bluuebunny/crossref_metadata_2025_split huggingface dataset:
Longitudinal assessment of sweat-based TNF-alpha in inflammatory bowel disease using a wearable device
Authors: Hirten Robert P. , Lin Kai-Chun , Whang Jessica , Shahub Sarah , Helmus Drew , Muthukumar Sriram , Sands Bruce E. , Prasad Shalini 
Published: February 2024

AbstractWearable devices can non-invasively monitor patients with chronic diseases. Sweat is an easily accessible biofluid for continuous sampling of analytes, including inflammatory markers and cytokines. We evaluated a sweat sensing wearable device in subjects with and without inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract. Participants with an IBD related hospital admission and a C-reactive protein level above 5 mg/L wore a sweat sensing wearable device for up to 5 days. Tumor necrosis factor-alpha (TNF-α) levels were continually assessed in the sweat via the sensor, and daily in the blood. A second cohort of healthy subjects without chronic diseases wore the device for up to 48 h. Twenty-eight subjects were enrolled. In the 16 subjects with IBD, a moderate linear relationship between serum and sweat TNF-α levels was observed (R2 = 0.72). Subjects with IBD were found to have a mean sweat TNF-α level of 2.11 pg/mL, compared to a mean value of 0.19 pg/mL in 12 healthy controls (p < 0.0001). Sweat TNF-α measurements differentiated subjects with active IBD from healthy subjects with an AUC of 0.962 (95% CI 0.894–1.000). A sweat sensing wearable device can longitudinally measure key sweat-based markers of IBD. TNF-α levels in the sweat of subjects with IBD correlate with serum values, suggesting feasibility in non-invasive disease monitoring.
Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis
Authors: Boubnovski Martell Marc , Linton-Reid Kristofer , Hindocha Sumeet , Chen Mitchell , Moreno Paula , Álvarez‐Benito Marina , Salvatierra Ángel , Lee Richard , Posma Joram M. , Calzado Marco A. , Aboagye Eric O. 
Published: February 2024

AbstractThe rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients’ prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Authors: Miller Robert J. H. , Shanbhag Aakash , Killekar Aditya , Lemley Mark , Bednarski Bryan , Van Kriekinge Serge D. , Kavanagh Paul B. , Feher Attila , Miller Edward J. , Einstein Andrew J. , Ruddy Terrence D. , Liang Joanna X. , Builoff Valerie , Berman Daniel S. , Dey Damini , Slomka Piotr J. 
Published: February 2024

AbstractEpicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90–0.98). During median follow-up of 2.7 years (IQR 1.6–4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.
Exploring the combinatorial explosion of amine–acid reaction space via graph editing
Authors: Zhang Rui , Mahjour Babak , Outlaw Andrew , McGrath Andrew , Hopper Tim , Kelley Brian , Walters W. Patrick , Cernak Tim 
Published: February 2024

AbstractAmines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine–acid reaction space via systematic enumeration of reactions involving a simple amine–carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.
Enhanced oxygen reduction reaction on caffeine-modified platinum single-crystal electrodes
Authors: Hoshi Nagahiro , Nakamura Masashi , Kubo Ryuta , Suzuki Rui 
Published: February 2024

AbstractEnhancing the activity of the oxygen reduction reaction (ORR) is crucial for fuel cell development, and hydrophobic species are known to increase the ORR activity. This paper reports that caffeine enhanced the specific ORR activity of Pt(111) 11-fold compared to that without caffeine in a 0.1 M HClO4 aqueous solution. Moreover, caffeine increased the ORR activity of Pt(110) 2.5-fold; however, the activity of Pt(100) was unaffected. The infrared (IR) band of PtOH (blocking species of the ORR) decreased for all the surfaces. Caffeine was adsorbed with its molecular plane perpendicular to the Pt(111) and Pt(110) surfaces and tilted relative to the Pt(100) surface. Thus, the effects of caffeine on the ORR activity can be rationalized by a decrease in PtOH coverage and the difference in adsorption geometry of caffeine.
Dynamics of inertialess sedimentation of a rigid U-shaped disk
Authors: Miara Tymoteusz , Vaquero-Stainer Christian , Pihler-Puzović Draga , Heil Matthias , Juel Anne 
Published: February 2024

AbstractWhen particles sediment in a viscous fluid, the character of their trajectories depends sensitively on the particles’ shape. Here we study the sedimentation of U-shaped rigid disks in a regime where inertia can be neglected. We show that, unlike the case of planar disks which settle in a fixed orientation relative to the direction of gravity, U-shaped disks tend to perform a periodic sequence of pitching and rolling motions which cause their centre of mass to sediment along complex trajectories that range from quasi-periodic spirals to helices. Thus, we demonstrate that particles of achiral shape can sediment along chiral paths whose handedness is determined by their initial orientation rather than their geometry. Our analysis provides a framework in which to interpret the motion of sedimenting particles of arbitrary shape.
Graph neural network‐based attack prediction for communication‐based train control systems
Authors: Zhao Junyi , Tang Tao , Bu Bing , Li Qichang 
Published: February 2024

