Search Results (47)

Iron deficiency is a global health issue, making the development of novel iron supplements to enhance iron absorption critically important. In this study, low molecular weight donkey-hide gelatin peptides (LMW DHGP) were enzymatically hydrolyzed from donkey-hide gelatin. Experimental results demonstrated that the iron chelating capacity of LMW DHGP reached 249.98 μg/mg. Key amino acids (Asn, Gly, Cys, Lys) may participate in chelation. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis showed rough, porous amorphous structures of LMW DHGP-iron complexes. The results of circular dichroism spectroscopy (CD) indicated that the self-assembly of LMW DHGP-iron complexes appears to be primarily mediated by peptide α-helical structural conformations. Fourier transform infrared (FTIR) spectroscopy further indicated that the interaction between LWM DHGP and Fe²⁺ likely occurs through carboxyl and amino functional groups. In vitro digestion stability studies demonstrated that LMW DHGP-iron complexes exhibited superior iron ion solubility compared to FeSO₄ in simulated gastrointestinal conditions. PGPAG-iron complexes exhibited the highest antioxidant activity, with scavenging rates of 71.64% (DPPH radical) and 88.79% (ABTS radical). These findings collectively suggest that LMW DHGP-iron complexes possess significant potential as a novel iron supplement in food applications, which provides valuable theoretical insights for the development of innovative iron supplementation strategies. Full article

Background: This single-center retrospective observational study reviewed data from 2305 persons examined for coronary artery calcium (CAC) with non-contrast-enhanced cardiac CT. Other cardiac structures, including chamber volumes, were evaluated besides the CAC scoring. We proposed a novel body size indexing measure that may outperform common indices for quantifying total heart volume (THV). Methods: This index is the sum of height and the difference between height (unitless) and body surface area (unitless), [h+(h-BSA)], and if the (h-BSA) equals “zero”, it is a feature of the “standard human”. Results: We found that, in subjects with a low cardiovascular (CV) risk, the THV normalized for the novel index was simply a function of BW gain, being the highest in obese. If high-CV-risk features (hypertension, diabetes) were present, the measured THV was larger than expected for BW gain, exceeding values observed in low-CV-risk ones. Differences were found to be sex-independent in all BMI categories. Conclusions: Common BSA correction hides these differences and makes the prognostication of CV risk error-introducing. The indexation we proposed might help distinguish the effects of body weight gain from the ones resulting from the presence of certain cardiovascular diseases. Full article

Inverse Distance Weighting (IDW) is a common method for spatial interpolation. Still, its accuracy decreases when there is a cluster of measurement stations or when some measuring stations are hidden behind others. This paper introduces Clusters Unifying Through Hiding Interpolation (CUTHI), a simple approach to enhance IDW accuracy. CUTHI calculates a weight for each station that considers its visibility from the interpolation point, reducing the influence of clustered or hidden stations. The method is tested in three cases: elevation data, rainfall measurements, and a mathematical function. Results demonstrate that CUTHI consistently outperforms traditional IDW, especially in areas with clustered measurement stations. CUTHI also treats the bull’s eye problem. This improved accuracy makes CUTHI a valuable tool for various applications requiring precise spatial interpolation. Full article

Two experiments were conducted to determine which part of the empty body of Boer × Saanen intact male kids can be used to predict the chemical composition of the whole body. In the first experiment, kids were fed ad libitum and slaughtered at 5, 10, and 15 kg body weight (BW). Eighteen animals were group-fed at three intake levels (ad libitum or restricted to 30% and 60% of the ad libitum level). When the ad libitum animal in the group reached 15 kg BW, all animals in the group were slaughtered. In the second experiment, kids were fed ad libitum and slaughtered at 15, 20, and 25 kg BW. Twenty-one animals were group-fed at three intake levels and slaughtered when the ad libitum animal within the group reached 25 kg BW. Analyzed body parts included head + feet, hide, organs, neck, shoulder, ribs, loin, leg, 9–11th ribs, and half carcass. Principal component and cluster analyses showed that the neck, 9–11th ribs, and loin had the highest frequency of grouping with the empty body. These body parts were used to develop prediction models for estimating body composition. The neck, loin, and 9–11th ribs accurately and precisely predicted the dry matter, ash, fat, protein, and energy body composition of goats, with most models also incorporating BW as a predictor variable. The equations showed root mean squared error (RMSE) lower than 13.5% and a concordance correlation coefficient (CCC) greater than 0.84. Fat and protein concentrations in the loin and neck were also reliable predictors of empty body energy composition (RMSE = 2.9% of mean and concordance correlation coefficient = 0.93). Removing the loin and 9–11th ribs could reduce the carcass retail price. Using the neck to estimate body composition in growing Boer × Saanen goats provides a valuable alternative for nutrition studies, given its low commercial value. Full article

