Rebar cages form the skeletal framework of reinforced concrete structural members. They comprise longitudinal and transverse reinforcing bars, usually connected by tie wires. Past studies have identified tie-wire connections as the "weak links" in large, prefabricated rebar cages, leading to their failure during site handling and construction stages. Recent research has explored the use of mechanical U-bolt connectors to bolster stability and safety of rebar cages while reducing their vulnerability and risk of failure. This paper presents a comprehensive experimental and numerical investigation of a new type of mechanical connector, referred to as "Cage Clamp", to enhance rebar cage stability and safety. Component-level experimental tests are conducted to determine the force-displacement response of Cage Clamp connectors for crossbar connections in different degrees of freedom. The results highlight the remarkable strength and stiffness of Cage Clamp connectors, significantly outperforming traditional tie-wire connections. Furthermore, full-scale experimental tests are performed on a circular underground pile-shaft rebar cage with Cage Clamp connectors to assess its behavior under various site handling conditions. The data obtained from these experiments are used to construct and refine detailed 3D finite element models of the rebar cages, capable of capturing the complex behavior of rebar cages with Cage Clamp connectors. Using the numerical model, a sensitivity analysis is performed to assess the effect of the number of Cage Clamps on the deflection and stability of the rebar cage. The findings show that using cage clamps to connect pick-up rebar with lateral reinforcement at every 8 to 10 feet is sufficient to ensure the stability of the cage during typical site handling conditions. The results of this study serve as a basis for establishing analysis, design, fabrication, and handling guidelines for rebar cages, contributing to safe and efficient construction practices.

Deep learning methods have been widely used in PolSAR image classification. To learn the polarimetric information, many deep learning methods expect to learn high-level semantic features from original PolSAR data. However, only original data cannot learn multiple scattering features and complex structures for extremely heterogeneous terrain objects. In addition, deep learning methods always cause edge confusion due to the high-level features. To overcome these shortages, we propose a double-channel CNN network combined with an edge-preserving MRF model(DCCNN-MRF) for PolSAR image classification. Firstly, to combine complex matrix data and multiple scattering features together, a double-channel convolution network(DCCNN) is developed, which consists of a Wishart-based complex matrix and multi-feature subnetworks. The Wishart-based complex matrix network can learn the statistical characteristics and channel correlation well, and the multi-feature network can learn high-level semantic features well. Then, a unified network framework is designed to fuse two kinds of features to enhance advantageous features and reduce redundant ones. Finally, an edge preserving MRF model is designed to combine with the DCCNN network. In the MRF model, a sketch map-based edge energy function is designed by defining adaptive weighted neighborhood for edge pixels. Experiments are conducted on four real PolSAR data sets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed DCCNN-MRF method.

Several coastal barriers experienced significant erosion and change in shape throughout the Mediterranean coasts over the past decades, and the issue has become of increasing concern for scientists and policymakers. With reference to a case study and by meteorological and geomorphological investigations, this note aims to infer recent shoreline dynamics and define coastal phenomena shaping the beach-dune barrier of Cesine Lagoon (South Italy), to finally point out the weather-driven processes. A challenge for this research was to obtain significant results from publicly available sources and simple and inexpensive methods. The retreat of the dune toe, the accretion of washover fans, and the formation of gravel beaches have been related to the analyzed wind-wave conditions. Net longshore transport was found in accordance with the direction of the more intense winds. The role of extreme events in the shaping of coastal landforms is yet to be established even if they greatly increase the vulnerability to flooding of the study area. The results achieved so far are starting points for further data collection and analysis in the perspective of assessing the impact of climate changes on the lagoon barrier.

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. The image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network(HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses the dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While the earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3428 from scratch. Thereby, we outperform state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.

Every biological image contains quantitative data that can be used to test hypotheses about how patterns were formed, what entities are associated with one another, and whether standard mathematical methods inform our understanding of biological phenomena. In particular, spatial point distributions and polygonal tessellations are particularly amendable to analysis with a variety of graph theoretic, computational geometric, and spatial statistical tools such as: Voronoi Polygons; Delaunay Triangulations; Perpendicular Bisectors; Circumcenters; Convex Hulls; Minimal Spanning Trees; Ulam Trees; Pitteway Violations; Circularity; Clark-Evans spatial statistics; Variance to Mean Ratios; Gabriel Graphs; and, Minimal Spanning Trees. Furthermore, biologists have developed a number of empirically related correlations for polygonal tessellations such as: Lewis’s Law (the number of edges of convex polygons are positively correlated with the areas of these polygons): Desch’s Law (the number of edges of convex polygons are positively correlated with the perimeters of these polygons); and Errara’s Law (daughter cell areas should be roughly half that of their parent cells’ areas). We introduce a new Pitteway Law that the number of sides of the convex polygons in a Voronoi tessellation of biological epithelia is proportional to the minimal interior angle of the convex polygons as angles less than 90 degrees result in Pitteway violations of the Delaunay dual of the Voronoi tessellation.

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale, thus one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images with the possibility of obtaining them in field conditions using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in combination, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. WFD2020 data are available freely at http://wfd.sysbio.ru/. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented by the authors as a bot on the Telegram platform, which allows assessing plants by lesions in the field conditions.

This article presents answers to the questions on superresolution and structured illumination microscopy as raised in the editorial of a recent publication [K. Prakash et al. arXiv, 2102.13649, 2021]. The answers are based on my personal views on superresolution in light microscopy, supported by reasoning. Discussed are the definition of superresolution, Abbe’s resolution limit and the classification of superresolution methods into non-linear-, prior-knowledge- and near-field-based superresolution. A further focus is put on capabilities and technical aspects of present and future structured illumination microscopy (SIM) methods.

Introduction: Despite the introduction of increasingly multifaceted diagnostic techniques and the general progress of emergency abdominal and vascular surgery, the outcome of treatment of patients with acute impaired intestinal circulation remains unsatisfactory. The non-invasive and high-resolution technique of optical coherence tomography (OCT) can be used intraoperatively to assess intestine viability and associated conditions that frequently emerge under conditions of impaired blood circulation. This study aims to demonstrate the effectiveness of multimodal (MM) OCT for intraoperative diagnostics of both the microstructure (cross-polarization OCT mode) and microcirculation (OCT angiography mode) of the small intestine wall in patients with acute mesenteric ischemia (AMI). Methods and Participants: A total of 18 patients were enrolled in the study. Nine of them suffered from AMI in segments II-III of the superior mesenteric artery (AMI group), where the ischemic segments of the intestine were examined. Nine others were operated for adenocarcinoma of the colon (control group), thus allowing areas of their normal small intestine to be examined for comparison. Data on the microstructure and microcirculation in the walls of the small intestine were obtained intraoperatively from the side of the serous membrane using the MM OCT system (IAP RAS, Russia) before bowel resection. The MM OCT data were compared with the results of histological examination. Results: The study finds that MM OCT visualized the damage to serosa, muscularis externa, and blood vessels localized in these layers in 100% of AMI cases. It also visualized the submucosa in 33.3% of AMI cases. The MM OCT image of non-ischemic (control group), viable ischemic and necrotic small intestines (AMI group) differed significantly across stratification of the distinguishable layers, the severity of intermuscular fluid accumulations and the type and density of the vasculature. Conclusion: The MM OCT diagnostic procedure optimally meets the requirements of emergency surgery. Data on the microstructure and microcirculation of the intestinal wall can be obtained simultaneously in real time without requiring contrast agent injections. The depth of visualization of the intestinal wall from the side of the serous membrane is sufficient to assess the volume of the affected tissues. However, the methodology for obtaining MM OCT data needs to be improved to minimize the motion artefacts generated in actual clinical conditions.

Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Through supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35% respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approaches uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approache combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%)

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

Legal consideration hold significant importance in the cloud migration process, encompassing contractual arrangements, data sovereignty concerns, and liability matters. Organizations need to make sure that their contracts with cloud service providers (CSPS) cover important aspects such as data ownership, usage rights, and indemnification clauses. In the ever-changing world of smart cities, the importance of ensuring secure, compliant, and efficient data migration across international borders has become more crucial than ever. This paper introduces a new framework that combines natural language processing (NLP), and blockchain smart contracts to tackle the intricate legal issues involved in moving data across borders in cloud settings. The framework starts by utilizing an NLP model to ensure compliance with data protection regulations, such as GDPR, CCPA, and DPDPA, which are specific to the destination jurisdiction of the data. After confirming the verification, the smart contract initiates the data transfer process, securely recording metadata such as file hash, timestamp, and transfer details on the blockchain, guaranteeing transparency and immutability. After the transfer, an international vendor at the destination verifies the data against the relevant legal requirements, guaranteeing compliance before storing it in the destination cloud. By adopting this approach, we can ensure the legal validity of cross-border data transfers, while also promoting trust and accountability among all parties involved in smart city ecosystems. The findings indicate that this framework has the potential to greatly reduce the risks associated with data sovereignty, liability, and contractual obligations when moving data to the cloud.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). This review paper provides a comprehensive overview of fNIRS, EEG, and fMRI, including their principles, advantages, limitations, and integration with other modalities for advanced BCIs. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Applications of advanced BCIs in clinical, assistive, gaming, and entertainment domains are also discussed. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature on data acquisition approaches and a comparison of the ease of use for each of the modalities.

Data Meshes is an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of Data Meshes and Data Products in response to user requests, ensuring that stakeholders will have access to specific portions of the Data Mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of Data Meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory.

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.

This cross-country four-year longitudinal study investigated the development of processing speed throughout primary school education. The analyses were conducted on data collected from 441 schoolchildren in Grades 1, 2, 3 and 4 (aged 6.42 to 11.85 years) in the Kyrgyzstan and Russia. Mixed effects growth modeling was applied to estimate average and individual growth trajectories for processing speed at two cross-country samples. Latent class growth modeling was conducted to identify different types of growth trajectories for processing speed and to compare the distribution of the types within the analyzed samples. According to the results, processing speed significantly increases across primary school years. The trajectory is characterized by nonlinear changes with more intensive growth between Grades 1 and 2, which later slows down until Grade 4. There were no significant cross-country differences in the initial score of processing speed or developmental changes in processing speed across primary school years. The development of processing speed is described by a model with three quadratic growth types but minimally differs. It is concluded that in both samples, the development of processing speed may be characterized by homogeneity, with the most intensive growth from Grade 1 to Grade 2 and subsequent linear improvement until Grade 4.

Brain-derived neurotrophic factor (BDNF) is a key molecule in promoting neurogenesis, dendritic and synaptic health, and neuronal survival, plasticity, and excitability, all of which are disrupted in neurological and cognitive disorders such as Alzheimer’s disease (AD). Extracellular aggregates of amyloid-β (Aβ) in the form of plaques and intracellular aggregates of hyperphosphorylated tau protein have been identified as major pathological insults in AD brain, along with immune dysfunction, oxidative stress, and other toxic stressors. Although aggregated Aβ and tau lead to decreased brain BDNF expression, early losses in BDNF prior to plaque and tangle formation may be due to other insults such as oxidative stress and contribute to early synaptic dysfunction. Physical exercise, on the other hand, protects synaptic and neuronal structure and function, with increased BDNF as a major mediator of exercise-induced enhancements in cognitive function. Here, we review recent literature on mechanisms behind exercise induced BDNF upregulation and its effects on improving learning and memory and on Alzheimer’s disease pathology. Mechanisms include elevations in peripheral BDNF-inducing hormones such as osteocalcin, FNDC5/irisin, and lactate. The fundamental mechanisms of how exercise impacts BDNF and cognition are unclear but are a prerequisite to developing new biomarkers and therapies to delay or prevent cognitive decline.

Accurately predicting electrical signals of three-phase Direct Torque Control (DTC) induction motors is crucial for optimal motor performance and effective condition monitoring. However, multiple DTC motors’ complexity and operating conditions’ variations pose challenges for traditional prediction methods. To overcome these challenges, we propose an innovative approach that combines Fast Fourier Transform (FFT) to transform electrical motor simulation data and a Bi-directional Long Short-Term Memory (Bi-LSTM) network to forecast processed motor data. By transforming the DTC induction motor signals from the time domain to the frequency domain, we enhance the capability of learning models to capture subtle differences and generate various input features. The Bi-LSTM model effectively captures both forward and backward dependencies in time series data, enabling accurate prediction of electrical signals. We evaluate the proposed approach using our simulated dataset and compare the proposed method to a state-of-the-art model, the Gated Recurrent Unit (GRU), demonstrating its effectiveness in improving the accuracy and reliability of induction motor signals forecasting. The finding provides valuable insights for advancing motor control and operation in industrial applications.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

Ethanol produced from various biobased sources (bioethanol) has been gaining high attention lately due to its potential to cut down net emissions of carbon dioxide while reducing burgeoning world dependence on fossil fuels. Global ethanol production has increased more than six-fold from 18 billion liters at the turn of the century to 110 billion liters in 2019 (1,2). Sugar cane and corn have been used as the major feedstocks for ethanol production. Lignocellulosic biomass has recently been considered as another potential feedstock. This paper reviews recent developments and current status of commercial production of ethanol across the world. The review includes the ethanol production processes used for each type of feedstock, both currently practiced at commercial scale and newly developed technologies, and production trends in various regions and countries in the world.

Vacuum drying can dehydrate materials further than dry heat methods while protecting sensitive materials from thermal degradation. Many industries have shifted to vacuum drying as cost- or time-saving measures. Small-scale vacuum drying, however, has been limited by high costs of specialty scientific tools. To make vacuum drying more accessible, this study provides design and performance information for a small-scale open source vacuum oven, which can be fabricated from off-the-shelf and 3-D printed components. The oven is tested for drying speed and effective-ness on both waste plastic polyethylene terephthalate (PET) and a consortium of bacteria developed for bioprocessing of terephthalate wastes to assist in distributed recycling of PET for both additive manufacturing as well as potential food. Both materials can be damaged when exposed to high temperatures, making vacuum drying a desirable solution. The results showed the open source vacuum oven was effective at drying both plastic and biomaterials, drying at a higher rate than a hot-air dryer for small samples or for low volumes of water. The system can be constructed for less than 20% of commercial vacuum dryer costs for several laboratory-scale applications including dehydration of bio-organisms, drying plastic for distributed recycling and additive manufacturing, and chemical processing.

This topical review describes the results of recent research into and development of silicon nitride, a ceramic material with unique properties. The outcome of this ongoing research strongly encourages the use of monolithic silicon nitride and coatings as contemporary and future biomaterial for a variety of medical applications. Crystallographic structure, synthesis and processing of monolithic structures and coatings, and examples of their medical applications are covered that relate to spinal, orthopedic and dental implants, bone grafts and scaffolds, platforms for intelligent synthetic neural circuits, antibacterial and antiviral particles and coatings, optical biosensors, and nano-photonic waveguides for sophisticated medical diagnostic devices. The examples provided show convincingly that silicon nitride is destined to become a leader to replace titanium and other entrenched biomaterials in many fields of medicine.

We propose an asynchronous acoustic chirp slope keying to map bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. Details of the implementation are disclosed and discussed, the performance verified on laboratory scale in a pool measurement, as well as simulated for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products of 50 in single band mode, a raw data rate of 100~bit/s is simulated to achieve bit error rates below 0.001 for signal-to-noise ratios above -6~dB. In dual-band mode and a data rate of 200~bit/s, this bit error level was achieved for signal-to-noise ratios above 0~dB for time-bandwidth product of 25. The packet error rates follow this behavior with an offset of 1~dB.

