Network Structure Research Articles

Effect of Pre-Emulsified Flaxseed Oil Containing Rutin on the Quality of Nemipterus virgatus Surimi Gel: Gelatinization Properties, Storage Stability, and Protein Digestibility.

Rinsing during surimi protein processing can result in the loss of essential nutrients, such as fats and minerals. Therefore, supplementing functional fats in a stable form can make up for the fat loss of surimi during the rinsing process. This research aimed to investigate the effects of incorporating pre-emulsified flaxseed oil with different concentrations of rutin (0, 0.5, 1.5, 2.5, and 3.5%, dissolved in flaxseed oil, w/v) to Nemipterus virgatus surimi on the gelatinization properties, lipid oxidation, and in vitro static simulated digestion characteristics of surimi gels. The results indicated that the addition of 1.5% rutin significantly improved the water-holding capacity and decreased the cooking loss rate of surimi gel (p < 0.05). The results of optical microscopy and scanning electron microscopy showed that the addition of 1.5% rutin promoted a denser network structure of surimi gel. Furthermore, the incorporation of rutin effectively slowed lipid oxidation in pre-emulsified flaxseed oil surimi gel. Compared with the gel group containing only pre-emulsified flaxseed oil, the addition of rutin significantly reduced the levels of volatile base nitrogen (TVB-N) and thiobarbituric acid reactive substances (TBARSs) in the gel and also mitigated the decline in acidity (p < 0.05). Moreover, the addition of rutin significantly inhibited the decrease in pH of surimi gel during storage (p < 0.05). In vitro static simulated digestion demonstrated that the addition of 1.5% rutin enhanced the protein digestibility from 71.2% to 77.2% of the surimi gel. Therefore, adding pre-emulsified oil containing an appropriate amount of rutin to surimi can not only compensate for the fat loss during the surimi rinsing process but also effectively improve the quality characteristics of surimi gels. This research will provide a theoretical basis for the effective addition of functional lipids in surimi products and the development of nutritious and healthy surimi products.

Cybersecurity investments and cyber insurance purchases in a non-cooperative game

Abstract The growing concern over cyber risk has become a pivotal issue in the business world. Firms can mitigate this risk through two primary strategies: investing in cybersecurity practices and purchasing cyber insurance. Cybersecurity investments reduce the compromise probability, while cyber insurance transfers potential losses to insurers. This study employs a network model for the spread of infection among interconnected firms and investigates how each firm’s decisions impact each other. We analyze a non-cooperative game in which each firm aims to optimize its objective function through choices of cybersecurity level and insurance coverage ratio. We find that each firm’s cybersecurity investment and insurance purchase are strategic complements. Within this game, we derive sufficient conditions for the existence and uniqueness of Nash equilibrium and demonstrate its inefficiency. These theoretical results form the foundation for our numerical studies, allowing us compute firms’ equilibrium decisions on cybersecurity investments and insurance purchases across various network structures. The numerical results shed light on the impact of network structure on equilibrium decisions and explore how varying insurance premiums influence firms’ cybersecurity investments.

Application research of convolutional neural network and its optimization in lightning electric field waveform recognition

Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%. The effects of various optimization terms and their different optimization orders on the training time of the model were studied. The results indicate that the CNN algorithm is suitable for the classification and recognition of lightning electric field (LEF) waveforms. Optimization can significantly improve recognition rate. The optimization method of fitting idealized waveforms can reduce noise in the dataset and significantly improve recognition rate, indicating that noise has a significant impact on waveform recognition. Therefore, it is necessary to perform noise preprocessing before recognition. The optimization has a huge impact on training efficiency, increasing training time by about 51% after optimization, but the influence of optimization order on it can be ignored.

Natural Protein-Based Multifunctional Hydrogel Dressing Formed by Rapid Photocuring and Zinc Ion Coordination to Accelerate Wound Healing.

This study explores the use of chicken egg white (EW), a rich source of natural proteins, to address challenges in wound healing management. Herein, a novel Zn2+-infused EW/GelMA (EW/Gel) hybrid hydrogel is developed, featuring an interpenetrating network (IPN) structure, where the first network consists of photo-cross-linked GelMA and the second network consists of Zn2+-infused EW (Zn-EW) through ion-protein binding. By optimizing the design and formulation, the resulting Zn-EW/Gel hydrogel exhibited enhanced mechanical stability and self-adhesive properties. In vitro experiments demonstrated that the combined effects of functional proteins and active ions within the Zn-EW/Gel hydrogel promote fibroblast proliferation and type I collagen expression, modulate the immune microenvironment, and enhance angiogenesis. The hydrogel also demonstrated excellent biocompatibility and bioactivity in vivo, showing strong promise for restoring the physiological properties of the damaged wound tissue.

