bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks
Authors: Abdallah Reham , Abdelgaber Sayed , Sayed Hanan Ali 
Published: December 2024

AbstractInfectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to effictively mitigate their impact. This study applies advanced machine learning techniques to forecast outbreaks of Dengue, Chikungunya, and Zika, utilizing a comprehensive dataset comprising climate and socioeconomic data. Spanning the years 2007 to 2017, the dataset includes 1716 instances characterized by 27 distinct features. The researchers adopt the Analytic Hierarchy Process (AHP) for feature selection and integrated transfer learning to boost the accuracy of the study’s predictions. The researchers’ approach involves the deployment of several machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and an ensemble of these methods. The result reveals that the ensemble model is particularly effective, achieving the highest accuracy rate of 96.80% and an AUC of 0.9197 for predicting Zika outbreaks. Furthermore, it exhibts consistent performance across various metrics. Notably, in the context of Chikungunya, this model achieves an optimal balance between precision and recall, with an accuracy of 93.31%, a precision of 57%, and a recall of 63%, highlighting its reliability for effective outbreak prediction.
Mycosynthesis of zinc sulfide/zinc oxide nanocomposite using Fusarium oxysporum for catalytic degradation of methylene blue dye, antimicrobial, and anticancer activities
Authors: Salaheldin Hosam , Aboelnga Aya , Elsayed Ashraf 
Published: December 2024

AbstractIn the present study, extracellular cell-free filtrate (CFF) of fungal Fusarium oxysporum f. sp. cucumerinum (FOC) species, was utilized to biosynthesize zinc oxide /zinc sulfide (ZnO/ZnS) nanocomposite. This was achieved by mixing the metal salt with the fungal CFF for 96 h at a temperature of 27 ± 1.0 °C and a pH of 6.5. Several analytical techniques, such as XRD, TEM, UV-Vis, FTIR, DLS, and zeta potential studies, have confirmed the synthesis of NPs. Fungal CFF enzymes and metabolites stabilized produced NPs, according to FTIR. The nanocomposite particle diameter (15–80 nm) was estimated using HR-TEM imaging. The DLS and XRD measurements verified those findings. The zone of inhibition diameter for MRSA was 35 ± 0.21 mm, while B. subtilis measured 33 ± 0.32 mm against the nanocomposite. For E. coli and S. typhi bacterial isolates, it was 25 ± 0.19 and 32 ± 0.36 mm, respectively. The determined MIC value for E. coli was 5,000 µg/mL and MRSA was 500 µg/mL. The ZnO/ZnS nanocomposite has a dose-dependent cytotoxic effect on breast cancer cells, with an IC50 of 197 ± 0.895 µg/mL. The Methylene blue dye was removed by 87.51% using the nanocomposite. Thus, green biosynthesized ZnO/ZnS nanocomposites are recommended for pharmaceutical, industrial, and biological applications.
Longitudinal changes in reinforcement learning during smoking cessation: a computational analysis using a probabilistic reward task
Authors: Montemitro Chiara , Ossola Paolo , Ross Thomas J. , Huys Quentin J. M. , Fedota John R. , Salmeron Betty Jo , di Giannantonio Massimo , Stein Elliot A. 
Published: December 2024

AbstractDespite progress in smoking reduction in the past several decades, cigarette smoking remains a significant public health concern world-wide, with many smokers attempting but ultimately failing to maintain abstinence. However, little is known about how decision-making evolves in quitting smokers. Based on preregistered hypotheses and analysis plan (https://osf.io/yq5th), we examined the evolution of reinforcement learning (RL), a key component of decision-making, in smokers during acute and extended nicotine abstinence. In a longitudinal, within-subject design, we used a probabilistic reward task (PRT) to assess RL in twenty smokers who successfully refrained from smoking for at least 30 days. We evaluated changes in reward-based decision-making using signal-detection analysis and five RL models across three sessions during 30 days of nicotine abstinence. Contrary to our preregistered hypothesis, punishment sensitivity emerged as the only parameter that changed during smoking cessation. While it is plausible that some changes in task performance could be attributed to task repetition effects, we observed a clear impact of the Nicotine Withdrawal Syndrome (NWS) on RL, and a dynamic relationship between craving and reward and punishment sensitivity over time, suggesting a significant recalibration of cognitive processes during abstinence. In this context, the heightened sensitivity to negative outcomes observed at the last session (30 days after quitting) compared to the previous sessions, may be interpreted as a cognitive adaptation aimed at fostering long-term abstinence. While further studies are needed to clarify the mechanisms underlying punishment sensitivity during nicotine abstinence, these results highlight the need for personalized treatment approaches tailored to individual needs.
Rocket launches threaten global biodiversity conservation
Authors: Ang Lee Ping , Kong Fanhao , Hernández-Rodríguez Enrique , Liu Qiuyu , Cerrejόn Carlos , Feldman Mariano J. , Shu Lei , Ye Li-Xin , Gao Lei , Ang Lee Ling , Yin Xiangbo 
Published: December 2024

