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The unauthorized replication and distribution of digital images pose significant challenges to copyright protection. While existing solutions incorporate blockchain-based techniques such as perceptual hashing and digital watermarking, they lack large-scale experimental validation and a dedicated blockchain consensus protocol for image copyright management. This paper introduces DRPChain, a novel digital image copyright management system that addresses these issues. DRPChain employs an efficient cropping-resistant robust image hashing algorithm to defend against 14 common image attacks, demonstrating an 85% success rate in watermark extraction, 10% higher than the original scheme. Moreover, the paper designs the K-Raft consensus algorithm tailored for image copyright protection. Comparative experiments with Raft and benchmarking against PoW and PBFT algorithms show that K-Raft reduces block error rates by 2%, improves efficiency by 300ms compared to Raft, and exhibits superior efficiency,decentralization, and throughput compared to PoW and PBFT. These advantages make K-Raft more suitable for digital image copyright protection. This research contributes valuable insights into using blockchain technology for digital copyright protection, providing a solid foundation for future exploration. [ABSTRACT FROM AUTHOR]

With the emergence of various forms of smart devices and new paradigms such as the Internet of Things (IoT) concept, the IT (Information Technology) service areas are expanding explosively compared to the provision of services by single systems. A new system operation concept that has emerged in accordance with such technical trends is the IT ecosystem. The IT ecosystem can be considered a special type of system of systems in which multiple systems with various degrees of autonomy achieve common goals while adapting to the given environment. The single systems that participate in the IT ecosystem adapt autonomously to the current situation based on collected data from sensors. Furthermore, to maintain the services supported by the whole IT ecosystem sustainably, the configuration of single systems that participate in the IT ecosystem also changes appropriately in accordance with the changed situation. In order to support the IT ecosystem, this paper proposes an architecture framework that supports dynamic configuration changes to achieve the goal of the whole IT ecosystem, while ensuring the autonomy of single systems through the collection of data from sensors so as to recognize the situational context of individual participating systems. For the feasibility evaluation of the proposed framework, a simulated example of an IT ecosystem for unmanned forest management was constructed, and the quantitative evaluation results are discussed in terms of the extent to which the proposed architecture framework can continuously provide sustainable services in response to diverse environmental context changes.

Kazachstania bulderi is a non-conventional yeast species able to grow efficiently on glucose and δ-gluconolactone at low pH. These unique traits make K. bulderi an ideal candidate for use in sustainable biotechnology processes including low pH fermentations and the production of green chemicals including organic acids. To accelerate strain development with this species, detailed information of its genetics is needed. Here, by employing long read sequencing we report a high-quality phased genome assembly for three strains of K. bulderi species, including the type strain. The sequences were assembled into 12 chromosomes with a total length of 14 Mb, and the genome was fully annotated at structural and functional levels, including allelic and structural variants, ribosomal array and mating type locus. This high-quality reference genome provides a resource to advance our fundamental knowledge of biotechnologically relevant non-conventional yeasts and to support the development of genetic tools for manipulating such strains towards their use as production hosts in biotechnological processes. A comprehensive genome assembly from three strains of the yeast, Kazachstania bulderi, serves as a resource to explore the biotechnological potential of this yeast, particularly as a low pH production host for biorefineries. [ABSTRACT FROM AUTHOR]

Much scholarship on the labor of Korean popular music (K-pop) fans has focused on the work they do to produce popularity of their favorite groups, such as campaigns to chart a new release. The focus of this article is how fans have become producers of items and services consumed by other fans, not just cheerleaders or consumers. In tandem with K-pop’s success, an entire secondary industry that is dependent on K-pop’s popularity, while also amplifying it, has emerged. The K-pop industry is now supporting and supported by a multitude of people, some of whom seek the lime-light and others who eschew it, who may be earning money through various K-pop-dependent activities—secondary yet autonomous industries. These participants support K-pop fandom, and may even become secondary stars. I conducted in-person and online interviews with producers and consumers of goods and services related to K-pop. These industries have become an integral part of K-pop today, introducing and enabling personal encounters with K-pop and Korea. As the young entrepreneurs of the K-pop adjacent industries pour energy into not just consumption but also creative endeavors, they make K-pop highly interactive. First, these industries have created a bridge—an area of interaction, exchange, and education between Korean musical stars and their non-Korean fans. Second, the industries have emerged as an economically significant aspect of the K-pop ecosystem. Third, this K-pop adjacent industry demonstrates a future area of tension over ownership of culture. [ABSTRACT FROM AUTHOR]

