bluuebunny/crossref_metadata_2025 huggingface dataset:
Emergent asymmetry in confined bioconvection
Authors: Bees Martin A. , Perlekar Prasad
Published: January 2025

Bioconvection is the prototypical active matter system for hydrodynamic instabilities and pattern formation in suspensions of biased swimming microorganisms, particularly at the dilute end of the concentration spectrum where direct cell–cell interactions are less relevant. Confinement is an inherent characteristic of such systems, including those that are naturally occurring or industrially exploited, so it is important to understand the impact of boundaries on the hydrodynamic instabilities. Despite recent interest in this area, we note that commonly adopted symmetry assumptions in the literature, such as for a vertical channel or pipe, are uncorroborated and potentially unjustified. Therefore, by employing a combination of analytical and numerical techniques, we investigate whether confinement itself can drive asymmetric plume formation in a suspension of bottom-heavy swimming microorganisms (gyrotactic cells). For a class of solutions in a vertical channel, we establish the existence of a first integral of motion, and reveal that asymptotic asymmetry is plausible. Furthermore, numerical simulations from both Lagrangian and Eulerian perspectives demonstrate with remarkable agreement that asymmetric solutions can indeed be more stable than symmetric; asymmetric solutions are, in fact, dominant for a large, practically important region of parameter space. In addition, we verify the presence of blip and varicose instabilities for an experimentally accessible parameter range. Finally, we extend our study to a vertical Hele-Shaw geometry to explore whether a simple linear drag approximation can be justified. We find that although two-dimensional bioconvective structures and associated bulk properties have some similarities with experimental observations, approximating near-wall physics in even the simplest confined systems remains challenging.
The elephant random walk in the triangular array setting
Authors: Roy Rahul , Takei Masato , Tanemura Hideki
Published: January 2025

Gut and Stadmüller (2021, 2022) initiated the study of the elephant random walk with limited memory. Aguech and El Machkouri (2024) published a paper in which they discuss an extension of the results by Gut and Stadtmüller (2022) for an ‘increasing memory’ version of the elephant random walk without stops. Here we present a formal definition of the process that was hinted at by Gut and Stadtmüller. This definition is based on the triangular array setting. We give a positive answer to the open problem in Gut and Stadtmüller (2022) for the elephant random walk, possibly with stops. We also obtain the central limit theorem for the supercritical case of this model.
(Re)Imagining the Polis: Audience Participation as Postdramatic Discourse
Authors: Lewis William W.
Published: January 2025

How much is enough? The relevance of this question comes from individual expectations regarding value. What is value and how does it manifest through our daily interactions? There is a qualitative difference between the concept of value and individual and collective values. Is there such a thing as a common good when it comes to either? Values are a social construct formed through a process of analysis, dialogue, and assessment within any given community. Though each individual's value system has varying degrees of difference, an agreed-upon system of values is created within and through communication, communion, and coalition. In contemporary societies, it seems the importance of unified community values has diminished in favor of the individual due to the rise of late capitalism, consumer culture, mediatization, political polarization, and the various signposts of neoliberalism. Postdramatic scholar Hans-Thies Lehmann states, “It is a fundamental fact of today's Western societies that all human experiences (life, eroticism, happiness, recognition) are tied to commodities or more precisely their consumption and possession (and not to a discourse).” Lehmann's assertion leads me to ask some striking questions relating to the theatrical practices that guide this essay. Namely, how have large-scale social systems of the contemporary era increasingly divested from community values, instead opting for smaller and smaller factions of identification? Without belief in a larger community good, what use is democracy?
Recursive recalibration and the social meaning of codeswitching
Authors: Fernández-Lizárraga Evelyn
Published: January 2025

Through analyzing Telemundo's Betty en NY (‘Betty in New York’, 2019), this study illustrates how insights from codeswitching contribute to sociolinguistic theories of stancetaking and style. Betty en NY features multiple characters that use Spanish-English codeswitching to invoke their epistemic rights, take stances, and craft distinct personae, thereby exploiting the agentive potential of linguistic boundaries. Thus, codeswitching serves as a key resource for signaling recursive recalibration—the process by which the alignment of individual stances connects to the repositioning of participant roles and personae. Drawing on data from multiple scenes, a discourse analysis of recursive recalibration at work demonstrates how stance alignment and personae are dialogically negotiated and constructed in interaction. (Stance, codeswitching, social meaning, epistemic rights, style, media)*
Hydrogen bonding in the crystal structure of molnupiravir Form I, C13H19N3O7
Authors: Ens Tawnee M. , Kaduk James A. , Rost Megan M. , Dosen Anja , Blanton Thomas N.
Published: January 2025

