Selenium (Se) is a micronutrient known for its essential role in human health and plant metabolism. Waxy maize (Zea mays L. sinensis kulesh)—known for its high nutritional quality and distinctive flavor—holds significant consumer appeal. Therefore, this study aims to assess the effects of foliar Se spraying on the nutritional quality of waxy maize grains, with a focus on identifying varietal differences and determining optimal Se dosage levels for maximizing nutritional benefits. We employed a two-factor split-plot design to assess the nutritional quality, trace elements, and pigment content of jinnuo20 (J20) and caitiannuo1965 (C1965) at the milk stage after being subjected to varying Se doses sprayed on five leaves. Our findings indicate superior nutrient content in J20 compared to C1965, with both varieties exhibiting optimal quality under Se3 treatment, falling within the safe range of Se-enriched agricultural products. JS3 (0.793) demonstrated the highest overall quality, followed by JS2 (0.606), JS4 (0.411), and JS1 (0.265), while CS0 had the lowest (−0.894). These results underscore the potential of foliar biofortification to enhance the functional component contents of waxy maize grains. Full article

This study focused on isolating tannin-tolerant yeasts from Miang, a fermented tea leaf product collected from northern Laos PDR, and investigating related food applications. From 43 Miang samples, six yeast isolates capable of ethanol production were obtained, with five isolates showing growth on YPD agar containing 4% (w/v) tannic acid. Molecular identification revealed three isolates as Saccharomyces cerevisiae (B5-1, B5-2, and C6-3), along with Candida tropicalis and Kazachstania humilis. Due to safety considerations, only Saccharomyces spp. were selected for further tannic acid tolerance study to advance food applications. Tannic acid at 1% (w/v) significantly influenced ethanol fermentation in all S. cerevisiae isolates. Notably, B5-2 and C6-3 showed high ethanol fermentation efficiency (2.5% w/v), while others were strongly inhibited. The application of tannin-tolerant yeasts in longan fruit wine (LFW) fermentation with longan seed extract (LSE) supplementation as a source of tannin revealed that C6-3 had the best efficacy for LFW fermentation. C6-3 showed promising efficacy, particularly with LSE supplementation, enhancing phenolic compounds, antioxidant activity, and inhibiting α-glucosidase activity, indicating potential antidiabetic properties. These findings underscore the potential of tannin-tolerant S. cerevisiae C6-3 for fermenting beverages from tannin-rich substrates like LSE, with implications for functional foods and nutraceuticals promoting health benefits. Full article

This study evaluated the biocontrol effect of isolated epiphytic yeasts (Papiliotrema terrestris, Hanseniaspora uvarum, and Rhodosporidium glutinis) against Botrytis cinerea and Alternaria alternata in blueberry fruits and its possible mechanisms. Our findings indicated that the three tested yeasts exerted a good biocontrol effect on postharvest diseases in blueberry, and that H. uvarum was the most effective. In addition, the three tested yeasts could improve the postharvest storage quality of blueberry fruits to some extent. H. uvarum demonstrated the strongest direct inhibitory effect on pathogens by suppressing spore germination, mycelial growth, and antifungal volatile organic compound (VOC) production. P. terrestris showed the highest extracellular lytic enzymes activities. It also had better adaptation to low temperature in fruit wounds at 4 °C. The biofilm formation capacity was suggested to be the main action mechanism of R. glutinis, which rapidly colonized fruit wounds at 20 °C. Several action mechanisms are employed by the superb biocontrol yeasts, while yeast strains possess distinctive characteristics and have substantially different action mechanisms. Full article

This paper aims to provide an updated review and current understanding of the impact of extreme temperatures—focusing on heat stress (HS)—on the quality of pork and poultry meat, particularly amidst an unprecedented global rise in environmental temperatures. Acute or chronic HS can lead to the development of pale, soft, and exudative (PSE) meat during short transportation or of dark, firm, and dry (DFD) meat associated with long transportation and seasonal changes in pork and poultry meat. While HS is more likely to result in PSE meat, cold stress (CS) is more commonly linked to the development of DFD meat. Methods aimed at mitigating the effects of HS include showering (water sprinkling/misting) during transport, as well as control and adequate ventilation rates in the truck, which not only improve animal welfare but also reduce mortality and the incidence of PSE meat. To mitigate CS, bedding on trailers and closing the tracks’ curtains (insulation) are viable strategies. Ongoing efforts to minimize meat quality deterioration due to HS or CS must prioritize the welfare of the livestock and focus on the scaleup of laboratory testing to commercial applications. Full article

