bluuebunny/crossref_metadata_2025 huggingface dataset:
New examples of non-polynomial integrals of two-dimensional geodesic flows
*
Authors: Agapov Sergei , Shubin Vladislav
Published: November 2023

In this paper, we continue to study integrable geodesic flows on 2-surfaces with non-polynomial first integrals which we started earlier in our previous papers. We construct explicitly new local examples of Riemannian metrics and such integrals via various approaches and methods such as the classical and the generalized hodograph methods, the method of separation of variables and some others.
Navigating Awareness: Factors Influencing Students’ Perception of Maritime Environmental Issues in Islamic Education
Authors: Dendi Yuda S , Solihin Muhtar , Murtado Dodo , Rudiana  , Subqi Ahmad , Suhayati Iis
Published: November 2023

This study employs a quantitative approach to explore the factors influencing students’ low awareness and attention to maritime environmental issues and recommends Islamic education management strategies to enhance maritime environmental awareness based on these factors. Given the backdrop of insufficient attention to maritime environmental issues within Islamic educational institutions, this study delves into various aspects related to the integration of maritime environmental concerns into the curriculum, the management of Islamic educational institutions, and the effectiveness of applied strategies. The sample for this study encompasses 80 students and 5 faculty members within the environment of one Islamic educational institution in Kabupaten Tasikmalaya, namely the University of Islam KH. Ruhiat. Data was collected through surveys and interviews. The research findings illuminate the primary factors influencing students’ limited awareness of and attention to maritime environmental issues. The distribution of student responses regarding these factors were as follows: (1) “Unfocused Studies that Neglect Environmental Aspects” (25%); (2) “Weak Discourse on Environmental Issues on Campus” (17.5%); and (3) “Non-Integrated Learning Curriculum” (15%). These findings underscore critical domains that educational institutions, particularly the University of Islam KH. Ruhiat, need to address in order to enhance students’ awareness of maritime environmental issues. Strategies encompass curriculum revisions that incorporate environmental themes, promoting academic discussions on environmental concerns, and creating platforms for community engagement that prioritize maritime environmental issues.
Sub-Contractor Selection in Shipyard Companies Using Fuzzy-AHP Method
Authors: Amrullah Haidar Natsir , Khairansyah Mades Darul , Mudjiono Urip , Hamzah Fais , Shah Muhammad
Published: November 2023

Shipyard industries is one of the very complex construction jobs because there is much work to be done in parallel. Activities in the shipyard industry mostly involve sub-contractors. Although many advantages are obtained, involving sub-contractors in the company also has an impact on the company’s performance. The selection of the right sub-contractor is a crucial activity and will determine the company’s performance. This research uses the Fuzzy Analytical Hierarchy Process method (Fuzzy AHP) to solve the problem of selecting sub-contractors in the Shipbuilding Company. Alternative sub-contractors were evaluated and pairwise comparisons were made between these alternatives using Fuzzy AHP with a Triangular Fuzzy Number (TFN) approach. The TFN scale can reduce the ambiguity of expert judgment on AHP. A case study was conducted on the selection of sub-contractors with the bottom cleaning and ship replating project at shipyard company in Surabaya, Indonesia. As a result, one of the sub-contractors selected as the best sub-contractor with a score of 0.167.
The Behaviour of Thin-Walled Cylinders with Reinforcing Inner Walls Subjected to High-Velocity Impact
Authors: Hardi Witono , Harbelubun Mohammad Muzni , Suyono Tri
Published: November 2023

Thin cylindrical tubes as impact energy absorbers have been widely used in various fields. Previous researchers have proposed many methods to increase the ability to absorb energy by modifying the geometry or using different materials. This study discusses using reinforcing walls in the cylinder to increase energy absorption capability. We make thin cylindrical specimens made of aluminium alloy with a length of 150 mm, a diameter of 50 mm, and a thickness of 1 mm. Reinforcing walls are fitted in the cylinder with a vertical position of 1, 2, 3, and without supporting walls. An impactor moving at high velocity hits the up-end cylinder in the axial direction. The finite element method modelling results show that installing reinforcing walls in thin-walled cylinders increase the maximum impact force, decreases the maximum deformation, changes the deformation mode, and increases the energy absorption capability. The amount of wall added to the thin wall will affect the impact behaviour of the specimen. However, in this study, the presence of a wall will have an effect when the deformation reaches the location of the wall. Nevertheless, using this reinforcing wall also makes the manufacturing process more complex. Therefore, this study’s results are recommendations for the design of the impact energy absorption structural system.
Experimental Study Influence of High Fin on Naca Airfoil 0018 On Performance Of H-Type Darrieus Wind Turbine
Authors: Santoso Emie , Awika Ikhtiar Syah , Ariwiyono Nopem
Published: November 2023

