Logarithm Of Number Research Articles

Evaluation of photocatalysis inactivation in indoor air purification of pathogenic microbes by using the different nanomaterials based on TiO2 nanomaterials

The photocatalytic technology for indoor air disinfection has been broadly studied in the last decade. Selecting proper photocatalysts with high disinfection efficiency remains to be a challenge. By doping with the incorporation of metals, the bandgap can be narrowed while avoiding the recombination of photogenerated charge. Three photocatalysts (Ag-TiO2, MnO2-TiO2, and MnS2-TiO2) were tested in photocatalytic sterilization process. The results revealed that Ag-TiO2 had the best antibacterial performance. Within 20 min, the concentration of Serratia marcescens (the tested bacteria) decreased log number of ln 4.04 under 640 w/m2 light intensity with 1000 µg/mL of Ag-TiO2. During the process of inactivating bacteria, the cell membranes of bacterial was destructed and thus decreasing the activity of enzymes and releasing the cell contents, due to the generation of reactive oxygen species (O2•– and •OH) and thermal effect. Spectral regulation has the greatest impact on the sterilization efficiency of MnO2-TiO2, which reduces the probability of photocatalytic materials being excited.

The time course of stimulus-specific perceptual learning.

Practice on perceptual tasks can lead to long-lasting, stimulus-specific improvements. Rapid stimulus-specific learning, assessed 24hours after practice, has been found with just 105 practice trials in a face identification task. However, a much longer time course for stimulus-specific learning has been found in other tasks. Here, we examined 1) whether rapid stimulus-specific learning occurs for unfamiliar, non-face stimuli in a texture identification task; 2) the effects of varying practice across a range from just 21 trials up to 840 trials; and 3) if rapid, stimulus-specific learning persists over a 1-week, as well as a 1-day, interval. Observers performed a texture identification task in two sessions separated by one day (Experiment 1) or 1 week (Experiment 2). Observers received varying amounts of practice (21, 63, 105, or 840 training trials) in session 1 and completed 840 trials in session 2. In session 2, one-half of the observers in each group performed the task with the same textures as in session 1, and one-half switched to novel textures (same vs.novel conditions). In both experiments we found that stimulus-specific learning - defined as the difference in response accuracy in the same and novel conditions - increased as a linear function of the log number of session 1 training trials and was statistically significant after approximately 100 training trials. The effects of stimulus novelty did not differ across experiments. These results support the idea that stimulus-specific learning in our task arises gradually and continuously through practice, perhaps concurrently with general learning.

Proximity Testing with Logarithmic Randomness

A fundamental result dating to Ligero (Des. Codes Cryptogr. '23) establishes that each fixed linear block code exhibits proximity gaps with respect to the collection of affine subspaces, in the sense that each given subspace either resides entirely close to the code, or else contains only a small portion which resides close to the code. In particular, any given subspace's failure to reside entirely close to the code is necessarily witnessed, with high probability, by a uniformly randomly sampled element of that subspace. We investigate a variant of this phenomenon in which the witness is not sampled uniformly from the subspace, but rather from a much smaller subset of it. We show that a logarithmic number of random field elements (in the dimension of the subspace) suffice to effect an analogous proximity test, with moreover only a logarithmic (multiplicative) loss in the possible prevalence of false witnesses. We discuss applications to recent noninteractive proofs based on linear codes, including Brakedown (CRYPTO '23).

A packing problem approach to grasping with a two-finger gripper with application in forest harvesting

Robotic Hand Grasping is a challenging task that is studied by different researchers, with contributions made to topics such as grasping quality measuring, synthesis, and mathematical models. However, these works usually consider grasping of only one object at time, which is not applicable when the hand must carry multiple objects. Multiple objects grasping problem can be approached as a packing problem, which consists in filling a container with objects of a determined geometry, using the space available in the most efficient way. The aim of this work is to obtain a design methodology for a two-finger gripper that optimizes the grasping capacity of slender cylindrical multiple objects, applying packing problem concepts. This case is of practical importance in grapple claws used in forest harvesting for picking logs. A mathematical model is presented that allows to obtain an optimized geometric configuration of the grapple claw for maximum grasping capacity and packing density for given numbers and sizes of logs. This model is analytically solved for the maximum radius of a set of logs that can be grasped. Alternatively, for a number of logs with a given diameter, the geometric dimensions of an optimized grapple claw can be obtained. The solutions are evaluated using packing density and Active Force Closure (AFC) as quality metrics, to show the benefits of adding them as design criteria in the design multiple objects grapple claws.

