bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Specialized Deep-Learning Models for Crop Freshness Assessment to Mitigate Post-Harvest Loss
Authors: Cunha Wellington , Kaur Arashdeep , Shih Frank Y.
Published: November 2024

Traditional methods for evaluating crop ripeness are critiqued for their inefficiency and potential harm to produce. The use of image-processing and deep-learning techniques can solve these issues as a trend in non-destructive methods. However, an overfitting problem arises when optimization and generalization are used to estimate the parameters of the next epoch. In this paper, we develop specialized models with a high volume of training images for a single type of crop to achieve the goal of 100% accuracy for both test and validation datasets. This development contributes insights into leveraging deep learning for crop assessment, emphasizing its potential application in diverse agricultural scenarios. Experimental results show that the proposed models are superior to several existing available methods.
A Novel Infogain and Multi-Axial Wavelet-Based Transformer for Personality Trait Question Answering
Authors: Biswas Kunal , Palaiahnakote Shivakumara , Bhattacharya Saumik , Pal Umapada , Sarkar Ram
Published: November 2024

Visual Question Answering (VQA) is one of the attractive topics in the field of multimedia, affective, and empathic computing to garner user interest. Unlike existing models which aim at addressing challenges of VQA for the scene images, this work aims at developing a new model for Personality Trait Question Answering (PQA). It uses Twitter account information, which includes shared images, profile pictures, banners, text in the images, and descriptions of the images. Motivated by the accomplishments of the transformer, for encoding visual features of the images, a new InfoGain Multi-Axial Wavelet Vision Transformer (IgMaWaViT) is explored here. For encoding textual features in the images and descriptions, a new Information Gain BERT (InfoBert) method is introduced, which can handle the variable length encoding of text by choosing the optimal discriminator. Furthermore, the model fuses encodings of images and text according to the questions on different personality traits for question answering. The model is called InfoGain Multi-Axial Wavelet Vision Transformer for Personality Traits Question Answering (IgMaWaViT-PQA). To validate the efficacy of the proposed model, a dataset has been constructed, and it is used along with standard datasets for experimentation. Comprehensive experiments show that the proposed model is better than the state-of-the-art models. The code is available at the link: https://github.com/biswaskunal29/InfoGain_MultiAxial_PQA .
Spatial-Frequency Based EEG Features for Classification of Human Emotions
Authors: Gornale Shivanand S. , Palaiahnakote Shivakumara , Unki Amruta , Vadera Sunil
Published: November 2024

Human emotion classification without bias and unfairness is challenging because most existing image-based methods are directly or indirectly affected by subjectivity. Therefore, we propose an EEG (Electroencephalogram) based model for an accurate emotion classification without the effect of subjectivity. The captured EEG signals are converted into Delta, Theta, Alpha, Beta, and Gama frequency bands. As emotions change, the frequency bands change and provide unique patterns for each emotion irrespective of different persons. With this observation, the statical features, namely, mean, standard deviation, variance, and kurtosis, and frequency-based features, namely, Power Spectral Density (PSD) and Petrosian Fractal Dimension (PFD) are extracted. To integrate the strength of spatial and frequency-based features, the features are supplied to quadratic discriminative analysis for the final classification. The experiments on the benchmark datasets, DEAP and SEED-IV, achieve 99.40% and 91.97% accuracy, respectively. A comparison with state-of-the-art methods shows that the method performs very well on some datasets.
RESEARCH NOTES: Design of a Distributed and Highly Scalable Fog Architecture for Heterogeneous IoT Infrastructures
Authors: Muñoz Lucía Arnau , Berná Martínez José Vicente , Asensi Carlos Calatayud , Pastor David Saavedra
Published: November 2024

Fog computing can provide an effective solution to the challenges presented by today’s ever-emerging Internet of Things (IoT) infrastructures. As the number of interconnected devices progressively increases, these infrastructures require better solutions to ensure high scalability and processing capacity, along with an efficient use of available resources. This is why this paper presents a distributed Fog architecture, specifically designed to address the challenges and difficulties presented by heterogeneous IoT environments. This Fog architecture is used as an intermediate layer between the IoT devices and the final layer, it has been designed after the previous analysis of the requirements to be met for the solution, then the modularization of the architecture has been carried out so that it can be easily distributed, and finally, an implementation has been generated on a real environment as a validation case of the proposal.
Davydov–Chaban Hamiltonian with deformation-dependent mass term for the sextic potential
Authors: Oulne M. , Tagdamte I.
Published: November 2024

This paper presents analytical expressions for spectra and wave functions derived from a Davydov–Chaban Hamiltonian, which models the collective motion of nonaxial nuclei within a sextic potential and incorporates the Deformation-Dependent Mass (DDM) formalism. The resulting solution, termed the Sextic and DDM Approach ([Formula: see text]-SDDMA), is attained through the combined application of Quasi-Exact Solvability (QES) and a Quantum Perturbation Method (QPM). Spectra and [Formula: see text] transition rates are provided for a selection of 15 nuclei: [Formula: see text]Xe, [Formula: see text]Pt and [Formula: see text]Pd, demonstrating a notable agreement with experimental data. Notably, the DDM parameter plays a crucial role in representing, for the first time, the [Formula: see text] band levels [Formula: see text] and [Formula: see text] as well as the higher levels within the natural ordering, resolving a previously identified anomaly present in all models related to the Davydov–Chaban Hamiltonian. Additionally, we explore the impact of DDM on the staggering effect of the [Formula: see text] band, moments of inertia, and shape phase transition, focusing on the prominent isotopic chains of Xe, Pt and Nd where some of the best candidates are obtained. Moreover, the DDM effect is found to facilitate the emergence of the backbending phenomenon in select nuclei in contrast with other models.
The weakness of the Erdős–Moser theorem under arithmetic reductions
Authors: Levy Patey Ludovic , Mimouni Ahmed
Published: November 2024

