The signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arose during the acute phase of COVID19 by an increased breakdown of haematocytes, to which the liver responds by reducing the production of albumin, in order to prevent a too large decrease in haematocrit.In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the intravenous administration of donor blood products, starting with albumin concentrate. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable. I suspect that most Long COVID patients will exhibit a high serum ferritin level as a result of internal haemolysis, and haematocrit and albumin values at the high and low extremes of the reference range because the liver can not keep up with the recovery of the red bone marrow.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

Convolutional Neural Networks (CNNs) are often criticized for their lack of transparency, acting as 'black boxes' in decision-making, a challenge compounded by class imbalance in ECG datasets, which limits their clinical application. This study introduces a CNN-based ECG signal classification model that enhances interpretability and addresses class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE). The model also integrates Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and SHAP value analysis, facilitating the visualization of decision boundaries and the assessment of feature contributions. Our evaluation using the MIT-BIH Arrhythmia Database highlights the model's high performance, with accuracy and precision nearing 1.00 for Normal ECG (NOR), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Ventricular Premature Beat (PVC) in the six-class task. In the ten-class task, the model demonstrated robustness, particularly with an accuracy of 0.9846, precision of 0.9783, recall of 0.9736, and F1 score of 0.9760 in Pacemaker Fusion Beat (PFHB), supported by an AUC of 0.9999 and AP of 0.9885. These results underscore the model's efficacy in cardiac rhythm recognition and resilience to class imbalance. Future research will explore sophisticated model architectures and feature extraction methods to enhance the model's generalization and clinical applicability for early heart disease diagnosis and personalized treatment.

In response to the burgeoning global demand for premium agricultural products, particularly within the competitive nut market, this paper introduces an innovative methodology aimed at enhancing the grading process for almonds and their shells. Leveraging state-of-the-art Deep Convolutional Neural Networks (CNNs), specifically the AlmondNet-20 architecture, our study achieves exceptional accuracy exceeding 99%, facilitated by the utilization of a 20-layer CNN model. To bolster robustness in differentiating between almonds and shells, data augmentation techniques are employed, ensuring the reliability and accuracy of our classification system. Our model, meticulously trained over 1000 epochs, demonstrates remarkable performance, boasting an accuracy rate of 99% alongside a minimal loss function of 0.0567. Rigorous evaluation through test datasets further validates the efficacy of our approach, revealing impeccable precision, recall, and F1-score metrics for almond detection. Beyond its technical prowess, this advanced classification system offers tangible benefits to both industry experts and non-specialists alike, ensuring globally reliable almond classification. The application of deep learning algorithms, as showcased in our study, not only enhances grading accuracy but also presents opportunities for product patents, thereby contributing to the economic value of our nation. Through the adoption of cutting-edge technologies such as the AlmondNet-20 model, we pave the way for future advancements in agricultural product classification, ultimately enriching global trade and economic prosperity.

The interaction between electromagnetic fields (EMFs) and biological systems has been a subject of intense research and debate. This study explores the potential influence of magnetic fields on neural circuits, which are the building blocks of the human brain. By developing a mathematical model and conducting computational simulations, we investigate how magnetic fields can affect the transmission of electrical signals within neural circuits. The results suggest that magnetic fields can alter the speed and direction of signal propagation, offering potential applications in medical treatments and brain-machine interfaces. However, the study also highlights the need for more comprehensive models that consider the complexity of real-world neural circuits and the multifaceted nature of magnetic fields. The interaction between EMFs and neural circuits is a rich and complex subject that requires a multidisciplinary approach, combining physics, biology, mathematics, and computer science. While this study provides valuable insights, further research is needed to fully understand the implications of this interaction and to realize its potential applications in medicine, neuroscience, and technology. The findings contribute to a growing body of knowledge that bridges the gap between physics and neuroscience, opening new horizons for understanding the human mind and its connection to the physical world.

: Schizophrenia and schizoaffective disorder are generally treated with second-generation antipsychotic drugs. These drugs are mostly D2 and 5-HT2A antagonists. They improve positive schizophrenia symptoms sufficiently well; however, they ameliorate negative schizophrenia symptoms and cognitive functions to a small extent. We review novel antipsychotic drugs exerting a partial agonism at dopaminergic and serotonergic receptors such as cariprazine, brexipiprazole and lumateperone. Besides, the mechanisms of actions of non-anti-dopaminergic antipsychotic drugs are pointed out. Updated neural networks are used to explain the mechanism of action of M4 and M1 receptor agonists, for example xanomeline combined with trospium or emraclidine, and trace-amine-associated receptor 1 agonists (TAAR1), for example ulataront. Phase 3 clinical trials of new third generation antipsychotic drugs are presented. Novel antipsychotic drugs with a partial agonism at D2 and D3 receptors improve positive and negative schizophrenia symptoms as well as cognitive symptoms better than second generation antipsychotic drugs. Besides, they are well tolerated. M4 and M1 receptor agonists, i.e. xanomeline combined with trospium or emraclidine, and TAAR1 agonists, i.e. ulataront, have promising results in clinical trials; they well improve negative schizophrenia symptoms and cognitive functions. Phase 3 clinical trials offer promising results for anti-dopaminergic and non-anti-dopaminergic novel antipsychotic drugs. These new non-anti-dopaminergic antipsychotic drugs better emend negative schizophrenia symptoms, and they better improve cognitive functions than second-generation antipsychotic drugs. Promising new antipsychotic drugs are cariprazine, brexipiprazole, lumateperone, ulataront, and xanomeline combined with trospium. Although phase 3 clinical studies are not yet completed, they showed a therapeutic effect superior to those achieved by second-generation antipsychotic drugs. They are tolerated very well, and they better treat negative schizophrenia symptoms and improve cognitive functions.

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 study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

In the ambitiously evolving construction industry of Kuwait, characterized by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. This research paper introduces a groundbreaking deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This meticulous analysis allows for the detection of deviations that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Additionally, our findings proactively sketch the future market trajectory and facilitate anomaly detection, thereby contributing substantially to the academic discussion on integrating machine learning into construction management. This research offers valuable insights for professionals within Kuwait, aiming to harness deep learning techniques to boost operational efficiency and strategic foresight in the construction sector.

The bivalve mollusc Pinna nobilis, endemic to the Mediterranean Sea, since 2016 is vanishing from the whole basin because of an infection by multiple pathogens that caused mass mortality events. In the Eastern Mediterranean some little populations seem to be resistant to the infection. These individuals could represent the only possibility for the species to recolonize now desert habitats. Thus, according to the recommendations of IUCN, the looking for living specimens of P. nobilis is a priority. As a result of a monitoring activity carried out in 2022-2024, along the Ionian cost of Apulia, in Southern Italy, the species has completely disappeared leaving only empty shells. Concurrent to the vanishing of P. nobilis, its congeneric P. rudis, resistant to the infection, seems to be taking advantage, becoming more common and colonizing habitats once exclusive of P. nobilis. In fact, from different areas of the Mediterranean, the sightings of P. rudis are increasing, together with the discovering of individuals exhibiting morphological traits that are a mixing between the two species. In some cases, these morphological features are not easy to detect, anyway the presence of these hybrids, resistant to the infection, is important for the conservation of the species.

Prochlorococcus, a cyanobacteria genus of the smallest and most abundant oceanic phototrophs, encompasses ecotype strains adapted to high-light (HL) and low-light (LL) niches. To elucidate the adaptive evolution of this genus, we analyzed 40 Prochlorococcus marinus ORFeomes, including two cornerstone strains, MED4 and NATL1A. Employing deep learning with robust statistical methods, we detected new protein family distributions in the strains and identified key genes differentiating HL and LL strains. HL strains harbor genes (ABC-2 transporters) related to stress resistance, such as DNA repair and RNA processing, while LL strains exhibit unique chlorophyll adaptations (ion transport proteins, HEAT repeats). We report variable, depth-dependent endogenous viral elements in the 40 strains. We constructed the ORFeomes of MED4 and NATL1A covering 99 % of the annotated protein-coding sequences of the two species, totaling 3976 cloned, sequence-verified ORFs. These comparative genomics analyses, paired with MED4 and NATL1A ORFeomes, will facilitate future genotype-to-phenotype mappings and systems biology exploration of Prochlorococcus ecology.

