Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II–IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan–Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine–Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II–IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829–0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.
High-Performance ECC Scalar Multiplication Architecture Based on Comb Method and Low-Latency Window Recoding Algorithm

Elliptic curve scalar multiplication (ECSM) is the essential operation in elliptic curve cryptography (ECC) for achieving high performance and security. We introduce a novel high-performance ECSM architecture over binary fields to meet the growing demand for performance and security. A low-latency window (LLW) recoding algorithm for hardware implementation is proposed to enhance the resistance toward side-channel attacks (SCAs). Based on the LLW algorithm, we propose an enhanced comb method for ECSM with a unified point addition (PA) and point doubling (PD) pattern. The theoretical analysis demonstrates that the enhanced comb method with <inline-formula> <tex-math notation="LaTeX">$w=4$ </tex-math></inline-formula> strikes the balance of computation burden for both extreme cases. To achieve short clock cycle latency and high frequency, the data dependency of ECSM is thoroughly analyzed, and we explore a timing schedule with one two-stage pipelined Karatsuba multiplier accumulator (MAC). The datapath of the proposed architecture is well-designed, ensuring that the critical path (CP) only contains minimal logic primitives apart from the MAC. Besides, the ideal placement of pipeline stages for MAC is illustrated. The proposed architecture has been implemented on Xilinx Virtex-7 series field-programmable gate arrays (FPGAs) and performs ECSM in 2.51, 4.93, and <inline-formula> <tex-math notation="LaTeX">$10.85 ~\mu \text { s}$ </tex-math></inline-formula> with 3422, 7983, and 20158 slices over <inline-formula> <tex-math notation="LaTeX">$\text {GF}(2^{163})$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\text {GF}(2^{283})$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\text {GF}(2^{571})$ </tex-math></inline-formula>, respectively. Implementation results reveal that our design shows 53.60%, 39.36%, and 32.64% performance improvement over the existing state-of-the-art works, respectively.
Deconvolution after migration

Deconvolution is a process that is normally applied before migration. However, there are data conditions where deconvolution should be performed after migration. These situations are reviewed with a final conclusion that deconvolution after migration should be applied and be tested, especially in areas with little structure, such as a sedimentary basin.
Regulatory Requirements and Banks' Level of Risk

This article evaluates the effect of prudential standards and good governance practices on the level of risk exposure of banks. Using banks in the West African Monetary Union (WAMU) as a framework for study, we conducted a study on a base of 14 banks from 2006 to 2017. Empirical analysis using a system of simultaneous equations reveals that changes in prudential regulation, as well as structural and functional changes at board level, result in an increase in banking risk.
Real Options Analysis for Investment Decisions in Geothermal Energy

Geothermal renewable energy can contribute to the reduction of greenhouse gas emissions. However, difficulties have been encountered during its development. It is characterized by high-risk investment and irreversibility, whereas traditional investment analysis techniques have limited applications. As a tool for evaluating energy investments, the real options theory allows flexibility to be incorporated into project design in the face of an uncertain environment and has demonstrated the ability to add economic value to investments. Based on simulations of a geothermal project implemented in Brazil in a real-options framework specifically adapted for the analysis of geothermal investments, this study demonstrates that the tool is efficient in adding economic value to the analysis. In addition, it provides a comprehensive view of the project, identifying the managerial flexibilities and uncertainties that influence profits the most. This model is expected to encourage researchers and investors to evaluate geothermal energy projects in Brazil.
Contrasting impacts of grazing on soil properties and plant communities between semiarid and temperate rangeland ecosystems.

We discuss how grazing by large herbivores as a land use option does not necessarily involve a trade-off in terms of soil carbon (C) storage, by presenting results from field grazing gradient experiments from rangeland ecosystems under different climatic conditions in semiarid grasslands from Central Mexico and temperate ecosystems from Northern England. In general, moderate grazing pressure did not reduce soil C in both ecosystems after comparisons with long-term grazing exclusions, and moderate grazing even showed higher soil C in the semiarid area. In the semiarid area, our results are likely explained by grazing tolerance of plant species in moderate grazing pressure, and by effects of herbivores on plant community structure and proportion of bare soil in heavy grazing pressure. In the temperate area, C losses might be more linked to temperature-limitation on heterotrophic soil C respiration. Our results indicate that moderate grazing is compatible with soil C storage, although we also provide warnings against this generalisation under scenarios of climate warming.
CMOS Variable-Gain Low-Noise Amplifier Adopting Transformer-Based Noise Cancelling Technique for 5G NR FR2 Applications

