bluuebunny/crossref_metadata_2025 huggingface dataset:
Probing the cosmology of f(Q,T) gravity with holographic background fluid
Authors: Chokyi Khandro K. , Chattopadhyay Surajit
Published: January 2024

In this work, we have investigated the cosmology of holographic origin within modified [Formula: see text] gravity framework by employing different forms of the scale factor viz. power law, intermediate, emergent, and logamediate forms. The reconstruction of [Formula: see text] gravity is done by assuming the generalized Holographic Dark Energy as the background fluid where the IR cut-off is considered [Formula: see text]. We consider two cosmological models corresponding to the intermediate and emergent scale factors. By choosing a linear combination of Q and T in each case, we analyze the equation of state (EoS) parameter. Considering the intermediate and logamediate scale factors, the reconstruction of [Formula: see text] gravity is achieved using the holographic fluid as background fluid without any functional form assumption for f. In some cases, it is observed that the EoS parameter could cross phantom boundary in later stages and thus indicate the possibility of avoidance of big rip.
Entropy and multifractality in ring-like and jet-like events produced in 11.6 A GeV/c 197Au-AgBr collisions
Authors: Ali Bushra , Singh Sweta , Chandra Anuj , Ahmad Shakeel
Published: January 2024

Physical quantities, such as entropy, dimensions and multifractal characteristics of multiplicity distributions of charged particles produced in [Formula: see text]Au–AgBr collisions, are examined and the findings are compared with the predictions of Monte Carlo model Ultra-Relativistic Quantum Molecular Dynamics (URQMD) and Heavy Ion Jet INteraction Generator (HIJING) and also with the results reported earlier in hadron–hadron and nucleus–nucleus collisions at different energies. Based on their azimuth distribution, the charged particles produced within narrow-bins exhibit two kinds of substructures, namely, ring-like and jet-like substructures. Thus, on applying the suitable criteria, the two different types of events are identified and analyzed separately. It is observed that the maximum entropy production occurs around a narrow mid-rapidity region. The analyses of ring-like and jet-like events suggest that the entropy production is much larger in ring-like events as compared to that in jet-like events. Furthermore, Rényi’s order-q information entropy is used to estimate the multifractal specific heat and to construct the spectrum of scaling indices. The findings reveal that the value of multifractal specific heat is higher in ring-like events as compared to that in jet-like events. The studies of generalized dimension and multifractal spectrum indicate that the multifractality is rather, more pronounced in ring-like events as compared to jet-like events. Various features of the experimental data are noticed to be nicely reproduced by the URQMD model.
Pseudospin symmetry in resonant states in deformed nucleus 154Dy
Authors: Shi Xin-Xing , Zheng Zhen-Yu
Published: January 2024

It is known that pseudospin symmetry plays a crucial role in formation of many physical phenomena. By combining the relativistic mean field theory with the complex momentum representation method, the pseudospin symmetry in the single particle resonant states in the deformed nucleus [Formula: see text]Dy is investigated through the energy and width splittings, the quadrupole deformation parameter, the radial density distributions and occupation probabilities of the pseudospin doublets. Near the continuum threshold, the pseudospin symmetry is well reserved in both bound and resonant states. The energy and width splittings of pseudospin doublets in resonant states exhibit correlations with the deformation and quantum numbers. The good pseudospin symmetry is expected with lower pseudo-orbital angular momentum projection [Formula: see text] and the main quantum number N. In general, an increase in deformation tends to weaken the quality of the pseudospin symmetry. The understanding of the evolution of the pseudospin doublets in the resonant states has been deepened by studying the pseudospin symmetry in the deformed nuclei.
OPTIMAL TIMES TO BUY AND SELL A HOME
Authors: LORIG MATTHEW , SUAYSOM NATCHANON
Published: January 2024

We consider a financial market in which the risk-free rate of interest is modeled as a Markov diffusion. We suppose that home prices are set by a representative homebuyer, who can afford to pay only a fixed cash flow per unit time for housing. The cash flow is a fraction of the representative homebuyer’s salary, which grows at a rate that is proportional to the risk-free rate of interest. As a result, in the long run, higher interest rates lead to faster growth of home prices. The representative homebuyer finances the purchase of a home by taking out a mortgage. The mortgage rate paid by the homebuyer is fixed at the time of purchase and equal to the risk-free rate of interest plus a positive constant. As the homebuyer can only afford to pay a fixed cash flow per unit time, a higher mortgage rate limits the size of the loan the homebuyer can take out. As a result, the short-term effect of higher interest rates is to lower the value of homes. In this setting, we consider an investor who wishes to buy and then sell a home in order to maximize his discounted expected profit. This leads to a nested optimal stopping problem. We use a nonnegative concave majorant approach to derive the investor’s optimal buying and selling strategies. Additionally, we provide a detailed analytic and numerical study of the case in which the risk-free rate of interest is modeled by a Cox–Ingersoll–Ross (CIR) process. We also examine, in the case of CIR interest rates, the expected time that the investor waits before buying and then selling a home when following the optimal strategies.
A New Method to Solve Matrix Game with Interval Payoffs and its MATLAB Code
Authors: Bisht Monika , Beg Ismat , Rawat Shivam
Published: January 2024

