bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical characteristics and mortality of patients with heart failure in Southern Sweden from 2013 to 2019: a population-based cohort study
Authors: Davidge Jason , Ashfaq Awais , Ødegaard Kristina Malene , Olsson Mattias , Costa-Scharplatz Madlaina , Agvall Björn
Published: December 2022

Objectives

To describe clinical characteristics and prognosis related to heart failure (HF) phenotypes in a community-based population by applying a novel algorithm to obtain ejection fractions (EF) from electronic medical records.

Design

Retrospective population-based cohort study.

Setting

Data were collected for all patients with HF in Southwest Sweden. The region consists of three acute care hospitals, 40 inpatient wards, 2 emergency departments, 30 outpatient specialty clinics and 48 primary healthcare.

Participants

8902 patients had an HF diagnosis based on the International Classification of Diseases, Tenth Revision during the study period. Patients <18 years as well as patients declining to participate were excluded resulting in a study population of 8775 patients.

Primary and secondary outcome measures

The primary outcome measure was distribution of HF phenotypes by echocardiography. The secondary outcome measures were 1 year all-cause mortality and HR for all-cause mortality using Cox regression models.

Results

Out of 8775 patients with HF, 5023 (57%) had a conclusive echocardiography distributed into HF with reduced EF (35%), HF with mildly reduced EF (27%) and HF with preserved EF (38%). A total of 43% of the cohort did not have a conclusive echocardiography, and therefore no defined phenotype (HF-NDP). One-year all-cause mortality was 42% within the HF-NDP group and 30% among those with a conclusive EF. The HR of all-cause mortality in the HF-NDP group was 1.27 (95% CI 1.17 to 1.37) when compared with the confirmed EF group. There was no significant difference in survival within the HF phenotypes.

Conclusions

This population-based study showed a distribution of HF phenotypes that varies from those in selected HF registries, with fewer patients with HF with reduced EF and more patients with HF with preserved EF. Furthermore, 1-year all-cause mortality was significantly higher among patients with HF who had not undergone a conclusive echocardiography at diagnosis, highlighting the importance of correct diagnostic procedure to improve treatment strategies and outcomes.
Trends in body mass index, overweight and obesity among adults in the USA, the NHANES from 2003 to 2018: a repeat cross-sectional survey
Authors: Li Mingxi , Gong Weijun , Wang Shidong , Li Zhe
Published: December 2022

Objectives

To analyse detailed trends in adult obesity from 2003 to 2018 in the USA, and provide the latest national estimates of adult obesity in 2017–2018.

Design, setting and participants

Analysis of data, including measured height and weight, obtained from 42 266 adults aged ≥20 years from the National Health and Nutrition Examination Survey, a cross-sectional, nationally representative sample of the US population.

Exposure

Survey period.

Primary outcome measures

The mean body mass index (BMI) and prevalence of overweight and obesity.

Results

In 2017–2018, the prevalence of overweight (including obesity, BMI ≥25 kg/m2) and obesity (BMI ≥30 kg/m2) was 73.8% (95% CI 71.1% to 76.4%) and 42.8% (95% CI 39.5% to 46.1%), respectively. From 2003 to 2018, a significant increase in the prevalence of overweight (including obesity, overall adjusted OR for 2017–2018 vs 2003–2004, 1.08 (95% CI 1.04 to 1.13) and obesity (overall adjusted OR for 2017–2018 vs 2003–2004, 1.15 (95% CI 1.10 to 1.21) was found among American adults. However, annual changes in mean BMI and the prevalence of overweight and obesity did not differ significantly before and after 2009–2010. The prevalence of overweight and obesity varied significantly by age, sex, race, education, daily total energy intake, economic conditions and physical activity status (all p<0.05).

Conclusions

Although the prevalence of adult obesity continues to rise, there have been no significant changes in the annual growth of adult obesity prevalence between 2003–2004 and 2017–2018. In 2017–2018, the prevalence of obesity was 42.8%, which equates to 76 million American adults at risk for serious and costly chronic conditions. The prevalence of obesity was higher among older adults (aged 60–69 years), females, non-Hispanic blacks, and those who did not graduate college, were physically inactive, reported lower daily total energy intake and had poor economic status.
Validation of a classification to identify emergency department visits suitable for subacute and virtual care models: a randomised single-blinded agreement study protocol
Authors: Strum Ryan P , Mondoux Shawn , Mowbray Fabrice , Worster Andrew , Griffith Lauren E , Tavares Walter , Miller Paul , Hanel Erich , Aryal Komal , Sivakumaran Ravi , Costa Andrew P
Published: December 2022

Introduction

Redirecting suitable patients from the emergency department (ED) to alternative subacute settings may assist in reducing ED overcrowding while delivering equivalent care. The Emergency Department Avoidance Classification (EDAC) was constructed to retrospectively classify ED visits that may have been suitable for safe management in a subacute or virtual clinical setting. The EDAC has established face and content validity but has not been tested against a reference standard as a criterion.

