Miracle-dz/UniFAR-FTUs huggingface dataset:
A CT-Based Clinical, Radiological and Radiomic Machine Learning Model for Predicting Malignancy of Solid Renal Tumors (UroCCR-75)

Background: Differentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic. Purpose: This study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations. Materials and methods: A unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC. Results: A total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/− 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and “intensity mean value” was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74. Conclusion: Our machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
Exploring the Companion of Ostrowski's Inequalities via Local Fractional Integrals

This paper investigates the companion of Ostrowski's inequality in the framework of fractal sets. First, a new identity related to local fractional integrals is introduced, serving as the foundation for establishing a set of inequalities applicable to functions with generalized $s$-convex and $s$-concave derivatives. An illustrative example is presented to validate the obtained results, demonstrating their accuracy. Additionally, the paper discusses several practical applications, highlighting the significance of the established inequalities. The research presented in this paper contributes to the growing field of studying functions on fractal sets, which has attracted considerable interest from scientists and engineers.
Family Needs Assessment of Patients with Cerebral Palsy Attending Two Hospitals in Accra, Ghana

Background: The family represents the most essential and supportive environment for children with cerebral palsy (CP). To improve children’s outcomes, it is crucial to consider the needs of families in order to offer family-centered care, which tailors services to these needs. Objective: We conducted a needs assessment to identify the family needs of patients with CP attending two hospitals in Accra. Methods: The study was a cross-sectional study involving primary caregivers of children with CP attending neurodevelopmental clinics. Structured questionnaires were used to collect data spanning an 8-month period. The data were summarized, and statistical inference was made. Results: Service needs identified were childcare, counseling, support groups, financial assistance, and recreational facilities. Information needs included adult education, job training/employment opportunities, education, health and social programs, knowledge about child development, and management of behavioral and feeding/nutrition problems. Reducing extensive travel time was desirable to improve access to healthcare. With the increasing severity of symptoms came the need for improved accessibility in the home to reduce the child’s hardship, as well as assistive devices, recreational facilities, and respite for the caregiver(s). Conclusion: Families of children with CP have information, service, and access needs related to their disease severity and family context.
Numerical solution stability of general stochastic differential equation

Abstract Stochastic differential equation has accumulated a large number of bases in numeral analysis over decades of development. It can accurately and truly reflect some development rules in the fields such as economics, physics and astronomy when being applied in practice. Stability of numeral solution is the key in numerical analysis of stochastic differential equation. In this study, the stability of numeral solution of stochastic differential equation was discussed and verified. Nonlinear stochastic equation was solved using stochastic-θ method under general decay rate, and two sufficient conditions were proposed for the obtained numerical solutions. It was verified that the numeral solution obtained using stochastic-θ method was p-th moment Φ(t)-stable and almost surely Φ(t)-stable under the sufficient conditions.
From Nature to Lab: A Review of Secondary Metabolite Biosynthetic Pathways, Environmental Influences, and In Vitro Approaches

Secondary metabolites are gaining an increasing importance in various industries, such as pharmaceuticals, dyes, and food, as is the need for reliable and efficient methods of procuring these compounds. To develop sustainable and cost-effective approaches, a comprehensive understanding of the biosynthetic pathways and the factors influencing secondary metabolite production is essential. These compounds are a unique type of natural product which recognizes the oxidative damage caused by stresses, thereby activating the defence mechanism in plants. Various methods have been developed to enhance the production of secondary metabolites in plants. The elicitor-induced in vitro culture technique is considered an efficient tool for studying and improving the production of secondary metabolites in plants. In the present review, we have documented various biosynthetic pathways and the role of secondary metabolites under diverse environmental stresses. Furthermore, a practical strategy for obtaining consistent and abundant secondary metabolite production via various elicitation agents used in culturing techniques is also mentioned. By elucidating the intricate interplay of regulatory factors, this review paves the way for future advancements in sustainable and efficient production methods for high-value secondary metabolites.
LEVERAGE, FREE CASH FLOW DAN PROFITABILITAS TERHADAP MANAJEMEN LABA DENGAN ENVIRONMENTAL, SOCIAL AND GOVERNANCE (ESG) SEBAGAI VARIABEL MODERASI

