bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward a quantum field theoretical description of oscillation effects
Authors: Libanov Maxim
Published: August 2024

In this paper, we propose an approach based on the theory of an axiomatic S matrix and partially switching on an interaction, which is extremely suitable for describing the phenomenon of oscillations within the framework of quantum field theory. We discuss the relation of the proposed approach with other approaches based on considering of wave packets as asymptotic states or introducing of “distance- or time-dependent propagators”.
Intelligent Management Framework for Internet Multimedia Resources Based on Deep Neural Network and Web Crawlers
Authors: Li Huizi , Zhang Chao
Published: August 2024

This work focuses on an intelligent management framework for vocal music media resources, with use of deep neural networks and web crawlers. The web driver module is used to simulate the Google browser access mode to collect and preprocess the data of the China vocal music network webpage project, and the delay code is added to solve the problem of Internet Protocol (IP) restrictions on crawling webpages. After parsing, the content of the project description is obtained and temporarily stored in the local file through the panda’s module to realize the automatic collection of project data. Membership functions of four basic types of emotions are designed. By utilizing the analytic hierarchy process, the experience and wisdom of many music experts are gathered. The weights of activity and the evoking force in determining the music emotion membership value are discussed, trying to make the membership function closer to reality. Through the application of the music emotion analysis model, the music emotion analysis technology is combined with the recommendation algorithm, and the music recommendation algorithm combined with the emotion analysis is realized. The experimental test proves that the algorithm has higher accuracy than the traditional music recommendation algorithm.
An Efficient Deep Learning Mechanism for Predicting Fake News/Reviews in Twitter Data
Authors: Devan K. Parimala Kanaga , Mala G. S. Anandha
Published: August 2024

Recently, social media platforms have been widely utilized as information sources due to their effortless accessibility and reduced costs. However, online platforms like Instagram, Twitter and Facebook get influenced by their users via fake news/reviews. The main intention of spreading fake news is to mislead other network users, which highly affects businesses, political parties, etc. Thus, an effective methodology is needed to predict fake news from social media automatically. The major objective of this proposed study is to identify and classify the given Twitter input data as real or fake through deep learning mechanisms. The proposed study involves four stages: pre-processing, embedded word analysis, feature extraction, and fake news/reviews prediction. Initially, pre-processing is performed to enhance the quality of data with the help of tokenization, stemming and stop word removal. Embedded word analysis is done using Advanced Word2Vec and GloVe modeling to enhance the performance of a proposed prediction model. Then, the hybrid deep learning model named Dense Convolutional assisted Gannet Optimal Bi-directional Network (DC_GO_BiNet) is introduced for feature extraction and prediction. A Dense Convolutional Neural Network (DCNN) is hybridized with a bi-directional long-short-term memory (Bi-LSTM) model to extract the essential features and predict fake news from the given input text. Also, the proposed model’s parameters are fine-tuned by adopting a gannet optimization (GO) algorithm. The proposed study used three different datasets and obtained higher classification accuracy as 99.5% in Fake News Detection on Twitter EDA, 99.59% in FakeNewsNet and 99.51% in ISOT. The analysis proves that the proposed model attains higher prediction results for each dataset than others.
From integrals to combinatorial formulas of finite type invariants — A case study
Authors: Brooks Robyn , Komendarczyk Rafał
Published: August 2024

We obtain a localized version of the configuration space integral for the Casson knot invariant, where the standard symmetric Gauss form is replaced with a locally supported form. An interesting technical difference between the arguments presented here and the classical arguments is that the vanishing of integrals over hidden and anomalous faces does not require the well-known “involution tricks”. The integral formula easily yields the well-known arrow diagram expression for regular knot diagrams, first presented in the work by Polyak and Viro. Moreover, it yields an arrow diagram count for the multicrossing knot diagrams, such as petal diagrams and gives a new lower bound for the übercrossing number. Previously, the known arrow diagram formulas were applicable only to the regular knot diagrams.
INVESTIGATION OF FRACTIONAL-ORDERED TUMOR-IMMUNE INTERACTION MODEL VIA FRACTIONAL-ORDER DERIVATIVE
Authors: ALI GAUHAR , MARWAN MUHAMMAD , RAHMAN UBAID UR , HLEILI MANEL
Published: August 2024

Dynamical analysis of system of ordinary differential equations (ODEs) is an interesting topic among researchers and several tools have been discovered since so far to deal with its several other features. There are many built-in functions designed in MATLAB that researchers can use as a tool for the system of ODEs with integer order but in the similar systems have fractional-order derivative is a difficult task. Therefore, the purpose of this work is to use the Caputo fractional-order derivative to qualitatively analyze and then numerically simulate the tumor-immune system interaction model. Moreover, the Schauder fixed point theorem is used to establish the condition for the existence of at least one solution, while the Banach fixed point theorem is utilized to guarantee the existence of a unique solution. Moreover, the stability in the trajectories of considered system achieved with the aid of Ulam–Hyers (UH) stability. In addition, the numerical computations are performed using Variational Iteration Method (VIM) and the visualized results are shown using MATLAB.
Probabilistic Dynamic Design Curves Optimized for High-Speed Reinforced Concrete Railway Bridges Using First-Order Reliability Method
Authors: Allahvirdizadeh Reza , Andersson Andreas , Karoumi Raid
Published: August 2024

