bluuebunny/crossref_metadata_2025_split huggingface dataset:
Practical relevance of charge transfer resistance at the Li metal electrode|electrolyte interface in batteries?
Authors: Stolz Lukas , Winter Martin , Kasnatscheew Johannes 
Published: January 2024

AbstractThe theoretically possible energy and power densities of rechargeable batteries are practically limited by resistances as these lead to overvoltages, particularly pronounced at kinetically harsher conditions, i.e., high currents and/or low temperature. Charge transfer resistance (Rct), being a major type of resistance alongside with Ohmic (RΩ) and mass transport (Rmt), is related with the activation hindrance of electrochemical reactions. Its practical relevance is discussed within this work via analyzing $$\mathrm{Li}\mid \,\, \mid\mathrm{Li}$$

Li
∣

∣
Li

cells with the galvanostatic/constant current (CC) technique. Rct at Li|electrolyte interfaces is shown to be relevantly impacted by electrode–electrolyte interphases; implying the electrolyte type, as well. While solid polymer electrolytes (SPEs), e.g., based on poly(ethylene) oxide (PEO), show negligible Rct, it is evident for commercial liquid electrolytes and readily increase during storage. Given the asymptotic overvoltage vs. current behavior of Rct, obeying Butler-Volmer equation, Rct gets less relevant at enhanced currents, as experimentally validated, finally pointing to the dominance of RΩ and (depending on system) Rmt in the overall resistance.
Graphical Abstract
Sex-dependent different clinicopathological characterization of Epstein–Barr virus-associated gastric carcinoma: a large-scale study
Authors: Kim Ji-Hyun , Kim Nayoung , Song Du Hyun , Choi Yonghoon , Jeon Eun-Bi , Kim Sihyun , Jun Yu Kyung , Yoon Hyuk , Shin Cheol Min , Park Young Soo , Lee Dong Ho , Oh Hyeon Jeong , Lee Hye Seung , Park Young Suk , Ahn Sang-Hoon , Suh Yun-Suhk , Park Do Joong , Kim Hyung Ho , Kim Ji-Won , Kim Jin Won , Lee Keun-Wook , Chang Won , Park Ji Hoon , Lee Yoon Jin , Lee Kyoung Ho , Kim Young Hoon , Ahn Soyeon 
Published: January 2024

Abstract
Background
Epstein–Barr virus (EBV)-associated gastric cancer (EBVaGC) has been reported to account for approximately 5–16% of all GCs with good prognosis compared to EBV-negative GC. We evaluated the clinicopathological characteristics of EBVaGC including survival rate in South Korea.

Methods
A total of 4,587 patients with GC who underwent EBV in situ hybridization (EBV–ISH) were prospectively enrolled at the Seoul National University Bundang Hospital from 2003 to 2021. Age, sex, smoking status, cancer type and stage, tumor size and location, histological type, molecular features and survival information were analyzed.

Results
A total of 456 patients with GC (9.9%) were positive for EBV. The EBVaGC group displayed a higher proportion of males (P < 0.001), a predominant presence in the proximal stomach (P < 0.001), a higher proportion of undifferentiated cancer (P < 0.001), and a lower cancer stage (P = 0.004) than the EBV-negative group. Cox multivariate analyses revealed age (hazard ratio [HR] = 1.025, P < 0.001), tumor size (HR = 1.109, P < 0.001), and cancer stage (stage2 HR = 4.761, P < 0.001; stage3 HR = 13.286, P < 0.001; stage4 HR = 42.528, P < 0.001) as significant risk factors for GC-specific mortality, whereas EBV positivity was inversely correlated (HR = 0.620, P = 0.022). Furthermore, the EBVaGC group displayed statistically significant survival advantages over the EBV-negative cancer group in terms of both overall (P = 0.021) and GC-specific survival (P = 0.007) on the Kaplan–Meier survival curve. However, this effect was evident only in males.