Molnupiravir Form I crystallizes in space group C2 (#5) with a = 6.48110(17), b = 8.71848(19), c = 27.0607(19) Å, β = 91.920(4)°, V = 1528.22(12) Å3, and Z = 4 at 295 K. The crystal structure consists of supramolecular double layers of molecules parallel to the ab-plane. The layer centers consist of hydrogen-bonded rings forming a 2D network and the outer surfaces of isopropyl groups, with van der Waals interactions between the layers. Each O atom acts as an acceptor in at least one hydrogen bond. A strong O–H⋯O hydrogen bond forms between the hydroxyl group of the oxolane ring and the carbonyl group of the oxopyrimidine ring. The other oxolane hydroxyl group forms bifurcated intra- and intermolecular hydrogen bonds. The hydroxylamino group forms an intramolecular O–H⋯N hydrogen bond with an N atom of the oxopyrimidine ring. The amino group forms an intermolecular N–H⋯N hydrogen bond to the same N atom of the ring. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Best Interests and Decisions to Withdraw Life-Sustaining Treatment from a Conscious, Incapacitated Patient
Authors: Johnson L. Syd M , Cerminara Kathy L.
Published: January 2025

Conscious but incapacitated patients need protection from both undertreatment and overtreatment, for they are exceptionally vulnerable, and dependent on others to act in their interests. In the United States, the law prioritizes autonomy over best interests in decision making. Yet U.S. courts, using both substituted judgment and best interests decision making standards, frequently prohibit the withdrawal of life-sustaining treatment from conscious but incapacitated patients, such as those in the minimally conscious state, even when ostensibly seeking to determine what patients would have wanted. In the United Kingdom, under the Mental Capacity Act of 2005, courts decide on the best interests of incapacitated patients by, in part, taking into account the past wishes and values of the patient. This paper examines and compares those ethicolegal approaches to decision making on behalf of conscious but incapacitated patients. We argue for a limited interpretation of best interests such that the standard is properly used only when the preferences of a conscious, but incapacitated patient are unknown and unknowable. When patient preferences and values are known or can be reasonably inferred, using a holistic, all-things-considered substituted judgment standard respects patient autonomy.
Re-examining democratization's impact on foreign borrowing costs during the first era of globalization
Authors: Funke Michael , Ho Tai-kuang , Tsang Andrew
Published: January 2025

This article re-examines the association between democratization and the cost of borrowing abroad in the first era of globalization. Using two representative datasets the literature offers but employing an improved method for panel event study, we find that democratization's impact on the costs of foreign borrowing is uncertain. In one case, the estimated coefficients are similar to the sign and magnitude of the original study but with larger standard errors, rendering the impact statistically insignificant. In the other case, the estimated coefficients hover around zero and are not statistically different from zero.
Beneath Greenland, Insights for Energy Transitions and Climate Models
Authors: Afonso Afonso , Wansing Agnes , Ajourlou Parviz , Hopper John , Ebbing J�rg
Published: January 2025

Emerging consensus on the structure and dynamics of Greenland’s lithosphere may help improve forecasts of climate and sea level change and develop solutions for sustainable resource use.
Advancing pancreatic cancer research and therapeutics: the transformative role of organoid technology
Authors: Xu Jihao , Pham Minh Duc , Corbo Vincenzo , Ponz-Sarvise Mariano , Oni Tobiloba , Öhlund Daniel , Hwang Chang-Il
Published: January 2025

Research on pancreatic cancer has transformed with the advent of organoid technology, providing a better platform that closely mimics cancer biology in vivo. This review highlights the critical advancements facilitated by pancreatic organoid models in understanding disease progression, evaluating therapeutic responses, and identifying biomarkers. These three-dimensional cultures enable the proper recapitulation of the cellular architecture and genetic makeup of the original tumors, providing insights into the complex molecular and cellular dynamics at various stages of pancreatic ductal adenocarcinoma (PDAC). We explore the applications of pancreatic organoids in dissecting the tumor microenvironment (TME); elucidating cancer progression, metastasis, and drug resistance mechanisms; and personalizing therapeutic strategies. By overcoming the limitations of traditional 2D cultures and animal models, the use of pancreatic organoids has significantly accelerated translational research, which is promising for improving diagnostic and therapeutic approaches in clinical settings, ultimately aiming to improve the outcomes of patients with pancreatic cancer.
Preterm growth assessment: the latest findings on age correction
Authors: Elmrayed Seham , Dai Susan , Lodha Abhay , Kumar Manoj , Fenton Tanis R.
Published: January 2025

Objective

To evaluate the effect of age correction up to 36 months of age for growth assessments of extremely preterm (<28 weeks) and very preterm (28 to <32 weeks) infants.

Study Design

This longitudinal analysis used data from the Preterm Infant Multicenter Growth Study (2001–2014).

Results

1,416 children were included (Median gestational age = 27 weeks). Chronological age-based weight, height, and head circumference z-scores were consistently lower than those based on corrected age for all ages (0, 4, 8, 21 and 36 months) by up to −5.2 (95% confidence interval −5.4, −5.1) z-scores for length at term. Using chronological age, higher proportions of children were misclassified as having suboptimal growth (up to 72.9% misdiagnosed as stunted and 89.8% misdiagnosed as underweight at term).

