bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on visual slam based on improved YOLOv5s in dynamic scenes
Authors: Shen Dong , Xu Haobo , Zhang Boxuan , Cao Meng , Ma Liang 
Published: December 2024

Abstract
To address inaccuracies in camera position computations induced by dynamic objects in visual simultaneous localization and mapping (vSLAM), a method for dynamic feature point detection and rejection is given. This method merges an improved YOLOv5 lightweight network with the lucas-kanade (LK) optical flow. In the GhostNet network architecture, the typical convolutional layer is initially replaced by GhostConv separable convolution. At the end of the feature extraction network, a spatial attention mechanism based on CA is incorporated to safeguard crucial positional information and improve detection precision. This method efficiently eliminates dynamic elements, resulting in a point cloud map that is denser. Evaluations against the TUM dataset indicate that the proposed approach considerably surpasses previous SLAM methods regarding real-time efficiency and localization accuracy.
A study of virtual reality control for digital twin systems of machining lines
Authors: Zhang Dashun , Zhang Xueyu , Wang Fuheng , Liang Huaidan , Li Xueguang 
Published: December 2024

Abstract
Based on the source of the processing production line twin data, the virtual and real interaction framework of the processing production line digital twin system was designed. Taking the data in the processing production line digital twin system as the research object, the equipment data acquisition transmission channel design was carried out through OPC UA communication technology, and the data acquisition function of the processing production line was realized by using the KEPServerEX 6 data acquisition platform. We chose the C# language and the Visual Studio platform to develop the monitoring module, and used TCP/IP protocol, S7.NET open source library, and motion control card dynamic link library, to achieve the control of the robotic arm, PLC, milling machine, monitoring and other functions. Utilizing the SQL Server database, the connection between the monitoring module and the digital twin system of the machining line was completed, and the basic data information of the whole system was studied. The virtual and real driving process of the whole system is studied to realize the function of this digital twin system of controlling the virtual with the real and controlling the real with the virtual.
Study on the dissolving performance of the Mg-(5, 8, 10wt.%)Sn-1.5wt.%Al alloys
Authors: Sun Zhaoqian , Li Yongjun , Ma Minglong , Li Xinggang , Shi Guoliang , Yuan Jiawei , Zhang Kui 
Published: December 2024

Abstract
A metallographic microscope and a scanning microscope were used to observe the extruded and corroded microstructure of the Mg-(5, 8, 10wt.%)Sn-1.5wt.%Al alloys. An electrochemical workstation was used to test the electrochemical performance of these samples. It shows that the Mg-Sn-Al alloys have good plasticity and dissolving properties after extrusion. Among them, the Mg-8wt.%Sn-1.5 wt.%Al alloy has the best plasticity, which is 22.5%. At the same time, it has the best-dissolving properties, with a double-layer capacitance in a solution of 9.448*10−6 F·cm−2. The dissolution process in the 3% kcl solution is a uniform intergranular corrosion.
New electrode structure vertical cavity surface emitting semiconductor laser and its array
Authors: Pang Chao , Yan Changling , Yang Jinghang 
Published: December 2024

Abstract
A new electrode structure is designed to enhance the uniformity of current distribution in vertical-cavity semiconductor lasers, thereby reducing device heat generation and improving both efficiency and output laser beam quality. Simulation software was employed to analyze the internal current distribution of both new electrode structure VCSEL and the traditional structure VCSEL. Subsequently, the new electrode structure VCSEL was successfully fabricated, and its output characteristics of the single VCSEL device were evaluated at room temperature. The laser wavelength measured 847nm, exhibiting a relatively uniform far-field distribution with a circular spot profile. The internal temperature distribution of a 1×3 array device is simulated.
An investigation of high-Z material for bolus in electron beam therapy
Authors: Das Indra J , Khan Ahtesham U , Lim Sara , Yadav Poonam , Donnelley Eric , Mittal Bharat B 
Published: December 2024

