bluuebunny/crossref_metadata_2025_split huggingface dataset:
TransitCrowd: Estimating Subway Stations Demand with Mobile Crowdsensing Data
Authors: Vitello Piergiorgio , Fiandrino Claudio , Connors Richard D. , Viti Francesco 
Published: December 2023

Abstract
Traditionally PT demand estimation relies on manual survey-based or, where available, smartcard passenger data. However, transport service providers and authorities make it rarely available to researchers. An additional challenge is the variety of formats and the low granularity in which such data is available. Recently, first steps towards the use of advanced ICT-based data-driven approaches have started to emerge. These new data sources can provide new opportunities for generating more data and insights into transit demand patterns and behaviour. In this paper, we propose a novel data-driven transit demand estimation process, TransitCrowd, and apply it to subway stations. TransitCrowd estimates the passengers entering and exiting each station using as proxy the subway crowdness provided by GPT crowdsensed information often available at sheer scale in any city. TransitCrowd's key component is its one-time calibration process, which creates temporal signatures of the stations based on historical GPT information, and regression-based machine learning and live GPT to predict passenger flows. We assess TransitCrowd's estimation accuracy for two cities across a two-months period, i.e., New York and Washington, showing very promising results for both estimation and real-time prediction of transit flows at subway stations.
Facilitators and Barriers to Vaccination Uptake in Pregnancy: A Qualitative Systematic Review
Authors: Razai Mohammad S , Mansour Rania , Ravindran Kavi , Freeman Samuel , Mason-Apps Charlotte , Morris Joan , Majeed Azeem , Ussher Michael , Hargreaves Sally , Oakeshott Pippa 
Published: December 2023

Abstract

Introduction
Vaccination during pregnancy protects both the mother and the foetus from vaccine-preventable diseases. However, uptake of the recommended vaccines (influenza, pertussis, COVID-19) by pregnant women remains low in Europe and USA. Understanding the reasons for this is crucial to inform strategies to increase vaccination rates in pregnant women. This qualitative systematic review aimed to identify the barriers and facilitators to vaccination against influenza, pertussis/whooping cough and COVID-19 during pregnancy and identify possible strategies to increase vaccination rates.
Methods
We conducted a comprehensive search of electronic databases, including Medline, PsycINFO, CINAHL, Web of Science, WHO database, Embase and grey literature to identify qualitative studies that explored barriers and facilitators to vaccine uptake among pregnant women (PROSPERO CRD42023399488). The search was limited to studies published between 2012 and 2022 conducted in high-income countries with established vaccination programmes during pregnancy. Studies were thematically analysed and underwent quality assessment using the Joanna Briggs Institute validated critical appraisal tool for qualitative research.
Results
Out of 2681 articles screened, 28 studies (n = 1573 participants) were eligible for inclusion. Five overarching themes emerged relating to personal, provider and systemic factors. Barriers to vaccine uptake included concerns about vaccine safety and efficacy, lack of knowledge about vaccines’ benefits and necessity, fear of adverse effects on the foetus or mother and low perception of disease severity. Facilitators included recommendations from trusted healthcare providers, easy access to vaccination, clear communication on the benefits and safety of vaccination, and positive social influences from family and friends. Strategies for increasing vaccination uptake included strong and proactive vaccine recommendations by trusted healthcare professionals, provision of vaccines during routine antenatal care, and clear and consistent communication about vaccines addressing pregnant women’s concerns.
Conclusion
This review highlights the need for interventions that address the identified barriers to vaccine uptake among pregnant women. Provider recommendation can play a significant role in promoting vaccine uptake, as can clear risk/benefit communication and convenient access to vaccination. Addressing concerns about vaccine safety and providing accurate information about vaccines is also important.
Biomarkers predict the efficacy of closed-loop rTMS treatment for refractory depression
Authors: Sajda Paul , Sun Xiaoxiao , Doose Jayce , Faller Josef , McIntosh James , Saber Golbarg , Huffman Sarah , Pantazatos Spiro , Yuan Han , Goldman Robin , Brown Truman , George Mark 
Published: December 2023

