Conventional Rules Research Articles

Enhancing Power Grid Data Analysis with Fusion Algorithms for Efficient Association Rule Mining in Large-Scale Datasets

Against the backdrop of the rapid development of information technology, the total amount of data has exploded, and efficient association rule mining methods for large-scale datasets have been studied. Conventional rule mining algorithms are subject to electrical constraints when working, and their convergence speed and data noise are currently the main problems they face. In order to accelerate the working process of the algorithm, this study introduces a data warehouse into the K-Means algorithm, and connects the time series and voltage interaction functions with the long-and-short-term memory network for efficient information analysis of power grid data, generating fusion algorithms. The study conducted experiments on the Netloss dataset and simultaneously conducted experiments on three models, including long-and-short-term memory networks, to verify the superiority of the fusion algorithm. Under the same experimental voltage, the circuit power flows of the four models were 0.37, 0.64, 0.79, and 0.82A, respectively, indicating that the algorithm effectively controlled the electrical dataset. Its measurement accuracy was the highest among the four models, at 91.7%. The experimental results show that the fusion algorithm proposed in the study has precise control ability in power grid datasets, and can effectively mine association rules on large-scale datasets.

Double-Step Shape Invariance of Radial Jacobi-Reference Potential and Breakdown of Conventional Rules of Supersymmetric Quantum Mechanics

The paper reveals some remarkable form-invariance features of the ‘Jacobi-reference’ canonical Sturm–Liouville equation (CSLE) in the particular case of the density function with the simple pole at the origin. It is proven that the CSLE under consideration preserves its form under the two second-order Darboux–Crum transformations (DCTs) with the seed functions represented by specially chosen pairs of ‘basic’ quasi-rational solutions (q-RSs), i.e., such that their analytical continuations do not have zeros in the complex plane. It is proven that both transformations generally either increase or decrease by 2 the exponent difference (ExpDiff) for the mentioned pole while keeping two other parameters unchanged. The change is more complicated in the latter case if the ExpDiff for the pole of the original CSLE at the origin is smaller than 2. It was observed that the DCTs in question do not preserve bound energy levels according to the conventional supersymmetry (SUSY) rules. To understand this anomaly, we split the DCT in question into the two sequential Darboux deformations of the Liouville potentials associated with the CSLEs of our interest. We found that the first Darboux transformation turns the initial CSLE into the Heun equation written in the canonical form while the second transformation brings us back to the canonical form of the hypergeometric equation. It is shown that the first of these transformations necessarily places the mentioned ExpDiff into the limit-circle (LC) range and then the second transformation keeps the pole within the LC region, violating the conventional prescriptions of SUSY quantum mechanics.

Defending dominant cooperative probabilistic attack in CRNs by JS-divergence-based improved reputation algorithm

Rapid advances in wireless communication services has made limited spectrum resources increasingly scarce. One promising solution for enhancing spectrum utilization is cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs). However CSS is vulnerable to Byzantine attack. Current researches show that Byzantine attack is easily defended for their fixed attack probability. In this context, we propose an improved attack model called the dominated cooperative probabilistic attack (DCPA) model in the actual situation, building upon the generalized collaborative probabilistic Byzantine attack model. This DCPA model contains auxiliary cooperative attackers (ACAs) who launch attacks and a dominant attacker (DA) who determines ACAs’ attack probability intervals based on their respective credibility. The DCPA model allows ACAs to flexibly launch attacks, without being identified by the traditional reputation defense algorithm, significantly compromising the sensing performance of CSS. To successfully resist the threat posed by the DCPA model to CSS, we propose a JS-divergence-based improved reputation algorithm that can distinguish honest users (HUs) from attackers. This algorithm analyzes two consecutive sensing reports to identify differences in sensing behavior between HUs and attackers. Through Python simulation analysis, we demonstrate that, compared to the generalized cooperative probabilistic attack (CPA) model and the attack-free CSS (AFC) model, the proposed DCPA model is more concealed and significantly more disruptive to the performance of traditional reputation defense algorithms. Furthermore, our approach greatly enhances the performance of CSS by promoting the participation of HUs and suppressing attackers during the final data fusion. And also compared with the PAM2 algorithm, the conventional voting rule (CVR) algorithm and the traditional reputation defense algorithm, our proposed algorithm improves the detection performance by at least 7%, 15% and 50%.

Crystal-Like Atomic Arrangement and Optical Properties of 25La2O3-75MoO3 Binary Glasses Composed of Isolated MoO42.

