Logical Dependencies Research Articles

Towards a Unified Temporal and Event Logic Paradigm for Multi-Hop Path Reasoning in Knowledge Graphs

Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dynamic, evolving data is essential for advancing topology design and application systems. Despite its importance, traditional approaches often encounter substantial limitations when applied to dynamic, time-sensitive scenarios. These models typically fail to adequately capture intricate logical dependencies and demonstrate suboptimal performance in data-constrained environments. To address these challenges, we introduce Path-Reasoning Logic (PRlogic), an innovative framework that seamlessly integrates rule-based logical reasoning with cutting-edge neural network methodologies. PRlogic enhances path inference by leveraging a context-aware logical association network adept at handling temporal and event-driven attributes, enabling improved reasoning for dynamic systems such as IoT-based power electronics and smart grids. This adaptability allows the framework to better accommodate evolving knowledge structures, significantly improving reasoning accuracy under resource-scarce conditions. Furthermore, PRlogic employs a multi-stage refinement strategy, harmonizing logic-based rules with learned contextual representations to achieve heightened robustness and scalability. Comprehensive experiments on widely-recognized benchmark datasets validate the superiority of PRlogic, demonstrating its consistent outperformance of existing models in path reasoning tasks. These results underscore the efficacy of incorporating logic-driven mechanisms into knowledge graph reasoning and highlight PRlogic’s potential as a powerful solution for applications in dynamic data environments.

Curating global datasets of structural linguistic features for independence

The increasing availability of cross-linguistic databases dedicated to documenting morphosyntactic, lexical and phonological features has proliferated the use of such data for studies on language evolution and human history. However, most of these databases were not designed to ensure independence of features, such that it is not valid to jointly use all their features in large-scale statistical analyses assuming independence of inputs. Here, we curate published data from five large linguistic databases to generate two global-scale cross-linguistic datasets: GBI (from the Grambank dataset), and TLI (using inputs from the World Atlas of Language Structures, AUTOTYP, PHOIBLE and Lexibank). The datasets minimize logical dependencies of features and forms of strong statistical dependencies that go beyond phylogenetic and geographical signal. They are also made available in densified form, reducing the proportion of missing data. We document our curation principles and workflows to ensure reusability of this framework with other inputs or thresholds of independence. Our curation steps on both datasets reveal robust and comparable global patterns of structural linguistic diversity.

Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems.

Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions. Extensive experiments on a large-scale synthetic distributed system log dataset show that TLAN outperforms existing methods by achieving a 9.4% improvement in F1-score and reducing false alarms by 15.3% while maintaining low latency in real-time detection. The framework demonstrates particular effectiveness in identifying complex anomalies that involve multiple interacting components and cascading failures. Through comprehensive empirical analysis and case studies, we validate that TLAN can effectively capture both temporal patterns and logical correlations in log sequences, making it especially suitable for modern distributed architectures. Our approach also shows strong generalization capability across different system scales and deployment scenarios, supported by thorough ablation studies and performance evaluations.

Diagnosing sucker-rod pumping units by means of a neural network module implemented on the basis of an oilwell controller

Currently, sucker-rod pumping (SRP) units are widely used in oil wells, due to two important factors: costs and ease of use. To optimize production using sucker rod pumps, well Rod Pump Controllers (RPC) have been used for more than 20 years. Today the main method of controlling the sucker-rod pumping unit is the analysis of dynagraph card (determination of the state and mode of operation of the pump based on the dependence of the rod’s load on its position). The qualitative analysis of a dynagraph (DMG) comes down to diagnostics, which detects and identifies signs of possible malfunctions of the pump and the well as a whole. The task of determining malfunctions by a dynagraph is considered difficult for a computer analysis and involves the evaluation of a dynagraph card by a qualified technologist. This article discusses methods and algorithms for determining malfunctions from obtained RPC dynagraphs, implemented directly on base of the well controller. These methods can be divided into analytical and neural network methods. Analytical algorithms describe the DMG defect in the form of formulas or logical dependencies. Neural network methods approach the problem of defect recognition as recognizing a set of unrelated data. This helps to work with a system with a large number of parameters, such as a (SRP). The Naftamatika took into account the pros and cons of previous approaches and developed its own neural network model. The development consisted of selecting the type of network, the number of its layers, creating a spatial structure of neurons, creating conditions for training the network and selecting an adequate training sample. To train the controller, a program was used in which thousands of dynamograms with various defects and conditions were loaded. This network model is implemented in a Wellsim pump controller, which provides sufficient performance, the algorithm successfully detects insufficient filling, gas factor and other faults with an accuracy of 96 %.