Abstract

Transcranial magnetic stimulation (TMS) is a non-invasive FDA-approved therapy for major depressive disorder (MDD), specifically for treatment-resistant depression (TRD). Though offering promise for those with TRD, its effectiveness is less than one in two patients (i.e., less than 50%). Limits on efficacy may be due to individual patient variability, but to date, there are no established biomarkers or measures of target engagement that can predict efficacy. Additionally, TMS efficacy is typically not assessed until a six-week treatment ends, precluding interim re-evaluations of the treatment. Here, we report results using a closed-loop phase-locked repetitive TMS (rTMS) treatment that synchronizes the delivery of rTMS based on the timing of the pulses relative to a patient's individual electroencephalographic (EEG) prefrontal alpha oscillation indexed by functional magnetic resonance imaging (fMRI). Among responders, synchronized rTMS produces two systematic changes in brain dynamics: a reduction in global cortical excitability and enhanced phase entrainment of cortical dynamics. These effects predict clinical outcomes in the synchronized treatment group but not in an active-treatment unsynchronized control group. The systematic decrease in excitability and increase in entrainment correlated with treatment efficacy at the endpoint and intermediate weeks during the synchronized treatment. Specifically, we show that weekly biomarker tracking enables efficacy prediction and dynamic adjustments through a treatment course, improving the overall response rates. This innovative approach advances the prospects of individualized medicine in MDD and holds potential for application in other neuropsychiatric disorders.
Halving nitrogen waste for global sustainable development goals
Authors: Gu Baojing , He Peiying , Zhang Xiuming , Zhang Chuanzhen , Chen Binhui , Wang Sitong , Cheng Luxi , Cui Jinglan , Deng Ouping , Reis Stefan , Masso Cargele , Pradhan Mahesh , Xu Jianming 
Published: December 2023

Abstract
Nitrogen waste poses substantial threats to global sustainable development through multiple pathways, prompting the United Nations (UN) to propose halving nitrogen waste as a means to achieve Sustainable Development Goals (SDGs). However, the pathways and potential to improve global SDGs through halving nitrogen waste are less known. Here we show that nitrogen waste is directly and indirectly linked to all 17 UN 2030 SDGs and that halving nitrogen waste could enhance global SDGs overall by 16%. The total social benefits of halving nitrogen waste could be as high as US$ 1,379 billion, considering improvements in human and ecosystem health and mitigation of climate change. While implementing an evenly halved nitrogen control strategy in systems may cost up to US$ 1,137 billion, adopting cost-effective strategies could slash these expenses by 72%. Our findings provide crucial insights for policymakers and underscore the urgency of developing cost-effective nitrogen waste reduction strategies to achieve global sustainable development.
Constrained Trajectory Optimization and Force Control for UAVs with Universal Jamming Grippers
Authors: Kremer Paul , Nohooji Hamed Rahimi , Voos Holger 
Published: December 2023

Abstract
This study presents a novel framework that integrates the Universal Gripper (UG) with Unmanned Aerial Vehicles (UAVs) to enable automated grasping with no human operator in the loop. Grounded in the principles of granular jamming, the UG exhibits remarkable adaptability and proficiency, navigating the complexities of soft aerial grasping with enhanced robustness and versatility. Central to this integration is a uniquely formulated constrained trajectory optimization using model predictive control, coupled with a robust force control strategy, underpinning enhanced levels of automation and operational reliability in aerial grasping. This paradigm, while simple, is a powerful conduit for various applications, ranging from material handling to disaster response, propelling advancements toward genuine autonomy in aerial manipulation tasks. The key contributions of this research include the introduction of a constrained trajectory planning algorithm, and a force control strategy ensuring robust grasping, validated through numerical simulations and virtual experiments.
Drug Resistance Mutations in the Polymerase Gene of Hepatitis B Virus in Three Generations of Patients with Chronic Hepatitis B
Authors: naderi Malihe , Hosseini Seyed Masoud , Behnampour Naser , Shahramian Iraj , Moradi Abdolvahab 
Published: December 2023

