bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Receiver Optics for Radio Astronomy
Authors: Yeap Kim Ho , Loh Mey Chern , Teh Peh Chiong , Dakulagi Veerendra
Published: February 2025

In a radio telescope, the sub-reflector is illuminated by a complex feed system, consisting of a feed horn and a pair of optics focusing elements which are usually a pair of mirrors. Although rigorous analysis of this system can be performed using Method of Moments (MoM) or physical optics (POs), design optimization using these methods may not be viable, since it requires lengthy computational time. In this paper, we describe an efficient optimization technique for the optics design which applies the quadratic on a pedestal distribution to compute the taper and aperture efficiencies. In our method, multimode Gaussian optics is employed to calculate the electromagnetic waves which scatter through the optical system. The edge taper associated with the optimum aperture efficiency is first identified. By setting the parameters of this edge taper and also the distance between mirror 2 and the antenna focus as the iteration targets, a root-searching routine is then applied to determine the distances of the optical paths between the mirrors and the feed. When an optimized feed design is established, the antenna performance indicators, such as the beam efficiency, co- and cross-polarization levels, and aperture efficiencies, are calculated using PO. In this way, we combine the accuracy of the quadratic function in determining the antenna efficiencies and the computational efficiency of Gaussian optics to optimize the design of the system with the rigor of PO to validate the final parameters of the antenna. The design procedure for the Atacama Large Millimeter/Submillimeter Array (ALMA) interferometric radio telescope’s feed optics system is used as an illustrative example. The results show that the co-polar beam efficiencies achieved with the proposed method are higher than those of the original method used for the ALMA feed optics system, while the cross-polar beam efficiencies are lower. This suggests a substantial improvement offered by the newapproach.
Performance Comparison Between Fingerprinting-based RSS Indoor Localization Techniques at WLAN Frequencies: Simulation Study
Authors: Obeidat Huthaifa , Alzuraiqi Eyad , Trrad Issam , Alhindawi Nouh , Rawashdeh Mohammad R.
Published: February 2025

This paper presents a performance comparison between two fingerprinting-based received signal strength (RSS) indoor localization techniques at wireless local-area network (WLAN) frequencies: 2.4 GHz and 5.8 GHz. The investigated algorithms include the comparative RSS (CRSS) and vector algorithms. The study was conducted using Wireless InSite ray-tracer software. The simulation was conducted in a simulated environment on the 3rd floor of the Chesham Building, University of Bradford, UK. Also, we presented an estimator which looks at the correlation between the test point RSS and the reference point RSS. The estimator performance is compared to the root mean square error (RMSE) performance. It was found that the CRSS algorithm suffers from the similarity problem while constructing the radio map, and it also suffers from ambiguity problems during localization. The vector algorithm outperforms CRSS algorithms in both frequencies and does not suffer from similarity or ambiguity problems. The proposed estimator shows better performance at both frequencies.
Analysis of Induction-polarization Response Characteristics of Marine Controlled-source Electromagnetic Multiphase Composite Medium
Authors: Gu Chunying , Li Suyi , Peng Silun
Published: February 2025

The complex composition and structure of the submarine reservoir and its secondary pyrite, which are multiphase composite media, can provoke the induced polarization (IP) effect, resulting in the change of the marine controlled-source electromagnetic (CSEM) induction-polarization response, which directly affects geological interpretation results. In this paper, the generalized effective-medium theory of induced polarization (GEMTIP) model is introduced to study the influence of composition, structure and geometric characteristics of submarine reservoirs and secondary pyrite on the 3D marine CSEM induction-polarization response. We first construct the 3D finite-difference frequency-domain (FDFD) electromagnetic field governing equation based on the GEMTIP model, apply the emission source on the non-uniform grid, and solve the linear equations by using the difference coefficient matrix. Then we perform forward calculation on a typical 1D reservoir model to verify the correctness and effectiveness of the proposed algorithm. Finally, we design the reservoir and secondary pyrite models, and analyze the influence laws of IP parameters and polarization layer geometry parameters on the 3D marine CSEM induction-polarization response. These studies are of great value for understanding the relationship between submarine multiphase composite medium and electromagnetic wavepropagation.
Comparing the performance of a large language model and naive human interviewers in interviewing children about a witnessed mock-event
Authors: Sun Yongjie , Pang Haohai , Järvilehto Liisa , Zhang Ophelia , Shapiro David , Korkman Julia , Haginoya Shumpei , Santtila Pekka
Published: February 2025

Purpose

The present study compared the performance of a Large Language Model (LLM; ChatGPT) and human interviewers in interviewing children about a mock-event they witnessed.

Methods

Children aged 6-8 (N =  78) were randomly assigned to the LLM (n =  40) or the human interviewer condition (n =  38). In the experiment, the children were asked to watch a video filmed by the researchers that depicted behavior including elements that could be misinterpreted as abusive in other contexts, and then answer questions posed by either an LLM (presented by a human researcher) or a human interviewer.

