Image denoising is regarded as an ill-posed problem in computer vision tasks that removes additive noise from imaging sensors. Recently, several convolution neural network-based image-denoising methods have achieved remarkable advances. However, it is difficult for a simple denoising network to recover aesthetically pleasing images owing to the complexity of image content. Therefore, this study proposes a multi-branch network to improve the performance of the denoising method. First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images. Subsequently, we integrate two modules into the network, including the Pyramid Context Module (PCM) and the Residual Bottleneck Attention Module (RBAM), to extract salient information for the training process. More specifically, PCM is applied at the beginning of the network to enlarge the receptive field and successfully address the loss of global information using dilated convolution. Meanwhile, RBAM is inserted into the middle of the encoder and decoder to eliminate degraded features and reduce undesired artifacts. Finally, extensive experimental results prove the superiority of the proposed method over state-of-the-art deep-learning methods in terms of objective and subjective performances. Full article

Throughout the nuclear power production process, the disposal of radioactive waste has consistently raised concerns about environmental safety. When the metal tanks used for waste disposal are corroded, radionuclides seep into the groundwater environment and eventually into the biosphere, causing significant damage to the environment. Hence, investigating the adsorption behavior of radionuclides on the corrosion products of metal tanks used for waste disposal is an essential component of safety and evaluation protocols at disposal sites. In order to understand the adsorption behavior of important radionuclides ⁶⁰Co, ⁵⁹Ni, ⁹⁰Sr, ¹³⁵Cs and ¹²⁹I on α-FeOOH, the influences of different pH values, contact time, temperature and ion concentration on the adsorption rate were studied. The adsorption mechanism was also discussed. It was revealed that the adsorption of key nuclides onto α-FeOOH is significantly influenced by both pH and temperature. This change in surface charge corresponds to alterations in the morphology of nuclide ions within the system, subsequently impacting the adsorption efficiency. Sodium ions (Na⁺) and chlorate ions (ClO₃⁻) compete for coordination with nuclide ions, thereby exerting an additional influence on the adsorption process. The XPS analysis results demonstrate the formation of an internal coordination bond (Ni–O bond) between Ni²⁺ and iron oxide, which is adsorbed onto α-FeOOH. Full article

Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated. Full article

Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods. Full article

The microstructure and texture evolution of Cu-Ni-P alloy after cold rolling and annealing at 500 °C was studied by electron backscattering diffraction (EBSD). The equiaxed grain is elongated and the dislocation density increases gradually after cold rolling. The grain boundaries become blurred and the structure becomes banded when the reduction in cold rolling reaches 95%. A typical rolling texture is formed with the increase in deformation amount in cold rolling. The deformation structure gradually disappeared and recrystallized new grains were formed after annealing at 500 °C. The recrystallization nucleation mechanism of Cu-Ni-P alloy at 60% reduction is mainly a bow nucleation mechanism. A shear band begins to form after annealing at 80% reduction. The shear band becomes the preferred nucleation location with the increase in reduction. Most adjacent recrystallized grains growing in the shear band have a twin relationship. Full article

Generative AI refers specifically to a class of Artificial Intelligence models that use existing data to create new content that reflects the underlying patterns of real-world data. This contribution presents a study that aims to show what the current perception of arts educators and students of arts education is with regard to generative Artificial Intelligence. It is a qualitative research study using focus groups as a data collection technique in order to obtain an overview of the participating subjects. The research design consists of two phases: (1) generation of illustrations from prompts by students, professionals and a generative AI tool; and (2) focus groups with students (N = 5) and educators (N = 5) of artistic education. In general, the perception of educators and students coincides in the usefulness of generative AI as a tool to support the generation of illustrations. However, they agree that the human factor cannot be replaced by generative AI. The results obtained allow us to conclude that generative AI can be used as a motivating educational strategy for arts education. Full article

