This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper’s structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT. Full article

Land use, as one of the major sources of carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national carbon emission management and control. Consequently, it is essential to explore the spatiotemporal effects and optimization strategies of land-use carbon emissions at the county scale to promote the achievement of regional dual carbon targets. This study, focusing on Shaanxi Province, analyzed the spatiotemporal characteristics of land use from 2000 to 2020. By establishing a carbon emission evaluation model, the spatiotemporal effects of county-level carbon emissions were clarified. Utilizing Geodetector and K-means clustering methods, the driving mechanisms and clustering characteristics of county-level carbon emissions were elucidated, and optimization strategies for land use carbon emission were explored. The results showed that during 2000–2020, land use in Shaanxi Province underwent significant spatiotemporal changes, with constructed land increasing by 97.62%, while cultivated land and grassland were substantially reduced. The overall county-level carbon emissions exhibited a pattern of North > Central > South. The total carbon emissions within the province increased nearly fourfold over 20 years, reaching 1.00 × 10⁸ tons. Constructed land was the primary source of emissions, while forest land contributed significantly to the carbon sink of the study area. Interactions among factors had significant impacts on the spatial differentiation of total county-level carbon emissions. For counties with different types of carbon emissions, differentiated optimization strategies were recommended. Low-carbon emission counties should intensify ecological protection and rational utilization, medium-carbon emission counties need to strike a balance between economic development and environmental protection, while high-carbon emission counties should prioritize profound emission reduction and structural transformation. Full article

In the aftermath of the COVID-19 pandemic, the intricate relationship between health and the environment has emerged with unparalleled significance. This investigation examines the effect of leisure-time physical activity (LTPA) on health and environmental consciousness and its influence on attitudes towards green/sustainable products among 533 individuals. Utilizing linear structural modeling and regression analysis, the findings reveal that participation in sports and leisure activities significantly mediates the connection between individual well-being and eco-responsible consumer behaviors. Highlighting LTPA’s crucial role in enhancing environmental awareness, this study offers invaluable perspectives for the green product sector. It advocates for the development of strategies that align with consumers’ environmental values, underscoring the essential function of sports and leisure in fostering sustainable consumer practices. Crucially, this underscores the intertwined nature of environmental sustainability and individual health, highlighting their mutual dependence. Full article

A considerable number of texts encountered daily are somehow connected. For example, Wikipedia articles refer to other articles via hyperlinks, or scientific papers relate to others via citations or (co)authors; tweets relate via users that follow each other or reshare content. Hence, a graph-like structure can represent existing connections and be seen as capturing the “context” of the texts. The question thus arises of whether extracting and integrating such context information into a language model might help facilitate a better-automated understanding of the text. In this study, we experimentally demonstrate that incorporating graph-based contextualization into the BERT model enhances its performance on an example of a classification task. Specifically, in the Pubmed dataset, we observed a reduction in balanced mean error from 8.51% to 7.96%, while increasing the number of parameters just by 1.6%. Full article

The key to high-dimensional clustering lies in discovering the intrinsic structures and patterns in data to provide valuable information. However, high-dimensional clustering faces enormous challenges such as dimensionality disaster, increased data sparsity, and reduced reliability of the clustering results. In order to address these issues, we propose a sparse clustering algorithm based on a multi-domain dimensionality reduction model. This method achieves high-dimensional clustering by integrating the sparse reconstruction process and sparse L1 regularization into a deep autoencoder model. A sparse reconstruction module is designed based on the L1 sparse reconstruction of features under different domains to reconstruct the data. The proposed method mainly contributes in two aspects. Firstly, the spatial and frequency domains are combined by taking into account the spatial distribution and frequency characteristics of the data to provide multiple perspectives and choices for data analysis and processing. Then, a neural network-based clustering model with sparsity is conducted by projecting data points onto multi-domains and implementing adaptive regularization penalty terms to the weight matrix. The experimental results demonstrate superior performance of the proposed method in handling clustering problems on high-dimensional datasets. Full article