Objectives

Our primary objective is to examine the agreement between the EDAC and ED physician judgements in retrospectively identifying ED visits suitable for subacute care management. Our secondary objective is to assess the validity of ED physicians’ judgement as a criterion standard. Our tertiary objective is to examine how the ED physician’s perception of a virtual ED care alternative correlates with the EDAC.

Methods and analysis

A randomised single-centre, single-blinded agreement study. We will randomly select ED charts between 1 January and 31 December 2019 from an academic hospital in Hamilton, Canada. ED charts will be randomly assigned to participating ED physicians who will evaluate if this ED visit could have been managed appropriately and safely in a subacute and/or virtual model of care. Each chart will be reviewed by two physicians independently. We compute our needed sample size to be 79 charts. We will use kappa statistics to measure inter-rater agreement. A repeated measures regression model of physician ratings will provide variance estimates that we will use to assess the intraclass correlation of ED physician ratings and the EDAC.

Ethics and dissemination

This study has been approved by the Hamilton Integrated Research Ethics Board (2022-14625). If validated, the EDAC may provide an ED-based classification to identify potentially avoidable ED visits, monitor ED visit trends, and proactively delineate those best suited for subacute or virtual care models.
Access, relationships, quality and safety (ARQS): a qualitative study to develop an Indigenous-centred understanding of virtual care quality
Authors: Roach Pamela , Ody Meagan , Campbell Paige , Bablitz Cara , Toth Ellen , Murry Adam , Henderson Rita , Kennedy Andrea , Montesanti Stephanie , Barnabe Cheryl , Crowshoe Lynden
Published: December 2022

Background

Among Indigenous peoples in Canada, access to high-quality healthcare remains an important determinant of health. The shift to virtual and remote-based approaches, expedited during the COVID-19 pandemic, influenced the ways in which individuals accessed care and the quality of care received. This study sought to determine which elements are required for effective and sustainable virtual care approaches for delivery of primary care to Indigenous patients and develop quality indicators grounded in Indigenous community and experience. We share a conceptual framework to understand how Indigenous patients access and define high-quality virtual care, grounded in Indigenous patient experiences and worldviews.

Methods

Using principles of patient-oriented research, we grounded this work in social justice and participatory action research. We sought to gain an in-depth understanding of the Indigenous experiences of virtual care and specifically of primary care. This was developed through semistructured interviews with Indigenous patients and Indigenous virtual primary care providers.

Results

Thirteen participants were interviewed between 5 August 2021 and 25 October 2021. Using Framework Analysis, we constructed four domains including access, relationships, quality and safety as being primary facets of defining high-quality Indigenous virtual primary care.

Discussion

The results presented here indicate that the shift to virtual care, largely seen in response to the COVID-19 pandemic, does not compromise quality of care, nor does it lead to negative patient experiences. Optimal care is possible in virtual settings for some care needs and types of appointments and has the potential to decrease barriers to access and improve patient experiences of safety and quality while facilitating patient/provider relationships.

Conclusion

In summary, high-quality Indigenous virtual care benefits from attention to patients’ experiences of access, relationships, safety and quality with their service providers and healthcare teams.
Functional connectivity of the default mode network subsystems in patients with major depressive episodes with mixed features
Authors: Liu Rui , Qi Han , Guan Lin , Wu Hang , Liu Jing , Li Xiaoya , Huang Juan , Zhang Ling , Zhou Yuan , Zhou Jingjing
Published: December 2022

Background

The neuroimaging mechanism of major depressive episodes with mixed features (MMF) is not clear.

Aims

This study aimed to investigate the functional connectivity of the default mode network (DMN) subsystems among patients with MMF and patients with major depressive disorder without mixed features (MDDnoMF).

Methods

This study recruited 47 patients with MDDnoMFand 27 patients with MMF from Beijing Anding Hospital, Capital Medical University, between April 2021 and June 2022. Forty-five healthy controls (HCs) were recruited. All subjects underwent resting-state functional magnetic resonance imaging scanning and clinical assessments. Intranetwork and internetwork functional connectivity were computed in the DMN core subsystem, dorsal medial prefrontal cortex (dMPFC) subsystem and medial temporal lobe (MTL) subsystem. Analysis of covariance method was performed to compare the intranetwork and internetwork functional connectivity in the DMN subsystems among the MDDnoMF, MMF and HC groups.