Energy is something that is needed in life, as technology develops the need for energy is increasing as well, while the role of New Energy Darrieus wind turbine is one of the solutions for renewable energy because it utilizes energy from wind and is converted into electrical energy, but this turbine has a low starting torque. In this study, the performance of the Darrieus wind turbine was improved by adding a fin to each blade with variations in height (0 cm, 1,5 cm, 2,5 cm, and 3,5 cm). In addition, testing was also carried out with variations in fluid flow speeds (3 m / s; 5 m / s; and 7 m / s). The subjects used in this study were H-type Darrieus wind turbines with a diameter of 40 cm and a height of 50 cm. Experiments were carried out to find out how the effect of fin addition with variations in height on the coefficient of power (Cp) and coefficient of torque (Ct) of the darrieus wind turbine. The final results showed the best performance improvement of the savonius wind turbine occurred at a variation in the fin height of 2.5 cm at a wind speed of 5 m/s. The coefficient of torque (Ct) increased by 7.64% and the coefficient of power (Cp) by 48.38% against conventional H-type darrieus wind turbines. This is because the addition of an effective fin is to reduce the tip of the vortex at the end of each blade so that the flow of fluid that flows becomes more regular and maximum.
Compressive Strength Investigations Of Foamed Mortar Incorporating Sandblasting Waste As Supplementary Cementitious Materials
Authors: Wulandari Kiki Dwi , Jannah Nabillah Rodhifatul , Wutsqo Umniati Huwaida Urwatul , Firdaussyah Aditya Tetra , Hayu Gati Annisa , Sutrisno Wahyuniarsih
Published: November 2023

Silica sand is one of the abrasive materials commonly used in the sandblasting process. The production of sandblasting waste is raising yearly, linear with the rapid development of the shipping industries. Silica sand was produced as by-product waste, approximately 400 ton per year. This research focused on compressive strength investigations of the foamed mortar incorporating silica sand as a supplementary cementitious material (SCM). Foamed mortar is a lightweight mortar made from a mixture of water, cement, sand, and foam, which can reduce the density of the mortar for construction purposes. Prior to use as SCM, the silica sand was pre-treated by using mechanical grinding to produce finer materials similar to cement. The observations were applied to pre-treated silica sand, such as chemical compositions, particle size analysis, normal consistency test, and setting time test. The specimens used in this research were mortar concrete with dimensions 5 x 5 x 5 cm and tested according to ASTM C579-01. The pre-treated silica sand varied from 10%, 20%, and 30% by weight of cement, were applied in this investigation. The compressive strength and spesific gravity were also observed. The results show that 20% cement replacement with pre-treated silica sand is the optimum composition and has 52.2 MPa in compressive strength at 28th days. These investigations conclude that pre-treated silica sand is potentially used as SCM in foamed mortars for sustainable concrete materials.
Effectiveness of Crew Boat Hull Form on Manouvering Performance
Authors: Santoso Mardi , Budianto  , Kusuma Arga Bangun , Rosidy Muhammad Ilham Yusuf
Published: November 2023

The condition of the crew boat is good hull shape with good operational conditions when maneuvering. In this case, the condition of the waveform at sea will have a significant impact on the ship’s motion. To provide the optimal type of hull, it was developed by conducting several simulations on the ship’s motion. Several simulation conditions were carried out, including analyses of ship speed, the shape of the waves formed, and ship maneuvering. At the simulation stage, several types of hulls were carried out, including the single U-hull, the single V-hull, the trimaran, and the catamaran. The simulation limits the length of the ship but not the width of the ship because the conditions in the multi-hull require sufficient space for its width, so that it can be ascertained that it provides effective and efficient results for the selection of the shape of the hull. The simulation analysis carried out provides a recommendation that the single V hull model gives good results compared to other types of hull selection for crew boat hulls.
Decreasing Demurrage Time by Increasing Port Service Level and Utilities Considering Crane Formation Using Discrete Simulation
Authors: Tama Mahya Indra , Aqsha Bayu Dwi
Published: November 2023