Leadership as a Fundamental Factor for Human Societies Progress – a Historical Review of the Leadership Theories

The article attempts to make a historical review of leadership theories and, on this basis, to derive their approximate systematization. The article emphasizes the importance of the phenomenon of leadership as a major factor in the management of human resources and security in human societies. The exposition goes through the various leadership theories that have existed to date. The research methodology includes etymological analysis, historical analysis, content analysis, the methods of induction and deduction, reveals logical connections and dependencies between related and similar concepts and phenomena.

Finite predicate-driven logic networks method for enhanced education data analysis

The subject matter of the study is intelligent data analysis in the field of academic information. The goal of the study is to create a mathematical model for analyzing students' academic information using the predicate-driven logic networks method, which allows taking into account both logical dependencies and probabilistic transitions between states. To achieve this goal, the following tasks were defined: analysis of the theoretical foundations of the logic networks method and predicate logic, integration of these approaches into a single mathematical model, development of approaches for its application in academic data analysis problems. The research used the methods of mathematical modeling, complex logical analysis, and method for constructing logic networks. The following results were obtained: a theoretical model was developed that integrates the principles of logic networks and predicate logic for analyzing student academic performance; the model accounts for both probabilistic transitions between states and logical dependencies among student parameters; the mathematical model also incorporates logical rules to enhance the accuracy of logical analysis within the academic context. The model was tested on a dataset of student performance, demonstrating its effectiveness in accurately predicting academic outcomes and confirming the validity of the integrated approach. Conclusions. The scientific novelty of the results obtained is as follows: 1) a theoretical model for analyzing student academic data was developed by integrating logic networks and predicate logic, allowing for the simultaneous consideration of probabilistic transitions and logical dependencies among student parameters; 2) the approach enhances the analysis process by incorporating logical rules into the probabilistic framework, providing a more nuanced and accurate tool for analyzing academic data; 3) this combined model offers a novel method for addressing complex logical analysis tasks in educational settings, paving the way for further research and practical applications in intelligent data analysis. The successful testing of the model on actual student data further underscores its potential as a powerful tool in educational data analysis.

Studi Manajemen Konstruksi Bangunan Spillway dan Intake pada Bendungan Bulango Ulu Provinsi Gorontalo Menggunakan Metode Fasttrack dan Crashing

The construction of the Bulango Ulu Dam consists of several stages of work involving many resources, both human, material, and heavy equipment. Therefore, good construction management is needed so that project can be completed on time, cost efficiently, and good quality. This research was conducted with the aim of knowing which method is optimal in accelerating the Bulango Ulu Dam project. In this study, rescheduling is carried out with 2 project acceleration optimization methods, namely the fasttrack and crashing methods. The fasttrack method is done by changing the work dependency logic of the work on the critical path. While the crashing method is done by adding overtime for 2 hours per day to the work on the critical path as well. After optimizing the project, there were changes in term of cost and duration. In the fasttrack method, it has a cost efficiency of 0,26% and a duration effectiveness of 5,90%. While the crashing method has a cost efficiency of 1,27% and duration effectiveness of 4,46%. Based on the research conducted, the fasttrack method has a superior duration effectiveness than the crashing method which is 5,90%. While in terms of cost efficiency, the crashing method is superior to the fasttrack method by 1,27%.

LORE++: Logical location regression network for table structure recognition with pre-training

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Current approaches address this issue by either predicting the adjacency of detected cells or direct generation of structural sequences. Nonetheless, these approaches either count on additional heuristic rules for post-processing, or involve the generation of extremely long-range sequences that lead to computational intricacy. In this paper, We redefine TSR as a LOgical location REgression paradigm, which effectively captures inherent logical dependencies and constraints among table cells. Correspondingly, we propose LORE, a novel approach for TSR. LORE simultaneously predicts accurate geometric coordinates of table cells and the logical structures of the entire table. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other TSR paradigms. Moreover, to enhance the model’s spatial and logical representation capabilities, we propose two pre-training tasks, resulting in an upgraded version named LORE++. The incorporation of pre-training is proven to enjoy significant advantages, leading to a substantial enhancement in terms of accuracy, generalization, and few-shot capability compared to its predecessor. Experiments on standard benchmarks demonstrate the superiority of LORE++, which highlights the potential and promising prospect of the logical location regression paradigm for TSR.