Results

Irrespective of condition, recommended (vs. not recommended) questions elicited more correct information. The LLM posed fewer questions overall, but no difference in the proportion of the questions recommended by the literature. There were no differences between the LLM and human interviewers in unique correct information elicited but questions posed by LLM (vs. humans) elicited more unique correct information per question. LLM (vs. humans) also elicited less false information overall, but there was no difference in false information elicited per question.

Conclusions

The findings show that the LLM was competent in formulating questions that adhere to best practice guidelines while human interviewers asked more questions following up on the child responses in trying to find out what the children had witnessed. The results indicate LLMs could possibly be used to support child investigative interviewers. However, substantial further investigation is warranted to ascertain the utility of LLMs in more realistic investigative interview settings.
Synthesis and Characterization of Cobalt(II) Complex of Polyamido-Amine (PAMAM) Decorated Azomethine Ligand: In Vitro Biological Evaluation
Authors: Patle R.Y. , Selokar Y.N. , Meshram K.K. , Dongre R.S.
Published: February 2025

A new cobalt(II) complex based on polyamido-amine (PAMAM) dendrimer was modified with 2-hydroxy-3-methoxybenzaldehyde. The structural elucidation of synthesized ligand and its complex were performed by the FTIR, NMR, Mass, EDX, TGA/DTA and UV-Vis analyses. The in vitro antimicrobial investigation of the synthesized complex was performed against Gram-positive Staphylococcus aureus, Gram-negative Escherichia coli bacterial strains and fungal strain Candida albicans by well-diffusion method. The antibiotic tetracycline (10 mg/mL) for antibacterial and fluconazole antibiotic for antifungal potency were used as reference standard. The results have revealed that the PAMAMM-G0-OV-Co(II) complex has shown excellent inhibitory antibacterial action against S. aureus with highest zone of inhibition of 16 mm and E. coli with highest zone of inhibition of 21 mm at 10 mg/mL of concentration. Similarly, the complex has shown exceptional antifungal activity with average zone of inhibition of 15 mm against Candida albicans fungal strain. Thus, the dendritic cobalt complex has shown antibacterial and antifungal activity at a considerable level at 10 mg/mL concentration.
Association of Body Mass Index (BMI) and Severity of COVID- 19: A Multicentric Study from Maharashtra, India
Authors: Talapalliwar M. , Menon P. , Padole W. , Thakur S. , Phadke M. , Saunik S.
Published: February 2025

Objective
The COVID-19 pandemic has led to the global research efforts to identify the risk factors which would lead to severe COVID-19 disease. This study aimed to investigate the relationship between body mass index (BMI) and severity of coronavirus disease 2019 (COVID-19).

Methods
A total of 2820 patients hospitalized with COVID-19 disease in nine Government Medical Colleges, COVID hospitals or COVID care centers of Maharashtra were included in the study. Interviews were conducted on the telephone and counselling was done.

Results
Among 2820, 2442 (86.60%) were asymptomatic or had a mild or moderate illness. More than half of the total COVID- 19 positive cases, 1591 (56.41%), were in the normal BMI range, and of these only 183 (11.50%) had severe COVID-19 disease. On the other hand, 647 (22.94%) patients were overweight and 106 (16.38%) had Severe COVID- 19 disease. Similarly, the patients who were obese, 363 (84.03%) were asymptomatic and 69 (15.97%) had severe COVID- 19 disease. Odds ratio for this association was found as 1.5, which indicates that patients who were obese (BMI ≥ 27.5) had one and half times increased odds of progression to severity as compared to patients having normal BMI. This association was found to be statistically significant (p- value < 0.05).

Conclusion
Our findings highlight the significant role of BMI in clinical progression of COVID-19 disease. Patients with underweight and obesity experienced more severe outcomes than those of normal weight when being hospitalized with COVID-19 disease.
Improving the Real-time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion
Authors: Lo Bobby , Møller Bjørn , Igel Christian , Wildt Signe , Vind Ida , Bendtsen Flemming , Burisch Johan , Ibragimov Bulat
Published: February 2025

Objective:

Endoscopic classification of ulcerative colitis (UC) shows high interobserver variation. Previous research demonstrated that artificial intelligence (AI) can match the accuracy of central reading in scoring still images. We now extend this assessment to longer colon segments and integrate AI into clinical workflows, evaluating its use for real-time, video-based classification of disease severity, and as a support system for physicians.

Methods:

We trained a convolutional neural network with the Mayo Endoscopic Subscores (MES) of 2,561 images and 53 videos from 645 patients. The model differentiated scoreable from unscoreable endoscopy sections through open-set recognition. Validation involved 140 video clips from 44 UC patients. Six inflammatory bowel disease (IBD) experts and 16 non-experts rated these videos, with expert scores as the gold standard. We assessed the model’s performance and the value as a supporting system. Lastly, the model underwent an alpha test on a real-world patient as a real-time endoscopic support.

Results:

The model achieved an accuracy of 82%, with no significant differences between the experts and the AI. When used as a supporting system, it improved non-IBD experts' performance by 12% and disagreed with the primary physician in 20-39% of cases. During the alpha test, it was successfully integrated into clinical practice, accurately distinguishing between MES 0 and MES 1, consistent with endoscopists' assessments.