Conclusions
EBVaGC patients showed better prognoses despite their association with proximal location and poorly differentiated histology in male, probably due to the difference in immunity between males and females.
Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review
Authors: van Hal S. T. , van der Jagt M. , van Genderen M. E. , Gommers D. , Veenland J. F. 
Published: January 2024

AbstractIntracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating “real-time model testing” stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.
Biochar: a high performance and renewable basic carbocatalyst for facilitating room temperature synthesis of 4H-benzo[h]chromene and pyranopyrazoles in water
Authors: Khalili Dariush , Ramjerdi Ali Asghar , Boostani Hamid Reza , Ghaderi Arash 
Published: January 2024

AbstractThis study has introduced a pioneering methodology by employing biochars as a basic carbocatalyst in the context of multicomponent reactions. Biochars were produced from different manures and organic wastes using the pyrolysis-carbonization process under limited oxygen conditions. The prepared biochars were well characterized using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), Brunauer–Emmett–Teller (BET) analysis, and powder X-ray diffraction (XRD). The chemical characteristics and potentiometric titration analysis provide compelling evidence of the intriguing basicity properties exhibited by the prepared biochars. The pH values, ash content, and potentiometric titration results confirmed the exceptional basicity characteristics of cow manure biochar formed at 600 oC (CB600), establishing it as the most basic carbocatalyst in this study. Encouraged by these initial results, the activity of the biochars as basic carbocatalysts was evaluated in multicomponent synthesis of 4H-benzo[h]chromene and pyranopyrazoles and 600 °C exhibited the most pronounced catalytic performance owing to its superior total basicity. By these findings, it can be asserted that this work introduces the groundbreaking application of biochars as potent basic carbocatalysts for the multicomponent synthesis of structurally diverse heterocycles. Unveiling the vital basic role of biochars will definitely open up new opportunities in organic chemistry and provide salient features for environmentally-friendly chemistry, including easy retrieval, non-toxicity, and widespread accessibility.
Graphical Abstract
Data-Based Energy Demand Prediction for Hybrid Electrical Vehicles
Authors: Fink Daniel , Maas Oliver , Herda Daniel , Ziaukas Zygimantas , Schweers Christoph , Trabelsi Ahmed , Jacob Hans-Georg 
Published: January 2024

AbstractTo achieve a resource-efficient automotive traffic, modern driver assistance systems minimize the vehicle’s energy demand through speed optimization algorithms. Based on predictive route data, the required energy for upcoming operation points has to be determined. This paper presents a method to predict the energy demand of a hybrid electrical vehicle. Within this method, data-based approaches, such as neural networks, Gaussian processes, and look-up tables, are applied and assessed regarding their ability to predict the behavior of separate powertrain parts. The applied approaches are trained using measured data of a test vehicle. As a result, for every separate powertrain part, the best-suited data-based approach is selected to obtain an optimal energy demand prediction method. On a validation data set, this method is able to predict the transmission ratio of the gearbox causing a rmse of 0.426. The combustion engine’s torque prediction results in an rmse of 19.01 Nm and the electric motor torque prediction to 19.11 Nm. The root mean square error of the motor voltage results to 1.211 V.
Robotics in Healthcare: A Survey
Authors: Silvera-Tawil David 
Published: January 2024

AbstractResearch and innovation in the area of robotics in healthcare has seen significant growth in recent years. Global trends indicate that patients are getting older and sicker, while demands in healthcare workers are increasing their chance of injury. Robotic technology has the potential to enable high levels of patient care, clinical productivity and safety for both patients and healthcare workers. This paper surveys the state-of-the-art in robotics in healthcare and well-being, with particular attention to the key barriers and enablers to the implementation of this technology in real-world settings. Desktop research was used to identify available and emerging robotic technology currently in use (or with potential use) in healthcare settings. Primary sources of information included: academic publications, international organisations, commercial websites and online news agencies. In this paper, applications of robots in healthcare were divided into five main areas: service, assistive, socially-assistive, teleoperated and interventional robots. The maturity and readiness of different products is still an open challenge, with service and interventional robots leading the way. Wide-spread adoption of robots is likely to happen as the cost of the technology reduces, and wide evidence of beneficial long-term impact is available. This manuscript identified the main drivers, challenges, opportunities and considerations for implementing robots in healthcare. We hope this manuscript will raise awareness about robotics in healthcare among a wider audience to maximise availability, quality, and acceptability this technology.
Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry
Authors: Kumar G. Kranthi , Bangare Manoj L. , Bangare Pushpa M. , Kumar Chanda Raj , Raj Roop , Arias-Gonzáles José Luis , Omarov Batyrkhan , Mia Md. Solaiman 
Published: January 2024

AbstractBecause there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.
Imaging Patterns in Breast Cancer for Women Under 40 Years: A Descriptive Cohort Study
Authors: Alhaidary Amal A. , Al-Qudimat Ahmad R. , Arabi Haitham , Al-Zoubi Raed M. 
Published: January 2024

Abstract
Background and Aim
Breast cancer is the most frequently occurring malignant disease in women and remains the leading cause of cancer-related deaths among females worldwide. The aim of this study is to evaluate the imaging findings of breast cancer in women under the age of 40 and analyze their pathological patterns.