AbstractThe Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions. GNN‐AP is proposed, a framework utilising an alert log to predict potential attack targets. Firstly, GNN‐AP uses causality to eliminate confounding elements from the alert dataset and then uses an encoder‐decoder model to reconstruct an attack scenario graph. Based on the chronological characteristics of APT attacks, GNN‐AP identifies APT attack sequences from attack scenario graphs and integrates these attack sequences with communication‐based train control (CBTC) devices topology information to construct an Attack‐Target Graph. Based on the attack‐target graph, a graph neural network approach is used to identify the attack intent and transforms the attack prediction problem into a link prediction problem that predicts the connected edges of the attack and target nodes. The simulation results obtained using DARPA data show that the proposed method can improve the comparison methods by 4% of accuracy in terms of prediction. Furthermore, the method was applied to the CBTC system dataset with a prediction accuracy of 88%, demonstrating the efficacy of the proposed method for industrial control systems.
A multi‐layer–multi‐player game model in electricity market
Authors: Kafshian Hajar , Monfared Mohammad Ali Saniee 
Published: February 2024

AbstractHere, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.
An reconstruction bidirectional recurrent neural network ‐based deinterleaving method for known radar signals in open‐set scenarios
Authors: Zheng Haiping , Xie Kai , Zhu Yingshen , Lin Jinjian , Wang Lihong 
Published: February 2024

AbstractIn electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.
Open space radar specific emitter identification using MSAK‐CNN‐LSTM network
Authors: Zheng Yuanhao , Wang Jiantao , Huang Jie 
Published: February 2024

AbstractTo enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.
Repair of Complex Esophageal Atresia with Tracheobronchial Remnant using Special Magnets
Authors: Reich Charlotte , Weigl Elena , Holler Anne-Sophie , Lee William , Harrison Michael , Muensterer Oliver J. 
Published: February 2024

AbstractEsophageal atresia (EA) repair can be complicated by associated malformations such as a tracheobronchial remnant in the distal esophagus. We describe our experience with a patient found to have long-gap EA with a distal cartilaginous ring who was managed using a combination of esophageal lengthening and magnetic compression anastomosis. A 5-month-old girl was referred to us from an outside hospital with type C EA including a very high upper pouch. She had undergone a prior thoracotomy with fistula ligation during which a clip was placed on the lower esophagus, leaving a 2-cm diverticulum on the trachea and a short lower esophageal pouch. Upon endoscopic evaluation at our center, we found a tracheobronchial remnant in the lower esophagus between the clip and the carina. An open thoracotomy was performed to approximate the esophageal pouches and a magnet anchor (Connect EA, Myka Laboratories, San Francisco, California, United States) was placed retrograde through the distal esophageal cartilaginous ring into the lower pouch. On postoperative day 8, after adequate growth and decreased pouch tension, a second magnetic anchor was placed endoscopically to the upper pouch to mate with the previously placed lower pouch anchor. The anastomosis formed within 14 days. Due to the tracheobronchial remnant, the device did not pass distally and was removed endoscopically. On postoperative day 8, balloon dilation of the anastomosis and tracheobronchial remnant was performed. Subsequently, the patient required a total of 6 dilations in an 18-month follow-up. This case report illustrates the utility of using magnets to create an esophageal anastomosis in complex cases of EA with concomitant esophageal malformations. The parents of the patient gave their written consent to publish this technical report.
Price optimization for manufacturers in a competitive retail market: imported products and online crowdfunding option
Authors: Parvasi Seyed Parsa , Taleizadeh Ata Allah , Thaichon Park 
Published: February 2024

Abstract
This study explores how manufacturers in the competitive supply chain can set prices and secure funding effectively. We use game theory to look at how competition between domestic and foreign manufacturers affects pricing decisions. Our research investigates how a domestic manufacturer can improve its market share by addressing financial challenges through modern financing methods. In this scenario, a domestic manufacturer competes with a foreign one to attract a retailer’s market share and profits. The retailer decides what products to buy and how to price them based on bid prices and demand. We also consider that the domestic manufacturer will use online crowdfunding platforms to tackle its financial problem. Hence, our study sets up a supply chain where competition revolves around both operational and financial decisions. Mathematical models are developed to analyze how costs, finances, market potential, and price sensitivity impact various parts of the supply chain. The results reveal that decisions made on the crowdfunding platform significantly influence other supply chain decisions. Manufacturers and retailers need to pay attention to the financial decisions made on this platform to maximize profits. Also, domestic and foreign manufacturers should consider customer preferences for their products when setting prices. Finally, the results demonstrate that a domestic manufacturer can gain a competitive edge in the retail market by carefully considering both product pricing and financial decisions, including those made on the lending platform.
Does intellectual capital impact the financial performance of Indian public sector banks? An empirical analysis using GMM
Authors: Barak Monika , Sharma Rakesh Kumar 
Published: February 2024