Results

The functional connectivity within the DMN core (F=6.32, pFDR=0.008) and MTL subsystems (F=4.45, pFDR=0.021) showed significant differences among the MDDnoMF, MMF and HC groups. Compared with the HC group, the patients with MDDnoMFand MMF had increased functional connectivity within the DMN MTL subsystem, and the patients with MMF also showed increased functional connectivity within the DMN core subsystem. Meanwhile, compared with the MDDnoMF, the patients with MMF had increased functional connectivity within the DMN core subsystem (mean difference (MDDnoMF−MMF)=−0.08, SE=0.04, p=0.048). However, no significant differences were found within the DMN dMPFC subsystem and all the internetwork functional connectivity.

Conclusions

Our results indicated abnormal functional connectivity patterns of DMN subsystems in patients with MMF, findings potentially beneficial to deepen our understanding of MMF’s neural basis.
Management of coronary artery disease in patients with aortic stenosis
Authors: Androshchuk Vitaliy , Patterson Tiffany , Redwood Simon R
Published: December 2022

Aortic stenosis (AS) is the most common valvular heart disorder in the elderly population. As a result of the shared pathophysiological processes, AS frequently coexists with coronary artery disease (CAD). These patients have traditionally been managed through surgical aortic valve replacement (SAVR) and coronary artery bypass grafting. However, increasing body of evidence supports transcatheter aortic valve implantation (TAVI) as an alternative treatment for severe AS across the spectrum of operative risk. This has created the potential for treating AS and concurrent CAD completely percutaneously. In this review we consider the evidence guiding the optimal management of patients with severe AS and CAD. While invasive coronary angiography plays a central role in detecting CAD in patients with AS undergoing surgery or TAVI, the benefits of complementary functional assessment of coronary stenosis in the context of AS have not been fully established. Although the indications for revascularisation of significant proximal CAD in SAVR patients have not recently changed, routine revascularisation of all significant CAD before TAVI in patients with minimal angina is not supported by the latest evidence. Several ongoing trials will provide new insights into physiology-guided revascularisation in TAVI recipients. The role of the heart team remains essential in this complex patient group, and if revascularisation is being considered careful evaluation of clinical, anatomical and procedural factors is essential for individualised decision-making.
Interactions between life history and the environment on changing growth rates of Chinook salmon
Authors: Buckner Jack H. , Satterthwaite William H. , Nelson Benjamin W. , Ward Eric J.
Published: December 2022

Fish in all the world's oceans exhibit variable body size and growth over time, with some populations exhibiting long-term declines in size. These patterns can be caused by a range of biotic, abiotic, and anthropogenic factors and impact the productivity of harvested populations. Within a given species, individuals often exhibit a range of life history strategies that may cause some groups to be buffered against change. One of the most studied declines in size-at-age has been in populations of salmon; Chinook salmon in the Northeast Pacific Ocean are the largest-bodied salmon species and have experienced long-term declines in size. Using long-term monitoring data, we develop novel size and growth models to link observed changes in Chinook size to life history traits and environmental variability. Our results identify three distinct trends in size across the 48 stocks in our study. Differences among populations are correlated with ocean distribution, migration timing, and freshwater residence. We provide evidence that trends are driven by interannual variation in certain oceanographic processes and competition with pink salmon.
Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG
Authors: Peh Wei Yan , Thangavel Prasanth , Yao Yuanyuan , Thomas John , Tan Yee-Leng , Dauwels Justin
Published: December 2022

Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures’ start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR/h and mFPR/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617–1.00, PRE of 0.534–1.00, aFPR/h of 0.425–2.002, and mFPR/h of 0–1.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment.
High transformer ratio electron acceleration for CEPC plasma injector
Authors: Wang Xiaoning , Gao Jie , An Weiming , Zhou Shiyu , Meng Cai , Wang Dou , Wang Jia , Li Dazhang , Zeng Ming , Lu Wei
Published: December 2022

The CEPC Plasma Injector is designed as the beam energy multiplier for avoiding the dipole magnet issue of the Circular Electron Positron Collider (CEPC) booster. Besides the final energy, other requirements including the energy stability, bunch charge and energy spread all make the injector design very challenging. With respect to the electron acceleration, a high transformer ratio plasma wakefield acceleration stage is necessary if we constrain the beam energy at the end of the linac and the plasma injector are 10[Formula: see text]GeV and 45.5[Formula: see text]GeV, respectively. In this work, we use the particle-in-cell code QuickPIC to design the electron acceleration stage. Based on the current conceptual design, a comprehensive analysis of single-parameter tolerance is performed. Through the tolerance studies, we obtain some useful guidance for the further optimization of the injector.
Evolution for Khovanov polynomials for figure-eight-like family of knots
Authors: Dunin-Barkowski Petr , Popolitov Aleksandr , Popolitova Svetlana
Published: December 2022