Most world countries are experiencing a remarkable aging process. Meanwhile, 50 million people are affected by Alzheimer’s disease (AD) and related dementia and there is an increasing trend in the incidence of these major health problems. In order to address these, the increasing evidence suggesting the protective effect of dietary interventions against cognitive decline during aging may suggest a response to this challenge. There are nutrients with a neuroprotective effect. However, Western diets are poor in healthy n-3 polyunsaturated fatty acids (n-3 PUFAs), such as docosahexaenoic acid (DHA), iodine (I), and other nutrients that may protect against cognitive aging. Given DHA richness in chub mackerel (Scomber colias), high vitamin B9 levels in quinoa (Chenopodium quinoa), and I abundance in the seaweed Saccorhiza polyschides, a functional hamburger rich in these nutrients by using these ingredients was developed and its formulation was optimized in preliminary testing. The effects of culinary treatment (steaming, roasting, and grilling vs. raw) and digestion on bioaccessibility were evaluated. The hamburgers had high levels of n-3 PUFAs in the range of 42.0–46.4% and low levels of n-6 PUFAs (6.6–6.9%), resulting in high n-3/n-6 ratios (>6). Bioaccessibility studies showed that the hamburgers could provide the daily requirements of eicosapentaenoic acid (EPA) + DHA with 19.6 g raw, 18.6 g steamed, 18.9 g roasted, or 15.1 g grilled hamburgers. Polyphenol enrichment by the seaweed and antioxidant activity were limited. The hamburgers contained high levels of Se and I at 48–61 μg/100 g ww and 221–255 μg/100 g ww, respectively. Selenium (Se) and I bioaccessibility levels were 70–85% and 57–70%, respectively, which can be considered high levels. Nonetheless, for reaching dietary requirements, considering the influence of culinary treatment and bioaccessibility, 152.2–184.2 g would be necessary to ensure daily Se requirements and 92.0–118.1 g for I needs. Full article

Millet is a small-seeded cereal crop with big potential. There are many different cultivars of proso millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are important for growers, processors, and consumers. Current methods of grain cultivar detection and classification are subjective, destructive, and time-consuming. Therefore, there is a need to develop nondestructive methods for sorting the cultivars of proso millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900–1700 nm) to discriminate between different cultivars of proso millet seeds was evaluated. A total of 5000 proso millet seeds were randomly obtained and investigated from the ten most popular cultivars in the United States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as spectral features for building the classification models because they had the largest variance. The classification performance showed prediction accuracy rates as high as 99% for classifying the different cultivars of proso millet using a Gradient tree boosting ensemble machine learning algorithm. Moreover, the classification was successfully performed using only 15 and 5 selected spectral features (wavelengths), with an accuracy of 98.14% and 97.6%, respectively. The overall results indicate that NIR hyperspectral imaging could be used as a rapid and nondestructive method for the classification of proso millet seeds. Full article

A hybrid free-space optical (FSO) and radio frequency (RF) communication system has been considered an effective way to obtain a good trade-off between spectrum utilization efficiency and high-rate transmission. Utilizing artificial intelligence (AI) to deal with the switching and rate adaption problems between FSO/RF links, this paper investigated their modulation adapting mechanism based on a machine learning (ML) algorithm. Hybrid link budgets were estimated for different modulation types in various environments, particularly severe weather conditions. For the adaptive modulation (AM) scheme with different order PPM/PSK/QAM, a rate-compatible soft-switching model for hybrid FSO/RF links was established with a random forest algorithm based on ML. With a given target bit error rate, the model categorized a link budget threshold of the hybrid FSO/RF system over a training data set from local weather records. The switching and modulation adaption accuracy were tested over the testing weather data set especially focusing on rain and fog. Simulation results show that the proposed adaptive modulation scheme based on the random forest algorithm can have a good performance for soft-switching hybrid FSO/RF communication links. Full article