The Erdős–Moser ([Formula: see text]) theorem says that every infinite tournament admits an infinite transitive subtournament. We study the computational behavior of the [Formula: see text] theorem with respect to the arithmetic hierarchy, and prove that [Formula: see text] instances of [Formula: see text] admit low[Formula: see text] solutions for every [Formula: see text], and that if a set [Formula: see text] is not arithmetical, then every instance of [Formula: see text] admits a solution relative to which [Formula: see text] is still not arithmetical. We also provide a level-wise refinement of this theorem. These results are part of a larger program of computational study of combinatorial theorems in Reverse Mathematics.
On a class of two-dimensional singular Douglas metrics
Authors: Yang Guojun
Published: November 2024

In this paper, we characterize a class of singular Douglas [Formula: see text]-metrics [Formula: see text] determined by [Formula: see text] on a two-dimensional manifold [Formula: see text], where [Formula: see text], [Formula: see text] is a Riemannian metric and [Formula: see text] is a 1-form on [Formula: see text]. When [Formula: see text] and under certain conditions about [Formula: see text], we show that any such a metric is a Douglas metric if and only if it is a Finsler metric derived from a [Formula: see text]-transformation of an [Formula: see text]-Kropina metric. For such a special class of singular [Formula: see text]-metrics, we further completely determine their local structure when they are, respectively, Douglasian and locally projectively flat. In particular, we show that a Kropina metric is always Douglasian and a Douglas [Formula: see text]-Kropina metric with [Formula: see text] is locally Minkowskian on a two-dimensional manifold. The main results in this paper are quite different from the results in the higher dimension case.
Effects of global monopole on thermodynamic properties and Shannon entropy under a Mie-type potential
Authors: Ahmed Faizuddin , Moreira Allan R. P. , Amadi Precious O. , Horchani Ridha , Ikot Akpan N.
Published: November 2024

In this analysis, we analytically obtain the eigenvalue solutions of non-relativistic quantum particles interacting with Mie-type potential in a topological defect geometry. Afterward, we study the thermodynamic properties for the quantum system, such as the vibrational free energy, mean energy, entropy, and specific heat using the partition unction and analyze the effects of topological defect. Furthermore, we calculated the Shannon entropy for this quantum system, thus measuring the loss of information about the particle’s location. This measure of information reveals the influence of the topological parameter on the location of the particle and provides us with more detailed parameters for a possible experimental detection of these quantum particles.
Innovative analytical and semi-analytical approaches to the nonlinear Fisher–KPP model: Accurate computational solutions and applications
Authors: Zhang Xiao , Vokhmintsev Aleksander , Nofal Taher A. , Khater Mostafa M. A.
Published: November 2024

This study investigates the nonlinear Fisher–Kolmogorov–Petrovsky–Piskunov (KPP) model, which is widely applied to phenomena such as population dynamics, combustion processes, wave propagation in excitable media, and other systems characterized by reaction–diffusion behavior. The primary objective is to examine the intricate interplay between diffusion and nonlinear growth, crucial for understanding processes where spatial spreading is coupled with local growth or decay, such as species invasion, disease transmission, and chemical reactions. The problem addressed is the challenge of obtaining accurate, analytical solutions to the nonlinear Fisher–KPP equation, which often resists conventional solution techniques due to its complex, coupled dynamics. This study employs two advanced analytical methods — the modified Khater (MKhat) and unified (UF) techniques — to derive new, exact solutions to the Fisher–KPP model. These solutions provide deeper insights into the underlying dynamics of the reaction–diffusion systems, showcasing the balance between diffusion-driven spreading and nonlinear growth effects. Numerical validation of the obtained solutions is carried out using He’s variational iteration (HVI) scheme, ensuring the reliability and accuracy of the analytical results. This combination of exact and numerical solutions strengthens the study’s findings and offers robust tools for future research. The expected results include a set of novel, exact analytical solutions that contribute significantly to the understanding of the diffusion and nonlinear growth interplay in reaction–diffusion systems. These solutions have practical applications in forecasting and modeling processes such as population growth, epidemic spread, combustion dynamics, and chemical reaction rates. The study’s innovative use of MKhat and UF methods introduces new approaches for solving nonlinear differential equations, expanding the toolkit available to researchers in applied mathematics and related fields. In conclusion, this research provides a comprehensive exploration of the nonlinear Fisher–KPP model, offering both theoretical advancements and practical applications. The findings have wide-ranging interdisciplinary implications, benefiting fields such as biology, physics, chemistry, and engineering, where reaction–diffusion systems play a central role.
Geometrothermodynamics study of specific black holes in extended Einstein–Gauss–Bonnet theory using Tsallis entropy
Authors: Jawad Abdul , Shahid Maryam , Chaudhary Shahid , Shaymatov Sanjar
Published: November 2024