Game theory is of substantial significance in diverse domains, acting as a potent instrument to comprehend and assess strategic engagements among rational decision-makers. It formulates mathematical models to represent strategic interactions among rational decision-makers in the competitive world. Due to ambiguity in the real-world problems, acquiring the precise payoff values of a matrix game proves challenging. However, in numerous scenarios, these payoffs fluctuate within specific ranges, making them suitable for consideration as interval numbers. This leads to the formulation of a special form of game problem known as the interval valued game problem (IVGP). Some methodologies exist in the literature to find the optimal strategies as well as the value of game for IVGP, but most of them possess some limitations, resulting in the need for proposing a new methodology to find the optimal strategies and value of game. Thus, in this paper, a new solution method for game problems with payoffs represented as interval numbers is presented, utilizing the fuzzy concept. The process begins by transforming the interval payoffs into fuzzy numbers using a ranking function. Subsequently, these fuzzy payoffs are converted into crisp values, leading to the formulation of the crisp matrix game. The resulting crisp matrix game is then solved using linear programming approach. Additionally, MATLAB code for the proposed method is developed and proposed to streamline the computation process, enhancing comparison and decision-making efficiency, particularly when dealing with large payoff matrices. Furthermore, three numerical examples are provided to illustrate the validity of the proposed approach as well as its MATLAB code. A real-life example of IVGP in the realm of tourism industry is also provided. Finally, a comparative analysis is conducted, comparing the proposed method with some existing methods.
Jump Risk Contagion and Determinants Driven by COVID-19 in Sino-US Stock Market: An Empirical Analysis from a Dependence Perspective
Authors: Du Xinyu , Lv Zhengyang , Yuan Ying , Xu Xinning , Zhao Chong
Published: January 2024

This paper examines the jump risk contagion between the US and China during the financial crisis driven by COVID-19, and the impact of a series of determinants to detect the transmission mechanisms. Specifically, we employ the ARJI–GARCH model to capture jump behavior and apply the Clayton Copula to construct lower tail jump contagion. Furthermore, we conduct regression analysis on variables related to economic fundamentals and investor behavior to explore the fundamental or pure contagion hypotheses. Our results suggest an increased jump dependence during major catastrophic crises such as COVID-19, but it is closer to a memory-less process. Besides, bilateral trade, similarity in fundamentals and investor behavior are all important determinants, and pure contagion hypothesis exists between the Chinese and US stock markets during the pandemic.
Quandles with one nontrivial column
Authors: Cazet Nicholas
Published: January 2024

The axioms of a quandle imply that the columns of its Cayley table are permutations. This paper studies quandles with exactly one non-trivially permuted column. Their automorphism groups, quandle polynomials, (symmetric) cohomology groups, and [Formula: see text] quandles are studied. The quiver and cocycle invariant of links using these quandles are shown to relate to linking number.
INVOLUTION GAME WITH SPECIALIZATION STRATEGY
Authors: LI BO
Published: January 2024

Involution now refers to the phenomenon that competitors in the same field make more efforts to struggle for limited resources but get lower individual “profit effort ratio”. In this work, we investigate the evolution of the involution game when competitors in the same field can adopt not only the strategy of making more efforts but also a specialization strategy which allows competitors to devote all their efforts to part of the competitive field. Based on the existing model, we construct the involution game with the specialization strategy and simulate the evolution of it on a square lattice under different social resource, allocation parameter (characterizing the intensity of social competition), effort and other conditions. In addition, we also conduct a theoretical analysis to further understand the underlying mechanism of our model and to avoid illusive results caused by the model settings. Our main results show that, when the total effort of the specialization strategy and the ordinary strategy is equal, the group composed of all the agents has a certain probability to choose the ordinary strategy if the allocation parameter is very large (that is to say, the intensity of competition is very weak), otherwise the group will choose the specialization strategy; when the total effort of the two strategies is not equal, the proportion of the specialization strategy adoption is related to the social resource, the effort and the allocation parameter. To some extent, our study can explain why division of labor appears in human society and provide suggestions for individuals on competition strategy selection and governments on competition policy development.
New asymptotic lower bound for the radius of analyticity of solutions to nonlinear Schrödinger equation
Authors: Getachew Tegegne , Belayneh Birilew
Published: January 2024