We look at how evolution method deforms, when one considers Khovanov polynomials instead of Jones polynomials. We do this for the figure-eight-like knots (also known as ’double braid’ knots, see arXiv:1306.3197) — a two-parametric family of knots which “grows” from the figure-eight knot and contains both two-strand torus knots and twist knots. We prove that parameter space splits into four chambers, each with its own evolution, and two isolated points. Remarkably, the evolution in the Khovanov case features an extra eigenvalue, which drops out in the Jones [Formula: see text] limit.
PTGAC Model: A machine learning approach for constructing phylogenetic tree to compare protein sequences
Authors: Pal Jayanta , Saha Sourav , Maji Bansibadan , Bhattacharya Dilip Kumar
Published: December 2022

This work proposes a machine learning-based phylogenetic tree generation model based on agglomerative clustering (PTGAC) that compares protein sequences considering all known chemical properties of amino acids. The proposed model can serve as a suitable alternative to the Unweighted Pair Group Method with Arithmetic Mean (UPGMA), which is inherently time-consuming in nature. Initially, principal component analysis (PCA) is used in the proposed scheme to reduce the dimensions of 20 amino acids using seven known chemical characteristics, yielding 20 TP (Total Points) values for each amino acid. The approach of cumulative summing is then used to give a non-degenerate numeric representation of the sequences based on these 20 TP values. A special kind of three-component vector is proposed as a descriptor, which consists of a new type of non-central moment of orders one, two, and three. Subsequently, the proposed model uses Euclidean Distance measures among the descriptors to create a distance matrix. Finally, a phylogenetic tree is constructed using hierarchical agglomerative clustering based on the distance matrix. The results are compared with the UPGMA and other existing methods in terms of the quality and time of constructing the phylogenetic tree. Both qualitative and quantitative analysis are performed as key assessment criteria for analyzing the performance of the proposed model. The qualitative analysis of the phylogenetic tree is performed by considering rationalized perception, while the quantitative analysis is performed based on symmetric distance (SD). On both criteria, the results obtained by the proposed model are more satisfactory than those produced earlier on the same species by other methods. Notably, this method is found to be efficient in terms of both time and space requirements and is capable of dealing with protein sequences of varying lengths.
Investigation of the Optimal PID-Like Fuzzy Logic Controller for Ball and Beam System with Improved Quantum Particle Swarm Optimization
Authors: Altinoz O. Tolga , Yilmaz A. Egemen
Published: December 2022

Fuzzy Logic Controllers (FLCs) are intelligent control methods, where membership functions and corresponding rules are defined to get a proper control signal. The parameters were defined for these controllers, and they are named as PID-like FLC since the input and output parameters are connected to the Fuzzy controller with integral and derivative action of the error signal to change the behavior/performance of FLC. In this research, three different rule sets for Fuzzy controllers; 3 × 3, 5 × 5, and 7 × 7 are used and parameters are optimized with; differential evolution, genetic algorithm, particle swarm optimization and quantum-behaved particle swarm optimization. In addition to these controllers, a novel algorithm named as improved quantum particle swarm optimization is proposed as a part of this research. The simulation and real-life implementation on the experimental set results of these controllers are discussed in this paper.
The Need for Emergency Management Models
Authors: Alrehaili Naif R. , Almutairi Yousef N. , Alghamdi Hamdan M. , Almuthaybiri Musaad S.
Published: December 2022

Emergency agencies can use emergency management models to enable them to better prepare for and respond to emergencies. This qualitative study aims to undertake a critical examination of emergency management models by thematic analysis to determine their significance to emergency management. A review and analysis of the existing literature were used in the study. The models were studied to explore their role in emergency management and to identify any significant constraints or challenges which could limit the ability of the emergency management model to carry out appropriate actions. The study found that such models are indispensable because they simplify and improve emergency management. Additionally, they may support planners, managers, and practitioners in reaching proper decisions, making them a valuable and necessary decision-making support tool. The study also showed that each model has an advantage that distinguishes it from the other models. Consequently, a comprehensive emergency management model should be designed to suit all cases and circumstances. The findings also confirmed the doubts raised about the limitations and concerns associated with the models. Concerns included future events’ unpredictability, the models’ prescriptive nature, the event’s cultural context, and the impact on businesses. The findings also indicated that certain planners, managers, and practitioners had a limited understanding of the use of models in emergency management. As such, they appear to have overlooked the use of models while dealing with emergencies. Hence, the study recommends that the models should be employed in all emergency management activities. The study also recommends that the findings are utilized as a basis for further research into the potential use of emergency management models.
Black-Box Attack using Adversarial Examples: A New Method of Improving Transferability
Authors: Wu Tao , Luo Tie , Wunsch Donald C.
Published: December 2022