Background/Objectives: Contradictory results have been reported regarding the influence of obesity on the prognosis of atrial fibrillation (AF). The present study aimed to explore the potential association of body mass index (BMI) with the clinical outcomes of hospitalized patients with AF. Methods: In this retrospective, post hoc analysis of the MISOAC-AF randomized trial, 1113 AF patients were included and stratified as the following: underweight (BMI < 18 kg/m²), normal weight (BMI 18–24.9 kg/m²), overweight (BMI 25–29.9 kg/m²), and obese (BMI ≥ 30 kg/m²). The primary outcome was all-cause mortality; the secondary composite outcome was any hospitalization related to AF, heart failure (HF), or stroke. Cox regression analysis, survival analysis, and spline curve models were utilized. Results: Of the patients (median age: 76 years (IQR: 13), male: 54.6%), the majority were overweight (41.4%), followed by obese (33%), normal weight (24%), and underweight (1.6%). During a median 31-month follow-up, 436 (39.2%) patients died and 657 (59%) were hospitalized due to AF, HF, or stroke. Underweight, overweight, and obesity groups were significantly associated with an increased risk of all-cause mortality (p-values 0.02, 0.001, and <0.001, respectively), while overweight and obesity were significantly associated with the composite endpoint (p-values 0.01, <0.001, respectively) compared to normal weight. The spline curve analyses yielded that BMIs > 26.3 and > 25 were incrementally associated with all-cause mortality and the composite endpoint, respectively. A J-shaped relationship between BMI and AF prognosis was deduced. Conclusions: In conclusion, in recently hospitalized AF patients, BMI values outside the normal range were independently associated with poorer prognosis; therefore, it is essential that AF patients maintain a normal weight. Full article

Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ${\ell }_{2,1}$ norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, ${\ell }_{2,p}$, is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the ${\ell }_{2,1}$ norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method. Full article

Photobiomodulation (PBM) therapy on the brain employs red to near-infrared (NIR) light to treat various neurological and psychological disorders. The mechanism involves the activation of cytochrome c oxidase in the mitochondrial respiratory chain, thereby enhancing ATP synthesis. Additionally, light absorption by ion channels triggers the release of calcium ions, instigating the activation of transcription factors and subsequent gene expression. This cascade of events not only augments neuronal metabolic capacity but also orchestrates anti-oxidant, anti-inflammatory, and anti-apoptotic responses, fostering neurogenesis and synaptogenesis. It shows promise for treating conditions like dementia, stroke, brain trauma, Parkinson’s disease, and depression, even enhancing cognitive functions in healthy individuals and eliciting growing interest within the medical community. However, delivering sufficient light to the brain through transcranial approaches poses a significant challenge due to its limited penetration into tissue, prompting an exploration of alternative delivery methods such as intracranial and intranasal approaches. This comprehensive review aims to explore the mechanisms through which PBM exerts its effects on the brain and provide a summary of notable preclinical investigations and clinical trials conducted on various brain disorders, highlighting PBM’s potential as a therapeutic modality capable of effectively impeding disease progression within the organism—a task often elusive with conventional pharmacological interventions. Full article

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems. Full article

The experimental data of core flow distribution are indispensable for obtaining licensing and facilitating the design of fluid systems of nuclear reactors. In this study, an Advanced power reactor Core flow and Pressure (ACOP) test facility was established to experimentally simulate the internal flow of the Advanced Power Reactor 1000 (APR1000) on a reduced length scale of 1/5. The core region was simulated by using 177 core simulators representing the fuel assemblies of the APR1000. The APR1000 flow distributions were synthetically identified by accurately measured parameters: the core inlet flow rate and outlet pressure under the four-pump balanced and unbalanced flow conditions. The overall inlet flow rates ranged from 87.7% to 112.0% relative to the averaged flow rate. Here, we scrutinize the flow distributions considering the flow conditions and internal structures and briefly describe the applied scaling method and design concept of the test facility. Full article

The present study deals with the reuse of agro-industrial waste with a specific focus on biochar (processed plant biomass or biochar) consisting of organic and inorganic waste biomass subjected to thermochemical processes. The objective of this work is to carry out a systematic review of the literature according to the Methodi Ordinatio methodology and select a bibliographic portfolio of high relevance to this study that makes it possible to present the concepts, applications and interest on the part of companies in including biochar in their processes, as well as addressing the environmental impacts linked to incorrect waste disposal. In this sense, biochar presents an interesting potential solution from both a waste management and environmental point of view. The current challenge is studies that prove economic viability. Full article

Over the past few decades, polymer composites have received significant interest and become protagonists due to their enhanced properties and wide range of applications. Herein, we examined the impact of filler and flame retardants in hemp seed oil-based rigid polyurethane foam (RPUF) composites’ performance. Firstly, the hemp seed oil (HSO) was converted to a corresponding epoxy analog, followed by a ring-opening reaction to synthesize hemp bio-polyols. The hemp polyol was then reacted with diisocyanate in the presence of commercial polyols and other foaming components to produce RPUF in a single step. In addition, different fillers like microcrystalline cellulose, alkaline lignin, titanium dioxide, and melamine (as a flame retardant) were used in different wt.% ratios to fabricate composite foam. The mechanical characteristics, thermal degradation behavior, cellular morphology, apparent density, flammability, and closed-cell contents of the generated composite foams were examined. An initial screening of different fillers revealed that microcrystalline cellulose significantly improves the mechanical strength up to 318 kPa. The effect of melamine as a flame retardant in composite foam was also examined, which shows the highest compression strength of 447 kPa. Significantly better anti-flaming qualities than those of neat foam based on HSO have been reflected using 22.15 wt.% of melamine, with the lowest burning time of 4.1 s and weight loss of 1.88 wt.%. All the composite foams showed about 90% closed-cell content. The present work illustrates the assembly of a filler-based polyurethane foam composite with anti-flaming properties from bio-based feedstocks with high-performance applications. Full article