We study the geometrothermodynamics of black holes within the framework of the five-dimensional Einstein–Gauss–Bonnet (EGB) theory and its various extensions, including Einstein–Maxwell–Gauss–Bonnet (EMGB), the inclusion of a cosmological constant in EMGB and Einstein–Yang–Mills–Gauss–Bonnet (EYMGB). To expand the scope of our research, we utilize Tsallis entropy, an extended form of entropy that surpasses the traditional Boltzmann–Gibbs entropy. We first obtain the relations for the Hawking temperature, specific heat and scalar curvature in terms of Tsallis entropy and then show that incorporating Tsallis entropy into the description of black holes allows us to explore their thermodynamic characteristics beyond conventional limits, providing valuable insight into the stability of the black holes. We analyze that in the scenario of Tsallis entropy, the stability regions and phase transition points of specific heat coincide with the divergence points of scalar curvatures which confirm the consistency of the results. We also provide a comparative examination of our findings for the considered models of black holes. This comparative analysis deepened our understanding of the thermodynamic behavior exhibited by these diverse black holes within the extended EGB framework.
Algebraic periodic points of transcendental entire functions
Authors: Krumm David , Marques Diego , Moreira Carlos Gustavo , Trojovský Pavel
Published: November 2024

We prove the existence of transcendental entire functions [Formula: see text] having a property studied by Mahler, namely that [Formula: see text] and [Formula: see text], and in addition having a prescribed number of [Formula: see text]-periodic algebraic orbits, for all [Formula: see text]. Under a suitable topology, such functions are shown to be dense in the set of all entire transcendental functions.
Exploring Nanotechnology: Current Uses, Cutting-Edge Innovations, and Future Opportunities, a Comprehensive Review
Authors: Habib Muhammad Bilal , Shah Naseer Ali , Amir Afreenish , Akbar Noreen Sher , Shahid Ameera , Jabeen Shaista , Batool Ghanwa
Published: November 2024

Regarding diagnosis, treatment, monitoring, and prognosis, nanomedicine will play a critical role in customized medicine in the future. Nanomedicine allows for better targeting, more accurate disease mapping, and fewer side effects by delivering drugs straight to the sick cells. The advantageous alterations that nanotechnology makes to the physiochemical, mechanical, magnetic, electrical, and optical properties of computing materials enable the development of new and innovative products. Food security, processing, coloring, nutritional absorption, flavor, nutrition, delivery, disease detection, food functioning, environmental protection, and cost-effective storage and distribution are a few of the important links between nanotechnology and food systems. Nanomaterials are used in biofuel production, wastewater treatment, biosensor pollution removal tools, photocatalysts, biomedical imaging and cancer treatments, and wearable chemical and environmental sensors, as shown by a number of academic publications. A few of the main challenges facing the application of nanotechnology are scalability, increased production rates, managing unwanted byproducts, repeatability and quality control of nanomaterials, and enhanced manufacturing rates.
Effect of Quantity of Plant Extract on Physicochemical Properties and Biomedical Applications of Phytoconstituent-Coated ZnONPs
Authors: Samanta Krishna Kanta , Mamgain Himanshu Prasad , Kumar Manoj , Ghosh Paulami , Bhunia Bibhas , Manna Suvendu , Pandey Jitendra Kumar
Published: November 2024

This study delved into how the quantity of extract (Withania somnifera root) affects the physicochemical properties and biomedical applications of ZnONPs. For this study, three samples of phytoconstituents-coated ZnONPs were prepared by altering the extract volume i.e., 10[Formula: see text]mL, 20[Formula: see text]mL and 30[Formula: see text]mL of 10% (w/v) extract solution (corresponding samples were marked as WS10, WS20 and WS30). UV–Vis spectroscopy preliminary confirms the formation of ZnONPs with surface plasmon resonance (SPR) peaks at 361[Formula: see text]nm, 359[Formula: see text]nm and 356[Formula: see text]nm for WS10, WS20 and WS30 samples, respectively. Fourier transform infrared data confirmed the presence of different functional groups. X-ray diffraction revealed the crystalline nature and high purity of samples. FESEM confirmed spherical-shaped ZnONPs with an average particle size of 72–88 nm. The agar well diffusion assay results indicated that out of three samples, WS30 has maximum antibacterial activity, with inhibition zones of [Formula: see text][Formula: see text]mm (against Escherichia coli) and [Formula: see text][Formula: see text]mm (against Streptococcus mutans). Synthesized samples also exhibited mild antioxidant activity. Additionally, we proposed a radical scavenging mechanism for phytoconstituents-coated ZnONPs. The MTT assay suggests WS30 exhibited excellent anticancer activity with IC[Formula: see text] of [Formula: see text] against human liver cancer cells (HepG2), while comparatively lower toxicity was observed against normal human melanoma cells (HaCaT) with IC[Formula: see text] of [Formula: see text]. This work is novel as it investigates the effects of phytoconstituents amount on the production of ZnONPs as reducing and coating agents that play a critical role in their overall biomedical applications.
Multifactorial Analysis of Ternary Magnetized Radiative Nanofluid with Waste Discharge Concentration and Microorganism Behavior: Multi-Hidden Layer Mechanism
Authors: Ayub Assad , Shah Syed Zahir Hussain , Sirisubtawee Sekson
Published: November 2024