This article presents a novel computational approach to visualizing memristor dynamics within a simulated neural network. Memristors, known for their ability to emulate synaptic plasticity due to their variable resistance characteristics, are key components in neuromorphic computing and hold potential for advancing our understanding of neural processes. Our study introduces a sophisticated memristor model that incorporates non-linear resistance changes, simulating the complex behavior of synaptic connections in a neural network.The neural network, consisting of multiple interconnected neurons with memristor-based synapses, is subjected to a series of electrical stimuli. Each memristor's resistance is modulated in response to the applied voltage, mimicking the synaptic weight adjustments that occur during learning and memory formation in biological neural networks. To effectively illustrate these changes, we employ a high-contrast color mapping scheme, where the varying resistance of each memristor is represented by distinct colors, providing a clear and intuitive visualization of synaptic modifications over time.Our simulation runs through multiple iterations, demonstrating how synaptic weights evolve in response to different input patterns. The use of an extended voltage range and increased scaling factors ensures pronounced changes in memristance, enhancing the visibility of synaptic adaptations. The resulting visualizations offer a compelling representation of how memristors can mimic the dynamic nature of biological synapses, contributing to the field of neuromorphic engineering and deepening our comprehension of neural mechanisms underlying learning and memory.This work not only showcases the potential of memristors in simulating neural behavior but also provides a valuable educational tool for illustrating complex concepts in neuroscience and neuromorphic computing. The insights gained from this study pave the way for further exploration into the development of advanced neural network models and the design of memristor-based computing systems.

To address the challenge of recognizing ship states accurately amidst the complexities of marine environments, this study proposes a novel ship state recognition approach leveraging a graph convolutional neural network (GCNN). Initially, the method extracts canonical and efficient ship motion trajectories from AIS data. Subsequently, a state recognition network tailored for ship motion trajectories is devised and implemented employing graph convolution. Notably, the accuracy of this model is enhanced through the introduction of novel weights and optimization of the Adj parameter. Experimental evaluations conducted on a ship state dataset demonstrate significant performance improvements. Specifically, the proposed recognition network achieves a recognition accuracy of 98.3% for regulated ship trajectories, marking an impressive 8.4% enhancement over traditional convolutional neural networks. This advancement holds promise for enhancing ship state recognition accuracy across diverse maritime applications including maritime supervision, navigation safety, and ship management.

Immunotherapy, particularly Immune Checkpoint Blockade (ICB), has demonstrated significant efficacy in treating melanoma in recent years. However, accurately predicting treatment success and avoiding ineffective therapies remains an unresolved challenge. Therefore, this study aims to develop statistical models utilizing neural networks to forecast the effectiveness of immune checkpoint therapies for melanoma patients. Our models primarily rely on Artificial Neural Networks (ANN) to anticipate both Overall Survival (OS) and Progression-Free Survival (PFS) among melanoma patients undergoing anti-CTLA4 and anti-PD1/anti-PDL1 therapy. We incorporate gene expression data, measured in Transcripts per Million (TPM), derived from bulk tumor RNA-sequencing datasets. Additionally, clinical variables such as gender, age, and treatment type are factored into our analysis. The ANN underwent optimization to attain the highest feasible precision in anticipating the predetermined survival outcome. Issues stemming from high-dimensional data, such as overfitting, were tackled through regularization and feature selection methods. Consequently, the ANN-based model incorporating feature selection exhibited the capacity to forecast survival (PFS) in response to ICB therapy with a maximal precision of 86%. Conversely, the ANN lacking feature selection but incorporating regularization achieved accuracies of up to 72% for PFS and 71% for OS, correspondingly. In order to confront the challenge posed by limited patient samples and to assess replicability, the model underwent training and validation based on the amalgamation of all five datasets. However, this amalgamation failed to enhance predictive performance, necessitating further investigations.

This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

Throughout the evolution of machine learning, the size of models has steadily increased as researchers strive for higher accuracy by adding more layers. This escalation in model complexity necessitates enhanced hardware capabilities. Today, state-of-the-art machine learning models have become so large that effectively training them requires substantial hardware resources, which may be readily available to large companies but not to students or independent researchers. To make the research on machine learning models more accessible, this study introduces a size reduction technique that leverages stages in Pyramid Training and Similarity Comparison. Our results demonstrate that pyramid training can reduce model complexity while maintaining accuracy of conventional full-sized models, offering a scalable and resource-efficient solution for researchers and practitioners in hardware-constrained environments.

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLME-Resnet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial and spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3\%) and Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and adjustment of treatment, by providing physicians with insights into the underlying pathological organization of gliomas.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real-time. Through precise prediction of the PV and wind power output, the proposed approach guarantees optimum scheduling. The precise prediction of the PV and wind power are achieved by employing a predictor of Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational cost of the hybrid power plants, and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the proposed dispatch engine feasibility. A real-time measurement data pertaining to load, wind, and PV power outputs is obtained from different locations in the Sultanate of Oman. The real-time data is utilized to predict the future output power from PV and the Wind farm over 24 hours. The predicted powers are then used in combination with a PV-Wind-DG-ESS-Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed Feed-Forward Neural Network’s (FFNN) powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce plant running expenses.

Digital twin technology is revolutionizing traditional manufacturing paradigms. In modern manufacturing systems, digital twin technology is fraught with challenges due to the scarcity of labeled data. Specifically, existing supervised machine learning algorithms, with their reliance on voluminous training data, find their applicability constrained in real-world production settings. This paper introduces an unsupervised 3D reconstruction approach tailored for industrial applications, aimed at bridging the data void in creating digital twin models. Our proposed model, by ingesting high-resolution 2D images, autonomously reconstructs precise 3D digital twin models without the need for manual annotations or prior knowledge. Through comparisons with multiple baseline models, we demonstrate the superiority of our method in terms of accuracy, speed, and generalization capabilities. This research not only offers an efficient approach to industrial 3D reconstruction but also paves the way for the widespread adoption of digital twin technology in manufacturing.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

Sublobar resection is a standard surgical procedure for small-sized non-small cell lung cancer (NSCLC). However, clinical role of adjuvant chemotherapy for small-sized NSCLC with pathological lymph node (LN) metastasis after sublobar resection is unknown. National Cancer Database was queried for NSCLC patients between 2004 and 2018. Eligibility included sublobar resection with pathological LN metastasis, R0 resection, Charlson comorbidity score = 0, clinical stage T1a-b, and tumor size ≤ 20 mm. Kaplan-Meier method with log-rank test and multivariable Cox proportional hazards analyses were used for assessing survival. Samples were evaluated before and after propensity score matching (PSM) with respect to age, sex, histologic type, and pathological LN status. Of 810 patients who met the eligibility criteria, 567 (70.0%) underwent adjuvant chemotherapy. After PSM, patients with adjuvant chemotherapy had a significantly longer survival than those without (median survival: 64.3 vs. 34.0 months, hazard ratio for death: 0.61, p < 0.0001). Multivariate analyses after PSM showed that younger age (p = 0.0206), female (p = 0.0005), and adjuvant chemotherapy (p < 0.0001) were independent prognostic factors for longer survival. Adjuvant chemotherapy has a prognostic impact in patients with small-sized NSCLC and pathological lymph node metastasis who undergo sublobar resection.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

This paper presents a methodology for the integration of Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to assess the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the global increase in the aging population, there is an increased demand for housing solutions tailored to the needs of elderly individuals. Focusing on the importance of the early phase of design, this study aims to improve the process of making efficient decisions by providing a comprehensive assessment of the life cycle costs associated with AIP homes. The study introduces a semi-automated model for the economic evaluation of AIP homes, enabling the estimation of costs throughout the houses’ entire life cycle, from design and construction to operation, maintenance, and eventual renovation or disposal. The said model facilitates the exploration of the long-term economic feasibility of design’s related decisions with an emphasis on the importance of considering the life cycle costs early during the design process to optimize the functionality and economic viability. By investing in accessible and universal design features upfront, the initial costs for modifications can lead to long-term savings by reducing the need for extensive retrofits. The model can easily do comparison between different design alternatives in terms of their lifecycle costs, allowing designers to assess the financial impact of using important features in their design such as wider doorways, accessible bathrooms, and elevators. Overall, this study provides valuable insights for designers and homeowners about the economic aspects of designing AIP homes as a support for efficient decision-making during the early stages of the design process.

