bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning Approach and Model for Predicting Proton Stopping Power Ratio and Other Parameters Using Computed Tomography Images
Authors: Chika Charles Ekene 
Published: December 2024

Purpose:
The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density ρ
e, effective atomic number (Z
eff), and mean excitation energy (I) using one simple robust model and design a machine learning algorithm that will lead to automation.

Methods:
Empirical relationships between computed tomography (CT) number and SPR, ρ
e (Z
eff) and I were used to formulate a model that predicts all the four parameters using linear attenuation coefficients which can be converted to CT numbers. The results of these models were compared with the results of other existing models. Thirty-three ICRU human tissues were used as modeling data and 12 Gammex inserts as testing data for the machine learning algorithm designed. More ways of tissue classification were introduced to improve accuracy. In the examples, the dual energy methods were implemented using 80 kVp and 150 kVP/Sn.

Results:
The proposed method gave modeling root mean square error (RMSE) near 1% at maximum for the case of SPR and ρ
e for both single and dual-energy CT approaches considered with modeling RMSE of 0.32% for ρ
e and 0.38% for SPR as modeling RMSE with room for improvement (this can be done by adjusting the model number of terms as well as the parameters). The method was able to achieve modeling RMSE of 1.11% for I and 1.66% for Z
ef
f. The mean error for all the estimated quantities was near 0.00%. In most cases, the proposed method has lower testing RMSE and mean error compare to the other methods presented in the study.

Conclusion:
The proposed method proves to be more flexible and robust among all presented methods since it has lower testing error in most cases and can be improved based on data using the machine learning algorithm. The algorithm can also improve estimation by adjusting the model as well as aid in automation and it’s easy to implement.
Comparison of Serum Zinc and Copper Concentrations in Females with Ovarian and Uterine Tumors
Authors: Adhab Hiba Ghmeedh , Mahdi Liwaa Hussein , Al-Hilo Eman M. 
Published: December 2024

Background and Aims:
Ovarian and uterine tumors are among the most serious gynecological diseases and the most common cause of mortality globally. In recent times, the role of trace elements in the onset and development of tumors has come under the review. This study aimed to assess the levels of Zn and Cu in the serum of female patients with benign and malignant uterine and ovarian tumors.

Materials and Methods:
One hundred and twenty-four women with benign and malignant ovarian and uterine tumors were eligible for the study. Blood samples were obtained and analyzed using flame-atomic absorption spectroscopy spectrometry in Najaf City, Iraq.

Results:
Serum zinc levels exhibited lower concentration (4.73 ± 1.92) in patients with malignant uterine tumors than those with benign uterine tumors (10.80 ± 1.87, P = 0.000). In contrast, the mean concentration of copper was higher in patients with malignant uterine tumors (110.37 ± 20.05 vs. 103.75 ± 14.34, P = 0.063). The serum zinc concentrations (12.73 ± 5.34 vs. 8.90 ± 2.77, P = 0.001) were higher in patients with malignant ovarian tumors. Furthermore, we found the mean serum copper levels in patients with benign ovarian tumors decreased significantly from (101.86 ± 15.44 to 86.77 ± 21.55, P = 0.002) in female patients with malignant ovarian tumors group.

Conclusions:
Serum concentrations of copper and zinc increased in some study groups and declined in others. The examination of serum trace element concentrations in patients with ovarian and uterus tumors would provide us with insight into a better understanding of the pathogenesis of the tumors and also to distinguish between them.
Magnetic Resonance Thermometry of Focused Ultrasound Using a Preclinical Focused Ultrasound Robotic System at 3T
Authors: Filippou Antria , Evripidou Nikolas , Georgiou Andreas , Georgiou Leonidas , Chrysanthou Antreas , Ioannides Cleanthis , Damianou Christakis 
Published: December 2024

Aim:
Focused ultrasound (FUS) therapies are often performed within magnetic resonance imaging (MRI) systems providing thermometry-based temperature monitoring. Herein, MRI thermometry was assessed for FUS sonications executed using a preclinical system on agar-based phantoms at 1.5T and 3T MRI scanners, using the proton resonance frequency shift technique.