Broad and alien is the field: dependence and heterogeneity in management research practices in Latin America

PurposeThis study aims to explore the ways in which management scholars affiliated with Peruvian universities navigate the tensions between global expectations and local realities in their research practices, drawing on their capitals and habitus.Design/methodology/approachDrawing on Bourdieu’s field theory, the authors analyse 25 in-depth interviews and a unique database of academic publications in the business and management field from 2000 to 2022. The analysis identifies the positions scholars occupy within the Peruvian management field and examines the factors influencing their research practices.FindingsThe authors find that the Peruvian management field is complex and unequal, where actors have different positions and interests, but are all influenced by a logic of academic dependency on the Global North. The authors identify three main positions held by scholars: transnational dominators, who accumulate greater resources and ignore local debates; dominated adaptors, who unsuccessfully try to imitate the dominant logic; and isolated innovators, who critique the dominant model but lack institutional support to develop alternatives.Originality/valueThis research presents an analysis of the Peruvian management field, a site often overlooked in international business studies. By examining scholarly practices, the authors reveal how academic inequalities are reproduced by the forces of globalization. The study underscores the urgent need for greater acknowledgement of regionally informed research, advocating for a more inclusive and diverse understanding in the field of management research.

OPTIMIZATION SCHEDULING PROJECT REHABILITATION BUILDING AND HALL DEKRANASDA AND KALIMANTAN BARAT MUSEUM FENCE USING CRITICAL PATH METHOD (CPM)

This study investigates the optimization of project scheduling using the Critical Path Method (CPM) in construction projects. Focusing on the Building Rehabilitation Project, Dekranasda Hall, and Kalimantan Barat Museum Fence, the research aims to reduce project duration without altering costs. CPM is employed to map out activities in a network, identifying the critical path to enhance time management. The initial project duration was 249 days. By breaking down work items and increasing the number of workers in critical activities, the duration was reduced to 125 days. Optimization involved dividing tasks into manageable segments and determining the appropriate workforce through trial and error. The study demonstrates that effective project scheduling and resource allocation can significantly reduce completion time, highlighting CPM's value in project management for achieving efficiency and meeting deadlines. The findings emphasize the importance of detailed planning, dependency logic, and continuous monitoring to optimize construction project timelines.

HD-LJP: A Hierarchical Dependency-based Legal Judgment Prediction Framework for Multi-task Learning

In real-world scenarios, multiple subtasks of legal judgment (such as law article, charge, and term of penalty) have strict task logical order and label topological relation, and their results influence and validate each other. However, most existing methods model them as simple classification problems, which ignores the logical and semantic constraints between subtasks. Besides, they mainly focus on the fact description for judgment results, and ignore the standard legal documents (i.e., the established law articles). To this end, we propose a Hierarchical Dependency-based Legal Judgment Prediction framework (HD-LJP), which integrates task judicial logic, label topological relation, and hierarchical semantics knowledge in legal text effectively. Specifically, HD-LJP employs consistency and distinction distillation to model label topological relation among multiple subtasks, and improve the differentiation of each subtask itself respectively. In addition, for simulating the judicial logic of human judges, we define logical dependencies between subtasks, and then utilize the results of intermediate subtasks to make auxiliary prediction of other subtasks. And, hierarchical semantics knowledge is fully integrated and applied in these two processes, which will profoundly affect the creditability and interpretability of the judgment results. The experimental results show that HD-LJP can improve the prediction performance of three LJP subtasks, especially in the term of penalty. Compared with the existing methods, the macro-F1 on CAIL-small is increased by 13.4%, and 6.9% on CAIL-big. In addition, through further case studies, this paper demonstrates that HD-LJP performs better for tail classes and confusing labels than the current SOTA R-former.