This study presents a complementary metal–oxide–semiconductor (CMOS) variable-gain low-noise amplifier (VGLNA) employing a transformer-based noise cancelling technique, applicable for fifth-generation new radio frequency range 2 communication. In the proposed design, gain controllability is realized by combining the in-phase main and current-steering path signals through a transformer load whose coupling coefficient is less than unity. Additionally, the noise contribution from cascode devices can be diminished through a transformer-based noise cancelling technique in low-gain modes. Consequently, an enhanced noise performance is achieved as the gain of the VGLNA is lowered. The proposed design is fabricated in a 65-nm CMOS process. At 28 GHz, the implemented VGLNA attains gains and noise figures of 12.1 to 2.7 dB and 3.55 to 4.3 dB, respectively. The design draws a bias current of 10.6 mA with a 1 V nominal supply and occupies a die size of 0.13 mm2, excluding bonding pads.
Design and electro-optical co-optimization of high-speed travelling wave silicon-organic hybrid modulators

Silicon-organic hybrid (SOH) modulators are promising as they have large bandwidth, high modulation efficiency and low optical loss. In this paper, we present the optical and electrical design and simulation of a high-speed travelling wave SOH modulator with multiple interplay parameters. We also implement the electro-optical co-optimization to delve into the influence of the key design parameters and propose the design guideline. The results demonstrate a modulator with 3 dB bandwidth of 72 GHz, modulation efficiency of 1.26 Vmm and optical loss less than 1 dB.
Customer Re-Use Model of Online Vegetables and Fruit Grocery Platform in Indonesia

: The online customer satisfaction including re-use behaviour concept has been examined in the literature since the development of mobile and digital activity accelerated for the past two years. In order to determine sustainability of online fruit and vegetables grocery platform utilization in practice, not only understanding adoptions but also it is becoming crucial to have detailed understanding of its drivers continuation of re-using behaviour. Unified Theory of Acceptance and Use of Technology (UTAUT) widely used to observe adoptions of technology more specifically online grocery. In this research we will try to use UTAUT used to identify key driving factors of re-use behaviour. We run primary data collection by survey with probabilistic sampling techniques for existing users of top 2 online vegetables and fruit grocery and test the model using hierarchical multiple regression analysis. Sampling is not only conducted for the one that continues to use the platform but also switchers or stop using the services to gain complete understanding and point of view. Several key observations such as expectation, trust, risk, time, enjoyment and innovativeness will become important variables to extend the baseline. Detailed understanding of key driver using behaviour can be utilized as important business metrics to improve customer stickiness and provide personalized offering.
Novel Definitions of α-Fractional Integral and Derivative of the Functions

An α-fractional integral and derivative of real function have been introduced in new definitions and then, they compared with the existing definitions. According to the properties of these definitions, the formulas demonstrate that they are most significant and suitable in fractional integrals and derivatives. The definitions of α-fractional derivative and integral coincide with the existing definitions for the polynomials for 0 ≤ α < 1. Furthermore, if α = 1, the proposed definitions and the usual definition of integer derivative and integral are identical. Some of the properties of the new definitions are discussed and proved, as well, we have introduced some applications in the α- fractional derivatives and integrals. Moreover, α-power series and α–rule of integration by parts have been proposed and implemented in this study.
30. CHARACTERIZATION AND CORRELATION OF MEGASCOPIC TEPHRAS IN SITE 792 CORES FROM THE IZU-OGASAWARA FOREARC BASIN (JAPAN) BY TRACE ELEMENTS