Conclusion

For extremely and very preterm children, age correction is required for all growth measures through 36 months of corrected age.
Realizing low voltage-driven bright and stable quantum dot light-emitting diodes through energy landscape flattening
Authors: Liu Yiting , Sun Yingying , Yan Xiaohan , Li Bo , Wang Lei , Li Jianshun , Sun Jiahui , Guo Yaqi , Liu Weipeng , Hu Binbin , Lin Qingli , Fan Fengjia , Shen Huaibin
Published: January 2025

Solution-processed quantum dot light-emitting diodes (QLEDs) hold great potential as competitive candidates for display and lighting applications. However, the serious energy disorder between the quantum dots (QDs) and hole transport layer (HTL) makes it challenging to achieve high-performance devices at lower voltage ranges. Here, we introduce “giant” fully alloy CdZnSe/ZnSeS core/shell QDs (size ~ 19 nm) as the emitting layer to build high-efficient and stable QLEDs. The synthesized CdZnSe-based QDs reveal a decreased ground-state band splitting, shallow valence band maximum, and improved quasi-Fermi level splitting, which effectively flatten the energy landscape between the QD layer and hole transport layer. The higher electron concentration and accelerated hole injection significantly promote the carrier radiative recombination dynamics. Consequently, CdZnSe-based device exhibits a high power conversion efficiency (PCE) of 27.3% and an ultra-low efficiency roll-off, with a high external quantum efficiency (EQE) exceeding 25% over a wide range of low driving voltages (1.8-3.0 V) and low heat generation. The record-high luminance levels of 1,400 and 8,600 cd m-2 are achieved at bandgap voltages of 100% and 120%, respectively. Meanwhile, These LEDs show an unprecedented operation lifetime T95 (time for the luminance to decrease to 95%) of 72,968 h at 1,000 cd m-2. Our work points to a novel path to flatten energy landscape at the QD-related interface for solution-processed photoelectronic devices.
Flexible micromachined ultrasound transducers (MUTs) for biomedical applications
Authors: Joshi Sanjog Vilas , Sadeghpour Sina , Kuznetsova Nadezda , Wang Chen , Kraft Michael
Published: January 2025

The use of bulk piezoelectric transducer arrays in medical imaging is a well-established technology that operates based on thickness mode piezoelectric vibration. Meanwhile, advancements in fabrication techniques have led to the emergence of micromachined alternatives, namely, piezoelectric micromachined ultrasound transducer (PMUT) and capacitive micromachined ultrasound transducer (CMUT). These devices operate in flexural mode using piezoelectric thin films and electrostatic forces, respectively. In addition, the development of flexible ultrasound transducers based on these principles has opened up new possibilities for biomedical applications, including biomedical imaging, sensing, and stimulation. This review provides a detailed discussion of the need for flexible micromachined ultrasound transducers (MUTs) and potential applications, their specifications, materials, fabrication, and electronics integration. Specifically, the review covers fabrication approaches and compares the performance specifications of flexible PMUTs and CMUTs, including resonance frequency, sensitivity, flexibility, and other relevant factors. Finally, the review concludes with an outlook on the challenges and opportunities associated with the realization of efficient MUTs with high performance and flexibility.
Hypertension at diagnosis of coarctation of the aorta as a risk factor for recoarctation
Authors: Berman Nofar , Pozailov Shani , Krymko Hanna , Slanovic Leonel , Murninkas Michael , Grunseid Michael , Levitas Aviva
Published: January 2025

Background

Coarctation of the aorta (CoA) is a narrowing of the aorta that affects 5–8% of congenital heart defects. Treatment options include surgical repair or transcatheter management with endovascular stenting or balloon dilatation. Late complications after operative repair include systemic hypertension, aortic valve abnormalities, aortic aneurysm, and recoarctation. This study examines the association between the presence of hypertension at the diagnosis of CoA and recoarctation.

Methods

This retrospective study analyzed medical records of patients at Soroka University Medical Center who underwent treatment for CoA between 1978 and 2021. The study included 128 patients diagnosed with CoA and who underwent repair; 9 were excluded, leaving 119 patients for analysis.

Results

Of the 119 patients, 28 developed recoarctation within 15 years of initial repair. Patients with hypertension at initial diagnosis were more likely to develop recoarctation, adjusted to sex and ethnicity. Other patient characteristics were not significantly associated with recoarctation.

Conclusions

Hypertension at the time of initial CoA diagnosis is a risk factor for the development of recoarctation within 15 years of initial repair. Close monitoring and management of blood pressure may be important for patients with CoA. Future research should investigate whether hypertension control can reduce recoarctation risk in this population.

Impact

Previous studies focus on post-repair hypertension in CoA.

Limited research on hypertension during CoA diagnosis.

Knowledge gap on its impact on recoarctation risk.

Hypertension at CoA diagnosis may predict recoarctation.

