Appl. Sci., Volume 13, Issue 7 (April-1 2023) – 576 articles

In recent years, millimeter-wave communication has played a crucial role in satellite communication, 5G, and even 6G applications. The millimeter-wave electro-optic modulator is capable of receiving and processing millimeter-wave signals effectively. However, the large attenuation of millimeter waves in the air remains a primary limiting factor for their future applications. Therefore, finding a waveguide structure with a high quality factor (Q-factor) is critical for millimeter-wave electro-optic modulators. This manuscript presents the demonstration of a double ring modulator made of lithium niobate with the specific goal of modulating an RF signal at approximately 35 GHz. By optimizing the microring structure, the double ring resonator with high Q-factor is studied to obtain high sensitivity modulation of the RF signal. This manuscript employs the transfer matrix method to investigate the operational principles of the double ring structure and conducts simulations to explore the influence of structural parameters on its performance. Through a comparison with the traditional single ring structure, it is observed that the Q-factor of the double ring modulator can reach 7.05 × 10⁸, which is two orders of magnitude greater than that of the single ring structure. Meanwhile, the electro-optical tunability of the double ring modulator is 6 pm/V with a bandwidth of 2.4 pm, which only needs 0.4 V driving voltage. The high Q double ring structure proposed in this study has potential applications not only in the field of communication but also as a promising candidate for a variety of chemical and biomedical sensing applications. Full article

Based on the statistics of 42 case histories, 732 finite element numerical simulations are conducted to determine the scope of the influenced zone of deep excavation under different conditions of excavation depth (H_e) and the maximum retaining wall deflection (δ_hm). On this basis, the effects of H_e and δ_hm on the scope of the influenced zone are studied, and a simplified prediction method for the scope of the influenced zone under any H_e and δ_hm conditions and the adjacent tunnel displacement is proposed. Then, the reliability of the proposed method is verified by comparing it with the current research and case histories. And finally, the proposed method is applied to an actual project, and the application effect is evaluated. The results show that the range outside the pit can be divided into “primary”, “secondary”, “general”, and “weak” influenced zones. The influenced zone can be simplified as a right-angled trapezoid shape, and the scope of influence zones can be quickly determined by defining three parameters: width coefficient M, depth coefficients N₁ and N₂. The parameters M and N₂ have a linear relationship with H_e and δ_hm, and N₁ varies between 1–2 with an average of about 1.5. In actual application, the effect of deep excavation on the adjacent tunnel can be alleviated by using the proposed method to predict the excavation-induced displacement of the adjacent tunnel and take some measures. Full article

In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training data are generated from a parametric and stochastic finite element model. The learning activity is performed by a support vector machine with Bayesian optimization of the hyperparameters, in which a penalty matrix is introduced to minimize the probability of missed alarms. Then, the estimated structural health conditions are used to update an exponential degradation model with random coefficients suitable for providing a prediction of the remaining useful life of the simulated corroded column. The results obtained show the potentiality of the proposed framework and its possible future extension for different types of damage and structural types. Full article

In the digital era, smart-home users face growing threats from cyberattacks that threaten their privacy and security. Hence, it is essential for smart-home users to prioritize cybersecurity education and training to secure their homes. Despite this, the high cost of such training often presents a barrier to widespread adoption and accessibility. This study aims to analyze the costs and benefits associated with various cybersecurity investment strategies for smart-home users in the context of cyberattacks. The study utilizes evolutionary game theory to model a game comprised of three populations: smart-home users, stakeholders, and attackers. We derive and analyze the replicator dynamics of this game to determine the evolutionarily stable strategy (ESS). Furthermore, we investigate the impacts of the costs and benefits of cybersecurity investment and cyberattack costs on the ESS. The findings indicate that incurring costs for cybersecurity training is beneficial for smart-home users to protect their homes and families. However, the training costs must be low and affordable for smart-home users in order to ensure their participation and engagement. Additionally, providing rewards for commitment to cybersecurity is crucial in sustaining interest and investment over the long term. To promote cybersecurity awareness and training for smart-home users, governments can incorporate it as a priority in national cybersecurity plans, provide subsidies for training costs, and incentivize good cybersecurity practices. Full article

Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, risk management, and trading strategies. Forecasting stock indices with noisy data is a complex and challenging task, but it plays an important role in the appropriate timing of buying or selling stocks, which is one of the most popular and valuable areas in finance. In this work, we propose novel hybrid models for forecasting the one-time-step and multi-time-step close prices of DAX, DOW, and S&P500 indices by utilizing recurrent neural network (RNN)–based models; convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent unit (GRU)-CNN, and ensemble models. We propose the averaging of the high and low prices of stock market indices as a novel feature. The experimental results confirmed that our models outperformed the traditional machine-learning models in 48.1% and 40.7% of the cases in terms of the mean squared error (MSE) and mean absolute error (MAE), respectively, in the case of one-time-step forecasting and 81.5% of the cases in terms of the MSE and MAE in the case of multi-time-step forecasting. Full article

The multi-objective optimization problem is difficult to solve with conventional optimization methods and algorithms because there are conflicts among several optimization objectives and functions. Through the efforts of researchers and experts from different fields for the last 30 years, the research and application of multi-objective evolutionary algorithms (MOEA) have made excellent progress in solving such problems. MOEA has become one of the primary used methods and technologies in the realm of multi-objective optimization. It is also a hotspot in the evolutionary computation research community. This survey provides a comprehensive investigation of MOEA algorithms that have emerged in recent decades and summarizes and classifies the classical MOEAs by evolutionary mechanism from the viewpoint of the search strategy. This paper divides them into three categories considering the search strategy of MOEA, i.e., decomposition-based MOEA algorithms, dominant relation-based MOEA algorithms, and evaluation index-based MOEA algorithms. This paper selects the relevant representative algorithms for a detailed summary and analysis. As a prospective research direction, we propose to combine the chaotic evolution algorithm with these representative search strategies for improving the search capability of multi-objective optimization algorithms. The capability of the new multi-objective evolutionary algorithm has been discussed, which further proposes the future research direction of MOEA. It also lays a foundation for the application and development of MOEA with these prospective works in the future. Full article

Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very widespread languages, such as Chinese and English, and rarely applied to speech recognition of Central and West Asian agglutinative languages. There are more network parameters in the Conformer end-to-end speech recognition model, so the structure of the model is complex, and it consumes more resources. At the same time, we found that there is a long-tail problem in Kazakh, i.e., the distribution of high-frequency words and low-frequency words is not uniform, which makes the recognition accuracy of the model low. For these reasons, we made the following improvements to the Conformer baseline model. First, we constructed a low-rank multi-head self-attention encoder and decoder using low-rank approximation decomposition to reduce the number of parameters of the multi-head self-attention module and model’s storage space. Second, to alleviate the long-tail problem in Kazakh, the original softmax function was replaced by a balanced softmax function in the Conformer model; Third, we use connectionist temporal classification (CTC) as an auxiliary task to speed up the model training and build a multi-task lightweight but efficient Conformer speech recognition model with hybrid CTC/Attention. To evaluate the effectiveness of the proposed model, we conduct experiments on the open-source Kazakh language dataset, during which no external language model is used, and the number of parameters is relatively compressed by 7.4% and the storage space is relatively reduced by 13.5 MB, while the training speed and word error rate remain basically unchanged. Full article

Soft broken surrounding rock exhibits obvious rheological properties and time-dependent weakening effects under the action of deep high-ground stress, leading to the increasingly prominent problem of sustained large deformation in deep roadways. In this study, with the II5 Rail Rise in Zhuxianzhuang Coal Mine as an example, the mechanism and control technology of time-dependent damage and instability in a deep soft-rock roadway were explored through a field observation and numerical simulation. The research results show that the range of the loose circle in the deep fractured surrounding rock can reach 3.0 m. The expansion of shallow and deep cracks causes the primary plastic deformation and secondary rheological deformation of the surrounding rock, with the rheological deformation rate increasing by 21.4% every 55 days on average, which ultimately induces the instability and failure of the surrounding rock. Based on the mechanism of roadway instability, a control technology of high-preload bolt + deep- and shallow-borehole crack filling was proposed. The technology reduces deformation and ensures the stability of the roadway surrounding rock by inhibiting the propagation of deep and shallow cracks and reinforcing the surrounding rock. Full article