Conclusions:

Our innovative AI model shows significant potential for enhancing the accuracy of UC severity classification and improving the proficiency of non-IBD experts. It is designed for clinical use and has proven feasible in real-world testing.
Impact of Surveillance Colonoscopy Timing on Colorectal Cancer Incidence Following Advanced Adenoma Resection
Authors: Honda Munenori , Oda Yasushi , Gushima Ryosuke , Miyamoto Hideaki , Naoe Hideaki , Hasuda Kiwamu , Sakurai Kouichi , Murakami Yoshitaka , Tanaka Yasuhito
Published: February 2025

Introduction:

Index colonoscopy findings can affect surveillance decisions that have an impact on the future risk of colorectal cancer (CRC). However, the effectiveness of surveillance colonoscopy in reducing CRC risk remains unclear. This study aimed to evaluate whether the timing of the first surveillance colonoscopy affected CRC incidence rates.

Methods:

We conducted a retrospective cohort study at two community endoscopy clinics between 2005 and 2021, including 20,397 individuals. Based on the findings at index colonoscopy, we categorized into four groups: advanced adenoma (AA), ≥3 non-AAs, 1–2 non-AAs, and no adenoma. We compared the cumulative incidence of CRC among these groups, focusing on whether the first surveillance interval was less than or greater than 3 years after index colonoscopy.

Results:

Index colonoscopy findings identified 11,601 subjects with no adenoma, 5,288 with 1–2 non-AA, 1,484 with ≥3 non-AA, and 2,024 with AA. There were 50 cases of CRC: 23 in the no adenoma, 10 in the 1–2 non-AA, six in the ≥3 non-AA, and 11 in the AA group. First surveillance after 3 years was associated with a higher incidence of subsequent CRC in the AA group (hazard ratio, 3.49; 95% confidence interval, 1.06–11.46; P=0.04).

Discussion:

In the AA group, surveillance colonoscopy within 3 years was associated with a lower risk of CRC. These findings suggest that a first surveillance within 3 years is particularly effective in patients with AA.
Accuracy of Visual Estimation for Measuring Colonic Polyp Size: A Systematic Review and Meta-Analysis
Authors: Cheloff Abraham Z. , Kim Leah , Pochapin Mark B. , Shaukat Aasma , Popov Violeta
Published: February 2025

Background:

Measurement of colorectal polyps is typically performed via visual estimation, which is prone to bias. Studies have evaluated the accuracy of visual estimation and utility of assistive tools, but results have been mixed. This study aims to clarify the accuracy of visual estimation as a measurement tool, and the benefits of artificial intelligence.

Methods:

MEDLINE and Embase were searched through October 2024. Extraction and quality assessment were performed independently by two authors. The primary outcome was the pooled absolute mean difference in size between visual estimation and control. Secondary outcomes included subgroup analysis of expert vs trainee status, accuracy of artificial intelligence, study origin (East vs. West), comparator type, definition of accuracy, polyp size, direction of estimation, and image type.

Results:

35 studies with 42,964 polyp measurements were included in our analysis. All studies were of high quality and there was no evidence of publication bias. The pooled absolute mean difference from comparator was 1.68mm (CI 1.21-2.15) with high variability explained by differences in the comparator, the direction of estimation, image type, and size of the polyp. Overall accuracy was 60% with high variability as well, with increased accuracy with video displayed over photos. Artificial intelligence improved accuracy with an odds ratio of 7.46.

Conclusion:

Visual estimation is an inaccurate and imprecise way to measure colorectal polyps. Further research is needed to determine the impact on clinical outcomes related to colorectal cancer. Investment in new technology to aid in polyp measurement is an important next step.
The current state of natural resources Ferula tadshikorum Pimenov in Uzbekistan
Authors: Mahmudov Azizbek , Tojibaev Komiljon , Abduraimov Ozodbek , Mavlanov Bekzod , Allamurotov Akmal
Published: February 2025

The article is devoted to studying natural reserves of raw materials of the endemic of Southwestern Pamir-Alai Ferula tadshikorum in the territory of the Republic of Uzbekistan. to excessive harvesting, the population of F. tadshikorum in the Republic of Uzbekistan is in a critical condition; the area of thickets and reserves of raw materials continues to decline. Compared with the indicators of 2020–2023, the presence of individuals in the monitoring sites (10×10 m2) of F. tadshikorum in the territory of Kashkadarya region revealed a decrease in the total number of individuals by 56.1%. Accordingly, these indicators correlate with operational individuals since by 2023, the maturity of the plants was reduced by 65.3% and operational by 71%. These indicators in the Surkhandarya region were reduced from 57.3% to 75%. Compared to resource indicators in 2015, in 2023, the area of commercial thickets decreased by 96% and the stock of raw materials decreased by 83%. Indicators of resource value F. tadshikorum in Uzbekistan is a biological reserve, and these reserves are not subject to exploitation today. Currently, the populations of the species are mainly preserved in the border zone, or the neutral zone of the state border of the republic and in some areas of forestry.
Increasing the shelf life of tomato fruits using physical, chemical and genetic modification methods
Authors: Murodov A A , Ayubov M S , Obidov N S H , Mamajonov B O , Yusupov A N , Bashirxonov Z H , Kamalova L K , Kushakov S O , Buriev Z T , Abdurakhmonov I Y
Published: February 2025