Glass shards from 26 megascopic tephras (1-29 cm thick) were separated and analyzed for 26 elements and 87Sr/86Sr and 143Nd/144Nd isotope ratios. The isotope data allowed us to assess the degree of alteration of the samples. The separates contain less than 10% of palagonitized glass or secondary alteration minerals. Correlation diagrams and multivariate analyses have been used to characterize the glass shards in a tectonic setting and group the tephras according to their magmatic affinity. Four tholeiitic groups, two calc-alkaline groups, and one group with glass of shoshonitic affinity can be distinguished. The tholeiitic groups form a time series; however, judging from the rare-earth-element patterns and the temporal variations of the Ce/Yb ratios, the evolution is not unidirectional from strong to weak tholeiitic affinity. The tephras have been correlated with volcanism along the Izu-Ogasawara and Ryukyu arcs by means of trace element distribution, 87Sr/86Sr ratios, and their magmatic affinity. The tholeiitic layers were derived from volcanoes on the Izu-Ogasawara Arc, most probably from the volcanic island Hachijojima. Only one group of the calc-alkaline layers can have derived from the Izu-Ogasawara Arc. The other calc-alkaline layers and the two layers with shoshonitic affinity probably derive from volcanism along the Ryukyu Arc.
PROPERTIES OF CONSTRUCTED SOILS ALONG FOUR-LANE HIGHWAYS IN WEST VIRGINIA 1

Four-lane highways have been constructed in West Virginia since the 1960's. During the early construction periods of four-lane highways, geologic information was not assessed, nor was the effect of the disturbance of these geologic materials on soil quality established prior to construction activities. Due to the mountainous topography of the Central Appalachian region, the construction of four-lane highways requires large, expansive cut and fill areas that may contain acid or alkaline producing materials. The disturbance and mixing of these materials with the original soil produces "new" soil that differs considerably from the surrounding native soil. In order to evaluate highway substrate conditions and the development processes of these new soils, study areas along sections of highways have been selected for comparison based on differences in age and geologic parent materials. The long-term goal of this research is to improve selective soil handling of earth excavated during highway construction similar to that of mine overburden materials. Cut, fill and on-grade areas within sections of Interstates 68, 79, and 81 and Corridor H (U.S. Route 33 and State Route 55) have been randomly selected as sampling sites. Soil pits located at 10- m increments along transects perpendicular to these four-lane highways have been sampled at 0-10 cm and 10-20 cm depths and described according to USDA methods. Surface samples also were taken near the edge of the highway. Field pH values of all sites ranged from 5 to 8. Preliminary analysis of the data suggests that thin A horizons develop within 1 to 2 years in rapidly weathering surface materials. In these young soils, little development is observed beneath a weakly developed A horizon, commonly creating an A-C1-C2 or A-AC-C horizon sequence. Soils of intermediate ages (9-12 yrs) were similar to young soils, although at some sites a more developed soil profile was occasionally observed (A-Bw-C1-C2). The most developed soil profiles were observed on sites where soils had been constructed 25 to 43 years ago. At these locations, multiple B horizon sequences were common creating A-Bw1-Bw2-C or A-Bw- BC-C horizons.
Head teacher perspectives on school lunch: At variance with national policy

Previous research indicates that head teachers in Sweden frequently fail to see school lunch as part of the educational activities of a school. This study contributes to an understanding of how head teachers in Sweden perceive and experience current national policy intentions related to school lunch. Qualitative inquiry. Ten municipal state schools in Sweden. Individual semi-structured interviews were conducted with 10 head teachers responsible for the compulsory education of 6- to 15-year-olds in state schools. Data from digitally recorded and transcribed interviews were thematically analysed. Head teachers primarily saw school lunch as a means to ensure pupils were fed and energised. Their focus tended to be on what followed after school lunch in the form of academic performance and mood, rather than the stated intentions of national school meal policy. Head teachers stressed the value of a free and nutritious school lunch for social equality, and the importance of good collaboration with food service managers and school meal personnel, despite the difficulty of achieving this. This paper highlights a gap between head teachers’ perspectives with respect to school lunch and official intentions stated in Swedish national school meals policy. The meanings head teachers saw as connected to school lunch were those of social equality and ensuring pupils were fed, rather than the wider potential envisioned by the authorities. This paper identifies factors that affect the possibility of realising national policy intentions for school lunch in Sweden.
Self-Supervised Deep Learning for Image Reconstruction: A Langevin Monte Carlo Approach