Abstract

Highlight. Electron beam treatment often requires bolus to augment surface dose to nearly 100%. There are no optimum bolus materials and hence a high-Z based clothlike material is investigated to reduce air column in treatment that provides optimum surface dose. This material is well suited as it can be used multiple times and can be sanitized. Characteristics of W-Si material is provided. Purpose /Objective(s). Electron beams are frequently used for superficial tumors. However, due to electron beam characteristics the surface dose is 75–95% of the prescribed dose depending on beam energy thus requiring placement of bolus to augment surface dose. Various types of boluses are commonly used in clinics, each having it’s own unique limitation. Most bolus devices do not conform to the skin contour and create airgaps that are known to produce dose perturbations creating hot and cold spots. A cloth-like high-Z materials; Tungsten, (Z = 74) and Bismuth, (Z = 83) impregnated in silicone gel is investigated for electron bolus. Materials/Methods. Super soft silicone-gel based submillimeter thin tungsten and bismuth sheets were investigated for bolus for 6–12 MeV. Parallel plate ion chamber measurements were performed in a solid water phantom on a Varian machine. Depth dose characteristics were measured to optimize the thickness for surface dose to be 100% for selected electron therapy and validated with Monte Carlo simulations. Results. Silicone-gel tungsten and bismuth sheets produce significant electrons thus increasing surface dose. Based on measured depth dose, our data showed that tungsten sheets of 0.14 mm, 0.18 mm and 0.2 mm and Bismuth sheets of 0.42 mm, 0.18 mm and 0.2 mm provide 100% surface dose for 6, 9 and 12 MeV beams, respectively without any significant changes in depth dose except increasing surface dose. Conclusions. The new high-Z clothlike sheets are extremely soft but high tensile metallic bolus materials that can fit flawlessly on any skin contour. Only 0.2 mm thick sheets are needed for 100% surface dose without degradation of the depth dose characteristics. These materials are reusable and ideal for bolus in electron beam treatment. This investigation opens a new frontier in designing new bolus materials optimum for patient treatment.
Dose optimization of extended collimators in boron neutron capture therapy
Authors: Zhu Yadi , Lian Chao , Ji Xiang , Zhang Xiaoxiang , Li Chunjing , Bai Yunqing , Gao Jun 
Published: December 2024

Abstract
In this paper, we propose the design of extending collimators aimed at reducing the radiation dose received by patients with normal tissues and protecting organs at risk in Boron Neutron Capture Therapy (BNCT). Three types of extended collimators are studied: Type 1, which is a traditional design; Type 2, which is built upon Type 1 by incorporating additional polyethylene material containing lithium fluoride (PE(LiF)); Type 3, which adds lead (Pb) to Type 1. We evaluated the dose distribution characteristics of the above-extended collimators using Monte Carlo methods simulations under different configurations: in air, in a homogeneous phantom, and a humanoid phantom model. Firstly, the neutron and gamma-ray fluxes at the collimator outlet of the three designs showed no significant changes, thus it can be expected that their therapeutic effects on tumors will be similar. Then, the dose distribution outside the irradiation field was studied. The results showed that, compared with Type 1, Type 2 has a maximum reduction of 57.14% in neutron leakage dose, and Type 3 has a maximum reduction of 21.88% in gamma-ray leakage dose. This will help to reduce the radiation dose to the local skin. Finally, the doses of different organs were simulated. The results showed that the neutron dose of Type 2 was relatively low, especially for the skin, thyroid, spinal cord, and left lung, with the neutron dose reduced by approximately 20.34%, 16.18%, 26.05%, and 18.91% respectively compared to Type 1. Type 3 collimator benefits in reducing gamma-ray dose for the thyroid, esophagus, and left lung organs, with gamma-ray dose reductions of around 10.81%, 9.45%, and 10.42% respectively. This indicates that attaching PE(LiF) or Pb materials to a standard collimator can suppress the dose distribution of patient organs, which can provide valuable insights for the design of extended collimators in BNCT.
Distributed quantum machine learning via classical communication
Authors: Hwang Kiwmann , Lim Hyang-Tag , Kim Yong-Su , Park Daniel K , Kim Yosep 
Published: December 2024