Novel RFID Multi-Label Network Location Measurement by Dual CCD and Non-Local Means-Harris Algorithm.

To improve the performance of Radio Frequency Identification (RFID) multi-label systems, the multi-label network structure needs to be quickly located and optimized. A multi-label location measurement method based on the NLM-Harris algorithm is proposed in this paper. Firstly, multi-label geometric distribution images are obtained through a label image acquisition system of a multi-label semi-physical simulation platform with two vertical Charge-Coupled Device (CCD) cameras, and Gaussian noise is added to the image to simulate thermoelectric interference. Then, a fast NLM algorithm that optimizes the kernel coefficient acquisition speed is used for image denoising. Finally, the Harris corner algorithm is used to obtain the corner points of the images. After screening the diagonal points, the pixel coordinates of the preset origin and the four corners of the labels are obtained. Furthermore, the actual coordinates of the labels are obtained according to the pixel relationship. The results show that the average absolute errors of x, y, and z coordinates are 0.773 mm, 0.782 mm, and 0.807 mm, respectively. In addition, the relative errors are 1.659%, 2.260%, and 0.258%, which shows the high location accuracy of the multi-label network. It is of great significance to measure and optimize the performance of multi-label systems.

Network modeling of problematic social media use components in college student social media users.

While the constitutive features of problematic social media use (PSMU) have been formulated, there has been a lack of studies in the field examining the structure of relationships among PSMU components. This study employed network analytic methods to investigate the connectivity among PSMU components in a large sample of 1,136 college student social media users (M age = 19.69, SD = 1.60). Components of PSMU were assessed by the Bergen Social Media Addiction Scale (BSMAS) derived from a components model of addiction. We computed two types of network models, Gaussian graphical models (GGMs) to examine network structure and influential nodes and directed acyclic graphs (DAGs) to identify the probabilistic dependencies among components. Relapse component consistently emerged as a central node in the GGMs and as a parent node of other components in the DAGs. Relapse and tolerance components exhibited strong mutual connections and were linked to the most vital edges within the networks. Additionally, conflict and mood modification nodes occupied more central positions within the PSMU network for the low-BSMAS-score subgroup compared with the high-BSMAS-score subgroup. Our findings shed new light on the complex architecture of PSMU and its potential implications for tailored interventions to relieve PSMU.

Peanut and Soy Protein-Based Emulsion Gels Loaded with Curcumin as a New Fat Substitute in Sausages: A Comparative Study.

The aim of this study was to evaluate the effects of the complete or partial substitution (0, 20, 40, and 100%) of the pork backfat in prepared sausage with protein emulsion gels loaded with curcumin. The effects of three protein emulsion gels (i.e., peanut proteins, ultrasound-modified peanut proteins, and soy proteins) on sausage characteristics (cooking loss, textural properties, microstructure, sensory characteristics, and antioxidant activity) were investigated and compared using a one-way analysis of variance and Duncan's multiple tests. The results revealed that the addition of each emulsion gel reduced cooking loss and improved the textural properties of the sausages in a dose-dependent manner. When 20% of pork backfat was substituted with untreated or ultrasound-modified peanut protein emulsion gel (PPEG), cooking loss decreased to a greater extent than when soy protein emulsion gel (SPEG) was used. However, the latter yielded higher cohesiveness and resilience at the same substitution levels. Compared with untreated PPEG, the sausages containing modified PPEG (at 200 W for 20 min) had significantly greater resilience and a denser microstructure. In addition, when 100% of pork backfat was substituted with modified PPEG, the sausages had desirable sensory characteristics. All sausages enriched with protein emulsion gels loaded with curcumin presented higher DPPH and ABTS radical scavenging capacities than the control sausages. The sausages prepared with the modified PPEG had the highest antioxidant activity (DPPH: 37.43 ± 0.35%; ABTS: 39.48 ± 0.50%; TBARS: 0.65 ± 0.05 mg MDA/Kg), which may be attributed to the increased stability of curcumin in the modified PPEG with a denser network structure. Therefore, ultrasound-modified PPEG loaded with curcumin can be used as a new fat substitute in functional sausages or other healthy meat products.