Manajemen laba merupakan suatu hal yang dilakukan manajemen perusahaan untuk memenuhi ekspektasi investor terhadap perusahaan. Regulator berusaha memberikan keterbatasan ruang gerak bagi perusahaan untuk melakukan manajemen laba. Salah satunya ialah dengan adanya suatu indeks penerapan beberapa aspek seperti environmental, social and governance (ESG). Penelitian ini ingin melihat ESG dapat memengaruhi hubungan beberapa indikator yang sering dianggap penting bagi investor dalam melihat laporan keuangan perusahaan seperti leverage, free cash flow, ataupun profitabilitas yang sering dihubungkan dengan manajemen laba. Populasi yang digunakan ialah perusahaan terbuka di Indonesia Stock Exchange tahun 2017-2019. Harapannya penelitian ini dapat digunakan sebagai salah satu pertimbangan dalam pengambilan keputusan, baik bagi manajer perusahaan yang hendak menerapkan manajemen laba dan melihat efektivitas environmental, social and governance (ESG), maupun bagi investor yang hendak menanamkan modalnya untuk berinvestasi di suatu perusahaan.
Evaluation of potting mixtures and humidity conditions for rooting and establishment of plagiotropic branches of black pepper (Piper nigrum L.)

Bush pepper grown from the lateral branches (plagiotrops) of black pepper ( Piper nigrum L.) has the advantage of growing as a garden plant without standards for trailing and there is surge in demand for planting materials. An experiment was conducted to standardize suitable rooting conditions for the production of bush pepper in which, potting mixture (soil and vermicompost in 3:1 proportion) and coir pith compost, alone and in combination were evaluated for the rooting and establishment of bush pepper with and without humid chambers. The laterals of black pepper variety Panniyur 1 were used. There were nine treatments with three replications each. After 50 days of planting it was observed that, the largest number of laterals established where coir pith compost in 8 x 5 cm black nursery bags and kept under humid chamber (T 2 / 63.3 %) followed by coir pith compost in 19” x 8” transparent LDPE bags, planted the cuttings and tied the mouth for retaining humidity (T 8 / 60.8%). In both the cases the planting medium used was coir pith compost. Hence it is inferred that coir pith compost could be used as medium for rooting and establishment of laterals for large scale production of bush pepper.
Numerical Boltzmann Equation Solutions for Secretly Asymmetric Dark Matter Scenarios

Everything that anyone interacts with on a daily basis is made up of ordinary matter: food, the Earth, and ourselves. However, during the 1900s people began to realize that the universe was not purely ordinary matter. When studying the motions of stars in other galaxies, scientists found that the velocity of the stars weren’t what one would expect based on Newtonian physics—instead of stellar velocities decreasing as the distance from the center grows, they remain constant [1]. They hypothesized that this discrepancy was a result of a large amount of unseen mass clustered around the galaxies, and dubbed this unseen mass “dark matter. ” Today, we know that dark
Polymeric Patches Based on Chitosan/Green Clay Composites and Hazelnut Shell Extract as Bio-Sustainable Medication for Wounds

Hazelnut shells, the main waste deriving from hazelnut processing, represent an interesting source of active molecules useful in pharmaceutics, although they have not yet been examined in depth. A hydrosoluble extract (hazelnut shell extract, HSE) was prepared by the maceration method using a hydroalcoholic solution and used as the active ingredient of patches (prepared by casting method) consisting of composites of highly deacetylated chitosan and green clay. In vitro studies showed that the formulation containing HSE is able to stimulate keratinocyte growth, which is useful for healing purposes, and to inhibit the growth of S. aureus (Log CFU/mL 0.95 vs. 8.85 of the control after 48 h); this bacterium is often responsible for wound infections and is difficult to treat by conventional antibiotics due to its antibiotic resistance. The produced patches showed suitable tensile properties that are necessary to withstand mechanical stress during both the removal from the packaging and application. The obtained results suggest that the developed patch could be a suitable product to treat wounds.
Timing and analysis of eclipsing black widow pulsar PSR J2256-1024

Pulsars are rapidly spinning neutron stars emitting radiation about their magnetic field axes. Misalignment of the spin and magnetic axes causes a “lighthouse” effect where we observe radiation pulses, in time with the pulsar’s rotation. Millisecond pulsars are those which have accreted material from a companion star, spinning themselves up to rotate faster. Some millisecond pulsars occur in tight orbits with low mass companions; this combination can lead to the companion losing material, due to bombardment by energetic particles from the pulsar, presumably eventually destroying the companion. These pulsars are known as Black Widows (BWs). This thesis is an analysis of radio observations of PSR J2256-1024, a BW pulsar with a spin period of 2.294531816964939(10)ms. Observing pulsars, we can calculate the arrival times of individual pulses and compare these with those predicted from various models to find the best-fitting one. This process is known as pulsar timing. We present the timing solution for PSR J2256-1024. We find it has a 5.1091831284(9)hr binary orbit with a semimajor axis of 4.1(3)ltsec and a 0.0312(9)M companion. PSR J2256-1024 shows a radio eclipse over 7.8 % of its orbit approximately twice the size of the Roche lobe calculated for the companion. This confirms the picture of a Black Widow pulsar with material being stripped from the companion and forming a trailing cloud which blocks the pulsar signal. We also find evidence for variable clumps of material in the system. We present polarization profiles and mean flux densities at 350 MHz, 820 MHz and 1500 MHz. We discuss polarization changes in the post-eclipse region, where the pulsar signal is transmitted through eclipsing material in the system, and find
ENERGY EFFICIENT MULTI-HOP CLUSTER BASED D2D COMMUNICATION FOR CATASTROPHIC AREA