In this paper, we show that the radius of analyticity [Formula: see text] of solutions to the one-dimensional nonlinear Schrödinger (NLS) equation [Formula: see text] is bounded from below by [Formula: see text] when [Formula: see text] and by [Formula: see text] when [Formula: see text] as [Formula: see text], given initial data that is analytic with fixed radius. This improves results obtained by Tesfahun [On the radius of spatial analyticity for cubic nonlinear Schrödinger equations, J. Differential Equations 263(11) (2017) 7496–7512] for [Formula: see text] and Ahn et al. [On the radius of spatial analyticity for defocusing nonlinear Schrödinger equations, Discrete Contin. Dyn. Syst. 40(1) (2020) 423–439] for any odd integers [Formula: see text], where they obtained a decay rate [Formula: see text] for larger [Formula: see text]. The proof of our main theorems is based on a modified Gevrey space introduced in [T. T. Dufera, S. Mebrate and A. Tesfahun, On the persistence of spatial analyticity for the beam equation, J. Math. Anal. Appl. 509(2) (2022) 126001], the local smoothing effect, maximal function estimate of the Schrödinger propagator, a method of almost conservation law, Schrödinger admissibility and one-dimensional Sobolev embedding.
Q-Learning-Based Priority Dispatching Rule Preference Model for Non-Permutation Flow Shop
Authors: Zhao Anran , Liu Peng
Published: January 2024

Non-permutation flow shop scheduling (NPFS) is an extension of the traditional permutation flow shop scheduling, with a broader solution space. The effectiveness of reinforcement learning in solving flow shop scheduling problems has been demonstrated through its powerful combinatorial optimization capabilities. However, the design and training of the end-to-end policy network is complex, leading to long online training time and limited adaptability of offline training. To overcome these problems, we introduced a NPFS dynamic decision-making process and then proposed a novel NPFS method that combines the Q-learning algorithm with the priority dispatching rule (PDR) set. The NPFS dynamic decision-making process involves decomposing the entire process into multiple sub-job queues for scheduling. The PDR demonstrates better scheduling performance when applied to smaller job queues. By utilizing the Q-learning algorithm, PDRs with superior performance are assigned to sub-scheduling queues based on the generation process of sub-job sequences, resulting in an optimized NPFS strategy. The limited number of PDRs in the PDR set and the small number of sub-job queues in the NPFS process contribute to the efficiency of Q-learning in solving NPFS problem. Finally, we demonstrate the superiority of the proposed NPFS method by a series of numerical experiments using the machine-based assignment PDR method and NSGA-II algorithms as performance benchmarks.
Investigation of the Additional Powder Effect on the Strength of Joined Aluminum Alloy Plates in Friction Stir Welding Using the Topsis-Game Theory Model
Authors: Yurdakul Mustafa , Ülke İbrahim , İÇ Yusuf Tansel
Published: January 2024

Friction stir welding (FSW) is a process that can join many materials by causing minimal internal stress without the need for a direct electric current, contrary to traditional welding methods. The effects of SiC and Al2O3 reinforcing powders on the joining of AA6061-T6 and AA7075-T6 plates, which are difficult to join with conventional welding methods by FSW, are investigated in this study. The metallurgical properties of the combined samples are examined in terms of strength characteristics to investigate the effects of the reinforcement powder. In addition, elemental analysis is carried out for the mixing behavior of the powders. Finally, we used the TOPSIS method to select the most appropriate powder types to improve welding quality. Furthermore, a game theory application is presented to determine which powder type is suitable considering the joined aluminum plate’s strength expectations.
Application of machine learning to experimental design in quantum mechanics
Authors: Belliardo Federico , Zoratti Fabio , Giovannetti Vittorio
Published: January 2024

The recent advances in Machine Learning hold great promises for the field of quantum sensing and metrology. With the help of reinforcement learning, we can tame the complexity of quantum systems and solve the problem of optimal experimental design. Reinforcement learning is a powerful model-free technique that allows an agent, which is typically a neural network, to learn the best strategy to reach a certain goal in a completely a priori unknown environment. However, in general, we know something about the quantum system the agent is interacting with, at least that it follows the rules of quantum mechanics. In quantum metrology, we typically have a model for the system and only some parameters of the evolution or of the initial state are unknown. We present here a general Machine Learning technique that can optimize the precision of quantum sensors, and in doing so it exploits the knowledge we have on the system. We have developed a Python package to automate a broad class of optimizations that can be found in the tasks of quantum parameter estimation, quantum metrology and quantum hypothesis testing. What the agent is learning here is an optimal adaptive strategy, that, on the basis of the previous outcomes, decides the next measurements to perform. It works both for Bayesian estimation and for frequentist estimation. The user is required to implement the physics of the system to be studied and state which parameters in the experiment are controllable and which are unknowns. The functions of the library allow then to easily train a neural network agent for optimizing the precision of the sensor, by simulating the experiment. We have explored some applications of this technique to magnetometry on NV centers (both DC and AC), to state discrimination in quantum optics and to phase estimation. So far, we were able to certify better results than the current state-of-the-art controls for many examples. The Machine Learning technique developed here can be applied in all those scenarios where the quantum system is well characterized and relatively simple and small. In these cases, we can squeeze every last bit of information from a quantum sensor by controlling it with a neural network appropriately trained.
A Novel Weighted Evidence Combination Method Based on Reliability and Credibility degree with an Application in Decision Making Process
Authors: Dutta Palash , Shome Sonom
Published: January 2024