Maritime logistics has a great influence on large-scale economic development. Maritime logistics has the largest logistics flow in Indonesia. Economic development can be seen by how much logistics flow in an area. The distribution of logistics using sea transportation must be controlled effectively and efficiently so that the flow of material does not stop or be disrupted. Teluk Lamong Terminal is the only green smart terminal in Surabaya, so there are many requests for ships to lean on the pier. The method of this research is to conduct a survey and collect information about the main activities of loading and unloading ships docking at the pier, the number of cranes used, the current crane formation (before improvement) and problems in the operation of containers. After that, create a simulation model that will be developed using Simulation Software that has inputted entities, activities, and model resources according to their actual activities. Scenario results improvements 1 to 4, the selection of crane formations in serving container ship loading and unloading is the most effective in scenario 3, namely by setting 3 cranes to serve 1 berthing ship and 2 cranes to serve 1 berthing ship. In scenario 3 with the simulation results obtained that the ship’s effective time at the dock was 9.19 hours and the demurrage time for container loading and unloading was 13.79 hours. Furthermore, crane utilization value is in scenario 3 with the results obtained from the simulation of 18.21%.
A machine learning approach for investigation of the natural frequency of a nitinol-reinforced composite beam
Authors: Rane Santosh B , Patil Ranjit Anil , Raykar N R , Kumbhar Samir B
Published: November 2023

Many applications are using composites to improve performance and reduce weight, but it is essential to know the different properties of the composite before manufacturing. Properties like natural frequency and elastic modulus are also crucial in many applications. The use of shape memory alloys (SMA) composite has increased in the last few years due to various advantages of the shape memory alloys, like a shift in natural frequency and elastic modulus during phase transformation. Hence it is essential to know the composite’s natural frequency and elastic modulus before constructing it. Although experimental and numerical methods for calculating natural frequency exist, they are time-consuming and infrastructure-dependent. This paper explores relationships between SMA composite construction parameters and natural frequency to predict it better. Nitinol-reinforced silicon rubber composite beams are investigated with various parametric combinations using an orthogonal array. Different machine-learning techniques are applied for natural frequency prediction after training models on numerical results from varied construction combinations. The study identifies the best-performing algorithm and provides tuning recommendations. Linear regression model, Ridge regression model, and Decision Tree regression are the best-performing algorithms for the dataset in this paper. A weighted sum method finds optimal construction parameters for maximum natural frequency. These models can predict natural frequency before construction and the shift during SMA phase transformation. The research aids in designing SMA-reinforced beams by identifying optimal parameters like volume fraction, location, and activation pattern, targeting maximum natural frequency. The composite studied in this research shows a maximum natural frequency of 19.58 Hz for a 3.53% volume fraction of SMA, 3 mm distance of reinforcement, all wires activated, and austenite temperature.
Improved decision making with similarity based machine learning: applications in chemistry
Authors: Lemm Dominik , Falk von Rudorff Guido , Anatole von Lilienfeld O
Published: November 2023

Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, ‘the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy.
Machine learning of microscopic structure-dynamics relationships in complex molecular systems
Authors: Crippa Martina , Cardellini Annalisa , Cioni Matteo , Csányi Gábor , Pavan Giovanni M
Published: November 2023

In many complex molecular systems, the macroscopic ensemble’s properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic-level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g. Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g. Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g. LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales.
Scalable quantum measurement error mitigation via conditional independence and transfer learning
Authors: Lee Changwon , Park Daniel K
Published: November 2023

Mitigating measurement errors in quantum systems without relying on quantum error correction is of critical importance for the practical development of quantum technology. Deep learning-based quantum measurement error mitigation (QMEM) has exhibited advantages over the linear inversion method due to its capability to correct non-linear noise. However, scalability remains a challenge for both methods. In this study, we propose a scalable QMEM method that leverages the conditional independence (CI) of distant qubits and incorporates transfer learning (TL) techniques. By leveraging the CI assumption, we achieve an exponential reduction in the size of neural networks used for error mitigation. This enhancement also offers the benefit of reducing the number of training data needed for the machine learning model to successfully converge. Additionally, incorporating TL provides a constant speedup. We validate the effectiveness of our approach through experiments conducted on IBM quantum devices with 7 and 13 qubits, demonstrating excellent error mitigation performance and highlighting the efficiency of our method.
A Bourgain-Brezis-Mironescu type result for the fractional relativistic seminorm
Authors: Ambrosio Vincenzo , Essebei Fares
Published: November 2023

We establish a version of the Bourgain-Brezis-Mironescu type formula on the limit as

s
→

1
−

s\rightarrow 1^-

of the fractional relativistic seminorm.
Playing with second language metaphor: An exploration with advanced Chinese learners of English
Authors: O’Reilly David , Yan Luling
Published: November 2023