Kingdonia uniflora is an endangered alpine herb that is distributed along an altitudinal gradient. The unique traits and important phylogenetic position make K. uniflora an ideal model for exploring how endangered plants react to altitude variation. In this study, we sampled nine individuals from three representative locations and adopted RNA-seq technology to sequence 18 tissues, aiming to uncover how K. uniflora responded to different altitudes at the gene expression level. We revealed that genes that responded to light stimuli and circadian rhythm genes were significantly enriched in DEGs in the leaf tissue group, while genes that were related to root development and peroxidase activity or involved in the pathways of cutin, suberin, wax biosynthesis, and monoterpenoid biosynthesis were significantly enriched in DEGs in the flower bud tissue group. All of the above genes may play an important role in the response of K. uniflora to various stresses, such as low temperatures and hypoxia in high-altitude environments. Furthermore, we proved that the discrepancy in gene expression patterns between leaf and flower bud tissues varied along the altitudinal gradient. Overall, our findings provide new insights into the adaptation of endangered species to high-altitude environments and further encourage parallel research to focus on the molecular mechanisms of alpine plant evolution. [ABSTRACT FROM AUTHOR]

This paper investigates a mathematical programming based methodology for solving the minimum sum-of-squares clustering problem, also known as the "k-means problem", in the presence of side constraints. We propose several exact and approximate mixed-integer linear and nonlinear formulations. The approximations are based on norm inequalities and random projections, the approximation guarantees of which are based on an additive version of the Johnson–Lindenstrauss lemma. We perform computational testing (with fixed CPU time) on a range of randomly generated and real data instances of medium size, but with high dimensionality. We show that when side constraints make k-means inapplicable, our proposed methodology—which is easy and fast to implement and deploy—can obtain good solutions in limited amounts of time. [ABSTRACT FROM AUTHOR]

Increasing concentrations of different essential and non-essential metals in the environment have posed a serious threat to all kinds of life. Bioremediation is considered as the best possible solution among the existing ones to reduce this kind of pollution. In the current study, copper resistant Klebsiella pneumoniae strains KW and CC were investigated for their bioremediation potential. Maximum uptake of Cu++ by mid log phase cultures of KW and CC was 19.26 and 28.77μg per mg cell dry weight, respectively. The strains also exhibited efflux ability; KW possessed more efficient efflux system as it expelled 94.46% of maximum stored copper within 24 h as compared to 56% by CC. These strains were also found substantially resistant to some other toxic heavy metals generally present in the industrial effluents. These strains exhibited good growth in the wide range of pH (5-10) and temperature (25-44°C). KW and CC were also investigated for their resistance potential against various antibiotics. KW was found to be resistant to broader range of antibiotics as compared to CC. All these characteristics viz., high tolerance against Cu++, excellent Cu++ uptake and efflux abilities, remarkable multi metal resistance potential and tolerance against substantial ranges of temperature and pH make K. pneumoniae KW an efficient tool for cleaning Cu++ rich industrial effluents. [ABSTRACT FROM AUTHOR]