The tomato is one of the most consumed vegetables and is rich in numerous beneficial and nutritious compounds. As climacteric fruits, tomatoes undergo significant metabolic changes during their growth and ripening. During fruit ripening, irreversible changes occur in the color, taste and appearance of the fruit. Shortly after ripening, the fruit begins to lose its shape and structural integrity. Approximately 50% of ripe tomatoes do not reach consumers. The primary cause of this loss is excessive fruit softening, which compromises the integrity of tomatoes during harvesting and transportation, making them susceptible to fungal and bacterial infections. Generally, fruit softening results from increased enzymatic activity that breaks down the fruit cell wall. Currently, chemical, physical and biotechnological methods are employed to extend tomato shelf life. These methods help reduce or inhibit the enzymatic activity responsible for fruit softening. The review provides a concise overview of these preservation methods. We focus on enhancing fruit preservation through plant genome modifications using modern biotechnological techniques, such as RNA interference (RNAi) and CRISPR/Cas9. Additionally, we will briefly discuss the advantages and limitations of these genetic engineering approaches.
Assessment of suitability of ber (Ziziphus mauritiana L.) cultivars for value addition through dehydration technology
Authors: Das U , Nidoni U R , Kurubar A R , Prasad S S , Balaji M
Published: February 2025

This novel experiment assessed the suitability of twelve Indian jujube (Ziziphus mauritiana L.) cultivars for value addition through dehydration technology. Fresh fruit samples were collected randomly from the plants and subjected to dehydration. Five qualitative characters such as total soluble solids (TSS), pH of fruit pulp, moisture content, ascorbic acid content and phenol content were analyzed along with organoleptic evaluation of fresh and dehydrated ber cultivars. Significantly higher TSS among fresh ber was found in Chhuhara (23.96 °Brix) and dehydrated ber was in Sanaur-2 (29.91 ° Brix). The highest pH among the fresh and dehydrated cultivars was recorded in Illaichi (4.73), followed by Dandan (4.70). The highest ascorbic acid content was recorded in fresh Seb (108.73 mg/100 g of fresh fruit) and dehydrated Kadaka (58.55 mg/100 g of dry fruit pulp). Significantly, the highest phenol content among fresh and dehydrated ber was in Seb 219.40 mg/100 g of fresh fruit pulp and 236.24 mg/100 g of dry fruit pulp, respectively. In the organoleptic evaluation, Kadaka ranked first, retaining a high ascorbic acid content even after dehydration. Based on the present study, Kadaka appears to be the best cultivar for dehydration, followed by Seb and Gola.
Quantifying the economic impact and management strategies for foot rot (Phytophthora capsici L.) disease on black pepper cultivation in West Coast India: Farm-level insights
Authors: Bhat S , Arunachalam V , Paramesha V , Gaonkar N
Published: February 2025

Foot rot disease, caused by the soil-borne fungus Phytophthora capsici, poses a severe threat to black pepper cultivation, particularly in the Western Ghats region of India. The current study aims to comprehensively assess the economic impact of foot rot disease on black pepper cultivation in Goa and coastal Karnataka, focusing on farm-level losses, considering both present year yield losses and replacement costs given the perennial nature of the vine. The findings reveal an average vine mortality rate of 9.64% due to foot rot, leading to an economic loss of USD 902.04/ha, equivalent to 56% of annual net returns. Using a dummy variable regression model, the effectiveness of various management practices employed by farmers in mitigating foot rot disease was assessed. The study shows that adopting management practices, such as avoiding water stagnation and ensuring good drainage, reduced losses caused by foot rot by 24% (USD 439/ha), while the use of fungicides led to a 20% reduction (USD 364/ha and in the absence of foot rot management strategies, the loss amounted to USD 1838/ha. In conclusion, educating farmers and promoting the adoption of integrated disease management practices can reduce foot rot losses, thereby enabling sustainable production and improved income from black pepper.
RED: Effective Trajectory Representation Learning with Comprehensive Information
Authors: Zhou Silin , Shang Shuo , Chen Lisi , Jensen Christian S. , Kalnis Panos
Published: February 2025

Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory classification, and travel-time estimation. However, existing TRL methods often produce vectors that, when used in downstream tasks, yield insufficiently accurate results. A key reason is that they fail to utilize the comprehensive information encompassed by trajectories. We propose a self-supervised TRL framework, called RED, which effectively exploits multiple types of trajectory information. Overall, RED adopts the Transformer as the backbone model and masks the constituting paths in trajectories to train a masked autoencoder (MAE). In particular, RED considers the moving patterns of trajectories by employing a