The Dempster–Shafer theory (DST), also known as Belief function theory (BFT), finds extensive application across various domains involving data fusion. However, it is not uncommon to encounter counterintuitive results when combining highly conflicting pieces of evidence using the Dempster combination rule (DCR). While the literature offers several combination rules, only a few address these shortcomings effectively. To address these limitations and rectify the issue of counterintuitive outcomes, a novel weighted evidence fusion rule has been introduced. This fusion approach comprises four key steps: First, the reliability degree of each evidence set, also known as body of evidence (BOE) is determined based on its compatibility degree and total correlation. This step helps to mitigate conflicts arising from unreliable sources. Second, the degree of credibility of each BOE is computed, taking into account distance of evidence and uncertainty degree, further enhancing the accuracy of the fusion process. Third, the reliability and credibility degrees are combined to derive weights for each BOE, ensuring that more weight is assigned to trustworthy sources of evidence. Finally, these weights for each BOE are used to compute a modified Basic Probability Assignment (BPA) for each proposition, which are then fused using the Dempster combination rule. Numerous numerical examples, as well as artificial and real datasets, have been employed to emphasize the effectiveness and potential of this innovative approach. Furthermore, through comparative analysis, it becomes evident that the proposed rule consistently outruns other existing methods, ensuring more accurate and reliable outcomes.
Categories of L-Fuzzy Internal (Enclosed) Relations
Authors: Ramadan A. A.
Published: January 2024

This paper reveals some relationships between [Formula: see text]-fuzzy internal (enclosed) relations and [Formula: see text]-fuzzy interior (closure) spaces in a categorical framework. Also, we prove that there exist many interesting Galois connections between the category of all discrete [Formula: see text]-fuzzy interior (closure) spaces and the category [Formula: see text]-fuzzy internal (enclosed) relations. Moreover, the category [Formula: see text]-fuzzy of internal relations, the category of [Formula: see text]-fuzzy quasi-uniform spaces and the category of [Formula: see text]-fuzzy quasi-proximity spaces are investigated.
On combinatorial properties of Cayley graphs of gyrogroups
Authors: Maungchang Rasimate , Khachorncharoenkul Prathomjit , Suksumran Teerapong
Published: January 2024

In this paper, we extend our research on Cayley graphs of gyrogroups by exploring connections between the algebraic characteristics of gyrogroups and the combinatorial characteristics of their Cayley graphs. We look into some automorphisms of right Cayley graphs, give sufficient and necessary criteria for a right Cayley graph to be vertex-transitive by left gyrotranslations, and prove a similar result for quotient gyrogroups. Some sufficient and necessary criteria for a left gyroaddition to preserve edge color and for a gyration to be an automorphism on the graph are also given. Lastly, we show that certain Cayley graphs of a gyrogroup encode the gyroaddition table and provide an algorithm to compute the gyroaddition with the aid of the gyration table.
Effect of annealing conditions on surface morphology and ferroelectric domain structures of BiFeO3 thin films
Authors: Liang Minchuan , Yang Jia , Yang Huayu , Liang Chen , Nie Zhenyue , Ai Hui , Zhang Tong , Ma Ji , Huang Houbing , Wang Jing
Published: January 2024

Ferroelectric materials are widely used in the applications of electronic devices due to their robust spontaneous polarization. The surface roughness of ferroelectric thin films, which is closely related to the morphology, can play an important role in determining the ferroelectric domain structures. In this work, we have investigated the influence of annealing conditions on the surface morphology of epitaxial BiFeO3 and SrRuO3 thin films prepared by pulsed laser deposition on SrTiO3 (001) substrates. It is found that the morphology of the thin films is sensitive to the annealing time and cooling rate, and the corresponding surface roughness decreases with increasing annealing time and decreasing cooling rate. In addition, the ferroelectric domain structures of BiFeO3 films have been investigated by piezoelectric force microscopy, which shows a significant improvement in domain size and reverse piezoelectric response in the thin films with decreasing surface roughness. This work provides a simple way to predict and improve the ferroelectric domain structures by in situ annealing.
Throughput Performance of Cellular-Connected UAV Coexisting with Terrestrial Users
Authors: Mohd Kamal N. L. , Sahwee Z. , Norhashim N. , Ahmad Shah S. , Hashim W.
Published: January 2024