Adversarial examples (AEs) are malicious test-data samples (typically images) generated by applying carefully calculated perturbations to clean samples. The added perturbations are usually human-imperceptible but the AEs can fool a machine learning (ML) model to make misclassifications. Although multiple methods were proposed to generate AEs, the ability to generalize is very limited; that is, they easily overfit to their source, single, white-box ML models and the generated AEs rarely work for other models. In this paper, we propose a black-box attack approach that crafts transferable AEs that can attack a wide range of ML models without knowing those model details. Our novel method consists of an elastic momentum (EM) that expedites gradient descent to avoid early overfitting, and a random erasure (RE) technique that increases the diversity of perturbations and reduces gradient fluctuations. Our method can be applied to any gradient-based attacks to make those attacks become more transferable. We evaluate our proposed method by attacking seven state-of-the-art (SOTA) deep learning models and comparing against five SOTA attacks; we also attack nine advanced defense mechanisms that are integrated into the above models. Our results demonstrate significant improvement on the attack success rate (ASR) and transferability when using our method alone, and that it can also be easily applied to other baseline methods (which are gradient-based) to substantially improve their performance.
Parallel Minimum Cuts in
O
(
m
log
2
n
) Work and Low Depth
Authors: Anderson Daniel , Blelloch Guy E.
Published: December 2022

We present a randomized
O
(
m
log
2
n
) work,
O
(polylog
n
) depth parallel algorithm for minimum cut. This algorithm matches the work bounds of a recent sequential algorithm by Gawrychowski, Mozes, and Weimann [ICALP’20], and improves on the previously best parallel algorithm by Geissmann and Gianinazzi [SPAA’18], which performs
O
(
m
log
4
n
) work in
O
(polylog
n
) depth.

Our algorithm makes use of three components that might be of independent interest. First, we design a parallel data structure that efficiently supports batched mixed queries and updates on trees. It generalizes and improves the work bounds of a previous data structure of Geissmann and Gianinazzi and is work efficient with respect to the best sequential algorithm. Second, we design a parallel algorithm for approximate minimum cut that improves on previous results by Karger and Motwani. We use this algorithm to give a work-efficient procedure to produce a tree packing, as in Karger’s sequential algorithm for minimum cuts. Last, we design an efficient parallel algorithm for solving the minimum 2-respecting cut problem.
Tamil Offensive Language Detection: Supervised versus Unsupervised Learning Approaches
Authors: Balakrishnan Vimala , Govindan Vithyatheri , Govaichelvan Kumanan N.
Published: December 2022

Studies on natural language processing are mainly conducted in English, with very few exploring languages that are under-resourced, including the Dravidian languages. We present a novel work in detecting offensive language using a corpus collected from YouTube containing comments in Tamil. The study specifically aims to compare two machine learning approaches—namely, supervised and unsupervised—to detect offensive patterns in textual communications. In the first setup, offensive language detection models were developed using traditional machine learning algorithms such as Random Forest, Logistic Regression, Support Vector Machine, and AdaBoost, and assessed based on human labeling. Conversely, we usedK-means (K= 2) to cluster the unlabeled data before training the same set of machine learning algorithms to detect offensive communications. Performance scores indicate unsupervised clustering to be more effective than human labeling with ensemble classifiers achieving an impressive accuracy of 99.70% and 99.87% respectively for balanced and imbalanced datasets, hence showing that the unsupervised approach can be used effectively to detect offensive language in low-resourced languages.
Swimming in Complex Fluids
Authors: Spagnolie Saverio E. , Underhill Patrick T.
Published: December 2022

We review the literature on swimming in complex fluids. A classification is proposed by comparing the length- and timescales of a swimmer with those of nearby obstacles, interpreted broadly, extending from rigid or soft confining boundaries to molecules that confer the bulk fluid with complex stresses. A third dimension in the classification is the concentration of swimmers, which incorporates fluids whose complexity arises purely by the collective motion of swimming organisms. For each of the eight system types that we identify, we provide a background and describe modern research findings. Although some types have seen a great deal of attention for decades, others remain uncharted waters still open and awaiting exploration.
Electrochemical Redox Processes for Wastewater Treatment and Resource Recovery Using Single-atom Catalysts
Authors: Nazemi Mohammadreza
Published: December 2022

