bluuebunny/crossref_metadata_2025 huggingface dataset:
Classification and Recognition of Music Curriculum Resources Based on Improved Fuzzy C-Means Clustering Algorithm
Authors: Xue Li
Published: December 2024

The classification and processing of multimedia audio and video teaching resources data is crucial for the development of the next generation of multimedia technology. In music courses, the traditional method of merging, classification, and identification of multimedia audio and video teaching resources uses functional traditional fuzzy C-average class methods, and treats the entire document as a systematic research object. However, this method cannot subdivide documents, let alone handle information about multimedia audio and video teaching resources that are specific to music courses. To address this issue, we propose a method of improving the fuzzy C-average polyet algorithm to classify and identify multimedia audio and video teaching resources with dual subtraction backgrounds. First, we use information entropy as a standard for classification and identification, and leverage the nonlinear mapping ability of neural networks to calculate and blur the weight fuzzy C-average polyet algorithm. This approach solves the issues of inaccurate classification and incompetence of classes. For actual test verification, we used five documents, with five documents in each category and three functional items. The results show that the improved fuzzy C-average polyet algorithm can more effectively identify and classify multimedia audio and video teaching resources in classified music courses. It is less characteristic, more distributed in the distribution of multimedia audio teaching resources in random music courses, and has strong convergence and high application value. Overall, this study demonstrates the effectiveness of the proposed method in classifying and identifying multimedia audio and video teaching resources in music courses. The improved fuzzy C-average polyet algorithm can be used as a valuable tool for researchers and practitioners in the field of multimedia technology.
Status and analysis perspectives of ICARUS at the Fermilab Short Baseline Neutrino program
Authors: Campani Alice
Published: December 2024

After a successful three-year physics run underground at the LNGS in Italy and a significant detector overhaul at CERN, the ICARUS-T600 detector was installed at Fermilab. In 2020, the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operation collecting the first neutrino events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, and started data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurements of neutrino cross-sections with the NuMI beam and several Beyond Standard Model searches. After the first year of operations, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND), within the Fermilab Short-Baseline Neutrino (SBN) program. In this paper, preliminary technical results from the ICARUS data with the BNB and NuMI beams are presented to highlight the performance of all ICARUS subsystems and its capability to select and reconstruct neutrino events.
Thermodynamics of charged four-dimensional hairy black holes
Authors: Dehghani M. , Baghbani R.
Published: December 2024

In this paper, we study the solutions of Einstein’s field equations in Einstein–Maxwell-dilaton gravity. By presenting the (3+1)-dimensional action in which gravity is coupled to a dilaton field, we derive the field equations. We present a new class of charged dilaton black hole solutions in Einstein–Maxwell-dilaton theory and investigate the effects of dilaton field on the properties of space–time. We show that these solutions are physical and describe spherically symmetric charged black holes. The calculated potential is Liouville type and has two rules. The exact solution of the field equations leads to the production of the metric function with three rules. By examining the physical properties of the solutions, we find the mass, charge, electric potential and temperature of charged dilaton black holes. Also, with the help of the canonical ensemble method, we study the thermodynamics and thermal stability of solutions and show the effects of dilaton and Maxwell fields on the thermodynamics of these solutions. Finally, by examining the phase transition of the solutions, we present thermal stability of the black hole in (3+1)-dimensional.
Dirac equation with a modified double ring shaped generalized Cornell potential: Extended Nikiforov–Uvarov method
Authors: Karayer Hale , Kamberli Arzu Küçüker , Yeşiltaş Özlem , Demirhan Doğan
Published: December 2024

The study reports on the attainment of bound state solutions and the associated relativistic energy eigenvalues of the Dirac equation in the presence of a modified double ring shaped generalized Cornell potential. This is accomplished by using the extended Nikiforov–Uvarov method, which prioritizes the elimination of complexities in determining ansatzs or employing series expansions. In particular, the wave functions are expressed in terms of Heun type polynomials.
The unification of gravity and the spin-1 field
Authors: Nash Gary
Published: December 2024

Unifying the massive spin-1 field with gravity requires the implementation of a regular vector field that satisfies the spin-1 Proca equation and is a fundamental part of the space–time metric. That vector field is one of the pair of vectors in the line element field [Formula: see text], which is paramount to the existence of all Lorentzian metrics and Modified General Relativity (MGR). Symmetrization of the spin-1 Klein–Gordon equation in a curved Lorentzian space–time introduces the Lie derivative of the metric along the flow of one of the regular vectors in the line element field. The Proca equation in curved space–time can then be described geometrically in terms of the line element vector, the Lie derivative of the Lorentzian metric and the Ricci tensor, which unifies gravity and the spin-1 field. Related issues concerning charge conservation and the Lorenz constraint, singularities in a spherically symmetric curved space–time and geometrical implications of MGR to quantum theory are discussed. A geometrical unification of gravity with quantum field theory is presented.
A cosmological holographic reconstruction of f(Q) theory
Authors: Saha Pameli , Rudra Prabir
Published: December 2024