AbstractSatellite technologies are essential for global conservation actions through providing continuous, real-time Earth monitoring. However, development of these technologies necessitates an increase in rocket launches, which introduces new threats to biodiversity. Here, we mapped rocket launch sites and assessed their threats on protected areas and terrestrial biodiversity. Our analysis revealed that over 90% launch sites are within areas where unprotected habitats excesses 50% and over 62% of operating sites are located within or near protected areas. The threats from rocket launches are potentially associated with biomes, coordinates, and proximity to oceans. In particular, threatened terrestrial species in Tropical and Subtropical Moist Broadleaf Forests are more vulnerable to these risks compared to species in other biomes. Without strategic planning, the continued growth of rocket launches could create conflicts between technological development and conservation efforts, undermining the achievement of UN Biodiversity Goals.
eDNA offers opportunities for improved biodiversity monitoring within forest carbon markets
Authors: Allen Michael C. , Lockwood Julie L. , Ibanez Rosa , Butler Josh D. , Angle Jordan C. , Jaffe Benjamin D. 
Published: December 2024

AbstractForest-based carbon sequestration projects incentivize reforestation and restoration activities while offering opportunities to realize co-benefits such as biodiversity conservation. While conservation aspects are increasingly emphasized in these projects, the rigor of biodiversity co-benefit verification has been highly variable. Recent advances in biodiversity monitoring based on shed DNA in the environment (eDNA) offer promise for improving effectiveness, standardization, and transparency. Here we analyze 129 forest carbon projects and 396 peer-reviewed studies to identify how biodiversity co-benefits are currently verified within forest carbon markets, and to evaluate the potential of eDNA for tracking biodiversity change. Our analysis revealed that eDNA studies focused more on smaller organisms (microbes and invertebrates) and on temperate ecosystems compared with biodiversity-focused forest carbon projects. Efforts to align these two worlds via investments into broadening the geographic and taxonomic scope could allow greater adoption and increased accountability in biodiversity monitoring within forest carbon markets (i.e. standardized, auditable biodiversity data trails). Adapting advancements in eDNA technology to the biodiversity monitoring needs of nature-based initiatives will aid countries and organizations striving to meet global conservation commitments.
Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory
Authors: Sun Yizhen , Yang Junyou , Zhao Donghui , Okonkwo Moses Chukwuka , Zhang Jianmin , Wang Shuoyu , Liu Yang 
Published: December 2024

AbstractThe advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.
Novel vision‐LiDAR fusion framework for human action recognition based on dynamic lateral connection
Authors: Yan Fei , Jin Guangyao , Mu Zheng , Zhang Shouxing , Cai Yinghao , Lu Tao , Zhuang Yan 
Published: December 2024

AbstractIn the past decades, substantial progress has been made in human action recognition. However, most existing studies and datasets for human action recognition utilise still images or videos as the primary modality. Image‐based approaches can be easily impacted by adverse environmental conditions. In this paper, the authors propose combining RGB images and point clouds from LiDAR sensors for human action recognition. A dynamic lateral convolutional network (DLCN) is proposed to fuse features from multi‐modalities. The RGB features and the geometric information from the point clouds closely interact with each other in the DLCN, which is complementary in action recognition. The experimental results on the JRDB‐Act dataset demonstrate that the proposed DLCN outperforms the state‐of‐the‐art approaches of human action recognition. The authors show the potential of the proposed DLCN in various complex scenarios, which is highly valuable in real‐world applications.
Maglev train levitation control via tracking differentiator with small phase lag
Authors: Zhang Louyue , Xiao Gaoxi , Zhai Chao , Zhang Hehong 
Published: December 2024

AbstractThe levitation control system is a critical subsystem in maglev trains, ensuring stable levitation of the train on the guideway. Achieving stable levitation requires providing the system with accurate levitation gap and corresponding velocity data while minimizing phase lag. This work proposes an enhanced tracking differentiator (TD) to reduce phase lag in both filtering and differentiation of input signals with varying noise levels. The improved performance is achieved by incorporating system damping and an amplitude factor into the control algorithm used to design the differentiator. Theoretical analysis guarantees the convergence of the proposed algorithm. Simulations and experiments conducted on the levitation gap data demonstrate the superior performance of the proposed TD in reducing phase lag for both filtered and differentiated signals. Furthermore, experimental results highlight the improved performance of the feedback controller when employing the proposed TD.
Code Parameter Summarization Based on Transformer and Fusion Strategy
Authors: Zhang Fanlong , Fan Jiancheng , Li Weiqi , Khoo Siau-cheng 
Published: December 2024