This manuscript presents three families of distributions, namely the Beta, Beta Prime and Beta Exponential families of distributions. From all the distributions of these families, 14 statistical distributions of three, four and five parameters are presented that have applicability in the analysis of extreme phenomena in hydrology. These families of distributions were analyzed regarding the improvement of the existing legislation for the determination of extreme events, specifically the elaboration of a norm regarding frequency analysis in hydrology. To estimate the parameters of the analyzed distributions, the method of ordinary moments and the method of linear moments were used; the latter conforms to the current trend for estimating the parameters of statistical distributions. The main purpose of the manuscript was to identify other distributions from these three families with applicability in flood frequency analysis compared to the distributions already used in the literature from these families, such as the Log–logistic distribution, the Dagum distribution and the Kumaraswamy distribution. The manuscript does not exclude the applicability of other distributions from other families in the frequency analysis of extreme values, especially since these families were also analyzed within the research carried out in the Faculty of Hydrotechnics and presented in other materials. All the necessary elements for their use are presented, including the probability density functions, the complementary cumulative distribution functions, the quantile functions and the exact and approximate relations for estimating parameters. A flood frequency analysis case study was carried out for the Prigor RiverRiver, to numerically present the proposed distributions. The performance of this distributions were evaluated using the relative mean error, the relative absolute error and the L-skewness–L-kurtosis diagram. The best fit distributions are the Kumaraswamy, the Generalized Beta Exponential and the Generalized Beta distributions, which presented a stability related to both the length of the data and the presence of outliers. Full article

The comfort of a bus running on different road conditions is a matter of public concern. In this paper, the differential equations of motion are established for a bus running on different road conditions and the whole driving process is mechanically analyzed. Firstly, the bump degree at different positions is quantitatively analyzed and it is found that the rear row is bumpier on different roads. Then, the relationship between the speed of the bus and the vertical displacement and acceleration is quantitatively described. Regardless of the speed, a similar displacement and acceleration will be eventually achieved, but the speed is higher, and the duration of maximum displacement and acceleration is longer. When the speed is 8 m/s, resonance occurs on the bus during road condition II. Finally, the change in vertical displacement and acceleration under the action of different spring stiffness coefficient ratios of the front and rear wheels is quantitatively analyzed. High stiffness ratios mean less displacement and acceleration. By establishing an actual excitation road surface, the differential equations and analytical solutions in this paper can be used to roughly analyze the mechanical response of a traveling bus. These results can provide some guidance for the design and driving of buses. Full article

The article suggests a system dynamics model for estimating the demand for public transport. Traditional scientific and technical transport modeling approaches involve coherent systems, meticulously considering other impactful variables for transport modeling. The vastness of the variables and their combinations hinder us from grasping all possible system interactions. This research aims at proposing a model that comprises decisive factors in relation to the supply and demand in various modes of transport, designing likely scenarios of the transport system development in a specific transport territory. The model uses system dynamics tools to explore the interaction between individual system elements and transport subsystems. A wise choice of crucial system elements, well-adjusted relationships and behavior settings, as well as system dynamics tools, allow for a considerable simplification of an otherwise complex system. The article works with a principle of stock and flow diagrams for forecasting supply and demand in public transport. We take into consideration the implementation of a ‘demand index’ in public and car passenger transport with a subsequent comparison. This innovative approach monitors the development of a regional or municipal transport system while assessing its sustainability. Suggested demand indexes may serve as indicators for a sustainable municipal system. The suggested model reflects data from the South Bohemian region in the Czech Republic and may involve other elements and indicators of a sustainable transport system. Full article

This retrospective study aimed to evaluate a possible correlation between the characteristics of the mandibular ramus and lower third molar impaction by comparing a group of subjects with an impacted lower third molar and a second group with normal eruption for an early prediction of this pathology. This comparison was made using linear and angular measurements, which were taken on digital panoramic radiographs. Materials and methods: A total of 726 orthopantomographs (OPT) were examined, and 81 were considered suitable for the present study. The results were divided into two groups: a control group and an experimental group. The control group comprised 38 cases in which patients had at least one lower third molar that had erupted, and the experimental group comprised 43 cases in which patients had at least one lower third molar that was impacted or partially impacted. In total, 16 variables (11 linear, 4 angular, and 1 ratio) were determined and measured by an experienced observer. Results: The control group had a larger retromolar space, a larger impaction angle and a higher ratio of retromolar area to the third molar, compared to the experimental group. In contrast, the experimental group showed a deeper sigmoid notch depth than the control group did. In the control group, moderate positive correlations were found between both the length of the coronoid and the width of the third molar, and the retromolar space. Furthermore, in the experimental group, moderate positive correlations were found between both the angular condyle–coronoid process and the inclination of the lower posterior teeth, and the retromolar space. Conclusion: this study showed that the angle of a lower third molar, in relation to mandibular pain, can be an index for predicting tooth inclusion. Full article

Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction methods, we created a zero-shot with a quick relation extraction task setup. Each triplet extracted from an input phrase consists of the subject, relation type, and object. This paper suggests generating structured texts by urging language models to provide related instances. Our model consists of two modules: relation generator and relation and triplet extractor. We use the Uyghur relation prompt in the relation generator stage to generate new synthetic data. In the relation and triple extraction stage, we use the new data to extract the relation triplets in the sentence. We use multi-language model prompts and structured text techniques to offer a structured relation prompt template. This method is the first research that extends relation triplet extraction to a zero-shot setting for Uyghur datasets. Experimental results show that our method achieves a maximum weighted average F1 score of 47.39%. Full article

While traditional industrial robots participate in repetitive manufacturing processes from behind caged safety enclosures, collaborative robots (cobots) offer a highly flexible and human-interactive solution to manufacturing automation. Rather than operating from within cages, safety features such as force and proximity sensors and programmed protection zones allow cobots to work safely, close to human workers. Cobots can be configured to either stop or slow their motion if they come in contact with a human or obstacle or enter a protection zone, which may be a high pedestrian traffic area. In this way, a task can be divided into sub-processes allocated to the cobot or the human based on suitability, capability or human preference. The flexible nature of the cobot makes it ideal for low-volume, ‘just-in-time’ manufacturing; however, this requires frequent reprogramming of the cobot to adapt to the dynamic processes. This paper reviews relevant cobot programming and control methods currently used in the manufacturing industry and alternative solutions proposed in the literature published from 2018 to 2023. The paper aims to (1) study the features and characteristics of existing cobot programming and control methods and those proposed in the literature, (2) compare the complexity of the task that the cobot is to perform with the skills needed to program it, (3) determine who is the ideal person to perform the programming role, and (4) assess whether the cobot programming and control methods are suited to that person’s skillset or if another solution is needed. The study is presented as a guide for potential adopters of cobots for manufacturing and a reference for further research. Full article

The preliminary sorting of plastic products is a necessary step to improve the utilization of waste resources. To improve the quality and efficiency of sorting, a plastic detection scheme based on deep learning is proposed in this paper for a waste plastics sorting system based on vision detection. In this scheme, the YOLOX (You Only Look Once) object detection model and the DeepSORT (Deep Simple Online and Realtime Tracking) multiple object tracking algorithm are improved and combined to make them more suitable for plastic sorting. For plastic detection, multiple data augmentations are combined to improve the detection effect, while BN (Batch Normalization) layer fusion and mixed precision inference are adopted to accelerate the model. For plastic tracking, the improved YOLOX is used as a detector, and the tracking effect is further improved by optimizing the deep cosine metric learning and the metric in the matching stage. Based on this, virtual detection lines are set up to filter and extract information to determine the sorted objects. The experimental results show that the scheme proposed in this paper makes full use of vision information to achieve dynamic and real-time detection of plastics. The system is effective and versatile for sorting complex objects. Full article

This study aims to produce renewable energy by applying a solar-energy-harvesting architectural design using solar panels on the facade of a building. To install as many solar panels as possible on the building elevation, the Signal Box auf dem Wolf, located in Basel, Switzerland, was selected as the research target. The solar panels to be installed on the facade of the Signal Box auf dem Wolf are planned such that they are able to move according to the optimal tilt angle every month to allow maximal energy generation. The kinetic photovoltaic facade system and the simulation of renewable energy generation were implemented using a parametric design. The novelty of this study is the development of a kinetic photovoltaic facade system using a parametric design algorithm. From the perspective of renewable energy in the field of architecture, the kinetic photovoltaic facade system developed in this study has the advantage of producing maximal renewable energy according to the optimal tilt angle of the solar panels. Additionally, building facades that move according to the optimal tilt angle will contribute to the expansion of the field of sustainable architectural design. Full article