We solve the problem of Apollonius by applying the Lorentz transformation within the frame- work of special relativity to go to a frame of simultaneity, where the solutions are the circum- center. We transform back to get the solutions in a general frame. We show that the solutions in a general frame are the foci of the ellipse going through the centers of the given 3 circles.

Millions of children in developing countries face preventable deaths due to inadequate vaccination and malnutrition, in part due to insufficient monitoring and the absence of official identification. A reliable fingerprint recognition system can be a practical solution to address this issue. However, the scarcity of longitudinal fingerprint datasets for young children leads to unresolved questions regarding the earliest age for fingerprint biometric use, the frequency of enrollment required for reliable recognition, and the methods to accommodate agerelated changes. A few recent studies introduced high-resolution fingerprint scanners and showed promising recognition performance for young children. However, these studies were conducted on a small dataset over a shorter period with limited diversity; further evaluation of their finding is essential. This study assessed the effectiveness of a high-resolution contactless scanner in a controlled, diverse longitudinal dataset of children (0-15 years). Our results indicate that infants can be enrolled at five days old and reliably recognized after two months with a TAR= 100% @ FAR = 0.1%, and children aged 4-15 years can be recognized after one year with a TAR= 98.72% @ FAR = 0.1%.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

We used light, electron microscope and teased fiber autoradiography to show that myelinating Schwann cells synthesize phospholipids and proteins in perinuclear cytoplasm and superficial cytoplasmic channels or Cajal bands (SCC/CB). Neither phospholipid nor protein syntheses occur in paranodal loops, Schmidt-Lanterman incisures or adaxonal cytoplasm. Autoradiographic studies also show that in support of local protein synthesis, RNAs move from the nucleus into perinuclear cytoplasm and out along SCC/CB. Unlike other phospholipids, phosphatidylinositol synthesis occurs extensively in axons. This finding indicates differences in the distributions of phospholipid synthesizing enzymes in smooth endoplasmic reticulum of Schwann cell processes and axoplasm.Autoradiographs of nerves at longer survival times show that tritiated phosphatidylcholine and fucose-labeled glycoproteins move from synthetic sites to outermost myelin lamellae. From there, phosphatidylcholine spreads inward reaching innermost lamellae of thicker sheaths in days. Coupled with retention of fucose glycoproteins in outer myelin lamellae is the presence of fucose glycoprotein synthesized during early myelination in inner lamellae of thicker sheaths, a result consistent with growth of myelin sheaths from inner lamellae out. In this review, we revisit these studies because they provide temporal and spatial perspectives needed in understanding dynamic aspects of myelinogenesis and how myelinating cells respond to injury and from altered gene expression.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Nitrogen is one of the most important macronutrients for crops, and in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and those with a correlation ≥ 0.7 were selected for subsequent use in Random Forest, Gradient Boosting, and Artificial Neural Networks to determine their relationship with nitrogen levels measured in the laboratory. Random Forest showed no relationship, yielding an R2 of zero, whereas Artificial Neural Networks yielded the best results with an R2 of 0.93. Thus, it is reliable to estimate nitrogen levels using this algorithm by feeding it with data from TCARI, MCARI, TCARI/OSAVI, and MCARI/OSAVI, assisted by technological tools.

According to Young Minds [1] ‘everyone gets angry sometimes’. Their website offers a number of de-escalating strategies, including staying calm, managing responses and setting limits to help young people who most deem resistive. Yet, Young Minds are not alone because such logical advice is ubiquitous in the literature about challenging behaviour despite the fact that in our experience when faced with high states of arousal, most young people tend to act first and deal with the consequences later. It’s not that they are stupid or non-caring, but they are human capable of great feats as well as stupidity. The same is true for any claims that solution focused (SF) conversations can put right the several decades of psychological theory suggesting resistance requires logic and better cognition. By giving the correct thinking skills, young people will walk away from risk, avoid physical confrontation, handle challenging situations like logical thinkers, and generally discount that the process of growing up is part of the challenge. It is with this in mind that over recent years in our current SF practice (with staff group supervision) we have examined the concept of ‘resistance’ and how it can be put to use as a process of collaboration. To do this, we have revisited SF theory that preoccupied many of its pioneers during the 1980’s and 1990’s and attempted to make it useful for the early 2020’s for professionals having conversations with young people, who, in traditional models, are labelled challenging. We want to introduce key differences of solution focused practice and how Anita started to formulate a 6D-SF model (details, dynamics, dimensions, dispositions, dislocations, descriptions) for contemplating how groups of professionals relate to each other and are triggered by challenging behaviour. We do not claim to have proof, logic or exactness on our side, but we’re happy to suggest how our resistance mirrors what many of the staff teams feel and describe when working through their work.

Strong-aroma Baijiu is a traditional Chinese spirit distilled from grains that undergo solid-state fermentation. At the end of each fermentation cycle, a significant quantity of Huangshui accumulates at the bottom of the fermentation pit, constituting one of the primary by-products in the brewing process of strong-flavor liquor. In this report, the chemical component analysis of Huangshui obtained from a strong-flavor liquor factory revealed a diverse array of organic acids, alcohols, esters, starch, reducing sugars, amino acids, and other compounds present in Huangshui. Additionally, two specialized solid-state fermentation models were developed to investigate the formation process of Huangshui. Our findings demonstrate a positive correlation between the yield of Huangshui and the initial moisture content of fermented grains. It is concluded that Huangshui is the percolate produced during the solid-state brewing process of strong-flavor liquor, with its water content originating from the initial moisture of fermented grains.

Gilbert's syndrome (GS) is a common hereditary condition characterized by mild increases in serum bilirubin levels due to inherited defects in bilirubin metabolism. This review, based on data from peer-reviewed articles and journals spanning from 1977 to January 2024, provides a comprehensive overview of over four decades of research on GS. Early studies primarily focused on defining the clinical and genetic characteristics of the syndrome, laying the foundation for subsequent investigations. More recent research has delved into the genetic mechanisms underlying the reduced expression of bilirubin UDP-glucuronosyltransferase, which has significantly enhanced our understanding of the pathogenesis of GS. Furthermore, recent studies have shed light on the clinical implications of GS, including its association with non-alcoholic fatty liver disease and mortality risk, highlighting the complex interplay between genetic factors, bilirubin metabolism, and clinical outcomes. Despite its generally benign nature, thorough research is essential to fully understand the impact of GS on patients' lives. This review emphasizes the importance of ongoing research to deepen our understanding of GS and its broader implications.

