This study aimed to assess and compare the therapeutic effectiveness of fresh latex derived from

male and female

Ficus

carica

L

trees as a natural

treatment for common warts

.

Fresh latex was

collected from immature green fruits of male and female trees and applied topically to 62 warts in

28 patients, three times weekly. Clinical responses were documented over a four

-

week period.

Both types of latex

produced measurable therapeutic effects, characterized by progressive

shrinking, drying, and detachment of warts. Male latex demonstrated significantly faster action:

initial flaking occurred by day 3, with complete resolution observed in most lesions wit

hin 12

–

21

days. Female latex required longer treatment times, with full regression occurring between 15 and

30 days

.

The superior efficacy of male latex may reflect differences in biochemical composition,

including higher or more active concentrations of p

roteolytic enzymes (e.g., ficin), organic acids,

phenolics, triterpenoids, and antioxidant

-

related proteins such as chitinase and peroxidase. These

compounds may synergistically produce antiviral, keratolytic, and cytotoxic effects on

human

papillomavirus

(

HPV)

-

infected keratinocytes. No severe adverse reactions were reported, and only

mild discomfort occurred in sensitive areas

.

This study provides the first comparative evidence

supporting fig latex, particularly from male tree, as a simple, inexpensive,

and natural therapeutic

option for common warts

Feature reduction is widely used in machine learning based phishing URL detection to lower model complexity while retaining detection performance. However, conclusions about how much reduction is acceptable may depend on how training and testing data are partitioned. This study examines whether conclusions about feature reduction remain stable when evaluation changes from random stratified to domain disjoint cross validation. Experiments were conducted on URL-Phish Version 2 using Mutual Information ranking within each training fold and four predefined feature budgets, from 22 to 5 features. Logistic Regression and Random Forest were evaluated under both protocols, with PR AUC as the primary metric. PR AUC decreased as the feature budget was reduced for both classifiers under both evaluation protocols, so the qualitative conclusion about feature reduction remained consistent within this experimental setting. However, the size of the protocol gap varied across classifiers and feature budgets: it was clearest for Random Forest under the smallest feature budget, whereas the Logistic Regression gaps were less clearly separated from fold-level variability. Domain disjoint evaluation also produced greater fold-level variability. The results show that claims about compact phishing URL representations should be interpreted together with the evaluation protocol used. Future work should examine whetherthis pattern persists across additional datasets, classifiers, and domain grouping rules.

Missing data are common in clinical prediction studies, yet their handling may affect not only predictive performance but also which model is judged best. This study examined the stability of classifier selection when missing data handling was changed under controlled, paired evaluation conditions. Using SUPPORT2 data from 9,105 patients, Logistic Regression, Random Forest, and XGBoost were evaluated with median, K nearest neighbor, and iterative imputation. The analysis retained natural missingness, added nested missing completely at random (MCAR) perturbations of 5%, 10%, 20%, and 30%, and included a separate 20% missing at random (MAR) condition. Repeated stratified five fold cross validation used the same patient partitions, model seeds, and artificial missingness masks across corresponding comparisons. Ranking stability was assessed through condition level winner changes, paired rank reversals, and agreement across receiver operating characteristic area under the curve (ROC AUC), Average Precision, and Brier Score. The condition level winner remained stable under natural and mild additional missingness, but became dependent on imputation at higher missingness. Winner changes occurred in 6 of 12 imputation comparisons and 9 of 15 missingness comparisons, while pairwise rank reversals occurred in about 44% of matched repeat comparisons. All three metrics selected the same winner in 8 of 18 conditions. Because competing winners were separated by small ROC AUC margins, the results indicate sensitivity of model selection rather than large performance advantages. Reporting ranking stability alongside conventional performance estimates may therefore provide a more cautious basis for comparative clinical prediction studies.

The current study explores the interconnectedness between financial development, information and communication technology (ICT), renewable energy and ecological sustainability in Tunisia over the 1991Q1–2021Q4 period. While there have been numerous studies that tested the determinants of ecological sustainability in Tunisia using indicators like carbon emissions (CO2) and ecological footprint (EF), a comprehensive evaluation of the load capacity curve (LCC) in context of Tunisia remains notably absent. This work fills the gap by examining the impact of financial development, ICT, and renewable energy on ecological sustainability within the LCC framework. The empirical findings reveal that the LCC hypothesis is valid in the context of Tunisia. In addition, ICT has been observed to have a positive effect on ecological sustainability by increasing load capacity factor (LCF). Furthermore, we found that natural resources and financial development have adverse effects on LCF. This study’s findings provide policymakers insights into the effects of financial development and ICT on LCF that is essential in formulating effective strategies and policies to achieve a sustainable environment.

Abstract

The effects of annealing temperature and holding time on recrystallization and grain growth in Nb-stabilized AISI 347H austenitic stainless steel containing 0.55 wt.% Nb were investigated under isothermal conditions. Specimens were annealed at 1000°C and 1200°C for 5, 10, and 30 min. Recrystallization at 1000°C was characterized by scanning electron microscopy–electron backscatter diffraction (SEM–EBSD), while grain size was quantified by optical metallography using ImageJ software. At 1000°C, the material was approximately 65% recrystallized after 5 min and approximately 95% recrystallized after 10 min; after 30 min, the microstructure was fully recrystallized and exhibited substantial grain growth. At 1200°C, rapid transition from precipitate-controlled boundary pinning to rapid grain growth where fully recrystallized structure was obtained within the first 5 min, followed by pronounced grain coarsening with increasing holding time. The measured mean grain sizes at 1200°C were 57, 85, and 235 μm after 5, 10, and 30 min, respectively. The accelerated grain growth at 1200°C is consistent with increased grain-boundary mobility and reduced effectiveness of Nb containing precipitates as boundary-pinning agents. A power-law grain-growth model was applied to the 1200°C data, giving a growth exponent of 1.597 and predicting a grain size of approximately 162 μm after 20 min. The results demonstrate the strong influence of annealing temperature and time on recrystallization and subsequent grain-boundary migration in AISI 347H. 

The rapid growth of Internet of Things (IoT) applications has increased the number of connected devices operating in shared wireless environments. IoT systems rely on sensors, actuators, embedded electronics, software, and internet connectivity to collect, exchange, and process data across sectors such as smart homes, healthcare, transportation, industrial systems, energy systems, and public infrastructure. However, the dense deployment of wireless IoT devices can create significant Electromagnetic Compatibility (EMC) challenges, especially when multiple transmitters and receivers operate in proximity or within the same or adjacent frequency bands. These conditions may lead to Electromagnetic Interference (EMI), which can degrade communication performance, reduce device reliability, and affect safety in critical environments. This study uses a regulatory review and conceptual framework development, drawing on literature synthesis, regulatory requirement extraction, analytical classification, regulatory mapping, and risk-based EMC categorisation. It identifies key EMC and EMI challenges in IoT applications, particularly in dense wireless and low-power communication environments. It analyses immunity requirements in critical sectors such as vehicular networks, healthcare, industry, and public infrastructure. It also develops a regulatory framework that defines stakeholder roles and classifies IoT connection methods, application areas, and device structures to support safer deployment and reduce interference risks.