Transparent and brown La2O3-MoO3 binary glasses were prepared in bulk form using a levitation technique. The glass-forming range was limited, with the primary composition being approximately 25 mol % La2O3. The 25La2O3-75MoO3 glass exhibited a clear crystallization at 546 °C, while determining its glass transition temperature was difficult. Notably, despite its amorphous nature, the glass possessed a density and packing density comparable to those of crystalline La2Mo3O12. X-ray absorption fine structure and Raman scattering analyses revealed that the glass structure closely resembles La2Mo3O12 due to the presence of isolated MoO42- units, whereas disordered atomic arrangement around La atoms was confirmed. The glass demonstrated transparency ranging from 378 to 5500 nm, and the refractive index at 1.0 μm was estimated to be 2.0. The optical bandgap energy was 3.46 eV, which was slightly smaller than that of La2Mo3O12. Additionally, the glass displayed a transparent region ranging from 6.5 to 8.0 μm. This occurrence results from the decreased diversity of MoOn units and connectivity of Mo-O-Mo, which resulted in the reduced overlap of multiphonon absorption. This glass formation, with its departure from conventional glass-forming rules, resulted in distinctive glasses with crystal-like atomic arrangements.

Unconventional features in the transcription and processing of spliceosomal small nuclear RNAs in the protozoan parasite Trichomonas vaginalis

Trichomonas vaginalis is a medically important protozoan parasite, and a deep-branching, evolutionarily divergent unicellular eukaryote that has conserved several key features of eukaryotic gene expression. Trichomonas vaginalis possesses a metazoan/plant-like capping apparatus, mRNAs with a cap 1 structure and spliceosomes containing the five small nuclear RNAs (snRNAs). However, in contrast to metazoan and plant snRNAs, the structurally conserved T. vaginalis snRNAs were initially identified as lacking the canonical guanosine cap nucleotide. To explain this unusual condition, we sought to investigate transcriptional and processing features of the spliceosomal snRNAs in this protist. Here, we show that T. vaginalis spliceosomal snRNA genes mostly lack typical eukaryotic promoters. In contrast to other eukaryotes, the putative TATA box in the T. vaginalis U6 snRNA gene was found to be dispensable for transcription or RNA polymerase selectivity. Moreover, U6 transcription in T. vaginalis was virtually insensitive to tagetitoxin compared with other cellular transcripts produced by the same RNA polymerase III. Most important and unexpected, snRNA transcription in T. vaginalis appears to bypass capping as we show that these transcripts retain their original 5′-triphosphate groups. In conclusion, transcription and processing of spliceosomal snRNAs in T. vaginalis deviate considerably from the conventional rules of other eukaryotes.

Widespread stable noncanonical peptides identified by integrated analyses of ribosome profiling and ORF features

Studies have revealed dozens of functional peptides in putative ‘noncoding’ regions and raised the question of how many proteins are encoded by noncanonical open reading frames (ORFs). Here, we comprehensively annotate genome-wide translated ORFs across five eukaryotes (human, mouse, zebrafish, worm, and yeast) by analyzing ribosome profiling data. We develop a logistic regression model named PepScore based on ORF features (expected length, encoded domain, and conservation) to calculate the probability that the encoded peptide is stable in humans. Systematic ectopic expression validates PepScore and shows that stable complex-associating microproteins can be encoded in 5’/3’ untranslated regions and overlapping coding regions of mRNAs besides annotated noncoding RNAs. Stable noncanonical proteins follow conventional rules and localize to different subcellular compartments. Inhibition of proteasomal/lysosomal degradation pathways can stabilize some peptides especially those with moderate PepScores, but cannot rescue the expression of short ones with low PepScores suggesting they are directly degraded by cellular proteases. The majority of human noncanonical peptides with high PepScores show longer lengths but low conservation across species/mammals, and hundreds contain trait-associated genetic variants. Our study presents a statistical framework to identify stable noncanonical peptides in the genome and provides a valuable resource for functional characterization of noncanonical translation during development and disease.

The Reasons to Follow Conventional Practices

ABSTRACT This article challenges a reductive analysis of social practices by distinguishing five kinds of reason for following the rules of conventional practices. Depending on one’s preferred intellectual tradition, conventional practices enable coordination, facilitate cooperation, constitute activities, fulfil reciprocity, or specify abstract rights. Instead of being rival theories of social practices, these different models complement one another in a normative analysis of social practices. By distinguishing five kinds of reasons to follow conventional rules, this paper supports a more dynamic conventionalist analysis of subjects like property, the law, professional obligation, civic responsibility, and culture.