Unmanned aerial vehicles (UAVs) have opened up various applications with significant socioeconomic benefits, but require reliable connectivity for control and data transmission. In situations where obstacles separate aerial vehicles and ground operators, the cellular network can be used to establish beyond the visual line of sight (BVLOS) communication links. However, the cellular network is designed to optimize terrestrial user performance, so investigating its connectivity performance for aerial users is crucial. This paper presents an investigation into the upper-bound throughput estimations of the cellular network based on practical RSS measurements for both aerial and terrestrial users occupying the network resources simultaneously, in a typical sub-urban environment in Malaysia. Actual measurements of received signal strength (RSS) were taken to quantify the throughput performance for each category of users. Results indicate that the cellular network is feasible as a communication link for UAVs at altitudes below 40[Formula: see text]m. However, the overall throughput performance degrades when aerial users are present, and the probability of aerial user arrival must be kept at a minimum level beyond this height. Despite this, the mean aerial throughput outperforms the terrestrial throughput below 40[Formula: see text]m.
EXPLORING THE NEXUS: WORK-LIFE QUALITY, EMPLOYEE SATISFACTION, AND PERFORMANCE IN THE HOSPITALITY SECTOR
Authors: NAIR GIRISH K.
Published: January 2024

The purpose of this research is to plug the research gap in the literature that lies in the association of quality of work life (QWL) and employee performance (EMP) in general, and hospitality industry. So, the research tries to develop and test a theoretical model to address the direct and indirect impact of QWL on EMP with job satisfaction (JSF) and life satisfaction (LSF) as the mediating variables. The research gap identified through the literature review was that there was no empirical study that explored this nexus. The research methodology uses interpretivist paradigm using structural equation modeling (SEM) to test the 10 hypotheses with a sample size of 206 middle-level managers chosen from 22 five-star hotels. Results indicated that QWL had positive significant relationship on JSF, LSF and EMP. LSF had a positive and significant relationship with EMP ([Formula: see text]; [Formula: see text]; [Formula: see text]), the QWL also had a significant and positive relationship with the LFS in the hospitality industry ([Formula: see text]; [Formula: see text]; [Formula: see text]), JFS and LSF had positive and significant relationship in hospitality industry ([Formula: see text]; [Formula: see text]; [Formula: see text]), QWL had a positive and significant relationship with EMP ([Formula: see text]; [Formula: see text]; [Formula: see text]), but JSF had no significant relationship with EMP. The indirect relationship has revealed that JSF mediates a significant positive relationship between QWL and life satisfaction.
Green Nanotechnology and Phytosynthesis of Metallic Nanoparticles: The Green Approach, Mechanism, Biomedical Applications and Challenges
Authors: Alomar Abdulrahman , Qassim Tabarak , AlNajjar Yusuf , Alqassab Alaa , Deen G. Roshan
Published: January 2024

The synthesis of nanoparticles is generally divided into bottom-up and top-down approaches which involve physical, chemical, and biological methods. The physical and chemical methods are associated with issues such as high cost, non-ambient reaction conditions, and toxicity. Biological methods or green-methods, using plants, bacteria, algae, and fungi have been developed in recent years to overcome the issues associated with conventional methods. The synthesis of nanoparticles using plants and plant-products as chemical reducing and stabilizing agents from metal precursors is termed phytosynthesis. Plants are available in plenty, safe to handle and contain a wide variety of water-soluble metabolites such as anthocyanins, flavonoids, polyphenols, alkaloids, and terpenoids, that act as excellent chemical reducing and stabilizing agents. These metabolites reduce the metal precursors to metal nanoparticles in a much shorter time as compared to bacteria and fungi. Furthermore, both bacteria and fungi require much longer incubation time for the chemical reduction process. A wide variety of plants have been used to synthesize nanoparticles, oxides, and alloys of gold, silver, titanium, platinum, palladium, copper, cobalt, selenium, zinc, titanium, and iron for various biomedical applications. The types of plants and parts used such as stem, leaf, flower, fruit, pods, and peel have significant effects on the size and shape of the synthesized nanoparticle. Although the phytosynthesis method is advantageous in many aspects of synthesis, there are challenges associated with scale-up process for larger scale production which could be overcome in the future. This review summarizes the phytosynthesis process, green nanotechnology, characterization methods, mechanisms, various biomedical applications, and challenges.
Local-to-Global Framework for Multi-target Frame Identification
Authors: Chen Xudong , Zheng Ce , Chang Baobao , Li Ru
Published: January 2024

Frame identification is a crucial and challenging task in frame semantic parsing, where the objective is to determine the most appropriate frame for a given target within its context. Existing approaches typically classify each target individually, overlooking the potential interactions among different targets. To address these limitations, we propose a novel two-step prediction framework that encompasses both local and global perspectives for target identification. Additionally, we leverage the frame ontology graph to enhance the interactions among multiple targets by incorporating rich frame ontology knowledge. Moreover, we introduce a dynamic masking strategy during model training. This strategy encourages our model to adopt a global view during prediction, avoiding local optimization. Experimental results demonstrate the superiority of our model compared to previous approaches on FrameNet1.5, and it achieves competitive performance on FrameNet1.7. Furthermore, supplementary experiments and analyses provide additional evidence of the effectiveness of our proposed model.
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability
Authors: Sakib Md Sadman , Sun Yu
Published: January 2024