Increasing the operating speed of trains in modern railway networks can induce greater actions on the infrastructure than was previously the case. This is due, in particular, to the occurrence of the resonance phenomenon in railway bridges, which is the focus of this paper and was not traditionally considered as a concern. In this context, the vibrations experienced by bridges, both vertical accelerations and displacements, are limited by design regulations to ensure that the safety of train passages over bridges and the comfort of passengers are guaranteed. However, previous studies have shown that the conventional dynamic design methods do not always result in conservative designs, nor is the achieved safety always consistent. Therefore, a probabilistic approach is adopted in this study to optimize the cross-section properties of various railway bridges in a wide design range including section types, span lengths, and number of spans. For this purpose, an iterative line search-based optimization problem is formulated to minimize the thickness of the cross-sections under consideration and consequently the linear mass of the bridges. Meanwhile, the associated failure probabilities of the above dynamic limit states are constrained to be less than the desired level of safety by incorporating them into the optimization constraint. In this regard, First-Order Reliability Method (FORM) is adopted to perform reliability analyses. Thus, the obtained results are presented in the form of design curves that may assist designers to select minimum cross-section dimensions satisfying the desired level of safety in terms of dynamic limit states. This objective can be achieved using the proposed design curves without the need to construct associated complex computational models and perform computationally expensive dynamic analyses.
Secondary Resonances of Asymmetric Gyroscopic Spinning Composite Box Beams
Authors: Bavi Reza , Sedighi Hamid M. , Shishesaz Mohammad
Published: August 2024

A comprehensive theoretical investigation on the occurrence of secondary resonances in parametrically excited unbalanced spinning composite beams under the stretching effects is conducted numerically and analytically. Based on an optimal stacking sequence and Rayleigh’s beam theory, the governing equations of the system are derived using extended Hamilton’s principle. The system’s partial differential equations are then discretized using the Galerkin method. Numerical (Runge–Kutta technique) and analytical (multiple scales method) approaches are exploited to solve the reduced-order equations, and their results are compared and verified accordingly. Comparison and convergence investigations are performed to guarantee the validity of the outcomes. Stability and bifurcation analyses are accomplished, and resonance effects are thoroughly studied utilizing frequency-response diagrams, phase portraits, Poincaré maps and time-history responses. It is observed that among the various types of secondary resonance, only a combination resonance can be observed in the system dynamics. The outputs reveal that, in this resonance, the gyroscopic coupling results in the steady-state time response consisting of three main frequencies. By examining the effects of damping, eccentricity, and beam length, it is exhibited that this resonance does not occur in the system’s dynamics for any combination of these parameters. Therefore, these parameters can be adjusted in the design of asymmetric beams to prevent this type of resonance.
Bridge Monitoring Using Smartphones Installed on Driving Vehicles: Oriented to Crowdsourcing Model
Authors: Liu Chengyin , Zhang Jun , Quan Yixin , Zeng Qing , Ren Zhicheng
Published: August 2024

The acquisition of bridge frequency by using the ubiquitous ordinary passenger vehicles and smartphones in cities has attracted an increasing amount of attention. This study proposes a bridge monitoring method and develops a vehicle–bridge crowdsourcing monitoring (VBCM) platform for public participation. It takes smartphones carried by vehicles as monitoring terminals for a long-term bridge monitoring task. To validate the feasibility of available smartphone brands as a signal collector, a small shake table test is carried out and the processing scheme for the smartphone sampling data is investigated. A smartphone sensor frequency gain technique is proposed to satisfy the fusion of multisensor data in different smartphones. Furthermore, a magnetic target identification technique is proposed to pick up and extract the bridge response signal segment corresponding to the vehicle’s entering and leaving, as well as to eliminate redundant data. To this end, the network of multiple smartphones is synchronized. For the sake of applicability and feasibility, a series of scaled experiments are conducted in the laboratory considering an adapted toy car driving over a simply supported steel bridge. The results demonstrated the practicality of the proposed methodology. The combination of easily accessible smartphones oriented to a crowdsourcing model and a driving vehicle lowers the threshold for the bridge monitoring process, which also pinpoints a potential for future structural health monitoring development.
Vibration Characterization of Permanent Magnet Levitation Vehicle System Based on Permanent Magnetic Track Relationships
Authors: Kuang Honglin , Shi Minming , Zhan Pengfei , Wu Gao , Deng Yongfang , Su Binbin
Published: August 2024