R
oad-aware masking strategy

that retains key paths of trajectories during masking, thereby preserving crucial information of the trajectories. RED also adopts a

spatial-temporal-user joint
E
mbedding

scheme to encode comprehensive information when preparing the trajectories as model inputs. To conduct training, RED adopts

D
ual-objective task learning

: the Transformer encoder predicts the next segment in a trajectory, and the Transformer decoder reconstructs the entire trajectory. RED also considers the spatial-temporal correlations of trajectories by modifying the attention mechanism of the Transformer. We compare RED with 9 state-of-the-art TRL methods for 4 downstream tasks on 3 real-world datasets, finding that RED can usually improve the accuracy of the best-performing baseline by over 5%.
Accurate and Fast Approximate Graph Pattern Mining at Scale
Authors: Arpaci-Dusseau Anna , Zhou Zixiang , Chen Xuhao
Published: February 2025

Approximate graph pattern mining (A-GPM) is an important data analysis tool for numerous graph-based applications. There exist sampling-based A-GPM systems to provide automation and generalization over a wide variety of use cases. Despite improved usability, there are two major obstacles that prevent existing A-GPM systems being adopted in practice. First, the termination mechanism that decides when to terminate sampling lacks theoretical backup on confidence, and performs significantly unstable and thus slow in practice. Second, they particularly suffer poor performance when dealing with the "needle-in-the-hay" cases, because a huge number of samples are required to converge, given the extremely low hit rate of their lazy-pruning strategy and fixed sampling schemes.

We build ScaleGPM, an accurate and fast A-GPM system that removes the two obstacles. First, we propose a novel on-the-fly convergence detection mechanism to achieve stable termination and provide theoretical guarantee on the confidence, with negligible online overhead. Second, we propose two techniques to deal with the "needle-in-the-hay" problem,
eager-verify
and
hybrid sampling.
Our eager-verify method drastically improves sampling hit rate by pruning unpromising candidates as early as possible. Hybrid sampling further improves performance by automatically choosing the better scheme between fine-grained and coarse-grained sampling schemes. Experiments show that our online convergence detection mechanism can precisely detect convergence, and results in stable and rapid termination with theoretically guaranteed confidence. We also show the effectiveness of eager-verify in improving the hit rate, and the scheme-selection mechanism in correctly choosing the better scheme for various cases. Overall, ScaleGPM achieves an geomean average of 565× (up to 610169×) speedup over the state-of-the-art A-GPM system, Arya. In particular, ScaleGPM handles billion-scale graphs in seconds, where existing systems either run out of memory or fail to complete in hours.
QueryArtisan: Generating Data Manipulation Codes for Ad-hoc Analysis in Data Lakes
Authors: Tang Xiu , Liu Wenhao , Wu Sai , Yao Chang , Yuan Gongsheng , Ying Shanshan , Chen Gang
Published: February 2025

Query processing over data lakes is a challenging task, often requiring extensive data pre-processing activities such as data cleaning, transformation, and loading. However, the advent of Large Language Models (LLMs) has illuminated a new pathway to address these complexities by offering a unified approach to understanding the diverse datasets submerged in data lakes. In this paper, we introduce QueryArtisan, a novel LLM-powered analytic tool specifically designed for data lakes. QueryArtisan transcends traditional ETL (Extract, Transform, Load) processes by generating just-intime code for dataset-specific queries. It eliminates the need for an intermediary schema, enabling users to query the data lake directly using natural language. To achieve this, we have developed a suite of heterogeneous operators capable of processing data across various modalities. Additionally, QueryArtisan incorporates a cost model-based query optimization technique, significantly enhancing its code generation capabilities for efficient query resolution. Our extensive experimental evaluations, conducted with real-life datasets, demonstrate that QueryArtisan markedly outperforms existing solutions in terms of effectiveness, efficiency and usability.
A Memory Guided Transformer for Time Series Forecasting
Authors: Cheng Yunyao , Guo Chenjuan , Yang Bin , Yu Haomin , Zhao Kai , Jensen Christian S.
Published: February 2025

Accurate long-term forecasting from multivariate time series has important real-world applications. However, achieving this so is challenging. Thus, analyses reveal that time series that span long durations often exhibit dynamic and disrupted correlations. State-of-the-art methods employ attention mechanisms to capture dynamic correlations, but they often do not contend well with disrupted correlations, which reduces prediction accuracy. We introduce local and global information concepts and then leverage these in a Memory Guided Transformer, called the Memformer. By integrating patch-wise recurrent graph learning and global attention, the Memformer aims to capture dynamic correlations and take disrupted correlations into account. We also integrate a so-called Alternating Memory Enhancer into the Memformer to capture correlations between local and global information. We report on experiments that offer insight into the effectiveness of the Memformer at capturing dynamic correlations and its robustness to disrupted correlations. The experiments offer evidence that the new method is capable of advancing the state-of-the-art in forecasting accuracy on real-world datasets.
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Authors: Merkel Nikolai , Toussing Pierre , Mayer Ruben , Jacobsen Hans-Arno
Published: February 2025

Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of high-dimensional features from neighboring vertices within sparse graph structures combined with neural network operations. The sparsity of graphs frequently results in suboptimal memory access patterns and longer training time.
Graph reordering
is an optimization strategy aiming to improve the graph data layout. It has shown to be effective to speed up graph analytics workloads, but its effect on the performance of GNN training has not been investigated yet. The generalization of reordering to GNN performance is nontrivial, as multiple aspects must be considered: GNN hyper-parameters such as the number of layers, the number of hidden dimensions, and the feature size used in the GNN model, neural network operations, large intermediate vertex states, and GPU acceleration.