This paper explores a cosmological reconstruction scheme in the background of [Formula: see text] gravity theory from a Holographic perspective. The basic motivation for this work is that the reconstruction is performed from a holographic origin, which has its roots in the black hole thermodynamics and quantum gravity. Dark energy models inspired by holographic prescription are used to reconstruct the [Formula: see text] gravity models. Two such models, namely, the Granda–Oliveros holographic dark energy model and its generalization, the Chen-Jing model, are considered for the study. Different scale factors are used and a thorough reconstruction scheme is set up using the dark energy models. The observationally constrained values of the free model parameters have been used to form the reconstructed models. Finally, a thorough investigation of the energy conditions has been performed to check the cosmological viability of the reconstructed [Formula: see text] models. As an outcome, we get some very promising and cosmologically viable [Formula: see text] models that present some interesting properties and demand further investigation. Finally, a method is discussed how the constructed [Formula: see text] models can be reconciled with a generalized holographic dark energy.
Collaborative Model for Task Scheduling and Resource Allocation in Fog–Cloud Network Using Game Theory
Authors: Sheela S. , Dilip Kumar S. M.
Published: December 2024

The deployment of fog computing has not only helped in task offloading for the end-users toward delay-sensitive task provisioning but also reduced the burden for cloud back-end systems to process variable workloads arriving from the user equipment. However, due to the constraints on the resources and computational capabilities of the fog nodes, processing the computational-intensive task within the defined timelines is highly challenging. Also, in this scenario, offloading tasks to the cloud creates a burden on the upload link, resulting in high resource costs and delays in task processing. Existing research studies have considerably attempted to handle the task allocation problem in fog–cloud networks, but the majority of the methods are found to be computationally expensive and incur high resource costs with execution time constraints. The proposed work aims to balance resource costs and time complexity by exploring collaboration among host machines over fog nodes. It introduces the concept of task scheduling and optimal resource allocation using coalition formation methods of game theory and pay-off computation. The work also encourages the formation of coalitions among host machines to handle variable traffic efficiently. Experimental results show that the proposed approach for task scheduling and optimal resource allocation in fog computing outperforms the existing system by 56.71% in task processing time, 47.56% in unused computing resources, 8.33% in resource cost, and 37.2% in unused storage.
HETEROGENEOUS INFLUENCE ADJUSTMENT CONSIDERING HISTORICAL STRATEGY PROMOTES COOPERATION IN SPATIAL PRISONER’S DILEMMA GAMES
Authors: MA JINLONG , ZHAO HONGFEI , WANG JIANCHAO
Published: December 2024

Inspired by the phenomenon that individuals tend to make decisions after gaining enough experience from past interactions, heterogeneous influence adjustment considering historical strategy mechanism is proposed. This mechanism adjusts the node’s strategy propagation influence by comparing the multi-round game income with that of selected neighbors. The results of the spatial prisoner’s dilemma game indicate that cooperation is substantially enhanced when the mechanism strength factor [Formula: see text] and the memory length m are suitably configured. Specifically, the mechanism strength factor [Formula: see text] exhibits a positive correlation with the frequency of cooperative behavior because stronger incentives or deterrents more effectively influence individuals to adopt cooperative strategies. Furthermore, the extended memory length m contributes to an enhancement in the network’s cooperation fraction. Longer memory length diminishes the fluctuation magnitude of the cooperation fraction because longer memory allows individuals to make more informed decisions based on a more comprehensive understanding of past interactions, leading to more stable and sustained cooperative behavior.
EXPLORING DATA ECOSYSTEMS IN SMART CITY SERVICE CREATION
Authors: OLDENBURG JENNA-RIIA , PUSSINEN PASI
Published: December 2024

Data platforms and data ecosystems play a key role in Smart City transformation, enabling data-intensive services and new value creation. As an emerging phenomenon, strategies, practices, and policies around data ecosystems are still forming. By utilising service-dominant logic and dynamic capabilities, this study maps out the roles and capabilities of cities in facilitating emerging data ecosystems, establishing data platforms, and identifying value proposals for data-intensive smart city services. We identify two main ecosystem leadership roles: business leader and innovation leader, their essential capabilities and activities that support establishing and growing a smart city data ecosystem.
Clustering-Based Hybrid Model for Predicting Symptoms for Colorectal Cancer: A Fuzzy Machine Learning Approach
Authors: Shafi M. A. , Jose Sayooj Aby , Rusiman M. S. , Jirawattanapanit Anuwat
Published: December 2024