Permanent magnetic levitation (PML) is an innovative levitated transportation system. Examining the dynamic response of PML vehicles based on their permanent magnetic track relationship is crucial for assessing system stability and safety. In this regard, this study first established the multibody dynamic equations of PML vehicles according to their magnetic trajectory relationship. A coupled dynamic model was then developed within the Simulink environment; finally, the dynamic characteristics of the PML vehicle engineering machine were analyzed. The simulation results were in good agreement with the experimental results, further verifying the accuracy and reliability of the dynamic model. The results show that the bogie keeps a certain attitude for a long time at a low speed. The vibration acceleration of the bogie and body was much smaller than the corresponding dynamic evaluation criteria. Notably, the spectral analysis of the vertical vibration acceleration of the body shows that the axis distance of the PML, lateral and axial distances of the second suspension system, and lateral distance of the guide wheels have a significant effect on the body vibration. This study provides a relevant reference for the engineering application of PML vehicles.
Two-sided zero product determined triangular algebras
Authors: Ghahramani Hoger , Hosseini Dana
Published: August 2024

Let [Formula: see text] be an algebra over the field [Formula: see text]. We say that [Formula: see text] is two-sided zero product determined if every bilinear functional [Formula: see text] the following holds: if [Formula: see text] whenever [Formula: see text], then there exist linear functionals [Formula: see text] and [Formula: see text] on [Formula: see text] such that [Formula: see text] for all [Formula: see text]. We show that the unital triangular algebra [Formula: see text] is a two-sided zero product determined algebra if and only if [Formula: see text] and [Formula: see text] are two-sided zero product determined algebras, and then we get various results about this property for generalized triangular algebras and block upper triangular matrix algebras. We also provide an application of the main result to determine the structure of commutativity preserving maps at commutative zero products on triangular algebras. We note that some of the previous results about the two-sided zero product determined property have been generalized.
Development of a Giant Human Riding Biped Humanoid Robot, Method II
Authors: Park Ill-Woo
Published: August 2024

Research in bipedal humanoid robots focuses on improving the walking performance of robots rather than designing them according to the presence or absence of human boarding. However, a bipedal robot can be operated by a human pilot. The pilot can control the robot’s arms, fingers, and bipedal locomotion using a wearable robot. As the wearable robot can allow physical interaction between the human and the robot, the control interface for the robot can be accurately described as a human–machine interface (HMI). In this regard, this study develops and assesses a new design of a humanoid robot that enhances load capacity and mobility through improved HMI. In particular, this study presents the design research of METHOD II, which is 4.2[Formula: see text]m tall, weighs 1.6 tons, and has 46 degrees-of-freedom (DoF). The robot can carry an adult weighing up to 100[Formula: see text]kg and is designed to execute both bipedal walking and arm movements. This giant humanoid mobility robot is inspired by AMP, a combat robot suit featured in the movie “Avatar.” This paper details the design philosophy, strategy, and results of METHOD II, highlighting the robot’s performance capabilities. A design method is proposed so that the giant robot can freely generate the motion of both arms of the robot through HMI while walking on its two legs.
Derivative Free King’s-Type Family for Solving System of Nonlinear Equations
Authors: Bhavna  , Bhatia Saurabh
Published: August 2024

There has been very less work done on developing methods for solving systems of nonlinear equations using the idea of memorization. Methods without memory can be extended to methods with memory without using any additional function evaluations. Improvement in the convergence order of methods with memory gives an added benefit. The stability of such methods is another important concept which needs to be considered. In this work, we deal with the development of such methods. We first construct a sixth-order derivative free family without memory and then further extend it to methods with memory of order [Formula: see text] and [Formula: see text]. Numerical results show the superiority of these methods over the existing ones. Basins of attraction have also been presented to observe the dynamical behavior of the proposed methods and the comparison validates that the proposed methods are much more stable than the existing methods.
CAN ESG PERFORMANCE DRIVE FIRM INNOVATION? THE MEDIATING ROLE OF AI ADOPTION AND DIGITAL TRANSFORMATION — EVIDENCE FROM CHINA
Authors: HE XIAN
Published: August 2024

The effectiveness of Environmental, Social, and Governance (ESG) on corporate performance is an area of growing interest with significant practical implications. However, the role of ESG in fostering corporate innovation has received limited attention, and the mechanisms through which ESG factors influence innovation performance remain inadequately understood. This paper empirically investigates the impact and mechanisms of ESG performance on corporate innovation capabilities using a sample of Chinese A-share listed companies from 2010 to 2021. The results indicate that ESG performance significantly enhances corporate innovation capacity, mediated by the degree of artificial intelligence (AI) adoption and digital transformation. By employing the concept of organisational innovation, this study elucidates the mechanisms of ESG factors on innovation performance, addressing a significant gap in the existing research. The findings offer valuable insights for enterprises aiming to improve their competitive advantage and provide a crucial foundation for policymakers seeking to enhance corporate innovation through targeted reforms.
Optimal control model for IVF treatment in women
Authors: Yeolekar Bijal , Shukla Nehal , Shukla Jagdish , Yeolekar Mahesh , Patil Surekha , Santra P. K.
Published: August 2024