Estimation of lumber recovery for small-scale mobile sawmills in Tanzania

Lumber recovery is an important parameter in determining the production efficiency and viability of mobile sawmilling operations. In this study, lumber recoveries for mobile sawmilling machines operating in four different locations in Tanzania were estimated. A total of 11 mobile sawmilling operations were studied. On each piece of mobile sawmilling machinery, data on log sizes, including bottom, mid and top diameter, and log lengths were recorded. Results showed that the number of logs per machine ranged from 5 to 40 with an average of 28 logs. The middle diameter of the logs ranged from 8 to 44.60 cm, with an average value of 20.58 cm. The number of lumbers produced for each log varied from 1 to 9 pieces, with an average of 3 pieces across all the sites. The lumber recovery (%), varied depending on the individual machine, machine type, source of logs and geographical site within Tanzania. For all the machines, the average lumber recovery (%) per machinery ranged from 16.15 to 62.79%. Among the three types of mobile sawmilling machinery, Wood-Mizer had relatively higher lumber recovery (56.2%) than Ameck and Ding-Dong. The overall average lumber recovery value for all the machines, irrespective of log sizes, site and source of lumber was 43.34%. Analysis of variance (ANOVA) indicated that site, source of lumber, machine type and size of logs had a statistically significant effect (p < 0.05) on lumber recovery (%). The study concluded that mobile sawmilling in Tanzania produces sawn timber with reasonable lumber recovery. However, further studies are recommended to examine technical aspects, such as productivity and taper, as well as cost of the mobile sawmilling operations.

Human movement avoidance decisions during Coronavirus disease 2019 in Japan

Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan’s Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.

Investigating optimal features in log files for anomaly detection using optimization approach

&lt;p&gt;&lt;span lang="EN-US"&gt;Logs have been frequently utilised in different software system administration activities. The number of logs has risen dramatically due to the vast scope and complexity of current software systems. A lot of research has been done on log-based anomaly identification using machine learning approach. In this paper, we proposed an optimization approach to select the optimal features from the logs. This will provide the higher classification accuracy on reduced log files. In order to predict the anomalies three phases are used: i) log representation ii) feature selection and iii) Performance evaluation. The efficacy of the proposed model is evaluated using benchmark datasets such as BlueGene/L (BGL), Thunderbird, spirit and hadoop distributed file system (HDFS) in terms of accuracy, converging ability, train and test accuracy, receiver operating characteristic (ROC) measures, precision, recall and F1-score. The results shows that the feature selection on log files outperforms in terms all the evaluation measures.&lt;/span&gt;&lt;/p&gt;

Analysis of Student Behavior Based on the History of Learning Activities in the Learning Management System Using the Pearson Correlation Method

The purpose of this study is to identify student learning behavior in online learning and to determine the relationship between student learning behavior and learning achievement based on learning history data (Learning logs) on the Learning Management System (LMS), including the performance of assignments and quizzes, utilization interaction features (forums and chat), as well as active access to learning resources (files and URLs). Pearson Correlation method is used to analyze the level of relationship between learning behavior and students’ achievement. The research object is 105 students at Muhammadiyah University of Enrekang who programmed introductory information technology (PTI) courses from 5 different classes but taught by the same lecturer. The total number of processed activity histories (after data preprocessing) is 6500 records, while the total number of logs before data preprocessing is 19386 records. Correlation analysis linking student behavior to student learning achievement is quite strong and unidirectional, as evidenced by the correlation value between learning behavior and student final grades which show an average number of 0.80 and all are positive, with confidence interval values reaching95%. This shows that the higher the learning activities that students participate in in online learning (the more active), the stronger the effect on student learning achievement. It also shows that student activity in completing assignments is the variable that most influences learning achievement with a correlation value of 0.88 (very strong).

Minimum collisions assignment in interdependent networked systems via defective colorings

In conjunction with the traffic overload of next-generation wireless communication and computer networks, their resource-constrained nature calls for effective methods to deal with the fundamental resource allocation problem. In this context, the Minimum Collisions Assignment (MCA) problem in an interdependent networked system refers to the assignment of a finite set of resources over the nodes of the network, such that the number of collisions, i.e., the number of interdependent nodes receiving the same resource, is minimized. Given the interdependent networked system's organization in the form of a graph, there already exists a randomized algorithm that converges with high probability to an assignment of resources having zero collisions when the number of resources is larger than the maximum degree of the underlying graph. In this article, differing from the prevailing literature, we investigate the case of a resource-constrained networked system, where the number of resources is less than or equal to the maximum degree of the underlying graph. We introduce two distributed, randomized algorithms that converge in a logarithmic number of rounds to an assignment of resources over the network for which every node has at most a certain number of collisions. The proposed algorithms apply to settings where the available resources at each node are equal to three and two, respectively, while they are executed in a fully-distributed manner without requiring information exchange between the networked nodes.

Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth

Various classic reasoning problems with natural hypergraph representations are known to be tractable if a hypertree decomposition (HD) of low width exists. The resulting algorithms are attractive for practical use in fields like databases and constraint satisfaction. However, algorithmic use of HDs relies on the difficult task of first computing a decomposition of the hypergraph underlying a given problem instance, which is then used to guide the algorithm for this particular instance. The performance of purely sequential methods for computing HDs is inherently limited, yet the problem is, theoretically, amenable to parallelisation. In this article, we propose the first algorithm for computing hypertree decompositions that is well suited for parallelisation. The newly proposed algorithm log- k -decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to so-called balanced separators. We provide a detailed experimental evaluation over the HyperBench benchmark and demonstrate that log- k -decomp outperforms the current state of the art significantly.

Early adapting to trends: self-stabilizing information spread using passive communication

AbstractHow to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of nagents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic $$\mathcal {PULL}$$ PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.

Cell counting to monitor swab efficiency.

Plastic bags, such as ziplock bags, have been used to transport illicit materials worldwide; however, very few studies have tried to optimize the recovery of DNA from these items. This study reports on the best combination of swabs and moistening solution for the greatest recovery of cellular material from ziplock bags. Five swabs, two different variations of Copan Diagnostics nylon 4N6FLOQSwabs, one Medical Wire rayon DRYSWAB, one IsoHelix rayon swab, and one Livingstone cotton swab, were evaluated with two moistening solutions, Triton X-100 in either distilled water or isopropanol. Fingermarks were deposited on ziplock bags and stained with Diamond™ Nucleic Acid Dye to allow visualization of the cells pre- and post-swabbing to determine the number of cells recovered. Based on cell counting data, swabs moistened with Triton X-100 in distilled water performed better than those moistened with isopropanol. Livingstone cotton swabs had the worst recovery of cellular material, while the other swabs tested had no significant difference in their respective solutions. A comparison of the best three swabs for cellular recovery yielded no differences in the DNA concentration extracted. A linear relationship was observed between the log number of cells recovered by swabbing and the DNA concentration following extraction and quantification. The process of monitoring cell collection using fluorescence microscopy on ziplock bags allowed evaluation of swabbing efficacy. Additionally, this study highlights the ability to evaluate cellular recovery independently of traditional extraction, quantification, or profiling techniques which may unequally affect samples.

Development of Circuits for Antilogarithmic Converters with Efficient Error–Area–Delay Product Using the Fractional-Bit Compensation Scheme for Digital Signal Processing Applications

Digital signal processing (DSP) has been widely adopted in sensor systems, communication systems, digital image processing, artificial intelligence, and Internet of Things applications. However, these applications require circuits for complex arithmetic computation. The logarithmic number system is a method to reduce the implementation area and transmission delay for arithmetic computation in DSP. In this study, we propose antilogarithmic converters with efficient error–area–delay products (eADPs) based on the fractional-bit compensation scheme. We propose three mathematical approximations—case 1, case 2, and case 3—to approximate the accurate antilogarithmic curve with different DSP requirements. The maximum percentage errors of conversion for case 1, case 2, and case 3 are 1.9089%, 1.7330%, and 1.2063%, respectively. Case 1, case 2, and case 3 can achieve eADP savings of 15.66%, 80.80%, and 84.61% compared with other methods reported in the literature. The proposed eADP-efficient antilogarithmic converters can achieve lower eADP and digitalized circuit implementation. The hardware implementation utilizes Verilog Hardware Description Language and the digital circuits are created via very-large-scale integration by the Taiwan Semiconductor Manufacturing Company with 0.18 µm CMOS technology. This proposed antilogarithmic converter can be efficiently applied in DSP.

Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention

BackgroundSelf-monitoring is crucial for behavioral weight loss. However, few studies have examined the role of self-monitoring using mixed methods, which may hinder our understanding of its impact.MethodsThis study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants’ daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants’ qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups.ResultsAfter the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories.ConclusionThe self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters’ weight loss motivation and promote adherence to self-monitoring practices.