The present study continues research that takes non-serious language more seriously (Cekaite and Aronsson 2005) by focusing on a central second language (L2) Metaphoric Competence factor, Metaphor Language Play (MLP). For willing learners, MLP offers a diversity of benefits (Bushnell 2009; Bell 2012a) despite being one of the most challenging Metaphoric Competence aspects (O’Reilly and Marsden 2021). While studies provide rich descriptions of naturally occurring MLP, elicitation approaches are needed to target comprehension/production of specific forms/meanings/usages and types of play, for example, comprehension of US sitcom humour (Dore 2015). With 69 advanced first-language Mandarin L2 English learners, we addressed these issues via an Exploratory Factor Analysis to uncover hitherto unknown relationships between written/spoken/receptive/productive MLP measures, and a thematic analysis of the linguistic, conceptual, and metalinguistic themes in learners’ MLP. The findings revealed three underlying MLP factors, two positively related, and a rich set of linguistic, conceptual, and metalinguistic themes. The implications of findings for future research and pedagogy are discussed.
deepFPlearn
+: enhancing toxicity prediction across the chemical universe using graph neural networks
Authors: Soulios Kyriakos , Scheibe Patrick , Bernt Matthias , Hackermüller Jörg , Schor Jana
Published: November 2023

Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task. Here, we present its extension deepFPlearn+ incorporating (i) a graph neural network to feed our AI with a more sophisticated molecular structure representation and (ii) alternative train-test splitting strategies that involve scaffold structures and the molecular weights of chemicals. We show that the GNNs outperform the previous model substantially and that our models can generalize on unseen data even with a more robust and challenging test set. Therefore, we highly recommend the application of deepFPlearn+ on the chemical inventory to prioritize chemicals for experimental testing or any chemical subset of interest in monitoring studies.

Availability and implementation

The software is compatible with python 3.6 or higher, and the source code can be found on our GitHub repository: https://github.com/yigbt/deepFPlearn. The data underlying this article are available in Zenodo, and can be accessed with the link below: https://zenodo.org/record/8146252. Detailed installation guides via Docker, Singularity, and Conda are provided within the repository for operability across all operating systems.
Diverse macrophage populations contribute to distinct manifestations of human cutaneous graft-versus-host disease
Authors: Strobl Johanna , Gail Laura M , Krecu Laura , Madad Shaista , Kleissl Lisa , Unterluggauer Luisa , Redl Anna , Brazdilova Kveta , Saluzzo Simona , Wohlfarth Philipp , Knaus Hanna A , Mitterbauer Margit , Rabitsch Werner , Haniffa Muzlifah , Stary Georg
Published: November 2023

Background

Graft-versus-host disease (GvHD) is a major life-threatening complication of allogeneic haematopoietic stem cell transplantation (HSCT), limiting the broad application of HSCT for haematological malignancies. Cutaneous GvHD is described as a post-transplant inflammatory reaction by skin-infiltrating donor T cells and remaining recipient tissue-resident memory T cells. Despite the major influence of lymphocytes on GvHD pathogenesis, the complex role of mononuclear phagocytes (MNPs) in tissues affected by GvHD is increasingly appreciated.

Objectives

To characterize the identity, origin and functions of MNPs in patients with acute cutaneous GvHD.

Methods

Using single-cell RNA sequencing and multiplex tissue immunofluorescence, we identified an increased abundance of MNPs in skin and blood from 36 patients with acute cutaneous GvHD. In cases of sex-mismatched transplantation, we used expression of X-linked genes to detect rapid tissue adaptation of newly recruited donor MNPs resulting in similar transcriptional states of host- and donor-derived macrophages within GvHD skin lesions.

Results

We showed that cutaneous GvHD lesions harbour expanded CD163+ tissue-resident macrophage populations with anti-inflammatory and tissue-remodelling properties including interleukin-10 cytokine production. Cell–cell interaction analyses revealed putative signalling to strengthen regulatory T-cell responses. Notably, macrophage polarization in chronic cutaneous GvHD types was proinflammatory and drastically differed from acute GvHD, supporting the notion of distinct cellular players in different clinical GvHD subtypes.

Conclusions

Overall, our data reveal a surprisingly dynamic role of MNPs after HSCT. Specific and time-resolved targeting to repolarize this cell subset may present a promising therapeutic strategy in combatting GvHD skin inflammation.
Clinical course in corticobasal syndrome and corticobasal degeneration: implications for diagnosis and management
Authors: McGeachan Robert I , King Declan
Published: November 2023

This scientific commentary relates to ‘Clinical course of pathologically confirmed corticobasal degeneration and corticobasal syndrome’, by Aiba et al. (https://doi.org/10.1093/braincomms/fcad296).
Subcortical grey matter volume and asymmetry in the long-term course of Rasmussen’s encephalitis
Authors: Bauer Tobias , Reiter Johannes T , Enders Selma , Keil Vera C W , Radbruch Alexander , Helmstaedter Christoph , Surges Rainer , Rüber Theodor
Published: November 2023

