bluuebunny/crossref_metadata_2025_split huggingface dataset:
Society’s material stocks as carbon pool: an economy-wide quantification of global carbon stocks from 1900–2015
Authors: Kaufmann Lisa , Wiedenhofer Dominik , Cao Zhi , Theurl Michaela Clarissa , Lauk Christian , Baumgart André , Gingrich Simone , Haberl Helmut 
Published: January 2024

Abstract
Societal activities massively alter the global carbon (C) cycle, thereby driving global climate heating. Socioeconomic material stocks - e.g. in buildings and infrastructures - have been identified as a C pool that can potentially store increasing amounts of C, thereby keeping C away from the atmosphere. However, little is known about the size, composition, distribution and development of global socioeconomic C stocks. Based on an established economy-wide C accounting approach from sociometabolic research, we consistently and comprehensively quantified the C contained in eight components of socioeconomic stocks in the period 1900–2015 at the level of nine world regions. We discern inert (aggregates and other gravel) and ‘active’ climate-relevant (i.e. biomass and fossil-fuel based) C pools. We find that global active components of socioeconomic C stocks grew by a factor of 9, from 1.9 (1.5–2.2) Pg of carbon (PgC) to 16.8 (13.7–20.2) PgC. The inert socioeconomic C stock in aggregates & other gravel amounted to 25.2 (6.1–48.0) PgC in 2015, however with high uncertainties. Absolute annual net additions to stock (NAS) of active stock components was 0.49 (0.40–0.59) PgC yr−1 which equaled 5% of the C emissions from fossil fuel combustion and industrial processes. However, raising NAS of components with biomass feedstock that sequester C from the atmosphere comes with biodiversity and food security trade-offs. This study contributes to a holistic perspective on social and natural C stocks that acknowledges their interactions. The global socioeconomic C stock reached a geologically relevant extent (approximately the size of C in coasts) and should therefore be integrated in the assessments of the global C cycle to acknowledge the Anthropocene.
Mobility, response and transport in non-equilibrium coarse-grained models
Authors: Jung Gerhard 
Published: January 2024

Abstract
We investigate two different types of non-Markovian coarse-grained models extracted from a linear, non-equilibrium microscopic system, featuring a tagged particle coupled to underdamped oscillators. The first model is obtained by analytically ‘integrating out’ the oscillators and the second is based on a derivation using projection operator techniques. We observe that these two models behave very differently when the tagged particle is exposed to external harmonic potentials or pulling forces. Most importantly, we find that the analytic model has a well defined friction kernel and can be used to extract work, consistent with the microscopic system, while the projection model corresponds to an effective equilibrium model, which cannot be used to extract work. We apply the analysis to two popular non-equilibrium systems, time-delay feedback control and the active Ornstein–Uhlenbeck process. Finally, we highlight that our study could have important consequences for dynamic coarse-graining of non-equilibrium systems going far beyond the linear systems investigated in this manuscript.
Synergetic effect of oleophilic textured surfaces and MoS2 on the tribological properties of Ti-6Al-4V alloy under dry and lubricated sliding conditions
Authors: Narayana Turali , Saleem Sheikh Shahid 
Published: January 2024

Abstract
In the present work, surface modification of Ti-6Al-4V alloy was carried out using Laser surface texturing (LST). The investigation aims to explore the synergetic impact of LST and molybdenum disulfide (MoS2), as a solid lubricant, on the tribological properties of Ti-6Al-4V alloy. Three types of LST textures (Circular, triangular, and square textures) were created on the Ti6Al4V alloy. Subsequently, tribological tests were conducted on a universal tribometer (ball-on-disc), at a load of 20 N with a frequency of 15 Hz. The worn surface was analyzed using various methods, including optical microscopy, 3D-profilometer, FESEM, EDAX analysis, and Raman spectroscopy. The study compared the coefficient of friction (COF) and wear behavior of un-textured surfaces (UTS) with those of textured surfaces (TS) under both dry sliding conditions (DSC) and lubricated sliding conditions (LSC). The results demonstrated a significant reduction in the COF and wear coefficients on the TS. Specifically, the circular texture exhibited significant results with a 10.30%, 10.42%, and 28.80% decrease in COF and a remarkable reduction of 39.22%, 47.51%, and 77.90% in wear coefficients compared to the UTS tested under DSC and LSC (using PAO-4 and PAO-4 + 1% wt MoS2). Severe abrasion, adhesion, and delamination as the primary wear mechanisms were observed on the UTS and TS under DSC, while the LSC was characterized by mild adhesive and delamination wear on the UTS and TS. The carbon layer formation and concentration of Mo and S particles resulted in lower friction and wear coefficients for the UTS, and TS under LSC (PAO-4 + 1% wt MoS2). The analysis indicated that the use of LST and solid lubricant nanoparticles on a Ti-6Al-4V alloy would result in improved service life and better endurance in cutting tools and tribo-mating parts.
Thermal activation and mechanical properties of high alumina coal gangue as auxiliary cementitious admixture
Authors: Zhang Mingjun , Li Liang , Yang Fan , Zhang Shigang , Zhang He , An Jian 
Published: January 2024