Interaction Effects of Tannic Acid and Gluten on Bread-Making and Its Starch Digestion.

In this study, we explored the binding mechanism between tannic acid (TA) and gluten to apply TA as an ingredient in bread-making to evaluate its baking performance and starch digestion. The interaction was systematically investigated by analyzing binding affinity, binding mode, and matrix structure of the TA-gluten complex using fluorescence quenching, molecular docking, and confocal laser scanning microscopy. TA strongly interacted with gluten via non-covalent interactions, mainly hydrogen bonds, and formed the major hydrogen bonds with six different glutamines (Q32, Q108, Q313, Q317, Q317, and Q349), which play a critical role in gluten network construction among amino acid residues of gluten. Additionally, TA showed lower binding affinity toward glutenin (-10.4 kcal/mol) compared to gliadin (-8.9 kcal/mol), implying stronger binding with glutenin. Consequently, the interaction between TA and gluten created a dense and compact gluten network structure. It influenced baking performance, causing a decrease in bread loaf volume while an increase in firmness and lowering the starch digestion rate, increasing slowly digestible starch and resistant starch fractions. This study identified the binding mechanism of TA toward gluten and provides better insights into how to apply TA or perhaps other polyphenols to design functional bakery products to control starch digestion rate.

The Optimization of River Network Water Pollution Control Based on Hydrological Connectivity Measures

Urbanization, driven by socio-economic development, has significantly impacted river ecosystems, particularly in plain city regions, leading to disruptions in river network structure and function. These changes have exacerbated hydrological fluctuations and ecological degradation. This study focuses on the central urban area of Changzhou using a MIKE11 model to assess the effects of four hydrological connectivity strategies—water diversion scheduling, river connectivity, river dredging, and sluice connectivity—across 13 different scenarios. The results show that water diversion, river dredging, and sluice connectivity scenarios provide the greatest improvements in water environmental capacity, with maximum increases of 54.76%, 41.97%, and 25.62%, respectively. The spatial distribution of improvements reveals significant regional variation, with some areas, particularly in Tianning and Zhonglou districts, experiencing declines in environmental capacity under sluice diversion and river-connectivity scenarios. In addition, the Lao Zaogang River is identified as crucial for improving the overall water quality in the network. Based on a multi-objective evaluation, combining environmental and economic factors, the study recommends optimizing water diversion scheduling at sluices (Weicun, Zaogang, and Xiaohe) with flow rates between 20–40 m3/s, enhancing connectivity at key river hubs, and focusing management efforts on the Lao Zaogang and Xinmeng rivers to strengthen hydrological and water quality linkages within the network.

An Advanced Energy Efficient Resource Allocation for Software‐Defined WSN Using Hybrid Optimization Algorithm

ABSTRACTSoftware‐Defined Wireless Sensor Networking (SDWSN) is termed a rising network structure that has become more important to the Internet of Things (IoT). In this network structure, the control planes effectively manage the sensor plane. Due to these kinds of separation, the management of networks became easier and also their efficacies are enhanced in the self‐motivated atmosphere. The common issue presented in the sensor atmosphere is minimal life in the network devices inspired by the maximal level of energy consumption rate. The current study provides a system architecture that intends to increase efficiency in an SDWSN by incorporating optimization techniques. A novel hybrid optimization strategy is created by merging the Election‐Based Optimization Algorithm (EBOA) with the Ladybird Beetle Optimization Algorithm (LBOA), and the result is known as Hybrid Election‐based Ladybird Beetle Optimization (HELBO). This work examines an energy‐efficient resource allocation mechanism in SDWSNs that have substantial computing power and memory. These algorithms optimize the bandwidth along with power allocation in the SDWSN to attain a considerable Signal to Interference plus Noise Ratio (SINR) under the individual constraint of quality of service. Finally, empirical findings show that the recommended HELBO method outperforms other present algorithms.