The growing importance of critical infrastructure systems (CIS) makes maintaining their normal operation against deliberate attacks such as terrorism a significant challenge. Combining game theory and complex network theory provides a framework for analyzing CIS robustness in adversarial scenarios. Most existing studies focus on single-layer networks, while CIS are better modeled as multilayer networks. Research on multilayer network games is limited, lacking methods for constructing incomplete information through link hiding and neglecting the impact of cascading failures. We propose a multilayer network Stackelberg game model with incomplete information considering cascading failures (MSGM-IICF). First, we describe the multilayer network model and define the multilayer node-weighted degree. Then, we present link hiding rules and a cascading failure model. Finally, we construct MSGM-IICF, providing methods for calculating payoff functions from the different perspectives of attackers and defenders. Experiments on synthetic and real-world networks demonstrate that link hiding improves network robustness without considering cascading failures. However, when cascading failures are considered, they become the primary factor determining network robustness. Dynamic capacity allocation enhances network robustness, while changes in dynamic costs make the network more vulnerable. The proposed method provides a new way of analyzing the robustness of diverse CIS, supporting resilient CIS design. Full article

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency. Full article

Agricultural areas can provide sources of food and hiding and nesting places for wild birds. Thus, the chemical load of potentially toxic elements (Cd, Cu, Pb) due to industrial and agricultural activities can affect not only the adult birds but also the embryos developing in the egg. The toxic effects of heavy metals applied alone were investigated on chicken embryos in the early and late stages of embryonic development using injection and immersion treatment methods. On day 3 of incubation, permanent preparations were made from the embryos to study the early development stage. There were no significant differences observed in embryo deaths and developmental abnormalities in this stage. On day 19 of incubation, the number of embryonic deaths, the body weight of the embryos, and the type of developmental abnormalities were examined. The embryonic mortality was statistically higher in the groups treated with cadmium and lead in the case of the injection treatment. A significant increase in developmental disorders was observed in the copper-treated group using the immersion application. The body weight significantly decreased in the cadmium- and lead-treated group using both treatment methods. However, a significant change in the body weight in the copper-treated group was only realized due to the injection method. Full article

Consider a finite directed graph without cycles in which the arrows are weighted by positive weights. We present an algorithm for the computation of a new distance, called path-length-weighted distance, which has proven useful for graph analysis in the context of fraud detection. The idea is that the new distance explicitly takes into account the size of the paths in the calculations. It has the distinct characteristic that, when calculated along the same path, it may result in a shorter distance between far-apart vertices than between adjacent ones. This property can be particularly useful for modeling scenarios where the connections between vertices are obscured by numerous intermediate vertices, such as in cases of financial fraud. For example, to hide dirty money from financial authorities, fraudsters often use multiple institutions, banks, and intermediaries between the source of the money and its final recipient. Our distance would serve to make such situations explicit. Thus, although our algorithm is based on arguments similar to those at work for the Bellman–Ford and Dijkstra methods, it is in fact essentially different, since the calculation formula contains a weight that explicitly depends on the number of intermediate vertices. This fact totally conditions the algorithm, because longer paths could provide shorter distances—contrary to the classical algorithms mentioned above. We lay out the appropriate framework for its computation, showing the constraints and requirements for its use, along with some illustrative examples. Full article

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO₂, SO₂, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. Full article