The space exploration landscape has transformed dramatically over the past several decades, with a significant rise in the number of annual launches. Key factors driving this increase include technological advancements, the miniaturization of satellites, and the surge in commercial satellite deployments for communication, Earth observation, and remote sensing. This paper examines the evolution of space activities from the launch of Sputnik 1 in 1957 to recent developments in commercial spaceflight and international collaborations. The paper discusses notable milestones in space exploration history, including early space missions, Apollo Moon landings, and the development of space stations like the International Space Station. It highlights the shift from state-led space programs to a more diverse landscape, characterized by private sector involvement and international partnerships. The "Space 4.0" phase, beginning around 2006, has seen the entry of commercial players like SpaceX, leading to a significant upsurge in orbital launches. Statistical data reveals an upward trend in launch activity, with over 200 launches in 2023 alone, driven by large satellite constellations like SpaceX's Starlink and the steady growth of China's space program. The increasing launch rate poses challenges such as space debris, emphasizing the need for mitigation strategies. Looking ahead, the paper projects a continued increase in launch activity, with over 150 launches expected annually by 2026 and 200-300 launches by 2040. Commercial space ventures, deep space exploration, lunar activities, and space tourism drive this growth. The paper concludes by emphasizing the importance of addressing space debris and promoting the long-term sustainability of space activities, which is crucial as humanity's presence in space continues to expand.

Ten new Schiff bases, including thiocarbohydrazone derivatives (1–10), were synthesized using an efficient, uncomplicated, and environmentally friendly approach using the reaction of alkyl-substituted aldehydes. The thiocarbohydrazones that were obtained were derived from o-phthalaldehyde. The chemical structures of the compounds were identified by the utilization of UV-Vis spectroscopy, Fourier transform infrared (FT-IR), ¹H nuclear magnetic resonance (¹H NMR), ¹³C nuclear magnetic resonance (¹³C NMR), and elemental analysis methods. The thermal stability of thiocarbohydrazones was examined using thermogravimetric analysis (TGA-DSC). Investigations were conducted on urease inhibition tests, pharmacokinetic property research, and molecular docking. The collection of samples with the urease enzyme revealed significant molecular docking findings, with MolDock scores ranging from -153 to -189. The compounds have the potential to be effective drug candidates for treating certain diseases, as indicated by the consistent results of molecular docking, urease inhibition tests, and pharmacokinetic characteristics investigations.

The rapid advancement of artificial intelligence (AI) and robotics has led to significant progress in various medical fields, including interventional radiology (IR). This review focuses on the research progress and applications of AI and robotics in IR, including deep learning (DL), machine learning (ML), and convolutional neural networks (CNNs) across specialties such as oncology, neurology, and cardiology. To ensure the breadth and depth of this review, we implemented a systematic literature search strategy, selecting research published within the last five years. We conducted searches in databases such as PubMed and Google Scholar to find relevant literature. Special emphasis was placed on selecting large-scale studies to ensure comprehensiveness and reliability of the results. We discuss the role of AI techniques used before, during, and after intervention treatments and the advantages of robotics in achieving precise operations and reducing intra-operative risks. Challenges faced in the implementation of these techniques are also addressed. Finally, we explore the future trends and potential applications of AI and robotics in interventional therapy, highlighting their potential to improve patient treatment outcomes and quality of life.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Background/Objectives: Although the role of F-18 FDG PET/CT imaging is well established in oncology, its diagnostic value in routine monitoring for recurrent colorectal cancer (CRC) is still controversial. The aim was to evaluate the diagnostic value of FDG PET/CT in detecting recurrent CRC in correlation with CEA, CA 19-9 levels, and conventional imaging modalities (CIM). Methods: Between 2009 and 2023, we retrospectively studied 134 CRC patients referred for FDG PET/CT imaging on the suspicion of recurrence based on elevated CEA and/or CA 19-9, and/or equivocal radiological findings. According to the Institutional Tumor Board CRC protocol, after initial treatment which was dependent on TNM stage (neoadjuvant therapy, primary resection or adjuvant treatment), patients underwent a standard 5-year surveillance including CEA and CA 19-9 measurements, CIM, and colonoscopy every 6 months. The statistics including univariate and multivariable analyses were conducted using the IBM SPSS 20.0 statistical software. P-values <0.05 were considered statistically significant. Results: Recurrent CRC was confirmed in 54/134 (40.3%) patients with elevated tumor markers. FDG PET/CT shows high diagnostic performance in detecting recurrent CRC with sensitivity, specificity, PPV, NPV, and accuracy of 94.4%, 82.5%, 78.5%, 95.7%, and 87.3%, respectively. The CEA shows a high sensitivity of 98.1%, but both low specificity and accuracy of 15% and 48.5%, respectively. The sensitivity, specificity, and accuracy, for CA 19-9 and CIM for the correct diagnosis in CRC patients were: 44.4%, 67.5%, 58.2%, and 51.9%, 98.8%, 79.9%, respectively. The AUC for FDG PET/CT, elevated CEA levels, CIM, and elevated CA 19-9 levels was 0.885 (95% CI: 0.824-0.946; p<0.001), 0.844 (95% CI: 0.772-0.916; p<0.001), 0.753 (95% CI: 0.612-0.844; p<0.001); and 0.547 (95% CI: 0.442-0.652; p=0.358). Univariate analysis shows that both FDG PET/CT- and CIM-positive results were highly associated with CRC recurrence (p<0.001 and p<0.001, respectively), while gender, mucinous tumor type, presence of initial lymph node metastasis (N+), and presence of initial distant metastasis (M+) have no significance (p=0.211, p=0.158, p=0.583, and p=0.201, respectively). Our multivariate analysis shows that independent predictors for CRC recurrence are: positive FDG PET/CT scans (p<0.001), positive CIM results (p=0.001) and elevated CA19-9 levels (p=0.023). Although CA 19-9 was not detected as a statistically significant predictor in the univariate analysis (p=0.358), in a multivariate analysis it was recognized as a significant predicting factor in detecting CRC recurrence (p=0.023). Conclusions: FDG PET/CT shows a high diagnostic efficacy in CRC recurrence detection, in correlation with CEA levels, CA 19-9 levels, and CIM. This imaging modality should be routinely integrated into the postoperative follow-op in patients with elevated tumor markers.

Antimicrobial resistance (AMR) poses a critical global health threat, leading to approximately 1.27 million deaths annually from infectious diseases resistant to existing antibiotics. Factors such as antibiotic misuse and environmental contamination have accelerated AMR, with the COVID-19 pandemic exacerbating the issue due to increased antibiotic usage. However, the development of new antibiotics has stagnated, with only a few approved in recent years, and many facing resistance. Among the priority pathogens identified by the WHO, Pseudomonas aeruginosa stands out as a significant concern due to its multidrug resistance and virulence. P. aeruginosa infections are challenging to treat due to intrinsic and acquired resistance mechanisms, as well as its ability to form biofilms. Additionally, P. aeruginosa produces a plethora of virulence factors, including proteases, siderophores, and toxins, further complicating treatment. Quorum sensing (QS) systems play a crucial role in regulating bacterial physiology and virulence factor expression. P. aeruginosa employs three QS systems, making it an attractive target for quorum quenching (QQ) strategies. Enzymes such as AHL lactonases, acylases, and oxidoreductases have shown promise in inhibiting QS, thus reducing virulence without impacting bacterial growth. Furthermore, natural QS inhibitors produced by various organisms offer potential therapeutic alternatives. Despite promising results in vitro, translating QQ strategies to clinical settings remains challenging. Nevertheless, QQ represents a novel approach in combating multidrug-resistant infections and warrants further investigation as a complementary therapeutic strategy alongside traditional antibiotics. This review summarizes enzymes with Qurorum Quenching activity evaluated against the pathogen P. aeruginosa and their potential applications in combination therapies.

Verification of inclusivity of molecular detection methods gives indications on the reliability of viral infections diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed to select inclusive probe based reverse transcription quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, detection methods previously described were re-evaluated in silico for specificity and inclusivity. Based on this evaluation, selected methods were modified or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared to previously described tests and two viral pathogens in a single PCR reaction.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. With reference to the industrial sector, the greatest need is the decarbonisation of industries energy-intensive, such as producers of iron and steel, aluminium, cement, petrochemicals, paper, etc. In particular, the steel industry is a hard to abate high-emissivity industry which due to chemical processes and high thermal demands (often above 500 °C) contributed to 7.4% of global CO₂ in the year 2021. This paper has explored the potential applications of hydrogen in the hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing how although there is potential for its inclusion as an element for the decarbonization, it is necessary to evaluate the energy balances of the various processes and the methods of hydrogen production very carefully, in order not to risk obtaining a marginal effect.

Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.

Models of quantum measurements are presented, including observers subject to the laws of quantum mechanics. Observers perform multiple measurements in an attempt to disprove the collapse hypothesis. It is shown that under reasonable assumptions the effort cannot succeed. Under a unitary evolution the end state is a correlation between the result of the first measurement and the result(s) of the others, consistent with the observer's collapse hypothesis. The observer may conclude that the laws of nature include a non-unitary collapse mechanism, even if one does not actually exist. Discard of information is the crucial factor which distinguishes entanglement from measurement.

The first member of the family, visual arrestin-1, was discovered in the late 1970s. Later the other three mammalian subtypes were identified and cloned. The first described function was regulation of GPCR signaling: arrestins bind active phosphorylated GPCRs, blocking their coupling to G proteins. It was later discovered that receptor-bound and free arrestins interact with numerous proteins, regulating GPCR trafficking and various signaling pathways, including those that determine cell fate. Arrestins have no enzymatic activity, they function by organizing multi-protein complexes and localizing their interaction partners to particular cellular compartments. Today we understand the molecular mechanism of arrestin interactions with GPCRs better than the mechanisms underlying other functions. However, even limited knowledge enabled the construction of signaling-biased arrestin mutants and extraction of biologically active monofunctional peptides from these multifunctional proteins. Manipulation of cellular signaling with arrestin-based tools has research and likely therapeutic potential: re-engineered proteins and their parts can produce effects that conventional small molecule drugs cannot.

The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

Major depressive disorder (MDD) and substance use disorder (SUD) are of immense medical and social concerns. Although significant progress on neuronal involvement in mood and reward circuitries has been achieved, only relatively recently the role of glia in these disorders have attracted attention. Detail understanding of the glial function in these devastating diseases could offer novel interventions. Here, following a brief review of circuitries involved in mood regulation and reward perception, specific contribution of the neurotrophic factors, neuroinflammation and gut microbiota to these diseases are highlighted. In this context, the role of specific glial cells (e.g., microglia, astroglia, oligodendrocytes and synantocytes) on phenotypic manifestation of MDD or SUD are emphasized. In addition, potential use of this knowledge in developing novel therapeutics is touched upon.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Coal bottom ash (CBA) is a byproduct of coal-burning power plants. This material can be used as a fine aggregate replacement in concrete to reduce global natural material depletion. Concrete usage is increasing rapidly; subsequently, the industry’s carbon footprint is increasing. Using CBA in the construction industry will reduce the technical and economic problems associated with power plants by reducing solid waste. This project aimed to determine if adding new sustainable materials, such as CBA, to concrete will reduce natural raw material usage, energy consumption, and greenhouse gas emissions while improving performance. This study included two projects: comparing increased CBA content to the two-control project and determining the optimum content based on the compressive strength. The optimum CBA content is 50%, which reduces fine aggregate usage in a concrete mix by 50% while maintaining equivalent or better concrete strength than a pure Portland concrete mix without CBA. The air content of the concrete mix containing % CBA decreased with increased CBA content and compressive strength, possibly due to the spacing factor of the voids in the mix and CBA’s porous and fineness behavior. The CBA optimum content had a unit weight lower than the controls for both projects, which makes the CBA mix a lightweight concrete. Using this material can significantly reduce natural material usage and environmental harm by reducing CBA waste disposal and improving concrete performance.

Mechanical equipment is composed of several parts, the interaction between the parts exists throughout whole life cycle leads to the widespread phenomenon of fault coupling and the diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the topological structure of graph neural network is used to describe the correlation of coupling faults and multiple fault types are regarded as network nodes respectively. The fault features are fused and extracted by graph convolution to achieve the classification of coupling faults. In this paper, dynamic vertexes are defined in data topology and the parameters of DIGNN classification model are optimized by changing the fault type of dynamic vertexes during training stage. The data is loaded into dynamic vertexes for classification and analysis during testing stage. The one-dimensional vibration data is converted into two-dimensional time-frequency domain data by wavelet transform making the input features of the model interpretable to reduce the uncertainty of model training. The data topology is then fed into DIGNN for fault classification, and GCN's node aggregation gives interpretability to each layer of network data processing. The method proposed in this paper can realize accurate diagnosis of independent faults on the data set, and can effectively judge the coupling mode of coupling faults, which is suitable for coupling fault analysis of mechanical equipment.

Mesenchymal stem cells due to their multipotent ability are considered one of the best candidates to be used in regenerative medicine. The most used source of those stem cells long the last decades was the bone marrow, despite its limited cell resources and the painful and very invasive surgical treatment. Therefore, the scientific community investigated many alternative sources for the harvesting of these stem cell subpopulations. Adipose tissue attracted the biggest interest thanks to special features, such as great tissue abundance, easy availability, and in the end the large amount of positive mesenchymal stem cells. Once it was verified that these stem cell subpopulations possess the same bone marrow differentiation potential, it became evident that they would be the ideal candidate to be used in regenerative and tissue engineering applications. Even the regenerative use of these cells long become a sensitive point because not all the acquired phenotypes resulted in the best source for plastic procedures. As documented in the literature, the ideal fillers for regenerative is represented by stem cell population. Therefore, it became crucial to investigate any kind of biological mechanism on the differentiative ability of those mesenchymal stem cells, especially their regulative factors. One of the most studied and important regulators are the miRNAs. Indeed, miRNA play a crucial role in the differentiation process of adipose stem cell cultures isolated from patients or obtained by commercial cell lines toward osteocytes, chondrocytes, and adipocytes differentiated populations.

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. However, traditional predictive models have often overlooked the spatial interdependencies that play a vital role in shaping housing prices. This study applies Graph Neural Networks (GNNs) on Open Statistics Knowledge Graphs to model spatial dependencies and predict house prices across Scotland’s 2011 data zones. To this end, integrated statistical indicators are retrieved from the official Scottish Open Government Data portal. The three representative GNN algorithms employed - ChebNet, GCN, and GraphSAGE - demonstrate higher prediction accuracy than traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP). In addition, local and global explainability are employed to increase transparency and trust in the predictions made by the most accurate GNN - GraphSAGE. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainaibility results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both global surrogate model and the SHAP approach underscored the Comparative Illness Factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

The dynamic landscape of AI adoption and its impact on employment in Malaysia demands a multifaceted approach to navigate the challenges and harness the opportunities presented. While the government's commitment to fostering AI adoption through initiatives like the National AI Framework and the Malaysia Digital Economy Blueprint is commendable, it is imperative to address concerns regarding job displacement and ensure inclusive growth. This study identifies the pressing research problem of workforce readiness for the AI-driven future, emphasizing the need to explore strategies for reskilling and upskilling affected workers. By employing critical lenses such as Marxist, feminist, and postcolonial perspectives, scholars highlight the power dynamics and societal implications inherent in AI deployment, guiding the development of policies and interventions that prioritize workers' interests and address social inequalities. Moving forward, a comprehensive approach combining policy interventions, educational reforms, and inclusive strategies is essential to maximize the benefits of AI adoption while mitigating its potential risks. Longitudinal studies, interdisciplinary research, and comparative analyses will further advance our understanding and inform evidence-based policymaking in this evolving field. Ultimately, by prioritizing workforce preparedness, social equity, and sustainable development, Malaysia can position itself as a leader in the AI-driven global economy, fostering a resilient, inclusive, and prosperous society for all.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

Given the increased provision of international exchange programmes in higher education, this paper examines students’ perceptions of their learning process in one Erasmus+ programme in Food Innovation and Product Design. Using evaluation model of international exchange programme and triple helix model as the analytical framework, students’ testimonials from 2015–2021 were analysed using a critical discourse analysis. EU and non-EU students had similar perceptions of learning outcomes related to knowledge of the food innovation industry, but non-EU students had a stronger sense of awareness of both the international context and multicultural interaction.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

Partial Differential Equations (PDEs) constitute a fundamental framework for modeling various physical phenomena across diverse fields such as physics, engineering, and finance. Solving PDEs accurately and efficiently remains a significant challenge, particularly when dealing with complex systems or high-dimensional data. In this paper, we present a novel approach based on deep learning techniques to investigate the effective factors in solving PDEs. Our methodology leverages the expressive power of deep neural networks to learn and represent the underlying relationships between input parameters and the solutions of PDEs. By incorporating domain knowledge into the network architecture and training process, our approach aims to provide insights into the key factors influencing the solution of PDEs. We demonstrate the effectiveness of our method through numerical experiments on various PDEs, showcasing its capability to identify important features and improve predictive accuracy. Our findings suggest that deep learning techniques offer a promising avenue for understanding and analyzing the complex dynamics inherent in PDEs, paving the way for enhanced computational methods in scientific and engineering applications.