Colorectal cancer (CRC) is a form of cancer that originates in the colon (large intestine) or rectum, which are components of the digestive system. It generally begins as small, benign growths known as polyps that can form on the inner lining of the colon or rectum. In Malaysia, colorectal cancer ranks among the most prevalent cancers, especially within the Chinese and Malay communities. A study released by the Malaysian National Cancer Registry indicates that colorectal cancer is the second most common cancer type following breast cancer, impacting both genders. The occurrence of colorectal cancer in Malaysia has been consistently increasing, with approximately 15–20% of cancer cases in the nation being colorectal cancer. This type of cancer arises when cells in the body start to multiply excessively, leading to various symptoms. In this research, a novel hybrid fuzzy linear regression with symmetric parameter clustering combined with a support vector machine (FLRWSPCSVM) model is employed to predict the high-risk symptoms associated with the onset of colorectal cancer in Malaysia. The study analyzed secondary data from 180 patients diagnosed with colorectal cancer and treated in a general hospital in Kuala Lumpur, considering twenty-five independent variables with various combinations of variable types. Furthermore, the model included parameters, errors, and explanations, along with two statistical measurement errors. The findings revealed that FLRWSPCSVM identified ovarian symptoms and a history of cancer symptoms as high-risk indicators for the development of colorectal cancer, with the lowest mean square error (MSE) recorded at 100.605 and a root mean square error (RMSE) of 10.030.
Averages of coefficients of the decomposable L-function of degree seven
Authors: Wang Dan
Published: December 2024

Let [Formula: see text] be a Hecke–Maass cusp form for [Formula: see text] with normalized Hecke eigenvalues [Formula: see text] and [Formula: see text] be a Hecke–Maass cusp form for [Formula: see text] with normalized Hecke eigenvalues [Formula: see text]. Under the generalized Ramanujan conjecture, we give a detailed proof of an asymptotic formula for averages of coefficients of degree seven [Formula: see text]-functions which can be factorized as a product of degree one and degree six [Formula: see text]-functions, namely [Formula: see text] We improve the results of Friedlander and Iwaniec [2] by changing the error term from [Formula: see text] to [Formula: see text]. Our proof is based on methods from the bilinear structure in the dual sum.
On the Glasner property of linear maps with prime entries on tori
Authors: Rajchert Andrew
Published: December 2024

We study the quantitative Glasner property in the context of maps between tori of differing dimension, instead of as a (semi-)group action. We also only consider matrices with entries being non-constant polynomials evaluated at primes, extending the work of Nair and Velani [Glasner sets and polynomials in primes, Proc. Amer. Math. Soc. 126(10) (1998) 2835–2840] and Bulinski and Fish [Glasner property for unipotently generated group actions on tori, Israel J. Math. 255 (2022) 109–122, 10.1007/s11856-022-2412-5 ] to a more general setting.
Analysis of SEIR network epidemic model considering self-protection awareness
Authors: Qin Jiaxin , Li Shuping
Published: December 2024

The susceptible population is categorized into two cohorts in proportion, and a network-based SEIR model is established to examine how self-protection awareness affects disease propagation. From the perspective of media reports and information dissemination among people, we carefully capture the enhancement of individuals’ awareness of self-protection amid the disease outbreak. The basic reproduction number [Formula: see text] is derived through the application of spectral analysis. We demonstrate the unique existence of the positive equilibrium, and then the stability of the two equilibria is determined by developing different Lyapunov functions. Sensitivity analysis determines the importance of parameters for [Formula: see text]. Our numerical simulations suggest that decreasing the proportion of unconscious susceptible people in the input population and strengthening self-protection awareness can effectively curb the expansion of infection.
Exploring gene mutation dynamics in lung carcinogenesis through multistage models in Japanese and Chinese populations
Authors: Nagah Ahmed , Liu Xinge , Ibrahim Muhammad , Abdelhamid Talaat
Published: December 2024

Lung cancer plays a significant role in the rising rates of cancer around the world, particularly in Japan and China. In Japan, it is the second most common type of cancer and the fourth leading cause of cancer deaths for both men and women. Meanwhile, in China, lung cancer is the top cause of cancer-related deaths, as highlighted by the National Cancer Center’s 2022 report. This study seeks to find out how many genetic mutations are necessary for lung cancer to develop in men and women from these countries. By examining age-specific lung cancer data, we construct multistage models to estimate mutation rates and how cancer cells grow. We also look into the effects of genomic instability and mutator phenotypes on cancer progression. To test our hypotheses, we apply various statistical methods like P-values, Chi-square tests and the Akaike Information Criterion (AIC), where lower P-values provide stronger evidence against the null hypothesis. Our findings indicate that reducing the rate at which cells proliferate could be more effective in lowering lung cancer risk than merely focusing on mutation rates. The number of driver mutations needed for lung cancer varies: in Japan, it ranges from two to nine mutations, around five in China, and between two and six in the United States. These variations are influenced by a mix of lifestyle choices, genetic backgrounds and environmental factors, with gender differences also playing a role in how lung cancer develops.
Strongly S-1-absorbing primary ideals of commutative rings
Authors: Pathak Kamal Krishna , Goswami Jituparna
Published: December 2024