Abstract
In order to use high alumina coal gangue as auxiliary cementitious admixture via a simple and convenient thermal activation technique, the thermal transformation, mineral phase transformation and structure changes of coal gangue at calcining temperatures of 500–1000 °C were analyzed using x-ray diffraction (XRD), differential thermal Analysis (DTA), thermogravimetry (TG), infrared analysis (IR) and scanning electron microscope (SEM). The mechanical properties of cement mortar with 30% coal gangue auxiliary cementitious admixture were also measured to determine the optimal calcining temperature. As calcining temperature was increased, the coal gangue experienced the following transformations: carbon combustion, dehydroxylation, metakaolin transformation and mullite transformation. The cement mortar with coal gangue auxiliary cementitious admixture calcined at 700 °C presented the highest 28-d flexural and compressive strength, increasing by 8.27% and 11.85% respectively as compared with the benchmark cement mortar. The maximum dosage of coal gangue auxiliary cementitious admixture in cement mortar was further identified to be less than 30% by mechanical properties testing. The activity of high alumina coal gangues at different calcining temperatures was explained from the view points of hydration degree and products. The present investigation can provide a useful reference to utilize high alumina coal gangue as auxiliary cementitious admixture by means of a simple thermal activation at 700 °C.
Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study
Authors: Saleh Radhwan A. A. , Metin ERTUNÇ H 
Published: January 2024

Abstract
Ensuring tire quality is crucial in the manufacturing industry, particularly for race cars, where defective tires present a significant safety risk. Visual inspection for defects in tires is crucial; however, identifying defects in complex, textured tires has been proven to be a challenging task. This paper tackles this challenge by introducing XAFCNN, an Explainable Attention-based Fused Convolutional Neural Network for tire defect detection. XAFCNN’s novel architecture, including a Special Attention Module (SAM) and custom CNN structure, coupled with Grad-CAM visualization, prevents overfitting, enhances local feature mapping, enables detection of small defects, and proffers valuable insights into the model’s reasoning, enabling confident interpretation of its predictions. The model was trained on a dataset from a leading global tire manufacturer, including 38,710 x-ray images of defective tires and 83,985 defect-free tire images, covering 15 defect types and 50 design patterns. The results demonstrate the model’s exceptional performance compared to literature, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. This research, with its substantial dataset and high-performing model, advances automated tire defect detection, satisfying the industry’s need for accurate and reliable inspections, ultimately enhancing human safety.
Enhancing person re-identification on RGB-D data with noise free pose-regularized color and skeleton distance features
Authors: Bilakeri Shavantrevva , Kotegar Karunakar A 
Published: January 2024

Abstract
Noisy features may introduce irrelevant or incorrect features that can lead to incorrect classifications and lower accuracy. This can be especially problematic in tasks such as person re-identification (ReID), where subtle differences between individuals need to be accurately captured and distinguished. However, the existing ReID methods directly use noisy and limited multimodality features for similarity measures. It is crucial to use robust features and pre-processing techniques to reduce the effects of noise and ensure accurate classification. As a solution, we employ a Gaussian filter to eliminate the Gaussian noise from RGB-D data in the pre-processing stage. For similarity measure, the color descriptors are computed using the top eight peaks of the 2D histogram constructed from pose regularized partition grid cells, and eleven different skeleton distances are considered. The proposed method is evaluated on the BIWI RGBD-ID dataset, which comprises still (front view images) and walking set (images with varied pose and viewpoint) images. The obtained recognition rates of 99.15% and 94% on still and walking set images demonstrate the effectiveness of the proposed approach for the ReID task in the presence of pose and viewpoint variations. Further, the method is evaluated on and RGBD-ID and achieved improved performance over the existing techniques.
Exploiting deep learning accelerators for neuromorphic workloads
Authors: Sun Pao-Sheng Vincent , Titterton Alexander , Gopiani Anjlee , Santos Tim , Basu Arindam , Lu Wei D , Eshraghian Jason K 
Published: January 2024