This study investigates a multifactorial analysis of the ternary infinite shear rate viscosity of a magnetized radiative Cross nanofluid with consideration of the waste discharge concentration of nanoparticles and of the microorganism behavior over a paraboloid surface. A three-hidden-layer supervised neural network mechanism is used in the analysis. Furthermore, this investigation includes the unique aspects of orthogonal/inclined magnetic field, thermal radiation, Brownian motion, thermophoresis, activation energy, and external source variation, with particular attention to the impact of waste discharge concentrations on viscosity and microbial dynamics. A set of partial differential equations is constructed with assumed physical assumptions and conversion is then made to ODEs on the basis of similarity variables. The MATLAB bvp4c program is used in combination with an artificial neural network (ANN) scheme scaled conjugate gradient neural network (SCG-NN) to predict the solution. It is found that the velocity power index, Rayleigh number, and volumetric percentage increase the magnitude of the velocity profile and that the Lorentz force tends to reduce the velocity magnitude of the nanofluid due to electromagnetic drag and resistive effect. Also, the temperature of nanofluid increases with radiation parameter and heat source/sink. The local pollutant external source parameter and local external pollution source variation parameter increase the concentration profile. Low mass transport of microorganisms can be seen for the bio-convection Lewis number, Peclet number and high local pollutant external source parameter.
Mathematical modeling of influenza dynamics: A novel approach with SVEIHR and fractional calculus
Authors: Alharbi Sana Abdulkream , Abdoon Mohamed A. , Degoot Abdoelnaser M , Alsemiry Reima Daher , Allogmany Reem , Guma Fathelrhman EL , Berir Mohammed
Published: November 2024

Viral infections pose a significant risk to public health all around the world. Knowledge of their transmission and epidemiology therefore forms the basis of prevention and control measures. Models are essential components to develop and quantify the characteristics of new pathogens, to evaluate the risks of emerging diseases, and to design strategies to control epidemics. This study investigates the validity of the fractional-order SVEIHR influenza model in predicting seasonal influenza outbreaks in Saudi Arabia using Atangana–Baleanu–Caputo (ABC) operators. The first part of our analysis was examining the positivity, boundedness, and global stability of the disease-free equilibrium of the model. The effective reproduction number of vaccination was calculated, indicating a decrease in the spread of the virus due to vaccination. Additionally, we determined that the reproduction number in the absence of vaccination suggests a higher rate of virus transmission. Therefore, we concluded that a reduction in [Formula: see text] occurs when vaccination rates increase. Our findings underscore the critical impact of vaccination on controlling the spread of influenza. Then raising coverage of protective vaccines becomes a logical goal for decreasing the basic reproduction number and thus mitigating the epidemic of influenza. We suggest that we extend this model by incorporating additional factors such as antiviral treatments and social behavior changes, which may further influence the dynamics of influenza transmission.
Dynamic of the air pollution pathogenic model and optimization control
Authors: Hippolyte Bessane Son-Ongaya Merveil , Qi Longxing
Published: November 2024

Air pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere. In this paper, the ability of air pollutants to affect the occurrence of infectious respiratory diseases in a population is studied by establishing an SISP respiratory disease model. It is found that the inhalation rate of pollutants, based on the daily emission level of pollutants, has a huge impact on the development of respiratory diseases. It is also found that the existence of multiple endemic equilibria, local stability and global stability of the equilibria, all depend on a certain quantity of pollutant produced in the environment. Moreover, the sensitivity analysis shows that the daily emission of pollutants and the inhalation rate of pollutants have significant positive correlation on the model while the clearance rate of pollutants have strong negative correlations with the model. Based on these results, an control strategy is produced in order to reduce the impact of air pollution on the occurrence of respiratory diseases in our environment and it goes as follows: the first step is to reduce the conversion rate of susceptible people to infective by reducing the inhalation rate of pollutant through raising public awareness on how to protect oneself from air pollutants in order to prevent respiratory infections; the second step is to reduce the daily emission of pollutants from all sources of emissions and try to keep it as low as possible; and the last step is to improve the clearance rate of pollutants in the environment.
Platform-enterprise collaborative scheduling in cloud manufacturing with deep reinforcement learning
Authors: Niu Wenbo , Liu Yongkui , Ping Yaoyao , Zhang Lin
Published: November 2024

The core value of cloud manufacturing is to enable optimal allocation of manufacturing resources across enterprises in a wide-area environment. Scheduling is the key technology to realize the core value of cloud manufacturing. At present, large-scale enterprises are integrated in the cloud platform, which poses great difficulties for cloud manufacturing scheduling. How to perform collaborative scheduling of platform and enterprises is therefore a key question. Moreover, in the cloud manufacturing environment, distributed enterprises with autonomy on the edge side access their own resources to the cloud manufacturing service platform and carry out collaborative scheduling with the cloud manufacturing platform. Platform-enterprise collaborative scheduling provides support for large-scale resources and services within the cloud. Given this, the paper provides a platform-enterprise collaborative model that is adopted to study the scheduling problem of large-scale resources and services in cloud manufacturing. The model considers the platform-based service scheduling and enterprise-based resource scheduling. The collaborative scheduling mechanisms of the cloud service and enterprise resource are investigated. The former completes the scheduling of cloud services while collaborating on tasks with the latter, and the latter completes the scheduling of enterprise resources while delivering scheduling information to the former. Moreover, deep reinforcement learning (DRL) has been widely applied to cloud manufacturing scheduling. A platform-enterprise collaborative scheduling algorithm based on dueling deep [Formula: see text]-Network with prioritized replay (CE-PDDQN) is proposed. To evaluate the effectiveness of our proposed algorithm, this paper selects DQN and dueling DQN for experiments. The experimental results show that the CE-PDDQN algorithm can obtain a better scheduling scheme after training and learning. And the CE-PDDQN algorithm is adaptive and scalable.
China’s Afghan Odyssey: From War to Prosperity in Taliban-Controlled Afghanistan
Authors: Kumar Amit , Ramachandran Veena
Published: November 2024