Let [Formula: see text] be a commutative ring with identity and [Formula: see text] be a multiplicative closed subset of [Formula: see text]. In this paper, we introduce and study the concept of strongly [Formula: see text]-1-absorbing primary ideals as subclass of primary ideals. We define a proper ideal [Formula: see text] disjoint with [Formula: see text] to be a strongly [Formula: see text]-1-absorbing primary ideal if there exists an [Formula: see text] such that for all nonunit elements [Formula: see text] and [Formula: see text], we have [Formula: see text] or [Formula: see text]. We characterize local rings and UN-rings with the help of strongly [Formula: see text]-1-absorbing primary ideals. Furthermore, we investigate the transfer of the above properties to trivial ring extensions, amalgamations and bi-amalgamations of rings along an ideal.
Economic Impact of Urban River Restoration on Resident Life Satisfaction
Authors: Dong Yinan , Lazareva Elena
Published: December 2024

Urban river restoration brings about ecological improvements to the urban environment and generates social benefits. However, previous studies have paid insufficient attention to investigating the social value of urban river restoration, especially its impacts on residents’ living conditions. This study aimed to examine how urban river restoration affects residents’ life satisfaction, using life satisfaction data to evaluate the value of river restoration. This study examines the effects of urban river restoration in Nanyang, China, from 2015 to 2019, combining public opinion surveys and water quality monitoring data. Using a difference-in-differences (DID) approach, the results demonstrate a significant positive impact of river restoration on life satisfaction. This improvement is attributed to enhanced water quality, increased recreational opportunities and better aesthetic environments. Robustness checks, including placebo tests and ordered probit models, confirm the validity of the findings. Economic valuation through the life satisfaction approach estimates residents’ willingness to pay (WTP) for river restoration at 24,780 CNY-27,129 CNY (3,591–3,928 USD) per year. For a one standard deviation improvement in water quality, the WTP is 12,631 CNY (1,830 USD) per year for biological oxygen demand (BOD) reduction and 13,116 CNY (1,900 USD) per year for chemical oxygen demand (COD) reduction. These findings underscore the importance of integrating urban river restoration with social well-being considerations, providing policymakers with evidence to prioritize urban river restoration as part of sustainable urban development strategies.
Research on the Sememe-Based Knowledge Representation Integrating Tibetan Features
Authors: Yao Zhou , Zhao Xiaobing
Published: December 2024

The sememe, as the smallest semantic unit, is widely used in knowledge bases to represent world knowledge. HowNet is one of the most well-known sememe-based knowledge bases. In theory, the HowNet sememe system is applicable to all human languages and capable of expressing all world knowledge. However, this claim has yet to be verified. This paper uses specific instances to highlight issues with the HowNet sememe system when applied to Tibetan, such as vague definitions, unclear features, and the lack of sufficient sememes to describe various linguistic phenomena. The paper integrates the Tibetan controllable–uncontrollable relationship into the HowNet sememe system, refining its definitions and features, and subsequently modifies the HowNet knowledge dictionary based on this integration.
A Flat-Span Contrastive Learning Method for Nested Named Entity Recognition
Authors: Liu Yaodi , Zhang Kun , Tong Rong , Cai Chenxi , Chen Dianying , Wu Xiaohe
Published: December 2024

In Natural Language Processing (NLP), it is common for one entity to contain another entity, i.e., nested entities. However, the most commonly used methods can only handle flat entities but not nested entities. To solve this problem, this paper proposes a flat-span contrastive learning method for nested Named Entity Recognition (NER), which consists of two sub-modules: a flat NER module and a candidate span classification module. The flat NER module is used to recognize the outermost entities, and we use star-transformer to capture the long-range dependencies of sentences, and the Conditional Random Field (CRF) to decode the outermost entity spans, contrastive learning is introduced, and the InfoNEC loss function is used to increase the difference between entity spans and nonentity spans. Finally, to improve the model performance and reduce error propagation, we jointly train the flat NER and candidate span classification modules through multi-task learning. Experimental results on the GENIA, GermEval2014, and JNLPBA datasets thoroughly verify the effectiveness of our model, and the ablation experiments further demonstrate the effectiveness of the model components. In the candidate span classification module, we generate all possible candidate spans based on the outermost entities by enumeration to better distinguish entity spans from nonentity.
AAC with Automated Vocabulary from Photographs: Insights from School and Speech-Language Therapy Settings
Authors: Fontana de Vargas Mauricio , Dai Jiamin , Moffatt Karyn
Published: December 2024

Traditional symbol-based AAC devices impose meta-linguistic and memory demands on individuals with complex communication needs and hinder conversation partners from stimulating symbolic language in meaningful moments. This work presents a prototype application that generates situation-specific communication boards formed by a combination of descriptive, narrative, and semantic related words and phrases inferred automatically from photographs. Through semi-structured interviews with AAC professionals, we investigate how this prototype was used to support communication and language learning in naturalistic school and therapy settings. We find that the immediacy of vocabulary reduces conversation partners’ workload, opens up opportunities for AAC stimulation, and facilitates symbolic understanding and sentence construction. We contribute a nuanced understanding of how vocabularies generated automatically from photographs can support individuals with complex communication needs in using and learning symbolic AAC, offering insights into the design of automatic vocabulary generation methods and interfaces to better support various scenarios of use and goals.
Camel:
Efficient Compression of Floating-Point Time Series
Authors: Yao Yuanyuan , Chen Lu , Fang Ziquan , Gao Yunjun , Jensen Christian S. , Li Tianyi
Published: December 2024