3D meso-scale structures that can reach up to centimeters in overall size but retain micro- or nano-features, proved to be promising in various science fields ranging from micro-mechanical metamaterials to photonics and bio-medical scaffolds. In this work we present synchronization of the linear and galvano scanners for efficient femtosecond 3D optical printing of objects at the meso-scale (from sub-μm to sub-cm spanning five orders of magnitude). In such configuration the linear stages provide stitch-free structuring at nearly limitless (up to tens-of-cm) working area, while galvo-scanners allow to achieve translation velocities in the range of mm/s-cm/s without sacrificing nano-scale positioning accuracy and preserving undistorted shape of the final print. The principle behind this approach is demonstrated, proving its inherent advantages in comparison to separate use of only linear stages or scanners. The printing rate is calculated in terms voxels/s, showcasing the capability to maintain an optimal feature size while increasing throughput. Full capabilities of this approach are demonstrated by fabricating structures that reach millimeters in size but still retain μm-scale features: scaffolds for cell growth, microlenses and photonic crystals. All this is combined into a benchmark structure: a meso-butterfly. Provided results show that synchronization of two scan modes is crucial for the end goal of industrial-scale implementation of this technology and makes the laser printing well aligned with similar approaches in nanofabrication by electron and ion beams.

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

The processed tomato is one of the major food products of Italy and characterized today many Italian Regions, in northern and southern Italy, even though the firms in the industry have had difficulties, in recent years, due to an increase in the cost of raw materials. Furthermore, tomato processing firms are often characterized by significant investment in fixed assets and working capital, with an consequent increase in equity and/or debt financing, with consequent increasing of the risk of bankruptcy, as happens to many firms in the sector in recent years. The aim of the research is than to analyze the risk of bankruptcy of tomato processing companies applying financial ratios. To achieve this goal, the research consider the annual data of a sample of 17 tomato processing firms operating in the Inter Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”; firms’ data are divided into still-active (not-distressed) and failed (distressed) firms, with the aim of analyzing the differences between financial data and management practices of the two groups of firms to evaluate the sustainability of the business cycle. The company data suggest that larger firms, with adequate financial structure, have been able to withstand the tomato market crisis in recent years, where distressed firms are on average smaller and suffer from a higher recourse to debt capital with lower profit margins than not-distressed firms. The research could then be applied by entrepreneurs, managers, bankers and public operator to define good practices of management to be achieved and measured with financial ratios even in order reduce the risk of distress for firms operating in tomato sector.

Gender recognition is trivial for physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during the stepping exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Nazarloo's work (90.34%) and other classifiers.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.

Homeostasis and its interaction with gene expression in the development and maintenance of living systems is discussed in order to formulate a new method to diagnose and treat diseases on this causative level. This includes an inquiry into the unsolved homeostasis problems of: location (where in the cell are calculations performed?), communication (what language used?), identity (set of plans/hierarchy of set points?) and computational (mechanisms used to make necessary adjustments?). As these functions require the processing of vast amounts of information which is communicated at every level of organization including the mechanisms of epigenetics, different computer models are discussed, with examples for each; analog, digital and the possibility of quantum biological. Each one allows certain properties and cell functions to emerge. The difference between functional homeostasis and dysfunctional homeostasis is discussed with strategies to indirectly study them in terms of epigenetic signatures. Hormesis is discussed as a mechanism to reverse the epimutations, leading to a method of treatment to almost any chronic disease. These topics are discussed as being a whole interconnected system.

Background Robotic-assisted thoracic surgery (RATS) has become the standard procedure for treating lung malignancy, offering numerous advantages over traditional methods. However, the minimally invasive nature of RATS presents unique challenges, notably a limited field of view and reduced tactile feedback for the surgeon. These limitations can significantly impact the surgeon's ability to navigate complex anatomical structures during surgery. To address these challenges, we recognized the need for enhanced preoperative familiarization with each patient’s specific anatomy. In response, we developed an innovative virtual reality (VR) surgical navigation system, utilizing head-mounted displays (HMDs), designed to provide surgeons with a more comprehensive and interactive understanding of the patient's anatomy before the actual surgery. This VR system aims to extend the surgeon's capabilities in both preoperative sumilation and intraoperative navigation, potentially mitigating the inherent limitations of RATS. Methods Three-dimensional (3D) volumized data, meticulously extracted from preoperative computed tomography (CT) scans, were seamlessly integrated into the Perspectus VR Education software. These data were then displayed on an HMD, facilitating an interactive 3D reconstruction of pulmonary structures. This reconstruction allowed for each structure to be visualized individually, enabling detailed preoperative resection simulations tailored to each patient's unique anatomy. During RATS, these specific 3D reconstructed images were made accessible within the surgeon's console through the TilePro™ multi-display input, providing real-time, intraoperative guidance. Results During the preoperative simulations, the 3D anatomic structure of the pulmonary vessels, as well as the spatial relationship between the lesion and adjacent tissues, were successfully visualized. This enhanced visualization enabled surgeons to thoroughly evaluate a patient's anatomy, significantly improving the safety and accuracy of the subsequent procedure. The integration of VR software with the HMD provided surgeons with a true 3D interaction with real patient data. This setup not only offered a unique perspective through the surgical console but also allowed for easy manipulation of the data on a mobile laptop, thereby enhancing the overall surgical planning process. Conclusions VR simulation using HMDs offers a viewing experience akin to that of a robotic 3D viewer, potentially providing an innovative avenue for surgeons to develop specific robotic surgical skills without the need for practicing on live patients. This technology, when integrated with routine imaging modalities, can significantly enhance preoperative planning. Such integration not only contributes to the safety but also improves the accuracy of anatomical resections. Our findings suggest that VR simulation with HMDs is particularly effective in assisting surgeons to accurately identify lesion locations in RATS, thereby optimizing surgical outcomes.

Deep learning heavily relies on statistical correlations to drive artificial intelligence (AI) innovations, particularly in computer vision applications like autonomous driving and robotics. However, despite providing a solid foundation for deep learning, these statistical correlations can be vulnerable to unforeseen and uncontrolled factors. The lack of prior knowledge guidance can result in spurious correlations, introducing confounding factors and affecting the model's robustness. To address this challenge, recent research efforts have focused on integrating causal theory into deep learning methodologies. By modelling the inherent and unbiased causal structure, causal theory can potentially mitigate the impact of spurious correlations effectively. Hence, this paper explores the basics of causal methodologies in image classification.

Abstract Big datasets are becoming increasingly common and can be challenging to understand and apply in public health. One method for lowering the dimensionality of these datasets and improving interpretability while minimizing information loss is data reduction using principal component analysis (PCA). It achieves this by successively maximizing variance through the creation of new, uncorrelated variables. PCA is an adaptive data analysis technique because it simplifies the process of finding new variables, or principal components, by solving an eigenvalue or eigenvector problem. These new variables are determined by the dataset being used, rather than by the analyst starting from scratch. It is also adaptable in another way because varieties of the method have been designed to adjust to various data structures and types. However, there are serious problems in the theoretical understanding and practical application of PCA in public health researchers, whereas its application is becoming popular in developing countries. Therefore, this article, which concentrated on using PCA to reduce data, began by outlining the fundamental concepts of PCA and going over what it can and cannot do, as well as when and how to use it. We also discussed the fundamental assumptions, benefits, and drawbacks of PCA. Furthermore, this article demonstrated and resolved PCA practical application problems in public health that most scholars are unaware of, such as variable preparation, variable inclusion and exclusion criteria for PCA, iteration steps, wealth index analysis, interpretation, and ranking.

We are introducing a highly efficient 48-channel ultrasound beamforming system ideal for ultrasound endoscopy applications. The system includes a transmitter and a receiver, which allow for low-area, high-resolution imaging acquisition. The transmitter uses a charge redistribution HV (high voltage) scheme to generate three-level pulses that actuate the transducer, implemented with the standard CMOS process for optimal cost and power savings. Meanwhile, the receiver features a sub-array structure and a delay generator that reduces area usage. To achieve high-resolution ultrasound imaging acquisition with low computational power, we developed the Shift Coherence Factor (SCF) algorithm, which is hardware-friendly. This approach delivers a lateral resolution over 20% better than the conventional delay and sum (DAS) algorithm, with a contrast ratio of over 30 dB. The system was implemented in a 180 nm standard CMOS process with an area of 24.98 mm2, power consumption of 8.23 mW per channel, achieving a delay resolution of 8.33 ns, and a low-area implementation of 0.52 mm2 per channel. The system offers high-quality imaging acquisition with minimal additional area and power consumption, which has great potential for 3D imaging or catheterized ultrasound systems.