Following the Taliban’s ascension to power, the Afghan government expresses a keen interest in affiliating with the Belt and Road Initiative (BRI). The prospective inclusion of Afghanistan in the BRI holds the promise of a profound impact on the Afghan economy, security dynamics, and overall stability. Concurrently, such an association can advance Beijing’s strategic interests within its domestic sphere and the broader regional context. Within the intricate geopolitical milieu of Afghanistan, characterized by ongoing legitimacy concerns, China’s escalating engagement assumes paramount importance, bearing substantial consequences for both Afghanistan and Beijing. This paper examines China’s investment and aid strategies in Afghanistan pre- and post-Taliban ascension to power in 2021. Additionally, it delves into the identification and analysis of the pivotal land transport network in Afghanistan, particularly in the context of its potential integration with the China-Pakistan Economic Corridor (CPEC) and participation in the BRI, assessing the consequential advantages thereof. Furthermore, it scrutinizes the dynamic diplomatic and security relations between China and Afghanistan, with a focus on safeguarding China’s strategic assets in the region. The authors utilize qualitative research, incorporating primary and secondary data from government documents, official speeches, interviews with experts, and various research works. The study concludes that China has increased investments in Taliban-led Afghanistan, confident that Afghanistan’s participation in BRI could reshape its economy, strengthen Beijing’s strategic position in minerals and energy, and considerably improve Afghanistan’s security environment. The research provides valuable insights for policy debates and international efforts to promote stability and long-term growth in a war-torn nation.
Camptodactyly
Authors: TAKAGI Takehiko
Published: November 2024

Camptodactyly is a congenital difference with flexion contracture of the proximal interphalangeal (PIP) joint. Camptodactyly limited to one finger is believed to be due to an anomaly of the lumbrical muscle that inserts into the flexor digitorum superficialis (FDS) tendon instead of the extensor expansion, whereas multiple finger camptodactyly is believed to be a result of shortage of soft tissues on the flexor surface of the fingers. It is important to differentiate camptodactyly from other causes of extension lag at the PIP joint. It is difficult to obtain good results after the release of flexion contractures in camptodactyly. The main goal of surgery is to prevent progressive contracture with appropriate postoperative therapy using night splinting and stretching. We should strive for a considered approach based on a thorough understanding of the pathophysiology of camptodactyly.

Level of Evidence: Level V (Therapeutic)
PERCEPTIONS AND VIABILITY OF ISLAMIC WEALTH MANAGEMENT AMONG STAFF AT THE ISLAMIC UNIVERSITY IN UGANDA, KAMPALA CAMPUS, KIBULI KAMPALA (IUIU-KC)
Authors: BUYONDO HAKIMU , JAMIL SERWANGA , MUSA MATOVU , MADINAH NABUKEERA
Published: November 2024

This study investigates the role of attitudes, behavioral beliefs, and normative beliefs in shaping the intention to adopt Islamic Wealth Management (IWM) among staff at the Islamic University in Uganda, Kampala Campus (IUIU-KC). Given the varied perceptions surrounding IWM understanding these influencing factors is crucial for its viability among stakeholders. By employing a qualitative exploratory approach, data were collected through interviews with purposively selected academicians from the Faculty of Management Studies. Thematic analysis revealed that positive attitudes, supportive behavioral beliefs, and favorable normative beliefs significantly encourage IWM adoption. The findings imply that fostering these positive perceptions can enhance IWM integration within educational and financial institutions. By understanding these key drivers, policymakers, educators, and financial institutions can develop targeted strategies to promote IWM adoption. This approach not only aids stakeholder engagement and curriculum refinement in Higher Education Institutions (HEIs) but also supports informed policy-making, advancing the broader acceptance and integration of IWM in Uganda and similar contexts. Further still IWM may be an option toward achieving the 17 UN Sustainable Development Goals if implemented.
The Good, the Bad, and the Ugly Sides of a Cashless Society
Authors: Akgiray Vedat , Aydogdu Ertunc , Sener Emir
Published: November 2024

The shift toward a cashless society is strongly endorsed by governments and businesses due to its potential to reduce cash handling costs, enhance transaction transparency, and improve operational efficiency. This transition is facilitated by various digital payment methods, including credit and debit cards, e-wallets, and mobile payment platforms. However, this shift also imposes significant financial burdens, with payment fees estimated at around $2 trillion annually. We investigate the origins of these steep fees as society moves away from cash transactions. We further explore the consequences of the lack of a public payment infrastructure, which has enabled profit-driven initiatives to develop proprietary systems and set up walled gardens within the payments industry. We then analyze the prospective transformative role of Central Bank Digital Currencies (CBDCs) — a public infrastructure — in establishing a more affordable and equitable payment system. Through the analysis of global CBDC trials, we outline six key parameters necessary for their successful deployment: scalable infrastructure, privacy protection, capability for offline transactions, digital identity verification, integration with smart contracts, and support for cross-border transactions.
RT-GNN: Accelerating Sparse Graph Neural Networks by Tensor-CUDA Kernel Fusion
Authors: Yan Jianrong , Jiang Wenbin , He Dongao , Wen Suyang , Li Yang , Jin Hai , Shao Zhiyuan
Published: November 2024