This project will address the critical bottleneck in electrochemical redox processes (i.e., decreasing cost and improving efficiency) for wastewater treatment and resource recovery by developing cost-effective and selective electrode materials that can treat or valorize ROC for safe discharge or reuse. The overarching goal is to develop an electrochemical prototype to degrade a wide range of organic contaminants, minimize electrodes’ fouling and scaling, and recover valuable resources (e.g., metals and salts), contributing to achieving “pipe parity” and “circular economy” using small-scale and modular electrochemical water treatment systems.
Ultrafast Preparation of Metal/Carbon Nanocomposite Electrocatalysts by Magnetic Induction Heating toward Efficient Hydrogen Evolution Reaction
Authors: Liu Qiming
Published: December 2022

Hydrogen, which can be readily produced by electrochemical water splitting, has been hailed as one of the most promising green energy sources. In a water electrolyzer, catalysts are needed for the hydrogen evolution reaction (HER) at the cathode, which are traditionally based on platinum-based materials. Recently, carbon-based nanocomposites have emerged as viable alternatives, which are mostly prepared by pyrolysis or wet chemistry methods, which are time- and energy-consuming. We have exploited magnetic induction heating (MIH) for ultrafast and green preparation of high-performance HER electrocatalysts. A novel MIH method was developed for the ultrafast and green preparation of carbon-supported Ru NPs toward HER in alkaline media, where the size and morphology of the Ru NPs can be readily manipulated by the heating current and duration. It is expected that MIH can be exploited for the design and engineering of high-performance nanocomposite catalysts for important electrochemical energy technologies.
(Re)Defining Clean Hydrogen: From Colors to Emissions
Authors: Kusoglu Ahmet
Published: December 2022

This article discusses the definition of “clean” hydrogen produced by various pathways with a focus on transitioning from color-coded definitions to one that is based on the emissions generated in its creation. Herein, we also discuss the limitations of color-coding by providing a refined critique and assessment. With increasing emphasis on decarbonization efforts and clean energy transition, clean hydrogen’s role has been recognized more than ever, from clean energy bills and roadmaps to net-zero scenarios, which necessitates a clear, unambiguous definition for clean hydrogen. This article aims to help stimulate discussions as some colors’ associations may fade in favor of carbon intensity of the hydrogen production, in light of recent developments and interests in re-categorizing hydrogen production pathways and reducing associated carbon intensity from a life-cycle perspective.
Reports From The Frontier-Strategies to Design Stable Layered Oxide Cathodes for Na-Ion Batteries
Authors: Cushing Scott , Rahman Muhammad Mominur , Lin Feng
Published: December 2022

This feature page is intended to let ECS award winning students and post-docs write a primary-author perspective on their field, their work, and where they believe things are going. This month we highlight the work of Muhammad Mominur Rahman, the Battery Division 2021 Student Research Award winner.
The Transistor was Invented 75 Years Ago: A Big Milestone in Human History
Authors: Iwai Hiroshi , Misra Durga
Published: December 2022

Seventy-five years ago, on December 16, 1947, the transistor was invented by William Shockley, John Bardeen, and Walter Brattain at Bell Telephone Laboratories. This was a big milestone in human history as it was the origin of the field of micro/nano electronics, which is now resulting in a super-intelligent society. The technological revolution that followed the invention of the transistor is astounding and has moved mankind forward in countless ways. This paper describes the process of the technological development behind the invention of the transistor, and discusses the meaning and impact of its invention on human history.
Concentrated Growth Factors Combined with Lipopolysaccharide Stimulate the In Vitro Regenerative and Osteogenic Activities of Human Dental Pulp Stem Cells by Balancing Inflammation
Authors: Firouzi Negin , Yavari Hamid Reza , Rahimi Saeed , Roshangar Leila , Chitsazha Ramtin , Amini Mohammad
Published: December 2022