Satellite remote sensing has entered the era of big data due to the increase in the number of remote sensing satellites and imaging modes. This presents significant challenges for the processing of remote sensing systems and will result in extremely high real-time data processing requirements. The effective and reliable geometric positioning of remote sensing images is the foundation of remote sensing applications. In this paper, we propose an optical remote sensing image matching method based on a simple stable feature database. This method entails building the stable feature database, extracting local invariant features that are comparatively stable from remote sensing images using an iterative matching strategy, and storing useful information about the features. Without reference images, the feature database-based matching approach potentially saves storage space for reference data while increasing image processing speed. To evaluate the performance of the feature database matching method, we train the feature database with various local invariant feature algorithms on different time phases of Gaofen-2 (GF-2) images. Furthermore, we carried out matching comparison experiments with various satellite images to confirm the viability and stability of the feature database-based matching method. In comparison with direct matching using the classical feature algorithm, the feature database-based matching method in this paper can essentially improve the correct rate of feature point matching by more than $30\%$ and reduce the matching time by more than $40\%$. This method improves the accuracy and timeliness of image matching, potentially solves the problem of large storage space occupied by the reference data, and has great potential for fast matching of optical remote sensing images. Full article

Increases in algal growth have been reported in rivers, reservoirs, and other water resources worldwide, including Korea. Algal overgrowth can result in algal bloom, which has several negative impacts, such as ecosystem degradation and economic losses. Mitigation measures employed in Korea include an algal warning system and survey-based water quality forecast systems. However, these methods are time-consuming and require sample collection from the site. On the other hand, remote sensing techniques that use chlorophyll a are unable to distinguish between different types of algal species. In this paper, we aimed to identify a classification technique based on remote sensing methods that can be used to distinguish between blue-green algae and green algae. We acquired and prepared an algal culture solution and used a hyperspectral sensor to obtain an algae spectrum. Thereafter, we measured the absorption and emission spectra of blue-green and green algae and distinguished them using the instantaneous slope change of the spectrum. The absorption spectra for green algae showed two peaks at 417–437 nm and 661–673 nm, whereas those of blue-green algae showed three peaks at 449–529 nm, 433–437 nm, and 669–677 nm. The results of this study could form a basis for developing mitigation measures for algal overgrowth. Full article

The growing demand for personalized treatments and the constant observation of vital signs for extended periods could positively solve the problematic concerns associated with the necessity for patient control and hospitalization. The impressive development in biosensing devices has led to the creation of man-made implantable devices that are temporarily or permanently introduced into the human body, and thus, diminishing the pain and discomfort of the person. Despite all promising achievements in this field, there are some critical challenges to preserve reliable functionality in the complex environment of the human body over time. Biosensors in the in vivo environment are required to have specific features, including biocompatibility (minimal immune response or biofouling), biodegradability, reliability, high accuracy, and miniaturization (flexible, stretchable, lightweight, and ultra-thin). However, the performance of implantable biosensors is limited by body responses and insufficient power supplies (due to minimized batteries/electronics and data transmission without wires). In addition, the current processes and developments in the implantable biosensors field will open new routes in biomedicine and diagnostic systems that monitor occurrences happening inside the body in a certain period. This topical paper aims to give an overview of the state-of-the-art implantable biosensors and their design methods. It also discusses the latest developments in material science, including nanomaterials, hydrogel, hydrophilic, biomimetic, and other polymeric materials to overcome failures in implantable biosensors’ reliability. Lastly, we discuss the main challenges faced and future research prospects toward the development of dependable implantable biosensors. Full article

Plastic pollution is one of the most serious environmental issues, causing severe environmental damage. It is of vital importance to find an efficient and eco-friendly approach to biodegrading plastics. The aim of this study was to isolate and characterize different bacterial isolates from water samples in the Al-Ahsa region of Saudi Arabia. The ability to degrade low-density polyethylene (LDPE) plastic was evaluated using multiple approaches, including changes in the media pH values, weight loss, Fourier transform infrared (FTIR), and gas chromatography–mass spectrometry (GC–MS). The water samples were collected from plastic-contaminated sites in Al-Ahsa, and bacterial isolates were obtained using a mineral nutrient medium (MNM) enriched with LDPE as the only carbon and energy source. Two bacterial isolates (APCK5 and APCZ14) were obtained and they showed potential LDPE degradation, as evidenced by changes in media pH (from 7.0 ± 0.03 to 6.17 ± 0.05 and 6.22 ± 0.03), LDPE weight reduction (8.1 ± 0.63% and 18.85 ± 0.96%, respectively), and FTIR and GC–MS analyses. Based on 16S rRNA gene similarities, APCZ14 and APCK5 were determined to be most closely related to the genus Brucella. APCZ14 exhibited a 99.48% homology with Brucella cytisi, whereas APCK5 showed a 99.33% similarity level to Brucella tritici. In conclusion, both bacterial strains had high efficiency in plastic biodegradation and could be developed for wide use as an eco-friendly method to remove or reduce plastic pollutants from the environment. Full article