Enzymatically derived selenium-enriched peptides from Cardamine violifolia (CV) can serve as valuable selenium supplements. However, the industrial application of free enzyme is impeded by its limited stability and reusability. Herein, this study explores the application of co-immobilized enzymes (Alcalase and Dispase) on amino resin for hydrolyzing CV proteins to produce selenium-enriched peptides. The successful enzyme immobilization was confirmed through scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FTIR). Co-immobilized enzyme at a mass ratio of 5:1 (Alcalase/Dispase) exhibited the smallest pore size (7.065 nm) and highest activity (41 U/mg), resulting in a high degree of hydrolysis of CV protein (27.2%), which was obviously higher than the case of using free enzymes (20.7%) or immobilized Alcalase (25.8%). In addition, after a month of storage, the co-immobilized enzyme still retained a viability level of 41.93%, showing fairly good stability. Encouragingly, the selenium-enriched peptides from co-immobilized enzyme hydrolysis exhibited uniform distribution of selenium forms, complete amino acid fractions and homogeneous distribution of molecular weight, confirming the practicality of using co-immobilized enzymes for CV protein hydrolysis. Full article

Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios. Full article

Leber congenital amaurosis (LCA)/early-onset severe retinal dystrophy (EOSRD) stand as primary causes of incurable childhood blindness. This study investigates the clinical and molecular architecture of syndromic and non-syndromic LCA/EOSRD within a Chilean cohort (67 patients/60 families). Leveraging panel sequencing, 95.5% detection was achieved, revealing 17 genes and 126 variants (32 unique). CRB1, LCA5, and RDH12 dominated (71.9%), with CRB1 being the most prevalent (43.8%). Notably, four unique variants (LCA5 p.Glu415*, CRB1 p.Ser1049Aspfs*40 and p.Cys948Tyr, RDH12 p.Leu99Ile) constituted 62.7% of all disease alleles, indicating their importance for targeted analysis in Chilean patients. This study underscores a high degree of inbreeding in Chilean families affected by pediatric retinal blindness, resulting in a limited mutation repertoire. Furthermore, it complements and reinforces earlier reports, indicating the involvement of ADAM9 and RP1 as uncommon causes of LCA/EOSRD. These data hold significant value for patient and family counseling, pharmaceutical industry endeavors in personalized medicine, and future enrolment in gene therapy-based treatments, particularly with ongoing trials (LCA5) or advancing preclinical developments (CRB1 and RDH12). Full article

Classic Hodgkin lymphoma (cHL) is a B-cell lymphoma in which tumour cells, the so-called Hodgkin Reed–Sternberg (HRS) cells, are admixed with non-malignant cell types that are a functional part of the disease. Immune cells, fibroblasts, specialised mesenchymal cells, and microvasculature together make up the tumour microenvironment and have functional interactions with tumour cells. HRS cells are surrounded by T and B cells admixed with plasma cells, macrophages, eosinophils, and mast cells. A cross-talk occurs between HRS cells and immune cells of the TME. This cross-talk is mediated either by a large network of cytokines and chemokines expressed by HRS cells or molecules produced by different cell types of the TME, i.e., CD30/CD30L, CD40/CD40L, OX40L/OX40, Il- 3/Il-3R, CCR5/CCL5, CD74 macrophage migration inhibitory factor/macrophages, and PD-L1/PD-1. The over-expression of CD30 and CD40, members of the TNF receptor family, is a hallmark of HRS cells. This review highlights the current development of newer therapeutic strategies as a means of immune checkpoint blockade and suggests that further research should explore innovative molecules aimed at targeting components of HL that are involved in cancer cell growth and/or immune escape. Hopefully, this will influence sensitivity or resistance to checkpoint inhibitor therapy in an individual patient. Full article