Recent media reports commented about a possible antidiabetics’ misuse issue related to molecules promoted as a weight-loss treatment in non-obese people. We evaluated here available pharmacovigilance misuse/abuse signals related to semaglutide, a glucagon-like peptide-1 (GLP-1) analogue, in comparison to other GLP-1 receptor agonists (albiglutide; dulaglutide; exenatide; liraglutide; lixisenatide; tirzepatide) and the phentermine-topiramate combination. To that aim, we analysed the Food and Drug Administration-FDA Adverse Events Reporting System (FAERS) dataset, performing a descriptive analysis of adverse event reports (AER) and calculating related pharmacovigilance measures, including the reporting odds ratio (ROR) and the proportional reporting ratio (PRR). During January 2018-December 2022, a total of 31,542 AER involving the selected molecules were submitted to FAERS; most involved dulaglutide (n=11,858; 37.6%) and semaglutide (n=8,249; 26.1%). In comparing semaglutide vs the remaining nolecules, the AER ‘drug abuse’, ‘drug withdrawal syndrome’, ‘prescription drug used without a prescription’ and ‘intentional product use issue’ respective PRR values were 4.05, 4.05, 3.60 and 1.80 (all<0.01). The same comparisons of semaglutide vs the phentermine-topiramate combination were not associated with any significant differences. To the best of our knowledge, this is the first study documenting the misusing/abusing potential of semaglutide in comparison with other GLP1 analogues and the phentermine-topiramate combination. Current findings will need to be confirmed by further empirical investigations to fully understand the safety profile of those molecules.

In modern security situations, tracking multiple human objects in real-time within challenging urban environments is a critical capability for enhancing situational awareness, minimizing response time, and increasing overall operational effectiveness. Tracking multiple entities enables informed decision-making, risk mitigation, and the safeguarding of civil-military operations to ensure safety and mission success. This paper presents a multi-modal electro-optical/infrared (EO/IR) and radio frequency (RF) fused sensing (MEIRFS) platform for real-time human object detection, recognition, classification, and tracking in challenging environments. By utilizing different sensors in a complementary manner, the robustness of the sensing system is enhanced, enabling reliable detection and recognition results across various situations. Specifically designed Radar tag and thermal tag can be used to discriminate friendly and non-friendly objects. The system incorporates deep learning-based image fusion and human object recognition and tracking (HORT) algorithms to ensure accurate situation assessment. After integrating into an all-terrain robot, multiple ground tests were conducted to verify the consistent HORT in various environments. The MEIRFS sensor system has been designed to meet the Size, Weight, Power, and Cost (SWaP-C) requirements for installation on autonomous ground and aerial vehicles.

Recent media reports commented about a possible antidiabetics’ misuse issue related to molecules promoted as a weight-loss treatment in non-obese people. We here evaluated available pharmacovigilance misuse/abuse signals related to semaglutide, a glucagon-like peptide-1 (GLP-1) analogue, in comparison to other GLP-1 receptor agonists (albiglutide; dulaglutide; exenatide; liraglutide; lixisenatide; tirzepatide) and the phentermine-topiramate combination. To that aim, we analysed the Food and Drug Administration-FDA Adverse Events Reporting System (FAERS) dataset, performing a descriptive analysis of adverse event reports (AER) and calculating related pharmacovigilance measures, including the reporting odds ratio (ROR) and the proportional reporting ratio (PRR). During January 2018-December 2022, a total of 31,542 AER involving the selected molecules were submitted to FAERS; most involved dulaglutide (n=11,858; 37.6%) and semaglutide (n=8,249; 26.1%). In comparing semaglutide vs the remaining antidiabetics, the AER ‘drug abuse’, ‘drug withdrawal syndrome’, ‘prescription drug used without a prescription’ and ‘intentional product use issue’ respective PRR values were 4.05, 4.05, 3.60 and 1.80 (all<0.01). The same comparisons of semaglutide vs the phentermine-topiramate combination were not associated with any significant differences. To our best, this is the first study documenting the misusing/abusing potential of semaglutide in comparison with other novel antidiabetics and the phentermine-topiramate combination. Current findings will need to be confirmed by further empirical investigations to fully understand the safety profile of those molecules.

Urethral pain syndrome (UPS) is still a pathology in which the diagnosis is formulated as a "diagnosis of exclusion". The exact pathogenetic mechanisms are not yet fully understood and clear recommendations for the prevention and treatment of UPS are absent. The goal of the study was to assess the condition of the tissues in the female urethra in UPS, by using transvaginal ultrasound (TVUS) and cross-polarization optical tomography (CP OCT). TVUS showed an expansion in the diameter of the internal lumen of the urethra, especially in the proximal region compared with the norm. Compression elastography revealed areas with increased stiffness (presence of fibrosis) in urethral and surrounding tissues. When studied with CP OCT it was shown that with UPS, the structure of the tissues in most cases was changed: trophic alterations in the epithelium (hypertrophy or atrophy) and fibrosis of underlying connective tissue were observed. The proximal fragment of the urethra with UPS underwent changes identical to those of the bladder neck. This paper showed that the introduction of new technology — CP OCT — in conjunction with TVUS will allow verification of structural changes in tissues of the lower urinary tract at the level of their architectonics and will help doctors understand better the basics of the UPS pathogenesis.

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Victor Vasarely’s (1906-1997) important legacy to the study of human perception is brought to the forefront and discussed. A large part of his impressive work conveys the appearance of striking three-dimensional shapes and structures in a large-scale pictorial plane. Current perception science explains such effects by invoking brain mechanisms for the processing of monocular (2D) depth cues. Here in this study, we illustrate and explain the local effects of 2D color and contrast cues on the perceptual organization in terms of figure-ground assignments, i.e. which local surfaces are likely to be seen as “nearer” or “bigger” in the image plane. Paired configurations are embedded in a larger, structurally ambivalent pictorial context inspired by some of Vasarely’s creations. The figure-ground effects these configurations produce reveal a significant correlation between perceptual solutions for “nearer” and “bigger” when no other monocular depth cues are given in the image. In consistency with previous findings on similar, albeit simpler visual displays, a specific color may compete with luminance contrast in resolving the planar ambiguity of a complex pattern context. Vasarely intuitively understood, and successfully exploited, this kind of subtle context effect in his art, well before empirical investigations had set out to study and explain their genesis in terms of information processing by the visual brain.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

The research is devoted to the acute problem of cinema influence on the young audience. With the continuous development of mass media, cinema is still one of the most popular leisure activities, remaining an influential tool on the picture of the world, values, stereotypes, the behavior of viewers, etc. The paper studies the effects of the cinema on young viewers in relation to their ideas about the elderly. Using a psychosemantic technique that includes 25 scales designed specifically to identify the content of representation of older people, respondents evaluated their various characteristics before and after watching the film. It should be noted that after watching a film about the elderly, the respondents changed their representations of motivational and regulatory, cognitive and emotional characteristics of the older people. However, the changes were not similar in different groups of young people: students seemed to have more negative representation, graduate students’ assessment, on the contrary, changed for a better one. The discovered opposite effects of the cinema influence can be explained by a number of reasons starting from age, personal experience of the respondents, the formation of ideas to the special features of the film itself. In this regard, the contribution of individual factors to the efficiency of the cinema impact and the prediction of its specific effects requires further study.

Several depth image based rendering (DIBR) watermarking methods have been proposed, but they have various drawbacks, such as non-blindness, low imperceptibility, and vulnerability to signal or geometric distortion. This paper proposes a template based DIBR watermarking method that overcomes the drawbacks of previous methods. The proposed method exploits two properties to resist DIBR attacks: the pixel is only moved horizontally by DIBR, and the smaller block is not distorted by DIBR. The one dimensional (1D) discrete cosine transform (DCT) and curvelet domains are adopted to utilize these two properties. A template is inserted in the curvelet domain to restore the synchronization error caused by geometric distortion. A watermark is inserted in the 1D DCT domain to insert and detect a message from the DIBR image. Experimental results of the proposed method show high imperceptibility and robustness to various attacks, such as signal and geometric distortions. The proposed method is also robust to DIBR distortion and DIBR configuration adjustment, such as depth image preprocessing and baseline distance adjustment.