Method
A retrospective study was conducted from 2013 to 2019, involving 120 patients below 40 years of age with pathologically confirmed primary epithelial breast cancers. The data were collected from the electronic records of a tertiary hospital in Riyadh, Saudi Arabia. Mammograms were performed for 115 patients, ultrasounds were conducted for all patients, and MRI scans were carried out for 47 patients.

Results
All radiological findings and clinical characteristics of the 120 cases were retrieved from our digital-based system. The majority of breast cancer patients (83.4%) were between 30 and 40 years old, and the most common clinical presentation was a mass (45.8%). Out of the 73 patients who underwent genetic tests, 32.9% tested positive for gene mutations. No statistically significant correlation was found between specific age groups and breast composition (P = 0.216), specific mammogram abnormalities such as masses (P = 0.262), or microcalcifications (P = 0.421). Ultrasonography was performed for all patients, with abnormalities detected in only one patient who was diagnosed with Paget’s disease of the nipple. Masses, with or without parenchymal changes, were the predominant feature in 88.3% of cases.

Conclusion
The imaging findings in breast cancer cases typically involve masses with suspicious features, irregular shape, and spiculated margins on mammograms, and irregular shape with microlobulated or angular margins on ultrasound. MRI features commonly include masses with irregular shape and heterogeneous enhancement. The luminal B subtype was identified as the most prevalent pathological feature, characterized by a high proliferative index (Ki-67%).

Graphical Abstract
Understanding the interplay between sustainability strategy and the approach to sustainability reporting in SMEs
Authors: Guidi Michele , Vitali Sonia , Giuliani Marco , Chiucchi Maria Serena 
Published: January 2024

Abstract
The study aims to understand the interplay between sustainability strategy and the approach to sustainability reporting in small- and medium-sized enterprises (SMEs). To achieve this purpose, a case study is developed, examining four companies that exemplify different sustainability strategy types delineated by Baumgartner: introverted, extroverted, conservative, and visionary. According to the findings of the research, while it is possible to identify sustainability strategies based on the key defining elements of the adopted conceptual map, and to interpret the company’s perception and approach to sustainability reporting considering the sustainability strategy, integrating owner-manager values, stakeholder proximity, and supply chain dynamics the conceptual map becomes a more adaptable and practical tool for SMEs seeking to thrive in an increasingly sustainability-focused business landscape. The incorporation of these insights extends Baumgartner’s work, enhancing the comprehension of sustainability strategies within SMEs and providing implications for regulatory bodies, consultants, and managers.
NON-TRIVIAL HIGHER HOMOTOPY OF FIRST-ORDER THEORIES
Authors: CAMPION TIM , YE JINHE 
Published: January 2024

Abstract
Let T be the theory of dense cyclically ordered sets with at least two elements. We determine the classifying space of

$\mathsf {Mod}(T)$

to be homotopically equivalent to

$\mathbb {CP}^\infty $

. In particular,

$\pi _2(\lvert \mathsf {Mod}(T)\rvert )=\mathbb {Z}$

, which answers a question in our previous work. The computation is based on Connes’ cycle category

$\Lambda $

.
Goldie dimension for C*-algebras
Authors: Rouzbehani Mohammad , Amini Massoud , Asadi Mohammad B. 
Published: January 2024

AbstractIn this article, we introduce and study the notion of Goldie dimension for C*-algebras. We prove that a C*-algebra A has Goldie dimension n if and only if the dimension of the center of its local multiplier algebra is n. In this case, A has finite-dimensional center and its primitive spectrum is extremally disconnected. If moreover, A is extending, we show that it decomposes into a direct sum of n prime C*-algebras. In particular, every stably finite, exact C*-algebra with Goldie dimension, that has the projection property and a strictly full element, admits a full projection and a non-zero densely defined lower semi-continuous trace. Finally we show that certain C*-algebras with Goldie dimension (not necessarily simple, separable or nuclear) are classifiable by the Elliott invariant.
Robust control and experimental validation of trajectory tracking for permanent magnet linear motors based on constraint-following under uncertainty
Authors: Zhen Shengchao , Huang Chenghui , Liu Xiaoli , Chen Ye-Hwa 
Published: January 2024