The green alga Keratococcus rhaphidioides was isolated in axenic culture from water samples of the extremely acid Lake Caviahue (Neuquén, Argentina). The lake pH is 3.0 and K. rhaphidioides is tolerant to conditions such as the very low pH and the high concentrations of different elements. In this work, bioassays were done to study the tolerance of the alga to different pH values and high contents of Fe, Al, and Mn combined with two different chelators, fulvic acids and nitrilotriacetic acid; in addition to the ability to grow with different organic and inorganic carbon, nitrogen and phosphorus sources. In addition to the ability to grow with different organic and inorganic carbon, nitrogen and phosphorus sources. Keratococcus rhaphidioides optimum pH range was 3.0 to 4.0 with a sub-optimum range from pH 5.0 to 7.0. Growth was completely inhibited at pH 2.0. The alga can grow on inorganic CO2, glucose, and acetate, while urea and amino acids did not work as carbon sources in axenic culture. Inorganic nitrogen such as nitrate and ammonium and organic nitrogen sources like urea and leucine induced algal growth, whereas nitrite and aspartic acid had an inhibitory effect. Aluminum had toxic effects when combined with both organic chelators, nitrilotriacetic acid and fulvic acids. Iron induced inhibition only with the latter. Finally, the alga was grown in a photobioreactor under the optimum conditions determined during this work with continuous air bubbling. Algal biomass production was 10 times higher than in the nutritional assays although the time frame was also larger. In summary, the broad nutritional and pH spectrum and the tolerance to low light intensities and to metals in acid medium make K. rhaphidioides a good prospect for acid effluents bioremediation. [ABSTRACT FROM AUTHOR]

Object sensitivity is widely used as a context abstraction for computing the points-to information contextsensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k-object-sensitive pointer analysis (under k-limiting), denoted k-obj, is often inefficient even when it is scalable for small values of k, where k - 2 holds typically. A recent popular approach for accelerating k-obj trades precision for efficiency by instructing k-obj to analyze only some methods in a program context-sensitively, determined heuristically by a pre-analysis. In this article, we investigate how to develop a fundamentally different approach, Eagle, for designing a pre-analysis that can make k-obj run significantly faster while maintaining its precision. The novelty of Eagle is to enable kobj to analyze a method with partial context sensitivity (i.e., context-sensitively for only some of its selected variables/allocation sites) by solving a context-free-language (CFL) reachability problem based on a new CFLreachability formulation of k-obj. By regularizing one CFL for specifying field accesses and using another CFL for specifying method calls, we have formulated Eagle as a fully context-sensitive taint analysis (without k-limiting) that is both effective (by selecting the variables/allocation sites to be analyzed by k-obj contextinsensitively so as to reduce the number of context-sensitive facts inferred by k-obj in the program) and efficient (by running linearly in terms of the number of pointer assignment edges in the program). As Eagle represents the first precision-preserving pre-analysis, our evaluation focuses on demonstrating its significant performance benefits in accelerating k-obj for a set of popular Java benchmarks and applications, with call graph construction, may-fail-casting, and polymorphic call detection as three important client analyses. [ABSTRACT FROM AUTHOR]

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system. [ABSTRACT FROM AUTHOR]

The demands for high-performance and low-cost batteries make K-ion batteries (KIBs) considered as promising supplements or alternatives for Li-ion batteries (LIBs). Nevertheless, there are only a small amount of conventional inorganic electrode materials that can be used in KIBs, due to the large radius of K+ ions. Differently, organic electrode materials (OEMs) generally own sufficiently interstitial space and good structure flexibility, which can maintain superior performance in K-ion systems. Therefore, in recent years, more and more investigations have been focused on OEMs for KIBs. This review will comprehensively cover the researches on OEMs in KIBs in order to accelerate the research and development of KIBs. The reaction mechanism, electrochemical behavior, etc., of OEMs will all be summarized in detail and deeply. Emphasis is placed to overview the performance improvement strategies of OEMs and the characteristic superiority of OEMs in KIBs compared with LIBs and Na-ion batteries. [ABSTRACT FROM AUTHOR]