In our work, we close this gap by performing an empirical evaluation of 12 reordering strategies in two state-of-the-art GNN systems, PyTorch Geometric and Deep Graph Library. Our results show that graph reordering is effective in reducing training time for CPU- and GPU-based training, respectively. Further, we find that GNN hyper-parameters influence the effectiveness of reordering, that reordering metrics play an important role in selecting a reordering strategy, that lightweight reordering performs better for GPU-based than for CPU-based training, and that invested reordering time can in many cases be amortized.
HyperBlocker: Accelerating Rule-Based Blocking in Entity Resolution Using GPUs
Authors: Zhu Xiaoke , Xie Min , Deng Ting , Zhang Qi
Published: February 2025

This paper studies rule-based blocking in Entity Resolution (ER). We propose HyperBlocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, HyperBlocker employs a pipelined architecture to overlap data transfer and GPU operations. It generates a data-aware and rule-aware execution plan on CPUs, for specifying how rules are evaluated, and develops a number of hardware-aware optimizations to achieve massive parallelism on GPUs.

Using real-life datasets, we show that HyperBlocker is at least 6.8× and 9.1× faster than prior CPU-powered distributed systems and GPU-based ER solvers, respectively. Better still, by combining HyperBlocker with the state-of-the-art ER matcher, we can speed up the overall ER process by at least 30% with comparable accuracy.
Calibrating Noise for Group Privacy in Subsampled Mechanisms
Authors: Jiang Yangfan , Luo Xinjian , Yang Yin , Xiao Xiaokui
Published: February 2025

Given a group size
m
and a sensitive dataset
D
, group privacy (GP) releases information about
D
(e.g., weights of a neural network trained on
D)
with the guarantee that the adversary cannot infer with high confidence whether the underlying data is
D
or a neighboring dataset
D
′ that differs from
D
by
m
records. GP generalizes the well-established notion of differential privacy (DP) for protecting individuals' privacy; in particular, when
m
= 1, GP reduces to DP. Compared to DP, GP is capable of protecting the sensitive
aggregate
information of a group of up to
m
individuals, e.g., the average annual income among members of a yacht club. Despite its longstanding presence in the research literature and its promising applications, GP is often treated as an afterthought, with most approaches first developing a differential privacy (DP) mechanism and then using a generic conversion to adapt it for GP, treating the DP solution as a black box. As we point out in the paper, this methodology is suboptimal when the underlying DP solution involves subsampling, e.g., in the classic DP-SGD method for training deep learning models. In this case, the DP-to-GP conversion is overly pessimistic in its analysis, leading to high error and low utility in the published results under GP.

Motivated by this, we propose a novel analysis framework that provides tight privacy accounting for subsampled GP mechanisms. Instead of converting a black-box DP mechanism to GP, our solution carefully analyzes and utilizes the inherent randomness in subsampled mechanisms, leading to a substantially improved bound on the privacy loss with respect to GP. The proposed solution applies to a wide variety of foundational mechanisms with subsampling. Extensive experiments with real datasets demonstrate that compared to the baseline convert-from-blackbox-DP approach, our GP mechanisms achieve noise reductions of over an order of magnitude in several practical settings, including deep neural network training.
Making CRDTs Not So Eventual
Authors: Mao Yunhao , Zhang Gengrui , Liu Zongxin , Nasirifard Pezhman , Tijanic Sofia , Jacobsen Hans-Arno
Published: February 2025

Conflict-free replicated data types (CRDTs) are highly available and performant data replication solutions for distributed applications. However, their eventual consistency guarantees are often insufficient for ensuring application correctness, especially in the presence of Byzantine failures. Naively applying traditional consensus and Byzantine fault tolerance (BFT) protocols to CRDT updates for stronger guarantees, while intuitive, negates the performance benefits of CRDTs.

We introduce a novel programming model called
reliable CRDTs
that expands CRDTs with additional guarantees: users can query strongly or eventually consistent values, enforce a total order among selected operations, and define data-type level invariants while remaining operational in the presence of Byzantine failures. Reliable CRDTs enable the use of CRDTs in scenarios where strong consistency is needed while maintaining their performance advantages.