A self-powered electrocoagulation system with a single-chamber aluminum–air fuel cell was employed for phosphate removal in this study. Electricity production and aluminum hydroxides in solution were also investigated. When the NaCl concentration increased from 2 mmol/L to 10 mmol/L, the phosphate removal increased from 86.9% to 97.8% in 60 min. An electrolyte composed of 10 mmol/L of NaCl was shown to obtain a maximum power density generation of 265.7 mW/m². When the initial solution pH ranged from 5.0 to 9.0, 98.5% phosphate removal and a maximum power density of 338.1 mW/m² were obtained at pH 6.0. Phosphate was mainly removed by aluminum hydroxide adsorption. These results demonstrate that the aluminum–air fuel cell can be applied as electricity-producing electrocoagulation equipment. Aluminum–air fuel cells provide an alternative method to meet the goal of carbon neutrality in wastewater treatment compared with traditional energy-consuming electrocoagulation systems. Full article

A new multi-constraint optimization model with the weighted objective function is proposed to design the multi-input-multi-output (MIMO) compliant mechanisms. The main feature of this work is that both the two notable problems related to the de facto hinge and the movement coupling are tackled simultaneously in the topological synthesis of MIMO compliant mechanisms. To be specific, the first problem is the severe stress concentration in the flexible hinge areas, and it is solved by the introduction of input and output compliances into the objective function, which could facilitate the optimization to strike a good balance between structural flexibility and stiffness. The second problem is the high degree of control complexity caused by the coupled outputs and inputs, and it is addressed by achieving the complete decoupling with two groups of extra constraints that are used to suppress the input coupling and the output coupling, respectively. As the most common and effective topology optimization method, the Solid Isotropic Material with Penalization (SIMP)-based density method is adopted here to obtain the optimized configurations. After the analytical sensitivity deduction related to the weighted objective function and constraints, two typical numerical examples are presented to demonstrate the validity of the proposed topology optimization framework in designing the hinge-free and completely decoupled MIMO compliant mechanisms. Full article

In order to safely and efficiently use soft rock aggregate cemented dams in red bed regions and promote the development of widely sourced cemented sand and gravel dam materials, the Jinjigou project in China applied soft rock for the first time in the construction of cemented material dams. This article further explores the concept of cemented material dams from conducting on-site direct shear tests and research on soft rock material ratios and explores and invents a new structure and construction method by combining soft rock cemented sand and gravel with cemented rockfill. This article also proposes a digital mixing and intelligent dynamic control method for cemented material dams with soft rock. The research results show that soft rock aggregate content not exceeding 60% can produce soft rock cemented gravel with a compressive strength of no less than 6 MPa. The stress on the dam body is small and does not produce tensile stress. The dam body with added soft rock has certain shear-bearing capacity, with a shear friction coefficient of 0.99~1.10 MPa, cohesion of 0.26~0.53 MPa, and high residual strength, accounting for 60~80% of the peak strength. At the same time, the problems of large fluctuations in moisture content and the uneven grading of the soft rock and riverbed gravel mix during the mixing and production process, and the significant influence on safety caused by the large strength dispersion of the cemented sand and gravel, are resolved, ensuring the quality of soft rock cemented sand and gravel preparation. The successful application of soft rock cemented material dams in Jinjigou has achieved a breakthrough in key technologies for soft rock cemented dam construction in red bed regions, proving the feasibility of soft rock cemented material dam construction and having broad prospects for application and promotion. Full article

Peer-to-peer (P2P) networks are distributed systems with a communication model in which no central authority governs the behavior of individual peers. These networks currently account for a considerable percentage of all bandwidth worldwide. However, this communication model also has a clear disadvantage: it has a multitude of vulnerabilities and security threats. The nature of the P2P philosophy itself means that there is no centralized server responsible for uploading, storing, and verifying the authenticity of the shared files and packets. A direct consequence of this is that P2P networks are a good choice for hackers for the spread of malicious software or malware in general since there is no mechanism to control what content is shared. In this paper, we present a mathematical model for P2P networks to study the effect of two different attacks on these systems, namely, malware and denial of service. To analyze the behavior of the cyber attacks and identify important weaknesses, we develop different Markov chains that reflect the main dynamics of the system and the attacks. Specifically, our model considers the case in which a certain number of nodes are infected with a cyber worm that is spread throughout the network as the file is shared among peers. This allows observation of the final number of infected peers when an initial number (we evaluate the system for from 1 to 14 initial nodes) of malicious nodes infect the system. For the DoS attack, our model considers the portion of peers that are unable to communicate and the average attack duration to study the performance degradation of such an attack. A two-pronged approach was used to study the impact of the attacks on P2P networks; the first focused only on the P2P network, and the second focused on the attacks and the network. Full article