We propose a novel moving median estimator specifically designed for online detection of threshold crossings in multi-channel signals, such as extracellular neural recordings. This estimator offers two key advantages: a reduced sensitivity to outliers and the elimination of memory requirements for storing arrival times. Furthermore, its design facilitates parallel implementation on FPGAs, making it ideal for real-time processing of multi-channel recordings.

This article conducts a bibliometric analysis focusing on the influence of globalization and technology in the academic context. Previous studies have focused on analyzing the relationship between the number of research citations and international collaboration, but no study has explored it in the scope of consumer behavior and retail. Leveraging the Scopus database, the research analyzes a significant corpus of 2,111 documents from 1971 to 2022. The findings reveal a distinct surge in academic output related to the topic of technology, reflecting a growing interest in understanding the interaction between technological factors and consumer behavior. The study also explores trends in international collaboration in publications as well as its impact on citation counts.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (&lt;50%), compared to those presented in the related literature where the whole set of channels is used.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Telecoupling, interactions between social-ecological systems across large, often global, distances, drives negative impacts from the forest-based carbon market. Yet, these negative impacts have been underreported; and therefore, likely vastly underestimated. We identify how these unintended negative impacts may occur and provide recommendations for the forest-based carbon market to better account for externalities by prioritising positive social impact, expanding comprehensive ecological outcomes and improving transparency of investments. We call for a carbon market that is designed to account for its global interconnectedness.

The integration of artificial intelligence (AI) into academic settings in Sabah, Malaysia, is transforming education and research landscapes. While AI technologies promise to enhance teaching, learning, and scholarly activities, they also pose challenges such as ethical concerns and skill gaps among academic staff. This study addresses the multifaceted impacts of AI adoption on academicians in Sabah, focusing on two key research questions: the impact of AI-based intelligent tutoring systems on student engagement, learning outcomes, and educational equity, and the challenges and opportunities in building academicians' capacity to integrate AI technologies effectively. Through a multidisciplinary approach, incorporating insights from education, computer science, social sciences, and ethics, this research aims to evaluate the effectiveness of AI tools in educational settings and identify strategies for skill development and infrastructure support. The study recommends empirical assessments within Sabah's educational institutions, considering contextual factors and fostering collaborative partnerships with local communities. Professional development programs, collaboration with industry partners, and improved access to AI infrastructure are proposed strategies. Longitudinal studies and interdisciplinary research initiatives are suggested for future research, along with qualitative methods to capture diverse perspectives. Ultimately, by embracing a collaborative and multidisciplinary approach, Sabah's academic community can harness the transformative potential of AI to advance education, research, and scholarship while promoting ethical and inclusive practices.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

Adult neurogenesis involves generation of functional neurons from neural progenitor cells, which have the potential to complement and restore damaged neurons and neural circuits. Therefore, the development of drugs that stimulate neurogenesis represents a promising strategy in stem cell therapy and neural regeneration, greatly facilitating the reconstruction of neural circuits in cases of neurodegeneration and brain injury. Our study reveals that compound A5, previously designed and synthesized by our team, exhibits remarkable neuritogenic activities, effectively inducing neurogenesis in neural stem/progenitor cells (NSPCs). Subsequently, transcriptome analysis using high-throughput Illumina RNA-seq technology was performed to further elucidate the underlying molecular mechanisms by which compound A5 promotes neurogenesis. Notably, comparative transcriptome analysis showed that the up-regulated genes were mainly associated with neurogenesis and the down-regulated genes were mainly concerned with cell cycle progression. Furthermore, we confirmed that compound A5 significantly affected the expression of transcription factors related to neurogenesis and cell cycle regulatory proteins. Collectively, these findings identify a new compound with neurogenic activity and may provide insights into drug discovery for neural repair and regeneration.

Diabetic foot is a serious complication that poses significant risks for diabetic people. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent the imminent ulceration and amputation. The primary method employed for evaluating this sensitivity loss is the 10 g f Semmes-Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this paper, a new equipment designed to address these limitations and enhance the assessment of the sensitivity loss using the 10 g f monofilament is introduced. The proposed system automates metrological verification and evaluation processes, ensuring accurate and reliable results. Furthermore, it simulates the practitioner’s procedure, serving both as a valuable training tool and a means to provide force-feedback information. An analytical model of the monofilament buckling and deflection is also presented. The obtained results showed that the tested monofilaments had a very high error compared to the 10 g f declared by the manufacturers. It is aimed, by introducing this advanced equipment, to improve the precision and reliability of assessing the sensitivity loss. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes such as ulceration and amputation.

Facial Emotion Recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This paper investigates the impact of histogram equalization, data augmentation and model optimization strategies on three well-known FER datasets using pre-trained VGG models. Additionally, this paper showcases the effectiveness of different regularization techniques, callbacks, and learning schedulers in enhancing model performance by conducting extensive experiments. The model evaluation is discussed in terms of the following metrics: Accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG models exhibit state-of-the-art performance compared to more complex conventional transfer learning models resulting in accuracies of 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

This paper introduces a novel architecture—bidirectional optical neural network (BONN)—for providing backward connections alongside forward connections in artificial neural networks (ANNs). BONN incorporates laser diodes and photodiodes and exploits the properties of Köhler illumination to establish optical channels for backward directions. Thus, it has bidirectional functionality that is crucial for algorithms such as the backpropagation algorithm. BONN has a scaling limit of 96 × 96 for input and output arrays, and a throughput of 8.5 × 1015 MAC/s. While BONN's throughput may rise with additional layers for continuous input, limitations emerge in the backpropagation algorithm as its throughput doesn't scale with layer count. Successful BONN-based implementation of the backpropagation algorithm requires the development of fast spatial light modulator to accommodate frequent data flow changes. A two-mirror-like BONN and its cascaded extension are alternatives for multilayer emulation, and they help save hardware space and increase the parallel throughput for inference. An investigation into the application of the clustering technique to BONN revealed its potential to help overcome scaling limits and to provide full interconnections for backward directions between doubled input and output ports. BONN's bidirectional nature holds promise for enhancing supervised learning in ANNs and increasing hardware compactness.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

Being able to express broad families of equivariant or invariant attributed graph functions is a popular measuring stick of whether graph neural networks should be employed in practical applications. However, it is equally important to learn deep local minima of losses (i.e., with much smaller loss values than other minima), even when architectures cannot express global minima. In this work we introduce the architectural property of attracting GNN optimization trajectories to local minima as a means of achieving smaller losses. We take first steps in satisfying this property for losses defined over attributed undirected unweighted graphs with a novel architecture, called Universal Local Attractor (ULA). The latter refines each dimension of end-to-end trained node feature embeddings based on graph structure to track the optimization trajectories of losses satisfying some mild conditions. The refined dimensions are then linearly pooled to create predictions. We experiment on 10 tasks, from node classification to clique detection, on which ULA is comparable with or significantly outperforms popular alternatives of similar or greater theoretical expressive power.