We present an implementation of reliable CRDTs named
Janus.
It enhances CRDTs with the aforementioned features by functioning as a middleware that facilitates CRDT communication and asynchronously runs a BFT consensus protocol. Our evaluation demonstrates that
Janus
achieves 21× higher throughput than naively applying state-of-the-art BFT protocols such as HotStuff achieves, and it remains responsive even under heavy loads.
The Cost of Representation by Subset Repairs
Authors: Liu Yuxi , Shen Fangzhu , Ghosh Kushagra , Gilad Amir , Kimelfeld Benny , Roy Sudeepa
Published: February 2025

Datasets may include errors, and specifically violations of integrity constraints, for various reasons. Standard techniques for "minimalcost" database repairing resolve these violations by aiming for a minimum change in the data, and in the process, may sway representations of different sub-populations. For instance, the repair may end up deleting more females than males, or more tuples from a certain age group or race, due to varying levels of inconsistency in different sub-populations. Such repaired data can mislead consumers when used for analytics, and can lead to biased decisions for downstream machine learning tasks. We study the "cost of representation" in subset repairs for functional dependencies. In simple terms, we target the question of how many additional tuples have to be deleted if we want to satisfy not only the integrity constraints but also representation constraints for given sub-populations. We study the complexity of this problem and compare it with the complexity of optimal subset repairs without representations. While the problem is NP-hard in general, we give polynomial-time algorithms for special cases, and efficient heuristics for general cases. We perform a suite of experiments that show the effectiveness of our algorithms in computing or approximating the cost of representation.
Editorial: In this issue
Authors: Editors
Published: February 2025

The editors are pleased to present issue 2024/II of the Pécs Journal of International and European Law, published by the Centre for European Research and Education of the Faculty of Law of the University of Pécs.
In the Articles section, Dimitris Liakopoulos provides an analysis of how EU law perceives and regulates renewable energy and its environmental impact. Gergely Kappel presents a comprehensive literature review as regards the risks of artificial intelligence-based decision support in companies’ executive-level decision- making.
In the Case Notes and Analysis section, Ágoston Mohay ponders the question whether the lengthy process of the EU’s accession to the ECHR is finally set to end thanks to the renegotiated draft accession agreement of 2023.
In the Book Review section, firstly, Maria Melikidou reviews the monograph ‘The European Arrest Warrant and EU Citizenship. EU Citizenship in Relation to Foreseeability Problems in the Surrender Procedure’ by Joske Graat, published by Springer in 2022. Secondly, Tiwai Mhundwa looks at the edited volume ‘International Labour Mobility: How Remittances Shape the Labour Migration Model’ published by Palgrave Macmillan in 2023.
A word of most sincere gratitude is due to the anonymous peer reviewers of the current issue.
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The Trap of Wish Fulfillment
Authors: Laczó Ferenc
Published: February 2025

How entrenched has Viktor Orbán’s rule become in Hungary after a decade and a half? How might domestic and international political shifts, especially Donald Trump’s second administration, impact the country’s trajectory? To answer those questions, this essay outlines the foreign policy strategy the Orbán regime has pursued and assesses its chances of success; discusses the recent emergence of a charismatic opposition leader and what that unexpected development may reveal about the domestic political situation; and considers the possibility and pitfalls of a liberal democratic restoration, including what building democracy back better would entail.
Three-Dimensional Concept of Critical Energy Infrastructure Risk Analysis
Authors:   G.E. Pukhov Institute for Modelling in Energy Engineering National Academy of Sciences of Ukraine, Kyiv, Shkarupylo V.V. ,   National University of Bioresources and Nature Management of Ukraine, Kyiv, Chemerys O.A. ,   G.E. Pukhov Institute for Modelling in Energy Engineering National Academy of Sciences of Ukraine, Kyiv, Zaiko T.A. ,   Zaporizhia Polytechnic National University of the Ministry of Education and Science of Ukraine, Zaporozhye, Dimitriieva D.O. ,   State enterprise "State scientific and technical center on nuclear and radiation       safety" of the National Academy of Sciences of Ukraine, Kyiv, Shkarupylo V.V. ,   Zaporizhia Polytechnic National University of the Ministry of Education and Science of Ukraine, Zaporozhye
Published: February 2025

A three-dimensional concept of energy infrastructure risk analysis has been developed. Named infrastructure is considered as a distributed safety-critical system. The result of the analysis conducted in accordance with the principles of the introduced concept is a taxonomy of the contemporary means focused on decreasing the level of risks taking place in critical distributed energy infrastructure, including the cyber risks in particular. Named taxonomy, in turn, is in-tended to serve as a background for developing an effective strategy for ensuring the resilience of energy infrastructure. Taking into consideration the circumstances Ukraine currently faces, caused by the unprovoked full-scale invasion of the aggressor from the East, the specified problem is of significant importance.
Obtained research results provide the grounding to consider that the share of cyber threats is growing nowadays. This, in turn, determines the importance of developing a contemporary strategy focused on delivering the resilience of energy infrastructure.
Connecting Coding and Clinical Practice Guidelines: Opioid Prescribing in Outpatient Settings
Authors:   American Academy of Pediatrics
Published: February 2025