Image conversion of malicious binaries, or binary visualisation, is a relevant approach in the security community. Recently, it has exceeded the role of a single-file malware analysis tool and has become a part of Intrusion Detection Systems (IDSs) thanks to the adoption of Convolutional Neural Networks (CNNs). However, there has been little effort toward image segmentation for the converted images. In this study, we propose a novel method that serves a dual purpose: (a) it enhances colour and pattern segmentation, and (b) it achieves a sparse representation of the images. According to this, we considered the R, G, and B colour values of each pixel as respective fuzzy sets. We then performed $\alpha$-cuts as a defuzzification method across all pixels of the image, which converted them to sparse matrices of 0s and 1s. Our method was tested on a variety of dataset sizes and evaluated according to the detection rates of hyperparameterised ResNet50 models. Our findings demonstrated that for larger datasets, sparse representations of intelligently coloured binary images can exceed the model performance of unprocessed ones, with 93.60% accuracy, 94.48% precision, 92.60% recall, and 93.53% f-score. This is the first time that $\alpha$-cuts were used in image processing and according to our results, we believe that they provide an important contribution to image processing for challenging datasets. Overall, it shows that it can become an integrated component of image-based IDS operations and other demanding real-time practices. Full article

The first detailed selected reagent ion-time-of-flight-mass spectrometric fundamental investigations of 2,6-diisopropylphenol, more commonly known as propofol (C₁₂H₁₈O), and its metabolite 2,6-diisopropyl-1,4-benzoquinone (C₁₂H₁₆O₂) using the reagent ions H₃O⁺, H₃O⁺.H₂O, O₂^+• and NO⁺ are reported. Protonated propofol is the dominant product ion resulting from the reaction of H₃O⁺ with propofol up to a reduced electric field strength (E/N) of about 170 Td. After 170 Td, collision-induced dissociation leads to protonated 2-(1-methylethyl)-phenol (C₉H₁₃O⁺), resulting from the elimination of C₃H₆ from protonated propofol. A sequential loss of C₃H₆ from C₉H₁₃O⁺ also through collision-induced processes leads to protonated phenol (C₆H₇O⁺), which becomes the dominant ionic species at E/N values exceeding 170 Td. H₃O⁺.H₂O does not react with propofol via a proton transfer process. This is in agreement with our calculated proton affinity of propofol being 770 kJ mol⁻¹. Both O₂^+• and NO⁺ react with propofol via a charge transfer process leading to two product ions, C₁₂H₁₈O⁺ (resulting from non-dissociative charge transfer) and C₁₁H₁₅O⁺ that results from the elimination of one of the methyl groups from C₁₂H₁₈O⁺. This dissociative pathway is more pronounced for O₂^+• than for NO⁺ throughout the E/N range investigated (approximately 60–210 Td), which reflects the higher recombination energy of O₂^+• (12.07 eV) compared to that of NO⁺ (9.3 eV), and hence the higher internal energy deposited into the singly charged propofol. Of the four reagent ions investigated, only H₃O⁺ and H₃O⁺.H₂O react with 2,6-diisopropyl-1,4-benzoquinone, resulting in only the protonated parent at all E/N values investigated. The fundamental ion-molecule studies reported here provide underpinning information that is of use for the development of soft chemical ionisation mass spectrometric analytical techniques to monitor propofol and its major metabolite in the breath. The detection of propofol in breath has potential applications for determining propofol blood concentrations during surgery and for elucidating metabolic processes in real time. Full article