Inability to become pregnant after 12 months of regular, unprotected intercourse is defined as Infertility. Couples who have not conceived after 12 months of unprotected vaginal intercourse should be offered further evaluation. Evaluation includes workup for anovulation (hormonal evaluations), hysterosalpingography, hysteroscopy, Laparoscopy. Treatment options vary from low-cost non-pharmacological therapy, like counseling on the timing of intercourse during the most fertile period, may wait for another year of unprotected intercourse, weight loss (target Body Mass Index [Formula: see text]), smoking cessation, limiting alcohol consumption, psychological interventions like cognitive behavioral therapy (CBT) to reduce stress, anxiety and depression related to infertility, to high-cost pharmacological approach including ovulation induction medication, intrauterine insemination (IUI), in vitro fertilization (IVF). Mathematical models are rising as a key factor to add to our knowledge of the fertility process and help us understand the intricacies in the reproductive system to be able to predict the possibilities of pregnancy precisely. We have created a mathematical model with five compartments to understand the success of treatment of infertility in women. We have carried out local stability, global stability at pregnancy-free and pregnancy exist equilibrium points and numerical analysis. We have also tried optimal control by maximizing fertility through non-pharmacological measures and applied cost control to IVF treatment. Our results showed non-pharmacological and pharmacological treatments have a positive impact on the overall success of treatment of infertility however cost is the important determining factor. We recommend maximizing non-pharmacological measures before opting for costly pharmacological measures. We also recommend that the government or other Non-Governmental Organizations (NGOs) help with the cost for women with infertility.
SIR–Bass modeling approach to quantify the effects of intervention adoption on disease transmission
Authors: Yadav Pramod Kumar , Goel Palak
Published: August 2024

This research paper proposes an integration of the Bass diffusion model with the classic Susceptible–Infectious–Recovered (SIR) compartmental model of infectious diseases. The combination of the Bass diffusion model and the SIR model is useful to gain insights into the intervention adoption behavior of the population based on imitation and innovation. The proposed model quantifies an optimal level of intervention adoption within the population, based on the epidemiological parameters. This optimal level serves as the threshold beyond which disease control within the community becomes achievable. In this work, we also identified influential adopters, encompassing both innovators and imitators. This identification is based on two distinct scenarios, providing a valuable avenue for the precise implementation of targeted interventions during disease outbreaks. The considered model presents a novel approach to comprehensively study intervention adoption behavior during infectious disease outbreaks, enhancing our understanding of the dynamics of disease transmission and adopting evidence-based interventions.
Bipolar resistive switching in MAPbI3−xClx mixed halide perovskite with polyvinylpyrrolidone additives
Authors: Park Byoungnam
Published: August 2024

In our study, we successfully demonstrated bipolar resistive switching in Resistive Random-Access Memory (RRAM) devices using MAPbI[Formula: see text][Formula: see text]Cl[Formula: see text] mixed halide perovskite, thereby underscoring the immense potential of this material for future memory applications. By incorporating polyvinylpyrrolidone (PVP) into the device structure, which consists of MAPbI[Formula: see text][Formula: see text]Cl[Formula: see text] sandwiched between top (Au) and bottom (ITO) electrodes, we achieved a remarkable morphological transition from elongated, needle-like structures to denser, more compact forms. This morphological evolution was pivotal in significantly lowering operational voltages, with “SET” and “RESET” voltages dramatically reduced from 7 V to 0.7 V and −2.2 V to −0.2 V, respectively. The incorporation of PVP proved to be a key factor in this achievement, as it enhanced charge transport within the device, minimized energy barriers, and markedly improved the dielectric properties of the perovskite material. These enhancements contributed directly to the observed reduction in switching voltages, marking a substantial leap forward in the quest for more efficient, stable, and low-power RRAM devices. Our research not only advances the field of memory technology but also emphasizes the critical aspects of reduced power consumption and the potential for enhanced device stability. This significant decrease in energy requirements, coupled with the prospect of increased device longevity, underscores the transformative impact of our findings, paving the way for the development of sustainable and reliable memory technologies that are well-suited to meet the demands of future electronic devices and systems.
Hybrid multilayer magnetoelectric coupling based on magnetostrictive materials
Authors: Sai Harsha S. , Kondaiah P. , Deepak K.
Published: August 2024