Time series compression encodes the information in a time-ordered sequence of data points into fewer bits, thereby reducing storage costs and possibly other costs. Compression methods are either general or XOR-based. General compression methods are time-consuming and are not suitable in streaming scenarios, while XOR-based methods are unable to consistently maintain high compression ratios. Further, existing methods compress the integer and decimal parts of floating-point values as a whole, thus disregarding the different characteristics of the two parts. We propose
Camel
, a new compression method for floating-point time series with the goal of advancing the compression ratios and efficiency achievable.
Camel
compresses the integer and decimal parts of the double-precision floating-point numbers in time series separately; and instead of performing XOR operations on values using their previous value,
Camel
identifies values that enable higher compression ratios.
Camel
also includes means of indexing compressed data, thereby making it possible to query compressed data efficiently. We report on an empirical study of
Camel
and 11 lossless and 6 lossy compression methods on 22 public datasets and three industrial datasets from AliCloud. The study offers evidence that
Camel
is capable of outperforming existing methods in terms of both compression ratio and efficiency and is capable of excellent compression performance on both time series and non-time series data.
Directional Queries: Making Top-k Queries More Effective in Discovering Relevant Results
Authors: Ciaccia Paolo , Martinenghi Davide
Published: December 2024

Top-
k
queries, in particular those based on a linear scoring function, are a common way to extract relevant results from large datasets. Their major advantage over alternative approaches, such as skyline queries (which return all the undominated objects in a dataset), is that the cardinality of the output can be easily controlled through the k parameter and user preferences can be accommodated by appropriately weighing the involved attributes.

In this paper we concentrate on two so-far neglected aspects of top-
k
queries: first, their general ability to return
all
the potentially interesting results, i.e., the tuples in the skyline; second, the difficulty that linear top-
k
queries might encounter in returning tuples with
balanced
attribute values that match user preferences more closely than tuples that are extremely good in one dimension but (very) poor in others. In order to quantify these undesirable effects we introduce four novel indicators for skyline tuples, which measure their robustness as well as the difficulty incurred by top-k queries to retrieve them.

After observing that real datasets usually contain many relevant results that are hardly retrievable by linear top-
k
queries, and with the aim of favoring balanced results, we extend the queries with a term that accounts for the
distance
of a tuple from the preference direction established by the attributes' weights. This novel query, which we call
directional query,
adds the flexibility needed to allow
each
skyline tuple to be ranked first for a proper choice of weights, with no extra burden on the user and, in the most adverse scenarios, only a minor computational overhead, as measured through an extensive experimental analysis on real and synthetic data.
SPID-Join: A Skew-resistant Processing-in-DIMM Join Algorithm Exploiting the Bank- and Rank-level Parallelisms of DIMMs
Authors: Lee Suhyun , Lim Chaemin , Choi Jinwoo , Choi Heelim , Lee Chan , Park Yongjun , Park Kwanghyun , Kim Hanjun , Kim Youngsok
Published: December 2024

Recent advances in Dual In-line Memory Modules (DIMMs) allow DIMMs to support Processing-In-DIMM (PID) by placing In-DIMM Processors (IDPs) near their memory banks. Prior studies have shown that in-memory joins can benefit from PID by offloading their operations onto the IDPs and exploiting the high internal memory bandwidth of DIMMs. Aimed at evenly balancing the computational loads between the IDPs, the existing algorithms perform IDP-wise global partitioning on input tables and then make each IDP process a partition of the input tables. Unfortunately, we find that the existing PID join algorithms achieve low performance and scalability with skewed input tables. With skewed input tables, the IDP-wise global partitioning incurs imbalanced loads between the IDPs, making the IDPs remain idle until the heaviest-load IDP completes processing its partition. To fully exploit the IDPs for accelerating in-memory joins involving skewed input tables, therefore, we need a new PID join algorithm which achieves high skew resistance by mitigating the imbalanced inter-IDP loads. In this paper, we present SPID-Join, a skew-resistant PID join algorithm which exploits two parallelisms inherent in DIMM architectures, namely bank- and rank-level parallelisms. By replicating join keys across the banks within a rank and across ranks, SPID-Join significantly increases the internal memory bandwidth and computational throughput allocated to each join key, improving the load balance between the IDPs and accelerating join executions. SPID-Join exploits the bank- and the rank-level parallelisms to minimize join key replication overheads and support a wider range of join key replication ratios. Despite achieving high skew resistance, SPID-Join exhibits a trade-off between the join key replication ratio and the join execution latency, making the best-performing join key replication ratio depend on join and PID system configurations. We, therefore, augment SPID-Join with a cost model which identifies the best-performing join key replication ratio for given join and PID system configurations. By accurately modeling and scaling the IDPs' throughput and the inter-IDP communication bandwidth, the cost model accurately captures the impact of the join key replication ratio on SPID-Join. Our experimental results using eight UPMEM DIMMs, which collectively provide a total of 1,024 IDPs, show that SPID-Join achieves up to 10.38x faster join executions over PID-Join, the state-of-the-art PID join algorithm, with highly skewed input tables.
CtxPipe: Context-aware Data Preparation Pipeline Construction for Machine Learning
Authors: Gao Haotian , Cai Shaofeng , Dinh Tien Tuan Anh , Huang Zhiyong , Ooi Beng Chin
Published: December 2024

