Modern enterprises increasingly depend on interconnected information systems, cloud services, software applications, third party platforms, and digital business processes. This dependence creates exposure to cyber incidents, abnormal behavior, service failures, and operational disruption. Existing cybersecurity approaches often focus on threat identification without translating technical risk into business impact or actionable response priorities. This study proposes an Explainable AI Driven Decision Intelligence framework for cyber resilient enterprise operations under uncertainty. The framework integrates cybersecurity telemetry, enterprise context, dual stream predictive modeling, service criticality, dependency analysis, Business Impact Score assessment, explainable reasoning, resource constrained prioritization, and resilience response. The predictive layer processes temporal technical signals and contextual severity attributes to estimate malicious activity and disruption probabilities. TreeSHAP and rule-based reasoning provide interpretable factors, affected services, estimated consequences, and recommended actions. The framework is designed for comparative evaluation against XGBoost, SVM, standard BiLSTM, and conventional threshold alerting using predictive, operational, explainability, resource allocation, and uncertainty measures. The study provides an integrated approach for connecting cyber threat prediction with transparent operational decisions and enterprise resilience.

Background: Adolescence is an important period of physical, emotional and sexual development during which young people may become vulnerable to sexually transmitted infections and unintended pregnancy. Adequate knowledge of safe sex strategies is important; however, knowledge may not necessarily translate into appropriate sexual health practices. Objective: To assess the knowledge and practice of safe sex strategies and identify factors influencing sexual health practices among senior secondary school students at University of Benin Demonstration Secondary School, Benin City, Nigeria. Methods: This was a cross-sectional study involving 243 senior secondary school students. A stratified random sampling technique was used to select participants from SS1 to SS3. Data were collected using a structured, self-administered questionnaire assessing sociodemographic characteristics, knowledge of safe sex strategies, sexual practices and factors influencing safe sex practices. The instrument underwent face and content validation and had a Cronbach’s alpha coefficient of 0.75 following pilot testing. Data were analysed using SPSS version 26 and summarised using descriptive statistics. Results: Of the 243 respondents, 139 (57.2%) were female and 126 (51.9%) were aged 14–16 years. Overall, 201 (82.9%) respondents had good knowledge of safe sex strategies, while 124 (51.0%) had good safe sex practices. One hundred and sixty (65.8%) had attended a class on safe sex education, while 159 (65.4%) had previously heard the term “safe sex.” Sixty-nine (28.4%) respondents reported having had sexual intercourse, 149 (61.3%) reported practising abstinence, and only 60 (24.7%) had ever undergone testing for a sexually transmitted infection. Only 112 (46.1%) had discussed sexual health with a parent, teacher or health professional. Conclusion: Knowledge of safe sex strategies was high among the students, but this was not matched by a similarly high level of safe sex practice. Strengthening comprehensive school-based sexuality education, improving parent-adolescent communication and increasing access to confidential youth-friendly sexual and reproductive health services may help bridge the gap between knowledge and practice.

Clients expect density, movement, proportional harmony, and results that hold up over weeks of ordinary care, yet the planning of volume and length remains largely intuitive. The same request for "more fullness" may call for entirely different solutions depending on the thickness, curvature, density, and chemical history of the client's natural hair. This article develops a structured strategy for modeling volume and length during hair extension procedures. It draws on twenty English-language sources covering hair morphology, fiber variability, biomechanics, cosmetic alteration, and extension-related disorders. It also incorporates the Hair Biomechanical Tolerance Dataset (Cowan Y, 2026), a synthetic set of 108 illustrative archetypes, not derived from clinical practice, that maps hair and scalp parameters to safe strand counts and wear cycle durations. In these illustrative data, the maximum safe number of donor attachments per scalp zone ranged from 23 strands for fine hair in the high-risk marginal hairline to 85 strands for coarse hair in the supportive crown zone, a 3.7-fold difference associated with hair profile and anatomical placement. Mean wear cycle fell from 8.1 weeks for virgin hair with high aftercare compliance to 4.3 weeks for chemically compromised hair with low compliance. On this basis, the article proposes the Comprehensive Extension Modeling Strategy, a five-stage authorial method integrating base profiling, aesthetic intention mapping, tolerance alignment, zonal distribution planning, and proportional validation. Volume and length must be co-modeled, and both must remain compatible with the structural profile of the natural base and the realistic wear cycle.