Multiferroic materials have a unique ability to combine two or more ferroic orders within the same phase. Specifically, magnetoelectric (ME) materials exhibit a unique combination of three ferroic orders in their response to magnetic, elastic, and electric fields. This study focuses on a finite element investigation of a hybrid multilayer ME coupling, incorporating two different magnetostrictive materials in a single composite. The composite includes Nickel, exhibiting negative magnetostriction, leading to an increase in pre-compressive stress, easy machinability, and cost-effectiveness. Additionally, Terfenol-D is utilized due to its characteristic property of high magnetostrictive strain when exposed to an external magnetic field. The result of this study shows that this hybrid ME structure has an ME coefficient of 804 mV/cm. Oe without pre-stress and ME coefficient is increased to 32.64 V/cm. Oe by applying pre-stress while retaining cost-effectiveness. The analysis in this particular work also suggests promising applications for energy harvesting and magnetic field sensing.
A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and Recommendations
Authors: Razzaq Abdul , Buckley Jim , Lai Qin , Yu Tingting , Botterweck Goetz
Published: August 2024

Context and Motivation
– Developer eXperience (Dev-X) is a recent research area that focuses on developers perceptions, feelings, and values with respect to software development and software quality. Research suggests that factors and practices related to Dev-X can have a substantial impact on developer productivity (Dev-P). However, despite a large and diverse body of literature on factors that can impact Dev-P in general, there is no coherent and comprehensive characterization of how Dev-X-specific insights can influence developer productivity.

Aims
– In the presented research, we aim to provide a coherent, comprehensive characterization of factors and practices related to Dev-X, with a particular focus on those factors and practices that potentially affect Dev-P.

Method
– To this end, we performed a systematic literature review and identified 218 relevant papers in this area. We characterize the papers based on the related frameworks and concepts common to Dev-X and Dev-P as presented in the existing literature. Dev-X factors such as “work fragmentation” and practices such as “collaboration with owner-developer” are identified using a grounded-in-the-literature, content-analysis method, guided by the theory. For each Dev-X factor, we identify attributes that might be used to assess/ measure the current status (of an organization or project) regarding that factor and how that factor and its effects on productivity have been evidenced in the literature (mentioned vs. considered in questionnaires vs. substantiated with a more positivist evaluation).

Results
– We identify 33 Dev-X-related factors and 41 Dev-X-related practices, which are organized into 10 themes to summarize their influence. The results suggest that the availability of required resources, relevant expertise re the allocated tasks, and fewer interruptions are among the top positively impacting factors. Conversely, factors such as code complexity, heterogeneous contexts of tasks, and non-adherence to standardization harm Dev-X and Dev-P. Top industrial practices employed to mitigate the negative influence of factors include characterization-based task assignments, mental model support, and the timely evolution of technologies.

Conclusions
– Overall, this research suggests that organizations can influence Dev-P through improved Dev-X, incorporating suitable practices to mediate relevant factors in their context. Important in this regard are practices such as fragmenting large tasks, highlighting the utility of proposed tasks/changes to the developers, and promoting (developer) ownership of artefacts. Finally, our results point to areas where further research seems appropriate, i.e., where Dev-X factors/practices have been proposed as being impactful on Dev-P but not yet fully substantiated or explored as such (factors like “Nature of Activity” and practices like choosing practices/protocols appropriately).
Efficient and Privacy-Enhanced Asynchronous Federated Learning for Multimedia Data in Edge-based IoT
Authors: Xiong Hu , Yan Hang , Obaidat Mohammad S. , Chen Jingxue , Cao Mingsheng , Kumar Sachin , Agarwal Kadambri , Kumari Saru
Published: August 2024

With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework shows promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous federated learning approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.
Don’t Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
Authors: Sun Zhensu , Du Xiaoning , Song Fu , Wang Shangwen , Ni Mingze , Li Li , Lo David
Published: August 2024

Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited and may conversely aggravate the workload of developers, as the code completions are automatically and actively generated in state-of-the-art code completion systems as developers type out once the service is enabled. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and energy, which severely goes against the sustainable development principle of AI technologies. However, such waste has never been realized, not to mention effectively addressed, in the research community for neural code completion. Hence, preventing such unhelpful code completions from happening in a cost-friendly way is of urgent need. To fill this significant gap, we first investigate the prompts of unhelpful code completions, called “low-return prompts.” We empirically identify four observable patterns in low-return prompts, each lacking necessary information, making it difficult to address through enhancements to the model’s accuracy alone. This demonstrates the feasibility of identifying such low-return prompts based on the prompts themselves. Motivated by this finding, we propose an early-rejection mechanism to turn down low-return prompts by foretelling the code completion qualities. The prompts that are estimated to receive unhelpful code completions will not be sent to the model. Furthermore, we investigated five types of estimators to demonstrate the feasibility of the mechanism. The experimental results show that the estimator can reject 20% of code completion requests with a 97.4% precision. To the best of our knowledge, it is the first systemic approach to address the problem of unhelpful code completions and this work also sheds light on an important research direction of large code models.
Measuring and Mining Community Evolution in Developer Social Networks with Entropy-Based Indices
Authors: Zhang Jierui , Wang Liang , Li Ying , Jiang Jing , Wang Tao , Tao Xianping
Published: August 2024