In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy. Full article

In the context of a burgeoning knowledge economy, enterprise intellectual capital has emerged as a pivotal asset for organizational growth. Evaluating it requires a comprehensive and robust index, yet there is no standard methodology for such assessments. Here, we propose an index for evaluating enterprise intellectual capital. We use the Delphi method to delineate a scientific decision structure. A grey-based decision-making trial and evaluation laboratory (DEMATEL) is coupled with an analytic network process (ANP)—i.e., grey DEMATEL-based ANP (GDANP)—to determine the relative weight of indicators. Then, we use the technique for order preference by similarity to an ideal solution to validate the effectiveness and applicability of the proposed evaluation index based on data on thirty new-technology companies in China. This study bridges a critical gap in academic discourse, and we discuss the practical implications for the strategic management of intellectual capital in corporate settings. Full article

In this research, we introduce and analyze a mathematical model for online social networks, incorporating two distinct delays. These delays represent the time it takes for active users within the network to begin disengaging, either with or without contacting non-users of online social platforms. We focus particularly on the user prevailing equilibrium (UPE), denoted as $P^{*}$, and explore the role of delays as parameters in triggering Hopf bifurcations. In doing so, we find the conditions under which Hopf bifurcations occur, then establish stable regions based on the two delays. Furthermore, we delineate the boundaries of stability regions wherein bifurcations transpire as the delays cross these thresholds. We present numerical simulations to illustrate and validate our theoretical findings. Through this interdisciplinary approach, we aim to deepen our understanding of the dynamics inherent in online social networks. Full article

The achievement and control of desired motions in active machines often involves precise manipulation of artificial muscles in a distributed and sequential manner, which poses significant challenges. A novel motion control strategy based on self-oscillation in active machines offers distinctive benefits, such as direct energy harvesting from the ambient environment and the elimination of complex controllers. Drawing inspiration from automobiles, a self-moving automobile designed for operation under steady illumination is developed, comprising two wheels and a liquid crystal elastomer fiber. To explore the dynamic behavior of this self-moving automobile under steady illumination, a nonlinear theoretical model is proposed, integrating with the established dynamic liquid crystal elastomer model. Numerical simulations are conducted using the Runge-Kutta method based on MATLAB software, and it is observed that the automobile undergoes a supercritical Hopf bifurcation, transitioning from a static state to a self-moving state. The sustained periodic self-moving is facilitated by the interplay between light energy and damping dissipation. Furthermore, the conditions under which the Hopf bifurcation occurs are analyzed in detail. It is worth noting that increasing the light intensity or decreasing rolling resistance coefficient can improve the self-moving average velocity. The innovative design of the self-moving automobile offers advantages such as not requiring an independent power source, possessing a simple structure, and being sustainable. These characteristics make it highly promising for a range of applications including actuators, soft robotics, energy harvesting, and more. Full article

Initial public offerings and mergers and acquisitions represent important opportunities for investors to exit and harvest their entrepreneurial success. Some firms are acquired shortly after their initial public offerings. This exit strategy is known as a double exit. In addition, issuing firms may choose to reincorporate in Delaware during their IPOs. In this study, we use hand-collected data from 1993 to 2020 to investigate whether and to what extent Delaware reincorporation may affect the M&As in the post-IPO stage. We use a Cox proportional hazard model to test the relation between Delaware reincorporation and the likelihood of being acquired for our sample IPOs. Recognizing that Delaware reincorporation is not a random decision, we adopt a Heckman switching regression method to estimate the relation between Delaware reincorporation and takeover premiums and announcement returns. We report that IPO firms choosing to reincorporate in Delaware experience a higher likelihood of being acquired compared to those IPO firms choosing to remain incorporated in their home states. We further document that IPO firms choosing to reincorporate in Delaware receive lower premiums in acquisitions, and experience lower abnormal returns on announcements. Full article

Making vocabulary stick in your memory involves dedicating attention to what needs to be learned. There are three main factors involved (focus, quantity, and quality) which can be expressed as six principles (focus, accuracy, repetition, time-on-task, elaboration, and analysis). When we include motivation in this description, then there are two more principles (motivation and self-efficacy). These principles apply to both incidental and deliberate learning, and apply to a wide range of learning focuses beyond vocabulary. These principles are well supported by research evidence. We can use the principles for re-examining teaching and learning, Technique Feature Analysis, understanding research, developing autonomy in learning, guiding curriculum design, and determining future research needs. The factors and principles provide a simple and clear view of what is needed for learning to occur from the viewpoint of attention. Full article