The application of electrochemical hydrogen evolution reaction (HER) for renewable energy conversion contributes to the ultimate goal of a zero-carbon emission society. Metal phosphides have been considered as promising HER catalysts in the alkaline environment, which, unfortunately, is still limited owing to the weak adsorption of H* and easy dissolution during operation. Herein, a bimetallic NiCoP-2/NF phosphide is constructed on nickel foam (NF), requiring rather low overpotentials of 150 mV and 169 mV to meet the current densities of 500 and 1000 mA cm⁻², respectively, and able to operate stably for 100 h without detectable activity decay. The excellent HER performance is obtained thanks to the synergetic catalytic effect between Ni and Co, among which Ni is introduced to enhance the intrinsic activity and Co increases the electrochemically active area. Meanwhile, the protection of the externally generated amorphous phosphorus oxide layer improves the stability of NiCoP/NF. An electrolyser using NiCoP-2/NF as both cathode and anode catalysts in an alkaline solution can produce hydrogen with low electric consumption (overpotential of 270 mV at 500 mA cm⁻²). Full article

This article focuses on the area of software development for microcontrollers and details the implementation of modern programming practices and principles in embedded systems and IoT applications. This article explains how we implemented previously unimplemented principles and applied design patterns for quality software design on microcontrollers, which are currently only used for developing applications on the higher layers of the IoT reference model. A custom modular framework for microcontrollers is presented, based on applying SOLID principles and adapting design patterns specific to the microcontrollers’ application development needs. The implemented framework enables independent communication between modules and flexible integration of hardware components. It is designed with platform independence in mind, contributing to its wide adaptability and ease of use in diverse development environments. By applying these technological approaches, we can create applications that are not only testable and extensible in terms of application logic but also allow for easy adaptation to changes in these hardware resources. Utilizing these capabilities represents an innovative approach to development for microcontrollers that fundamentally improves the long-term sustainability and scalability of applications. Full article

Bat coronaviruses cause a wide range of illnesses in humans and animals. Bats are known to harbor a wide diversity of Alphacoronaviruses and Betacoronaviruses. Betacoronaviruses have been linked to Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and other diseases such as gastroenteritis, bronchiolitis, and pneumonia. In the last 20 years, three betacoronaviruses emerged and caused widespread outbreaks in humans, including two deadly betacoronavirus epidemics, SARS-CoV, with mortality rate of 10%, and MERS-CoV, with mortality rate of 34.7%, and SARS-CoV-2, which caused the COVID-19 pandemic, with mortality rate of 3.4%. Studies have shown that bats are the main natural reservoirs for these viruses or their ancestral viruses. Observed variations in bat coronavirus genomes indicate that these viruses may have a potential to transmit to other hosts in close contact with humans and subsequently transmit to humans. As of today, there are no reported cases of direct coronavirus transmission from bats to humans. One reason for this might be that intermediate hosts are required for the transmission of bat coronaviruses to humans. Further studies are needed to map the amino acids and genomic regions responsible for the interactions between the spike of coronavirus and its receptors. Full article

Lettuce is susceptible to high-temperature stress during cultivation, leading to bolting and affecting yield. Plant-specific transcription factors, known as GRAS proteins, play a crucial role in regulating plant growth, development, and abiotic stress responses. In this study, the entire lettuce LsGRAS gene family was identified. The results show that 59 LsGRAS genes are unevenly distributed across the nine chromosomes. Additionally, all LsGRAS proteins showed 100% nuclear localization based on the predicted subcellular localization and were phylogenetically classified into nine conserved subfamilies. To investigate the expression profiles of these genes in lettuce, we analyzed the transcription levels of all 59 LsGRAS genes in the publicly available RNA-seq data under the high-temperature treatment conducted in the presence of exogenous melatonin. The findings indicate that the transcript levels of the LsGRAS13 gene were higher on days 6, 9, 15, 18, and 27 under the high-temperature (35/30 °C) treatment with melatonin than on the same treatment days without melatonin. The functional studies demonstrate that silencing LsGRAS13 accelerated bolting in lettuce. Furthermore, the paraffin sectioning results showed that flower bud differentiation in LsGRAS13-silenced plants occurred significantly faster than in control plants. In this study, the LsGRAS genes were annotated and analyzed, and the expression pattern of the LsGRAS gene following melatonin treatment under high-temperature conditions was explored. This exploration provides valuable information and identifies candidate genes associated with the response mechanism of lettuce plants high-temperature stress. Full article