This work presents four novel entropy-based indices for measuring the community evolution of developer social networks (DSNs) in open source software (OSS) projects. The proposed indices offer a quantitative measure of community split, shrink, merge, and expand events. The indices have proven properties like monotonicity, and they have defined maximum and minimum values that signify meaningful scenarios. These indices can be combined to describe complex community evolution events such as emergence and extinction. Expanding upon these indices, this research proposes a novel machine learning approach, leveraging shapelet mining, to unearth representative patterns of community evolution. The results from real-world OSS projects show that these indices effectively capture various community evolution behaviors with a 94.1% accuracy compared to existing work. They also predict OSS team productivity with a 0.718 accuracy. With the shapelet mining and learning framework, the indices can identify patterns of community evolution and predict the survival of OSS projects with 93% accuracy 3 months before the projects’ last observed commits. The findings highlight the potential of these entropy-based indices for understanding OSS project status and predicting future trends, which are valuable for supporting future research on DSNs and OSS communities.
Solving the
t
-Wise Coverage Maximum Problem via Effective and Efficient Local Search-Based Sampling
Authors: Luo Chuan , Song Jianping , Zhao Qiyuan , Sun Binqi , Chen Junjie , Zhang Hongyu , Lin Jinkun , Hu Chunming
Published: August 2024

To meet the increasing demand for customized software, highly configurable systems become essential in practice. Such systems offer many options to configure, and ensuring the reliability of these systems is critical. A widely used evaluation metric for testing these systems is

\(t\)

-wise coverage, where

\(t\)

represents testing strength, and its value typically ranges from 2 to 6. It is crucial to design effective and efficient methods for generating test suites that achieve high

\(t\)

-wise coverage. However, current state-of-the-art methods need to generate large test suites for achieving high

\(t\)

-wise coverage. In this work, we propose a novel method called
LS-Sampling-Plus
that can efficiently generate test suites with high

\(t\)

-wise coverage for

\(2\leq t\leq 6\)

while being smaller in size compared to existing state-of-the-art methods.
LS-Sampling-Plus
incorporates many core algorithmic techniques, including two novel scoring functions, a dynamic mechanism for updating sampling probabilities, and a validity-guaranteed systematic search method. Our experiments on various practical benchmarks show that
LS-Sampling-Plus
can achieve higher

\(t\)

-wise coverage than current state-of-the-art methods, through building a test suite of the same size. Moreover, our evaluations indicate the effectiveness of all core algorithmic techniques of
LS-Sampling-Plus
. Furthermore,
LS-Sampling-Plus
exhibits better scalability and fault detection capability than existing state-of-the-art methods.
An Exploratory Study on Machine Learning Model Management
Authors: Latendresse Jasmine , Abedu Samuel , Abdellatif Ahmad , Shihab Emad
Published: August 2024

Effective model management is crucial for ensuring performance and reliability in Machine Learning (ML) systems, given the dynamic nature of data and operational environments. However, standard practices are lacking, often resulting in
ad hoc
approaches. To address this, our research provides a clear definition of ML model management activities, processes, and techniques. Analyzing 227 ML repositories, we propose a taxonomy of 16 model management activities and identify 12 unique challenges. We find that 57.9% of the identified activities belong to the maintenance category, with activities like refactoring (20.5%) and documentation (18.3%) dominating. Our findings also reveal significant challenges in documentation maintenance (15.3%) and bug management (14.9%), emphasizing the need for robust versioning tools and practices in the ML pipeline. Additionally, we conducted a survey that underscores a shift toward automation, particularly in data, model, and documentation versioning, as key to managing ML models effectively. Our contributions include a detailed taxonomy of model management activities, a mapping of challenges to these activities, practitioner-informed solutions for challenge mitigation, and a publicly available dataset of model management activities and challenges. This work aims to equip ML developers with knowledge and best practices essential for the robust management of ML models.
Inositol Pyrophosphates as Versatile Metabolic Messengers
Authors: Nagpal Latika , He Sining , Rao Feng , Snyder Solomon H.
Published: August 2024