Modification Mechanism and Performance of High-Content Polyurethane-Modified Asphalt

To explore the influence of the polyurethane blending ratio on the micro-characteristics of polyurethane-modified asphalt, three samples of the modified asphalt with different blending ratios (40%, 50%, and 60%) were prepared. Fourier transform infrared spectroscopy (FTIR), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were employed to elucidate the modification mechanism of polyurethane-modified asphalt. To investigate how the preparation method affects the performance of polyurethane-modified asphalt mixtures, two different preparation methods, namely internal blending and external blending, were adopted. The road performance of the polyurethane-modified asphalt mixtures was evaluated through the utilization of rutting tests, low-temperature trabecular bending tests, and freeze–thaw splitting tests. The FTIR test results indicate that during the modification of polyurethane, there is a change in both the intensity and position of the absorption peak, which affects the local arrangement of the molecular structure. Upon reaching a polyurethane blending ratio of 50%, a cross-linked network structure that is similar to polyurethane is formed. The results of the AFM test demonstrate that an increase in polyurethane content results in a corresponding increase in surface roughness. At a polyurethane content of 50%, the curing reaction is most effective, which is beneficial for enhancing the bonding performance between the asphalt and the aggregate, thereby enhancing the overall water stability of the mixture. The results of the scanning electron microscopy (SEM) tests indicate that the microstructure is more stable when the polyurethane content is 50%. The results of the performance test of the polyurethane-modified asphalt mixture indicate that the dynamic stability of the polyurethane-modified asphalt mixture is approximately four times that of the SBS-modified asphalt mixture. The flexural tensile strength and maximum flexural strain of the polyurethane-modified asphalt mixture are, respectively, 1.5 and 3.2 times those of the SBS-modified asphalt mixture, indicating that its anti-deformation ability is stronger in a low-temperature environment, and it is found that the low-temperature performance of the mixture prepared with the internal blending method is better than that with the external blending method. The splitting strength of the polyurethane asphalt mixture before and after freezing and thawing is greater than that of an SBS asphalt mixture: before freezing and thawing, by about 3.8 times; after freezing and thawing, by about 3 times. Although the freezing and thawing of the polyurethane mixture has damage, it still meets the requirements of the use of pavement materials. It can be observed that the incorporation of polyurethane alters the internal structure of the asphalt, which markedly enhances the properties of the asphalt mixture and offers a novel perspective on the development of modified asphalt materials.

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments. The model demonstrates superior performance across various prediction tasks of materials' properties, achieving particularly notable results in properties sensitive to many-body interactions. For band gap prediction, EOSnet achieves a mean absolute error of 0.163 eV, surpassing previous state-of-the-art models. The model also excels in predicting mechanical properties and classifying materials, with 97.7% accuracy in metal/nonmetal classification. These results demonstrate that embedding GOM fingerprints into node features enhances the ability of GNNs to capture complex atomic interactions, making EOSnet a powerful tool for materials' discovery and property prediction.

The Role of Technical Car Features in Managing and Promoting New Peer-to-Peer Car-Sharing Systems: Insights from Potential Users and Strategic Implications for Service Providers

Peer-to-peer car-sharing systems are an evolving branch of urban mobility, aligning with global trends focused on sustainable development and reducing congestion in cities. A research gap has been identified concerning the specific vehicle attributes that would encourage the public to potentially use these services. Addressing this gap, and in the context of launching a new peer-to-peer car-sharing service in Katowice, Poland, this article investigates the technical features influencing the choice of vehicles in peer-to-peer car-sharing systems, particularly from the perspective of individuals who currently do not use such platforms. The study employs Social Network Analysis (SNA) to examine the interrelationships between vehicle attributes. The analysis reveals that key factors influencing users’ decisions include fuel/energy consumption, safety features, and technological advancement, with a particular emphasis on driver assistance systems, including autonomous driving capabilities. The network structure, characterized by a relatively low density (0.2536) and a short average path length (1.872), suggests that a few central vehicle features dominate user decisions, and improvements in these key areas can quickly propagate through the decision-making process, enhancing overall user satisfaction. To validate the findings, a Gradient Boosting Regression (GBR) analysis was conducted, confirming the significance of the key factors identified by the SNA, such as fuel efficiency, battery capacity, and safety systems, thus strengthening the reliability of the results. This study underscores the growing importance of sustainability and technological innovation in the automotive industry, particularly in the context of the sharing economy. It suggests that car-sharing platforms and vehicle manufacturers should prioritize these features to meet user expectations and preferences. These findings provide valuable insights for the strategic and operational management of peer-to-peer car-sharing services, emphasizing the importance of targeted vehicle selection and user-centered innovations to improve platform performance and scalability.