Materials and Methods:
Sonications were executed at 1.5T and 3T to assess the system and observe variations in magnetic resonance (MR) thermometry temperature measurements. MR thermometry was assessed at 3T, for identical sonications on three agar-based phantoms doped with varied silica and evaporated milk concentrations, and for sonications executed at varied acoustic power of 1.5–45 W. Moreover, echo time (TE) values of 5–20 ms were used to assess the effect on the signal-to-noise ratio (SNR) and temperature change sensitivity.

Results:
Clearer thermal maps with a 2.5-fold higher temporal resolution were produced for sonications at 3T compared to 1.5T, despite employment of similar thermometry sequences. At 3T, temperature changes between 41°C and 50°C were recorded for the three phantoms produced with varied silica and evaporated milk, with the addition of 2% w/v silica resulting in a 20% increase in temperature change. The lowest acoustic power that produced reliable beam detection within a voxel was 1.5 W. A TE of 10 ms resulted in the highest temperature sensitivity with adequate SNR.

Conclusions:
MR thermometry performed at 3T achieved short temporal resolution with temperature dependencies exhibited with the sonication and imaging parameters. Present data could be used in preclinical MRI-guided FUS feasibility studies to enhance MR thermometry.
Virtual Mentoring for Medical Physicists: Results of a Global Online Survey
Authors: Van Dyk Jacob , Jalink Matt , Schreiner L. John , Jeraj Robert 
Published: December 2024

Purpose:
Medical physics professional development is limited in parts of the globe and can be aided by virtual mentoring. A global online perception survey was conducted to elucidate the characteristics of the preferred virtual mentoring program.

Methods:
Informed by a literature review and pilot testing by focus groups, the survey was electronically disseminated to multiple medical physics organizations, list servers, and professional contacts. It addressed issues including factors and barriers influencing successful mentoring; mentors’/mentees’ matching preferences; frequency and length of meetings; importance of defining expectations; formal agreement; and assessment of the mentoring process. Descriptive statistics were used to characterize responses including comparisons by country income level.

Results:
The 396 responders (68% male and 32% female) were from 76 countries with 66% from high-income countries (HICs) and 34% from low- and middle-income countries (L&MICs). Data were provided on experience level as mentors (43% “little [occasional]”, 38% “lot [regular or ongoing]”) and mentees (53% “little [occasional]”, and 23% “lot [regular or ongoing]”), and interest in participating in mentorship program (83% as mentor, mentee, or both). L&MIC responders were generally younger with less work experience (55% <10 years versus 28% for HIC responders). Differences between L&MIC and HIC responses occurred when considering the perceived limitations and barriers to virtual mentoring. Preferences were given to mentoring logistics (formal agreement, frequency, length, and format of meetings).

Conclusions:
Factors to consider in developing a virtual mentorship program are informed by the survey results and are applicable to both HIC and L&MIC contexts, to medical physicists, and to other related professions.
Quantitative Assessment of Photoneutron-induced Secondary Radiation Dose in Prostate Treatment Using an 18 MV Medical Linear Accelerator: A Monte Carlo Study
Authors: Assalmi Mustapha , Alshreef Abdullah , Diaf EL Yamani , Arectout Assia , Ade Nicholas , El Berhdadi El Hassan 
Published: December 2024

Purpose:
This study aims to quantify the secondary radiation dose caused by photoneutrons during prostate cancer treatment using an 18 MV medical linear accelerator (LINAC) through Monte Carlo simulations and experimental validation.

Methods:
Monte Carlo simulations were performed using G4Linac_MT to model the 18 MV photon beam of an Elekta LINAC. The simulation results were validated against experimental measurements. Neutron characteristics, including penetration, cross-section interactions, Linear Energy Transfer (LET), and dose contributions, were analyzed using an adult male ICRP phantom. Prostate treatment scenarios involved 3D-CRT plans with 4-fields, 5-fields, and 7-fields. Specific absorbed fractions (SAFs) in various organs were also evaluated.

Results:
Simulation and experimental measurements showed strong agreement, with a dose error of approximately 0.74%, and 97% of dose points passed a 2%/2 mm gamma index. Intermediate neutrons constituted 87.05%, while 12.95% were fast neutrons. Neutron dose contributions were 0.63%, 0.33%, and 0.77% for the 3D-CRT 4-field, 5-field, and 7-field plans, respectively. SAF values decreased as neutron energy increased, highlighting reduced neutron interaction efficiency at higher energies.