: Earth has been experiencing different types of disasters since time immemorial, causing death and destruction (to all types of infrastructure). Different parts of the world have been devastated by cyclones, earthquakes, landslides, and tornadoes. Over the past few years, energy and resource management has been the goal of research. Device-to-device communication is one of the emerging technologies that have the potential to play a crucial role in natural disasters by utilizing direct D2D communication to help achieve specific goals. Communication between D2D devices was designed for short-range use but can be applied to longrange applications through multi-hop relays. In addition, it is applicable to a number of practical applications including disaster management, uncoverage areas in cells, and battlefields. In order to optimize energy balancing and resource utilization, a cluster-based channel allocation scheme was used. Our communication infrastructure can work independently in case of any natural disaster, where primary infrastructure is damaged or not available. The proposed scheme "relays the communication traffic to the nearest base station through multiple hops. We applied the Dijkstra algorithm for the shortest path calculation. In order to calculate linkages from the source to the destination and to send data over reliable channels, the Link Calculation Method (LCM) is employed before transmitting frames. Our RF energy harvesting mechanism HSEH ensures energy is harvested from the most efficient source for maximum efficiency. The high strength signal of the RF source continues to ensure that energy is being harvested. Finally, we compared our simulation results with existing schemes. Results indicate that the proposed scheme outperforms the existing schemes in terms of consuming less energy while transmitting the data through a more reliable channel.
Stigma and related factors among renal dialysis patients in China

Background Stigma is an important psychological concept that is being studied in many diseases. However, there have been few studies on stigma in renal dialysis patients in China. This study aimed to investigate the level of stigma and its potential influencing factors among Chinese renal dialysis patients. Methods A cross-sectional study was conducted among renal dialysis patients in two Chinese dialysis centers between April 2022 and July 2022. Two hundred four renal kidney patients were interviewed with a questionnaire on demographic variables using the Social Impact Scale (SIS), Resilience Scale-14 (RS-14), Herth Hope Index(HHI), Multidimensional Scale of Perceived Social Support (MSPSS), Revised Life Orientation Test(LOT-R), Perceived Stress Scale (PSS-4) and Fear of Progression (FoP). T-test/univariate one-way ANOVA, Pearson’s R, and hierarchical linear regression analysis were used to investigate the factors that influence stigma. Results Renal dialysis patients in China experienced a moderate level of stigma (52.36 ± 8.16). Stigma was negatively correlated with resilience, hope, and perceived social support, whereas it was positively associated with perceived stress and fear of progression. However, it showed no significant relationship between optimism and stigma. Hierarchical linear regression analysis showed that hope (β = -0.318, P < 0.001), social support (β = -0.193, P < 0.01), perceived stress (β = 0.197, P < 0.01), and fear of progression (β = 199, P < 0.01) were found to be associated with stigma among the renal dialysis patients. All four variables in the model could explain 34.6% of the variance in stigma among renal dialysis patients in China. Conclusion According to this study, renal dialysis patients in China face a moderate level of stigma. Stigma was found to be negatively related to hope and social support but positively associated with perceived stress and fear of progression. Future research on the stigma of renal dialysis patients should include hope-based interventions, proper and specific social support strategies, stress management interventions, and more disease-related information.
Harmonic Phase Processing In Magnetic Resonance Susceptibility Imaging