Abstract
Hepatitis B virus polymerase proofreading defects lead to frequent DNA mutations in the hepatitis B virus genome and treatment resistance. This research aimed to investigate drug-resistant mutations in three generations of chronic hepatitis B patients. Based on the inclusion criteria, 90 HBV patients in northeastern Iran were divided into three groups. HBV DNA, liver function tests, serological markers, and liver stiffness measurements were also evaluated. Polymerase gene sequencing was used to identify mutations linked with resistance to NAs. P-values less than 0.05 were used to statistical significance. All samples were genotyped as genotype D/subtype ayw2. HBeAg was detected positive in 12.3% of samples, viral loads (P-value = 0.02) and LFT (P-value = 0.007) considerably higher than HBeAg-negative. YMDD, YINN and FLMAQ mutations were found in 26.7%, 4.5% and 5.5% of CHB patients, respectively. YIDD and FLIPH mutations occur together in 3.4% of three generations and 10% of two generations of patients. The three-generation group had a greater mean LSM (4.2 ± 1.6 KPa) than the other groups. Detection of mutations is critical for physicians to make decisions about antiviral medication selection and management. These results imply that polymerase resistance mutations in three and two generations of patients should be investigated before beginning the antiviral medication.
Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection
Authors: Padmanandam Kayal , Khilar Rashmita , Pitla Nikitha , Dhanaraj Rajesh Kumar , Pamucar Dragan , Balusamy Balamurugan 
Published: December 2023

Abstract
Lung cancer is a significant and frequent types of cancer globally both in patients and mortality rate. Computed Tomography (CT) test is efficient for early lung cancer detection due to potentiality in constructing lung images, generating nodules both in high resolution and tumor detection. But, early detection of lung cancer is obtained as difficult and time-consuming task. Also, detection of pulmonary nodules is a main method for early detection of lung cancer that can even heavily improve survival rate of lung cancer patients by reduced mortality rate to better level. Moreover, correct precision and incorrect detection of positive cancerous cell for lung nodules is both low and high. A lung cancer detection method using deep learning called, Residual Neural and Partial Derivative Multilayer Perceptron (RN-PDMP) is proposed. Initially the aid of lung cancer images acquired from IQ-OTH/NCCD Lung Cancer Dataset addresses class imbalance issue, therefore reducing the noise to a greater extent using Cross Entropy Focal Loss-based preprocessing model. Secondly preprocessed images as input, robust features are extracted by means of Census Transformed Residual Neural Network-based feature extraction algorithm. Finally, classification between normal, benign and malignant are made using Partial Derivative Multilayer Perceptron Deep Learning-based Classifier. The performance analysis of results describe to lung cancer detection model based on RN-PDMP integrating Forward Propagation-based Residual Learning has achieved a precision of 99% and recall of 97%. The results are comparatively better than existing methods in RN-PDMP method of lung cancer detection based on Partial Derivative-based back propagation is better recall for small samples and improves true positive pulmonary nodules.
Evaluating Heat Transfer in Electronic Cabinet Applications: An Analytical and Experimental Approach
Authors: Fadavian Hossein , Fadavian Ahmad , Fadavian Heidar 
Published: December 2023

Abstract
In this study, an investigation was conducted into the heat transfer dynamics of electronic cabinets cooled using a vortex tube in two scenarios: without insulation and with insulation. The study was conducted both analytically and experimentally, with the aim of calculating the operational time and operational cycles of a compressor as a vortex tube feeder. The findings of the study reveal that the implementation of insulation leads to a considerable reduction in the time required to cool the electronic cabinet, and the proposed analytical relationships proved to be highly accurate.
Towards carbon neutrality: a study on the spatial spillover effects of FCS in the Yellow River Basin, China
Authors: Ma Mingjuan , Ke Shuifa , Li Qiang 
Published: December 2023