Construction of Supramolecular Polymer Network Elastomers Based on Pillar[5]arene/Alkyl Chain Host-Guest Interactions.

As a special kind of supramolecular compound with many favorable properties, pillar[n]arene-based supramolecular polymer networks (SPNs) show potential application in many fields. Although we have come a long way using pillar[n]arene to prepare SPNs and construct a series of smart materials, it remains a challenge to enhance the mechanical strength of pillar[n]arene-based SPNs. To address this issue, a new supramolecular regulation strategy was developed, which could precisely control the preparation of pillar[n]arene-based SPN materials with excellent mechanical properties by adjusting the polymer network structures. Specifically, we utilized the host-guest interaction between pillar[5]arene and the alkyl chain of butyl acrylate monomer to form a supramolecular polymer network and achieved the transformation of different states by regulating the cross-linking density of the polymer networks. Additionally, the polymer networks exhibited good stimuli responsiveness as well as excellent dynamic properties and reconfigurable characteristics through a temperature change and the addition of competitive hosts or guests. The research provided new possibilities for the development of polymer materials.

DSF-GAM: a location inference model in social network Twitter

Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds applications in various domains, such as targeted advertising and public health monitoring. Inference techniques through social interactions, such as co-mentions, remain a favored method for constructing user social networks. Co-mentions, however, introduce a substantial number of noisy relationships that impact the overall network structure and prediction of the location of a user. Furthermore, previous approaches have overlooked the examination of cohesive groups or community structures within an individual's ego network for location prediction. This paper introduces a predictive model called DSF-GAM (DS stand for document similarity, F stands for frequencies and GAM stands for Generalized Additive Model). which redesigns social interactions based on mentions to infer user location on Twitter. DSF-GAM introduces a novel and innovative social graph, termed a user relation graph. DSF-GAM employs a generalized additive model (GAM) for location inference. The fundamental contribution of DSF-GAM lies in the representation of data using a new graph structure with enriched community features and edges. The experimental results on well-known datasets demonstrate that DSF-GAM outperforms other state-of-the-art methods based on both content and network approaches and mixed results in hybrid approaches.

Manifestation of Rouse and Entanglement Dynamics in Non-Cross-Linked and Cross-Linked Polymers Studied by Field-Cycling and Multiple Quantum NMR.

Rubbers prepared from technical poly(butadiene) and natural poly(isoprene) are studied by field-cycling (FC) 1H NMR relaxometry to elucidate the changes of the relaxation spectrum. Starting with the non-cross-linked polymer successively cross-links are introduced via sulfur or peroxide vulcanization. Applying an advanced home-built relaxometer allows one to probe entanglement dynamics in addition to Rouse dynamics. We show that entanglement dynamics evidenced in terms of a characteristic power-law in the NMR susceptibility is still observed with an exponent identical to that in non-cross-linked linear polymers. Yet, the entanglement regime disappears more and more from the accessible frequency window upon increasing the cross-link density and a spectrally enlarged Rouse regime is revealed. Adding a swelling agent, the manifestation of the Rouse and entanglement regimes virtually does not change, yet, the apparent power-law exponents increase. Concomitant multiple-quantum (MQ) 1H NMR experiments provide information on the structure of the rubber network in terms of the residual dipolar coupling and the fraction of the network defects, i.e., persisting entangled or nonentangled chains, introduced upon cross-linking and swelling.

Guns and butter: Measuring spillover and implications for technological competition

ABSTRACT Technological spillover – the transfer and use of knowledge between defence and civilian sectors – is desired by many countries, and particularly salient under intensifying US-China competition. Yet to date, no systematic measure of spillover exists. This paper presents a novel measure of spillover using a dataset of over 116,000 defence-related patents, and argues that (1) concerns over China’s ‘authoritarian advantage’ should be moderated, and (2) the composition and structure of knowledge transfer networks in the US and China should inform the appropriate channels of managing competition, which I demonstrate through case studies of aerial drones and missile technology spillover networks.

Conductive Eutectogels Fabricated by Dialdehyde Xylan/Liquid Metal-Initiated Rapid Polymerization for Multi-Response Sensors and Self-Powered Applications.