Selection process based on new building construction work using square root vague sets and their aggregated operators

This study discusses novel approaches for solving multi-attribute decision-making problems using logarithmic square root vague sets. Logarithmic square root vague sets can be considered as an extension of vague sets and square root fuzzy sets provides a more profitable method to express the uncertainties in the data to deal with decision making problems. We developed some novel logarithmic square root vague sets, which can overcome the weaknesses of algebraic operations and capture the relationship between various square root vague sets and vague sets. The purpose of this study is to examine several aggregation operators using logarithmic square root vague sets. The weighted logarithmic averaging distance and weighted logarithmic geometric distance operators are distance measure that is based on aggregating model. We discuss the concepts of logarithmic square root vague weighted averaging, logarithmic square root vague weighted geometric, generalized logarithmic square root vague weighted averaging and generalized logarithmic square root vague weighted geometric operators. We communicate that algebraic structures such as associative, distributive, idempotent, bounded, commutativity and monotonicity properties are satisfied by logarithmic square root vague numbers. We discusses some mathematical properties of logarithmic square root vague sets, as well as the Hamming distance and Euclidean distance measures. It measures the similarity of two strings of information by measuring the Hamming distance. An analysis of the proposed operators is presented, along with alternative formulations and families, as well as their main properties. The weighting vector is characterized by multiple classical measures and we propose alternative solutions for dealing with the logarithmic properties of the operators. As a result of studying the weighting vectors of the operators, we present generalizations of them. We examine vague sets with important properties using algebraic operations in more detail. Furthermore, we develop an algorithm to solve multi-attribute decision-making problems using these operators. We provide real-life examples to illustrate how enhanced Euclidean distances, Hamming distances and score values can be applied in real-world situations. To show the superiority and the validity of the proposed aggregation operations, we compared it with the existing method, and concluded from the comparison and sensitivity analysis that our proposed technique is more effective and reliable. Based on the expert assessments, we compared the criteria with the most appropriate options.

Abstract A137: Drive time to physicians and outcomes for bladder cancer

Abstract Introduction: Poor geographic access to cancer care providers can delay treatment and limit access to high quality care. Previous research has shown that for patients with bladder cancer, time to cystectomy and receipt of neoadjuvant chemotherapy are associated with improved survival. Travel burden may be an important barrier to timely guideline-concordant treatment and is particularly relevant for residents of rural areas, which are often medically underserved. This study examines the association between drive time to medical oncologists and urologists and clinical outcomes for muscle-invasive bladder cancer (MIBC) patients. Data and Methods: We used information on physician location from the Centers for Medicare and Medicaid Services and patient-level data from the 2010-2016 Pennsylvania Cancer Registry linked to Pennsylvania Healthcare Cost Containment Council (PHC4) inpatient discharge data through 2018. We examined patients in Pennsylvania with a first lifetime bladder cancer diagnosis who underwent radical cystectomy at a non-federal short-term general hospital identified in PHC4. Outcomes examined were surgery within 90 days, receipt of neoadjuvant chemotherapy, 90-day mortality and 90-day readmission. We estimated multivariable linear regression models at the patient level. Our primary exposures were the natural log number of minutes from the patient’s home census tract centroid to the nearest medical oncologist and the nearest urologist. We controlled for patient age, sex, race/ethnicity, tumor size, tumor node involvement, and Elixhauser comorbidity index. We included a control for whether the patient received neoadjuvant chemotherapy in models estimating the association between drive times and cystectomy within 90 days. We also controlled for area-level characteristics, including ZIP code median and area deprivation index quartile. Standard errors were clustered at the county level.Results: The study included 954 patients with MIBC, 1,520 medical oncologists, and 976 urologists in Pennsylvania. Mean (SD) drive time to the nearest oncologist was 17.1 (11.4) minutes and median drive time was 14.4 minutes. Mean drive time to the nearest urologist was 13.9 (9.2) minutes and median drive time was 11.7 minutes. A 30-minute increase in drive time to the nearest urologist was associated with a 12.5 percentage point lower likelihood of undergoing cystectomy within 90 days (95% CI: -24.3 to -0.6). A 30-minute increase in drive time to the nearest oncologist was associated with an 11.9 percentage point lower likelihood of receiving neoadjuvant chemotherapy (95% CI: -23.4 to -0.4). Drive time was not significantly associated with 90-day mortality or readmission within 90 days. Conclusions: Drive time to both oncologists and urologists is associated with timely receipt of guideline-recommended care for patients with MIBC. Understanding the impact of geographic access to medical oncologists and urologists on clinical outcomes for patients with MIBC can inform providers and policymakers in addressing geographic equity in cancer care. Citation Format: Sruthi Muluk, Coleman Drake, Zhaojun Sun, Manisha Bhattacharya, Bruce Jacobs, Lindsay M. Sabik. Drive time to physicians and outcomes for bladder cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A137.