The rapid advancement toward smart cities has accelerated the adoption of various Internet of Things (IoT) devices for underground applications, including agriculture, which aims to enhance sustainability by reducing the use of vital resources such as water and maximizing production. On-farm IoT devices with above-ground wireless nodes are vulnerable to damage and data loss due to heavy machinery movement, animal grazing, and pests. To mitigate these risks, wireless Underground Sensor Networks (WUSNs) are proposed, where devices are buried underground. However, implementing WUSNs faces challenges due to soil heterogeneity and the need for low-power, small-size, and long-range communication technology. While existing radio frequency (RF)-based solutions are impeded by substantial signal attenuation and low coverage, acoustic wave-based WUSNs have the potential to overcome these impediments. This paper is the first attempt to review acoustic propagation models to discern a suitable model for the advancement of acoustic WUSNs tailored to the agricultural context. Our findings indicate the Kelvin–Voigt model as a suitable framework for estimating signal attenuation, which has been verified through alignment with documented outcomes from experimental studies conducted in agricultural settings. By leveraging data from various soil types, this research underscores the feasibility of acoustic signal-based WUSNs. Full article

Background: Obesity is a public health problem which prevalence has increased worldwide and is associated with different degrees of hemodynamic alterations and structural cardiac changes. The aim of the study is to investigate the impact of body mass index (BMI) on left atrial function using standard and advanced echocardiography in a population of patients with non-valvular atrial fibrillation (AF). Methods: 395 adult patients suffering from non-valvular AF, divided into three tertiles based on BMI value, carry out a cardiological examination with standard and advanced echocardiography. Results: Peak atrial longitudinal strain (PALS), a measure of left atrial function, is lower in the tertile with highest BMI (14.3 ± 8.2%) compared to both the first (19 ± 11.5%) and the second tertile (17.7 ± 10.6%) in a statistically significant manner (p < 0.002). Furthermore, BMI is significantly associated independent with the PALS by multilinear regression analysis, even after correction of the data for CHA₂DS₂-VASc score, left ventricular mass index, left ventricular ejection fraction, E/E’ ratio and systolic pulmonary arterial pressure (coefficient standardized β = −0.127, p < 0.02; Cumulative R² = 0.41, SEE = 0.8%, p < 0.0001). Conclusions: BMI could be considered an additional factor in assessing cardiovascular risk in patients with non-valvular atrial fibrillation, in addition to the well-known CHA₂DS₂-VASc score. Full article

The low availability of phosphorus has become a common problem worldwide. Phosphorus is essential for phenotypic morphology and ginsenoside synthesis. However, the effects of Pi stress on ginseng phenotype and ginsenoside synthesis remain unclear. Phenotypic analyses and transcriptomics revealed the phenotypic construction and regulation of differential genes involved in the physiological metabolism of ginseng under low-Pi stress. Root length and stem length were found to be significantly inhibited by phosphate-deficiency stress in the half-phosphate (HP) and no-phosphate (NP) treatment groups; however, the number of fibrous roots, which are regulated by phytohormones, was found to increase. In ginseng leaves, the indexes of physiological stress, superoxide anion (221.19 nmol/g) and malonaldehyde (MDA) (0.05 μmol/min/g), reached the maximum level. Moreover, chlorophyll fluorescence images and chlorophyll content further confirmed the inhibition of ginseng photosynthesis under low-Pi stress. A total of 579 and 210 differentially expressed genes (DEGs) were shared between NP and total phosphate (TP) and HP and TP, respectively, and only 64 common DEGs were found based on the two comparisons. These DEGs were mainly related to the synthesis of phosphate transporters (PHTs), phytohormones, and ginsenosides. According to KEGG analyses, four DEGs (Pg_s 0368.2, Pg_s3418.1, Pg_s5392.5 and Pg_s3342.1) affected acetyl-CoA production by regulating glycometabolism and tricarboxylic acid cycle (TCA). In addition, related genes, including those encoding 13 PHTs, 15 phytohormones, and 20 ginsenoside synthetases, were screened in ginseng roots under Pi-deficiency stress. These results indicate that changes in the ginseng phenotype and transcriptional regulation of DEGs are involved in the Pi-deficiency stress environment of ginseng, thereby providing new information regarding the development of ginseng for low-Pi tolerance. Full article

