bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electrochemical performance study of nano-Fe3O4/C modified separator for lithium-sulfur batteries
Authors: Li Lei , Ma Long , Guo Xin , Li Xiaoming , Liu Tiejun 
Published: November 2023

Abstract
In order to solve the problems of low cathode conductivity, shuttle effect, and poor electrochemical performance of Li-S batteries, we designed a porous carbon/nano-sized ferric oxide (Fe3O4) composite modified membrane. This improved both conductivity and adsorption capacity of lithium polysulfides (LiPSs) prepared with mesopores and high specific surface area C-Fe3O4@PE. The membrane can bind polysulfide via chemical and physical adsorption. Owing to these advantages, a Li-S battery based on C-Fe3O4@PE and with a separator exhibited excellent rate performance (965.9 mA h g–1 at 0.2 C and 437.5 mA h g–1 at 5 C), as well as a long cycle life (416.6 mA h g–1 after 300 cycles at 0.5 C, with a capacity retention rate of 50.5%).
Applied machine learning for stroke differentiation by electrical impedance tomography with realistic numerical models
Authors: Culpepper Jared , Lee Hannah , Santorelli Adam , Porter Emily 
Published: November 2023

Abstract
Electrical impedance tomography (EIT) may have potential to overcome existing limitations in stroke differentiation, enabling low-cost, rapid, and mobile data collection. Combining bioimpedance measurement technologies such as EIT with machine learning classifiers to support decision-making can avoid commonly faced reconstruction challenges due to the nonlinear and ill-posed nature of EIT imaging. Therefore, in this work, we advance this field through a study integrating realistic head models with clinically relevant test scenarios, and a robust architecture consisting of nested cross-validation and principal component analysis. Specifically, realistic head models are designed which incorporate the highly conductive layers of cerebrospinal fluid in the subarachnoid space and ventricles. In total, 135 unique models are created to represent a large patient population, with normal, haemorrhagic, and ischemic brains. Simulated EIT voltage data generated from these models are used to assess the classification performance of support vector machines. Parameters explored include driving frequency, signal-to-noise ratio, kernel function, and composition of binary classes. Classifier accuracy at 60 dB signal-to-noise ratio, reported as mean and standard deviation, are (79.92% ± 10.82%) for lesion differentiation, (74.78% ± 3.79%) for lesion detection, (77.49% ± 15.90%) for bleed detection, and (60.31% ± 3.98%) for ischemia detection (after ruling out bleed). The results for each method were obtained with statistics from 3 independent runs with 17,280 observations, polynomial kernel functions, and feature reduction of 76% by PCA (from 208 to 50 features). While results of this study show promise for stroke differentiation using EIT data, our findings indicate that the achievable accuracy is highly dependent on the classification scenario and application-specific classifiers may be necessary to achieve acceptable accuracy.
Optical properties of charged defects in monolayer MoS2
Authors: Aghajanian Martik , Mostofi Arash A , Lischner Johannes 
Published: November 2023

Abstract
We present theoretical calculations of the optical spectrum of monolayer MoS2 with a charged defect. In particular, we solve the Bethe–Salpeter equation based on an atomistic tight-binding model of the MoS2 electronic structure which allows calculations for large supercells. The defect is modelled as a point charge whose potential is screened by the MoS2 electrons. We find that the defect gives rise to new peaks in the optical spectrum approximately 100–200 meV below the first free exciton peak. These peaks arise from transitions involving in-gap bound states induced by the charged defect. Our findings are in good agreement with experimental measurements.
Optimization of TIG welding process parameters on 304 austenitic stainless steel sheet metal using fuzzy logic based Taguchi method
Authors: Abebe Demeyesus Gizaw , Bogale Teshome Mulatie 
Published: November 2023