Enables tailored monitoring and timely intervention.
Melanoma bone metastasis-induced osteocyte ferroptosis via the HIF1α-HMOX1 axis
Authors: Jia Yewei , Li Rui , Li Yixuan , Kachler Katerina , Meng Xianyi , Gießl Andreas , Qin Yi , Zhang Fulin , Liu Ning , Andreev Darja , Schett Georg , Bozec Aline
Published: January 2025

Osteocytes are the main cells in mineralized bone tissue. Elevated osteocyte apoptosis has been observed in lytic bone lesions of patients with multiple myeloma. However, their precise contribution to bone metastasis remains unclear. Here, we investigated the pathogenic mechanisms driving melanoma-induced osteocyte death. Both in vivo models and in vitro assays were combined with untargeted RNA sequencing approaches to explore the pathways governing melanoma-induced osteocyte death. We could show that ferroptosis is the primary mechanism behind osteocyte death in the context of melanoma bone metastasis. HMOX1 was identified as a crucial regulatory factor in this process, directly involved in inducing ferroptosis and affecting osteocyte viability. We uncover a non-canonical pathway that involves excessive autophagy-mediated ferritin degradation, highlighting the complex relationship between autophagy and ferroptosis in melanoma-induced osteocyte death. In addition, HIF1α pathway was shown as an upstream regulator, providing a potential target for modulating HMOX1 expression and influencing autophagy-dependent ferroptosis. In conclusion, our study provides insight into the pathogenic mechanisms of osteocyte death induced by melanoma bone metastasis, with a specific focus on ferroptosis and its regulation. This would enhance our comprehension of melanoma-induced osteocyte death.
A dataset of Uniswap daily transaction indices by network
Authors: Chemaya Nir , Cong Lin William , Jorgensen Emma , Liu Dingyue , Zhang Luyao
Published: January 2025

Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.
StreetSurfaceVis: a dataset of crowdsourced street-level imagery annotated by road surface type and quality
Authors: Kapp Alexandra , Hoffmann Edith , Weigmann Esther , Mihaljević Helena
Published: January 2025

Road unevenness significantly impacts the safety and comfort of traffic participants, especially vulnerable groups such as cyclists and wheelchair users. To train models for comprehensive road surface assessments, we introduce StreetSurfaceVis, a novel dataset comprising 9,122 street-level images mostly from Germany collected from a crowdsourcing platform and manually annotated by road surface type and quality. By crafting a heterogeneous dataset, we aim to enable robust models that maintain high accuracy across diverse image sources. As the frequency distribution of road surface types and qualities is highly imbalanced, we propose a sampling strategy incorporating various external label prediction resources to ensure sufficient images per class while reducing manual annotation. More precisely, we estimate the impact of (1) enriching the image data with OpenStreetMap tags, (2) iterative training and application of a custom surface type classification model, (3) amplifying underrepresented classes through prompt-based classification with GPT-4o and (4) similarity search using image embeddings. Combining these strategies effectively reduces manual annotation workload while ensuring sufficient class representation.
The ECOLANG Multimodal Corpus of adult-child and adult-adult Language
Authors: Gu Yan , Donnellan Ed , Grzyb Beata , Brekelmans Gwen , Murgiano Margherita , Brieke Ricarda , Perniss Pamela , Vigliocco Gabriella
Published: January 2025

Communication comprises a wealth of multimodal signals (e.g., gestures, eye gaze, intonation) in addition to speech and there is a growing interest in the study of multimodal language by psychologists, linguists, neuroscientists and computer scientists. The ECOLANG corpus provides audiovisual recordings and ELAN annotations of multimodal behaviours (speech transcription, gesture, object manipulation, and eye gaze) by British and American English-speaking adults engaged in semi-naturalistic conversation with their child (N = 38, children 3-4 years old, face-blurred) or a familiar adult (N = 31). Speakers were asked to talk about objects to their interlocutors. We further manipulated whether the objects were familiar or novel to the interlocutor and whether the objects could be seen and manipulated (present or absent) during the conversation. These conditions reflect common interaction scenarios in real-world communication. Thus, ECOLANG provides ecologically-valid data about the distribution and co-occurrence of multimodal signals across these conditions for cognitive scientists and neuroscientists interested in addressing questions concerning real-world language acquisition, production and comprehension, and for computer scientists to develop multimodal language models and more human-like artificial agents.
High-resolution X-ray phase-contrast tomography of human placenta with different wavefront markers
Authors: Savatović Sara , Laundon Davis , De Marco Fabio , Riedel Mirko , Hammel Jörg U. , Busse Madleen , Salomé Murielle , Pascolo Lorella , Zanette Irene , Lewis Rohan M. , Herzen Julia , Thibault Pierre
Published: January 2025