Machine learning models are only as good as their training data. Simple models trained on well-chosen features extracted from the raw data often outperform complex models trained directly on the raw data. Data preparation pipelines, which clean and derive features from the data, are therefore important for machine learning applications. However, constructing such pipelines is a resource-intensive process that involves deep human expertise.

Our goal is to design an efficient framework for automatically finding high-quality data preparation pipelines. The main challenge is how to explore a large search space of pipeline components with the objective of computing features that maximize the performance of the downstream models. Existing solutions are limited in terms of feature quality, which results in low accuracies of the downstream models, while incurring significant runtime overhead. We present CtxPipe, a novel framework that addresses the limitations of previous works by leveraging contextual information to improve the pipeline construction process. Specifically, it uses pre-trained embedding models to capture the data semantics, which are then used to guide the selection of pipeline components. We implement CtxPipe with deep reinforcement learning and evaluate it against state-of-the-art automated pipeline construction solutions. Our comprehensive experiments demonstrate that CtxPipe outperforms all of the baselines in both model performance and runtime cost.
Connecting the Unconnected
Authors: Beck Micah D. , Bentley Brenna , Roskowski Anika
Published: December 2024

Micah D. Beck, Brenna Bentley, and Anika Roskowski look to LiteLoad to increase the resilience of the World Wide Web.
Wireless Charging Scheduling for Long-term Utility Optimization
Authors: Xu Jia , Chen Wenbin , Dai Haipeng , Xu Lijie , Xiao Fu , Liu Linfeng
Published: December 2024

Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have investigated the problem of long-term charging scheduling with random variable. This article designs an optimization model for the long-term scheduling of chargers to maximize the time-averaged charging utility while ensuring its time-averaged constraints of budget and response rate. The Lyapunov optimization technique is adopted to transform the stochastic optimization problem into a deterministic optimization problem, which remains NP-hard. Thus, an approximation algorithm following greedy approach is proposed to solve the deterministic optimization problem. We further provide the theoretical analysis of feasibility and performance guarantee of the proposed scheduling algorithm. The simulation results show that our algorithm outperforms three comparison algorithms by 6.53%, 20.04%, and 19.97% in terms of time-averaged charging utility, as well as by 11.25%, 4.42%, and 3.73% in terms of time-averaged response rate on average.
Data Mining-Driven Shift Enumeration for Accelerating the Solution of Large-Scale Personnel Scheduling Problems
Authors: Rastgar-Amini Farin , Aloise Daniel , Contardo Claudio , Desaulniers Guy
Published: December 2024

This study addresses large-scale personnel scheduling problems in the service industry by combining mathematical programming with data mining techniques to enhance efficiency. The studied problem aims at efficiently scheduling skilled employees over a one-week planning horizon, minimizing costs while meeting diverse job demands. In service industries, shift planning is intricately tied to customer presence, leading to a multitude of potential shifts and a difficult optimization problem that cannot be easily solved using a commercial mixed-integer programming solver. Nevertheless, these problems are categorized as recurrent problems, where distinct instances share common characteristics and solution structures that differ only in a few parameters over time. We propose to use a data mining technique, namely, the

\(k\)

-nearest neighbors algorithm, to expedite the solution process while upholding solution quality. We suggest using schedules of past solutions to reduce the problem size. Thus, for an upcoming instance, we identify similar historical instances and streamline the enumeration of shifts to align with the comparable historical instances’ schedules. This approach allows us to solve the problem using a commercial solver within a reasonable timeframe while preserving solution quality. Moreover, our methodology offers decision-makers the flexibility to determine the extent to which they wish to scale down the problem. Our experiments conducted on instances generated from real historical data with up to 12 jobs and 252 employees, yield an average removal of up to 85.5% of decision variables. This resulted in an average speedup factor of up to 15.5, with a marginal average cost increase of approximately 1.2%.
RUL Prediction of Lithium-Ion Batteries based on Combined Network Model Considering Partial Charge and Discharge Data
Authors: Sun Jing , Yan Huiyi
Published: December 2024