Abstract
Developing forest carbon sinks (FCS) is significant for China to achieve carbon neutrality. The Yellow River Basin is a principal area for China’s energy consumption, and the forest resource distribution of this vast basin is spatially dependent, determining that the development of FCS cannot be separated geographically. Based on the spatial panel data of 69 prefecture-level cities in the Yellow River basin in 1988-2018, firstly, we depict a picture of the spatial-temporal trajectories for FCS variations using geospatial analysis. Secondly, fully considering the economic and geographical attributes of forest resources, we develop a spatial econometrics model to investigate the inter-regional correlations and spatial spillover effects of FCS from the spatial dimension and comprehensively explore the direct and indirect impact of different explanatory variables on FCS. Thirdly, pertinent policy suggestions are put out to serve as a guide for increasing FCS.
Application of drug-coated balloons for coronary artery de novo lesions in non-diabetes patients vs. diabetes patients with good/poor glycemic control
Authors: Lyu Huanran , Chi Runze , Yi Yingfan , Wang Zhengzhong 
Published: December 2023

Abstract
Background: The application of drug-coated balloons (DCBs) for coronary artery disease has attracted increasing attention, as they do not leave any polymorphic matrix residue or do not require implantation. However, only few studies have evaluated this application in patients with coronary artery de novo lesions complicated with diabetes mellitus. This study investigated the application of DCB for coronary artery de novo lesions, as well as determined the prognosis, in non-diabetes patients vs. diabetes patients with good/poor glycemic control.
Methods: Patients with coronary artery de novo lesions and who received DCBs were divided into the no-diabetes group, diabetes group with good glycemic control, and diabetes group with poor glycemic control according to whether they had diabetes and the admission glycated hemoglobin level of diabetes patients. Data of clinical parameters, relevant laboratory tests, and intraoperative percutaneous coronary intervention were collected, and patients were followed up after discharge. The prognosis of the three groups was compared and analyzed.
Results: The incidence of major adverse cardiovascular events was significantly higher in the diabetes poor glycemic control group than in the diabetes good control group and the no-diabetes group (P<0.001). The incidence of target lesion failure was higher in the diabetes poor glycemic control group than in the no-diabetes group (P<0.05).
Conclusion: Diabetes mellitus and poor glycemic control negatively affect the prognosis of patients undergoing DCB for coronary artery de novo lesions. The use of aspirin plus ticagrelor in addition to intensive glycemic control therapy can be a better treatment option.
A MEMS based Real-Time Structured Light 3-D Measuring Architecture on FPGA
Authors: Zhou Wenbiao , Jia Yunfei , Fan Luyao , Fan Gongyu , Lu Fengchi 
Published: December 2023

Abstract

With its its ability to non-contact measure three-dimensional information of objects and its extremely high accuracy advantage in close range, structured light 3-D measurement is widely used in various fields. However, some application scenarios, such as measuring moving objects and performing measurements in confined spaces, impose requirements for high speed and miniaturization in structured light 3-D measurement. Therefore, we propose a real-time structured light 3-D measurement system on FPGA. This system employs a four-step phase-shifting method to compute wrapped phases, complemented Gray code for phase unwrapping, and a cubic polynomial fitting approach for calculating the 3-D coordinates of points. We have proposed the optimized pipeline structure for each module. We have also proposed an optimized on-chip buffer structure to further improve throughput. The 3-D measurement speed of the proposed method can reach 76.9 fps, when the clock frequency is 100 MHz, and the image size is 2448*2048.
Hybrid Quantum Neural Network in High-dimensional Data Classification
Authors: Chen Hao-Yuan , Chang Yen-Jui , Liao Shih-Wei , Chang Ching-Ray 
Published: December 2023

Abstract
The research explores the potential of quantum deep learning models to address challenging machine learning problems that classical deep learning models find difficult to tackle. We introduce a novel model architecture that combines classical convolutional layers with a quantum neural network, aiming to surpass state-of-the-art accuracy while maintaining a compact model size. The experiment is to classify high-dimensional audio data from the Bird-CLEF 2021 dataset. Our evaluation focuses on key metrics, including training duration, model accuracy, and total model size. This research demonstrates the promising potential of quantum machine learning in enhancing machine learning tasks and solving practical machine learning challenges available today.
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
Authors: Li Yiyang , Li Lei , Hu Dingxin , Li Yuze , Zhou Yanquan , Litvak Marina , Vanetik Natalia 
Published: December 2023