Abstract
Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantum machine learning demonstrates substantial advantages over classical counterparts, but a reliable scale-up is hindered by the fragile nature of quantum systems. Here we present an experimentally accessible distributed quantum machine learning scheme that integrates quantum processor units via classical communication. As a demonstration, we perform data classification tasks on eight-dimensional synthetic datasets by emulating two four-qubit processors and employing quantum convolutional neural networks. Our results indicate that incorporating classical communication notably improves classification accuracy compared to schemes without communication. Furthermore, at the tested circuit depths, we observe that the accuracy with classical communication is no less than that achieved with quantum communication. Our work provides a practical path to demonstrating large-scale quantum machine learning on intermediate-scale quantum processors by leveraging classical communication that can be implemented through currently available mid-circuit measurements.
Techno-economic analysis of large-scale battery energy storage system for stationary applications in South Africa
Authors: Borerwe Christopher , Longe Omowunmi Mary 
Published: December 2024

Abstract
South Africa’s transition to renewable energy sources (RES), particularly solar photovoltaics (PV), requires robust energy storage solutions to counterbalance intermittency and meet low-carbon objectives. This study offers a comparative techno-economic analysis of three large-scale battery energy storage systems (BESS): lithium iron phosphate (LFP), lead-acid (Pb-acid), and vanadium redox flow batteries (VRFB). These technologies were selected for technical maturity, cost-effectiveness, and suitability in stationary applications. Using HOMER Pro software, two BESS capacity scenarios A (1.17 MWh) and B (2.34 MWh) were simulated and evaluated on key performance metrics: such as levelized cost of electricity (LCOE), efficiency, environmental impact, and cycle life. The results indicate LFP BESS as the optimal choice for both scenarios, achieving the lowest LCOE values (R4.05/kWh for 1.17 MWh and R4.25/kWh for 2.34 MWh), up to 34 percent (%) lower than Pb-acid BESS and 30% lower than VRFB. LFP also demonstrated significant advantages, including a high round-trip efficiency of up to 95%, a cycle life of 10–20 years, and a reduced environmental impact. Moreover, LFP requires 30% less installed capacity than Pb-acid, resulting in greater cost savings. The environmental emissions evaluation in this case study further demonstrated that a hybrid system with LFP achieved the lowest carbon dioxide (CO2) emissions, with reductions of 10.5% compared to Pb-acid and 23.4% compared to VRFB. While VRFB provides high durability and long-term efficiency, its higher LCOE and additional energy requirements make it less suitable for cost-sensitive, short-term applications. Consequently, the analysis identifies LFP batteries as the most techno-economically efficient option for large-scale stationary storage, underscoring their critical role of BESS in stabilizing energy supply, enhancing grid reliability, and reducing greenhouse gas emissions, pivotal for South Africa’s sustainable energy transition.
Interactional flow physics of freely falling sphere on stagnant water
Authors: Gupta Nishant , Raj Prince Raj Lawrence , Debnath Koustuv , Bhowmik Joydeep 
Published: December 2024

Abstract
The process of water impact and subsequent water entry of rigid objects is essential in engineering applications, including marine, offshore, and aerospace technologies. However, most studies have focused on the impact analysis of the object itself, with limited attention given to its interaction with water and the resulting flow dynamics. This work aims to address this gap by examining the effect of buoyancy on the hydrodynamics of a sphere in freefall, particularly its interaction with stagnant water. The investigation uses computational methodologies validated against experimental results to quantify flow and turbulence parameters, including pressure distribution, flow velocity, and other turbulence parameters for different buoyancy regimes. The study also explores the temporal evolution of the free surface profile to gain insight into the deformation and displacement of water resulting from the impact. The analysis reveals that buoyant spheres generate localized turbulent kinetic energy near the surface, while non-buoyant spheres induce higher, more dispersed turbulence. Pressure peaks at the bottom of the sphere, influenced by fall height but independent of density, while buoyancy affects the pressure distribution over time. Furthermore, buoyancy significantly influences the temporal evolution of pressure distribution and the formation of cavities compared to non-buoyant spheres, which exhibit more concentrated velocity streamlines. These results significantly affect designing and optimizing structures interacting with fluid environments, such as underwater vehicles and offshore platforms. Understanding the interplay between buoyancy and flow characteristics can enhance predictions of hydrodynamic behaviour, improving performance and safety in engineering applications.
Development of eDART based online diameter prediction systems in injection molding with multiple linear regression and fuzzy logic
Authors: Chen Joseph C , Guo Gangjian , Jala Stephen 
Published: December 2024