Rasmussen’s encephalitis is characterized by drug-resistant focal seizures and chronic inflammation of one hemisphere leading to progressive loss of hemispheric volume. In this cohort study, we aimed to investigate subcortical grey matter volumes and asymmetries in Rasmussen’s encephalitis longitudinally in clinically relevant subgroups. We retrospectively included all T1-weighted MRI scans of all people with Rasmussen’s encephalitis who were treated at the University Hospital Bonn between 1995 and 2022 (n = 56, 345 scans, median onset 8 years, 36 female). All cases were classified as type 1 (onset ≤ 6 years) or type 2 (onset > 6 years). Subcortical segmentations were performed using FreeSurfer. Longitudinal trajectories of subcortical volumes and hemispheric ratios (ipsi-/contralesional) were assessed using linear mixed-effect models. Unihemispheric cortical degeneration was accompanied by ipsilesional atrophy of the nucleus accumbens, caudate nucleus, putamen, thalamus and contralesional atrophy of the nucleus accumbens and caudate nucleus both in type 1 (all P ≤ 0.014) and type 2 (all P < 0.001). In type 1, however, contralesional volume increase of the amygdala, hippocampus, pallidum and thalamus was found (all P ≤ 0.013). Both ipsilesional and contralesional subcortical atrophies, like cortical atrophy, are most probably caused by neurodegeneration following chronic neuroinflammation. We speculate that contralesional volume increase in type 1 could be related to either neuroplasticity or ongoing acute neuroinflammation, which needs to be investigated in further studies.
Dupilumab-associated inflammatory arthritis: a literature review
Authors: Napolitano Maddalena , Ruggiero Angelo , Patruno Cataldo
Published: November 2023

Dupilumab is a fully human monoclonal antibody that acts by inhibiting the interleukin (IL)-4 receptor subunit α, and hence the IL-4 and IL-13 signalling pathway. Dupilumab treatment has been linked to the onset of T helper 17-driven inflammatory diseases, including cases of seronegative arthritis and enthesitis. To date, dupilumab-associated inflammatory arthritis (DAIA) represents a relatively unknown adverse event, initially reported in single cases or case series reports. Indeed, the onset of DAIA may not be promptly recognized, and is probably underestimated. Here we have reviewed the available English literature regarding arthritis and enthesitis onset during dupilumab treatment for atopic dermatitis, aiming to improve rapid recognition and thus prompt treatment of these diseases.
Characterization of skin barrier defects using infrared spectroscopy in patients with atopic dermatitis
Authors: Williams Samuel F , Wan Helen , Chittock John , Brown Kirsty , Wigley Andrew , Cork Michael J , Danby Simon G
Published: November 2023

Background

Atopic dermatitis (AD) is characterized by skin barrier defects that are often measured by biophysical tools that observe the functional properties of the stratum corneum (SC).

Objectives

To employ in vivo infrared spectroscopy alongside biophysical measurements to analyse changes in the chemical composition of the SC in relation to AD severity.

Methods

We conducted an observational cross-sectional cohort study where attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy measurements were collected on the forearm alongside surface pH, capacitance, erythema and transepidermal water loss (TEWL), combined with tape stripping, in a cohort of 75 participants (55 patients with AD stratified by phenotypic severity and 20 healthy controls). Common FLG variant alleles were genotyped.

Results

Reduced hydration, elevated TEWL and redness were all associated with greater AD severity. Spectral analysis showed a reduction in 1465 cm–1 (full width half maximum) and 1340 cm–1 peak areas, indicative of less orthorhombic lipid ordering and reduced carboxylate functional groups, which correlated with clinical severity (lipid structure r = –0.59, carboxylate peak area r = –0.50).

Conclusions

ATR-FTIR spectroscopy is a suitable tool for the characterization of structural skin barrier defects in AD and has potential as a clinical tool for directing individual treatment based on chemical structural deficiencies.
Psychiatric comorbidities associated with rosacea: a propensity score-matched case–control study in the All of Us database
Authors: Abdi Parsa , Haq Zaim , Diaz Michael J , Maibach Howard I
Published: November 2023

Using the ethnically diverse All of Us dataset, this study investigated the association between rosacea and various psychiatric disorders. The analysis identified a significant prevalence of major psychiatric conditions, most notably phobic disorder, OCD, dysthymia and generalized anxiety disorder in participants with rosacea. This association highlights the importance of addressing the psychological wellbeing of people with rosacea and calls for further research into the underlying pathophysiological connections.
Left-sided primary cardiac lymphoma diagnosed by needle biopsy with total endoscopic anterolateral mini-thoracotomy: a case report
Authors: Sano Madoka , Okada Taiji , Yamashita Daisuke , Koyama Tadaaki , Furukawa Yutaka
Published: November 2023

Background

Primary cardiac lymphoma (PCL) is an extremely rare tumour that typically affects the right heart chamber. It is a life-threatening tumour presenting with rapid growth; therefore, early diagnosis and treatment are crucial for improving the prognosis of patients with PCL.