This article develops algorithms for the characterization and the visualization of micro-scale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. We implement the algorithms to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) surface. Such images are highly dense; therefore, traditional image processing techniques might be computationally expensive. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

Signal approximation, is the basic idea of signal processing and mathematical analysis where complex signals can be represented with simpler ones. This paper explores unifying two mathematical fundamentals, Dirichlet-type problems, and hyperbolic series identities, into signal approximation. In this paper, we address a well-known classic topic in mathematical analysis - the Dirichlet problem - to improve (the approximation of) signal using Fourier series. We also discuss using hyperbolic series representations to increase signal modeling flexibility. In this way, we hope to create new links that span some distance along this dimension and result in a somewhat theoretical view of signal behavior - present with the mathematical rigor needed for speculation but absent from an understanding capable of appropriately handling real-world complexity. This paper provides a theoretical investigation of the mechanics at play and reveals by analogy how these mathematical tools can potentially advance signal processing techniques and understanding. This paper illustrates Fourier series and fuzzy logic methods by two case studies on signal processing, satellite communication and financial risk measurement. The first case study performs signal approximation using the Fourier series and hyperbolic function identities to provide insight into how signals behave and are modeled. The second case study deals with applying fuzzy logic for risk evaluation of investment portfolios, emphasizing the utility in evasion systems solving uncertainty and imprecision problems related to financial decisions.

The cognitive and affective impacts of video games are subjects of ongoing debate, with recent research recognizing their potential benefits. This study employs the Gaming Skill Questionnaire (GSQ) to evaluate participants' gaming skills across six genres and overall proficiency. Eighty-eight individuals aged 20-40 participated, completed assessments of empathy and six cognitive abilities: verbal short-term memory, verbal working memory, visuospatial short-term memory, visuospatial working memory, psychomotor speed (hand-eye coordination), and attention. Cognitive abilities were examined using the Digit Span Test, Corsi Block Test, and Deary-Liewald Reaction Time Task, while empathy was assessed using the Empathy Quotient Questionnaire. Findings indicate that high video game skill levels correlate with improvements in visuospatial short-term and working memory, psychomotor speed, and attention. Different genres enhanced specific skills: RPGs positively influenced verbal working and visuospatial short-term memory but negatively affected empathy; action games improved psychomotor speed and attention; and puzzle games benefited visuospatial working memory. These promising results contribute positively to ongoing research on the cognitive and affective effects of video games, highlighting the potential for video games to enhance certain cognitive functions while also underscoring the complexity of their impact on empathy. Future research should further investigate genre-specific effects and long-term outcomes.

As fatigue properties of as-built components of additively manufactured (AM) metals are considerably weaker than those of wrought metals because of rougher surface, post-processing is necessary to improve fatigue properties. To demonstrate improvement of fatigue properties of AM metals by post-processing, powder bed fusion (PBF) using laser sintering (LS) AlSi10Mg, i.e., PBF-LS/AlSi10Mg was treated by “submerged laser peening (SLP)” using a fiber laser and/or a Nd:YAG laser, then evaluated by plane bending fatigue tests. At SLP, “laser ablation (LA)” is generated by a pulsed laser, and a bubble is generated after LA, which behaves like a cavitation bubble that is named as “laser cavitation (LC)”. In the present paper, LA-dominated SLP is called as “laser treatment (LT)”, while LC-collapse-dominated SLP is called as “laser cavitation peening (LCP)”, as impact at LC collapse is used for peening. It was revealed that SLP using the fiber laser was LT rather than LCP. It was demonstrated that the fatigue strength at N = 10^7 was 85 MPa for LCP, and 103 MPa for combined process of blasting (B) + LT + LCP, whereas the fatigue strength of as-built specimen was 54 MPa.

This comprehensive review explores the advancements in processing-in-memory (PIM) techniques for deep learning applications. It addresses the challenges faced by monolithic chip architectures and highlights the benefits of chiplet-based designs in terms of scalability, modularity, and flexibility. The review emphasizes the importance of dataflow-awareness, communication optimization, and thermal considerations in designing PIM-enabled manycore architectures. It discusses different machine learning workloads and their tailored dataflow requirements. Additionally, the review presents a heterogeneous PIM system for energy-efficient neural network training and discusses thermally efficient dataflow-aware monolithic 3D (M3D) NoC architectures for accelerating CNN inferencing. The advantages of TEFLON (Thermally Efficient Dataflow-Aware 3D NoC) over performance-optimized SFC-based counterparts are highlighted. Overall, this review provides valuable insights into the development and evaluation of chiplet and PIM architectures, emphasizing improved performance, energy efficiency, and inference accuracy in deep learning applications.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

This paper proposes a novel concept within Quantum Natural Language Processing (QNLP) to encode the interpersonal metafunction of Systemic Functional Linguistics (SFL), particularly tenor, into quantum circuits by treating Japanese honorifics seriously. Because of English language bias in the literature, the incorporation of nuanced aspects of interpersonal communication evident in languages, such as the aforesaid Japanese, remains under-developed and overlooked. Utilizing lambeq, monoidal categories, and string diagrams, this study extends lambeq’s quantum computing framework to capture not only grammar but also social context—specifically the roles and relationships that define interpersonal interactions in quantum circuits. This approach represents a significant step toward that end simply by defining the honorific type h in lambeq. This innovative strategy exposes the capability of quantum circuits to model the complex structures of language, moving beyond grammar and even semantics—the horizontal, textual dimension of language—to embrace the vertical, hierarchical dimension of human communication and social interaction. Through this lens, the paper underscores the potential of QNLP to transcend traditional linguistic analysis, advocating for a broader and more nuanced understanding of language that includes not only what is said but also the social persona of the interlocutors. An algorithm for parsing Japanese grammar into pregroup diagrams containing the h type is introduced along with a codebase for other researchers to use and to which to contribute. Lastly, a toy experiment is performed to demonstrate that the circuits generated from these pregroup diagrams are suitable for Quantum Machine Learning (QML) applications.

In this work, we studied light-output properties, efficiency function as well as PSD capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a size of (45 x 45) mm with PSD properties. Recently presented studies of light-output functions have been measured only for low neutron energies. Our motivation has been to determine more accurate the light output function for p-Terphenyl scintillator over a wider neutron energy range.

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services exerting major impact upon all aspects of human activity, be it social interactions as well as interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity and connectivity. F6G networks differ from those of previous generations (F1G-F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches etc. such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose: a) use of all-optical processing to mitigate performance barriers of analog to digital and digital to analog converters (ADC/DAC); b) exploitation of optical multi-band transmission, space division-multiplexing and adoption of more efficient modulation formats.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Despite the development of technologies, modern methods of disposal of end-of-life tires most often represent either the incineration in cement kilns or the destruction of tires in special landfills, showing a lack of sustainable recycling of this valuable material. The fundamental role of recycling is evident, and the development of high-efficiency processes represents a priority for the European market. Therefore, investigation of end-of-life rubber processing methods is of high importance for manufacturers and recyclers of rubber materials. In this paper, methods of processing of end-of-life tires are reviewed in order to obtain rubber crumb, which can later be used in the production of new industrial rubber goods and composite The processes of separation end-of-life tire into fractions by type of materials using mechanical processing methods along with mechanochemical and mechanical processes of processing the materials of used tires in order to obtain crumb rubber of various fractions and chemical reactivity are considered.

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Multiphase systems are important in minerals processing, and usually include solid-solid and solid-fluid systems. Examples of operations in multiphase systems include flotation, dewatering, and magnetic separation, among several other unit operations. In this paper, the current trends in the process system engineering tasks of modeling, design, and optimization, in multiphase systems, are analyzed. Different scales of size and time are included, and therefore the analysis includes modeling at the molecular level and unit operation level, and the application of optimization for the design of a plant. New strategies for the modeling and optimization of multiphase systems are also included, with a strong focus on the application of artificial intelligence (AI) and the combination of experimentation and modeling with response surface methodology (RSM). The paper finishes with tools to study the uncertainty, both epistemic and stochastic, which is present in all mineral processing operations. It is shown that all these areas are very active and can help to understand, operate, design, and optimize mineral processing that involves multiphase systems.