Abstract
TIG welding can be used to produce excellent weld quality and precise welding operation for sheet metals. The aim of this study was to get the best welding parameters for enhancing ultimate tensile strength, bending strength and Rockwell hardness of the butt-weld joint. The experimental work was used, and the experiment was carried out on 2 mm thickness of 304 austenitic-stainless steel sheet metal using the L9 orthogonal array of the Taguchi design. Automated TIG welding fixture was developed to control the welding speed accurately. The selected welding parameters were welding speed, current, voltage and gas flow rate with their three levels. Based on the fuzzy logic based Taguchi method, the best optimal levels of parameters were found at the values of 110 A of current, 13.86 cm/min of speed, 17.5 V of voltage and 7.5 L/min of gas flow rate. The analysis results of ANOVA showed that gas flow rate and current were found as the significant factors, and the contributions of the gas flow rate, current, speed and error were 47.63%, 34.34%, 16.49% and 1.54%, respectively. According to the confirmation tests, the multi response performance index mean value of the confirmatory test of 0.6068 was found between the 95% confidence interval of 0.5028 and 0.7314, and the maximum ultimate tensile strength, bending strength and Rockwell hardness were obtained 614.8 MPa, 765.32 MPa and 95.3 HRB, respectively.
Throttling effect on the availability and sustainability of a gasoline-fuelled spark ignited multi-cylinder engine
Authors: Hamada Khalaf I , Ibrahim Thamir K , Rahim M F , Rahman M M , Bakar Rosli A 
Published: November 2023

Abstract
Large energy waste is one of the internal combustion engine challenges, making it unsustainable and unfriendly to the environment. Charging the spark ignition engine with the required fuel-air mixture is associated with a particular quantity of energy loss due to flow throttling. Throttle position effect on the availability, sustainability, and environmental trends of a gasoline, spark-ignited engine was evaluated based on thermodynamic analyses of real experiments. The availability and sustainability analyses were performed utilizing the relations governing the thermodynamic system under various engine speeds (1500–4000 rpm) at part and wide-open throttle (POT and WOT) conditions. The results indicated that the WOT position mostly ensures the best sharing of useful availability, i.e., 2% greater, more sustainable, i.e., 3% better, and environmentally friendly, i.e., 16% healthier, engine operation for the entire tested speed range. On the other hand, an optimum engine speed, mostly 3000 rpm, of the tested engine yielded the maximum efficiencies, i.e., 3% higher, lowest entropy production, i.e., 15% lower, and best availability proportions, as well as the most sustainable and environmentally friendly operation mode. Furthermore, a considerable perspective of the waste heat recovery of the associated availability with exhaust gases was achieved under POT and WOT conditions. The acquired improvement in engine efficiency based on the recovery potential was in the range of 3% to 19.7% and 13.6% to 24% for the WOT and POT conditions, respectively. However, the detected recovery rate and efficiency improvement were randomly tended with engine speed variation. In terms of the recovery potential, the highest engine efficiencies were about 46% and 50% at 3000 rpm, for the POT and WOT conditions, respectively. Finally, in average around 10% and 52% increment with 20% and 24% reduction was detected for the environmental impact of the CO2, NOx, CO and HC, respectively, under the WOT condition in comparison with POT condition.
Improved multi objective particle swarm optimization based reactive power optimization for ensuring voltage security of power systems
Authors: Hilawie Ahadu , Shewarega Fekadu 
Published: November 2023

Abstract
In this study an improved multi objective particle swarm optimization (IMOPSO) algorithm is proposed for power system reactive power optimization with the objective of ensuring voltage security. The multi objective particle swarm optimization (MOPSO) is improved by introducing an adapted binary crossover (ABX) to the new positions obtained by the basic particle swarm optimization (PSO) algorithm. Additionally, diversity maintenance strategy is added to the algorithm by employing crowding distance (CD) calculation. The developed algorithm is tested and compared with standard MOPSO and non dominated sorting genetic algorithm (NASGA II). The comparison is made based on the degree of closeness to the true pareto front, as measured by the inverted generational distance (IGD), and based on diversity, as measured by the CDs . The test is made using ZDT1, ZDT2, and ZDT3 test functions. The IMOPSO showed improved performance over MOPSO and NASGA II algorithms in terms of convergence to the true pareto front (PF) and in terms of the speed of convergence as well as in maintaining diversity. The algorithm is then implemented to reactive power optimization of IEEE 14 bus test system. For the implementation purpose, the voltage stability and voltage deviation components of voltage security are formulated as a multi objective functions. The implementation has resulted diverse options of optimal settings of reactive power controlling parameters. The optimal settings proved to produce an improved voltage security as measured in terms of voltage deviation and voltage stability.
Calibration of uncertainty in the active learning of machine learning force fields
Authors: Thomas-Mitchell Adam , Hawe Glenn , Popelier Paul L A 
Published: November 2023