Case summary

An 81-year-old female with a history of dermatomyositis and interstitial pneumonia was referred to the cardiology department for cardiomegaly detected on chest radiography and computed tomography (CT). She experienced shortness of breath on exertion. Electrocardiography revealed negative T-waves in various leads. Transthoracic and transoesophageal echocardiography revealed a large mass on the epicardial free wall of the left atrium and ventricle. Coronary CT angiography showed feeding vessels from the left circumflex artery and the posterolateral branch of the right coronary artery. Positron emission tomography showed elevated mass uptake and no systemic metastasis. Needle biopsy with total endoscopic anterolateral mini-thoracotomy was performed. Histopathological examination revealed diffuse large B-cell lymphoma. She received systemic chemotherapy and achieved a complete metabolic response.

Discussion

Herein, we report an extremely rare case of PCL located on the left side of the heart. Owing to the location of the tumour, percutaneous or transcatheter biopsy could not be performed. Early diagnosis with needle biopsy via anterolateral mini-thoracotomy and systemic chemotherapy resulted in good outcomes.
Recurrent giant cell myocarditis following orthotopic heart transplant resulting in urgent redo orthotopic heart transplantation: a case report
Authors: AlJohani Abdullah , Solomon Joshua , Joury Abdulaziz , Cecere Renzo , Giannetti Nadia
Published: November 2023

Background

Giant cell myocarditis (GCM) is a severe and rapidly progressing condition that can lead to end-stage heart failure. We present a case of a 51-year-old male with a history of orthotopic heart transplantation (OHTx) for GCM, who experienced recurrent GCM in the allograft, leading to progressive heart failure and the need for a second heart transplant.

Case summary

A 51-year-old male with a history of OHTx for GCM presented with rapidly worsening heart failure symptoms. Despite initial stability, he deteriorated to cardiogenic shock and required intensive support. His clinical course was complicated by recurrent COVID-19 infections, worsened left ventricular ejection fraction, and withdrawal of guideline-directed medical therapy. Imaging showed extensive scar burden, and subsequent investigations ruled out coronary artery disease. With declining functional status and worsening cardiogenic shock, he was re-listed for OHTx and successfully underwent a second heart transplant.

Discussion

Giant cell myocarditis poses challenges due to its aggressive nature. Early, aggressive immunosuppression and mechanical circulatory support are crucial. The recurrence rate of GCM post-OHTx is notable, often within the first year, and the optimal immunosuppressive regimen remains uncertain. In this case, GCM recurrence following OHTx led to continued deterioration despite treatment, necessitating a second heart transplant. This unique case emphasizes the complexity of managing recurrent GCM post-OHTx.
International evaluation of an artificial intelligence–powered electrocardiogram model detecting acute coronary occlusion myocardial infarction
Authors: Herman Robert , Meyers Harvey Pendell , Smith Stephen W , Bertolone Dario T , Leone Attilio , Bermpeis Konstantinos , Viscusi Michele M , Belmonte Marta , Demolder Anthony , Boza Vladimir , Vavrik Boris , Kresnakova Viera , Iring Andrej , Martonak Michal , Bahyl Jakub , Kisova Timea , Schelfaut Dan , Vanderheyden Marc , Perl Leor , Aslanger Emre K , Hatala Robert , Wojakowski Wojtek , Bartunek Jozef , Barbato Emanuele
Published: November 2023

Aims

A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non–ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria.

Methods and results

An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924–0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7–92.0), sensitivity 80.6% (95% CI: 76.8–84.0), and specificity 93.7 (95% CI: 92.6–94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1–85.1), sensitivity 32.5% (95% CI: 28.4–36.6), and specificity 97.7% (95% CI: 97.0–98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5–91.9), sensitivity 73.0% (95% CI: 68.7–77.0), and specificity 95.7% (95% CI: 94.7–96.6)].

Conclusion

The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
Exploring “Equigenesis” in the Associations Between Green Space and Kidney Health Among Middle-Aged and Older Adults Using Street View Data
Authors: Wang Ruoyu , Dong Guoping , Cao Mengqiu , Zhou Yang , Dong Guang-Hui
Published: November 2023

Background and Objectives

This study systematically explores the association between community green space and preventing kidney failure among middle-aged and older adults in China, using street view data.

Research Design and Methods

The 33 Chinese Community Health Study was used to conduct the analysis. We used street view data to assess street view green space (SVG) exposure and clearly distinguished the difference between grass (SVG-grass) and trees (SVG-tree). The normalized difference vegetation index (NDVI) was also used. Kidney failure was defined as a serum creatinine concentration of above 177 mol/L. We used multilevel logistic regression models (controlled for a series of covariates) to examine the associations between SVG and the odds of middle-aged and older adults having kidney failure. We also tested whether middle-aged and older adults from socioeconomically disadvantaged groups are likely to derive greater benefits from the effects of green space (“equigenesis”).