In this paper, simulation modeling was carried out using Sentaurus Technology Computer-Aided Design. Two types of high electron mobility transistors (HEMT), an AlGaN/GaN/AlGaN double heterojunction and AlGaN/GaN single heterojunction, were designed and compared. The breakdown characteristics and damage mechanisms of the two devices under the injection of high-power microwaves (HPM) were studied. The variation in current density and peak temperature inside the device was analyzed. The effect of Al components at different layers of the device on the breakdown of HEMTs is discussed. The effect and law of the power damage threshold versus pulse width when the device was subjected to HPM signals was verified. It was shown that the GaN HEMT was prone to thermal breakdown below the gate, near the carrier channels. A moderate increase in the Al component can effectively increased the breakdown voltage of the device. Compared with the single heterojunction, the double heterojunction HEMT devices were more sensitive to Al components. The high domain-limiting characteristics effectively inhibited the overflow of channel electrons into the buffer layer, which in turn regulated the current density inside the device and improved the temperature distribution. The leakage current was reduced and the device switching characteristics and breakdown voltage were improved. Moreover, the double heterojunction device had little effect on HPM power damage and high damage resistance. Therefore, a theoretical foundation is proposed in this paper, indicating that double heterojunction devices are more stable compared to single heterojunction devices and are more suitable for applications in aviation equipment operating in high-frequency and high-voltage environments. In addition, double heterojunction GaN devices have higher radiation resistance than SiC devices of the same generation. Full article

Diatoms are important primary producers in marine and freshwater environments, but little is known about the signalling mechanisms they use to detect changes in their environment. All eukaryotic organisms use Ca²⁺ signalling to perceive and respond to environmental stimuli, employing a range of Ca²⁺-permeable ion channels to facilitate the movement of Ca²⁺ across cellular membranes. We investigated the distribution of different families of Ca²⁺ channels in diatom genomes, with comparison to other members of the stramenopile lineage. The four-domain voltage-gated Ca²⁺ channels (Ca_v) are present in some centric diatoms but almost completely absent in pennate diatoms, whereas single-domain voltage-gated EukCatA channels were found in all diatoms. Glutamate receptors (GLRs) and pentameric ligand-gated ion channels (pLGICs) also appear to have been lost in several pennate species. Transient receptor potential (TRP) channels are present in all diatoms, but have not undergone the significant expansion seen in brown algae. All diatom species analysed lacked the mitochondrial uniporter (MCU), a highly conserved channel type found in many eukaryotes, including several stramenopile lineages. These results highlight the unique Ca²⁺-signalling toolkit of diatoms and indicate that evolutionary gains or losses of different Ca²⁺ channels may contribute to differences in cellular-signalling mechanisms between species. Full article

Considering the characteristics of both quality and quantity losses in fresh produce as well as the existence of spot markets, optimal retailer ordering, pricing, and freshness-keeping decisions through the single ordering policy (firm ordering only or option ordering only) and the mixed ordering policy (firm ordering and option ordering simultaneously) are constructed based on option contracts and analyzed for the retailer under different ordering policies. The results show that there is a unique optimal pricing, ordering, and freshness-keeping decision under all three ordering policies, but there is no joint decision. The optimal freshness-keeping and retail price under the mixed ordering policy are lower than those under the option ordering only but higher than those under the firm ordering only. When only a single order can be placed, the retailer’s optimal ordering policy is determined by demand risk. When all three ordering policies are available, the optimal ordering policy for the retailer is the mixed ordering policy. A spot market will weaken the role of option contracts in mitigating supply chain risks, and the larger the risk, the more significant the role of the spot market. Full article

Human skills are a critical factor in the success or failure of a digital project. Limited studies have been conducted to identify the industry demand for skills of scrum roles (product owner, scrum master, web developer) and levels (entry, associate, mid-senior). The evaluation of skills over time benefits both decision-makers and associated team members, which leads to successful project completions. The aim of this research is to improve decision making concerning the level-specific skills of selected scrum roles for digital projects. The study identifies major and minor skills, patterns, and relationships between levels, and formulates the mathematical equations as the most important inputs to the skill-driven model’s implementation and evaluation. Both qualitative and quantitative research methods were used to analyse 900 surveyed job advertisements published on LinkedIn in Europe. Descriptive analysis was used to analyse quantitative data while the deductive approach was followed with thematic analysis. There are required skill sets for each level of roles, level-specific skills, industry-demanded skills, and formulas related to the initial and individual skill ratings that are investigated. A new mechanism for evaluation is introduced based on “the time spent with skills”. As a result, the proposed model is implemented by feeding research findings into the Mendix programming platform. The skill-driven model is a decision-support solution in software project management to evaluate skills which assist in assigning the right person to the right digital project. Further investigation on different job portals can help to improve the accuracy of industry standards and reduce the lack of progression skills by overcoming limitations identified in this paper. Full article