Abstract
FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training. MEPE uses the predictive uncertainty of a Gaussian process (GP) to balance exploration and exploitation when selecting the next training sample. However, the predictive uncertainty of a GP is unlikely to be accurate or precise immediately after training. We hypothesize that calibrating the uncertainty quantification within MEPE will improve active learning performance. We develop and test two methods to improve uncertainty estimates: post-hoc calibration of predictive uncertainty using the CRUDE algorithm, and replacing the GP with a student-t process. We investigate the impact of these methods on MEPE for single sample and batch sample active learning. Our findings suggest that post-hoc calibration does not improve the performance of active learning using the MEPE method. However, we do find that the student-t process can outperform active learning strategies and random sampling using a GP if the training set is sufficiently large.
High-uniformity atomic layer deposition of superconducting niobium nitride thin films for quantum photonic integration
Authors: Lennon C T , Shu Y , Brennan J C , Namburi D K , Varghese V , Hemakumara D T , Longchar L A , Srinath S , Hadfield R H 
Published: November 2023

Abstract
Atomic layer deposition (ALD) has been identified as a promising growth method for high-uniformity superconducting thin films for superconducting quantum photonic applications, offering superior uniformity, thickness control and conformality to techniques such as reactive sputtering. The potential scalability of ALD makes this method especially appealing for fabrication of superconducting nanowires and resonators across large areas. We report on the growth of highly uniform superconducting NbN thin films via plasma-enhanced atomic layer deposition (PEALD) with radio frequency substrate biasing, on a 200 mm (8 inch) Si wafer, specifically for superconducting nanowire single-photon detector applications. Niobium nitride films were grown using (tert-butylimido)-tris(diethylamido)-niobium(V) precursor and an H2/Ar plasma. The superconducting properties of a variable thickness series of films (5.9–29.8 nm) show critical temperature (T
c) of 13.5 K approaching bulk thickness (28.8 nm) with low suppression down to the ultrathin regime (5.9 nm), with T
c = 10.2 K. T
c across the 200 mm wafer with 8 nm thick NbN, measured in 15 mm intervals, exhibits minimal variation (<7%). Microbridge structures fabricated on 8 nm thick NbN films also exhibit high critical current densities (J
c), > 10 MA cm−2 at 2.6 K. PEALD could therefore be a pivotal technique in enabling large-scale fabrication of integrated quantum photonic devices across a variety of applications.
Performance evaluation of heparin-induced platelet aggregation vs serotonin release assay
Authors: Barouqa Mohammad , Matta Milad , Reyes-Gil Morayma 
Published: November 2023

Abstract

Objectives
Heparin-induced thrombocytopenia (HIT) is a rare but life-threatening condition that requires rapid diagnosis for proper management. Laboratory testing should only be performed on patients with intermediate- or high-risk pretest probability. The platelet factor 4 (PF4) enzyme-linked immunosorbent assay (ELISA) is the screening test that should be confirmed by higher specificity testing such as the heparin-induced platelet aggregation (HIPA) or the serotonin release assay (SRA). This study aims to evaluate the performance of the HIPA in comparison to the SRA, establish cutoffs of the PF4 ELISA to predict positivity for HIPA/SRA, and study the mortality rate between patients with suspected HIT confirmed as HIT positive vs negative.

Methods
All positive PF4 ELISA cases with confirmatory HIPA and SRA testing were included. As the SRA was considered the gold standard, the HIPA performance was evaluated in comparison to SRA before and after the implementation of a new standardized interpretation guide in 2022. The mortality of these cases was also documented by chart reviews.