RepFTI: Representation-Fused Function-Type Inference for Vehicular Secure Software Systems

To enhance the security of vehicular software systems, inversely identifying the underlying function types of binary files plays a key role in threat discovery. However, existing function-type inference (FTI) methods can only provide a suboptimal performance because of splitting binary files into multiple sub-blocks as inputs, which results in breaking the program context logic and complete data dependency. To solve this problem, we propose a novel representation-fused function-type inference (RepFTI) framework for secure vehicular software systems. First, the RepFTI learns semantic representations of assembly codes and then extracts node representations in the function call graph by the multi-head attention mechanism of Graph-Attention Transformer (GAT) models. Second, the RepFTI fuses these representations to accurately infer the function type. With RepFTI, the specific limits of in-vehicle software will be bypassed, which proposes a promising direction for other work that relies on reverse engineering to improve software security.

Development and implementation of a digital control system for a former-vulcanizer

When modernizing the control system for the tire vulcanization process at CJSC «Voronezh Tire Plant», it was necessary to ensure control and regulation of technological parameters, as well as control of the vulcanizer equipment based on a control controller. The structure of an automated control system was developed, temperature and pressure sensors, an actuator, input and output modules, as well as a SIMATIC S7-1500 control controller were selected. A project for an automated workstation for a vulcanization section operator for the SIMATIC 6АV6647–0АF11–3АХ0 touch panel was developed in the WinCC environment. As part of the project, information data channels were formed, mnemonic diagrams of tire vulcanization units were developed, logical dependencies for picking up and moving tires were implemented, as well as an algorithm for digital control of the temperature of the slabs during vulcanization (the controller programming was performed in the TIA Portal environment). At the same time, information about completed operations is archived, and emerging emergency situations are recorded. A synthesis of an algorithm for digital cascade pressure control (an intermediate controlled parameter) with steam flow with correction for plate temperature has been carried out. The settings of the digital controllers of the internal and external loops were calculated using optimization programs using the numerical gradient method according to the criterion of minimum integral-square error based on the discrete dynamic models of the channels of the control object obtained as a result of identification (according to experimental acceleration curves). Calculation using models showed a higher efficiency of the cascade control algorithm compared to a single-circuit scheme for stabilizing the temperature of the plates (temperature fluctuations during regulation decreased by 5-7 °C). The system has been put into operation.

Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance

Microservice Extraction Based on a Comprehensive Evaluation of Logical Independence and Performance

Artificial Intelligence and Fact Checking in Africa: Between the Logic of Dependency and the Limits of Automation

This article focuses on fact-checking initiatives in the context of the rise of artificial intelligence. With reference to theories of the political economy of communication and platform studies, this study sheds light on the very confusing evolution of initiatives in Africa. The approach combines content analysis and distanced observation of two fact-checking platforms, chosen on the basis of their local roots and the experimentation of smart tools: Africa Check and Check4Decision. The results highlight the economic and technological dependencies of African platforms on GAFAM via factchecking services and an automation process that is far from complete with regard to local realities. It appears that the African context provides a different perspective with structural constraints and “cultural” algorithmic biases.

Artificial Intelligence and Fact Checking in Africa: Between Logic of Dependency and the Limits of Automation

This article focuses on fact-checking initiatives in the context of the rise of artificial intelligence. With reference to theories of the political economy of communication and platform studies, this study sheds light on the very confusing evolution of initiatives in Africa. The approach combines content analysis and distanced observation of two fact-checking platforms, chosen on the basis of their local roots and the experimentation of smart tools: Africa Check and Check4Decision. The results highlight the economic and technological dependencies of African platforms on GAFAM via factchecking services and an automation process that is far from complete with regard to local realities. It appears that the African context provides a different perspective with structural constraints and "cultural" algorithmic biases.

Fluent Restoration Modelling Applied to a Real Power System Using Colored Petri Nets

In this article the Colored Petri Nets (CPN) and the Critical Path Method along with the Program Evaluation Review Technique (CPM/PERT) are described and their characteristics compared for application on the real-time fluent restoration of power systems. A CPN based method is proposed and has been found more suitable since it is able to represent the temporal sequencing and the logical dependency between restoration activities. The restoration planning of electrical power systems are based on a worksheet drawn up by experts, which contains a list of procedures for restoration. The restoration process has been improved and a real case study is presented using the CPN modeling applied with success to an operative fluent restoration post-outage in the Brazilian interconnected system

Forging a sustainable path: improving India’s (Bharat’s) coal mining operations for a better tomorrow

PurposeSustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.Design/methodology/approachAn integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.FindingsThe study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.Practical implicationsThe proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.Originality/valueThe current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.