Results

The results showed that both SVG (OR = 0.353; 95% CI = 0.171–0.731) and SVG-trees (OR = 0.327; 95% CI = 0.146–0.736) were negatively associated with the likelihood of middle-aged and older adults experiencing kidney failure, but there was no significant evidence of any links between either SVG-grass (OR = 0.567; 95% CI = 0.300–1.076) or the NDVI (OR = 0.398; 95% CI = 0.237–1.058) and kidney failure. Furthermore, the moderation analysis indicated that income and educational attainment have a moderating effect on the association between green space and the improvement of kidney health, which suggests that green space has greater positive effects on the kidney health of disadvantaged groups.

Discussion and Implications

To reduce inequalities in relation to kidney disease through urban planning, policymakers are advised to provide more visual green space—especially trees—within the community and to focus in particular on socioeconomically disadvantaged population groups.
How Have Formal Firms Recovered From the Pandemic? Insights From Survey and Tax Administrative Data in Zambia
Authors: Hoy Christopher , Simbeye Laban , Koivisto Aliisa , Maboshe Mashekwa , Abdullah Ali Malik Muhammad
Published: November 2023

This paper examines how formal firms have been impacted by and recovered from the COVID-19 pandemic, by drawing on two distinct but complementary data sources. This is the first attempt to use both survey and tax administrative data to measure the impact of the pandemic in a developing country. The findings of three rounds of follow-up surveys to a standard World Bank Enterprise Survey completed immediately before the pandemic are compared to the universe of value-added tax and personal income tax returns filed by firms in Zambia. Despite substantial differences in the breadth and depth of these data sources, they show a very similar pattern. The sales of formal firms recovered from the pandemic far more strongly than their employment levels. By July 2021, both the survey and tax administrative data show that most firms experienced a complete recovery in sales, while levels of employment worsened throughout the pandemic for many firms. The tax administrative data show that even two and a half years following the start of the pandemic, employment in formal firms remained well below pre-pandemic levels. The key insight that emerges from this analysis is that formal firms appear to have adjusted their operations in a way that reduced their need for as much labor to achieve the same (or higher) level of sales. As such in response to an unprecedented shock, productivity gains within formal firms were possible through the shredding of somewhat unproductive labor inputs, which can often be more challenging in a stable setting.
Introduction and Setting the Stage
Authors: Jaywant Abhishek , Oberlin Lauren E. , Cherestal Stephanie , Bueno Castellano Christina , Wilkins Victoria M. , Kanellopoulos Dora
Published: November 2023

This chapter discusses how the devastation of COVID-19 affected people, explaining that anxiety, depression, cognitive symptoms, and fatigue are common sequelae of COVID-19. Treatment programs, based on strategies like cognitive behavioral therapy (CBT), were developed and implemented rapidly during the initial waves of the COVID-19 pandemic. The COVID-19 pandemic highlighted many existing structural and systemic inequities in the United States healthcare system. The chapter also covers the topic of therapist burnout, which became widespread due to the external stressors and uncertainties that the pandemic brought. It explains the importance of ascertaining the client's cultural preferences to determine the most effective way to build rapport and trust in therapy.
Module 5: Mindfulness
Authors: Jaywant Abhishek , Oberlin Lauren E. , Cherestal Stephanie , Bueno Castellano Christina , Wilkins Victoria M. , Kanellopoulos Dora
Published: November 2023

This chapter introduces the practice of mindfulness, which is defined as paying attention in a particular way: on purpose, in the present moment, and non-judgmentally. Clients can practice mindfulness by engaging in culturally relevant activities or by using mobile apps that offer mindfulness practice outside of the therapy session. Mindfulness practice is not necessarily about relaxation or clearing the mind; rather, therapists should encourage clients to notice or verbalize their experiences and let go of unhelpful evaluations. It is important for therapists to explain to their clients that they can benefit from additional mindfulness sessions and daily practice of mindfulness at home between sessions.
Module 6: Relaxation Skills
Authors: Jaywant Abhishek , Oberlin Lauren E. , Cherestal Stephanie , Bueno Castellano Christina , Wilkins Victoria M. , Kanellopoulos Dora
Published: November 2023

This chapter explains how relaxation skills can help clients manage acute emotional distress resulting from persistent COVID-19 symptoms. It includes psychoeducation about the acute stress response and the psychological basis behind relaxation strategies. Psychoeducation about the fight-or-flight response can help clients recovering from COVID-19 understand why certain physiological sensations arise and continue to be present in moments of acute distress. Clients might require additional sessions if they present with acute emotional distress or need more practice of relaxation skills. The chapter explains that relaxation strategies are behavioral approaches to counteract the acute stress response and to decrease physiological arousal and alleviate symptoms of acute stress.
Comptonization by reconnection plasmoids in black hole coronae – III. Dependence on the guide field in pair plasma
Authors: Gupta Sanya , Sridhar Navin , Sironi Lorenzo
Published: November 2023