This study aimed to address the crop growth and development issues caused by environmental factors in the area of the Liupan Mountains in Ningxia. In this area, there is a large temperature difference between day and night due to drought and low rainfall from spring to summer. The effects of farmland mulching for cabbage on soil environment, water and fertilizer use efficiency, and on cabbage were studied by comparing white common mulch (WCM), black common mulch (BCM), white and black biodegradable mulch (WBM and BBM), black permeable mulch (BPM), and black-and-white composite mulch (BWCM). The types of mulch suitable for application in the region were selected after a comprehensive comparative analysis. The results suggested that soil temperature and water content decreased in the mulch of the two biodegradable mulches and the permeable mulch compared with the control (WCM). Meanwhile, soil water content significantly increased into the rainy season in the mulch of BPM. The overall index of soil enzyme activity was 11.8% and 5.2% higher in WBCM and BBM than that in WCM. The soil overall fertility index of WCM exceeded the other treatments by 16.3%, 33.0%, 25.6%, 36.6%, and 25.4%. The water use efficiency and fertilizer bias productivity of BBM and BPM mulch treatments were the highest among all treatments. The economic yield and economic efficiency of cabbage in BBM, BPM, and WBCM mulch treatments were among the best. A comprehensive analysis of the indicators by completing principal components and affiliation functions revealed that WBCM, BBM, and BPM ranked in the top three in comprehensive scores. In conclusion, black biodegradable mulch, permeable mulch, and black-and-white composite mulch can be applied to replace the white common mulch, with black biodegradable mulch treatment performing the best. Full article

Two important goals of sustainable agriculture are food production and preserving and improving soil health. The soil organic carbon content is considered an indicator of soil health. The evaluation of the methods to increase the soil organic carbon content in long-term experiments is usually carried out without considering its environmental effects, (e.g., CO₂–C soil emission). This study hypothesized that sandy soils have a low carbon storage potential, and that the carbon accumulation in the soil is accompanied by increased CO₂–C emissions into the atmosphere. The study was carried out as a long-term fertilization experiment in Central Poland using a rye monoculture. The changes in the soil organic carbon content (SOC), CO₂–C emissions from soil, and plant yields were examined for two soil treatments: one treated only with mineral fertilizers (CaNPK) and one annually fertilized with manure (Ca + M). Over the 91 years of the experiment, the SOC content of the manure-fertilized treatment increased almost two-fold, reaching 10.625 g C kg⁻¹ in the topsoil, while the content of the SOC in the soil fertilized with CaNPK did not change (5.685 g C kg⁻¹ in the topsoil). Unlike mineral fertilization, soil manuring reduced the plant yields by approximately 15.5–28.3% and increased the CO₂–C emissions from arable land. The CO₂–C emissions of the manured soil (5365.0 and 5159.2 kg CO₂–C ha⁻¹ in the first and second year of the study, respectively) were significantly higher (by 1431.9–2174.2 kg CO₂–C ha⁻¹) than those in the soils that only received mineral fertilizers (3933.1 and 2975.0 kg CO₂–C ha⁻¹ in the first and second year of the study, respectively). The results from this experiment suggest that only long-term fertilization with manure might increase the carbon storage in the sandy soil, but it is also associated with higher CO₂–C emissions into the atmosphere. The replacement of mineral fertilizers with manure, predicted as a result of rising mineral fertilizer prices, will make it challenging to achieve the ambitious European goal of carbon neutrality in agriculture. The increase in CO₂–C emissions due to manure fertilization of loamy sand soil in Central Poland also suggests the need to research the emissivity of organic farming. Full article

Alginate gels are extensively tested in biomedical applications for tissue regeneration and engineering. In this regard, the modification of alginate gels and solutions with dispersed magnetic particles gives extra options to control the rheo-elastic properties both for the fluidic and gel forms of alginate. Rheological properties of magnetic suspensions based on Na-alginate water solution with embedded magnetic particles were studied with respect to the interfacial adhesion of alginate polymer to the surface of particles. Particles of magnetite (Fe₃O₄), metallic iron (Fe), metallic nickel (Ni), and metallic nickel with a deposited carbon layer (Ni@C) were taken into consideration. Storage modulus, loss modulus, and the shift angle between the stress and the strain were characterized by the dynamic mechanical analysis in the oscillatory mode. The intensity of molecular interactions between alginate and the surface of the particles was characterized by the enthalpy of adhesion which was determined from calorimetric measurements using a thermodynamic cycle. Strong interaction at the surface of the particles resulted in the dominance of the “fluidic” rheological properties: the prevalence of the loss modulus over the storage modulus and the high value of the shift angle. Meanwhile, poor interaction of alginate polymer with the surface of the embedded particles favored the “elastic” gel-like properties with the dominance of the storage modulus over the loss modulus and low values of the shift angle. Full article