DECOMPOSITION OF FAILURE STATES OF GRAIN HARVESTING COMBINERS

The article discloses methodological approaches to the formation of the validity of the application of the simulation model of the decomposition of failure-free states of combine harvesters. The authors reproduce the functional decomposition of the reliability of grain harvesters obtained through the analysis of system functions. The authors reveal analytical approaches to the question of what the system does, regardless of how it works. As a basis, the authors formed a division into functional subsystems in the community of functions performed by groups of elements. When conducting the experiment, a set of random events that occur with the module under consideration is first generated - the event of failure of the blocked module, the event of activation of the blocking of the module and the event of the transmission of the module. Then, by assigning the corresponding probabilities, states are defined and logical dependencies between them are found. The experiment consisted in the sequential generation of events and obtaining the final states of the functional module. This experiment was repeated several times, then it was calculated, in several cases the functional module of the grain harvester was in good condition. The ratio of the number of experiment results in which the functional module was found to be working to all results shows the possibility of the functioning of the functional module. By performing a certain number of calculations of each state, summing the results of each calculation, taking the true value as one and the false value as zero, and then dividing the result by the length of the set, we obtain the value of the probability of blocked failure calculated by the Monte Carlo method.When performing the calculations and with the original data, it was equal to 0.885. The similarity of the result to the result accepted as true proves the truth. The method given in the article allows to improve the quality of work of the fail-safe system of grain harvesters, providing more accurate failure-free accounting, expanding the list of analyzed reliability parameters and making fuller use of technical means of monitoring technical condition parameters.

Weather Prediction: Predicting Rain Using Weather Conditions

Weather forecasting is a major discipline that plays an important role in fields such as agriculture, transport, and emergency management, and it largely depends on accurate forecasts. Concerning this problem, this work aimed to analyze the effectiveness of recurrent neural networks, particularly the Long Short-Term Memory (LSTM), for estimating rainfall depending on precipitation, maximum temperature, minimum temperature, and wind speed. We will therefore use a large database containing recorded weather data obtained over several years to calibrate accurate predictive models designed to distinguish between drizzle, rain, sun, snow, and fog. The main idea of the work is to teach LSTM models that are capable of revealing temporal relations and patterns in sequential data, which makes them suitable to work on various time series forecasting such as weather prediction. The data is preprocessed effectively to clean it and make it ideal for our analysis to accurately compare the performance of one model against the others, we have divided the data into training, validation, and testing sets. The concurrency of the proposed LSTM model is then evaluated with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to measure the forecasting accuracy. The findings show a better predictive performance uplift whereby the best-performing LSTM model has an MSE of 8.74, RMSE of 2.96, MAE of 2.35, and R² of 0.83. Such metrics represent logical dependence between the predicted and actual weather conditions, proving thus the efficiency of the model. Also, the evaluation shows how hyper parameters’ optimization, features’ selection, and normalization, make a huge difference in the model’s performance and indicate that the precise management of weather parameters can result in better forecasts. However, the contributors of this research are not recluded to theoretical perspective; the present study can be useful for various subjects since the dependability of weather forecasts can be improved. They will be advantaged to have more precise weather data for crop growing, road networks, and other transport systems to prepare for the worst conditions, and emergency, rescue operations to be in a better position to handle certain disasters. Consequently, this study improves the academic literature on weather peculiarities with unforeseen downpours through a demonstration and explanation of the potential of LSTM networks to analyze key meteorological characteristics for rainfall prediction. Possible future study directions are outlined, proposing the expansion of features beyond those analyzed in the existing study to improve the predictive models, the usage of continuous rather than weekly data, as well as considering the mixed-ingredients approach for increasing the prediction accuracy. This inclusive strategy seeks to enhance the realistic stages in the phased meteorological prognosis and also timely resource allocation and management tactics within climate volatility.