The popularity of magnetic resonance imaging (MRI) owes much to its flexibility. Sensitive to a host of different biophysical phenomena, parameters of the scan can be fine-tuned to highlight specific pathologies. One such mechanism for generating image contrast is magnetic susceptibility—the material property that defines how an object will distort an applied magnetic field such as that of the MR scanner. In functional MRI (fMRI), for instance, the unique magnetic signatures of oxygenated (diamagnetic) and deoxygenated (paramagnetic) blood are what permit the indirect measure of neuronal activity. However, rather than measuring the susceptibility itself, fMRI registers haemodynamic change as subtle gains and losses in the signal magnitude due to haem-iron induced dephasing of the proton spins. Susceptibility mapping, an emerging area in MRI, attempts to retrieve a direct measure of bulk susceptibility itself. Generally this is done, not by means of the standard magnitude image, but through the phase component of the signal which, in the idealized case, relates the magnetic field perturbation by a simple multiplicative constant. Several issues interfere with the construction of accurate susceptibility maps. Foremost is the obfuscation of the small-scale “local” field (i.e., that pertaining to susceptibility variation within tissue) by the so-called “background” field, which owes predominantly to the comparatively substantial susceptibility shift between tissue and air. Whether the goal is to produce qualitatively useful images of the local field, or to map the susceptibility itself, the local field must first be isolated from the background. To isolate overlapping signals which are a priori unknown, the unique characteristics of the expected signals need to first be codified. One family of methods for isolating the local field begins by asserting that, away from air-tissue interfaces, the background field should satisfy the partial differential equation of Laplace, whereas the local field should satisfy that of Poisson (viz., the background field should be harmonic, the local field non-harmonic). This classification informs a filtering technique based on the spherical mean value (SMV) property of harmonic functions: the mean of a harmonic function calculated over a spherical surface equates to the specific value taken by the function at the centre of the sphere.
Patient specific channel optimization using entropy and CNN deep learning for epileptic seizure prediction

Predicting epileptic seizures in advance improves greatly the life of epileptic patients. In this paper we present a new approach based on patient specific channel optimization using four different features namely entropy, variance, kurtosis and skewness. After selecting three best channels for each method, we then use Convolutional Neural Network (CNN) to classify raw EEG signal in order to discriminate between interictal and preictal state. With entropy, our method achieves a good degree of prediction in terms of accuracy 97.09%, sensitivity 97.67% and specificity 96.51% for patient 01 using channels 4, 8 and 20.
University of Birmingham Scaling up sustainable innovation: Stakeholder ties, eco‐product innovation, and new product performance

Deriving insights from the stakeholder theory, this article investigates the impact of industry stakeholders on product innovation through eco-innovation. It further examines the moderating role of environmental R&D expenditure on this relationship. Using data from 212 firms, the results from this article reveal that (1) both intra and extra-stakeholder ties predict eco-innovation, (2) eco-innovation mediates the relationship between industry stakeholder ties and new product performance, and (3) environmental R&D expenditure has a positive moderating effect on the linkage between intra and extra-industry stakeholder ties and new product performance through eco-innovation. These findings contribute to our understanding of the role of industry stakeholders in the context of emerging markets by being a driver of new product outcomes. Implications for theory and practice are discussed.
Posttraumatic Stress Disorder in the wake of heart disease

Background: There is increasing recognition that patients after a cardiac event may be at risk of posttraumatic stress disorder (PTSD). The present article reviews studies looking at PTSD as a sequel of heart disease with a focus on prevalence, risk factors, and future research directions. Methods: We conducted a search on PsychInfo and MEDLINE from 1980 to the present. Studies were included in the review if they looked at PTSD after a cardiac event, reported on the number of cases with PTSD, and had been published in English. Results: We identified 25 studies that fulfilled the inclusion criteria, of which 7 reported on the follow-up of previously published studies. The prevalence of PTSD after heart disease varied from 0% to 38% across studies. PTSD has been most rigorously researched after myocardial infarction with the best-powered studies finding a prevalence rate of 15%. Studies including control groups showed that cardiac patients were at risk of developing PTSD. Risk factors included sociodemographic and psychological characteristics and aspects related to the cardiac event. Conclusion: Despite substantial heterogeneity in the methodology of studies and differences in prevalence across studies, this review indicates that subgroups of patients are at risk of PTSD after a cardiac event. Future studies investigating PTSD as a sequel of heart disease should be more systematic, use a prospective study design with multiple assessments, and include sufficiently large samples. PTSD should not be ignored as a sequel of heart disease, given preliminary evidence that PTSD may be associated with nonadherence with medication and an increased risk of clinical adverse events.
UvA-DARE (Digital Academic Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

Can we make Bayesian posterior MCMC sampling more efﬁcient when faced with very large datasets? We argue that computing the likelihood for N datapoints twice in order to reach a single binary decision is computationally inefﬁcient. We introduce an approximate Metropolis-Hastings rule based on a sequential hypothesis test which allows us to accept or reject samples with high conﬁdence using only a fraction of the data required for the exact MH rule. While this introduces an asymptotic bias, we show that this bias can be controlled and is more than offset by a decrease in variance due to our ability to draw more samples per unit of time.