In circumstances influenced by freeze–thaw cycles, the strength of rock diminishes, necessitating an in-depth investigation into its corresponding anchoring support schemes. This study conducted experiments on rocks with and without fractures at angles of 0°, 45°, and 90° subjected to freeze–thaw cycles of 0, 10, 20, and 30 iterations. It explored the effects of fracture inclination, anchoring conditions, and freeze–thaw cycles on the mechanical properties of rock. The primary findings from the experiments are as follows: (1) fracture inclination significantly impacts rock strength, with the most pronounced deterioration observed in samples with a 45° fracture, exhibiting strengths and elastic moduli at 28.4% and 73.4%, respectively, of those of fracture-free samples; (2) anchoring effectively controls deformation but concurrently induces stress concentrations, resulting in Y-shaped crack formation around the anchoring rod; (3) the degree of strength reduction due to freeze–thaw cycles is angle-dependent, with fracture-free and 90° fracture samples exhibiting diminished strength post freezing, while the 45° fracture samples’ strength remains largely unchanged. Additionally, this study employed a numerical model, coupling a discrete element method (DEM) with a finite difference method (FDM), to simulate experimental conditions, yielding conclusions consistent with experimental outcomes, and notably revealing a prevalence of tensile cracks over shear cracks within samples under uniaxial compression. Full article

With the rapid development of artificial intelligence, computer vision techniques have been successfully applied to concrete defect diagnosis in bridge structural health monitoring. To enhance the accuracy of identifying the location and type of concrete defects (cracks, exposed bars, spalling, efflorescence and voids), this paper proposes improvements to the existing Mask Region Convolution Neural Network (Mask R-CNN). The improvements are as follows: (i) The residual network (ResNet101), the backbone network of Mask R-CNN which has too many convolution layers, is replaced by the lightweight network MobileNetV2. This can solve the problem that the large number of parameters leads to a slow training speed of the model, and improve the ability to extract features of smaller targets. (ii) Embedding attention mechanism modules in Feature Pyramid Networks (FPNs) to better extract the target features. (iii) A path aggregation network (PANet) is added to solve the problem that the model Mask R-CNN lacks the ability to extract shallow layer feature information. To validate the superiority of the proposed improved Mask R-CNN, the multi-class concrete defect image dataset was constructed, and using the K-means clustering algorithm to determine the aspect ratio of the most suitable prior bounding box for the dataset. Following, the identification results of improved Mask-RCNN, original Mask-RCNN and other mainstream deep learning networks on five types of concrete defects (cracks, exposed bars, spalling, efflorescence and voids) in the dataset were compared. Finally, the intelligent identification system for concrete defects has been established by innovatively combining images taken by unmanned aerial vehicles (UAVs) with our improved defect identification model. The reinforced concrete bridge defects images collected by UAVs were used as test set for testing. The result is the improved Mask R-CNN with superior accuracy, and the identification accuracy is higher than the original Mask-RCNN and other deep learning networks. The improved Mask-RCNN can identify the new untrained concrete defects images taken by UAVs, and the identification accuracy can meet the requirements of bridge structural health monitoring. Full article

An interconnected sponge structure and porous surface poly (acrylonitrile-co-methyl acrylate) (P(AN-MA)) microfiltration membranes (MF) were fabricated via thermally induced phase separation (TIPS) by using caprolactam (CPL), and acetamide (AC) as the mixed diluent. When the ternary system was composed of 15 wt.% P(AN-MA), 90 wt.% CPL, and 10 wt.% AC and formed in a 25 °C air bath, the membrane exhibited the highest water flux of 8107 L/m²·h. The P(AN-MA) membrane contained hydrophobic groups (-COOCH₃) and hydrophilic groups (-CN), leading it to exhibit oleophobic properties underwater and hydrophobic properties in oil. The membrane demonstrates efficient separation of immiscible oil/water mixtures. The pure water flux of the petroleum ether/water mixture measured 870 L/m²·h, and the pure oil flux of the petroleum tetrachloride/water mixture measured 1230 L/m²·h under the influence of gravity. Additionally, the recovery efficiency of diluents through recrystallization was 85.3%, significantly reducing potential pollution and production costs. Full article