Discovered in 1993, inositol pyrophosphates are evolutionarily conserved signaling metabolites whose versatile modes of action are being increasingly appreciated. These include their emerging roles as energy regulators, phosphodonors, steric/allosteric regulators, and G protein–coupled receptor messengers. Through studying enzymes that metabolize inositol pyrophosphates, progress has also been made in elucidating the various cellular and physiological functions of these pyrophosphate-containing, energetic molecules. The two main forms of inositol pyrophosphates, 5-IP7 and IP8, synthesized respectively by inositol-hexakisphosphate kinases (IP6Ks) and diphosphoinositol pentakisphosphate kinases (PPIP5Ks), regulate phosphate homeostasis, ATP synthesis, and several other metabolic processes ranging from insulin secretion to cellular energy utilization. Here, we review the current understanding of the catalytic and regulatory mechanisms of IP6Ks and PPIP5Ks, as well as their counteracting phosphatases. We also highlight the genetic and cellular evidence implicating inositol pyrophosphates as essential mediators of mammalian metabolic homeostasis.
FBXL19 in endothelial cells protects the heart from influenza A infection by enhancing antiviral immunity and reducing cellular senescence programs
Authors: Xia Boyu , Chen Huilong , Taleb Sarah J. , Xi Xiaoqing , Shaheen Nargis , Baoyinna Boina , Soni Sourabh , Mebratu Yohannes A. , Yount Jacob S. , Zhao Jing , Zhao Yutong
Published: August 2024

Our study reveals a novel facet of IAV infection, demonstrating that it can trigger cellular senescence within the heart. Intriguingly, upregulation of endothelial FBXL19 promotes host innate immunity, reduces cardiac senescence, and diminishes inflammation. These findings highlight the therapeutic potential of targeting FBXL19 to mitigate IAV-induced cardiovascular complications.
Dental Stem Cell‐Derived Exosomes: A Review of Their Isolation, Classification, Functions, and Mechanisms
Authors: Ning Xiner , Liu Rui , Huang Yingying , Huang Zhilong , Li Haodi , Li Qiqi , Sheng Zengyan , Wu Junjie
Published: August 2024

The scientific field concerned with the study of regeneration has developed rapidly in recent years. Stem cell therapy is a highly promising therapeutic modality for repairing tissue defects; however, several limitations exist, such as cytotoxicity, potential immune rejection, and ethical issues. Exosomes secreted by stem cells are cell‐specific secreted vesicles that play a regulatory role in many biological functions in the human body; they not only have a series of functional roles of stem cells and exert the expected therapeutic effects, but they can also overcome the mass limitations of stem cells and are thus considered in the research as an alternative treatment strategy for stem cells. Since dental stem cell‐derived exosomes (DSC‐Exos) are easy to acquire and present modulating effects in several fields, including neurovascular regeneration and craniofacial soft and hard tissue regeneration processes, they are served as an emerging cell‐free therapeutic strategy in various systematic diseases. There is a growing body of research on various types of DSC‐Exos; however, they lack systematic elaboration and tabular summarization. Therefore, this review presents the isolation, characterization, and phenotypes of DSC‐Exos and focuses on their current status of functions and mechanisms, as well as the multiple challenges prior to clinical applications.
Licensed Practical Nurses’ (LPNs’) Evaluations of the Attractiveness of Work and Wellbeing at Work: A Cross‐Sectional Nationwide Study
Authors: Roos Mia , Kopra Juho , Tevameri Terhi , Viinikainen Sari , Kuosmanen Lauri
Published: August 2024

Background. Severe challenges in recruiting and retaining healthcare workers exist on a global level and are especially noticeable in elderly care services. Previous studies have not assessed how the attractiveness of a job is related to wellbeing at work among licensed practical nurses (LPNs) even though these professionals are vital in providing care to the elderly. Objective. The purpose of this study is to define factors that affect LPNs’ attractiveness of work and wellbeing at work. Design. A cross‐sectional survey study. Participants. A large‐scale nationwide sample of 10 848 licensed practical nurses (LPNs) working in Finland. Methods. An online survey for LPNs working in Finland was conducted. Criteria for a good workplace (CFGW), measuring the attractiveness of work and wellbeing at work, were utilized. Descriptive and comparative analyses were performed. Cronbach’s alpha values were tested to assess internal consistency (α = 0.739–0.915). An ANOVA or t‐test result which indicated a statistically significant between‐group difference (which was in line with difference ≥0.1) was considered as both statistically significant and significant in practice. Results. The core group, LPNs’ wellbeing at work (m = 4.36), is extremely important factor of attractiveness of work. Regarding the subgroups high quality of care (m = 4.61), reconciling of work and private life (m = 4.59) and well‐functioning practices (m = 4.55) were most important. Well‐functioning practices associated with several background variables, e.g., with working for full‐time (p ≤ 0.001, mean = 3.10) and age (p < 0.001). LPNs over 56 years old, especially, regard well‐functioning practices important (p = 3.17). LPNs’ who were 56 years old or older (m = 3.12) and those who had work experience 1 year or less (m = 3.19) stated rewarding work most important. Conclusions. The core group, wellbeing, is an exceptionally important for the LPNs attractiveness of work. The LPNs’ wellbeing at work is supported by the quality of care and reconciliation of work and private life.
On Alpha Power Transformation Generalized Pareto Distribution and Some Properties
Authors: Bleed Salma Omar , Attwa Rasha Abd-Elwahab , Ali Rabeea Farag Meftah , Radwan Taha
Published: August 2024