Deep learning‐based response spectrum analysis method for building structures

AbstractThe response spectrum method has gained widespread acceptance in practical applications owing to its favorable compromise between accuracy and practical efficiency. The method predicts the peak responses of multi‐degree‐of‐freedom (MDOF) systems by combining modal responses. The Square Root of the Sum of Squares (SRSS) and Complete Quadratic Combination (CQC) rules are commonly used for modal combinations. However, it has been widely known that these rules have limitations in accurately predicting responses influenced by higher modes and cross‐modal correlations. To improve the accuracy of the response spectrum analysis method for building structures, this paper proposes a Deep learning‐based modal Combination (DC) rule by introducing modal contribution coefficients predicted by a deep neural network (DNN) model. The DC rule enhances prediction accuracy by considering the characteristics of ground motion and the dynamic properties of a structural system. The DC rule provides more accurate predictions than the conventional rules, particularly for irregular response spectra and responses affected by higher modes. The efficiency and applicability of the DC rule are demonstrated by numerical investigations of multistory shear buildings and steel frame structures with regular and irregular shapes. The source codes, data, and trained models are available for download at https://github.com/tyongkim/ERD2.

Robust Reward-Free Actor-Critic for Cooperative Multiagent Reinforcement Learning.

In this article, we consider centralized training and decentralized execution (CTDE) with diverse and private reward functions in cooperative multiagent reinforcement learning (MARL). The main challenge is that an unknown number of agents, whose identities are also unknown, can deliberately generate malicious messages and transmit them to the central controller. We term these malicious actions as Byzantine attacks. First, without Byzantine attacks, we propose a reward-free deep deterministic policy gradient (RF-DDPG) algorithm, in which gradients of agents' critics rather than rewards are sent to the central controller for preserving privacy. Second, to cope with Byzantine attacks, we develop a robust extension of RF-DDPG termed R2F-DDPG, which replaces the vulnerable average aggregation rule with robust ones. We propose a novel class of RL-specific Byzantine attacks that fail conventional robust aggregation rules, motivating the projection-boosted robust aggregation rules for R2F-DDPG. Numerical experiments show that RF-DDPG successfully trains agents to work cooperatively and that R2F-DDPG demonstrates robustness to Byzantine attacks.

Unravelling the Sufferings of Black Women: A Feministic Study of Angelou’s Poetry through the lens of Transitivity mode of Analysis

The present study aims to explore the way Angelou in her poetry, seems to challenge the patriarchal standards of beauty and the expectations that a woman needs to fulfil being a member of that society. Meanwhile, the researcher has applied mixed methods along with the implementation of Systemic functional linguistics as the theoretical framework of this study. However, the data of this investigation has been comprised of two poems entitled “Still I Rise" and Phenomenal Woman”. The purposive sampling technique has been applied by the researcher to get the particular data. In the data analysis section, first, the researcher did the quantitative analysis of the data by applying Halliday's (2004) model of transitivity and after that, she gave the qualitative interpretation of the data with the use of Simon de Beauvoir’s perspective of feminism given in her work entitled “The Second Sex” (1949). The findings revealed five types of processes in both poems, out of them material process was dominant throughout. Further, this transitivity system successfully explored the struggle of the speaker against the conventional rules to oppress women in the patriarchal society of America.

Perkembangan Akhir Masa Anak-Anak

Late childhood or what is known as elementary school age (SD) is a period of life development that lasts from the age of six until a child moves into adolescence and becomes sexually mature. The end of childhood is marked by dominant situations that can influence the child's personal adjustment and social adjustment. The research uses a critical review method by reading various books, journals or other publications related to the research topic discussed. The journals chosen consist of national and international journals, as well as several official websites. The research source comes from articles obtained via the internet in the form of research articles discussing late childhood development. The research results are as follows; a) Cognitive development is a person's ability to acquire, process and use information obtained from the surrounding environment b) Piaget and named the second level of moral development at the end of childhood as the level of conventional morality or morality from conventional rules and adjustments. c) Implications for children's end-of-life development in counseling services which aim to help them get to know themselves, develop their potential, overcome difficulties, and prepare children's mental and social development, as well as overcome problems experienced by children such as obstacles, disturbances and difficulties.

