Philadelphia University

In this paper, we introduce and investigate the notion of soft co-compact spaces as a natural generalization of classical co-compact spaces within the framework of soft topology. The aim of this new concept is to provide researchers with a flexible structure through which advanced topological properties-such as generalized compactness, separation axioms, and continuity-can be studied in the context of soft sets. We begin by presenting the formal definition of a soft co-compact space and demonstrating how it extends the classical idea of co-compactness to parameterized environments. Several fundamental properties of this new class are established, and we show that soft co-compact spaces form a distinct category that is not reducible to previously known soft topological constructs. We further explore the interaction between soft co-compactness and various soft separation axioms, thereby revealing new characterizations and criteria that govern their relationships. Motivated by these findings, we introduce associated soft operators-such as soft co-compact interior and soft co-compact closure-and describe their behaviors and structural roles. The developed framework opens a new approach in soft topology, allowing for refined analysis of soft continuity, decomposition theorems, and transitions between different soft topological settings.

Background: Topical Corticosteroids (TCS) are potent therapeutic agents associated with severe local and systemic adverse effects if misused. In Jordan, the unauthorized supply of TCS for cosmetic purposes and the mismanagement of dermatological conditions remain significant public health concerns. This study utilized a repeated-measures simulated patient (SP) methodology to evaluate community pharmacists' stewardship of TCS across a spectrum of clinical risks. Methods: A national cross-sectional study was conducted across 380 randomly selected community pharmacies in Jordan. Each pharmacy received four covert visits (N = 1520) corresponding to four distinct clinical scenarios representing different risk levels: cosmetic whitening, acne management, fungal infection, and pediatric diaper rash. The primary outcome was appropriate Practice, defined as the refusal to dispense unsafe medication or the provision of evidence-based alternatives. Results: Stewardship behavior varied significantly by clinical context (p < 0.001). Pharmacists demonstrated a hierarchy of safety, adhering to guidelines most strictly in the Pediatric scenario (82.1% appropriate refusal) but frequently abandoning safety standards in the Cosmetic scenario (30.0% appropriate refusal). Notably, 70.0% of pharmacists dispensed potent steroids for facial whitening, and 26.1% voluntarily offered to compound unauthorized steroid mixtures (Khaltat). In the acne scenario, 52.1% dispensed the contraindicated TCS, while only 37.9% appropriately switched the patient to an evidence-based alternative. In the fungal scenario, 60.0% failed to visually inspect the lesion, leading to a 40.0% rate of inappropriate TCS dispensing. Multivariable regression indicated that pharmacists working in chain pharmacies (aOR: 2.15, 95% CI: 1.68-2.75) and those holding advanced degrees (PharmD/MSc) (aOR > 1.38) were significantly more likely to practice appropriate TCS stewardship. High workload (>200 prescriptions/day) was a significant barrier to safety (aOR: 0.55). Conclusions: Community pharmacists in Jordan exhibited selective TCS stewardship, demonstrating high vigilance for pediatric safety, but widespread illegal practice regarding cosmetic misuse and differential diagnosis that may be unethical. The study results warrant the need for further urgent research to understand why these practices are occurring and how best to address them.

Management of chronic diseases like diabetes mellitus (DM) requires patient activation. Although many studies have associated patient activation with outcomes like increased medication adherence and better blood glucose control, evidence for these outcomes is scarce in the Middle East, particularly in the Jordanian population. To address this issue, the current study examined the association between patient activation and both medication adherence and glycemic control among patients with diabetes in Jordan. A descriptive longitudinal method was used, and convenience sampling was adopted. Newly diagnosed adult patients with diabetes were assessed utilizing the Patient Activation Measure (PAM-13), Adherence to Refills and Medications Scale (ARMS), and glycemic control was evaluated by HbA1c levels. Data were collected from 140 patients at baseline, 3 months, and 6 months from hospital inpatient departments and outpatient clinics. PAM scores increased from 44.7 at baseline to 54.5 at 3 months and 60.0 at 6 months. ARMS scores dropped from 34.4 to 29.7 and 25.3, indicating progressive improvement in medication adherence, and HbA1c from 10.6% to 9.6% and 8.7%. Better activation was significantly associated with higher adherence (F(1, 335.1) = 315.43, p < 0.001) and lower HbA1c (F(1, 362.2) = 414.38, p < 0.001). Time and activation interplay were prominent for adherence (F(2, 177.1) = 3.15, p = 0.045) and HbA1c (F(2, 165.2) = 6.04, p = 0.002). These findings suggest that patient activation is associated with improved health outcomes at 6 months. This finding thus supports prioritizing structured education and support in diabetes care to enhance and maintain patient activation.

Mycotoxins are toxic compounds produced by certain fungi, whose health effects may be significant when they contaminate fresh eggs. Conventional methods of mycotoxin analysis, while accurate, are labor-intensive, time-consuming, and impractical for large-scale screening applications. This study attempts to use using machine learning techniques to predict the concentration and presence of deoxynivalenol (DON), aflatoxin B1 (AFB1), and ochratoxin A (OTA) in fresh eggs from Jordan. Rather than replacing analytical detection methods, the proposed approach can enable a risk-based prioritization of samples for laboratory testing by identifying high-risk samples based on environmental and production factors. A dataset consisting of 1250 poultry egg samples collected between January and July 2024 under several factors involving environmental conditions and chemical assay results regarding mycotoxin content in eggs was used. Several machine learning algorithms were used in this study to build predictive models, including decision trees, support vector machines, and neural networks. The results indicate that machine learning can accurately and reliably predict mycotoxin contamination, which demonstrates the potential for integrating machine learning into food safety protocols. This study contributes toward developing predictive analytics for food safety and lays the groundwork for future research aimed at improving contamination monitoring systems.

Scaling Agile in industrial software projects is a process control problem that must balance governance, scalability, and adaptability while keeping decisions auditable. We present a hybrid fuzzy multi-criteria decision-making (MCDM) framework that combines Fuzzy Analytic Hierarchy Process (FAHP) for uncertainty-aware weighting with a tunable VIKOR-PROMETHEE ranking stage. Weighting and ranking are kept distinct to support traceability and parameter sensitivity. A three-layer hierarchy organizes twenty-two criteria across organizational, project, group, and framework levels. In a single-enterprise validation with two independent expert panels (n = 10 practitioners), the tuned hybrid achieved lower rank error than single-method baselines (mean absolute error, MAE = 1.03; Spearman rho = 0.53) using pre-specified thresholds and a transparent alpha+beta = 1 control. The procedure is practical for process governance: elicit priorities, derive fuzzy weights, apply the hybrid ranking, and verify stability with sensitivity analysis. The framework operationalizes modeling, optimization, control, and monitoring of scaling decisions, making trade-offs explicit and reproducible in industrial settings.