bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital and Smart Assembly of Laser Target Designation Based on Digital Twin
Authors: Gu Ye , Wu Weiwei , Huang Lanxin , Zhang Chunfeng , Wei Xiaoyu 
Published: January 2025

Abstract
To solve the problems of low production efficiency and poor quality consistency in the assembly of laser target designation (ALTD), a digital and smart assembly method of laser target designation based on digital twin was proposed. Through real-time data collection of automatic assembly machines and digital test instruments, we can dynamically optimize the assembly process and debugging parameters, and control the output quality of each process. We can simulate in virtual space by physical data and control physical space by virtual entity to achieve virtual-reality integration digital and smart assembly. In this paper, we expound the digital and smart assembly of laser rod components to show that the digital twin of ALTD can realize the resource optimization, process optimization, quality control, cost reduction and efficiency improvement in the assembly workshop.
Design and Implementation of a Modular Underwater Brush-Clearing Robot and Its Observation Module
Authors: Li Jintian , He Yanjin , Tao Wenmei 
Published: January 2025

Abstract
This paper focuses on designing a modular unmanned underwater ROV for efficiently scrubbing marine vehicles. The modular ROV can carry various tool modules for targeted brushing, while an observation robot assesses the effectiveness of commands using image sonar. By incorporating intelligent driving and fouling target recognition, the ROV adapts to diverse environments, enabling multi-purpose use.
Lightweight Design for Interval Finite Element Structure with Improved Grey Wolf Optimization Algorithm
Authors: Pei Chunyan , Zhang Yingshuang 
Published: January 2025

Abstract
The interval finite element model is used to design an uncertain structure. Using interval extension, the interval element stiffness matrix of the structure is recorded as median matrix plus increment one of the element. They are assembled as an interval global stiffness matrix. Then the interval displacement vectors can be derived from the structural equation to obtain the interval constraints. Improved grey wolf optimization algorithm is applied to the interval finite element structure for optimal design. The strategy is adopted to determine the individual position, which makes the convergence factor show nonlinear fluctuation, thus speeding up convergence of the program. The optimal result of an interval space finite element truss proves the feasibility of the method.
Optimum Design of Lifting Mechanism for Disinfection Cabinet Bowl and Basket Based on Multi Body Dynamics
Authors: Qin Si , Wang Jihui 
Published: January 2025

Abstract
At present, there is a lower position of the lower bowl basket in the household disinfection cabinet on the market, which requires long-term bending or squatting when picking up and placing utensils, which may cause discomfort in the user's waist in the long term. To solve this problem, this article designs a lifting mechanism for the disinfection cabinet bowl and basket. Through this structure, the lower bowl basket can be raised to a certain height and pulled out, making it easier for items to be placed and taken out. Based on the principle of multi body dynamics, ADAMS software is used to simulate and analyze the dynamics of the bowl basket lifting mechanism. The results indicate that the improved bowl basket lifting mechanism can achieve a labor-saving effect during the lifting process, and also verify the feasibility of the mechanism design.
Biomechanical Analysis of Forward Driving Posture Based on Human Musculoskeletal Model
Authors: Wang Shanlin , Wang Kun , Wang Tianhui , Zhang Jianjun 
Published: January 2025

Abstract
Lower back pain in drivers of specialized vehicles may be related to excessive lumbar spine loading. The purpose of this study was to investigate human lumbar spine loads and lumbar muscle forces in a forward-flexed driving position in a specialty vehicle to better understand lower back pain and improve the design of automotive seats. The results of the study showed that the driver's lumbar load and lumbar muscle strength tended to increase and then decrease with the increase of the forward flexion angle at different forward flexion angles, and the lumbar spine load and lumbar muscle strength reached the peak value when the forward flexion angle was5°. The results of the study provide a theoretical basis for the correlation between lower back pain and sitting posture and help prevent low back pain in drivers.
A model for elastic half space under a visco-elastic layer in generalized thermoelasticity
Authors: Khader S. E. , Marrouf A. A. , Khedr M. 
Published: January 2025