Deep learning has proved to be a powerful tool for solving inverse problems in imaging, and most of the related work is based on supervised learning. In many applications, collecting truth images is a challenging and costly task, and the prerequisite of having a training dataset of truth images limits its applicability. This paper proposes a self-supervised deep learning method for solving inverse imaging problems that does not require any training samples. The proposed approach is built on a re-parametrization of latent images using a convolutional neural network (CNN), and the reconstruction is motivated by approximating the minimum mean square error (MMSE) estimate of the latent image using a Langevin-dynamics-based Monte Carlo (MC) method. To efficiently sample the network weights in the context of image reconstruction, we propose a Langevin MC scheme called Adam-LD, inspired by the well-known optimizer in deep learning, Adam. The proposed method is applied to solve linear and nonlinear inverse problems, specifically, sparse view CT image reconstruction and phase retrieval. Our experiments demonstrate that the proposed method outperforms existing unsupervised or self-supervised solutions in terms of reconstruction quality.
Corporate social responsibility, new challenges with new development, and new structural work of management framework business development cycles

The study focuses on the impact Of Corporate Social Responsibility on Sustainable Enterprise Development. In addition, it also focuses on Corporate Social Responsibility and institutional theory for new Perspectives on private governance. Lastly, it examines reimagining Corporate Social Responsibility in the Era of COVID-19 by Promoting Corporate Social Competence.As this paper is based on reviews so that secondary data collection method will be used in which various past articles, books, journals, and reviews will be used to collect all the reliable data based on the topic. This study is qualitative in nature.The integration of CSR into the core business strategy is more efficacious than its treatment as a distinct initiative. This strategy aims to synchronize corporate social responsibility endeavors with the objectives of the organization, thereby guaranteeing enduring viability. Nevertheless, it presents difficulties as enterprises must devise strategies to integrate CSR deliberations into all facets of their activities.Corporate Social Responsibility (CSR) initiatives are frequently characterized by their voluntary nature and self-regulatory approach, which may not be subject to enforceable standards. This phenomenon may lead to incongruity and fluctuating degrees of dedication among corporations. In the absence of well-defined guidelines, certain organizations may resort to "greenwashing" tactics, whereby they purport to exhibit social responsibility without effecting significant alterations.Through a comprehensive comprehension and proficiency in tackling emerging challenges in Corporate Social Responsibility (CSR), enterprises can augment their standing and foster confidence among their stakeholders. A robust corporate social responsibility (CSR) strategy that is in line with societal expectations has the potential to enhance brand reputation, entice customers, and cultivate enduring relationships with stakeholders. The academic perspective suggests that the study of Corporate Social Responsibility (CSR) enables businesses to comprehend the wider societal and ecological consequences of their operations. The aforementioned statement highlights the significance of comprehending the effects of corporate choices on stakeholders, thereby facilitating judicious and conscientious decision-making. Consequently, this facilitates the alignment of corporate strategies with sustainable development objectives and enables decision-making that is mutually advantageous for the enterprise and the community.
Effect of exercise interventions on sarcopenic obesity in middle-aged and older adults: a comprehensive review

Purpose: This study examined the definitions, diagnostic criteria, and measurements of sarcopenic obesity and identified effective exercise interventions that improve cardiometabolic outcomes in middle-aged and older adults, in whom the prevalence of sarcopenic obesity is increasing. Methods: This comprehensive review followed the principles of literature search, data extraction, and review, as described in the PRISMA 2009 guidelines. Results: The 11 articles included in this study presented different concepts of sarcopenic obesity. Exercise interventions for sarcopenic obesity varied in their effects. Resistance exercise improved muscle mass and physical function, while aerobic exercise primarily impacted obesity and cardiometabolic indicators. Combined exercise had mixed results across indicators.Conclusion: This study addressed a pressing public health concern in the context of an aging population, acknowledged the unique challenges of sarcopenic obesity, and attempted to clarify definitions and assessment methods, while identifying effective exercise interventions to reduce cardiometabolic risk. Sarcopenic obesity is a multifaceted condition with varying definitions and diagnostic criteria. Its association with cardiometabolic risk underscores the need for comprehensive assessments considering both muscle and obesity indicators. While exercise interventions hold promise for managing sarcopenic obesity, further research is required to establish effective strategies.