Background: Porcine Parvovirus (PPV) is a Parvovirinae virus that can cause embryonic and fetal loss and death and mummification in affected fetal pigs. Unlike conventional vaccines, virus-like particles (VLPs) inherit the natural structure of their authentic virions and highly immunostimulatory that can induce strong humoral immune and T cell responses with no risk of pathogenicity. The production of PPV VLPs is still a challenge based on traditional expression platforms due to their low yields and high culture costs. Kluyveromyces marxianus is a safe and fast-growing eukaryote that can get high biomass with low-cost cultures. In this study, we investigated the expression and downstream processes of PPV VLPs in K. marxianus, and the potential for effective stand-alone vaccines. Results: After optimization according to the codon bias of K. marxianus, the VP2 protein from Kresse strain was highly expressed. In a 5 L fermentator, the yield of PPV VLPs reached 2.5 g/L, quantified by HPLC, using a defined mineral medium after 48 h fermentation. Two strategies were established to purify intracellular PPV VLPs: (i) Using the cation exchange chromatography coupled with Sephacryl® S-500 HR chromatography to purify VLPs from the supernatants of pH adjusted cell lysates. (ii) Using anion exchange chromatography followed by cross-flow diafiltration to recover the VLPs precipitated in pH adjusted cell lysates. The purity of PPV VLPs reached about 95%, and total recovery was more than 60%. Vaccination of mice with the purified PPV VLPs induced high titers of specific IgG antibodies in sera, and showed hemagglutination inhibitions on both swine and guinea pig erythrocytes. Spleen lymphocyte proliferation and cytokines detection suggested the PPV VLPs produced by K. marxianus provoked the cellular immune and humoral immunity responses in mice. Conclusions: This is the highest production of recombinant PPV VLPs achieved to date. The superiorities, Generally Recognized As Safe (GRAS), high production, short lead time, and low cost, make K. marxianus a greatly competitive platform for bioproduction of PPV VLPs vaccine. [ABSTRACT FROM AUTHOR]

Bioethanol is considered an excellent alternative to fossil fuels, since it importantly contributes to the reduced consumption of crude oil, and to the alleviation of environmental pollution. Up to now, the baker yeast Saccharomyces cerevisiae is the most common eukaryotic microorganism used in ethanol production. The inability of S. cerevisiae to grow on pentoses, however, hinders its effective growth on plant biomass hydrolysates, which contain large amounts of C5 and C12 sugars. The industrial-scale bioprocessing requires high temperature bioreactors, diverse carbon sources, and the high titer production of volatile compounds. These criteria indicate that the search for alternative microbes possessing useful traits that meet the required standards of bioethanol production is necessary. Compared to other yeasts, Kluyveromyces marxianus has several advantages over others, e.g., it could grow on a broad spectrum of substrates (C5, C6 and C12 sugars); tolerate high temperature, toxins, and a wide range of pH values; and produce volatile short-chain ester. K. marxianus also shows a high ethanol production rate at high temperature and is a Crabtree-negative species. These attributes make K. marxianus promising as an industrial host for the biosynthesis of biofuels and other valuable chemicals. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) is the method of choice for understanding the structure, function, and interactions of molecules. However, MD simulations are limited by the strong metastability of many molecules, which traps them in a single conformation basin for an extended amount of time. Enhanced sampling techniques, such as metadynamics and replica exchange, have been developed to overcome this limitation and accelerate the exploration of complex free energy landscapes. In this paper, we propose Vendi Sampling, a replica-based algorithm for increasing the efficiency and efficacy of the exploration of molecular conformation spaces. In Vendi sampling, replicas are simulated in parallel and coupled via a global statistical measure, the Vendi Score, to enhance diversity. Vendi sampling allows for the recovery of unbiased sampling statistics and dramatically improves sampling efficiency. We demonstrate the effectiveness of Vendi sampling in improving molecular dynamics simulations by showing significant improvements in coverage and mixing between metastable states and convergence of free energy estimates for four common benchmarks, including Alanine Dipeptide and Chignolin. [ABSTRACT FROM AUTHOR]