Abstract
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units this becomes more expensive than non-spiking networks. The emergence of Graphcore’s intelligence processing units (IPUs) balances the parallelized nature of deep learning workloads with the sequential, reusable, and sparsified nature of operations prevalent when training SNNs. IPUs adopt multi-instruction multi-data parallelism by running individual processing threads on smaller data blocks, which is a natural fit for the sequential, non-vectorized steps required to solve spiking neuron dynamical state equations. We present an IPU-optimized release of our custom SNN Python package, snnTorch, which exploits fine-grained parallelism by utilizing low-level, pre-compiled custom operations to accelerate irregular and sparse data access patterns that are characteristic of training SNN workloads. We provide a rigorous performance assessment across a suite of commonly used spiking neuron models, and propose methods to further reduce training run-time via half-precision training. By amortizing the cost of sequential processing into vectorizable population codes, we ultimately demonstrate the potential for integrating domain-specific accelerators with the next generation of neural networks.
A framework for informing context-sensitive sustainable management of organic waste in rural agricultural regions
Authors: Zaki Mohammed T , Wilson Matthew E , Yates Jarred , Orner Kevin D 
Published: January 2024

Abstract
Resource recovery can provide opportunities to mitigate the negative impacts of conventional organic waste management such as landfilling to the environment, economy, and society in rural agricultural regions. However, region-specific assessment of such opportunities can be challenging due to lack of data, limited economic resources, and inadequate policy support to meet community needs. Therefore, we developed a framework utilizing open-source data and methods informed by community engagement to assist stakeholders in rural agricultural regions in considering strategies to recover resources from organic waste. The framework was applied in Hardy County (one of the largest agricultural regions in rural West Virginia) to compare the sustainability of current management practices (landfilling of sewage sludge and organic municipal solid waste such as food and yard waste, land application of sewage sludge and poultry litter, and transportation of poultry litter out of watershed) with different anaerobic digestion scenarios. The results indicated that co-digesting alum-treated poultry litter with organic municipal solid waste and sewage sludge would result in the most sustainable organic waste management under stakeholder-preferred weighting of environmental (global warming and eutrophication potential), economic (annual worth), and social (potential to benefit vulnerable households) factors. Interestingly, the current management practices received the second-highest score. The results were further utilized to develop recommendations for relevant policies related to nutrient management and decarbonization. Overall, the framework can be a useful tool for rural agricultural regions to promote sustainable organic waste management.
The Underlying Structure of Preventive Behaviors and Related Factors During the COVID-19 Pandemic: A Comprehensive Network Analysis
Authors: Liang Yan , Zhu Qianqian , Yang Yuqi , Gu Xiaoqing , Yan Yuge , Gu Jie , Huang Jiaoling 
Published: January 2024

Abstract

Background
Various strategies against COVID-19 have been adopted in different countries, with vaccination and mask-wearing being widely used as self-preventive interventions. However, the underlying structure of these behaviors and related factors remain unclear.

Purpose
In this study, we aimed to explore the network structure of preventive behaviors during the COVID-19 pandemic and their underlying factors, incorporating age and sex in the network.

Methods
We used a multi-center sample of 20,863 adults who were vaccinated against COVID-19 in China between April 1, 2021, and June 1, 2021. Networks were estimated using unregularized partial correlation models. We also estimated the accuracy and stability of the network.

Results
The preventive behaviors related to network factors revealed that self-initiated vaccination was more connected with cognition factors, and mask-wearing was more connected with personal profiles. The two clusters were linked through information-seeking and political beliefs. Moreover, self-initiated vaccination was negatively connected with vaccine hesitancy and concerns about COVID-19 vaccines and positively connected with trust in the vaccines, pandemic-related altruism, political beliefs, and being married. Mask-wearing was negatively connected with being a professional/white collar worker and higher education level and positively connected with regular physical examination, self-rated health, migration, being married, and better family relationships. Incorporation of age and sex into the network revealed relevant associations between age and mask-wearing and age and self-initiated vaccination. The network was highly accurately estimated. The subset bootstrap showed that the order of node strength centrality, betweenness, and closeness were all stable. The correlation stability coefficient (CS-coefficient) also showed the stability of this estimate, with 0.75 for node strength, 0.75 for betweenness, and 0.67 for closeness.