Abstract
Injection molding is a versatile technique for processing a wide range of thermoplastic and thermosetting polymers, as well as their composites. Dimensional defects are a critical issue in injection molding. This research focuses on developing online diameter prediction systems via multiple linear regression (MLR) and fuzzy logic. The systems are developed using Delrin 311 DP material, with the Taguchi methodology employed to define optimized process parameters that ensure adequate process capability. Processing data were collected from the sensors embedded in the surface of mold cavity by the eDART system. Regression analysis was employed to build and test the relationship between the real-time data from in-mold sensors and the diameter of molded part. The real time data from the sensor-based monitoring system, including end of cavity, hydraulic injection pressure, and efficient viscosity, were selected as the inputs for the predictive model. Both MLR and fuzzy logic models were established to predict the outcomes, based on the data retrieved from the sensors, achieving the prediction accuracies of 99.09% for MLR and 99.98% for fuzzy logic, respectively. Fuzzy logic demonstrated its reliability in predicting diameters and minimizing dimensional defects in injection molding.
Leveraging optuna for hyperparameter tuning in GANs: a novel solution for class imbalance in IoT datasets
Authors: Mouiti Mohammed , El Hariri Ayyoub , Lazaar Mohamed 
Published: December 2024

Abstract
The Internet of Things (IoT) has become more prevalent in recent years, generating a huge amount of data from several interconnected devices. These datasets frequently experience severe class imbalance, where certain classes are significantly underrepresented compared to others, resulting in biased machine learning (ML) models. Addressing the class imbalance in IoT datasets is critical for achieving accurate and reliable predictions. In this paper, we propose a novel approach for handling imbalanced IoT datasets using Optimized Generative Adversarial Networks (OGAN). The proposed approach relies on the powerful capabilities of GANs to generate synthetic data for minority classes and balance the dataset, resulting in enhanced model performance. The approach involves using a GAN to generate synthetic data for the minority class, thereby balancing the dataset. This balanced dataset is then used to test the performance of four different machine learning models. The entire process is optimized using Optuna, which maximizes performance by testing various hyperparameters of the GAN. This approach ensures that the models are trained on a more representative dataset, potentially improving their accuracy and robustness. We demonstrate the efficacy of our method by performing extensive experiments on real-world IoT datasets and comparing them with existing methods for imbalanced data handling. The results reveal that our optimized GAN-based approach outperforms previous methods with an accuracy of 99% for all models and effectively handles the class imbalance problem in IoT datasets.
Recommender systems may enhance the discovery of novelties
Authors: De Marzo Giordano , Gravino Pietro , Loreto Vittorio 
Published: December 2024

Abstract
Recommender systems are vital for shaping user online experiences. While some believe they may limit new content exploration and promote opinion polarization, a systematic analysis is still lacking. In this paper we present a model that explores the influence of recommender systems on novel content discovery. Surprisingly, analytical and numerical findings reveal that these techniques can enhance novelty discovery rates. Also, distinct algorithms with similar discovery rates yield different outcomes, with the matrix factorization algorithm producing opinion polarization. Our approach shed light on the interplay between algorithmic recommendations and novelties discovery, offering a framework to enhance recommendation techniques beyond accuracy metrics.
Focusing Φ⁴₃-model with a Hartree-type Nonlinearity
Authors: Oh Tadahiro , Okamoto Mamoru , Tolomeo Leonardo 
Published: December 2024

Lebowitz, Rose, and Speer (1988) initiated the study of focusing Gibbs measures, which was continued by Brydges and Slade (1996), Bourgain (1997, 1999), and Carlen, Fröhlich, and Lebowitz (2016) among others. In this paper, we complete the program on the (non-)construction of the focusing Hartree Gibbs measures in the three-dimensional setting. More precisely, we study a focusing