Aim. We investigated the long-term effects of exposure to concentrated growth factors (CGFs) on the regenerative properties of dental pulp stem cells (DPSCs) in the presence and absence of lipopolysaccharide (LPS) as a proinflammatory agent. Methods. DPSCs were cultured with CGF at different concentrations of LPS (0.1, 1, and 10 µg/ml) for 21 days. Then, using MTT and scratch assays, the cell viability and migration were examined. Osteogenic stimulation was evaluated by alkaline phosphatase (ALP) staining and Sirius Red staining, which determined the ALP activity and collagen levels, respectively. The expression levels of osteogenic markers were quantified using the qRT-PCR method. One-way analysis of variance (ANOVA) and Tukey’s HSD test were used to analyze differences between groups. Results. Long-term treatment of DPSCs with CGFs reduced LPS-induced cell death. Moreover, CGF and LPS (1 µg/ml), either in combination or alone, improved the DPSC migratory ability and caused a significant increase in osteogenic stimulation through the upregulation of collagen levels and ALP activity. Additionally, CGFs significantly upregulated RUNX2, DSPP, OCN, and OPN mRNA levels (as osteogenic markers), while LPS (1 µg/ml) only significantly increased OCN overexpression. Conclusion. Our findings are evidence that CGF could be a promising agent to induce dentin-pulp complex healing in long-term chronic inflammation.
Soil Parameters Model Prediction via Resistivity Value Limit to Shallow Subsurface Areas
Authors: Jusoh Muhammad Noor Hisyam , Syamsunur Deprizon , Abd Rahman Norinah , Olisa Emmanuel , Rahim Abdur , Md Yusoff Nur Izzi
Published: December 2022

Geotechnical engineering design necessitates a study of soil parameters for calculation purposes. As a complement to the conventional method, a resistivity survey can give preliminary results in a short amount of time. This study uses a statistical analysis program to show a correlation and regression for predicting soil parameters based on the resistivity value. Sixty-six data points for each soil parameter and resistivity were used for statistical analysis from six profiles of the study area as a part of the investigation. The soils are sampled by drilling the borehole up to a depth of 3 meters. The Wenner array was applied to conduct a resistivity survey. The collected data were utilized to establish a correlation between resistivity (ρ) and soil parameters such as moisture content (

w

), clay content (clay), plasticity index (

P
I

), friction angle (ϕ), bulk density (γ), and porosity (n). The generated statistic model was improved through several iterations for each correlation. For the ρ-

w

, ρ-clay, ρ-PI, ρ-ϕ, ρ-γ, and ρ-n relationship, the total iterations are 5, 4, 7, 6, 5, and 4 for each correlation, respectively. Resistivity and moisture content showed a significant power correlation, followed by a modest relationship between resistivity and porosity. Between resistivity and other parameters, relationships such as clay content, bulk density, friction angle, and plasticity index were found to have very weak or no association. However, some trends can be seen clearly. This study aims to offer the geotechnical engineer a quick preliminary way of obtaining the associated soil parameters for various geotechnical calculations employing the resistivity approach based on the developed numerical equation.
Knowledge, Attitude, and Practice on Pediatric Tuberculosis Management among Healthcare Workers in the Centre Region of Cameroon: A Cross-Sectional Study
Authors: Vukugah Thomas Achombwom , Akoku Derick Akompab , Tchoupa Micheline Mekemnang , Lambert Edward
Published: December 2022

Background. This study was designed to assess the level of knowledge, attitude, and practice (KAP) of healthcare workers (HCWs) on pediatric TB management and its associated factors in the Centre Region of Cameroon. Methods. A cross-sectional study was conducted between January and March 2022. HCWs, selected through a multistage sampling technique in 21 health facilities, were interviewed using a KAP questionnaire on pediatric TB management. Logistic regression analyses were used to test associations between HCWs’ characteristics and knowledge, attitudes, and practice levels at a 0.05 level of significance. Results. The median age of the participants was 35 years (IQR = 30–42), and the majority (73.2%) were females. About half (50.9%) of the participants (173/340) had good knowledge, 55.6% (189/340) had a good attitude, and 57.1% (194/340) had good practice scores on pediatric TB management. Having a bachelor’s degree and above, working in the TB unit, and having received training on pediatric TB in the last five years were significantly associated with good knowledge of pediatric TB management. Similarly, having a bachelor’s degree or higher and more than five years of experience providing TB services were significantly associated with a good attitude towards pediatric TB management. Being a general practitioner, nurse, and lab technician was significantly associated with good practice in pediatric TB management. Conclusion. The level of knowledge, attitude, and practice on pediatric TB management among HCWs was suboptimal, as substantial gaps were identified. The Ministry of Health and other international organizations need to prioritize training, coaching, and mentoring support to help HCWs improve their knowledge, attitude, and practice to detect, diagnose, and treat pediatric TB.
Avelumab-Induced Scleroderma in a Patient with Metastatic Squamous Cell Carcinoma of the Lung
Authors: Dobrzynski Jeffrey L. , Vest Blake E. , Swick Brian L.
Published: December 2022