Some composite materials have been prepared, constituted by a cyclodextrin-bis-urethane-based nanosponge matrix in which a reduced graphene oxide/silver nanoparticles photocatalyst has been dispersed. Different chain extenders were employed for designing the nanosponge supports, in such a way as to decorate their hyper-cross-linked structure with diverse functionalities. Moreover, two different strategies were explored to accomplish the silver loading. The obtained systems were successfully tested as catalysts for the photodegradation of emerging pollutants such as model dyes and drugs. Enhancement of the photoactive species performance (up to nine times), due to the synergistic local concentration effect exerted by the nanosponge, could be assessed. Overall, the best performances were shown by polyamine-decorated materials, which were able to promote the degradation of some particularly resistant drugs. Some methodological issues pertaining to data collection are also addressed. Full article

Thalassemia a common hereditary blood disorder resulting in anemia. It is an important public health problem, with a high prevalence in Southeast Asia and Mediterranean countries, and preventable through screening programs. However, due to its chronic nature, permanent physical changes, troublesome complications, and lifelong treatment, pediatric patients with thalassemia major are more prone to mental disorders and cognitive impairment. Internalizing and externalizing problems are higher in pediatric patients with thalassemia. Children with β-thalassemia major exhibit lower IQ scores than healthy children. Neurophysiology and neuroimaging examinations have shown abnormal results in children with thalassemia. Co-morbidity with mental disorders increases the mortality, morbidity, and total healthcare costs of patients with thalassemia. Therefore, routine evaluation of mental health problems is recommended to accommodate the early detection and prompt treatment of mental disorders. A multidisciplinary approach for thalassemia patients and families should be delivered by providing appropriate medical care, psychosocial support, and good transition care to improve survival and well-being, assist good social integration and daily functioning, and cope with the stress of chronic disease. Full article

To compare apple aroma intensities, apples were analyzed from the calyx side (on the opposite side of the stem) using an electronic nose (e-nose) sensor device and direct mass spectrometry. The results indicated that the sensor value tended to increase in accordance with the total intensity of apple aroma components measured by direct mass spectrometry. In addition, the e-nose sensor values for apple aroma did not correlate with the sugar content and ripeness measurements using optical sensors. Moreover, the relative standard deviations of repeatability and intermediate precision in the measurement of apple flavor (apple lip balm) were within 1.36–9.96%. Similar to the utilization of sugar content and ripeness values, the aroma measured from the calyx side can be potentially used for apple evaluation. Full article

Background: A fundamental grasp of the variability observed in healthy individuals holds paramount importance in the investigation of neuropsychiatric conditions characterized by sex-related phenotypic distinctions. Functional magnetic resonance imaging (fMRI) serves as a meaningful tool for discerning these differences. Among deep learning models, graph neural networks (GNNs) are particularly well-suited for analyzing brain networks derived from fMRI blood oxygen level-dependent (BOLD) signals, enabling the effective exploration of sex differences during adolescence. Method: In the present study, we introduce a multi-modal graph isomorphism network (MGIN) designed to elucidate sex-based disparities using fMRI task-related data. Our approach amalgamates brain networks obtained from multiple scans of the same individual, thereby enhancing predictive capabilities and feature identification. The MGIN model adeptly pinpoints crucial subnetworks both within and between multi-task fMRI datasets. Moreover, it offers interpretability through the utilization of GNNExplainer, which identifies pivotal sub-network graph structures contributing significantly to sex group classification. Results: Our findings indicate that the MGIN model outperforms competing models in terms of classification accuracy, underscoring the benefits of combining two fMRI paradigms. Additionally, our model discerns the most significant sex-related functional networks, encompassing the default mode network (DMN), visual (VIS) network, cognitive (CNG) network, frontal (FRNT) network, salience (SAL) network, subcortical (SUB) network, and sensorimotor (SM) network associated with hand and mouth movements. Remarkably, the MGIN model achieves superior sex classification accuracy when juxtaposed with other state-of-the-art algorithms, yielding a noteworthy 81.67% improvement in classification accuracy. Conclusion: Our model’s superiority emanates from its capacity to consolidate data from multiple scans of subjects within a proven interpretable framework. Beyond its classification prowess, our model guides our comprehension of neurodevelopment during adolescence by identifying critical subnetworks of functional connectivity. Full article