Graph Neural Networks (GNNs) have achieved remarkable successes in various graph-based learning tasks, thanks to their ability to leverage advanced GPUs. However, GNNs currently face challenges arising from the concurrent use of advanced Tensor Cores (TCs) and CUDA Cores (CDs) in GPUs. These challenges are further exacerbated due to repeated, inefficient, and redundant aggregations in GNN that result from the high sparsity and irregular non-zero distribution of real-world graphs. We propose RT-GNN, a GNN framework based on the fusion of advanced TC and CD units, to eliminate the aforementioned redundancies by exploiting the properties of an adjacency matrix. First, a novel GNN representation technique, hierarchical embedding graph (HEG) is proposed to manage the intermediate aggregation results hierarchically, which can further avoid redundancy in intermediate aggregations elegantly. Next, to address the inherent sparsity of graphs, RT-GNN places the blocks (a.k.a tiles) in HEG onto TCs and CDs according to their sparsity by a new block-based row-wise multiplication approach, which assembles TCs and CDs to work concurrently. Experimental results demonstrate that HEG outperforms HAG by an average speedup of 19.3 × for redundancy elimination performance, especially up to 72 × speedup on the dataset of ARXIV. Moreover, for overall performance, RT-GNN outperforms state-of-the-art GNN frameworks (including DGL, HAG, GNNAdvisor, and TC-GNN) by an average factor of 3.1 × while maintaining or even improving the task accuracy.
TreeHouse: An MLIR-based Compilation Flow for Real-Time Tree-based Inference
Authors: Su ChiaHui , Ku Chia-Hua , Lee Jenq Kuen , Chen Kuan-Hsun
Published: November 2024

Tree-based ensembles stand as the prominent resource-efficient approaches for real-time inference. To optimize their performance, researchers have developed several solutions to accommodate their unique program structure, i.e., consecutive branches and eliminate the floating-point arithmetic operations, which are usually costly at the embedded computing systems. Most of them are realized at the level of source code and a standard compilation is applied subsequently. Therefore, an end-to-end compilation flow may consolidate these methods and provide a holistic optimization. In this work, we introduce TreeHouse, a compilation flow based on MLIR designed for real-time inference of tree ensembles. First, we optimize the layout of basic blocks to reduce the expected branches during inference. Moreover, we provide a solution to facilitate LLVM register allocation for further efficiency. In addressing the suboptimal performance of floating-point operations in edge systems, we incorporate methods to convert data into integer format. We implemented and evaluated TreeHouse using two tree-based ensembles: boosting trees and random forests. Overall, boosting trees exhibited performance gains ranging from 1.38 × to 8.24 × on x86, 1.70 × to 6.61 × on ARMv8, and 1.75 × to 4.52 × on RISC-V compared to state-of-the-art decision tree research. Random forests achieved performance gains of up to 1.6 × on x86, 2.03 × on ARMv8, and 2.9 × on RISC-V.
Molecular Innovations Shaping Beak Morphology in Birds
Authors: Cheng Yalin , Miller Matthew J. , Lei Fumin
Published: November 2024

The beak, a pivotal evolutionary trait characterized by high morphological diversity and plasticity, has enabled birds to survive mass extinction events and subsequently radiate into diverse ecological niches worldwide. This remarkable ecological adaptability underscores the importance of uncovering the molecular mechanisms shaping avian beak morphology, particularly benefiting from the rapidly advancing archives of genomics and epigenomics. We review the latest advancements in understanding how genetic and epigenetic innovations control or regulate beak development and drive beak morphological adaptation and diversification over the past two decades. We conclude with several recommendations for future endeavors, expanding to more bird lineages, with a focus on beak shape and the lower beak, and conducting functional experiments. By directing research efforts toward these aspects and integrating advanced omics techniques, the complex molecular mechanisms involved in avian beak evolution and morphogenesis will be deeply interpreted.
A One Health Approach to Reducing Livestock Disease Prevalence in Developing Countries: Advances, Challenges, and Prospects
Authors: Lane Jennifer K. , Kelly Terra , Bird Brian , Chenais Erika , Roug Annette , Vidal Gema , Gallardo Rodrigo , Zhou Huaijun , VanHoy Grace , Smith Woutrina
Published: November 2024

Challenges in livestock production in developing countries are often linked to a high disease prevalence and may be related to poor husbandry, feeding, and nutrition practices, as well as to inadequate access to preventive veterinary care. Structural barriers including chronic poverty, gender roles, inadequate supply chains, and limitations in surveillance infrastructure further complicate progress. Despite many challenges, the livestock sector substantially contributes to agricultural GDP, and reducing livestock disease prevalence is a goal for many countries. One Health initiatives that work across disciplines and sectors to reduce livestock diseases are underway around the world and use integrated approaches that consider the connections between humans, animals, and their shared environments. The growing recognition of the role livestock play in sustainability and livelihoods, as well as their involvement in zoonotic disease transmission and global health security, has highlighted the need for disease reduction strategies as described in this review.
The Physiology of Enteric Glia
Authors: Gonzales Jacques , Gulbransen Brian D.
Published: November 2024