The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper presents a novel approach designed to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.
Fitting Algorithms for Conjunctive Queries
Authors: ten Cate Balder , Funk Maurice , Christoph Jung Jean , Lutz Carsten
Published: January 2024

A fitting algorithm for conjunctive queries (CQs) is an algorithm that takes as input a collection of data examples and outputs a CQ that fits the examples. In this column, we propose a set of desirable properties of such algorithms and use this as a guide for surveying results from the authors' recent papers published in PODS 2023, IJCAI 2023, and Inf. Proc. Letters 2024. In particular, we explain and compare several concrete fitting algorithms, and we discuss complexity and size bounds for constructing fitting CQs with desirable properties.
Once upon a Time, in a Computer Engineering Department ....
Authors: Catarci Tiziana
Published: January 2024

When I read Tamer's introduction, talking about advice from senior researchers, I had to agree that I am now, indeed in that category. I am presently department chair and used to be vice-rector in the past, acting more like a manager than a researcher. However, I confess that I still have some small personal spaces in which I permit myself to feel like an enthusiastic young post-doc, stop reading documents and start thinking about new research issues. Actually, this is my first advice for those who are in their mid-career: do not just look ahead ("ad maiora" used to say in the ancient Roma), always keep within you the curiosity of the PhD student that you used to be.
Peer-Reviewing Processes and Incentives: Data Management Community Survey Results
Authors: Meliou Alexandra , Bhowmick Sourav , Aberer Karl , Agrawal Divy , Bonifati Angela , Braganholo Vanessa , Geerts Floris , Lehner Wolfgang , Srivastava Divesh
Published: January 2024

Reviewing papers for conferences is an important and hard task that brings several challenges. The Data Management community has been increasingly struggling with high reviewer load, low-quality reviews and low reviewer engagement, unethical reviewing practices as well as undeclared and under-declared conflicts of interest. In this article, we report the results of a survey we conducted to gather the opinion of the Data Management community regarding what could be done to address these challenges. We reached out to about 1,200 members of the data management community with relevant reviewing experience and collected 345 anonymous responses. We plan to follow up with a subsequent report, discussing in more depth particular proposals, inspired by the collective feedback from the community.
Fault Diagnosis of PV Array Based on Time Series and Support Vector Machine
Authors: Zhong Ying , Zhang Bo , Ji Xu , Wu Jieping
Published: January 2024

This paper proposes a diagnosis method based on time series and support vector machine (SVM) to improve the timeliness, accuracy, and feasibility of fault diagnosis for photovoltaic (PV) arrays. It obtains the nominal output power of the PV array based on real-time collected data such as voltage, current, radiation, and temperature and normalizes the power values at different time points throughout the day to form a time series. Using the time series values as input data for a “one-to-one” multiclass classifier, we can identify and classify typical operational faults such as random shading, fixed shading, and aging degradation of PV arrays. The developed algorithmic model is trained and tested for different fault conditions using the data sets generated by the PV array simulation device. The experimental results show that our model has fairly good reliability and accuracy, and to some extent, it solves the problem of classifying shading and aging faults, two of which exhibit rather similar degradation characteristics.
Experimental Study on Shear Characteristics of Coal Samples in Different Water‐Bearing States
Authors: Wu Yang , Wang Lujun , Wu Baoyang , Xia Ze
Published: January 2024

During the establishment of the coal mine goaf reservoir, the strength of the coal pillar dam was weakened by various water–rock interactions triggered by water movement. To explore the influence of the water‐bearing state on the shear characteristics of coal pillar dams, the 2–2 coal seam of Shangwan Coal Mine in the Shendong Mining area was taken as the research object and was prepared to coal samples with different moisture contents and different drying‐saturation cycles, and the variable angle shear test of coal samples was carried out. The law of water absorption of coal samples under natural and pressurized conditions is obtained. The research results show that the shear characteristics of coal samples change significantly under the action of water, with the increase of moisture content and drying‐saturation cycles, the lower the shear strength of coal samples is, the larger the peak shear strain is, and the cohesion and internal friction angle of coal samples decrease linearly. According to the relationship between cohesion, internal friction angle, moisture content, and drying‐saturation cycle times, the Mohr–Coulomb model of coal samples considering moisture content and immersion times is established, which contributes to the load‐bearing capacity estimation of water‐bearing coal mass. Additionally, scanning electron microscope analysis revealed significant changes in the microstructure of coal samples under different moisture states: as the moisture content and dry‐saturation cycles increase, the microstructure of the coal samples gradually transforms from orderly and compact to disordered and murky, elucidating the mechanism by which moisture weakens the mechanical properties of the coal samples. The research results provide a useful reference for engineering problems related to the stability evaluation of coal bodies involving the repeated erosion effect of water.
In Silico Identification and Characterization of Drug Targets in Streptococcus pneumoniae ATCC 700669 (Serotype 23F) by Subtractive Genomics
Authors: Duguma Tolossa , Dinka Hunduma
Published: January 2024