Multi-factor authentication (MFA) is a system for authenticating an individual’s identity using two or more pieces of data (known as factors). The reason for using more than two factors is to further strengthen security through the use of additional data for identity authentication. Sequential MFA requires a number of steps to be followed in sequence for authentication; for example, with three factors, the system requires three authentication steps. In this case, to proceed with MFA using a deep learning approach, three artificial neural networks (ANNs) are needed. In contrast, in parallel MFA, the authentication steps are processed simultaneously. This means that processing is possible with only one ANN. A convolutional neural network (CNN) is a method for learning images through the use of convolutional layers, and researchers have proposed several systems for MFA using CNNs in which various modalities have been employed, such as images, handwritten text for authentication, and multi-image data for machine learning of facial emotion. This study proposes a CNN-based parallel MFA system that uses concatenation. The three factors used for learning are a face image, an image converted from a password, and a specific image designated by the user. In addition, a secure password image is created at different bit-positions, enabling the user to securely hide their password information. Furthermore, users designate a specific image other than their face as an auxiliary image, which could be a photo of their pet dog or favorite fruit, or an image of one of their possessions, such as a car. In this way, authentication is rendered possible through learning the three factors—that is, the face, password, and specific auxiliary image—using the CNN. The contribution that this study makes to the existing body of knowledge is demonstrating that the development of an MFA system using a lightweight, mobile, multi-factor CNN (MMCNN), which can even be used in mobile devices due to its low number of parameters, is possible. Furthermore, an algorithm that can securely transform a text password into an image is proposed, and it is demonstrated that the three considered factors have the same weight of information for authentication based on the false acceptance rate (FAR) values experimentally obtained with the proposed system. Full article

The leather and footwear cluster in Tungurahua state has a main role in the country’s production chain, supplying 76% of the country’s tanned hides for the textile, footwear, and furniture industries, among others. The processes involved in leather tanning generate liquid, gaseous, and solid waste, the latter including the shavings from the leather trimming process, which, due to their composition and volume, are compressed and disposed of in sanitary landfills. Through Strategic Design and circular processes, as axes of change in production, new processes and strategies are established for the creation of products derived from the reuse of tannery waste; as a result, a decorative block is obtained for the design of architectural spaces with dimensions of 150 × 75 × 355 mm, 300 g in weight, and a compressive strength of 15.72 MPa. This is subjected to physicochemical tests for its validation. Full article

This paper presents an optimal weighted modulus (OWM) algorithm able to conceal secret messages in a high dynamic range image encoded via the RGBE format, consisting of the red, green, blue, and exponent channels. In contrast to current state-of-the-art schemes, which mainly employ limited and vulnerable homogeneous representations, our OWM scheme exploits four channels and an embedding weight to conceal secret messages, thereby offering more embedding capacities and undetectability against steganalytic tools. To reduce the impact on the luminance variation, we confine the maximal change incurred in the exponent channel when embedding secret messages. In addition, we propose an SEC scheme to eliminate the pixel saturation problem, even though a pixel contains values close to the boundary extreme. As a result, the stego images produced not only exhibit high quality but also comply with the RGBE encoding format, making them able to resist malicious steganalytic detection. The experimental results show that our scheme offers larger embedding rates, between 2.8074 and 5.7549 bits per pixel, and the average PSNR value for twelve tone-mapped images is over 48 dB. In addition, the HDR VDP 3.0 metric demonstrates the high fidelity of stego HDR images, where the average Q value is close to the upper bound of 10.0. Our scheme can defeat RS steganalytic attacks and resist image compatibility attacks. A comparison result confirms that our scheme outperforms six current state-of-the-art schemes. Full article

This study aimed to explore the structural characteristics and antioxidant mechanism of donkey-hide gelatin peptides. After hydrolysis and ultrafiltration treatment, five gelatin peptides with different molecular weights (MWs) were obtained. Amino acid analysis showed that gelatin peptides with different MWs contained a large number of amino acids, including G, P, E, N, A, and R, and differences were noted in the content of various amino acids. Fourier transform infrared spectroscopy and circular dichroism revealed that these gelatin peptides differed in terms of the peak strength of functional groups and number of secondary structures. Moreover, 26 pentapeptides/hexapeptides were identified. Among them, we investigated by molecular docking how PGPAP, which has the best antioxidant activity, may interact with the Keap1 protein. The results showed that the PGPAP-Keap1 complex had a stable conformation, and Arg415, Gly462, Phe478, and Tyr572 were the key residues involved in the binding of the peptide PGPAP to Keap1. Our results demonstrated that PGPAP could serve as a bioactive peptide with antioxidant activity. Full article

Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes. Full article

With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to life patterns, and obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices are different from check-in data that have semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose a Self-Attention model for next location prediction based on semantic mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k-nearest POI, and then use the reciprocal of the distance from the stay point to the k-nearest POI and the number of categories as weights. Finally, we use the probability to express the semantic without losing other important semantic information. Furthermore, this research, combined with sequential pattern mining, can result in richer semantic features. In order to better perceive the trajectory, temporal features learn the periodicity of time series by the sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1. Full article