Conclusions:
Monte Carlo simulation is a reliable approach for evaluating neutron dose contributions in high-energy X-ray LINACs. Optimization of treatment plans is essential to minimize neutron-induced dose contributions.
Ensemble Learning for Three-dimensional Medical Image Segmentation of Organ at Risk in Brachytherapy Using Double U-Net, Bi-directional ConvLSTM U-Net, and Transformer Network
Authors: Pal Soniya , Singh Raj Pal , Kumar Anuj 
Published: December 2024

Aim:
This article presents a novel approach to automate the segmentation of organ at risk (OAR) for high-dose-rate brachytherapy patients using three deep learning models combined with ensemble learning techniques. It aims to improve the accuracy and efficiency of segmentation.

Materials and Methods:
The dataset comprised computed tomography (CT) scans of 60 patients obtained from our own institutional image bank and 10 patients from the other institute, all in Digital Imaging and Communications in Medicine format. Experienced radiation oncologists manually segmented four OARs for each scan. Each scan was preprocessed and three models, Double U-Net (DUN), Bi-directional ConvLSTM U-Net (BCUN), and Transformer Networks (TN), were trained on reduced CT scans (240 × 240 × 128) due to memory limitations. Ensemble learning techniques were employed to enhance accuracy and segmentation metrics. Testing and validation were conducted on 12 patients from our institute (OID) and 10 patients from another institute (DID).

Results:
For DID test dataset, using the ensemble learning technique combining Transformer Network (TN) and BCUN, i.e., TN + BCUN, the average Dice similarity coefficient (DSC) ranged from 0.992 to 0.998, and for DUN and BCUN (DUN + BCUN) combination, the average DSC ranged from 0.990 to 0.993, which reflecting high segmentation accuracy. The 95% Hausdorff distance (HD) ranged from 0.9 to 1.2 mm for TN + BCUN and 1.1 to 1.4 mm for DUN + BCUN, demonstrating precise segmentation boundaries.

Conclusion:
The proposed method leverages the strengths of each network architecture. The DUN setup excels in sequential processing, the BCUN captures spatiotemporal dependencies, and transformer networks provide a robust understanding of global context. This combination enables efficient and accurate segmentation, surpassing human expert performance in both time and accuracy.
Evaluating the Predictive Value of the Dose-volume Parameters and Vascular Endothelial Growth Factor Expression on Rectal Toxicity in Prostate Cancer Patients
Authors: Mirzaeiyan Marziyeh , Khanahmad Hossein , Hemati Simin , Etehadtavakol Mahnaz , Sharifonnasabi Zahra , Shokrani Parvaneh 
Published: December 2024

Background:
The response to radiation varies widely among people. Despite recent advances to conform the dose distribution to the tumor volume, it is still impossible to perform radiotherapy based on the biological characteristics of each individual. In this case, identifying a biomarker can be a step toward personalizing treatment. This research was carried out to evaluate the predictive value of dose-volume parameters and vascular endothelial growth factor (VEGF) expression on rectal proctitis toxicity in prostate cancer patients.

Materials and Methods:
Eighteen patients with prostate cancer who underwent helical tomotherapy were included in the study. VEGF serum level before and after treatment was obtained. Furthermore, dosimetric parameters, including rectal volume, maximum dose, V50, V60, V65, V70, and V75 were extracted. Spearman’s correlation coefficient of VEGF-related and dosimetric parameters with the grade ≥1 rectal proctitis was calculated.

Results:
In the rectal toxicity group, the mean value of VEGF increased significantly after treatment compared to before (P = 0.008). Despite lower values of pre- and post-treatment VEGF in the toxicity group, this difference was not statistically significant (P > 0.05). Among the dosimetric parameters, only V65 had a significantly higher value in the toxicity group (P = 0.033). The highest correlation coefficients were obtained for pretreatment VEGF values and V65 (−0.446 and 0.450).

Conclusion:
The results of the study confirm the correlation of VEGF expression with the pathobiology process of rectal radiation proctitis. However, the pathobiology process of radiation proctitis is complicated. More research is needed to prove the involvement of VEGF expression in the early detection of proctitis.
Industrial Ecosystems Sustainable Development Management Conceptual Model
Authors: Mityakov Evgenii , Kulikova Natalia , Ladynin Andrey , Yudin Alexander , Grosheva Polina 
Published: December 2024

Objective: The paper is devoted to industrial ecosystems management sustainable development theoretical basis and conceptual model creation. Industrial ecosystem concept author's vision is presented.