Streptococcus pneumoniae (S. pneumoniae) is an important pathogen worldwide that causes pneumococcal infections which are related to high rates of morbidity and mortality especially in young children, older adults, and immune-compromised persons. Antibiotic resistance in S. pneumoniae is a serious problem across the world from time to time, resulting in treatment failure and diminished value of older medicines. Therefore, the objective of this study was to identify new putative drug targets against S. pneumoniae serotype 23F by using subtractive genomics. By using bioinformatics tools such as NCBI, UniProt KB, PDB, KEGG, DEG, PSORTb, CD hit, DrugBank database, and other softwares, proteins involved in unique metabolic pathways of S. pneumoniae serotype 23F were studied. The result indicates that this serotype consists of 97 metabolic pathways of which 74 are common with that of human, and 23 pathways are unique to the serotype 23F. After investigation and analysis of essentiality, nonhomology, subcellular localization, having drug targets, and enzymatic activity, four proteins were prioritized as druggable targets. These druggable proteins include UDP-N-acetylglucosamine 1-carboxyvinyltransferase, UDP-N-acetyl muramate dehydrogenase, D-alanine-D-alanine ligase, and alanine racemase that are found in S. pneumoniae serotype 23F. All these four proteins are essential, are nonhomologous with human proteins, have drug targets, and are located in cell cytoplasm. Therefore, the authors recommend these proteins to be used for efficient drug design against S. pneumoniae serotype 23F after experimental validation for essentiality and druggability.
Huaju Xiaoji Formula Regulates ERS‐lncMGC/miRNA to Enhance the Renal Function of Hypertensive Diabetic Mice with Nephropathy
Authors: Zhang Zeng , Fu Xiaodong , Zhou Fengzhu , Zhang Duanchun , Xu Yanqiu , Fan Zhaohua , Wen Shimei , Shao Yanting , Yao Zheng , He Yanming
Published: January 2024

Background. Better therapeutic drugs are required for treating hypertensive diabetic nephropathy. In our previous study, the Huaju Xiaoji (HJXJ) formula promoted the renal function of patients with diabetes and hypertensive nephropathy. In this study, we investigated the therapeutic effect and regulation mechanism of HJXJ in hypertensive diabetic mice with nephropathy. Methods. We constructed a mouse hypertensive diabetic nephropathy (HDN) model by treating mice with streptozotocin (STZ) and nomega‐nitro‐L‐arginine methyl ester (LNAME). We also constructed a human glomerular mesangial cell (HGMC) model that was induced by high doses of sugar (30 mmol/mL) and TGFβ1 (5 ng/mL). Pathological changes were evaluated by hematoxylin and eosin (H&E) staining, periodic acid Schiff (PAS) staining, and Masson staining. The fibrosis‐related molecules (TGFβ1, fibronectin, laminin, COL I, COL IV, α‐SMA, and p‐smad2/3) were detected by enzyme‐linked immunosorbent assay (ELISA). The mRNA levels and protein expression of endoplasmic reticulum stress, fibrosis molecules, and their downstream molecules were assessed using qPCR and Western blotting assays. Results. Administering HJXJ promoted the renal function of HDN mice. HJXJ reduced the expression of ER stress makers (CHOP and GRP78) and lncMGC, miR379, miR494, miR495, miR377, CUGBP2, CPEB4, EDEM3, and ATF3 in HDN mice and model HGMCs. The positive control drugs (dapagliflozin and valsartan) also showed similar effects after treatment with HJXJ. Additionally, in model HGMCs, the overexpression of CHOP or lncMGC decreased the effects of HJXJ‐M on the level of fibrosis molecules and downstream target molecules. Conclusion. In this study, we showed that the HJXJ formula may regulate ERS‐lncMGC/miRNA to enhance renal function in hypertensive diabetic mice with nephropathy. This study may act as a reference for further investigating whether combining HJXJ with other drugs can enhance its therapeutic effect. The findings of this study might provide new insights into the clinical treatment of hypertensive diabetic nephropathy with HJXJ.
Genital Abnormalities and Growth Retardation as Early Signs of Dilated Cardiomyopathy with Ataxia Syndrome
Authors: Papadopoulou-Legbelou Kyriaki , Ntoumpara Maria , Kavga Maria , Kotanidou Eleni P. , Papoulidis Ioannis , Galli-Tsinopoulou Assimina , Fotoulaki Maria
Published: January 2024

Dilated cardiomyopathy with ataxia syndrome is a rare mitochondrial disease caused by autosomal recessive mutations in the DNAJC19 gene. The disease has been described in detail in the Canadian Hutterite population, but a few sporadic cases with de novo mutations have been published worldwide. We describe a homozygous pathogenic variant in the DNAJC19 gene, diagnosed in Northern Greece, presenting with genital anomalies, growth failure, cardiomyopathy, and ataxia, but without increased urinary 3-methylglutaconic acid and additional presence of vitamin D disorders, hypercalciuria, and osteopenia. This case not only expands the clinical characteristics of 3-methylglutaconic aciduria type V (MGCA5) but also highlights the power of genetic analysis for detecting a diagnosis when the metabolic screen is negative.
Multimodal Imaging in Diagnosing Multiple Evanescent White Dot Syndrome following Human Papillomavirus Vaccine Immunization
Authors: Yu Jing , Ji Yuying , Zeng Yunkao , Li Huihui , Liao Hailan , Wen Feng
Published: January 2024