Wind power generation is expected to greatly contribute to the future of humanity as a promising source of renewable energy. However, the high variability inherent in wind is a challenge that hinders stable power generation. To utilize wind power as a primary energy source, integration with a polymer electrolyte membrane water electrolysis (PEMWE) system is proposed. Yet, PEMWE is known to suffer from degradation when exposed to input power patterns with high variability. This poses challenges to its commercialization. This necessitates stress testing with various wind power fluctuations during the production process of the devices. This study investigates representative patterns of wind power fluctuation so that these patterns can be used for the stress testing process. We employ data-mining techniques, including the swing door algorithm and k-means clustering, to identify these patterns by analyzing wind power generation data at a 10-s interval. As a result, the five most representative wind power ramps are presented. This study provides practical guidelines for the development process of expensive devices for wind power generation, thereby promoting the active utilization of wind power generation. [ABSTRACT FROM AUTHOR]

In order to improve the damage prediction effect of concrete structural column and beam structure, this paper uses multimedia technology to improve the damage prediction effect of reinforced concrete structural column and beam structure. Moreover, this paper presents the intelligent transformation model and the corresponding solution of the general damage problem, and deduces the error relationship between the result estimator and the importance function according to the error of the importance function. In addition, by analyzing the relationship between the importance function and the dual transport calculation, this paper proposes a complementary dual calculation process that can provide the importance function to each other, and builds an intelligent prediction model. Through the experimental research, it can be seen that the multimedia technology algorithm proposed in this paper can play an important role in the damage prediction of concrete structural columns and beams. [ABSTRACT FROM AUTHOR]

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment. [ABSTRACT FROM AUTHOR]

The use of biofertilizers is becoming an economical and environmentally friendly alternative to promote sustainable agriculture. Biochar from microalgae/cyanobacteria can be applied to enhance the productivity of food crops through soil improvement, slow nutrient absorption and release, increased water uptake, and long-term mitigation of greenhouse gas sequestration. Therefore, the aim of this study was to evaluate the stimulatory effects of biochar produced from Spirulina (Arthrospira platensis) biomass on the development and seed production of rice plants. Biochar was produced by slow pyrolysis at 300°C, and characterization was performed through microscopy, chemical, and structural composition analyses. Molecular and physiological analyses were performed in rice plants submitted to different biochar concentrations (0.02, 0.1, and 0.5 mg mL-1) to assess growth and productivity parameters. Morphological and physicochemical characterization revealed a heterogeneous morphology and the presence of several minerals (Na, K, P, Mg, Ca, S, Fe, and Si) in the biochar composition. Chemical modification of compounds post-pyrolysis and a highly porous structure with micropores were observed. Rice plants submitted to 0.5 mg mL-1 of biochar presented a decrease in root length, followed by an increase in root dry weight. The same concentration influenced seed production, with an increase of 44% in the number of seeds per plant, 17% in the percentage of full seeds per plant, 12% in the weight of 1,000 full seeds, 53% in the seed weight per plant, and 12% in grain area. Differential proteomic analyses in shoots and roots of rice plants submitted to 0.5 mg mL-1 of biochar for 20 days revealed a fine-tuning of resource allocation towards seed production. These results suggest that biochar derived from Arthrospira platensis biomass can stimulate rice seed production. [ABSTRACT FROM AUTHOR]

In this paper, we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge (zk-SNARKs). The delegable zk-SNARK is parameterized by (μ,k,k′,k″). The delegable property of zk-SNARKs allows the prover to delegate its proving ability to μ proxies. Any k honest proxies are able to generate the correct proof for a statement, but the collusion of less than k proxies does not obtain information about the witness of the statement. We also define k′-soundness and k″-zero knowledge by taking into consider of multi-proxies. We propose a construction of (μ,2t + 1,t,t)- delegable zk-SNARK for the NPC language of arithmetic circuit satisfiability. Our delegable zk-SNARK stems from Groth’s zk-SNARK scheme (Groth16). We take advantage of the additive and multiplicative properties of polynomial-based secret sharing schemes to achieve delegation for zk-SNARK. Our secret sharing scheme works well with the pairing groups so that the nice succinct properties of Groth’s zk-SNARK scheme are preserved, while augmenting the delegable property and keeping soundness and zero-knowledge in the scenario of multi-proxies. [ABSTRACT FROM AUTHOR]