Performance Assessment of a Rehabilitation Transportation Reservation Matching Service with Market Design Mechanisms

Government agencies provide huge amount of subsidies to support the rehabilitation transportation service over the past decade in eastern Taiwan; however, low demand request fulfillment rate, limited medical and transportation resources, long travel distances, and an extremely high percentage of dead mileages are still the main challenges faced by rehabilitation transportation service providers. This study applies the market design theory to match the rehabilitation buses with the requests of patients, so as to improve resource utilization efficiency in rural areas. The developed market design mechanisms aim to allocate resources to those who need them most in a matching manner, by using the deferred acceptance algorithm and the top trading cycle approach. The model is initialized with the requests of those who choose the rehabilitation bus based on their desired boarding time slots. On the other hand, the service providers of the rehabilitation bus would determine patients’ schedule based on their disability level, willingness to share the ride, number of fulfilled appointments during this month, and the travel distance of this trip as the order of preference. Since the current vehicle dispatching rule is to reserve seats of a rehabilitation bus on the “first-come-first-served” basis, and it cannot fully satisfy patients need. In accordance with the historical data, 63 of 72 demand requests could successfully reserve the seats. In the “first-come, first-served” mode, 48 requests obtained the first-ranking shift (i.e., their desired time slots), and the sum of their disability level score is 155. In the market design matching mode, 57 requests obtained the first-ranking shift, and the sum of their disability level score is 170, which demonstrates that the proposed market design matching mechanism outperforms than the conventional rules.

Eksplorasi Bentuk Tempurung Kura-Kura Alligator Snapping Dalam Karya Seni Rupa Kontemporer

Turtles are reptiles that have uniqueness and beauty, which radiates from the character of the shell, the beauty of color and shape, and the history of the turtle's life. The snapping alligator is an extraordinary beauty because it has the characteristics of a shell that is like a tapered and jagged hill. The problem discussed in this paper is how the character of the snapping alligator turtle shell is, as well as how to formulate and visualize this character in contemporary works of art. The final result of the process of creating this work, the author presents in the form of an alligator snapping turtle shell which has been changed based on the results of the exploration of the author's imagination. With this new form created, the author feels that he has found more freedom in creativity which is not only limited by conventional rules and rules that exist in realist form.

Scale-Up Investigation of a Pilot and Industrial Scale Semi-Autogenous Mill Using a Particle Scale Model

A particle scale model based on a full two-way coupling of the Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPHs) methods is applied to SAG mills. Motion and collisions of resolved coarser particles within an SAG mill are performed by the DEM component. Fine particles in the feed combine with the water to form a slurry, which is represented by the SPH component of the model. Slurry rheology is controlled by solid loading and fine particle size distribution for each volume of slurry. Transport, dispersion, and grinding of the slurry phase particle size distribution are predicted by solving additional coupled advection–diffusion equations in the SPH component of the model. Grinding of the finer particles in the slurry due to collisions and shear of the coarser particles (rocks and grinding media) is achieved via the inclusion of population balance terms in these equations for each SPH particle. This allows prediction of the transport of both coarser and finer material within the grinding and pulp chambers of an SAG mill, including the discharge performance of the mill. This particle-scale model is used to investigate the relative performance (throughput, product size distribution, resident particle size distribution, net power draw, wear) for an SAG mill at a pilot scale and a 36 ft industrial scale. The 36′ SAG mill considered is a geometrically scaled-up version of the 1.8 m Hardinge pilot scale mill but with a longer belly length, reflecting current SAG mill design preferences. The belly lifters are scaled to a lesser degree with a larger number of lifters used (but still many fewer liners than would typically be used in a large SAG mill based on conventional liner selection rules). The model shows that despite reasonable qualitative similarities, many aspects of the charge structure, slurry transport, coarse particle and slurry discharge through the grates, and the collision energy spectra vary in important ways. This demonstrates that a near purely geometric scale-up of an SAG mill is not sufficient to produce a comparable performance at the two physical mill scales.

Designing an adaptive and deep learning based control framework for modular production systems

AbstractIn today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.