Phase-contrast micro-tomography (
$$\upmu$$
CT) with synchrotron radiation can aid in the differentiation of subtle density variations in weakly absorbing soft tissue specimens. Modulation-based imaging (MBI) extracts phase information from the distortion of reference patterns, generated by periodic or randomly structured wavefront markers (e.g., gratings or sandpaper). The two approaches have already found application for the virtual inspection of biological samples. Here, we perform high-resolution
$$\upmu$$
CT scans of an unstained human placenta specimen, using MBI with both a 2D grating and sandpaper as modulators, as well as conventional propagation-based imaging (PBI). The 3D virtual representation of placenta offers a valuable tool for analysing its intricate branching villous network and vascular structure, providing new insights into its complex architecture. Within this study, we assess reconstruction quality achieved with all three evaluated phase-contrast methods. Both MBI datasets are processed with the Unified Modulated Pattern Analysis (UMPA) model, a pattern-matching algorithm. In order to evaluate the benefits and suitability of MBI for virtual histology, we discuss how the complexities of the technique influence image quality and correlate the obtained volumes to 2D techniques, such as conventional histology and X-ray fluorescence (XRF) elemental maps.
Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns
Authors: Handousa Aya Mohsen , Abdellatief Mohamed , Salem Fikry Abdo , Mahmoud Nabil , Ghannam Mohamed
Published: January 2025

Concrete-filled double-skin steel tubular (CFDST) columns have become widely utilized in building construction and bridges, thanks to their exceptional structural capabilities. Therefore, this study investigates the axial compressive behavior of square CFDST columns. The study aims to explore the influence of external and internal plate shapes (flat or corrugated plates) and different widths of internal steel tubes on the axial compressive behavior. The effects of varying internal widths of the internal steel tube (60 mm, 116 mm, and 160 mm) on the performance of CFDST columns were examined. Additionally, the study compared the performance of concrete-filled steel tubular (CFST) and CFDST columns with external flat or corrugated plates. The findings indicated that incorporating internal corrugated plates notably improved both the load-carrying capacity and ductility of the specimens. Notably, CFDST columns featuring corrugated internal plates (116 mm width) exhibited strength enhancements of 25.3% and 7.4% compared to internal widths of 160 mm and 60 mm, respectively. Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. The findings indicated that the GPR model outperformed the ANN model in predicting the bearing capacity of square CFDST columns. Additionally, the Shapley Additive Explanation technique was employed for feature analysis. The outcomes of this analysis revealed that parameters such as section width and concrete strength positively influence the compressive strength index.
Charge doping into spin minority states mediates doubling of TC in ferromagnetic CrGeTe3
Authors: Trzaska Liam , Qiao Lei , Watson Matthew D. , Ciomaga Hatnean Monica , Marković Igor , Abarca Morales Edgar , Antonelli Tommaso , Cacho Cephise , Balakrishnan Geetha , Ren Wei , Picozzi Silvia , King Phil D. C.
Published: January 2025

The recent discovery of the persistence of long-range magnetic order when van der Waals magnets are thinned towards monolayers provides a tunable platform for engineering of novel magnetic structures and devices. Here, we study the evolution of the electronic structure of CrGeTe3 as a function of surface electron doping. From angle-resolved photoemission, we observe spectroscopic fingerprints that this electron doping drives a marked increase in T
C, reaching values more than double that of the undoped material, in agreement with recent studies using electrostatic gating. Together with density functional theory calculations and Monte Carlo simulations, we show that, surprisingly, the increased T
C is mediated by the population of spin-minority Cr t
2g
states, forming a half-metallic 2D electron gas. This promotes a novel variant of double exchange, and unlocks a significant influence of Ge – which was previously thought to be electronically inert in this system – in mediating Cr-Cr exchange.
A machine learning approach to leveraging electronic health records for enhanced omics analysis
Authors: Mataraso Samson J. , Espinosa Camilo A. , Seong David , Reincke S. Momsen , Berson Eloise , Reiss Jonathan D. , Kim Yeasul , Ghanem Marc , Shu Chi-Hung , James Tomin , Tan Yuqi , Shome Sayane , Stelzer Ina A. , Feyaerts Dorien , Wong Ronald J. , Shaw Gary M. , Angst Martin S. , Gaudilliere Brice , Stevenson David K. , Aghaeepour Nima
Published: January 2025

Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.
First-in-class ultralong-target-residence-time p38α inhibitors as a mitosis-targeted therapy for colorectal cancer
Authors: Rudalska Ramona , Harbig Jule , Forster Michael , Woelffing Pascal , Esposito Aylin , Kudolo Mark , Botezatu Adelina , Haller Vanessa , Janssen Nicole , Holzmayer Samuel , Nahidino Philipp , Trompak Omelyan , Pantsar Tatu , Kronenberger Thales , Yurttas Can , Rist Elke , Weber Alexander N. R. , Dahlke Marc H. , Ott German , Koenigsrainer Alfred , Rothbauer Ulrich , Maerklin Melanie , Muerdter Thomas , Schwab Matthias , Singer Stephan , Zender Lars , Laufer Stefan , Dauch Daniel
Published: January 2025