Introduction: Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients. Areas covered: This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases. Expert opinion: Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics. [ABSTRACT FROM AUTHOR]

In order to mitigate the nonlinear effects of Mach-Zehnder modulator (MZM) on optical transmission signals in intensity modulation and direct detection (IM-DD) systems, a combined approach utilizing sinusoidal subcarrier modulation (SSM) and the Levenberg-Marquardt back propagation (LM-BP) neural network is proposed in this paper. The method employs a sine wave as the subcarrier to carry the 4 pulse amplitude modulation (PAM4) signals, aiming to equalize the distorted signals after MZM modulation. Subsequently, the LM-BP algorithm eliminates any remaining inter-symbol interference (ISI). This scheme uses sine wave modulation to solve the problem of additional ISI caused by triangular wave modulation. Furthermore, this combined approach simplifies the algorithm complexity compared to solely relying on a neural network equalizer. In this paper, the performance of SSM-LM-BP scheme is simulated and analyzed in IM-DD system. The results show that the joint scheme outperforms the triangular wave modulation scheme as well as the neural network algorithm after transmitting 50 Gbit/s PAM4 signals for 80 km without relays under the conditions of dispersion compensation, and the symbol error rate (SER) can be as low as 10−5. [ABSTRACT FROM AUTHOR]

A standard practice in statistical hypothesis testing is to mention the P-value alongside the accept/reject decision. We show the advantages of mentioning an e-value instead. With P-values, it is not clear how to use an extreme observation (e.g. P << α) for getting better frequentist decisions. With e-values it is straightforward, since they provide Type-I risk control in a generalized Neyman-Pearson setting with the decision task (a general loss function) determined post hoc, after observation of the data--thereby providing a handle on "roving α's." When Type-II risks are taken into consideration, the only admissible decision rules in the post hoc setting turn out to be e-value-based. Similarly, if the loss incurred when specifying a faulty confidence interval is not fixed in advance, standard confidence intervals and distributions may fail, whereas e-confidence sets and e-posteriors still provide valid risk guarantees. Sufficiently powerful e-values have by now been developed for a range of classical testing problems. We discuss the main challenges for wider development and deployment. [ABSTRACT FROM AUTHOR]

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A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes. [ABSTRACT FROM AUTHOR]

The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm. For this purpose, an experimental dataset representing standard ISO specimens with piston aluminum alloy material, which was subjected to rotational bending fatigue tests under fully reversed cyclic load conditions, was utilized. Subsequently, the stress and fatigue lifetime data were employed to fit the algorithm of K-means clustering. Then, to enhance the K-means performance, various preprocessing methods and Kernel functions were employed to cluster fatigue lifetime and stress data. Furthermore, following the division of the data into multiple clusters, the middle cluster, which represents fatigue lifetime and stress, was identified as the transition fatigue region, and its center defines the estimated transition fatigue lifetime. Ultimately, the transition fatigue lifetimes were determined using the Coffine--ansone--asquin equation for piston aluminum alloys and compared to the estimated transition fatigue lifetimes, along with the calculation of relative errors. The obtained results indicated that, among the different models employed in this study, the polynomial Kernel K-means clustering algorithm proved to be the most efficient for clustering data within stress and number of cycles plots (S-N plots). Moreover, employing the K-means algorithm with a polynomial Kernel function and five cluster numbers yielded the most accurate estimation of transition fatigue lifetime for piston aluminum alloys, exhibiting the lowest relative error. [ABSTRACT FROM AUTHOR]

This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks. This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types. Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies. This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands. The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation. Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios. [ABSTRACT FROM AUTHOR]