Method: Key methods include comparative analysis to explore definitions, approaches, and management models; decomposition to break down sustainable development factors into sub-factors for detailed management; system analysis to identify essential sustainable development factors; and modeling to create a structural framework that aligns goal-setting, management, and evaluation.

Results and Discussion: The resulting model integrates key factors, determinants, indicators, and management tools, aiming for long-term sustainability by balancing environmental, social, and economic goals while fostering innovation and collaboration among stakeholders. Future research may refine the model by examining specific cases to develop precise sustainability metrics, supporting both theoretical and practical advancements in industrial management.

Research Implications: The research implications of this study highlight the potential for advancing sustainable management practices within industrial ecosystems by developing an integrated model that balances economic, social, and environmental sustainability. The model underscores the importance of digital maturity, collaboration, and innovation as drivers of ecosystem resilience and adaptability. Future research can apply this model to specific industrial ecosystems, enabling the identification of actionable metrics for sustainable performance and fostering evidence-based practices across diverse sectors.
PENERAPAN HUKUM PIDANA TERHADAP PENCUCIAN UANG DALAM TINDAK PIDANA KEJAHATAN NARKOTIKA (Putusan Mahkamah Agung Nomor 250 K/Pid.Sus/2018)
Authors: Manalu Arisman Freddy , Purba Nelvitia , Affan Ibnu 
Published: December 2024

The crime of money laundering as a crime has a distinctive feature, namely that this crime is not a single crime but a multiple crime. The crime of money laundering does not stand alone because the assets placed, transferred, or transferred by means of integration are obtained from a criminal act, meaning that there has been another criminal act that preceded it. The formulation of the problem in this thesis is how to regulate the crime of money laundering, how to apply the law of money laundering in narcotics crime cases, how the legal basis for judges to return the assets of the defendant in the Supreme Court decision Number 250 K / Pid.Sus / 2018. The research method used is descriptive analysis which leads to normative juridical legal research, namely research carried out by referring to legal norms, namely examining library materials or secondary materials. Secondary data by processing data from primary legal materials, secondary legal materials and tertiary legal materials. The results showed that the regulation of money laundering in case Number 250 K / Pid.Sus / 2018 is regulated in Article 3 of Law Number 8 of 2010. Murtala Ilyas Bin Ilyas was legally and convincingly proven guilty of committing the crime of money laundering and imposing a crime. to the defendant with imprisonment for 8 (eight) years and a fine of Rp. 5,000,000,000.00 (five billion rupiah) provided that if the fine is not paid, then it is replaced by imprisonment for (three) months. The legal consideration of the judge returning the defendant's assets was because the evidence was obtained between 2002 and 2006, namely the period before the tempus delicti of 2009 to 2016, so the evidence had to be returned to the defendant Murtala Ilyas.
The Value of Generative AI for Qualitative Research: A Pilot Study
Authors: Pattyn Frédéric 
Published: December 2024

This mixed-methods approach study investigates the potential of introducing generative AI (ChatGPT 4 and Bard) as part of a deductive qualitative research design that requires coding, focusing on possible gains in cost-effectiveness, coding throughput time, and inter-coder reliability (Cohen’s Kappa). This study involved semi-structured interviews with five domain experts and analyzed a dataset of 122 respondents that required categorization into six predefined categories. The results from using generative AI coders were compared with those from a previous study where human coders carried out the same task. In this comparison, we evaluated the performance of AI-based coders against two groups of human coders, comprising three experts and three non-experts. Our findings support the replacement of human coders with generative AI ones, specifically ChatGPT for deductive qualitative research methods of limited scope. The experimental group, consisting of three independent generative AI coders, outperformed both control groups in coding effort, with a fourfold (4x) efficiency and throughput time (15x) advantage. The latter could be explained by leveraging parallel processing. Concerning expert vs. non-expert coders, minimal evidence suggests a preference for experts. Although experts code slightly faster (17%), their inter-coder reliability showed no substantial advantage. A hybrid approach, combining ChatGPT and domain experts, shows the most promise. This approach reduces costs, shortens project timelines, and enhances inter-coder reliability, as indicated by higher Cohen’s Kappa values. In conclusion, generative AI, exemplified by ChatGPT, offers a viable alternative to human coders, in combination with human research involvement, delivering cost savings and faster research completion without sacrificing notable reliability. These insights, while limited in scope, show potential for further studies with larger datasets, more inductive qualitative research designs, and other research domains.