Mediterranean climate is marked by arid climate conditions in summer, therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, also triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (Southern Italy), close to the Adriatic sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was arranged to perform, on daily basis, a water and salt balance along the soil profile. Long-term weather data and soil physical parameters representative of the considered area were utilized in applying the model, also considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn-winter rainfall resulted frequently insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were prospected.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

A useful and increasingly common additive manufacturing (AM) process is the selective laser melting (SLM) or direct metal laser sintering (DMLS) process. SLM/DMLS can produce full-density metal parts from difficult materials, but it tends to suffer from severe residual stresses introduced during processing. This limits the usefulness and applicability of the process, particularly in the fabrication of parts with delicate overhanging and protruding features. The purpose of this study was to examine the current insight and progress made toward understanding and eliminating the problem in overhanging and protruding structures. To accomplish this, a survey of literature was undertaken, focusing on process modeling (general, heat transfer, stress and distortion, and material models), direct process control (input and environmental control, hardware-in-the-loop monitoring, parameter optimization, and post-processing), experiment development (methods for evaluation, optical and mechanical process monitoring, imaging, and design-of-experiments), support structure optimization, and overhang feature design; approximately 140 published works were examined. The major findings of this study were that a small minority of the literature on SLM/DMLS deals explicitly with the overhanging stress problem, but some fundamental work has been done on the problem. Implications, needs, and potential future research directions are discussed in-depth in light of the present review.

- Early and accurate detection of pneumonia from chest X-ray images is crucial for timely treatment and patient care. In study presents a robust computational framework leveraging the CheXnet algorithm, a deep learning model, to identify pneumonia from chest X-rays. Developed a neural network architecture trained on a substantial dataset of chest X- ray images labeled as 'Normal' and 'Pneumonia'. The user- friendly interface of the system allows for seamless uploading of images, with the model subsequently providing predictions, a confusion matrix, accuracy metrics, and layer activations visualizations. The algorithm's performance was rigorously evaluated, with a confusion matrix indicating a high number of true positives and true negatives. Specifically, the model detected pneumonia with an accuracy of 92.47%, as shown by the results displayed in the system's interface. The precision, recall, and F1-score for pneumonia detection were 0.90, 0.96, and 0.93 respectively, with the overall system achieving an accuracy of 0.91 across the test set of 624 images. Visualizations of the neural network layers reveal the complex feature mappings the model uses to differentiate between normal and pathological findings. These insights could be vital for enhancing the interpretability of deep learning models in medical diagnostics. The Results indicate that the CheXnet-based neural network is a promising tool for automating pneumonia detection in clinical settings, which can assist radiologists in the diagnostic process and potentially improve patient outcomes through earlier intervention.

Ecosystemic services are those that provide material or immaterial benefits to the inhabitants of a landscape from its water, food, medicines and raw materials. They are classified into four types: provisioning, regulating, supporting and cultural. The latter can be defined as those non-material benefits that humans obtain through ecosystems. They include educational value, cultural diversity, sources of inspiration, spirituality and religious values, aesthetic value, social relations, rootedness or belonging, cultural heritage, recreational and ecotourism services, and scientific knowledge, among others. Characterising such diverse factors requires a systematisation in study facets for their management. We propose three: memory, image and socio-system, as differentiated spheres of approach to the urban landscape. The memory facet would include the ecosystemic services of belonging, cultural heritage and, in certain themes, cultural diversity, sources of inspiration and religious values. The image facet includes the aesthetic value and perceptual aspects of ecotourism. In the socio-system we would study the educational value, scientific knowledge and recreational or spirituality services, reflected in the endowments; but also social relations, rootedness and another vision of ecotourism.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a Specular-Light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the Multi-Scale Normalized Cross Correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used nearby living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnosis and image-guided medical interventions. Following to an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the imager, the features of MRI-compatible materials and the conformity control of MRI-compatibility, are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary shielding protection technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations, the body numerical virtual models, are conferred and reviewed. A qualitative methodology, case study, simple example illustrating the mentioned methodology is presented. The last Section of the paper discussed potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues.

Deep learning classification is the state-of-the-art of machine learning approach. Earlier work proves that the deep convolutional neural network has successfully and brilliantly in different applications such as images or video data. Recognizing and clarifying the remote sensing aspect of the earth's surface and exploit land cover and land use (LCLU). First, this article summarized the remote sensing emerging application and challenges for deep learning methods. Second, we propose four approaches to learn efficient and effective CNNs to transfer image representation on the ImageNet dataset to recognize LCLU datasets. We use VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 models to extract features from the EACC dataset. We use pre-trained CNNs on ImageNet to extract features. For feature selection we proposed principal component analysis (PCA) to improve accuracy and speed up the model. We train our model by multi-layer perceptron (MLP) as a classifier. Lastly, we apply the multi-granularity encoding ensemble model. We achieve an overall accuracy of 92.3% for the nine-class classification problem. This work will help remote sensing scientists understand deep learning tools and apply them in large-scale remote sensing challenges

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

This study introduces a data obfuscation technique, leveraging the exponential map associated with the generators of Lie groups. Originating from quantum machine learning frameworks, our method illustrates the practical application of quantum mechanics principles in data processing. Specifically, it employs the exponential map of a generator algebra to introduce controlled noise into the data, achieving obfuscated data while preserving its utility for machine learning tasks. This strategy is shown to safeguard privacy in sensitive datasets, such as discussed medical records, and to enhance dataset volume and diversity through augmentation. Our empirical analysis, benchmarked against standard machine learning approaches, demonstrates that our method can maintain or even improve the predictive accuracy of the original data. This research highlights the potential of Lie group theory for advancing data privacy in medicine, marking a significant contribution to machine learning methodologies by offering the dual benefits of data obfuscation and enrichment. Through this synthesis of algebraic structures and machine learning, we propose new pathways for the secure and effective use of data in sensitive areas.

This study investigates the properties of solution processed hybrid pol-yimide, PI nanocomposites containing a variety of nanofillers, including polyaniline copolymer modified clay, PNEA, nanographene sheets, NGS, and carbon nanotube sheet, CNT-PVDF, respectively. Through a series of experiments, the flow behavior of poly(amic acid), PAA solution and PAA suspension containing polyaniline copolymer modified clay, PNEA was determined as a function of the shear rate, processing temperature and polymerization time. It is shown that the neat PAA solution exhibited a complex rheological behavior ranging from shear thickening to New-tonian behavior with increasing shear rate and testing temperature. The presence of modified clay in PAA solution significantly reduced the viscosity of PAA. Differential Scanning Calorimetry (DSC) analysis show that polyimide-nanographene sheets, PI NGS nanocomposites processed at high spindle speed (100 rpm) had lower total heat of decomposition which is indicative of improved fire retardancy. The effect of processing temperature on the specific capacitance of polyimide-CNT-PVDF composites containing electrodeposited polypyrrole fillers was determined by using cyclic voltammetry, CV. It was shown that the hybrid composite working electrode material processed at 90˚C produced a remarkably higher overall stored charge when compared to the composite electrode material processed at 250˚C. Consequently, the specific capacitance ob-tained at a scan rate of 5 mV/s for the hybrid nanocomposite processed at 90°C was about 858 F/g after 1 cycle, which is about 6.3 times higher than the specific capacitance of 136 F/g produced by the hybrid nanocomposite processed at 250°C. These findings show that the properties of the hybrid nanocomposites are remarkably influenced by the processing conditions and highlights the need for process optimization.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

We investigated how Artificial Intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1,970 comments from VH participants and trained two Machine Learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three Health Belief Model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

The growing dependence on large language models (LLM)s highlights the urgent need to deepen trust in these technologies. Regular, rigorous validation of their expertise, especially in nuanced and intricate scenarios, is essential to ensure their readiness for clinical applications. Our study pioneers the exploration of LLM utility in the field of cardiology. We stand at the cusp of a transformative era where mature AI and LLMs, notably ChatGPT, GPT-4, and Google Bard, are poised to influence healthcare significantly. Recently, we put three available LLMs, OpenAI's ChatGPT-3.5, GPT-4.0, and Google's Bard, to the test against a significant Polish medical special-ization licensing exam (PES). The exams cover the scope of completed specialist training, focusing on diagnostic and therapeutic procedures, excluding invasive medical procedures and interven-tions. In our analysis, GPT-4 consistently outperformed the others, ranking first, with and Google Bard and ChatGPT- 3.5 following, respectively. The performance metrics underscore GPT-4's no-table potential in medical applications. Given a score improvement of over 23.5 % between two AI models released just four months apart, clinicians must stay informed and up-to-date about these rapidly evolving tools and their potential applications to clinical practice. Our results provide a snapshot of the current capabilities of these models, highlighting the nuanced performance dif-ferences when confronted with identical questions