Epithelial barrier damage plays a central role in the development and maintenance of allergic inflammation. The rise in airways epithelial barrier permeability alters the tissue homeostasis and allows the allergen and other external agents penetration. Different factors contribute to barrier impairment, such as eosinophilic infiltrate and allergens proteases action: the eosinophilic cationic proteins and the allergens' proteolytic activity have a central role in contributing to epithelial damage. In the airways, the allergens proteases degrade the epithelial junctional proteins allowing the allergen penetration and the uptake by dendritic cells. The increase in allergen-immune system interactions induce the release of the alarmins and the activation of type 2 inflammatory pathways causing, or worsening, the main symptoms at skin, bowel and respiratory levels. We aim to highlight the molecular mechanisms underlying allergenic proteases-induced epithelial barrier damage and the role of immune response in allergic asthma onset, maintenance and progression. We further will explore clinical and radiological potential biomarkers of airways remodeling in allergic asthma patients.

Though nowadays germanium do not reach the range of popularity than other metals, i.e. rare earth elements, its utility in target industries makes of it also a strategic metal. Though germanium can be find in a series of raw materials, the principal source for its recovery is from secondary wastes of the zinc industry, and also, the recycle of germanium-bearing waste materials is becoming of interest. In this recovery, Hydrometallurgy is performing a key role to the successful achievement of this goal. The present work reviews the most recent applications (2023 and 2024 years) of hydrometallurgical operations on the recovery of germanium from different solid and liquid sources.

Objective To investigate nailfold capillary parameters in community-dwelling individuals aged over 60 years who have hypertension and do not exercise regularly. Furthermore, the study examined the correlations between capillary function and other health-related indicators. Design A single- center pilot trial Setting Faculty of Health, Tsukuba University of Technology, Japan Participants hypertensive community-dwelling elderly people Intervention Microcirculation was observed before and 1 min after arm-curl exercise by means microscopy of capillary with non-exercised limb. Additionally, we examined other health-related indicators. Outcome measures: Acute effects of reperfusion on nailfold density, flow, and diameters. Secondary outcomes included the correlations between microvascular parameters and other health-related indicators. We hypothesized that brief exercise could enhance microcirculation reperfusion and correlate with other health-related parameters.　 Results: There were 20 participants with a mean (SD) age of 67.1 (5.8) years. The capillary flow rate changed from 2.3±6.7 to 2.7±0.2 log µm/s (P

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

The article presents an innovative method for synthesizing radiation patterns efficiently by 1 combining the Taguchi method and neural networks, while validating the results on a 10-element 2 antenna array. The Taguchi method aims to minimize product and process variability, while neural 3 networks are used to model the relationship between antenna design parameters and radiation 4 pattern characteristics. This approach utilizes Taguchi parameters as inputs for the neural network, 5 which is then trained on a dataset generated by the Taguchi method. After training, the network is 6 validated using a real 10-element antenna array. Analytical results demonstrate that this method 7 enables efficient synthesis of radiation patterns with a significant reduction in computation time 8 compared to traditional approaches. Furthermore, validation on the antenna array confirms the 9 accuracy and robustness of the approach, showing a high correlation between predicted performances 10 by the neural network model and actual measurements on the antenna array.In summary, our article 11 highlights that the combined use of the Taguchi method and neural networks, with validation on a 12 real antenna array, offers a promising approach for efficient synthesis of antenna radiation patterns. 13 This approach combines speed, accuracy, and reliability in antenna system design.

Tunicate orthologs in the human genome comprise just 84 genes of the 19,872 protein-coding genes, yet they stand at the base of the Olfactores clade - which radiated to generate thousands of tunicate and vertebrate species. What were the powerful drivers among the 84 that enabled this process? Many of these orthologs are present in gene families. We discuss the biological role of each family and the orthologs’ quantitative contribution to the family. Most important was the evolution of a second type of cadherin. This, a Type II cadherin, had the property of detaching the cell containing that cadherin from cells that expressed the Type I class. The set of such Type II cadherins could now detach and move away from their Type I neighbours, a process which would eventually evolve into the formation of the neural crest, “ the fourth germ layer”, providing a wide range of possibilities for further evolutionary invention. A second important contribution were key additions to the broad development of the muscle and nerve protein and visual perception tool-kits. These developments in mobility and vision provided the basis for the development of the efficient predatory capabilities of the Vertebrata.

Avian influenza viruses (AIVs) are highly contagious respiratory viruses of birds, lead-ing to significant morbidity and mortality globally and causing substantial economic losses to the poultry industry. Since their first isolation in 2013, the Asian-origin H5N1 highly pathogenic avian influenza viruses (HPAI) of clade 2.3.4.4b have undergone unprecedented evolution and re-assortment of internal gene segments and have spread to Asia, Europe, Africa, and America, caus-ing outbreaks in all the poultry categories. Novel epidemiological and pathobiological character-istics, distinct from other clades, are specific of clade 2.3.4.4b viruses. Wild waterfowl, the natu-ral reservoir of AIVs, are frequently found infected with clade 2.3.4.4b viruses, which can also cause high morbidity and mortality in these birds. The sustained clade 2.3.4.4b virus circulation in waterfowl has also led to virus infection in other wild bird species, with implications for the conservation of endangered species. Furthermore, clade 2.3.4.4b viruses have been isolated in var-ious wild and domestic mammals worldwide, and critical mutations related to virus adaptation to mammalian species have been identified, raising concerns about virus spillover to humans. The main clinical signs, and anatomopathological findings associated to clade 2.3.4.4b virus infection in birds and non-human mammals are hereby summarized.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been thought as a universal dogma controlling developmental and pathological processes. More than half a century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. Moreover, EMT and MET effects should not be a cause, but rather a consequence of or an accompanying effect during developmental and pathological processes. EMT and MET represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

The affinity constant, also known as the equilibrium constant, binding constant, equilibrium association constant, or the reciprocal value, the equilibrium dissociation constant (K_d), can be considered as one of the most important characteristics for any antibody-antigen pair. Many methods based on different technologies have been proposed and used to determine this value. However, since a very large number of publications and commercial datasheets do not include this information, significant obstacles in performing such measurements seem to exist. In other cases where such data are reported, the results have often proved to be unreliable. This situation may indicate that most of the technologies available today require a high level of expertise that does not seem to be available in many laboratories. In this paper, we present a simple approach based on standard immunoassay technology that is easy and quick to perform. It relies on the effect that the molar IC₅₀ approaches the K_d value in the case of infinitely small concentrations of the reagent concentrations. A two-dimensional dilution of the reagents leads to an asymptotic convergence to K_d. The approach has some similarity to the well-known checkerboard titration used for the optimization of immunoassays. A well-known antibody against the FLAG peptide, clone M2, was used as a model system and the results were compared with other methods. This approach could be used in any case where a competitive assay is available or can be developed. The determination of an affinity constant should belong to the crucial parameters in any quality control of antibody-related products and assays and should be mandatory in papers using immunochemical protocols.

The increasing use of Convolutional Neural Networks (CNN) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model for classifying images of sericea lespedeza (SL; Lespedeza cuneata) from weed images, four architectures were explored: CNN-Model Variant 1, CNN-Model Variant 2, VGG16, and ResNet50. The CNN-Model Variant 1 demonstrated 100 % validation accuracy, while Variant 2 achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50's steady learning pattern indicated potential for better generalization. A detailed evaluation of these models revealed that Variant 1 achieved a perfect score in precision, recall, and F1-score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. The VGG16 mirrored the behavior of Variant 2, both maintaining around 90% accuracy, but ResNet50's results revealed a conservative approach for class 0 predictions. Overall, Variant 1 stood out in performance, while both Variant 2 and VGG16 showed balanced results. The reliability of CNN model Variant 1 is highlighted by the significant accuracy percentages, which demonstrate its potential for practical implementation in agriculture. Smartphone application for the identification of SL in a field-based trial has shown promising results with an accuracy of 98%-99%. Using a CNN model with batch normalization has the potential to play a crucial role in redefining and optimizing the management of undesirable vegetation in the future.