Conclusions
The internal structures of vaccination and mask-wearing behaviors were quite different, the latter of which were mainly affected by socioeconomic status and health-related behaviors and the former by knowledge about vaccines and political beliefs. Information-seeking and family relationships were the bridge factors connecting these two self-preventive behavior clusters, suggesting the direction of future efforts.
Principal stratification analysis of noncompliance with time-to-event outcomes
Authors: Liu Bo , Wruck Lisa , Li Fan 
Published: January 2024

ABSTRACTPost-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. The existing literature on principal stratification lacks generally applicable and accessible methods for time-to-event outcomes. In this paper, we focus on the noncompliance setting. We specify 2 causal estimands for time-to-event outcomes in principal stratification and provide a nonparametric identification formula. For estimation, we adopt the latent mixture modeling approach and illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional hazards model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters. We provide analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness) trial to evaluate the causal effect of taking 81 versus 325 mg aspirin on the risk of major adverse cardiovascular events. We develop the corresponding R package PStrata.
Immunoproteasome deficiency results in age-dependent development of epilepsy
Authors: Leister Hanna , Krause Felix F , Gil Beatriz , Prus Ruslan , Prus Inna , Hellhund-Zingel Anne , Mitra Meghma , Da Rosa Gerbatin Rogerio , Delanty Norman , Beausang Alan , Brett Francesca M , Farrell Michael A , Cryan Jane , O’Brien Donncha F , Henshall David C , Helmprobst Frederik , Pagenstecher Axel , Steinhoff Ulrich , Visekruna Alexander , Engel Tobias 
Published: January 2024

Abstract
The immunoproteasome is a central protease complex required for optimal antigen presentation. Immunoproteasome activity is also associated with facilitating the degradation of misfolded and oxidized proteins, which prevents cellular stress. While extensively studied during diseases with increasing evidence suggesting a role for the immunoproteasome during pathological conditions including neurodegenerative diseases, this enzyme complex is believed to be mainly not expressed in the healthy brain. In this study, we show an age-dependent increase in polyubiquitination in the brains of wild-type mice, accompanied by an induction of immunoproteasomes, which was most prominent in neurons and microglia. In contrast, mice completely lacking immunoproteasomes (triple-knockout mice), displayed a strong increase in polyubiquitinated proteins already in the young brain and developed spontaneous epileptic seizures, beginning at the age of 6 months. Injections of kainic acid led to high epilepsy-related mortality of aged triple-knockout mice, confirming increased pathological hyperexcitability states. Notably, the expression of the immunoproteasome was reduced in the brains of patients suffering from epilepsy. In addition, the aged triple-knockout mice showed increased anxiety, tau hyperphosphorylation and degeneration of Purkinje cell population with the resulting ataxic symptoms and locomotion alterations. Collectively, our study suggests a critical role for the immunoproteasome in the maintenance of a healthy brain during ageing.
Interhemispheric imbalance and bradykinesia features in Parkinson’s disease
Authors: Paparella Giulia , De Riggi Martina , Cannavacciuolo Antonio , Costa Davide , Birreci Daniele , Passaretti Massimiliano , Angelini Luca , Colella Donato , Guerra Andrea , Berardelli Alfredo , Bologna Matteo 
Published: January 2024

Abstract
In patients with Parkinson’s disease, the connectivity between the two primary motor cortices may be altered. However, the correlation between asymmetries of abnormal interhemispheric connections and bradykinesia features has not been investigated. Furthermore, the potential effects of dopaminergic medications on this issue remain largely unclear. The aim of the present study is to investigate the interhemispheric connections in Parkinson’s disease by transcranial magnetic stimulation and explore the potential relationship between interhemispheric inhibition and bradykinesia feature asymmetry in patients. Additionally, we examined the impact of dopaminergic therapy on neurophysiological and motor characteristics. Short- and long-latency interhemispheric inhibition was measured in 18 Parkinson’s disease patients and 18 healthy controls, bilaterally. We also assessed the corticospinal and intracortical excitability of both primary motor cortices. We conducted an objective analysis of finger-tapping from both hands. Correlation analyses were performed to explore potential relationships among clinical, transcranial magnetic stimulation and kinematic data in patients. We found that short- and long-latency interhemispheric inhibition was reduced (less inhibition) from both hemispheres in patients than controls. Compared to controls, finger-tapping movements in patients were slower, more irregular, of smaller amplitudes and characterized by a progressive amplitude reduction during movement repetition (sequence effect). Among Parkinson’s disease patients, the degree of short-latency interhemispheric inhibition imbalance towards the less affected primary motor cortex correlated with the global clinical motor scores, as well as with the sequence effect on the most affected hand. The greater the interhemispheric inhibition imbalance towards the less affected hemisphere (i.e. less inhibition from the less to the most affected primary motor cortex than that measured from the most to the less affected primary motor cortex), the more severe the bradykinesia in patients. In conclusion, the inhibitory connections between the two primary motor cortices in Parkinson’s disease are reduced. The interhemispheric disinhibition of the primary motor cortex may have a role in the pathophysiology of specific bradykinesia features in patients, i.e. the sequence effect.