This article reviews coding for the identification, assessment, and treatment of pain in children as addressed in the American Academy of Pediatrics (AAP) “Opioid Prescribing for Acute Pain Management in Children and Adolescents in Outpatient Settings: Clinical Practice Guideline” (https://doi.org/10.1542/peds.2024-068752). The following topics are discussed: Diagnosis codes for painPain and underlying causeReporting opioids used in pain managementMedical decision-making (MDM) related to painDevelopment of care plans for pain
Compliance Corner: Refunds, Self-disclosure, and Investigations
Authors:   American Academy of Pediatrics
Published: February 2025

Billing and coding errors happen in every practice. This article discusses the options for addressing identified errors and possible consequences of not addressing errors, including the following: Overpayments and refundsSelf-disclosureExternal investigation
April 2025 ICD-10-CM Updates
Authors:   American Academy of Pediatrics
Published: February 2025

International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) updates have been announced, with changes effective April 1, 2025.
The relationship between trait anger, psychological maladjustment, psychological resilience and loneliness levels of athletes and physical inactive individuals
Authors: Kan Emirhan , Ozan Murat
Published: February 2025

Background and Study Aim. Trait anger, psychological maladjustment, resilience, and loneliness are crucial aspects influencing mental health in both athletes and physically inactive individuals. Exploring these factors provides valuable insights into their psychological well-being. The study aims to investigate the relationship between trait anger, psychological maladjustment, psychological resilience, and loneliness among athletes and physically inactive individuals, with a particular focus on the predictive role of psychological resilience in these relationships.
Material and Methods. The sample consisted of 793 individuals divided into three groups. These included 176 participants selected randomly, 120 team athletes, and 497 sedentary individuals. Data collection involved the following tools: the Demographic Information Form, the Trait Anger and Anger Expression Style Scale, the Psychological Maladjustment Scale, the Psychological Resilience Scale, and the Loneliness Scale. Cronbach's Alpha coefficients were calculated to assess the reliability of the scales. Multiple regression analysis was performed to explore the relationship between loneliness, psychological resilience, anger traits, anger expression styles, and levels of depression, stress, and anxiety. The statistical analysis was conducted using SPSS 24.
Results. For combat athletes, outward anger, anger control, and stress significantly predicted psychological resilience (R = .653, F = 15.557, p < .05), explaining 43% of the variance. Among team athletes, anger control emerged as a significant predictor (R = .605, F = 7.784, p < .05), accounting for 37% of the variance. For physically inactive individuals, outward anger, anger control, anxiety, and stress predicted psychological resilience (R = .545, F = 25.768, p < .05), explaining 30% of the variance. Across all groups, the key predictors were anxiety (β = -.219, p < .05), stress (β = -.190, p < .05), anger control (β = .196, p < .05), and outward anger (β = .189, p < .05).
Conclusions. Targeted interventions focused on anger control and stress reduction can effectively enhance psychological resilience. These strategies may support both athletes and physically inactive individuals in managing psychological maladjustment and reducing loneliness.
Scientific support for strength sports: analysis of scientific resources from the Web of Science Core Collection
Authors: Podrigalo Leonid , Tymchenko Kostiantyn , Perevoznyk Volodymyr , Semeniv Bogdan , Paievskyi Volodymyr , Halashko Olexandr
Published: February 2025

Background and Study Aim.  Strength is a key physical attribute that contributes to success in sports. Strength training plays a crucial role in enhancing athletes' physical condition, improving skill levels, and achieving competitive success. This study aims to analyze publications on strength sports using bibliometric methods to identify priority research areas in this field.
Material and Methods. The Web of Science Core Collection (WoS) bibliometric database was analyzed. A total of 589 sources published between 2021 and 2025 that met the search criteria were selected for primary analysis. Bibliometric methods were applied to process the data. VOSviewer 1.6.18 software was used for keyword analysis and direct citation analysis, including the construction of bibliometric maps, cluster density visualization, and weighted citation analysis.
Results. The leading publication categories in WoS were Sports Sciences, Physiology, Orthopedics, Hospitality, Leisure, Sport, Tourism, and Rehabilitation. The highest number of publications appeared in 2022 and 2024. The top five countries in terms of publication output were Spain, Brazil, England, the USA, and Australia. The top five universities with the highest publication activity were identified. The most cited authors were also determined. The constructed bibliometric maps helped identify the main research themes and current directions in strength sports. Six research clusters were identified, covering the following topics: strength training in sports, rehabilitation and return to sport after injuries, athlete performance dynamics under strength training, strength training in fitness, strength development across different sports, and the impact of nutrition on training effectiveness. The keywords associated with each cluster were analyzed.
Conclusions. A bibliometric analysis of the WoS database on strength sports has been conducted. The priority research directions in this field have been identified. These include sports-related topics, such as strength training methods, testing, and evaluation of athletes' strength, as well as rehabilitation and recreational aspects, including strength training for recovery, rehabilitation after injuries, and injury prevention. Various tests and assessment methods are used to evaluate athletes' strength. These methods must meet the principles of specificity, simplicity, reliability, and practicality. The most commonly used assessments include body composition indices, anthropometric criteria, and strength performance measures. Among strength exercises, the most frequently analyzed are the bench press, grip strength, squats, and jumps. The effectiveness of these tests for athlete monitoring, selection, and performance prediction has been confirmed.