Φ
3
4

\Phi ^4_3

-model with a Hartree-type nonlinearity, where the potential for the Hartree nonlinearity is given by the Bessel potential of order

β
\beta

. We first construct the focusing Hartree

Φ
3
4

\Phi ^4_3

-measure for

β
>
2

\beta > 2

, while we prove its non-normalizability for

β
>
2

\beta > 2

. Furthermore, we establish the following phase transition at the critical value

β
=
2

\beta = 2

: normalizability in the weakly nonlinear regime and non-normalizability in the strongly nonlinear regime. We then study the canonical stochastic quantization of the focusing Hartree

Φ
3
4

\Phi ^4_3

-measure, namely, the three-dimensional stochastic damped nonlinear wave equation (SdNLW) with a cubic nonlinearity of Hartree-type, forced by an additive space-time white noise, and prove almost sure global well-posedness and invariance of the focusing Hartree

Φ
3
4

\Phi ^4_3

-measure for

β
>
2

\beta > 2

(and

β
=
2

\beta = 2

in the weakly nonlinear regime). In view of the non-normalizability result, our almost sure global well-posedness result is sharp. In Appendix, we also discuss the (parabolic) stochastic quantization for the focusing Hartree

Φ
3
4

\Phi ^4_3

-measure.

We also consider the defocusing case. By adapting our argument from the focusing case, we first construct the defocusing Hartree

Φ
3
4

\Phi ^4_3

-measure and the associated invariant dynamics for the defocusing Hartree SdNLW for

β
>
1

\beta > 1

. By introducing further renormalizations at

β
=
1

\beta = 1

and

β
=

1
2

\beta = \frac 12

, we extend the construction of the defocusing Hartree

Φ
3
4

\Phi ^4_3

-measure for

β
>
0

\beta > 0

, where the resulting measure is shown to be singular with respect to the reference Gaussian free field for

0
>
β
≤

1
2

0 > \beta \le \frac 12

.
Conclusion
Authors: Romanis Elizabeth Chloe 
Published: December 2024

Abstract
This chapter summarizes key themes that emerge in the analysis of technologies enabling gestation undertaken in all the chapters of this book. Novel gestations are exciting prospects. Technologies enabling gestation, and the fundamental shifts in the modalities of gestation that they make possible, require a careful conceptual analysis of procreative ‘facts’ that have been taken for granted, and also how we describe these ‘facts’. Looking at technologies enabling technologies as a genus requires the asking of questions about the very nature of gestation and what it means—in legal, social, and political terms—since different modalities of gestation are possible. Gestation beyond bodies assigned female at birth, and beyond bodies at all, could challenge our preconceptions about so many fundamental aspects of our social lives including our identities and our conceptions of relatedness and parenthood. However, the transformative potential of these technologies—in how they may enhance the procreative possibilities for individuals in different and new ways—is, and will be, limited by social and legal regulation that limits their emancipatory potential. Imagining the possibilities of uterus transplantation and ectogestation in contemporary contexts reveals how biological essentialism is the fault line that underpins procreative regulation in England and Wales and, as such, has created significant friction and limitation where new procreative technologies that could challenge cis-heteronormative nuclear family models come into being.
Meet Supplier-Induced Demand
Authors: Sytch Maxim 
Published: December 2024

Abstract
Supplier-induced demand refers to the demand generated by the discretionary influence of the supplier, which is unrelated to the buyer’s needs. In the weak form of supplier-induced demand, procuring an additional service may result in no change in the buyer’s outcome relative to not doing so; in the strong form, the buyer may be left worse off. This chapter traces the intellectual origins of supplier-induced to healthcare, where James Alison Glover in the 1930s uncovered evidence that the frequency of tonsillectomies—surgical procedures to remove tonsils from one’s throat—varied greatly across different school districts and depended on the surgeons’ discretion. Disturbingly, higher rates of tonsillectomy did not translate into improved health outcomes for schoolchildren. Studying supplier-induced demand in professional services has profound implications for organizations and markets. This is because the economy is moving toward, rather than away from, a higher level of supplier-induced demand.