Hair extension practice rarely fails in a single dramatic way. It fails gradually when the cumulative load on natural hair exceeds its structural reserve, when an attachment zone absorbs repeated tension it was not assessed to sustain, or when a client's actual maintenance behavior falls short of what the installation required. This article develops a literature-based algorithm for gradual hair extensions that treats the procedure as a staged adaptation process rather than a one-time intervention. The study draws on 20 verified English-language sources covering hair and scalp diagnostics, hair camouflage, scalp reactions, traction-related damage, and material exposure. It also references the Hair Biomechanical Tolerance Dataset (Cowan Y, 2026), a synthetic illustrative dataset of 108 hypothetical archetypes with rule-generated action directives. Three patterns from this model informed the algorithm. First, scalp condition was encoded as an absolute procedural gate: all archetypes with active erythema or pre-traction markers were assigned refusal, regardless of hair thickness, density, or zone. Second, staged extension is exclusively a Supportive and Conditional zone procedure: none of the 24 Staged Extension profiles is located in the high-risk marginal zone. Third, aftercare compliance reduces safe wear duration by 1.3 to 1.7 weeks across all scalp zones, making it a planning variable rather than post-procedural advice. On this basis, the article proposes a Gradual Hair Extension Algorithm with five decision blocks: structural diagnosis, zonal tolerance assessment, aftercare capacity assessment, phased load planning, and follow-up correction logic. Graduality is not a stylistic preference. It is a principle of biological proportionality.

Healthcare organizations face challenges in workforce allocation because fixed staffing approaches may not adequately reflect changing demand, workflow bottlenecks, staff qualifications, workload, and operational constraints. This study develops an Explainable Process Intelligence framework for Workforce Allocation and Operational Governance in Digital Healthcare Systems to connect healthcare process analysis with transparent staff-to-task recommendations. A quantitative, simulation-based design was used with a simulated healthcare event log and workforce records. The framework integrates process discovery, conformance analysis, bottleneck identification, demand estimation, constrained workforce allocation, explainable artificial intelligence, and governance monitoring. The allocation model was compared with a historical baseline roster using identical demand periods and eligibility rules. The proposed approach increased demand coverage from 78.1% to 88.7%, reduced median waiting time from 1.70 to 1.29 hours, decreased workload coefficient of variation from 0.31 to 0.21, and reduced overtime exposure from 12.4% to 8.9%. Explanations were available for accepted and rejected recommendations, while human reviewers retained authority to modify or reject recommendations. The findings indicate that integrating process intelligence, explainable allocation, and governance can support more transparent workforce planning, workload management, and operational oversight in digital healthcare environments.

Background: Regenerative endodontic treatment uses a scaffold within a disinfected canal to support healing in immature permanent teeth. This case series describes the use of two scaffold approaches in mandibular premolars. Case Presentations: A 12-year-old girl received treatment of tooth 34 using an induced intracanal blood clot. A 12-year-old boy received treatment of tooth 45 using platelet-rich fibrin (PRF). At the first visit, both canals were irrigated with sodium hypochlorite and medicated with antibiotic paste. At the second visit, the medicaments were removed and the canals were irrigated with EDTA. Bleeding was induced beyond the apex of tooth 34 and allowed to clot; PRF was placed in tooth 45. Mineral trioxide aggregate was placed over each scaffold, followed by coronal restoration. Immediate postoperative, two-week, and three-month follow-up radiographs documented the treated teeth. Conclusion: These cases illustrate the use of an induced blood clot and PRF as scaffolds in regenerative endodontic treatment. Radiographic follow-up was available up to three months. Longer clinical and radiographic follow-up is needed to assess sustained healing and continued root development, and these two cases cannot establish comparative scaffold effectiveness.

Candida albicans is a predominant fungal commensal of the oral cavity that normally maintains a balanced relationship with the host without causing tissue damage. However, alterations in local and systemic host conditions can disrupt this equilibrium, enabling C. albicans to transition from a commensal organism to an opportunistic pathogen and cause oral candidiasis. This review aims to provide a comprehensive overview of the major virulence mechanisms that facilitate this commensal-to-pathogen transition, with particular emphasis on adhesion, morphological switching, biofilm formation, epithelial invasion, and host tissue damage. Initial adherence to oral epithelial surfaces is mediated by fungal adhesins, particularly the agglutinin-like sequence proteins and hyphal wall protein 1. Subsequent yeast-to-hyphal transition enhances adhesion, biofilm development, and tissue invasion through highly regulated polarized hyphal growth. Biofilm formation further promotes fungal persistence and resistance to antifungal agents and host immune responses. Epithelial invasion occurs through induced endocytosis and active penetration and is facilitated by fungal invasins and secreted hydrolytic enzymes, particularly secreted aspartyl proteinases. Understanding the coordinated interplay of these virulence mechanisms provides insight into the pathogenesis of oral candidiasis.