Enteric glia are the partners of neurons in the enteric nervous system throughout the gastrointestinal tract. Roles fulfilled by enteric glia are diverse and contribute to maintaining intestinal homeostasis through interactions with neurons, immune cells, and the intestinal epithelium. Glial influences optimize physiological gut processes such as intestinal motility and epithelial barrier integrity through actions that regulate the microenvironment of the enteric nervous system, the activity of enteric neurons, intestinal epithelial functions, and immune response. Changes to glial phenotype in disease switch glial functions and contribute to intestinal inflammation, dysmotility, pain, neuroplasticity, and tumorigenesis. This review summarizes current concepts regarding the physiological roles of enteric glial cells and their potential contributions to gut disease. The discussion is focused on recent evidence that suggests important glial contributions to gastrointestinal health and pathophysiology.
A Case of Pediatric Myopia Complicated by Vitreous Cyst: A Unique Ophthalmic Challenge
Authors: Al Qattan Abdulaziz J. , Alasqah Abdulrahman , Babgi Rafaa
Published: November 2024

Vitreous cysts represent uncommon ophthalmological conditions. Most patients are asymptomatic, but a minority may experience symptoms such as floaters or blurred vision. Here, we report the case of a 2‐year‐old girl who was incidentally found to have a vitreous cyst in her left eye during a routine outpatient clinic visit. The cyst was observed to move with eye movements, was pigmented, lobulated, and measured 3 mm in diameter. Our patient exhibited several systemic manifestations. We recommended regular follow‐up through clinical examinations and monitoring of the cyst using B‐scan ultrasound.
The Association of Salivary Cortisol Levels With Depression, Anxiety, and Quality of Life in Male Relapsing‐Remitting Multiple Sclerosis Patients: A Case‐Control Study
Authors: Mokary Yousef , Vaheb Saeed , Yazdan Panah Mohammad , Afshari-Safavi Alireza , Moases Ghaffary Elham , Barzegar Mahdi , Shirbacheh Ali , Shaygannejad Vahid , Mirmosayyeb Omid
Published: November 2024

Background: Dysfunctions of the hypothalamic‐pituitary‐adrenal (HPA) axis can trigger multiple sclerosis (MS) symptoms. Mood disorders comorbid with MS are implicated in the HPA axis activation in most people with MS (pwMS). This study purposed to examine salivary cortisol (SC) levels and their association with mood disorders in pwMS.

Methods: Forty‐three men as pwMS and sixteen men as healthy controls (HC) were included in this study. pwMS and HC completed the Beck Depression Inventory (BDI), 36‐Item Short Form Survey (SF‐36), Fatigue Severity Scale (FSS), Depression Anxiety and Stress Scale, and Hospital Anxiety and Depression Scale questionnaires. SC levels were also measured in pwMS and HC. A linear regression model was used to analyze the relationship between the Expanded Disability Status Scale (EDSS) and mood disorders and SC levels. Pearson’s or Spearman’s tests examined the correlation between SC levels and mood disorders.

Results: The SC level, anxiety, depression, fatigue, and stress were significantly higher in pwMS than in HC (p < 0.05). pwMS also showed significant correlations between SC levels and role physical (r = 0.3, p < 0.05) and emotional (r = 0.34, p < 0.05) in SF‐36. Among SF‐36 domains, only general health (β = −0.72, p < 0.05) and physical functioning (β = −1.61, p < 0.05) were negatively associated with disease duration. No correlation was found between SC levels and anxiety, depression, and fatigue (p > 0.05).

Conclusions: The pwMS showed mild to moderate depression, anxiety, and fatigue. Only general health and physical functioning as aspects of quality of life were related to disease duration, while SC levels, mood disorders, and other quality‐of‐life domains were not associated with clinical characteristics. Also, this study failed to find a significant role for SC as a valuable marker in approaching mood disorders in pwMS.
Data from The Neurodevelopmental Protein POGZ Suppresses Metastasis in Triple-Negative Breast Cancer by Attenuating TGFβ Signaling
Authors: Heath John , Mirabelli Caitlynn , Annis Matthew G. , Sabourin Valerie , Hebert Steven , Findlay Steven , Kim HaEun , Witcher Michael , Kleinman Claudia L. , Siegel Peter M. , Orthwein Alexandre , Ursini-Siegel Josie
Published: November 2024

Abstract
The pogo transposable element–derived zinc finger protein, POGZ, is notably associated with neurodevelopmental disorders through its role in gene transcription. Many proteins involved in neurological development are often dysregulated in cancer, suggesting a potential role for POGZ in tumor biology. Here, we provided experimental evidence that POGZ influences the growth and metastatic spread of triple-negative breast cancers (TNBC). In well-characterized models of TNBC, POGZ exerted a dual role, both as a tumor promoter and metastasis suppressor. Mechanistically, loss of POGZ potentiated TGFβ pathway activation to exert cytostatic effects while simultaneously increasing the mesenchymal and migratory properties of breast tumors. Although POGZ levels are elevated in human breast cancers, the most aggressive forms of TNBC tumors, including those with increased mesenchymal and metastatic properties, exhibit dampened POGZ levels, and low POGZ expression was associated with inferior clinical outcomes in these tumor types. Taken together, these data suggest that POGZ is a critical suppressor of the early stages of the metastatic cascade.