In our research, we broaden the scope of Fourier–Stieltjes transforms to encompass locally compact groups, denoted as G. We achieve this extension by leveraging the induced representation from a closed subgroup K. From this, we deduce the Fourier transform f ^ of a Haar-integrable function f defined on G. Specifically, we express f ^ as the Fourier–Stieltjes transform μ ^ of the measure μ = f λ , where λ denotes the Haar measure of G. Our work is significant because when applied to Lie groups with compact subgroups K, our Fourier–Stieltjes transform m ^ exhibits more nuanced characteristics compared to the traditionally defined one via the Gel’fand transform, which is standard in the context of Lie groups. We rigorously substantiate this observation. One of the principal challenges we confront is the construction of the “trigonometric functions”, which serve as the foundation for building the Fourier transform. [ABSTRACT FROM AUTHOR]

Situated in AsianCrit, which emphasizes centrality of racism among Asians in education as well as youth activism scholarship that denotes the engagement of youth in informal, communal, and everyday political spheres, the current qualitative study aims to center and uplift the voices of Korean American and migrant students who were enthusiastically involved in a Korean Student Organization (pseudonym) since the COVID-19 pandemic. More specifically, this study explores Korean American and migrant students' motivations, perspectives, and aspirations to participate in the organization at a predominantly white university in the Midwest since the onset of the COVID-19 pandemic. By highlighting how Korean American and migrant students foregrounded their experiences of racism and marginalization at a predominately white university during the COVID-19 pandemic by creating a variety of collective activities, events, and opportunities within and across the campus to survive, resist, and flourish amidst of heightened racist climates, this study will provide several implications for AsianCrit and youth activism scholarship. Together, the goal of this study is to bring attention to everyday youth activism and agency among Asian and Asian American students regarding racial justice and complicate and challenge the hegemonic representations of Asian and Asian Americans as model minorities, forever foreigners, or victims of anti-Asian hate crimes in contemporary U.S. society. [ABSTRACT FROM AUTHOR]

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI's within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies.† [ABSTRACT FROM AUTHOR]

Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three individual algorithms are used as the base models in the first layer, with the multiple linear regression (MLR) algorithm serving as the meta-model in the second layer, merging the demand prediction model based on the hybrid model into the overall demand prediction model. This study demonstrates that the accuracy and stability of demand prediction using the LSTM–RF–XGBoost model significantly outperform traditional single models, highlighting the significant advantages of using a combined model. This research offers practical and innovative solutions for enterprises seeking rational resource allocation through demand prediction. [ABSTRACT FROM AUTHOR]

Machine learning (ML) approaches, widely used in civil engineering, have the potential to reduce computing costs and enhance predictive capabilities. However, many ML methods have yet to be applied to develop models that accurately analyze the nonlinear dynamic response of cross-fault hydraulic tunnels (CFHTs). To predict CFHT models and fragility curves effectively, we identify the most effective ML techniques and improve prediction capacity and accuracy by initially creating an integrated multivariate earthquake intensity measure (IM) from nine univariate earthquake IMs using principal component analysis. Structural reactions are then performed using incremental dynamic analysis by a multimedium-coupled interaction system. Four techniques are used to test ML–principal component analysis (PCA) feasibility. Meanwhile, mathematical statistical parameters are compared to standard probabilistic seismic demand models of expected and computed values using ML-PCA. Eventually, multiple stripe analysis–maximum likelihood estimation (MSA-MLE) is applied to assess the seismic performance of CFHTs. This study highlights that the Gaussian process regression and integrated IM can improve reliable probability and reduce uncertainties in evaluating the structural response. Thorough numerical analysis, using the suggested methodology, one can efficiently assess the seismic fragilities of the tunnel by the predicted model. ML-PCA techniques can be viewed as an alternate strategy for seismic design and CFHT performance enhancement in real-world engineering. [ABSTRACT FROM AUTHOR]