Colorectal cancer (CRC) constitutes the second leading cause of cancer-related death worldwide and advanced CRCs are resistant to targeted therapies, chemotherapies and immunotherapies. p38α (Mapk14) has been suggested as a therapeutic target in CRC; however, available p38α inhibitors only allow for insufficient target inhibition. Here we describe a unique class of p38α inhibitors with ultralong target residence times (designated ULTR-p38i) that robustly inhibit p38α downstream signaling and induce distinct biological phenotypes. ULTR-p38i monotherapy triggers an uncontrolled mitotic entry by activating Cdc25 and simultaneously blocking Wee1. Consequently, CRC cells undergo mitotic catastrophe, resulting in apoptosis or senescence. ULTR-p38i exhibit high selectivity, good pharmaco-kinetic properties and no measurable toxicity with strong therapeutic effects in patient-derived CRC organoids and syngeneic CRC mouse models. Conceptually, our study suggests ultralong-target-residence-time kinase inhibitors as an alternative to covalent inhibitors, which, because of the lack of cysteine residues, cannot be generated for many kinase cancer targets.
Regioselective synthesis of 5-azaindazoles based on the intramolecular amination reaction of 5-acyl-4-pyridones with hydrazines
Authors: Viktorova Viktoria V. , Obydennov Dmitrii L. , Mustafina Alsu F. , Ulitko Maria V. , Kornev Mikhail Y. , Sosnovskikh Vyacheslav Y.
Published: January 2025

A general and convenient approach for the synthesis of biologically important pyrazolo[4,3-c]pyridines has been developed via amination of 5-acyl-4-pyridones with hydrazines.
On-surface gold-catalyzed hydroamination/cyclization reaction of alkynes
Authors: Jiménez-Martín Alejandro , Munteanu Tatiana , Chen Qifan , Pascal Simon , Tintaru Aura , Mallada Benjamin , Mutombo Pingo , Siri Olivier , Jelínek Pavel , de la Torre Bruno
Published: January 2025

On-surface synthesis under ultra-high vacuum (UHV) conditions facilitates the fabrication of unique molecular compounds, replicating established in-solution protocols.
Collective motion and its connection to the energy landscape in 2D soft crystals
Authors: Hassan Md. Rakib , Aronow Sam R. , Douglas Jack F. , Starr Francis W.
Published: January 2025

We visualize the dynamical heterogeneity of 2D soft crystals. Collective motion occurs in discrete excited energy states. Excitations arise due to discrete defect clusters. The size and duration of excitations grow approaching crystal melting.
A study on device physics of deep ultraviolet light emitting diodes leveraging machine learning
Authors: Lin Na , Liu Zhiqiang , Jiang Zhuoying , Jiang Ying , Zhao Shanshan , Yan Jinjian , Jiang Sijie , Yun Yikai , Wei Wenjie , Li Shaoqun , Wan Ziang , Du Jianfeng , Li Jinchai , Tao Tao , Huang Kai , Li Lin , Chen Mengyu , Li Cheng , Zhang Rong
Published: January 2025

An effective approach to optimizing the luminous performance of AlGaN-based DUV LEDs is demonstrated using machine learning. A CNN model predicts LOPD with high accuracy, revealing key features that influence LED performance.
Highly linear wearable ionic gel based on self-assembled discoid liquid crystal towards human motion monitoring
Authors: Chen Jie , Feng Rui , Su Peng , Zhou Tong , Dong Lijie
Published: January 2025

A novel type of ionic gel, constructed from self-assembled discoid liquid crystals, is demonstrated to exhibit high linearity, outstanding stability, and holds promise as a flexible strain sensing material.
Robust and Arbitrary Pole Placement using Static Output Feedback
Authors: El Faiz Samira , Guerbaoui Mohammed , Ed-Dahhak Abdelali , Lachhab Abdeslam
Published: January 2025

This paper presents the output feedback pole assignment methods that attempt to achieve a robust solution where the arbitrary choose of the poles to be assigned are as insensitive as possible to perturbations in the closed loop system. The robust and arbitrary pole placement problem is formulated as a nonlinear and non-convex programming problem with a nonlinear constraint on matrix functions as well as a linear constraint. The numerical treatment is the subject of a comparative study of the proposed methods.
From Data to Decisions: A Smart IoT and Cloud Approach to Environmental Monitoring
Authors: Guerbaoui Mohammed , El Faiz Samira , Ed-Dahhak Abdelali , Lachhab Abdeslam , Benhala Bachir , Bakziz Zakaria , Ichou Ismail , Selmani Abdelouahed
Published: January 2025