We perform non-radiative two-dimensional particle-in-cell simulations of magnetic reconnection for various strengths of the guide field (perpendicular to the reversing field), in magnetically dominated electron–positron plasmas. Magnetic reconnection under such conditions could operate in accretion disc coronae around black holes. There, it has been suggested that the transrelativistic bulk motions of reconnection plasmoids containing inverse-Compton-cooled electrons could Compton-upscatter soft photons to produce the observed non-thermal hard X-rays. Our simulations are performed for magnetizations 3 ≤ σ ≤ 40 (defined as the ratio of enthalpy density of the reversing field to plasma enthalpy density) and guide field strengths 0 ≤ Bg/B0 ≤ 1 (normalized to the reversing field strength B0). We find that the mean bulk energy of the reconnected plasma depends only weakly on the flow magnetization but strongly on the guide field strength – with Bg/B0 = 1 yielding a mean bulk energy twice smaller than Bg/B0 = 0. Similarly, the dispersion of bulk motions around the mean – a signature of stochasticity in the plasmoid chain’s motions – is weakly dependent on magnetization (for σ ≳ 10) but strongly dependent on the guide field strength – dropping by more than a factor of two from Bg/B0 = 0 to Bg/B0 = 1. In short, reconnection in strong guide fields (Bg/B0 ∼ 1) leads to slower and more ordered plasmoid bulk motions than its weak guide field (Bg/B0 ∼ 0) counterpart.
Connection between SDSS galaxies and ELUCID subhaloes in the eye of machine learning
Authors: Xu Xiaoju , Yang Xiaohu , Xu Haojie , Zhang Youcai
Published: November 2023

We explore the feasibility of learning the connection between Sloan Digital Sky Survey (SDSS) galaxies and ELUCID subhaloes with random forest (RF). ELUCID is a constrained N-body simulation constructed using the matter density field of SDSS. Based on a SDSS-ELUCID matched catalogue, we build RF models that predict Mr magnitude, colour, stellar mass M*, and specific star formation rate (sSFR) with several subhalo properties. While the RF can predict Mr and M* with reasonable accuracy, the prediction accuracy of colour and sSFR is low, which could be due to the mismatch between galaxies and subhaloes. To test this, we shuffle the galaxies in subhaloes of narrow mass bins in the local neighbourhood using galaxies of a semi-analytic model (SAM) and the TNG hydrodynamic simulation. We find that the shuffling only slightly reduces the colour prediction accuracy in SAM and TNG, which is still considerably higher than that of the SDSS. This suggests that the true connection between SDSS colour and subhalo properties could be weaker than that in the SAM and TNG without the mismatch effect. We also measure the Pearson correlation coefficient between the galaxy and subhalo properties in SDSS, SAM, and TNG. Similar to the RF results, we find that the colour–subhalo correlation in SDSS is lower than in both SAM and TNG. We also show that the galaxy–subhalo correlations depend on subhalo mass in the galaxy formation models. Advanced surveys with fainter galaxies will provide new insights into the galaxy–subhalo relation in the real Universe.
Estimation of redshift and associated uncertainty of Fermi/LAT extragalactic sources with Deep Learning
Authors: Gharat Sarvesh , Borthakur Abhimanyu , Bhatta Gopal
Published: November 2023

With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active galactic nuclei (AGN), which are a class of supermassive black hole systems known for their intense multi-wavelength emissions and violent variability. Determining the redshifts of AGNs is essential for understanding their distances, which, in turn, sheds light on our current understanding of the structure of the nearby universe. However, the task involves a number of challenges, such as the need for meticulous follow-up observations across multiple wavelengths and astronomical facilities. In this study, we employ a simple yet effective deep learning model with a single hidden layer having 64 neurons and a dropout of 0.25 in the hidden layer on a sample of AGNs with known redshifts from the latest AGN catalogue, 4LAC-DR3, obtained from Fermi-LAT. We utilized their spectral, spatial, and temporal properties to robustly predict the redshifts of AGNs as well quantify their associated uncertainties by modifying the model using two different variational inference methods. We achieve a correlation coefficient of 0.784 on the test set from the frequentist model and 0.777 and 0.778 from both the variants of variational inference, and, when used to make predictions on the samples with unknown redshifts, we achieve mean predictions of 0.421, 0.415, and 0.393, with standard deviations of 0.258, 0.246, and 0.207 from the models, respectively.