Abstract
We consider a model of two layers for two cases. In the first case, a viscoelastic upper layer over an elastic half-space. In the second case, an elastic upper layer over a viscoelastic half-space. The upper layer’s surface is taken to be traction-free and is subjected to a constant thermal shock. This model is solved in the context of the generalized thermoelasticity theory with one relaxation time. Laplace transform techniques are used. The inverse Laplace transforms are obtained using a numerical method based on the Fourier expansion technique. Numerical results are computed and represented graphically for the temperature, displacement, and stress distributions. This work may be useful in the design of materials used in thermal insulation, vibration reduction, and applications in microelectronics.
Can Local Optimality Be Used for Efficient Data Reduction?
Authors: Komusiewicz Christian , Morawietz Nils 
Published: January 2025

Abstract
An independent set S in a graph G is k-swap-optimal if there is no independent set

$$S'$$

S
′

such that

$$\varvec{|S'|>|S|}$$

|

S
′

|
>
|
S
|

and

$$\varvec{|(S'\setminus S)\cup (S\setminus S')|\le k}$$

|

(

S
′

\
S
)

∪

(
S
\

S
′

)

|
≤
k

. Motivated by applications in data reduction, we study whether we can determine efficiently if a given vertex v is contained in some k-swap-optimal independent set or in all k-swap-optimal independent sets. We show that these problems are NP-hard for constant values of k even on graphs with constant maximum degree. Moreover, we show that the problems are

$$\varvec{\Sigma ^{\text {P}}_{2}}$$

Σ
2
P

-hard when k is not constant, even on graphs of constant maximum degree. We obtain similar hardness results for determining whether an edge is contained in a k-swap optimal max cut. Finally, we consider a certain type of edge-swap neighborhood for the Longest Path problem. We show that for a given edge we can decide in

$$\varvec{f(\Delta +k)\cdot n^{\mathcal {O}(1)}}$$

f

(
Δ
+
k
)

·

n

O
(
1
)

time whether it is in some k-optimal path.
Prognostic 18F-flotufolastat PET parameters for outcome assessment of 177Lu-labeled PSMA-targeted radioligand therapy in metastatic castration-resistant prostate cancer
Authors: Karimzadeh Amir , Hansen Kimberley , Hasa Ergela , Haller Bernhard , Heck Matthias M. , Tauber Robert , D`Alessandria Calogero , Weber Wolfgang A. , Eiber Matthias , Rauscher Isabel 
Published: January 2025

Abstract

Purpose
This retrospective analysis evaluates baseline 18F-flotufolastat positron emission tomography (PET) parameters as prognostic parameters for treatment response and outcome in patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing treatment with [177Lu]Lu-PSMA-I&T.

Methods
A total of 188 mCRPC patients with baseline 18F-flotufolastat PET scans were included. Tumor lesions were semiautomatically delineated, with imaging parameters including volume-based and standardized uptake value (SUV)-based metrics. Outcome measures included prostate-specific antigen (PSA) response, PSA-progression-free survival (PSA-PFS), and overall survival (OS). Univariate and multivariate regression analyses assessed the impact of baseline imaging and pretherapeutic clinical parameters on outcome. Event time distributions were estimated with the Kaplan-Meier method, and groups were compared with log-rank tests.

Results
Significant prognostic parameters for PSA response and PSA-PFS included log-transformed whole-body SUVmax (odds ratio (OR), 3.26, 95% confidence interval (CI), 2.01–5.55 and hazard ratio (HR), 0.51, 95% CI, 0.4–0.66; both p < 0.001) and prior chemotherapy (OR 0.3, 95% CI, 0.12–0.72 and HR 1.64, 95% CI, 1.07–2.58; p = 0.008 and p = 0.028, respectively). For OS, significant prognosticators were the following log-transformed parameters: number of lesions (HR 1.38, 95% CI, 1.24–1.53; p < 0.001), TTV (HR 1.27, 95% CI, 1.18–1.37; p < 0.001), and ITLV (HR 1.24, 95% CI, 1.16–1.33; p < 0.001), with log-transformed TTV (HR 1.15, 95% CI, 1.04–1.27; p = 0.008) remaining significant in multivariate analysis.