Results
In total, 232 cases with positive or indeterminate anti-PF4 IgG ELISA had confirmatory testing with HIPA and SRA. The sensitivity of the HIPA improved from 55.4% in 2018 to 2021 to 73.8% in 2022. The specificity remained similarly high in 2018 to 2021 vs 2022 (94.9% vs 87.5%). The negative predictive value (NPV) improved in 2022. The PF4 optical density cutoff to predict the positivity of SRA was 0.85 vs 1.47 to predict the positivity of HIPA but decreased to 0.83 when combining HIPA and/or SRA. There was no significant difference in mortality between patients with suspected HIT confirmed positive vs negative.

Conclusions
Although the HIPA has a lower sensitivity than the SRA, the new standardized interpretation guide improved its sensitivity and NPV in 2022. Future improvements are needed to use the HIPA as a stand-alone confirmatory test with the goal to shorten hospital length of stay and expedite proper anticoagulation management.
Identifying local associations in biological time series: algorithms, statistical significance, and applications
Authors: Ai Dongmei , Chen Lulu , Xie Jiemin , Cheng Longwei , Zhang Fang , Luan Yihui , Li Yang , Hou Shengwei , Sun Fengzhu , Xia Li Charlie 
Published: November 2023

Abstract
Local associations refer to spatial–temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.
Inferring delays in partially observed gene regulation processes
Authors: Hong Hyukpyo , Cortez Mark Jayson , Cheng Yu-Yu , Kim Hang Joon , Choi Boseung , Josić Krešimir , Kim Jae Kyoung 
Published: November 2023

AbstractMotivationCell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality.ResultsWe develop a simulation-based Bayesian MCMC method employing an approximate likelihood for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: an activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. With prior information about observed fluorescent protein synthesis, our method successfully infers the dynamics of the unobserved regulatory protein. We can estimate the delay and kinetic parameters characterizing target regulation including transcription, translation, and target searching of an unobserved protein from experimental measurements of the products of its target gene. Our method is scalable and can be used to analyze non-Markovian models with hidden components.Availability and implementationOur code is implemented in R and is freely available with a simple example data at https://github.com/Mathbiomed/SimMCMC.
Klotho: a potential therapeutic target in aging and neurodegeneration beyond chronic kidney disease—a comprehensive review from the ERA CKD-MBD working group
Authors: Kanbay Mehmet , Copur Sidar , Ozbek Lasin , Mutlu Ali , Cejka Daniel , Ciceri Paola , Cozzolino Mario , Haarhaus Mathias Loberg 
Published: November 2023

ABSTRACT
Klotho, a multifunctional protein, acts as a co-receptor in fibroblast growth factor 23 and exerts its impact through various molecular pathways, including Wnt, hypoxia-inducible factor and insulin-like growth factor 1 pathways. The physiological significance of Klotho is the regulation of vitamin D and phosphate metabolism as well as serving as a vital component in aging and neurodegeneration. The role of Klotho in aging and neurodegeneration in particular has gained considerable attention. In this narrative review we highlight several key insights into the molecular basis and physiological function of Klotho and synthesize current research on the role of Klotho in neurodegeneration and aging. Klotho deficiency was associated with cognitive impairment, reduced growth, diminished longevity and the development of age-related diseases in vivo. Serum Klotho levels showed a decline in individuals with advanced age and those affected by chronic kidney disease, establishing its potential diagnostic significance. Additionally, multiple medications have been demonstrated to influence Klotho levels. Therefore, this comprehensive review suggests that Klotho could open the door to novel interventions aimed at addressing the challenges of aging and neurodegenerative disorders.
Responses to systemic therapy in metastatic pheochromocytoma/paraganglioma: a retrospective multicenter cohort study
Authors: Fischer Alessa , Kloos Simon , Remde Hanna , Dischinger Ulrich , Pamporaki Christina , Timmers Henri J L M , Robledo Mercedes , Fliedner Stephanie M J , Wang Katharina , Maurer Julian , Reul Astrid , Bechmann Nicole , Hantel Constanze , Mohr Hermine , Pellegata Natalia S , Bornstein Stefan R , Kroiss Matthias , Auernhammer Christoph J , Reincke Martin , Pacak Karel , Grossman Ashley B , Beuschlein Felix , Nölting Svenja 
Published: November 2023