Abstract
The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task. Most of the current metrics are adopted from the question answering (QA) or natural language inference (NLI) task. However, the application of QA-based metrics is extremely time-consuming in practice while NLI-based metrics are lack of interpretability. In this paper, we propose a cloze-based evaluation framework called ClozE and show its great potential. It inherits strong interpretability from QA, while maintaining the speed of NLI-level reasoning. We demonstrate that ClozE can reduce the evaluation time by nearly 96$\%$ relative to QA-based metrics while retaining their interpretability and performance through experiments on six human-annotated datasets and a meta-evaluation benchmark GO FIGURE\cite{gabriel2021go}. Finally, we discuss three important facets of ClozE in practice, which further shows better overall performance of ClozE compared to other metrics. The codes and models are released at https://github.com/Mr-KenLee/ClozE.
Multi-flavor quantum criticality
Authors: Shekhter Arkady , Khansili Akash , Bangura Alimamy , McDonald Ross , Ramshaw Brad , Rydh Andreas 
Published: December 2023

Abstract

The electronic density of states, and, hence, the quasiparticle mass on the Fermi surface, is strongly enhanced through electronic correlations in quantum-critical metals [1, 2]. The nature of electronic correlations in such systems can be constrained by comparing different probes of the electronic density of states. Comparative studies in high-Tc superconductors present a significant challenge because of the masking effect of the superconducting phase [3]. In contrast, the normal state can be readily accessed in the unconventional superconductor CeCoIn5, because the energy scale associated with superconductivity is small [4]. Here we use thermal impedance spectroscopy [5] to simultaneously access the electronic density of states in CeCoIn5 in two independent ways; via the nuclear spin-lattice relaxation rate and via the electronic specific heat. We establish that the temperature- and magnetic field dependence of the nuclear spin-lattice relaxation rate is determined entirely by the electronic density of states on the Fermi surface, where mass enhancement is cut off at high magnetic fields. Surprisingly, the specific heat reveals excess entropy in addition to that associated with the density of states on the Fermi surface. The electronic nature of this excess entropy is evidenced by its suppression in the superconducting state. We postulate that a second “flavor” of boson generates the observed quantum critical physics beyond the mass renormalization on the Fermi surface in CeCoIn5. We suggest such a multi-flavor character for a broader range of quantum critical metals.
Shifting from traditional infrastructure planning to a collaborative approach: lessons from the Freetown sanitation master plan
Authors: Bateganya Najib L. , Beardsley Rachel , Lebu Sarah , Atim Janet , Lilian Kente S. , Lahai Francis , Madeira Malick M. , Mulenga Osward C. , Manga Musa 
Published: December 2023

Abstract
Planning frameworks, usually in the form of master plans, provide the direction for investment priorities necessary to achieve development objectives in many sub-Saharan African countries. Traditionally, master plans were developed using engineering methodologies with little stakeholder participation. There is little understanding of how master plans for sanitation infrastructure and services can be collaboratively designed. Through the application of a case study from Freetown, Sierra Leone, the paper proposes a framework for shifting infrastructure planning from conventional models to participatory, evidence-based methods. Findings highlight that the commitment of government and funding institutions created a sense of urgency and importance around sanitation issues and unlocked opportunities for increased financing and a better enabling policy and regulatory environment. Active engagement of the community and evidence-based decision-making ensured that the master plan aligned with the specific needs, preferences, and aspirations of all stakeholders. It was however challenging to use the principles of citywide inclusive sanitation (CWIS) for participatory planning because the implementing agencies and stakeholders lacked adequate capacity to do so. These findings hold significant implications for decision-makers, sanitation planners, and municipal authorities engaged in the development of sanitation master plans.