Context: As more time has been spent on code comprehension activities during software development, automatic code summarization has received much attention in software engineering research, with the goal of enhancing software comprehensibility. In the meantime, it is prevalently known that a good knowledge about the declaration and the use of method parameters can effectively enhance the understanding of the associated methods. A traditional approach used in software development is to declare the types of method parameters.Objective: In this work, we advocate parameter‐level code summarization and propose a novel approach to automatically generate parameter summaries of a given method. Parameter summarization is considerably challenging, as neither do we know the kind of information of the parameters that can be employed for summarization nor do we know the methods for retrieving such information.Method: We present paramTrans, which is a novel approach for parameter summarization. paramTrans characterizes the semantic features from parameter‐related information based on transformer; it also explores three fusion strategies for absorbing the method‐level information to enhance the performance. Moreover, to retrieve parameter‐related information, a parameter slicing algorithm (named paramSlice) is proposed, which slices the parameter‐related node from the abstract syntax tree (AST) at the statement level.Results: We conducted experiments to verify the effectiveness of our approach. Experimental results show that our approach possesses an effective ability in summarizing parameters; such ability can be further enhanced by understanding the available summaries about individual methods, through the introduction of three fusion strategies.Conclusion: We recommend developers employ our approach as well as the fusion strategies to produce parameter summaries to enhance the comprehensibility of code.
The coherent magnetic field of the Milky Way halo, the Local Bubble, and the Fan region
Authors: Korochkin Alexander , Semikoz Dmitri , Tinyakov Peter 
Published: December 2024

Context. A recent catalogue of the Faraday rotation measures (RMs) of extragalactic sources, together with the synchrotron polarisation data from WMAP and Planck, provide us with a wealth of information on the magnetic fields of the Galaxy. However, the integral character of these observables, together with our position inside the Galaxy, make the inference of the coherent Galactic magnetic field (GMF) complicated and ambiguous.
Aims. We combine several phenomenological components of the GMF – the spiral arms, the toroidal halo, the X-shaped field, and the field of the Local Bubble – to construct a new model of the regular GMF outside the thin disc.
Methods. We use the binned χ2 approach to fit the parameters of the model to the data. To have control over the relative contributions of the RM and polarisation data to the fit, we pay special attention to the estimation of errors in data bins. To this end, we developed a systematic method that is uniformly applicable to different data sets. This method takes into account individual measurement errors, the variance in the bin, and fluctuations in the data at angular scales that are larger than the bin size. This leads to a decrease in the errors and, as a result, to better sensitivity of the data to the model content. We cross checked the stability of our method with the new LOFAR data, which have very small errors on the measurements of individual sources.
Results. We find that the four components listed above are sufficient to fit both the RM and polarisation data over the whole sky with only a small fraction masked out. Moreover, we have achieved several important improvements compared to previous approaches. Due to our location inside of the Local Bubble, our model does not require the introduction of striated fields. For the first time, we show that the Fan region can be modelled as a Galactic-scale feature. The pitch angle of the magnetic field in our fit converges to a value of around 20 degrees. Interestingly, this value is very close to the direction of the spiral arms inferred recently from Gaia data on upper-main sequence stars.
Efficacy and Tolerability of Lacosamide in Pediatric and Young Adult Epilepsy Patients with Severe Motor and Intellectual Disabilities
Authors: Ishikawa Nobutsune , Suzuki Shin , Komori Ryota , Izumo Hiroki , Goda Satoshi , Tsuboi Ayana , Jinno Kazuhiko 
Published: December 2024

Abstract
Objective Epilepsy is common among patients with severe motor and intellectual disabilities (SMID) patients, often taking a prolonged and intractable course. Lacosamide (LCM) is widely used to treat epilepsy in both adults and children. We assess the efficacy and tolerability of LCM among pediatric and young adult epilepsy patients with SMID who suffer from intractable seizures.
Methods This retrospective analysis reviewed the medical records of SMID patients undergoing LCM treatment for more than a year. The study included 24 patients (14 males) aged 3 to 29 years. Epilepsy was classified as focal in 17 patients, generalized in 4 patients, and combined generalized and focal in 3 patients.
Results The retention rates were 70.8%, 65%, and 52.9% at 1, 2, and 3 years after LCM initiation, respectively. The 50% responder rate (achieving >50% seizure reduction) for LCM treatment was 50%, with two patients experiencing complete seizure control (absence of seizures for 6 months before the follow-up visit). The 50% responders included a higher proportion of patients with focal epilepsy (58.8%) compared to those with generalized epilepsy (25.0%). Treatment-emergent adverse events (TEAEs) included somnolence in five patients and nausea in two patients. TEAEs, particularly nausea, developed within 1 month after treatment initiation in two patients, leading to LCM discontinuation.
Conclusion LCM demonstrated good efficacy for intractable epilepsy in pediatric and young adult SMID patients. It was generally well-tolerated, resulting in a favorable retention rate. LCM emerged as a useful antiseizure medication for epilepsy treatment in pediatric and young adult SMID patients.
Photodriven Site-Selective Deuteration of Pyridyl Ketones with Deuterium Oxide
Authors: Li Yanzhong , Dong Kaiwu , Tian Wen-Qiang , Shen Chaoren 
Published: December 2024