Immune checkpoint inhibitors are associated with a spectrum of cutaneous immune-related adverse events. While maculopapular eruptions are the most common cutaneous adverse event, scleroderma can rarely develop. Herein, we report a case of new-onset scleroderma associated with avelumab treatment in the setting of metastatic squamous cell carcinoma of the lung. The pathophysiology of immune checkpoint inhibitor-induced scleroderma is not completely understood. A proposed mechanism is discussed along with the clinical presentation of symptoms and associated therapeutic response in cancer treatment. This case contributes to the few existing reports of immune checkpoint inhibitor-induced scleroderma to better understand the implications in the management of cutaneous immune-related adverse events.
Estimating the Potential Modal Split of Any Future Mode Using Revealed Preference Data
Authors: de Clercq Gijsbert Koen , van Binsbergen Arjan , van Arem Bart , Snelder Maaike
Published: December 2022

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.
Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network
Authors: Wang Hongbin , Li Hongyi , Gao Shitao , Zhou Lianke , Lao Zhenyu
Published: December 2022

For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate.
Complex Trauma Symptoms after Childhood Maltreatment: Strengths and Challenges of the Therapy Program STAIR-NT
Authors: Rojas Roberto , Hitzler Melissa , Palmer Sebastian , Sauer Karoline Sophie , Wendler-Bödicker Christine , Boos Anne , Hoyer Jürgen , Niemeyer Helen
Published: December 2022

The number and type of traumatic experiences (especially childhood maltreatment) appear to increase the risk of developing complex trauma symptoms. There is initial evidence that patients with complex trauma benefit from phase-based therapeutic approaches. STAIR-NT (Skills Training in Affective and Interpersonal Regulation with Narrative Therapy) is a two-phase trauma therapy approach: emotion regulation and interpersonal skills are learned in the STAIR phase, and in the narrative therapy phase (NT) the exposure to trauma aims at recognizing and changing dysfunctional, trauma-associated assumptions. Experiences with STAIR-NT in the context of a current treatment study show that the two phases provide a clear structure and more security for the treatment. First clinical experiences with STAIR-NT as well as short case vignettes are presented and recommendations for the treatment of patients with complex trauma are given. Concrete practical recommendations are offered for the implementation and individualization on interventions, which among others relate to offender contact, motivational work, handling dissociative states, therapeutic methods (especially exposure), and therapy completion. The close connection between theory and practice facilitates the transfer of learning experiences into everyday life and promotes the strengthening of patients’ self-efficacy and autonomy.
Artifacts Can Be Deceiving: The Actual Location of Deep Brain Stimulation Electrodes Differs from the Artifact Seen on Magnetic Resonance Images
Authors: Nuzov Noa B. , Bhusal Bhumi , Henry Kaylee R. , Jiang Fuchang , Vu Jasmine , Rosenow Joshua M. , Pilitsis Julie G. , Elahi Behzad , Golestanirad Laleh
Published: December 2022

Introduction: Deep brain stimulation (DBS) is a common treatment for a variety of neurological and psychiatric disorders. Recent studies have highlighted the role of neuroimaging in localizing the position of electrode contacts relative to target brain areas in order to optimize DBS programming. Among different imaging methods, postoperative magnetic resonance imaging (MRI) has been widely used for DBS electrode localization; however, the geometrical distortion induced by the lead limits its accuracy. In this work, we investigated to what degree the difference between the actual location of the lead’s tip and the location of the tip estimated from the MRI artifact varies depending on the MRI sequence parameters such as acquisition plane and phase encoding direction, as well as the lead’s extracranial configuration. Accordingly, an imaging technique to increase the accuracy of lead localization was devised and discussed. Methods: We designed and constructed an anthropomorphic phantom with an implanted DBS system following 18 clinically relevant configurations. The phantom was scanned at a Siemens 1.5 Tesla Aera scanner using a T1MPRAGE sequence optimized for clinical use and a T1TSE sequence optimized for research purposes. We varied slice acquisition plane and phase encoding direction and calculated the distance between the caudal tip of the DBS lead MRI artifact and the actual tip of the lead, as estimated from MRI reference markers. Results: Imaging parameters and lead configuration substantially altered the difference in the depth of the lead within its MRI artifact on the scale of several millimeters − with a difference as large as 4.99 mm. The actual tip of the DBS lead was found to be consistently more rostral than the tip estimated from the MR image artifact. The smallest difference between the tip of the DBS lead and the tip of the MRI artifact using the clinically relevant sequence (i.e., T1MPRAGE) was found with the sagittal acquisition plane and anterior-posterior phase encoding direction. Discussion/Conclusion: The actual tip of an implanted DBS lead is located up to several millimeters rostral to the tip of the lead’s artifact on postoperative MR images. This distance depends on the MRI sequence parameters and the DBS system’s extracranial trajectory. MRI parameters may be altered to improve this localization.