Purpose. This study presents a case of multiple evanescent white dot syndrome (MEWDS) following the administration of the second dose of a human papillomavirus vaccine (HPV). We conducted a review of the literature on vaccine-associated MEWDS. Observations. A 23-year-old Chinese female reported central scotomata in the left eye persisting for 3 weeks. Upon further inquiry, she had received the second dose of the human papillomavirus vaccine (Gardasil-9) three days before the onset of symptoms. A diagnosis of MEWDS was established based on clinical and multimodal imaging (MMI) data. Symptoms resolved after twelve weeks of oral prednisone treatment. Conclusion and Importance. This case highlights a typical case of MEWDS closely associated with HPV vaccination, demonstrating a favorable prognosis with MMI. Given the self-limiting nature of MEWDS, there is a risk of clinical misdiagnosis or oversight. While further studies are warranted to establish a definitive link between the HPV vaccine and MEWDS, this case suggests a potential connection. Healthcare practitioners should remain vigilant regarding possible ocular side effects associated with immunizations.
Development of Inhibition Control in Children during the Transition from Kindergarten to School during the COVID-19 Pandemic: Longitudinal Study
Authors: Bukhalenkova Daria A. , Nechaeva Darina M.
Published: January 2024

Background. The COVID-19 pandemic is a unique situation in the global community which emerged for the first time. Research shows that the pandemic may have a long-term effect on children's development. Inhibition control is one of the main components of executive functioning and a predictor to a child's further academic success. However, there are few works devoted to the study of the impact of the pandemic on inhibition control in preschoolers. The objective of the study was to identify dynamics of the development of inhibition control in children aged 5-7 years (from senior preschool to primary school age) during the COVID-19 pandemic. Indicators of the development of inhibition control in first-graders before and during the pandemic were compared. Methods. Inhibition control was assessed three times (at 5, 6, and then at 7 years of age) with the NEPSY-II Inhibition subtest. Sample. This longitudinal study involved children aged 5 to 8 years at the time of follow-up from 2019 to 2021 (N=101). Additionally, the sample included first-grade children who were trained in kindergarten and first grade before the pandemic (March 2019, N=84). Results. The results of the analysis indicate that inhibition control successfully developed in children from the senior kindergarten group to the first grade of school during the pandemic. At the same time, the level of development of inhibition control in girls after the pandemic is higher than in girls before the pandemic, while no significant differences were found for boys. Conclusion. The level of inhibitory control in preschoolers, whose education in the preparatory group of the kindergarten took place during the COVID-19 pandemic, despite the restrictions introduced and changes in the usual way of life, corresponds to the norms. It has been shown that the results of the development of inhibitory control in children who were caught up in the pandemic and those who were not caught up in the pandemic differ depending on gender.
Regularly Functions in Preschoolers Aged 4–7: the Impact of Kindergarten Attendance Span
Authors: Veraksa Aleksander N. , Gavrilova Margarita N. , Karimova Aleksandra I. , Solopova Oksana V. , Yakushina Anastasia A.
Published: January 2024

Background. Executive functions are one of the factors that contribute to the balanced development of children and their success in the future. In this regard, the study of factors that may influence the development of executive functions is an urgent task. The study examines the role of the kindergarten attendance duration in the development of working memory, cognitive flexibility and inhibition as components of executive functions in preschool children. Methods. The NEPSY-II battery subtests were used to test the level of development of regulatory functions. Data on the period of children's kindergarten attendance were collected according to preschool attendance journal. Sample. The study involved 947 children aged 53 to 86 months (Mage = 70.3; SDage = 4.3), 515 boys and 495 girls from Moscow, Kazan, and Sochi. Results. As a result of comparison of averages using Welch's criterion, significant differences were found in the development of regulatory functions in children with different periods of kindergarten attendance. The level of visual and verbal working memory development was significantly higher in children who attended kindergarten for more than two years as compared to children with shorter time of attendance. Physical inhibitory control is significantly lower in children who attend kindergarten for less than a year compared to those who attend kindergarten for longer periods of time. Significant differences were shown in the level of visual working memory development depending on the region and on the duration of attendance. Conclusion. The study demonstrated significant differences in the level of visual working memory, verbal working memory, and physical inhibitory control in children depending on the duration of kindergarten attendance. The highest scores were found in preschoolers attending kindergarten for more than two years. Thus, the results of the study emphasize the importance of kindergarten educational environment for successful cognitive development of preschoolers.