This study assesses the usability of natural materials available in Australia’s remote communities for making fibre-reinforced mudbricks. The present construction cost for housing in remote areas is too high to maintain the level of housing required for the remote Australian population. As this includes mostly First Nations communities, more culturally appropriate housing materials and construction methods are being considered. This study looks at mudbricks made from laterite soil reinforced by spinifex fibre, both available in abundance in remote communities. Hence, this material is more acceptable to communities as it is more sustainable, and the construction methods are more suited for First Nations engagement. Various mixes were tested for compressive strength and erosion resistance. Results suggest that spinifex can significantly improve compressive strength and reduce erosion effects; however, spinifex showed adverse effects at the early stage of the spray test. The results satisfy the minimum strength and erosion resistance requirements for construction and suggest that spinifex-reinforced mudbricks could potentially be considered as an alternative material in remote housing. Full article

We conducted a comparative study on the characterization of Ga-polar and N-polar GaN metal–insulator–semiconductor (MIS) Schottky contact with a SiN_x gate dielectric. The correlation between the surface morphology and the current–voltage (I–V) characteristics of the Ga- and N-polar GaN Schottky contact with and without SiN_x was established. The insertion of SiN_x helps in reducing the reverse leakage current for both structures, even though the leakage is still higher for N-polar GaN, consistent with the Schottky barrier height calculated using X-ray photoelectron spectroscopy. To optimize the electric property of the N-polar device, various substrate misorientation angles were adopted. Among the different misorientation angles of the sapphire substrate, the GaN MIS Schottky barrier diode grown on 1° sapphire shows the lowest reverse leakage current, the smoothest surface morphology, and the best crystalline quality compared to N-polar GaN grown on 0.2° and 2° sapphire substrates. Furthermore, the mechanism of the reverse leakage current of the MIS-type N-polar GaN Schottky contact was investigated by temperature-dependent I–V characterization. FP emissions are thought to be the dominant reverse conduction mechanism for the N-polar GaN MIS diode. This work provides a promising approach towards the optimization of N-polar electronic devices with low levels of leakage and a favorable ideality factor. Full article

The rapid evolution of technology has given rise to a connected world where billions of devices interact seamlessly, forming what is known as the Internet of Things (IoT). While the IoT offers incredible convenience and efficiency, it presents a significant challenge to cybersecurity and is characterized by various power, capacity, and computational process limitations. Machine learning techniques, particularly those encompassing supervised classification techniques, offer a systematic approach to training models using labeled datasets. These techniques enable intrusion detection systems (IDSs) to discern patterns indicative of potential attacks amidst the vast amounts of IoT data. Our investigation delves into various aspects of supervised classification, including feature selection, model training, and evaluation methodologies, to comprehensively evaluate their impact on attack detection effectiveness. The key features selected to improve IDS efficiency and reduce dataset size, thereby decreasing the time required for attack detection, are drawn from the extensive network dataset. This paper introduces an enhanced feature selection method designed to reduce the computational overhead on IoT resources while simultaneously strengthening intrusion detection capabilities within the IoT environment. The experimental results based on the InSDN dataset demonstrate that our proposed methodology achieves the highest accuracy with the fewest number of features and has a low computational cost. Specifically, we attain a 99.99% accuracy with 11 features and a computational time of 0.8599 s. Full article

Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms. Full article

Deep convolutional neural networks (CNNs) can achieve good performance in image denoising due to their superiority in the extraction of structural information. However, they may ignore the relationships between pixels to limit effects for image denoising. Transformer, focusing on pixel to pixel relationships can effectively solve this problem. This article aims to make a CNN and Transformer complement each other in image denoising. In this study, we propose a dynamic network with Transformer for image denoising (DTNet), with a residual block (RB), a multi-head self-attention block (MSAB), and a multidimensional dynamic enhancement block (MDEB). Firstly, the RB not only utilizes a CNN but also lays the foundation for the combination with Transformer. Then, the MSAB adds positional encoding and applies multi-head self-attention, which enables the preservation of sequential positional information while employing the Transformer to obtain global information. Finally, the MDEB uses dimension enhancement and dynamic convolution to improve the adaptive ability. The experiments show that our DTNet is superior to some existing methods for image denoising. Full article