On the number of different variables required to define the n-density or the bounded n-width of Kripke frames with some consequences for Sahlqvist formulae

Abstract We show that both the $n$-density and the bounded $n$-width of Kripke frames can be modally defined not only with natural and well-known Sahlqvist formulae containing a linear number of different propositional variables but also with formulae of polynomial length with a logarithmic number of different propositional variables and then we prove that this exponential decrease in the number of variables leads us outside the class of Sahlqvist formulae.

Token Games and History-Deterministic Quantitative-Automata

A nondeterministic automaton is history-deterministic if its nondeterminism can be resolved by only considering the prefix of the word read so far. Due to their good compositional properties, history-deterministic automata are useful in solving games and synthesis problems. Deciding whether a given nondeterministic automaton is history-deterministic (the HDness problem) is generally a difficult task, which can involve an exponential procedure, or even be undecidable, as is the case for example with pushdown automata. Token games provide a PTime solution to the HDness problem of B\"uchi and coB\"uchi automata, and it is conjectured that 2-token games characterise HDness for all $\omega$-regular automata. We extend token games to the quantitative setting and analyse their potential to help deciding HDness of quantitative automata. In particular, we show that 1-token games characterise HDness for all quantitative (and Boolean) automata on finite words, as well as discounted-sum (DSum), Inf and Reachability automata on infinite words, and that 2-token games characterise HDness of LimInf and LimSup automata, as well as Sup automata on infinite words. Using these characterisations, we provide solutions to the HDness problem of Safety, Reachability, Inf and Sup automata on finite and infinite words in PTime, of DSum automata on finite and infinite words in NP$\cap$co-NP, of LimSup automata in quasipolynomial time, and of LimInf automata in exponential time, where the latter two are only polynomial for automata with a logarithmic number of weights.

Research on Fault Detection for Microservices Based on Log Information and Social Network Mechanism Using BiLSTM-DCNN Model

The microservice architecture breaks through the traditional cluster architecture mode based on virtual machines and uses containers as carriers to interact through lightweight communication mechanisms to reduce system coupling and provide more flexible system service support. With the expansion of the system scale, a large number of system logs with complex structures and chaotic relationships are generated. How to accurately analyze the system logs and make efficient fault prediction is particularly important for building a safe and reliable system. By studying neural network technology, this paper proposes an Attention-Based Bidirectional Long Short-Term Memory Network (Bi-LSTM). Combined with the dual channel convolutional neural network model (DCNN), it uses the attention mechanism to explore the differences between dimensional features, realizes multi-dimensional feature fusion, and establishes a BiLSTM-DCNN deep learning model that integrates the attention mechanism. From the perspective of social network analysis, a data preprocessing method is proposed to process fault redundant data and improve the accuracy of fault prediction under Microservices. Compare BiLSTM-DCNN with the mainstream system log analysis machine learning models SVM, CNN and Bi-LSTM, and explore the advantages of BiLSTM-DCNN in processing microservice system log text. The model is applied to simulation data and HDFS data set for experimental comparison, which proves the good generalization ability and universality of BiLSTM-DCNN.

System-level data management for endpoint advanced persistent threat detection: Issues, challenges and trends

Advanced persistent threat (APT) attacks pose significant security threats to governments and large enterprises. Endpoint detection and response (EDR) methods, which are standard solutions to combat APT attacks, can efficaciously respond to associated security threats by leveraging the semantic richness of provenance graphs and clear causality relations to resist illegal tampering. However, the large number of audit logs produced over time, which provide key supporting information for EDR methods, lead to substantial computational overhead and increased storage costs. Therefore, a robust data management framework must be developed. However, most existing reviews discuss data collection, compression, and storage methods independently. Due to the lack of a comprehensive, structured survey of data management strategies, current data management analyses tend to be separated into individual modules, making it difficult to obtain prompt and precise guidance for higher-level security analysis tasks from these analyses. In this paper, a comprehensive and structured survey of data management strategies based on provenance graphs is conducted, the core ideas of the mainstream approaches to each aspect of data management are summarized, and existing approaches are systematically classified and compared. Then, the problems with individual data management modules are investigated, and potential complementary and collaborative strategies are examined based on the insights and challenges of existing work as a basis for recommending best practices for practical deployment. Finally, an ideal data management framework is described to guide future research.