Received: 30 March 2024 | Revised: 17 May 2024 | Accepted: 18 June 2024

Conflicts of Interest
The author declares that he has no conflicts of interest to this work.

Data Availability Statement
The data that support the findings of this study are not publicly available due to privacy concerns. However, anonymous data are available on request from the corresponding author upon reasonable request.

Author Contribution Statement
Frédéric Pattyn: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization, Project administration.
ПЕРСПЕКТИВНЫЕ НАПРАВЛЕНИЯ СНИЖЕНИЯ ЭМИССИИ CO2 ПРИ ПРОИЗВОДСТВЕ ВАНАДИЕВОГО ЧУГУНА
Authors: Половец М.В. , Загайнов С.А. , Пыхтеева К.Б. , Сидоров Е.А. , Бижанов А.М. 
Published: December 2024

. Современный этап развития металлургии характеризуется беспрецедентным вниманием к вопросам снижения эмиссии CO2. В качестве одного из путей снижения эмиссии CO2 рассматривается замена углерода водородом при выплавке чугуна при этом следует учитывать, что получение водорода также связано с выбросами углекислого газа. Степень использования CO при косвенном восстановлении в доменной печи составляет 28–38% в отличие от прямого восстановления, где степень использования в большей степени зависит от площади контакта восстановителя и оксидов. Перспективным направлением увеличения степени использования восстановительной способности углерода в доменной печи является применение рудоугольных брикетов. В работе рассмотрена эффективность использование рудоугольных брикетов при производстве ванадиевого чугуна с точки зрения снижения эмиссии СО2. Показано, что затраты углерода на прямое восстановление титаномагнетитового концентрата в 6 раз меньше, чем при косвенном. Применение рудоугольных брикетов в шихте доменных печей позволит снизить удельный расход углерода топлива при производстве ванадиевого чугуна, что приведет к снижению эмиссии CO2.
The current stage of development of metallurgy is characterized by unprecedented attention to issues of reducing CO2 emissions. As one of the ways to reduce CO2 emissions, the replacement of carbon with hydrogen in the smelting of cast iron is considered; however, it should be taken into account that the production of hydrogen is also associated with carbon dioxide emissions. The CO utilization rate for indirect reduction in a blast furnace is 28–38%, in contrast to direct reduction, where the utilization rate is more dependent on the contact area between the reducing agent and the oxides. A promising direction for increasing the degree of utilization of the reducing power of carbon in a blast furnace is the use of ore-coal briquettes. The paper examines the effectiveness of using ore-coal briquettes in the production of vanadium cast iron from the point of view of reducing CO2 emissions. It has been shown that carbon consumption for direct reduction of titanomagnetite concentrate are 6 times less than for indirect reduction. The use of ore-coal briquettes in the blast furnace charge will reduce the specific consumption of fuel›s carbon in the production of vanadium cast iron, which will lead to a reduction in CO2 emissions.
ПРИМЕНЕНИЕ СИСТЕМ ЭЛЕКТРОМАГНИТНОГО ПЕРЕМЕШИВАНИЯ НА СЛЯБОВЫХ МНЛЗ И ИХ ВЛИЯНИЕ НА КАЧЕСТВО МАКРОСТРУКТУРЫ ЗАГОТОВОК
Authors: Кононыхин Г.Н. , Глебов В.П. , Шахов С.И. , Рогачиков Ю.М. 
Published: December 2024

Электромагнитное перемешивание является инструментом, способным оказывать влияние на характер кристаллизации непрерывнолитой заготовки и подавлять развитие ряда дефектов макроструктуры. Представлено описание технологических схем использования электромагнитных полей в МНЛЗ и проявляющихся при этом физических эффектов. Приведены фактические результаты по качеству стали, полученные при применении системы электромагнитного перемешивания роликового типа в зоне вторичного охлаждения слябовой МНЛЗ ПАО «НЛМК».
Electromagnetic stirring (EMS) is a tool capable of influencing the crystallization pattern of a continuously cast workpiece and suppressing the development of a number of macrostructure defects. The description of technological schemes is presented for the use of electromagnetic fields in the CCM and the physical effects manifested in this case. The actual results on the quality of steel are presented obtained by using a roller-type EMS in the secondary cooling zone of NLMK’s slab CCM.