This research paper investigates the potential of FinTech solutions in promoting sustainable finance in Mongolia, an emerging market economy with a burgeoning FinTech landscape. This study explores how Mongolia leverages FinTech innovations to advance sustainable development goals. Utilizing secondary data sourced from Mongolian government agencies, global financial organizations, academic institutions, market research firms, and industry associations, we conducted an integrated SWOT-MLP analysis of Mongolia’s FinTech industry, which includes the FinTech business models, ecosystem, regulatory frameworks, traditional financial institutions, and stakeholders. This study presents a comprehensive analysis of the opportunities and challenges facing Mongolia in integrating FinTech into its sustainable finance agenda, and SWOT-based strategies. Full article

Metastasis in breast cancer is the major cause of death in females (about 30%). Based on our earlier observation that Vitamin D3 downregulates mTOR, we hypothesized that Vitamin D3 conjugated to gold nanoparticles (VD3-GNPs) reduces breast cancer aggressiveness by downregulating the key cancer controller PI3K/AKT/mTOR. Western blots, migration/invasion assays, and other cell-based, biophysical, and bioinformatics studies are used to study breast cancer cell aggressiveness and nanoparticle characterization. Our VD3-GNP treatment of breast cancer cells (MCF-7 and MDA-MB-231) significantly reduces the aggressiveness (cancer cell migration and invasion rates > 45%) via the simultaneous downregulation of ETV7 and the Hippo pathway. Consistent with our hypothesis, we, indeed, found a downregulation of the PI3K/AKT/mTOR pathway. It is surprising that the extremely low dose of VD3 in the nano formulation (three orders of magnitude lower than in earlier studies) is quite effective in the alteration of cancer invasiveness and cell signaling pathways. Clearly, VD3-GNPs are a viable candidate for non-toxic, low-cost treatment for reducing breast cancer aggressiveness. Full article

Background: Electronic cigarettes or vapes are battery-operated devices that heat a liquid, often containing nicotine and flavouring substances, to produce an inhalable aerosol. Despite being used as an alternative to traditional smoking, many studies have reported their health risks and ineffectiveness in smoking cessation. The impact of e-cigarettes on weight control behaviours, a known effect of traditional cigarette smoking, is unclear. Herein, a systematic review was conducted to explore the relationship between e-cigarette use and body weight changes in adolescents and young adults. Methods: The existing literature from databases such as PubMed, Cochrane Library, Embase, Science Direct, Web of Science, Scopus, and Google Scholar until October 2023 was searched and included in the review. The methodological quality of all selected studies was assessed using the Joanna Briggs Institute’s (JBI) Critical Appraisal Checklists for Studies. Results: Out of 5117 citations, 20 publications featuring cross-sectional studies with adolescent participants were qualitatively analysed. The high rates of e-cigarette usage seemed to correlate with increased weight concerns, particularly among females. Regular e-cigarette users who reported being overweight and used calorie restriction for weight reduction were more likely to view vaping as a weight loss or control strategy. Young adults (<24 years) may consume more flavoured e-cigarettes than older users (>25 years). Conclusions: This study revealed a significant use of e-cigarettes among high school students, driven by taste preferences, weight management, and perceived harm reduction. Particularly among girls facing body image pressures, vaping serves as a weight control method. This highlights the need to assess cardiovascular risks and advocate for further research, including longitudinal studies, to inform public health strategies effectively. Full article