AbstractThis study examines the impact of intellectual capital (IC) on the financial performance (FP) of Indian public sector banks (IPSBs) between 2010 and 2021. This paper utilizes the modified value-added intellectual coefficient (MVAIC) approach to measure IC. It employs a panel data estimation approach, specifically the generalized method of moments (GMM), to investigate the connection between IC and the FP of public sector banks in India. The findings indicate that IC, along with its sub-elements of human capital (HC), capital employed (CE), structural capital (SC), and Relational capital (RC) have a notable positive or negative influence on various FP indicators, including return on assets (ROA), return on equity (ROE), return on capital employed (ROCE), earnings per share (EPS), and market performance (Tobin’s Q). Furthermore, the combined IC, as evaluated through the modified value-added intellectual coefficient (MVAIC), has a notable and favorable impact on ROA and ROE. The leverage variable exhibits a negative and significant impact on the financial indicators, indicating that an increase in debt leads to a decrease in the bank’s value. This study aims to assist stakeholders and policymakers in identifying the key elements of IC and determining an appropriate distribution among them to achieve sustainable FP and value generation in Indian IPSBs.
AI chatbots contribute to global conservation injustices
Authors: Urzedo Danilo , Sworna Zarrin Tasnim , Hoskins Andrew J. , Robinson Cathy J. 
Published: February 2024

AbstractArtificial Intelligence (AI)-driven language models (chatbots) progressively accelerate the collection and translation of environmental evidence that could be used to inform planetary conservation plans and strategies. Yet, the consequences of chatbot-generated conservation content have never been globally assessed. Drawing on distributive, recognition, procedural, and epistemic dimensions of environmental justice, we interviewed and analysed 30,000 responses from ChatGPT on ecological restoration expertise, stakeholder engagements, and techniques. Our results show that more than two-thirds of the chatbot’s answers rely on the expertise of male academics working at universities in the United States, while largely ignoring evidence from low- and lower-middle-income countries (7%) and Indigenous and community restoration experiences (2%). A focus on planting and reforestation techniques (69%) underpins optimistic environmental outcomes (60%), neglecting holistic technical approaches that consider non-forest ecosystems (25%) and non-tree species (8%). This analysis highlights how biases in AI-driven knowledge production can reinforce Western science, overlooking diverse sources of expertise and perspectives regarding conservation research and practices. In the fast-paced domain of generative AI, safeguard mechanisms are needed to ensure that these expanding chatbot developments can incorporate just principles in addressing the pace and scale of the worldwide environmental crisis.
Wasta in business management: a critical review of recent developments and future trends in the tourism sector
Authors: Khalfan Shaikha 
Published: February 2024

AbstractWasta is a concept that is widespread across the Middle East, and practiced as a common and acceptable culture in most Middle East based business organizations. While the wasta practice is common and is followed as part of the usual organizational culture in the Middle East, yet, its implications are not all positive for the overall employee and organizational performance, which will be highlighted in the current paper in detail. It encourages interpersonal connections and networking of individuals to be used in order to obtain jobs, based on personnel connection rather than merit, which has huge negative impacts on the business. This paper, therefore, presents a detailed discussion on wasta and its implications considering business organizations across the Middle East. The tourism sector will be the area of the focus for the present work. The impacts of such activities on the tourism sector are significant on recruitment, teamwork, training, reward system, and overall HRM practices, which raise questions on whether this concept should indeed be as common in practice as it is currently. This paper studied the phenomena of wasta in the Middle East in details and proposed framework for Wasta-free Management. Also, recommendation for practice for tourism sector in the middle East along with suggestions for further research are presented.
Alexander Hamilton on Executive Authority
Authors: Wurman Ilan 
Published: February 2024

Abstract
The ‘residuum’ theory of executive power maintains that Article II’s Vesting Clause grants to the president of the United States a residuum of royal prerogative powers that have not been assigned to other departments of the national government or otherwise limited elsewhere in the text of the Constitution. This theory is often traced to Alexander Hamilton’s Pacificus essay, in which he defended President Washington’s proclamation of neutrality with a version of that theory. Two years earlier, however, in his opinion on the constitutionality of the Bank of the United States, Hamilton appears to have rejected the residuum theory; at a minimum, he had incentive to propound that theory but did not do so. Although not the only possible way to interpret Hamilton’s opinion, scholars of executive power must contend with this possibility before concluding that Hamilton believed in a residual vesting of prerogative powers.