Conclusion
At baseline, SUV-based 18F-flotufolastat PET metrics (e.g., whole-body SUVmax) serve as significant positive prognosticators for short-term outcomes (PSA response and PSA-PFS). In contrast, volume-based metrics (e.g., TTV) are significant negative prognosticators for long-term outcome (OS), in mCRPC patients treated with [177Lu]Lu-PSMA-I&T.
Towards shock absorbing hyperelastic metamaterial design (II): A prospective multiscale buckling-lattice computational model
Authors: Cante J. , Nuñez-Labielle A. , Huespe A. E. , Oliver J. 
Published: January 2025

Abstract
As a continuation of a previous work of the authors, on Computational Design of Shock-absorbing Metamaterials (Part I) (NunezLabielle et al. in Comput Methods Appl Mech Eng 393:114732, 2022), this work explores the potential of computational multiscale methods, in combination with massive buckling-lattice structures at the metamaterial core (meso/micro scale), to render a suitable framework for designing such a shock-absorbing metamaterials focusing on industrial applications. In this context, a prospective computational setting is considered under the hypothesis that, for a sufficiently complex microlattice topology, some localized regions might buckle within the lattice-structure core and propagate through it, giving rise to different loading-unloading paths, in such a way that hysteretic-like structural behaviours would take place, thus arising dissipative behaviours, even if the base material at the buckling micro-lattice behaves in a hyperelastic (thus intrinsically non-dissipative) manner. Using the standard Hill-Mandel homogenization principle, and assuming that the necessary separation of scales holds, the homogenized body, now living in a classical solid-mechanics setting, displays a homogenized non-convex behaviour which, in agreement with the conclusions of Part (I) of the work, exhibits extrinsic dissipation and, thus, could be potentially used (at reduced computational cost) for shock absorbing metamaterials analysis and design purposes. A tentative industrial application, to a sneaker’s insole design, has been then considered as a work’s target for evaluation of the room offered by the explored setting in the context of shock-absorbing metamaterial design. Finally, remarks on the scope and limitations of the work, and its significance for further advances in the field are emphasized.
Deep learning at the service of metaheuristics for solving numerical optimization problems
Authors: Oyelade Olaide N. , Ezugwu Absalom E. , Saha Apu K. , Thieu Nguyen V. , Gandomi Amir H. 
Published: January 2025

Abstract
Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms.
Discovering ship maneuvering models from data
Authors: Hasan Agus 
Published: January 2025

Abstract
In this paper, we introduce a methodology to discover ship maneuvering models from data, leveraging Wide-Array of Nonlinear Dynamics Approximation (WyNDA) framework. WyNDA operates by utilizing basis functions and estimation algorithms to discern the ship maneuvering behaviors. Specifically, we employ a discrete-time exponential forgetting factor observer to accurately estimate both the structures and parameters inherent in the maneuvering models. Through extensive numerical simulations, we demonstrate the efficacy of our proposed approach in solving system identification and data-driven discovery problems within this domain. Moreover, we assess the robustness of our method with respect to noise levels and system excitation. This research contributes to advancing data-driven discovery of ship maneuvering dynamics and provides a practical tool for applications requiring accurate modeling.
The learning curve for the Shouldice Repair: a pilot analysis of post-training specialized surgeons at the Shouldice Hospital
Authors: Paasch Christoph , Hunger Richard , Szasz Peter , Yilbas Ayse , Netto Fernando A. C. Spencer , Mantke Rene , Mainprize Marguerite 
Published: January 2025

Abstract

Purpose
The aim of the study was to evaluate operative time and postoperative complications of 4 post-training specialized surgeons.