The kinematics of the human hand exhibit complex and diverse characteristics unique to each individual. Various techniques such as vision-based, ultrasonic-based, and data-glove-based approaches have been employed to analyze human hand movements. However, a critical challenge remains in efficiently analyzing and classifying hand grasp types based on time-series kinematic data. In this paper, we propose a novel sparse coding feature extraction technique based on dictionary learning to address this challenge. Our method enhances model accuracy, reduces training time, and minimizes overfitting risk. We benchmarked our approach against principal component analysis (PCA) and sparse coding based on a Gaussian random dictionary. Our results demonstrate a significant improvement in classification accuracy: achieving 81.78% with our method compared to 31.43% for PCA and 77.27% for the Gaussian random dictionary. Furthermore, our technique outperforms in terms of macro-average F1-score and average area under the curve (AUC) while also significantly reducing the number of features required. Full article

Rooftop solar modules are usually held in place by racks or frames that are mechanically attached to a roof structure and/or by heavyweight, ballasted footing mounts. These mounts ensure that the panel system remains in position against wind load. However, mechanical connectors create penetrations into the water-resistant layer of the roof, whereas ballasted footing mounts cause a significant additional load on the load-bearing structure of roof. For these reasons, adhesive connection seems to be a beneficial solution. Acrylic adhesive tapes, marked as VHB^TM, may provide sufficient strength, and they have no need for mechanical fasteners or ballast. Acrylic adhesive tapes also provide a comfortable, fast, and efficient bonding process with no curing compared to liquid adhesives. On the other hand, resistance to water at load-bearing joints has not been sufficiently studied yet and could be critical for connections exposed to the outdoor environment. The present study aims at the determination of water resistance and durability of the VHB^TM tapes from the GPH series, which are typically used to bond a variety of substrates including many metals. The mechanical properties and failure modes are compared for the specimens before and after a 21-day immersion in water. A significant reduction in strength was observed, depending on the substrate material. The study of chemical changes in the acrylic tape and in its leachate through infrared spectroscopy (FT-IR), X-ray fluorescence, and X-ray diffraction analyses clarified the reduction in mechanical properties. The selected VHB^TM tape demonstrated strong resistance to the effects of water. However, the overall strength of the joint after immersion was significantly impacted by the decrease in adhesion to a specific substrate. Full article

Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. This paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explore these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects. Full article

We prove a fundamental attribution error connecting rule-breaking behavior to entrepreneurs. We do so in the research context of the US, where we recently sampled from medium-sized venture entrepreneurs and their corporate executive peers (as an applicable reference point). We chose the US not only for its high entrepreneurial activity, but also because of the not uncommon relationship between business leaders and religion. By including various measures of religiosity in the study, we could control for factors that would likely influence rule-breaking, which standard models like the fraud triangle do not explicitly consider. In fact, we add contingency theory ideas to the fraud triangle to determine whether it is the decision conditions that determine rule-breaking rather than the role of the person (i.e., as an entrepreneur). We find that once demographic, religious, firm and industry contingencies are controlled for, any statistically significant influence of being an entrepreneur (relative to being a corporate executive with similar opportunity, motivation, capability and rationalization) disappears when it comes to self-admitted value-bending behaviors at work. Our contribution consists of a novel analysis, results and discussion of the ‘bent’repreneur—adding to conversations on the under-researched nexus of entrepreneurship with religiosity and ethical decision-making. Full article

Measuring pH in oxidizing solutions is a crucial issue in areas such as aquaculture, water treatment, industrial chemistry, and environmental analysis. For this purpose, a low-cost potentiometric flexible sensor using a polymer film as a pH-sensitive material has been developed in this study. The sensor consists in a polyaniline film electrodeposited from a sulfuric acid solution on a gold electrode previously deposited on a flexible polyimide substrate. The resulting polyaniline-based pH sensors showed an interesting performance detection in aqueous solution, leading to sensitive (73.4 mV per unit pH) and reproducible (standard deviation of 1.75) responses over the entire pH range from 3 to 8. On the contrary, they were inoperative in the presence of oxidizing hypochlorite ions. Thus, other polyaniline films were electrodeposited in the presence of cetyltrimethylammonium bromide or Tritonx100 surfactant in an attempt to improve the sensing performance of the pH sensors in oxidizing solutions. The pH sensors based on polyaniline and Tritonx100 surfactant were then found to be sensitive (62.3 mV per unit pH) and reproducible (standard deviation of 1.52) in aqueous solutions containing hypochlorite ions. All polyaniline films were also characterized by profilometry and electronic microscopy to correlate the physicochemical features with the performance of the sensors. Full article