Lithium-ion batteries are widely used in new energy vehicles, but capacity regeneration and fluctuations during aging affect the accuracy of remaining useful life (RUL) prediction. Complete charge/discharge data are often unavailable during actual usage. To address these issues, this paper proposes a combined model for RUL prediction using partial charge/discharge data. Five health indicators are extracted from the voltage vs time curve and processed using variational mode decomposition to remove outliers and noise, improving the correlation between HIs and battery capacity. Spearman’s correlation coefficient verifies the relationship between HIs and capacity. The Kolmogorov-Arnold Networks-Structured State Space model (KAN-S4) is then developed, capturing spatial correlations and long-term degradation patterns. Experimental validation using data from our laboratory and the University of Maryland's CALCE center shows that the KAN-S4 model achieves accurate RUL predictions, even under complex conditions like capacity regeneration and rapid decline. The model demonstrates strong robustness and generalization across varying usage scenarios.
Shear stress-induced restoration of pulmonary microvascular endothelial barrier function following ischemia reperfusion injury requires VEGFR2 signaling
Authors: Walsh Don , Kostyunina Daria S. , Blake Aoife , Boylan John , McLoughlin Paul
Published: December 2024

Critical illnesses are frequently complicated by noncardiogenic pulmonary edema. Many such illnesses include periods of reduced blood flow, often accompanied by hemodilution, which together reduce endothelial shear stress. We report that in ischemia-reperfusion injury reduced shear stress contributes to increased permeability of the in situ pulmonary microvascular endothelium and worsens alveolar edema. Restoring shear stress toward normal reduces endothelial permeability and edema formation, an effect that requires the normal mechanoreceptor function of VEGFR2.
Skeletal muscle inosine monophosphate formation preserves ΔGATP during incremental step contractions in vivo
Authors: Smith Zoe H. , Hayden Christopher M. T. , Hayes Kate L. , Kent Jane A.
Published: December 2024

An ∼9% decline in [ATP] led to the formation of inosine monophosphate (IMP) during submaximal muscular contractions. Modeling revealed IMP formed to preserve a favorable energy state (ΔGATP) by minimizing large increases in [ADP], whereas the loss of [ATP] did not alter ΔGATP. [ATP] did not recover by 10 min, and the loss of [ATP] was associated with a reduced oxidative capacity, providing a new link between [ATP] loss and an impaired energetic capacity in vivo.
Intestinal barrier function declines during polycystic kidney disease progression
Authors: Sedaka Randee , Lovelady Caleb , Hallit Emily , Duyvestyn Branden , Shinde Sejal , Moran-Reyna Aida , Lee Goo , Yamaguchi Shinobu , Maynard Craig L. , Saigusa Takamitsu
Published: December 2024

Though autosomal dominant polycystic kidney disease (ADPKD) is a multisystem disorder, this is the first study to explore a kidney-gut contribution to disease progression. We identified a tightened intestinal epithelial barrier in early PKD, which becomes leaky as kidneys become more cystic, accompanied by a sustained loss of fecal water. Given the only approved ADPKD therapeutic yields adverse aquaretic events, this study emphasizes the need to evaluate extrarenal water loss in patients before prescribing.
Genetic tools that target mechanoreceptors produce reliable labeling of bladder afferents and altered mechanosensation
Authors: Tran Emily L. , Stuedemann Sara A. , Ridlon Monica , Link Olivia D. , Keil Stietz Kimberly P. , Crawford LaTasha K.
Published: December 2024

Most mechanosensitive afferents do not express markers of peptidergic nociceptors and therefore remain largely overlooked in studies of bladder dysfunction and disease. TrkB-mediated labeling of putative Aδ mechanoreceptors emerged as a valuable tool for the study of neuropeptide-negative bladder afferents with a confirmed role in bladder mechanosensation. Targeted neuronal ablation likewise validated an intersectional genetic strategy that can now directly test the role of TrkB mechanoreceptors in bladder physiology and disease.
Expression and distribution of MUC1 in the developing and adult kidney
Authors: Manrique-Caballero Carlos L. , Barasch Jonathan , Zaidi Syed K. , Bates Carlton M. , Ray Evan C. , Kleyman Thomas R. , Al-Bataineh Mohammad M.
Published: December 2024

MUC1 is a transmembrane glycoprotein expressed on the apical surface of polarized epithelial tissues and most carcinomas. MUC1 may play novel roles in the kidney including defense against infections. Here, we examine the expression of MUC1 in mouse and human kidneys. We show that the distal nephron and the urinary system are the predominant sites of expression of both message and protein, implicating segment-specific roles including distal nephron defense against ascending bacteria.
Remote organ cancer induces kidney injury, inflammation, and fibrosis and adversely alters renal function
Authors: Hammouri Dana , Orwick Andrew , Doll Mark A. , Sanchez Vega Dianet , Shah Parag P. , Clarke Christopher J. , Clem Brian , Beverly Levi J. , Siskind Leah J.
Published: December 2024

Patients with cancer frequently experience kidney complications, often attributed to antineoplastic therapies. This emphasis on therapy-induced nephrotoxicity has led to the underestimation of the impact of cancer on the kidney. Our study demonstrates that distant organ cancer is sufficient to induce nephrotoxicity, highlighting the existence of cancer-kidney crosstalk. Our findings underscore a gap in our understanding of renal complications in patients with cancer and provide a rationale for identifying the underlying mechanisms for the development of nephroprotective agents.
The discharge characteristics of motor units innervating functionally paralyzed muscles
Authors: Oliveira Daniela Souza De , Carbonaro Marco , Raiteri Brent James , Botter Alberto , Ponfick Matthias , Del Vecchio Alessandro
Published: December 2024