Environmental monitoring plays a crucial role in various domains, including agriculture, healthcare, and manufacturing, where optimal environmental conditions are essential for productivity and safety. In this project, we present a smart environmental monitoring system that leverages IoT (Internet of Things) technology and data analytics to monitor temperature and humidity levels in real-time. The system consists of a network of sensor nodes deployed in the target environment, comprising ESP32 microcontrollers and DHT11 sensors for data collection. The sensor nodes transmit data using the MQTT (Message Queuing Telemetry Transport) protocol to a cloud-based MQTT Broker hosted on HiveMQ Cloud. Data processing and visualization are handled by Node-RED, which subscribes to MQTT topics, processes incoming data streams, and stores them in a time-series database, InfluxDB Cloud. The collected data is then visualized in real-time using Grafana dashboards, which are embedded within a Flask web application, providing stakeholders with seamless access to actionable insights into environmental conditions. The smart environmental monitoring system offers numerous benefits, including improved decisionmaking, proactive maintenance, and enhanced productivity. Future enhancements could include the integration of additional sensors and the application of machine learning algorithms for predictive analytics. Overall, the project demonstrates the potential of IoT and data analytics in addressing real-world challenges related to environmental monitoring.
Advancements in CNN Architectures for Offline Handwritten Arabic Character Recognition
Authors: El Ibrahimi Aissam , Elzaar Abdellah , El Akchioui Nabil , Benaya Nabil , El Allati Abderrahim
Published: January 2025

Analyzing and classifying images of Arabic handwritten characters is crucial for text understanding and interpretation from image data. The recognition of handwritten Arabic characters not only preserves the integrity of the Arabic language but also enhances computer vision applications tailored for Arabic script. Existing literature often proposes complex architectures, which can hinder real-time prediction speed and accuracy. In this paper, we propose a novel Deep Learning architecture based on Convolutional Neural Networks (CNNs) for accurate classification of Arabic handwritten characters. Our approach offers simplicity without compromising accuracy, making it suitable for online recognition tasks. We validate our method on the Arabic Handwritten Characters Database (AHCD) and achieve a high recognition rate of 99%. The trained model demonstrates robust performance, indicating its potential for practical applications in Arabic character recognition.
Power system operation enhancement modelling using MATLAB/Simulink
Authors: Somefunn Tobiloba Emmanuel , Ayodeji Olaoye Daniel , Longe Omowunmi Mary
Published: January 2025

This paper focuses on enhancing power system operations at the injection substation through the integration of renewable energy, modelled and simulated using MATLAB/Simulink. The study explores the incorporation of photovoltaic (PV) systems and energy storage solutions (ESS) to improve the substation’s operational efficiency and reliability. By simulating different scenarios, including PV integration and ESS operations, the results show a significant improvement in voltage stability, with an average voltage profile enhancement of 13.33% across all feeders. Additionally, the energy storage system scenario demonstrated the capability to continue supplying power to the feeders even after the 33 kV grid supply was cut off, further proving its effectiveness in ensuring grid stability during disruptions. These findings suggest that renewable energy integration, coupled with advanced control mechanisms, holds great potential for broader application in enhancing the reliability and sustainability of power systems.
Emerging Trends and Future Directions in Fused Deposition Modeling: A Bibliometric Analysis (2013-2023)
Authors: Rabbaje Fatiha , Ait Taleb Abdelmajid , Lasri Larbi
Published: January 2025

This bibliometric overview provides a detailed and comprehensive analysis of the research conducted on Fused Deposition Modeling (FDM) technology from 2013 to 2023. The analysis includes a thorough examination of publications, conference papers, and patents related to FDM technology. The overview offers valuable insights into the evolution, current state, and prospective trajectories of FDM technology. The analysis covers a wide range of topics related to FDM technology, including its applications, advancements, limitations, and prospects. This bibliometric overview aims to help researchers gain a deeper understanding of the FDM technology and its potential applications. It offers a comprehensive overview of the current state of research in the field and identifies new areas of research that could lead to significant advancements in FDM technology. The study utilized bibliometric indicators and network analysis methodologies, encompassing keyword analysis, citation metrics, journal productivity, associated publications, and author-related metrics. To conduct this analysis, two freely available software tools, VOSviewer and Bibliometrix [1], were employed.
Enhanced Air Traffic Control Safety Using low Earth orbit Satellites: Non-TCAS Aircraft
Authors: Raziq Omayma , Nhaila Hasna , El Khaili Mohamed , Khiat Azeddine
Published: January 2025

This paper presents a study on a novel TCAS design that combines the exploitation of low-orbit satellites with the existing TCAS system to improve operational efficiency and overcome challenges. With the continuous growth of air traffic, ensuring safety remains a top priority. TCAS was developed to mitigate the risks of aircraft collisions and is mandatory on large transport aircraft. TCAS uses information and data to determine the altitude and relative positions of nearby aircraft. However, despite the advances in air traffic control (ATC) systems, non-TCAS-equipped aircraft continue to operate in the airspace, potentially increasing the risk of mid-air collisions. In addition, existing TCAS systems often generate frequent and unnecessary alarms, especially in densely populated terminal areas, leading to erroneous pilot actions. The proposed solution aims to detect non-TCAS-equipped aircraft by other aircraft, whether they are equipped with TCAS or not. Therefore, the goal is to optimize the efficiency of TCAS to reduce the risk of mid-air collision and improve overall aviation safety. The management applications are distributed on the cloud to save resource exploitation, including energy consumption by processing and air traffic control-related exchanges.
A comparative analysis of photovoltaic system using three algorithms: P&O, Hill Climbing and Fuzzy Logic
Authors: Benkirane Yassine , Benhala Bachir , Raihani Abdelhadi
Published: January 2025