AbstractThis paper proposes a robust control approach to achieve high-precision trajectory tracking for permanent magnet linear motor (PMLM) system containing uncertainties by describing the dynamic model of PMLM based on the Udwadia-Kalaba equation combined with constraint-following method. First, the system of PMLM is described as a constraint-following system by adding the generalized constraint force to the unconstrained Udwadia-Kalaba equation of PMLM system. Second, the robust constraint-following controller is designed based on the proposed model after uncertainty analysis. Moreover, the proposed controller is verified to guarantee deterministic performance for uncertain systems: uniformly bounded and uniformly ultimately bounded. Third, the numerical simulation and experimental validation demonstrate the effectiveness of proposed controller. Finally, the design approach of constraint-following can be applied to other systems with uncertainties.
Asperity Size and Neighboring Segments Can Change the Frictional Response and Fault Slip Behavior: Insights From Laboratory Experiments and Numerical Simulations
Authors: Corbi F. , Mastella G. , Tinti E. , Rosenau M. , Sandri L. , Pardo S. , Funiciello F. 
Published: January 2024

AbstractAccurate assessment of the rate and state friction parameters of rocks is essential for producing realistic earthquake rupture scenarios and, in turn, for seismic hazard analysis. Those parameters can be directly measured on samples, or indirectly based on inversion of coseismic or postseismic slip evolution. However, both direct and indirect approaches require assumptions that might bias the results. Aiming to reduce the potential sources of bias, we take advantage of a downscaled analog model reproducing megathrust earthquakes. We couple the simulated annealing algorithm with quasi‐dynamic numerical models to retrieve rate and state parameters reproducing the recurrence time, rupture duration and slip of the analog model, in the ensemble. Then, we focus on how the asperity size and the neighboring segments' properties control the seismic cycle characteristics and the corresponding variability of rate and state parameters. We identify a tradeoff between (a–b) of the asperity and (a–b) of neighboring creeping segments, with multiple parameter combinations that allow mimicking the analog model behavior. Tuning of rate and state parameters is required to fit laboratory experiments with different asperity lengths. Poorly constrained frictional properties of neighboring segments are responsible for uncertainties of (a–b) of the asperity in the order of per mille. Roughly one order of magnitude larger uncertainties derive from asperity size. Those results provide a glimpse of the variability that rate and state friction estimates might have when used as a constraint to model fault slip behavior in nature.
A Comparison of Regional Climate Projections With a Range of Climate Sensitivities
Authors: Barnes Clair R. , Chandler Richard E. , Brierley Christopher M. 
Published: January 2024

AbstractTo investigate the extent to which differences in regional model projections can be explained by differences in the warming rates of their driving models, we compare projections of temperature and precipitation over the UK from two regional climate ensembles—the EuroCORDEX multi‐model ensemble and UKCP18 perturbed parameter ensemble—along with projections produced by the “parent” GCMs from which boundary conditions were taken. We evaluate the ensembles in terms of their representation of recent climate, then compare the changes simulated between 1981–2010 and 2050–2079. While both ensembles exhibit seasonal biases with similar magnitudes and spatial patterns during the evaluation period, the UKCP18 ensemble exhibits a somewhat stronger change signal in future simulations, due to a combination of higher climate sensitivity of the driving models, variations in the forcings applied, and—in the regional simulations—the inclusion of time‐varying aerosols. In order to reconcile the two sets of projections, we compare two periods corresponding to fixed global warming levels in the driving models, to constrain the variability within and between the ensembles which can be ascribed to differing rates of global warming: the discrepancy between the ensembles is greatly reduced, although some differences in the local response remain, with the UKCP18 runs slightly warmer and drier than the EuroCORDEX runs, particularly in summer. We also highlight potential pitfalls of comparing warming levels with a reference time period, due to uncertainty about the warming that has already occurred in the driving models prior to the reference period.