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT, a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the SEVIRI instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day ALbedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a Climate Data Record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: one for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This has prompted numerous researchers to investigate different methodologies and techniques suitable for intrusion detection systems (IDS) requirements. Over the years, many studies have proposed various solutions in this regard including signature-based and machine-learning (ML) based systems. More recently, researchers are considering deep learning (DL) based anomaly detection approaches. Most proposed works in this research field aimed to achieve either one or a combination of high accuracy, considerably low false alarm rates (FARs), high classification specificity and detection sensitivity, achieving lightweight DL models, or other ML and DL-related performance measurement metrics. In this study, we propose a novel method to convert a raw dataset to an image dataset to magnify patterns. Based on this we devise an anomaly detection for IDS using a lightweight convolutional neural network (CNN) that classifies denial of service and distributed denial of service. The proposed methods were evaluated using a modern dataset, CSE-CIC-IDS2018, and a legacy dataset, NSL-KDD. We have also applied a combined dataset to assess the generalization of the proposed model across various datasets. Our experimental results have demonstrated that the proposed methods achieved high accuracy and considerably low FARs with high specificity and sensitivity. The resulting loss and accuracy curves have also demonstrated the excellent generalization of the proposed lightweight CNN model, effectively avoiding overfitting. This holds for both the modern and legacy datasets, including their mixed version.

Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of Cone Beam Computed Tomography with Magnetic Resonance Imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

Sensory processing challenges are crucial yet often neglected aspects in the care of children with neurodevelopmental disorders and genetic conditions. They represent a key area of interest in neuroscience, as they significantly impact children's daily functioning and quality of life. This observational study examines the characteristics of these challenges in a group of 614 children, aged 3 to 14 years and 11 months, divided into three groups: 183 with neurodevelopmental disorders (Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, developmental delays, learning disorders), 89 with genetic conditions (22q11.2 Deletion Syndrome, Williams Syndrome, Pseudohypoparathyroidism), and 342 controls. Sensory processing was assessed using the Sensory Profile 2 (SP2). Results indicated that children with neurodevelopmental disorders and genetic conditions exhibited significant sensory processing difficulties compared to controls. The SP2 identified sensory challenges across different sensory systems, with varying configurations depending on the diagnosis. Children with neurodevelopmental disorders and genetic conditions show marked sensory processing challenges impacting daily lives and well-being. These findings highlight the importance of early identification and tailored evidence-based interventions to address these specific sensory processing issues. Further research should explore the long-term impact of these interventions in these different populations and their integration into broader therapeutic programs.

The spread of fake news poses a serious threat to public trust and informed decision-making. The emergence of AI-generated fake news exacerbates this issue, as it can be produced more rapidly and convincingly than human-generated content. Addressing this issue, our paper introduces a robust approach to detect AI-generated fake news that is applicable across major disinformation domains: Politics and Elections, Health and Medicine, Science and Technology, and Entertainment and Celebrities. Our models differentiate between AI-generated and human-generated news articles while assessing their truthfulness. Utilizing large language models such as ChatGPT, we curated datasets spanning both AI and human generated fake and genuine news. Following extensive data preprocessing and the application of diverse feature extraction techniques, our models were trained using multiple machine learning algorithms. Our results show the feasibility of accurately identifying AI-generated fake news, thereby adding a significant layer of defense in combating misinformation across various sectors. In addition, we have explored the potential utility of transferable machine learning models trained on data from one domain (e.g., politics), when tested on another domain (e.g., health).

As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.

A simple method is presented for the continuous generation of carbon nanotube forests stably anchored on stainless-steel surfaces using a reactive-roll-to-roll (RR2R) configuration. No addition of catalyst nanoparticles is required for the CNT-forest generation, the stainless-steel substrate itself being tuned to generate the catalytic growth sites. The process enables very larges surfaces covered with CNT forests having individual CNT roots anchored to the metallic ground through primary bonds. Fog water harvesting is demonstrated and tested as one potential application using long CNT-covered wires. The RR2R is performed in the gas phase, no solution processing of CNT suspensions is used contrary to usual R2R CNT-based technologies. Full or partial CNT-forest coverage provides tuning of the ratio and shape of hydrophobic and hydrophilic zones on the surface. This enables optimization of fog water harvesters for droplet capture through the hydrophobic CNT forest, and water removal from the hydrophilic SS surface. Water recovery tests using small harp-type harvesters with CNT-forest generate water capture of up to 2.2 g/cm2h under ultrasound-generated fog flow. The strong CNT root anchoring on the stainless steel surfaces provides opportunities for (i) robustness and easy transport of the composite structure, (ii) chemical functionalization and/or nanoparticle decoration of the structures, and opens the road for a series of applications on large scale surfaces, including fog harvesting.

Newcastle Disease (ND) remains a critical disease affecting poultry in sub-Saharan Africa. In some countries, repeated outbreaks have a major impact on local economies and food security. Recently, we developed an adenovirus-vectored vaccine encoding the Fusion protein from an Ethiopian isolate of Newcastle Disease Virus (NDV). The adenoviral vector was designed and a manufacturing process developed in the context of the Livestock Vaccine Innovation Fund initia-tive funded by the International Development Research Centre (IDRC) of Canada. The industrial-ly-relevant recombinant vaccine technology platform is being transferred to the National Veteri-nary Institute (Ethiopia) for veterinary applications. Here, we demonstrate that the instillation of the adenoviral vector through the nasal cavity can confer protection to chickens against a lethal challenge with NDV. A manufacturing process using HEK293 suspension cells cultured in stirred-tank bioreactor for the vaccine production is proposed. Taking into consideration supply chain limitations, options for serum-free media selection have been evaluated. A streamlined downstream process including a filtration, an ultrafiltration and a concentration step was de-veloped. With high volumetric yields (infectious titers up to 5 x 109 TCID50/mL) in the culture su-pernatant, the final formulations were prepared at 1010 TCID50/mL, either in liquid or lyophi-lized forms. The liquid formulation was suitable and safe for mucosal vaccination and was sta-ble for 1 week at 37˚C. Both liquid and lyophilized formulations were stable after 6 months of storage at 4˚C. Overall, a manufacturing process for adenovirus vectored vaccine was developed and protective doses were determined using a convenient route of delivery. Formulation and storage conditions were established, and quality control protocols were implemented.

Remote patient monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine are usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enables the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature. The ubiquitous problem of wearable devices is power demand for signal transmission, such devices require frequent battery charge which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report for collecting kinetic energy from human daily activities for monitoring Human vital signs, the harvested energy is used to sustain the battery autonomy of wearable devices, which enables longer monitoring time of vital data. A thorough review of available commercial ECG devices is first provided, and different methods evaluated in other literature to improve the monitoring time of wearable IOT devices. Besides, a novel type of Stress or exercise ECG monitoring device based on Microcontroller PIC18F4550 and Wi-Fi device ESP8266 is proposed in this study, which is cost effective and enables real time monitoring of heart rate on cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has smallest structure and low friction. Neodymium magnets are chosen for their highest magnetic force. Due to nonlinear magnetic force interaction of the magnets, the nonlinear part of the dynamic equation has inverse quadratic form. Electromechanical damping is considered in this study and the quadratic non linearity is approximated using MacLaurin expansion, which enables to find the law of motion for general case of studies using classical methods for dynamic equations and find the suitable parameters for the harvester. The oscillations are enabled by applying an initial force and there is loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab software and ODE45 solver is used to verify the accuracy of the method.