Methods
This was a pilot retrospective chart review to determine the learning curve of a Shouldice primary inguinal hernia repair (Shouldice Repair) of 4 post-training specialized surgeons, at the Shouldice Hospital. The first 300 Shouldice Repairs (early learning block) were compared to their 900-1,000 repairs as the primary operating surgeon (late learning block). Data was collected from the hospital’s database. The learning curve was examined using cumulative sum analysis (CUSUM).

Results
During the early learning block cases, the surgeons had a mean operating time of 59.2 ± 11.2 min. The late learning block cases had significantly reduced operative time (53.4 ± 10.5 min, p = 0.001). According to the CUSUM analysis all four surgeons had a plateau after 78 to 88 operations in terms of operative time. A nonsignificant reduction in the rate of reported recurrences (n = 16 vs. n = 0) and surgical site occurrences (haematoma, seroma, infection; n = 27 vs. n = 2) was found between the early and late learning block cases.

Conclusion
The operating time plateaued after 78–88 Shouldice Repairs for the 4 surgeons trained and working at the Shouldice Hospital. A nonsignificant trend towards fewer complications were noted among late learning block cases.
Structured hernia surgery training program for general practitioners in Rwanda - feasibility and evaluation
Authors: Lorenz Ralph , Hubert Lea , Paasch Christoph , Conze Joachim , Oppong Chris , Akoh Jacob A. , Sedgwick David M. , Nsabimana Venuste , Mantke René 
Published: January 2025

Abstract

Background
Hernias are among the most common surgical conditions worldwide, with significant prevalence in Africa. However, according to recent WHO statistics, Africa faces a critical shortage of trained surgeons. Structured surgical training programs are also scarce. Since 2016, Surgeons for Africa in collaboration with Operation Hernia have developed structured training course on hernia surgery specifically for surgeons in Rwanda. Due to the severe shortage of surgeons, a new initiative was launched in 2023 to train general practitioners (GPs) to support the country’s surgical care needs. This study aims to assess the feasibility and effectiveness of these training programs for general practitioners.

Methodology
Six standardized questionnaires were used to evaluate the structured training program before, during, and after the one-and-a-half-week courses conducted in Rwanda. Both trainees and trainers completed the relevant evaluations. The results were anonymized, ummarized using descriptive statistics, and statistically analysed. Trainers also assessed the surgical competence of each participant at the end of the course.

Results
Between 2023 and 2024, 47 general practitioners received hernia surgery training in several Rwandan hospitals. The course significantly improved both the theoretical knowledge and practical surgical skills of the participants. Of the 47 GPs, 22 were able to independently perform simple inguinal hernia surgeries after the training. Three were able to perform complex inguinal hernia repairs independently. 12 participants required minimal supervision, while 22 required full supervision.

Conclusion
This study confirms the feasibility and effectiveness of a standardized hernia surgery training program for general practitioners in Rwanda. The results demonstrate the rogramme’s potential to address the surgical care gap by enabling GPs to perform basic hernia surgeries.
The efficiency of single-session photobiomodulation on healing of periapical bone lesions using CBCT: a randomized clinical trial
Authors: Hamdy Ismail Hend , Obeid Maram Farouk , ElSaid Hassanein Ehab 
Published: January 2025