Abstract

Objective
The therapeutic options for metastatic pheochromocytomas/paragangliomas (mPPGLs) include chemotherapy with cyclophosphamide/vincristine/dacarbazine (CVD), temozolomide monotherapy, radionuclide therapies, and tyrosine kinase inhibitors such as sunitinib. The objective of this multicenter retrospective study was to evaluate and compare the responses of mPPGLs including those with pathogenic variants in succinate dehydrogenase subunit B (SDHB), to different systemic treatments.

Design
This is a retrospective analysis of treatment responses of mPPGL patients (n = 74) to systemic therapies.

Methods
Patients with mPPGLs treated at 6 specialized national centers were selected based on participation in the ENSAT registry. Survival until detected progression (SDP) and disease-control rates (DCRs) at 3 months were evaluated based on imaging reports.

Results
For the group of patients with progressive disease at baseline (83.8% of 74 patients), the DCR with first-line CVD chemotherapy was 75.0% (n = 4, SDP 11 months; SDHB [n = 1]: DCR 100%, SDP 30 months), with somatostatin peptide receptor-based radionuclide therapy (PPRT) 85.7% (n = 21, SDP 17 months; SDHB [n = 10]: DCR 100%, SDP 14 months), with 131I-meta-iodobenzylguanidine (131I-MIBG) 82.6% (n = 23, SDP 43 months; SDHB [n = 4]: DCR 100%, SDP 24 months), with sunitinib 100% (n = 7, SDP 18 months; SDHB [n = 3]: DCR 100%, SDP 18 months), and with somatostatin analogs 100% (n = 4, SDP not reached). The DCR with temozolomide as second-line therapy was 60.0% (n = 5, SDP 10 months; SDHB [n = 4]: DCR 75%, SDP 10 months).

Conclusions
We demonstrate in a real-life clinical setting that all current therapies show reasonable efficacy in preventing disease progression, and this is equally true for patients with germline SDHB mutations.
Towards English language learners’ wellbeing: Contributing factors and stressors
Authors: Khajavy Gholam Hassan , Vaziri Mahdieh 
Published: November 2023

Abstract
Understanding and enhancing wellbeing are at the heart of positive psychology. Both ELT teachers and researchers have recently been urged to prioritize wellbeing in their teaching and research. However, far too little attention has been paid to wellbeing among ELT learners, especially from their own perspective. Accordingly, this study aimed at developing an understanding of ELT learners’ perceptions of their stressors in class and recommendations to improve their wellbeing. Out of eighty survey participants, we selected nine participants with the highest and nine participants with the lowest scores on a wellbeing measure. Then, we conducted oral survey interviews with each of these participants, and the data were analysed using qualitative content analysis. The findings indicated four main stressors for ELT learners: lack of preparation, assessment, speaking English, and lack of understanding. Participants also made several recommendations to teachers, the university, and for themselves which they believe would help enhance their wellbeing.
EAM highlights in FEMS 2023: from the Petri dish to planet Earth
Authors: Vogel Jörg , de Lorenzo Victor 
Published: November 2023

Abstract
On 9–13 July 2023, the 10th FEMS Congress took place in Hamburg, Germany. As part of this major event in European microbiology, the European Academy of Microbiology (EAM) organized two full sessions. One of these sessions aimed to highlight the research of four recently elected EAM fellows and saw presentations on bacterial group behaviours and development of resistance to antibiotics, as well as on new RNA viruses including bacteriophages and giant viruses of amoebae. The other session included five frontline environmental microbiologists who showcased real-world examples of how human activities have disrupted the balance in microbial ecosystems, not just to assess the current situation but also to explore fresh approaches for coping with external disturbances. Both sessions were very well attended, and no doubt helped to gain the EAM and its fellows more visibility.