Recently, the need to develop statistical distributions has become the most important spot. In this context, we employ the α‐power transformation (APT) method to convert the generalized Pareto distribution (GPD) into a new distribution. Some statistical properties of the proposed distribution are being studied, such as moments, arithmetic mean, moment‐generating function, random variables, entropy, reliability, and hazard function (HF). In addition, the proposed distribution is compared with the Pareto distribution and some other forms of alpha power distributions, such as the alpha power Pareto (APP) distribution, alpha power Rayleigh (APR) distribution, and the alpha power Lomax (APL) distribution. Finally, we demonstrate the benefits of the proposed distribution through a simulation study and two real data sets. It was found that the results showed the MLE method is reliable, the APTGP distribution is a competitive distribution for the aforementioned data set, and it is a mirror image of the Pareto distribution.
Multiplicity Results for a (p1(x), p2(x))‐Laplacian Equation via Variational Methods
Authors: Rezvani A.
Published: August 2024

We prove the existence and multiplicity of nontrivial weak solutions for the following (p1(x), p2(x))‐Laplacian equation involving variable exponents: . Using Ricceri’s variational principle, we show the existence of at least three weak solutions for the problem. We also apply the variational method and genus theory to establish the existence of infinitely many solutions. Then, we prove the closedness of the set of eigenfunctions, such that p(x) ≡ p.
A Rare Case of Pseudomonas aeruginosa‐Associated Diarrhea in a Cirrhotic Patient
Authors: Chapman Malcolm , Lodh Atul , Tristeza Joven , Yang Alexander
Published: August 2024

While rare, cases of community‐acquired Pseudomonas aeruginosa (PsA) enterocolitis have been reported and are associated with recent antibiotic use and immunocompromised hosts. Here we present a 29‐year‐old immunocompetent female with newly diagnosed alcoholic cirrhosis that presents with bloody diarrhea and was found to have PsA in her stool culture. This case is unique as our patient does not have a history of recent antibiotic use or prior history of immunosuppression. However, immune dysfunction in cirrhosis results in defects in both innate and acquired immunity, thus predisposing our patient to PsA‐associated diarrhea. Overall, this case showcases that, while considered immunocompetent, cirrhotic patients are predisposed to rare infections such as PsA in the community due to defects in their immune system.
Remote Neuroinflammation in Newly Diagnosed Glioblastoma Correlates with Unfavorable Clinical Outcome
Authors: Bartos Laura M. , Quach Stefanie , Zenatti Valerio , Kirchleitner Sabrina V. , Blobner Jens , Wind-Mark Karin , Kolabas Zeynep Ilgin , Ulukaya Selin , Holzgreve Adrien , Ruf Viktoria C. , Kunze Lea H. , Kunte Sebastian T. , Hoermann Leonie , Härtel Marlies , Park Ha Eun , Groß Mattes , Franzmeier Nicolai , Zatcepin Artem , Zounek Adrian , Kaiser Lena , Riemenschneider Markus J. , Perneczky Robert , Rauchmann Boris-Stephan , Stöcklein Sophia , Ziegler Sibylle , Herms Jochen , Ertürk Ali , Tonn Joerg C. , Thon Niklas , von Baumgarten Louisa , Prestel Matthias , Tahirovic Sabina , Albert Nathalie L. , Brendel Matthias
Published: August 2024

Purpose:

Current therapy strategies still provide only limited success in the treatment of glioblastoma, the most frequent primary brain tumor in adults. In addition to the characterization of the tumor microenvironment, global changes in the brain of patients with glioblastoma have been described. However, the impact and molecular signature of neuroinflammation distant of the primary tumor site have not yet been thoroughly elucidated.

Experimental Design:

We performed translocator protein (TSPO)-PET in patients with newly diagnosed glioblastoma (n = 41), astrocytoma WHO grade 2 (n = 7), and healthy controls (n = 20) and compared TSPO-PET signals of the non-lesion (i.e., contralateral) hemisphere. Back-translation into syngeneic SB28 glioblastoma mice was used to characterize Pet alterations on a cellular level. Ultimately, multiplex gene expression analyses served to profile immune cells in remote brain.

Results:

Our study revealed elevated TSPO-PET signals in contralateral hemispheres of patients with newly diagnosed glioblastoma compared to healthy controls. Contralateral TSPO was associated with persisting epileptic seizures and shorter overall survival independent of the tumor phenotype. Back-translation into syngeneic glioblastoma mice pinpointed myeloid cells as the predominant source of contralateral TSPO-PET signal increases and identified a complex immune signature characterized by myeloid cell activation and immunosuppression in distant brain regions.

Conclusions:

Neuroinflammation within the contralateral hemisphere can be detected with TSPO-PET imaging and associates with poor outcome in patients with newly diagnosed glioblastoma. The molecular signature of remote neuroinflammation promotes the evaluation of immunomodulatory strategies in patients with detrimental whole brain inflammation as reflected by high TSPO expression.