Abstract
As photobiomodulation is growing in the dental field the aim of this prospective, two-arm clinical trial was to assess the radiographic changes for chronic periapical bone lesions related to mandibular molars after primary root canal therapy with or without applying Diode laser on soft tissue. The samples were randomly divided into a Laser group and a mock laser (ML) group. Preoperative CBCT images were compared 12 months later with postoperative CBCT to gauge the changes in the volume of the bony lesion by two observers. The kappa coefficient was calculated to assess the intra-observer agreement. Data were presented as median, mean, and standard deviation (SD) values. For parametric data (age), a Student’s t-test was used. Qualitative data (Gender) was presented as frequencies and percentages and then analyzed by Chi-square test. The Wilcoxon signed-rank test was used to compare CBCT measurements pre- and post-treatment. The significance level was set at P ≤ 0.05. The collected data in this study showed that there was no statistically significant difference between median volumes of the periapical lesion in the Laser and ML groups (P-value = 0.564, Effect size = 0.237) and (P-value = 0.452, Effect size = 0.310), respectively while, there was a statistically significant decrease in the volume of periapical lesion post-treatment within each group (P-value < 0.001). The size of the periapical bony lesion decreased significantly after conventional endodontic treatment whether Laser was applied or not. The study protocol was enrolled as NCT04311905 at www.clinicaltrials.gov database on (17/03/2020) after the approval of the Ethics Committee, Faculty of Dentistry, Ain Shams University (FDASURecID091703).
Unveiling educators’ readiness to teach through Digital Media (DM): The case of South Africa
Authors: Muyambi Godfrey Chitsauko , Ramorola Mmankoko Ziphorah 
Published: January 2025

Abstract
South African schools are increasingly integrating digital technology into their curricula to enhance educational outcomes. This study aims to evaluate educators' readiness to teach using digital media (DM) and the effectiveness of the e-learning policy in addressing disparities across schools. The hypothesis is that disparities in access to digital resources, training, and policy implementation hinder equitable integration of DM in teaching. Eleven secondary schools in Tshwane North District, Gauteng Province, were purposively selected. Data from 180 educators were collected using structured questionnaires designed around the Technological, Pedagogical, and Content Knowledge (TPACK) framework and the ICT Competency for Teachers framework (ICT-CFT version 3). The findings revealed significant discrepancies in educators' access to digital resources, technical skills, and knowledge of e-learning policies. Many educators lacked digital subject-specific knowledge, hindering effective technology integration. Schools in underprivileged areas faced acute shortages of digital resources, limited upskilling opportunities, severe time constraints, and reluctance from educators to adopt digital technology. The study establishes a direct relationship between the successful use of DM in teaching and the availability of resources, training, and educators' competence. It underscores the urgent need for equitable funding, access to digital tools, and targeted professional development for educators. Furthermore, the research advocates for stakeholder collaboration—including parents, learners, government, businesses, and NGOs—to promote DM integration. It proposes developing a tailored digital framework and a Digital Readiness Index to help schools measure progress in embedding digital resources into teaching practices.
Multimorbidity and Depressive Symptoms and their Association with Self-Reported Health and Life Satisfaction Among Adults Aged ≥ 50 Years in Mexico
Authors: García Pérez Alvaro , Villanueva Gutiérrez Teresa 
Published: January 2025

Abstract
Identify the association between multimorbidity and depressive symptoms (DS) with self-reported health (SRH) and life satisfaction in a national sample of Mexican ≥ 50 years older adults. Data are drawn from the Mexican Health and Aging Study (MHAS), a cross-sectional study conducted in 2018 involving 14,230 older adults aged 50 years and older living in urban and rural areas of Mexico. Depressive symptoms were measured using the Center for Epidemiological Studies depression scale (CES-D) and life satisfaction using the Life Satisfaction Scale (LSS), examined both as a categorical and continuous variable. Logistic and Poisson regression analyses were used to assess the association adjusting for confounders. The prevalence of multimorbidity was 25.8%. Age (≥ 65 years) was significantly associated with increased odds of fair/poor SRH [OR = 1.17 (95% CI 1.09–1.27)]. Older adults with multimorbidity and high DS were more likely to present fair/poor SRH [OR = 7.83 (95% CI 6.48–9.45)]. Older adults with multimorbidity and high DS were 37% [RR = 1.37 (95% CI 1.35–1.40)] more likely to present low life satisfaction than those that did not present multimorbidity. Multimorbidity and high DS were found to be associated with fair/poor SRH and low life satisfaction. The early identification of factors related to multimorbidity, DS, and low life satisfaction are important in order to reduce possible complications and improve quality of life.