Creep–Fatigue Life Estimation of Gr.91 Steel and Its Welded Joints

A series of creep–fatigue tests of Gr.91 steel were performed at 600 °C. Fatigue life was reduced by tensile strain holding. The minimum life reduction factor was approximately 0.3. The creep–fatigue life could not be estimated properly via the conventional linear summation rule of the fatigue damage and creep damage. Since this material is considered to have a large creep–fatigue interaction, it was proposed that the creep–fatigue life should be estimated using the improved linear summation rule of the fatigue damage, the creep damage and the creep–fatigue interaction damage. In the future, it will be necessary to clarify the creep–fatigue interaction mechanism and define its damage value. On the other hand, a series of creep–fatigue tests for Gr.91 steel welded joints were also performed in the strain range of 0.5% at 600 °C. Again, the fatigue life was shortened by the tensile strain holding. The minimum fatigue life reduction factor was approximately 0.2. All the test pieces fractured in the fine-grained HAZ of the welded joints. The creep–fatigue life could not be estimated properly using the linear summation rule of the fatigue damage and creep damage in the HAZ. One possible reason was thought to be the effects of the elastic follow-up phenomena peculiar to welded joints. The creep strain of the HAZ might increase due to the transfer of the elastic strain from both the base metal and the weld metal, according to the elastic follow-up phenomena during strain holding. In the future, it will be important to quantitatively estimate the effects of the elastic follow-up phenomena.

HERONIAN MEAN DERIVATIVE-BASED OPEN NEWTON-COTES QUADRATURE RULES

A novel family of open Newton-Cotes (ONC) formulas is devised for evaluating the definite integrals. The new family is developed by using the Heronian mean in the first-order derivatives of the integrand within the interval [a, b]. The devised Heronian mean derivative-based quadrature rules (HRMDONC) achieve two orders of accuracy enhancement over the conventional ONC quadrature rules. These formulas are derived using the idea of degree of precision. Theorems regarding the degree of precision and order of accuracy are also derived along with the local and global error terms. In addition, the computational order of accuracy of each method is computed confirming the theoretical results. Computational cost and absolute error drops are also determined for three different integrals from the literature which demonstrate the superiority of the proposed HRMDONC methods over the classical ONC.

On Characterization of Shear Viscosity and Wall Slip for Concentrated Suspension Flows in Abrasive Flow Machining.

In the realm of abrasive flow machining (AFM), precise finishing and maintaining dimensional accuracy have remained challenging due to non-uniformities in the AFM process and complexities associated with the abrasive media's shear viscosity and wall slip behavior. By addressing these challenges, this study introduces a comprehensive framework, combining theoretical foundations, measurement techniques, and experimental setups. Utilizing capillary flow, a novel compensation strategy is incorporated within the Mooney method to counter entrance pressure drop effects. This enhanced capillary flow method emerges as a promising alternative to the conventional Cox-Merz empirical rule, enabling precise characterization of wall slip behavior and shear viscosity, particularly at elevated shear rates. The abrasive media exhibit a Navier nonlinear wall slip, as highlighted by the Mooney method. Rigorous verification of the proposed methodologies and models against supplemental experiments showcases a high degree of congruence between predicted and observed results, emphasizing their accuracy and broad application potential in AFM. This research illuminates the intricacies of the abrasive media's behavior, accentuating the need for meticulous characterization, and provides a robust foundation for genuine modeling and predictions in material removal within AFM.

Vicarious liability or liability for the Acts of others in Tort A Comparative perspective

Vicarious liability is a rudimentary concept in Law of Torts, It is a legal principle that holds an individual or business accountable for the deeds of their agents or employees. This research paper would explore vicarious liability in a comparative view, analyzing how different legal systems around the world approach this doctrine and its implications. This paper explores the historical development of vicarious liability and how it has changed across different jurisdictions. The study also examines current topics including the gig economy, the expansion of remote employment, and developing technology while examining recent advances and trends in vicarious liability. It assesses the adaptability of conventional vicarious liability rules to the changing nature of contemporary work relationships. This research paper seeks to advance knowledge of this significant area of Law of Torts by offering a thorough comparative examination of vicarious liability. It provides insightful information.

Auditory sensory augmentation to support table tennis games for people with vision loss

People with vision loss often face limitations in regular sports games with standard rules and equipment. For example, in current blind table tennis, conventional rules are modified so that the ball rolls along the table instead of bouncing. In this work, we propose an auditory sensory augmentation system to support traditional table tennis in three dimensions. We capture the trajectory of the table tennis ball using two neuromorphic event cameras and sonify the path of the ball using loudspeakers mounted near the left and right edges of the playing table. The two event cameras capture rapid changes in brightness allowing fast and precise ball tracking. The ball's 3D trajectory is then sonified using four lines of loudspeakers mounted at two different heights near the left and right edges of the playing table. We present a preliminary implementation and investigation of the proposed sensory augmentation system with a focus on the technical and perceptual challenges.