Significance: The POGZ neurodevelopmental protein plays dual functions in triple-negative breast cancers as a tumor promoter and metastasis suppressor, inhibiting TGFβ-regulated EMT to limit breast cancer metastatic progression.
Data from Low–Molecular Weight Cyclin E Confers a Vulnerability to PKMYT1 Inhibition in Triple-Negative Breast Cancer
Authors: Li Mi , Lulla Amriti R. , Wang Yan , Tsavaschidis Spyros , Wang Fuchenchu , Karakas Cansu , Nguyen Tuyen D.T. , Bui Tuyen N. , Pina Marc A. , Chen Mei-Kuang , Mastoraki Sofia , Multani Asha S. , Fowlkes Natalie W. , Sahin Aysegul , Marshall C. Gary , Hunt Kelly K. , Keyomarsi Khandan
Published: November 2024

Abstract
Cyclin E is a regulatory subunit of CDK2 that mediates S phase entry and progression. The cleavage of full-length cyclin E (FL-cycE) to low–molecular weight isoforms (LMW-E) dramatically alters substrate specificity, promoting G1–S cell cycle transition and accelerating mitotic exit. Approximately 70% of triple-negative breast cancers (TNBC) express LMW-E, which correlates with poor prognosis. PKMYT1 also plays an important role in mitosis by inhibiting CDK1 to block premature mitotic entry, suggesting it could be a therapeutic target in TNBC expressing LMW-E. In this study, analysis of tumor samples of patients with TNBC revealed that coexpression of LMW-E and PKMYT1-catalyzed CDK1 phosphorylation predicted poor response to neoadjuvant chemotherapy. Compared with FL-cycE, LMW-E specifically upregulates PKMYT1 expression and protein stability, thereby increasing CDK1 phosphorylation. Inhibiting PKMYT1 with the selective inhibitor RP-6306 (lunresertib) elicited LMW-E–dependent antitumor effects, accelerating premature mitotic entry, inhibiting replication fork restart, and enhancing DNA damage, chromosomal breakage, apoptosis, and replication stress. Importantly, TNBC cell line xenografts expressing LMW-E showed greater sensitivity to RP-6306 than tumors with empty vector or FL-cycE. Furthermore, RP-6306 exerted tumor suppressive effects in LMW-E transgenic murine mammary tumors and patient-derived xenografts of LMW-E–high TNBC but not in the LMW-E null models examined in parallel. Lastly, transcriptomic and immune profiling demonstrated that RP-6306 treatment induced interferon responses and T-cell infiltration in the LMW-E–high tumor microenvironment, enhancing the antitumor immune response. These findings highlight the LMW-E/PKMYT1/CDK1 regulatory axis as a promising therapeutic target in TNBC, providing the rationale for further clinical development of PKMYT1 inhibitors in this aggressive breast cancer subtype.

Significance: PKMYT1 upregulation and CDK1 phosphorylation in triple-negative breast cancer expressing low–molecular weight cyclin E leads to suboptimal responses to chemotherapy but sensitizes tumors to PKMYT1 inhibitors, proposing a personalized treatment strategy.
Supplementary Table S1 from FDA Approval Summary: Belzutifan for Patients with Advanced Renal Cell Carcinoma
Authors: Fallah Jaleh , Heiss Brian L. , Joeng Hee-Koung , Weinstock Chana , Gao Xin , Pierce William F. , Chukwurah Benjamin , Bhatnagar Vishal , Fiero Mallorie H. , Amiri-Kordestani Laleh , Pazdur Richard , Kluetz Paul G. , Suzman Daniel L.
Published: November 2024

Demographics and baseline disease characteristics
Supplementary Fig. S3 from Hepatocellular Carcinoma Immune Microenvironment Analysis: A Comprehensive Assessment with Computational and Classical Pathology
Authors: Ercan Caner , Renne Salvatore Lorenzo , Di Tommaso Luca , Ng Charlotte K.Y. , Piscuoglio Salvatore , Terracciano Luigi M.
Published: November 2024

Supplementary Fig. S3. Interobserver variability, and intratumoural homogeneity-heterogeneity.
Supplementary Table S3 from MEK Inhibitors Lead to PDGFR Pathway Upregulation and Sensitize Tumors to RAF Dimer Inhibitors in NF1-Deficient Malignant Peripheral Nerve Sheath Tumor
Authors: Miranda-Román Miguel A. , Lee Cindy J. , Fishinevich Eve , Ran Leili , Patel Amish J. , Yan Juan , Khudoynazarova Makhzuna N. , Warda Sarah , Pachai Mohini R. , Chen Yu , Chi Ping
Published: November 2024

Supplementary Table S3. Antibodies used for WB and IP.
Supplementary Figure S7 from MEK Inhibitors Lead to PDGFR Pathway Upregulation and Sensitize Tumors to RAF Dimer Inhibitors in NF1-Deficient Malignant Peripheral Nerve Sheath Tumor
Authors: Miranda-Román Miguel A. , Lee Cindy J. , Fishinevich Eve , Ran Leili , Patel Amish J. , Yan Juan , Khudoynazarova Makhzuna N. , Warda Sarah , Pachai Mohini R. , Chen Yu , Chi Ping
Published: November 2024

Supplementary Figure S7. Change in total tumor volume of MPNST tumor models after trametinib and ripretinib treatment.
Supplementary Figure S4 from MEK Inhibitors Lead to PDGFR Pathway Upregulation and Sensitize Tumors to RAF Dimer Inhibitors in NF1-Deficient Malignant Peripheral Nerve Sheath Tumor
Authors: Miranda-Román Miguel A. , Lee Cindy J. , Fishinevich Eve , Ran Leili , Patel Amish J. , Yan Juan , Khudoynazarova Makhzuna N. , Warda Sarah , Pachai Mohini R. , Chen Yu , Chi Ping
Published: November 2024

Supplementary Figure S4. Effect of PDGFRβ KO on MPNST cells sensitivity to trametinib and ripretinib treatment.