This article presents a unique set of voltage and current data from a public building and acquired using a hybrid measurement solution that combines Python and Grafana. The transversal purpose consists of contributing to the community with a vision of the quality of the supply more oriented to the monitoring of the state of the network, providing a more realistic vision, which allows a better understanding, and the adoption of the best decisions to achieve the efficient energy management and thus optimize the operation and maintenance of power systems. The work focuses on higher order statistical estimators that, combined with exploratory data analysis techniques, improve the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination in the line of low-cost measurement solutions. It also incorporates the underlying benefit of the contribution to industrial benchmarking. The paper also includes a computational comparison between Python and LabVIEW to elicit the performance of the measurement solution.

Since the early 1980s, a large number of research works on enzymes from the red king crab hepatopancreas have been conducted. These studies have been relevant both from a fundamental point of view for studying the enzymes of marine organisms and in terms of the rational management of nature to obtain new and valuable products from the processing of crab fishing waste. Most of these works were performed by Russian scientists due to the area and amount of waste of red king crab processing in Russia (or the Soviet Union). However, the close phylogenetic kinship and the similar ecological niches of commercial crab species and the production scale of the catch provide the bases for the successful transfer of experience in the processing of red king crab hepatopancreas to other commercial crab species mined worldwide. This review describes the value of recycled commercial crab species, discusses processing problems, and suggests possible solutions to these problems. The main emphasis is placed on the enzymes of the hepatopancreas as the most highly salubrious product of waste processed from red king crab fishing.

The dominant paradigm holds that spontaneous and therapeutically induced anti-tumor responses are mediated mainly by CD8 T cells and directed against tumor-specific antigens (TSAs). The presence of specific TSAs on cancer cells can only be proven by mass spectrometry analyses. Bioinformatic predictions and reverse immunology studies cannot provide this type of conclusive evidence. Most TSAs are coded by unmutated non-canonical transcripts that arise from cancer-specific epigenetic and splicing aberrations. When searching for TSAs, mass spectrometry analyses must therefore interrogate not only the canonical reading frame of annotated exome but all reading frames of the entire translatome. The majority of aberrantly expressed TSAs (aeTSAs) derive from unstable short-lived proteins that are good substrates for direct MHC I presentation but poor substrates for cross-presentation. This is an important caveat because cancer cells are poor antigen-presenting cells and the immune system therefore depends on cross-presentation by dendritic cells (DCs) to detect the presence of TSAs. We therefore postulate that, in the untreated host, most aeTSAs are undetected by the immune system. We present evidence suggesting that vaccines inducing direct aeTSA presentation by DCs represent an attractive strategy for cancer treatment.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

Sensory Processing Sensitivity (SPS) is a trait describing inter-individual differences in sensitivity to environments, both positive and negative ones. SPS has attracted growing societal interest. However, (neuro)scientific evidence is lagging behind. We critically discuss how to measure SPS, how it relates to other theories of Environmental Sensitivity and other temperament and personality traits, how SPS interacts with environments to influence (a)typical development, what the underlying aetiologies and mechanisms are, and its relation to mental disorders involving sensory sensitivities. Drawing on the diverse expertise of the authors, we set an agenda for future research to stimulate the field. We conclude that SPS is a heritable, evolutionarily conserved trait, linked to increased risk for psychopathology and stress-related problems in response to negative environments, as well as to greater benefits (e.g., intervention responsivity, positive mood) in positive environments. We need advances in objective assessment of SPS, understanding mechanisms, differentiating it from (seemingly) related mental disorders, to exploit the potential of SPS to improve mental health, preserve human capital, and prevent adverse effects.

Background: Ice formation on power transmission lines presents significant risks, including structural damage and power outages. Traditional methods for detecting ice on these lines have limitations, particularly in challenging environmental conditions. This study aims to enhance ice detection accuracy by introducing a novel image segmentation and edge detection methodology using improved multi-scale Retinex and advanced morphological operations. Method: The proposed method integrates multi-stage image processing techniques, including image enhancement, grayscale conversion, thresholding, segmentation, object isolation, and edge detection. High-resolution binocular cameras were used to capture images of ice formations on transmission lines. The effectiveness of the method was assessed through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methodologies. Results: The proposed method demonstrated superior performance in both ice detection and thickness measurement. The method effectively identified and isolated ice formations under various conditions with an average accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03%. Additionally, the analysis of ice thickness measurements further highlighted the method’s accuracy, with a notably low root mean squared error (RMSE) of 1.20 mm and mean absolute error (MAE) of 1.10 mm, along with a strong R-squared value of 0.95. These statistical metrics show that the proposed method not only outperforms previous methods in consistency and alignment with manual measurements but also stands out as the most reliable option for tasks requiring precise measurements, offering significant advancements in accuracy and reliability over existing methodologies. Conclusions: The enhanced image segmentation and edge detection approach provides a more accurate and reliable method for monitoring ice formations on power transmission lines.

The shortage of physicians in rural communities contributes to increased mortality rates among Medicare beneficiaries. This increase in mortality risk is commonly due to diseases such as heart disease and cancer, including melanoma and non-melanoma skin cancer (NMSC). Mohs micrographic surgery (MMS) is considered the gold-standard treatment for NMSC tumors, however, due to a shortage of dermatologists and Mohs surgeons in rural areas, access to MMS is limited. In these areas, image guided superficial radiation therapy (IGSRT) serves as a viable option for the treatment of NMSC lesions, with fewer recurrences and 99% cure rates, which are comparable to MMS cure rates. IGSRT is performed in dermatology clinics with support from a multidisciplinary team, including radiation therapists, medical physicists, radiation oncologists, and dermatologists. As IGSRT is able to be performed by general dermatologists, Medicare beneficiaries in rural communities will have an increase in access to treatment options and a decrease in delayed skin cancer treatment.

(1) Background: Stereological estimations significantly contributed to our anatomical and physiological understanding of the lung by providing an average distribution of measured parameters over the entire lung parenchyma. However, most structural lung diseases show a very inhomogeneous pattern of alterations. (2) Methods: We developed a novel protocol for the analysis of all pulmonary airspaces. Our pipeline starts with high-resolution synchrotron radiation-based X-ray tomographic microscopy (SRXTM) and consists of (i) image segmentation with the combination of the free machine-learning tool Ilastik and ImageJ and (ii) calculation of the airspace diameter distribution of an entire lung using a diameter map function. To evaluate the new pipeline, lungs from adult mice with cystic fibrosis (CF)-like lung disease (ENaC-transgenic mice) or mice with elastase-induced emphysema were compared to healthy controls. (3) Results: We were able to show the distribution of airspace diameters throughout the entire lung, as well as separately for the conducting airways and the gas-exchange area. In the pathobiological context, we observed an irregular widening of parenchymal airspaces in mice with CF-like lung disease and elastase-induced emphysema. Comparable results were obtained when analyzing lungs imaged with μCT, suggesting that our pipeline is applicable to different kinds of imaging modalities. (4) Conclusions: We conclude that the airspace diameter map is well suited for a detailed analysis of unevenly distributed structural alterations in chronic muco-obstructive lung diseases such as cystic fibrosis and COPD.

Eating disorder (ED) symptoms often co-occur with nonsuicidal self-injury (NSSI). This comorbidity is consistent with evidence that trait negative urgency increases risk for both of these phenomena. We previously found that impaired late-stage negative emotional response inhibition (i.e., negative emotional action termination or NEAT) might represent a neurocognitive mechanism for heightened negative urgency among people with NSSI history. The current study evaluated whether relations between negative urgency and ED symptoms similarly reflect deficits in this neurocognitive process. One hundred and five community adults completed an assessment of ED symptoms, negative urgency, and an emotional response inhibition task. Results indicated that, contrary to predictions, negative urgency and NEAT contributed independent variance to the prediction of ED symptoms, while controlling for demographic covariates and NSSI history. Worse NEAT was also uniquely associated with restrictive eating, after accounting for negative urgency. Our findings suggest that difficulty inhibiting ongoing motor responses triggered by negative emotional reactions (i.e., NEAT) may be a shared neurocognitive characteristic of ED symptoms and NSSI. However, negative urgency and NEAT dysfunction capture separate variance in the prediction of ED-related cognitions and behaviors, distinct from the pattern of results we previously observed in NSSI.