Conductive eutectogels have emerged as candidates for constructing functional flexible electronics as they are free from the constraints posed by inherent defects associated with solvents and feeble network structures. Nevertheless, developing a facile, environmentally friendly, and rapid polymerization strategy for the construction of conductive eutectogels with integrated multifunctionality is still immensely challenging. Herein, a conductive eutectogel is fabricated through a one-step dialdehyde xylan (DAX)/liquid metal (LM)-initiated polymerization of a deep eutectic solvent. DAX acts as a stabilizer for the preparation of LM nanodroplets and plays a crucial role in facilitating ultrafast gelation (less than 2 min) by virtue of its reducing dialdehyde groups. Notably, this fabrication strategy obviates the use of toxic chemical initiators and cross-linkers. The resultant eutectogels exhibit extremely high stretchability (2860%), desirable self-healing ability, high conductivity (0.72 S m-1), biocompatibility, excellent environmental stability, and exceptional responsiveness to tensile strain (GF = 4.08) and temperature (TCR = 5.35% K-1). Benefiting from these integrated features, the conductive eutectogels serve as multifunctional flexible sensors for human motion recognition and temperature monitoring. Furthermore, the eutectogel serves as a pliable electrode in the assembly of a triboelectric nanogenerator (TENG), designed to harvest mechanical energy, convert it into stable electrical outputs, and enable self-powered sensing. This study offers an approach to fabricating multifunctional integrated conductive eutectogels, making it a step closer to the development of intelligent flexible electronics.

Simulation Research on Data Acquisition and Supervisory Control System of Distribution Network Based on Vector Regression Model

With the gradual maturity of deep learning and the Internet of Things, a smart grid is urgently needed to be established. For the intellectualization of the power grid, non-intrusive load monitoring (NILM) technology is the key step of intellectualization. The traditional load monitoring method is an invasive scheme, in which monitoring sensors are usually installed at the output end of each grid load. This method requires a lot of manpower and material resources in terms of equipment installation and maintenance, which is difficult to sustain. Therefore, monitoring electrical appliances are installed at the entrance of the distribution network to decompose the classification and operation status of the individual electrical appliances in the grid. Aiming at the problem of long-term multi-state electrical apparatus monitoring, this paper mainly optimizes the two optimal deep learning models Seq2Seq and WindowGRU based on activation function optimization, regular function optimization and neural network structure optimization. PReLU, Leaky-ReLU and ELU are used and tested. For the optimization based on regular functions, the value of F1-score is 0.9700, which is 0.1534 more than the optimization algorithm Seq2SeqLR in this paper. Meanwhile, it can also reach 0.8100 for other devices, which is 0.2373 more than the nilmTCN designed in this paper. The Recall value of the identification is improved to 0.6673, which is significantly higher than that of other models. It is proved that the research contents of this paper can improve the effective analysis of load energy consumption data, and can minimize unnecessary energy consumption to achieve the purpose of saving electricity.

Bayesian predictive system for assessing the damage intensity of residential masonry buildings under the impact of continuous ground deformation

The paper introduces a method for predicting damage intensity in masonry residential buildings situated in mining areas, focusing on the impact of large-scale continuous ground deformation. The research utilizes in situ data collected in a database, encompassing structural and material features, as well as information on maintenance quality and building durability. In addition to this information, the database collected data on the intensity of continuous deformation of the mining area at the location of the building, as well as the range and intensity of damage identified in buildings. The information included in the database was the result of many years of observations of buildings during the disclosure of impacts from mining exploitation and was based on: the results of in-situ building inventory, analysis of available building documentation and information provided by mining companies. The archived data were categorized variables labeled. The transformation of the data to a labeled value was dictated directly by the assumptions of the GOBNILP algorithm. Ultimately, a predictive model, represented by an optimal Bayesian network structure, is established. The optimisation of the network structure is achieved through the adaptation of the GOBNILP Bayesian network learning algorithm from data. This optimisation process is executed through the Gurobi Optimizer. It is worth noting that this interdisciplinary approach represents one of the first applications of such a methodology in the field of civil and environmental engineering. The results obtained can therefore be of significant value given the fact that the methodology of detecting the structure of Bayesian networks from data is still developing intensively in other scientific fields. In the course of the analyses, metric scores are examined, and various network structures are assessed based on their complexity. Great values of classification accuracies over 91% were obtained. This meticulous evaluation allows for the selection of the optimal Bayesian network that best generalises the knowledge acquired during the learning process. The paper also demonstrates the potential application of the obtained model in diagnosing damage causes and predicting future occurrences, highlighting the versatility of the proposed approach for addressing issues in the field.