Here, we observed a similar pattern of motor unit activation during attempted hand movements in individuals with complete SCI, who cannot move their fingers, and in a control group, who performed the prescribed movements. Despite differences in individual motor unit behavior between these groups, such as a higher proportion of nonmodulated motor units in SCI, movement intention can still be decoded from paralyzed muscles.
Associative brain-computer interface training increases wrist extensor corticospinal excitability in patients with subacute stroke
Authors: Svejgaard Benjamin , Modrau Boris , Hernández-Gloria José Jesús , Wested Carina L. , Dosen Strahinja , Stevenson Andrew J. T. , Mrachacz-Kersting Natalie
Published: December 2024

We have demonstrated that a single training session with an associative brain-computer interface increased corticospinal excitability in patients suffering from upper limb weakness following stroke. This is the first time such an effect is described in the upper limb, which paves the way for effect augmentation of existing upper limb rehabilitation protocols.
Prevalence and Influencing Factors of Severe Depression in Nurses During and After the COVID‐19 Pandemic: A Large‐Scale Multicenter Study
Authors: Liu Jiao , Liu Qinghe , Ji Lijie , Yang Yuan , Zhang Ran , Ding Yu , Luo Guoshuai , Sun Daliang
Published: December 2024

Background: During the COVID‐19 epidemic, nurses are facing tremendous psychological pressure. However, there is a lack of research on severe depression and its related factors in nurses after COVID‐19. This study aimed to compare the prevalence and explore the influencing factors of severe depression among Chinese nurses during and after the COVID‐19 pandemic.

Methods: This study recruited 937 frontline nurses during the outbreak and 784 frontline nurses who had participated in the initial recruitment after the pandemic. The self‐rating depression scale (SDS), self‐rating anxiety scale (SAS), and Pittsburgh Sleep Quality Index (PSQI) were used to assess subjects’ depression, anxiety, and sleep disorders, respectively. Chi‐square test, t‐test, and binary logistic regression were used to identify factors influencing severe depression. Receiver operating characteristic (ROC) curves were used to assess the predictive value of severe depression‐related variables.

Results: The study found that the incidence of severe depression among nurses after the epidemic (4.9%) was significantly higher than that during the epidemic (1.7%). In addition, academic qualifications, health status, frequency of exercise per week, anxiety, and sleep disorders were associated with severe depression among nurses after the COVID‐19 epidemic. ROC analyses showed that SAS scores and PSQI scores had good predictive value for nurses’ severe depression after the pandemic.

Conclusions: The prevalence of severe depression among nurses after COVID‐19 is higher than that during COVID‐19. Nurses continue to face severe psychological stress after the COVID‐19 pandemic. Therefore, it is desperately needed for nurses to provide timely psychological support and establish a mental health support system after the COVID‐19 epidemic.
Affective and Clinical Outcomes Related to Pain After Graded Motor Imagery in Patients With Chronic Shoulder Pain: A Pre–Post‐Single‐Group Study
Authors: Araya-Quintanilla Felipe , Gutiérrez-Espinoza Héctor , Méndez-Rebolledo Guillermo , Cavero-Redondo Iván , Álvarez-Bueno Celia , Stasinopoulos Dimitrios
Published: December 2024

Objective: The aim of this study was to assess at 6‐month and 1‐year follow‐up the effect of graded motor imagery (GMI) in addition to usual care on the affective and clinical outcomes in patients with chronic shoulder pain.

Methods: A pre–post‐intervention single‐group study was conducted. One hundred forty‐eight patients with chronic shoulder pain were included. All participants received a 6‐week GMI program in addition to usual care. The primary outcome assessed was pain intensity using visual analog scale (VAS), the secondary outcomes were fear of movement with the Tampa Scale of Kinesiophobia (TSK), catastrophization with the pain catastrophization scale (PCS), shoulder flexion active range of motion (AROM) with a goniometer, and central sensitization with the central sensitization inventory (CSI). All outcomes were assessed at baseline and 6‐month and 1‐year follow‐up.

Results: At 6 months, GMI showed to be statistically significant for all outcomes assessed (p < 0.001). At 1‐year follow‐up, the VAS showed a decrease of 3.3 cm (p < 0.001), TSK showed a decrease of 16.1 points (p < 0.001), PCS showed a decrease of 17.4 points (p < 0.001), AROM showed an increase of 29.9° (p < 0.001), and CSI showed a decrease of 17.9 (p < 0.001).

Conclusions: At medium‐ and long‐term follow‐up, the individuals who received the GMI program in addition to usual care showed a clinically and statistically significant change for all outcomes assessed. Further studies, including clinical trials, are needed to confirm our findings.