AbstractIn this work, a photodriven phosphine/D2O-mediated method for the reductive deuteration of pyridyl ketones was developed. By regulating the reaction conditions, site-selective deuteration of pyridyl diketones was achieved with high deuterium-incorporation rates.
Homeopathic Management of Bulky Uterus with Uterine Fibroid in a 40-year-old Patient: An Evidence-Based Case Report
Authors: Wadhwa Bharti , Kureel Shweta Singh 
Published: December 2024

Abstract
Background Uterine fibroids are benign gynecological tumours that arise in the uterine smooth muscle tissue and are characterized by the production of excessive quantities of extracellular matrix. Various therapeutic options, from medical management to surgical intervention, exist for uterine fibroids, for which homeopathy has emerged as a promising therapeutic option in patient-centered care.
Case History A 40-year-old woman presented with complaints of heavy menstrual bleeding, with pain in the pelvic region and back. She was diagnosed as a case of bulky uterus with uterine fibroid. The patient opted for homeopathic management and was prescribed Thuja occidentalis based on individualization, miasmatic analysis and repertorial totality.
Results After treatment, there was regression in size of the fibroid within 5 months, with complete resolution and restoration of the uterus to normal size within 9 months. Changes were validated through ultrasonography, which added objective evidence to the positive outcome. The Modified Naranjo Criteria for Homeopathy (MONARCH) inventory total score was +9, which indicated causal attribution of clinical outcome to the homeopathic intervention.
Conclusion This case study has illustrated the clinical benefits of homeopathy in the management of a patient with uterine fibroids, reaffirming the need for further research to explore its value as a therapeutic option for individuals seeking alternative or complementary approaches for appropriate medical conditions.
Beverage-Induced Surface Changes in Biomimetic Dental Resin Composite: AFM and Bacterial Analysis
Authors: Basheer Rasha R. , Hamza Nermeen K. 
Published: December 2024

Abstract
Objective Continuous advancements in composite resin materials have revolutionized and expanded its clinical use, improving its physical and mechanical properties. Attaining and retaining surface texture and gloss are crucial for the long-term durability of the composite resin material. This study investigated the supra-nanospherical filler composite material compared with different composite resin materials immersed in different beverages. The study evaluated their surface roughness and subsequent adhesion of bacteria.
Materials and methods A total of 144 specimens were made, using Teflon mold from different composite materials. Eighty-four specimens were used for surface roughness testing, using four different resin composite materials, Tetric N-Ceram (Ivoclar Vivadent, Schaan, Liechtenstein), Multichrome (Harvard Dental, Germany), Filtek Z350 XT (3M ESPE, Minnesota, United States), and Palfique LX5 (Tokuyama Dental Corporation, Taitō-Ku, Tokyo, Japan; n = 21). They were further subdivided into three subgroups according to the immersion solution (n = 7) for Monster, Gatorade, and deionized water, which served as the control group. Surface roughness values were tested via atomic force microscopy (AFM). Then, for biofilm testing the bacterial count was performed on the remaining 60 composite specimens from the four tested composite materials (n = 15), that were subdivided randomly based on the immersion solutions into three subgroups (n = 5).
Statistical analysis Data were collected and statistically analyzed using the Kruskal–Wallis test followed by Dunn's post hoc test with Bonferroni's correction (p ≤ 0.05). The intergroup comparison showed a significant difference among different composite materials (p < 0.05), with the Multichrome showing the highest roughness values. Also, there was a significant difference between all composite materials with different beverages, with Palfique LX5 showing the lowest average roughness (Ra) values. All studied materials' average surface roughness, however, remained below the crucial Ra value of 0.2 μm. For the bacterial count, there was a significant difference between different materials in different beverages (p < 0.05), with Z350 XT and Palfique LX5 showing the lowest bacterial count.
Conclusion Supra-nanospherical composite (Palfique LX5) exhibited better resistance to different beverage challenges regarding surface roughness, while nanohybrid composite (Z350) showed the least bacterial adherence.