Photovoltaic solar energy plays a crucial role in our lives, as it provides electricity from the sun, a renewable source. The objective of this paper is to create a comprehensive model of a photovoltaic solar system, representing its various components, with the aim of maximizing power output using a control technique called MPPT (Maximum Power Point Tracking). Additionally, the work involves using algorithms to manage energy within this solar system to optimize power generation. We focus on the following algorithms: Perturb and Observe (A simple and highly responsive algorithm with a quick response that adjusts the voltage), Hill Climbing (It is a simple algorithm to implement and is effective for solving combinatorial problems by adjusting the duty cycle), and fuzzy logic (Using linguistic rules, it is possible to intelligently adjust the parameters, improving the connection between the photovoltaic generator and the load to ensure optimal operation). This work will be conducted using MATLAB/SIMULINK.
A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning
Authors: Belarbi Zaid , El Younoussi Yacine
Published: January 2025

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing water scarcity and climate variability. Accurate estimation of evapotranspiration (ET), a key component in determining water requirements for crops, is essential for effective irrigation management. Traditional methods of measuring and estimating ET, such as eddy-covariance systems and lysimeters, provide valuable data but often face limitations in scalability, cost, and complexity. Recent advancements in machine learning (ML) offer promising alternatives to enhance the precision and efficiency of ET estimation and smart irrigation systems. This review explores the integration of machine learning techniques in optimizing irrigation water usage, with a particular focus on ET prediction and smart irrigation technologies. We examine various ML models, that have been employed to predict ET using diverse datasets comprising meteorological, soil, and remote sensing data. In addition to ET estimation, the review highlights smart irrigation systems that optimize irrigation schedules based on real-time data inputs. Through this review, we aim to provide a comprehensive overview of the state-of-the-art in ML-based ET estimation and smart irrigation technologies, contributing to the development of more resilient and efficient agricultural water management strategies.
Study of the stability of a high-power PV conversion chain for isolated site supply
Authors: Akoro Edjadessamam , Bokovi Yao , Agbessi Akueté Pierre , Ateyo Gnakou Mazama
Published: January 2025

The aim of this paper is to design and study the stability of a 500 kW PV string connected to a DC-AC load. To achieve this, a MPPT method based on a hybrid Artificial Neural Network (ANN)-Synergistic Control Algorithm (SCA) already developed in the literature will be used to find the system's maximum power point. This control will enable the PV generator to deliver maximum energy. Next, the power control approach will be developed to control the inverter, and the stability of the overall system will be studied.
CFD Analysis for Aerodynamic Performance Optimization of a Vertical Axis Helical Wind Turbine Using Rigid PVC
Authors: Outar Hamza , Ennadafy Hamza , Jammoukh Mustapha , El Khaili Mohamed
Published: January 2025

In the context of sustainable development, and faced with the dual global threat of the depletion of fossil energy resources and their harmful environmental impact, the use of renewable energies is emerging as a promising alternative. The number of wind turbines being installed around the world is growing rapidly, and research into the aerodynamics of wind turbines has become highly specialised. Computational fluid dynamics (CFD) is used to analyse the effect of loads on the aerodynamic performance of wind turbines. To optimise this performance and ensure greater reliability, it is essential to model the airflow around the turbine. This study focuses on evaluating the first comparative part of the dynamic performance of a vertical axis wind turbine made of innovative bio-composite and rigid PVC, using the finite element method. The simulations revealed that bio-composite blades offer a significant improvement in energy performance compared with rigid PVC blades. In addition, the adoption of bio-composite further reduced the overall carbon footprint of the blades, making them more environmentally friendly. The results of this research underline the superior potential of bio-composites over rigid PVC in the wind energy sector, paving the way for more sustainable technologies.
Advanced Facial Recognition Systems for Real-Time Embedded Applications
Authors: Guerbaoui Mohammed , Misbah Nouhaila , Selmani Abdelouahed , El Faiz Samira , Benhala Bachir , Ed-Dahhak Abdelali , Lachhab Abdeslam
Published: January 2025

This article explores several classical techniques of facial recognition, assessing their suitability for real-time embedded systems such as digital cameras. It also includes a thorough discussion on the training requirements for various facial recognition methods. Addressing mounting concerns in security and surveillance, this project aims to develop an advanced facial recognition system using a 2D detection approach. Leveraging the Intel RealSense 415 camera connected to a Raspberry Pi 3. An introduction to Haar Cascading models is provided, highlighting their advantages, particularly their ability to ensure acceptable levels of accuracy for facial recognition in unseen image collections. Lastly, the article offers a detailed description and implementation of a functional platform, accompanied by preliminary results.