Sublingual vaccines offer the benefits of inducing mucosal immunity to protect against respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and influenza, while also enabling needle-free self-administration. Previously, we developed a sublingual vaccine formulated with the SARS-CoV-2 receptor binding domain antigen and Poly(I:C) adjuvant in nonhuman primates, cynomolgus macaques. This study examined the immune and inflammatory responses elicited by the sublingual influenza vaccine containing hemagglutinin (HA) antigen and Poly(I:C) adjuvant and assessed the safety of this vaccine in nonhuman primates. The Poly(I:C)-adjuvanted sublingual vaccine induced both mucosal and systemic immunities. Specifically, the sublingual vaccineproduced HA-specific secretory IgA antibodies in saliva and nasal washings, and HA-specific IgA and IgG were detected in the blood. This vaccine appeared to be safe, as judged from the results of blood tests and plasma C-reactive protein levels. Notably, sublingual vaccination neither increased the production of inflammation-associated cytokines—IFN-alpha, IFN-gamma, and IL-17—in the blood, nor upregulated the gene expression of proinflammatory cytokines—IL12A, IL12B, IFNA1, IFNB1, CD69, and granzyme B—in white blood cells. Moreover, DNA microarray analyses revealed that sublingual vaccination evoked both enhancing and suppressing expression changes in genes associated with immune-related responses in cynomolgus monkeys. Therefore, the sublingual vaccine with Poly(I:C) adjuvant is safe and creates a balanced state of enhancing and suppressing the immune-related response.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

The liver diseases are posing serious threat to human health associated with abusive use of allopathy medicines, especially in developing economies like Pakistan. The strengthening of detoxification system using natural products might be a suitable option to minimize the liver problems. In this current research, Zingiber officinale and Azadirachta indica were evaluated for their hepatoprotective role. For the purpose, 20 Sprague Dawley rats were equally divided into four groups i.e., negative control, paracetamol positive control, 2.5% ginger and 2% neem powder, respectively. During 42 days study, the levels of bilirubin and liver enzymes significantly elevated due to liver damage. Although, the values dropped (20-25%) in treatment group. Serum glucose and serum lipid profile increased in positive control group, but reduced (5-10%) in both ginger and neem groups. The serum protein profile declined in liver damage group, however, improved (~5%) in treatment groups. The hematological parameters slightly declined in liver damage group, but increased in ginger and neem groups. Moreover, the white blood cells differential count and renal parameters showed abnormal values in liver damage group, but improved after treatment. This study is promising and highlighted the hepato-protective role of neem and ginger that may open several avenues of research.

Camouflage evaluation typically involves human visual search and detection experiments that are time-consuming and expensive. Hence, there is a need for models that compute camouflage effectiveness from digital imagery. Convolutional neural networks are a powerful tool for automatic object detection and recognition. We investigated whether such a network (YOLO) can also provide a measure of camouflage effectiveness that is related to human perception. To this end, human performance measures of camouflage effectiveness such as detection time and target conspicuity were obtained in observer experiments and compared with the performance of the (standard, i.e. pre-trained) YOLO-V4-tiny algorithm. YOLO provides the probability that a detected object is correctly classified, and this is adopted as our measure of camouflage effectiveness. We compared the YOLO-predicted classification probability for a person in camouflage clothing moving through rural and urban scenes to human detection performance. Overall, camouflage effectiveness predicted by YOLO correlates with human observer performance. At close distances, YOLO’s classification performance appears sensitive to high-contrast shape-breaking elements of a camouflage pattern. This suggests that YOLO may be adapted to assess camouflage effectiveness for a wide range of applications, such as evaluating or optimizing one’s signature and predicting optimal hiding locations in a given environment. Further research is required to fully establish YOLO’s limitations and applicability for this purpose.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% mAP@0.5, which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

A proof is given showing the Collatz Conjecture is true for all positive integers. The proof was possible once the perspective of the proof was changed from looking at the pattern of the positive integers to looking at the conjecture rules. The conjecture rule for even numbers organizes all positive integers into unique sets and the rule for odd numbers interconnects the unique sets into dendritic pathways to “1.” Infinite loops, other than the minor 4-2-1 loop after reaching “1,” and pathways to infinity are shown to be mathematically impossible. The proof predicted a general equation that shows all positive integers ultimately reach a final value of “1," and calculates the values and locations of the odd positive integers during the iterations for each tested positive integer.

Remote sensing technology is crucial for accurate rooftop detection, benefiting urban planning, disaster management, and solar resource estimation. This study employs Efficient Interactive Segmentation (EISEG) to enhance the efficiency of remote sensing image labeling, with a particular focus on rooftop detection. It is necessary to use modern technology because traditional manual labelling methods are labor-intensive and complicated. The study introduces a novel framework on deep learning semantic segmentation models, facilitating an efficient approach to rooftop identification using high-resolution UAV remote sensing datasets. Large dataset of labeled UAV rooftop building images, in which each superpixel region is assigned a binary label indicating rooftop presence. Advanced methods including Asymmetric Neural Network (ANN), Dual At-tention Network (DANet), PP-LiteSeg, and Deeplab3 are implemented for automatic rooftop detection due to their higher performance and advanced architectures. These models are executed on the Baidu deep learning platform PaddlePaddle, generating initial rooftop segmentation maps crucial for estimating photovoltaic resources. The ANN model emerges with the highest accuracy at 96%, followed by DANet at 95.09%, PP-LiteSeg at 94.54%, and Deeplab3 at 81.61%. The outcomes presenting efficient models for automated rooftop identification, and demonstrating the continuous need for improving deep learning techniques in smart and sustainable cities.

This paper examines the transformative journey of Artificial Intelligence (AI) agents from copilots to integral coworkers within diverse sectors. Beginning with a foundational overview, we delve into the historical evolution of AI agents, highlighting key technological milestones and the shift from simple automated tools to sophisticated decision-making systems capable of learning and adapting over time. We explore the capabilities of AI agents, including problem-solving, predictive analytics, and automation, and their applications across healthcare, business, and education, showcasing their role in diagnostics, customer service, and personalized learning, among other areas. We also address the integration of AI agents in the workplace, presenting strategies for collaboration between humans and AI, and outline the challenges and ethical considerations that accompany the development and deployment of AI technologies. A comprehensive tutorial provides insight into the development process of AI agents, emphasizing goal definition, data handling, algorithm selection, and ethical guidelines. We also consider the pathway towards Artificial General Intelligence (AGI) and the societal and ethical implications of widespread AI integration, proposing strategies for equitable and ethical AI use.

India has pledged ambitious initiatives to mitigate its growing economies’ impact on the climate, including a goal of net zero emissions by 2070. The transportation sector is one of the key sources of emissions. Therefore, fostering e-mobility within this sector is a key priority for the Indian government. The lifecycle of LiBs raises numerous concerns regarding its sustainability in production, use of rare materials, and most importantly waste management. Furthermore, this paper will highlight the case for EV battery recycling as a growing sector that also has the potential to foster circularity in the EV sector. Through the introductory section, this paper lays out rationale for battery recycling in a circular EV economy. The subsequent sections discuss the current and emerging battery technologies and their link to recycling methods whilst overviewing domestic and foreign battery recycling companies. Finally, the paper concludes with a gap assessment of the battery recycling ecosystem in India and makes recommendations to further integrate this industry into the EV supply chain. As India steers towards a sustainable future, it is imperative that the country integrates battery recycling to enhance circularity in the EV ecosystem, closing the gap between a growing number of EVs and waste management.