Organizations that want to gain a competitive advantage have focused on customer retention. However, traditional approaches to customer relationship management do not account for changing customer behaviour in the digital realm. This work suggests the Virtual Consumer Twin Framework (VCTF) for customer retention and predictive marketing through delivering behavioural analytics, customer segmentation, predictive modeling, and the process of continuous updating of digital twins. It seeks to achieve higher accuracy in terms of retention predictions and to improve personalized means of marketing through being able to engage with customers online in real time. A quantitative research design was utilized for examining transaction, demographic, and online engagement data from 1,320 individuals over the period of 36 months. Thus, Structural Equation Modeling (SEM) was adopted to study the connections between digital engagement, quality of personalization, customer satisfaction, customer trust, efficiency of predictive marketing activities, and retention, whereas predictive validation was based on past behaviour and binary classification indicators. The model suggested in the study was successful in predicting with 94.1% accuracy, AUC being 0.952, precision at 92.8%, recall rate being 91.6%, the score of F1 was 92.2%, and RMSE is 0.182, and MAPE equals 5.12%. After SEM analysis has been conducted it was revealed that personalization quality has a positive influence on consumer satisfaction (β=0.81, p<0.001), satisfaction has a positive influence on client trust (β=0.76, p<0.001), and trust influences customer retention (β=0.78, p<0.001), the efficiency of marketing efforts provides customer retention (β=0.84, p<0.001), and the level of digital engagement influences the efficiency of marketing efforts (β=0.79, p<0.001). The results showed that the measurement model demonstrated good reliability and validity, with CFI being 0.957, TLI being 0.949, RMSEA being 0.039, SRMR = 0.036, and Cronbach’s α being 0.84 to 0.92. The data confirm that Virtual Consumer Twin (VCT) technologies provide a solid framework for analysing customer behaviour through data-based personalization that enables companies to make better marketing decisions, strengthen ties with customers, and enhance long- term customer retention.
Problem: Academic events, which require considerable amounts of energy, transportation, catering, water, material usage, and waste generation, lack integrated computational methodologies for assessment and optimization of their ecological footprint. Approach: The paper suggests a framework for computational engineering analysis and sustainability optimization of academic events, which involves activity-based modeling of carbon emissions, circular economy principles, IoT-Edge-Cloud sensing and monitoring architecture, and multi-objective optimization. A constrained four-objective problem (minimization of carbon emissions, energy, and waste, maximization of Circularity Index) is solved by MGWO and SPEA2 algorithms, and a Java-based Green Event Planner tool calculates initial, minimized and residual emissions based on activity information and emission factors. 1,000 people scenario is used to compare a conventional setup (Scenario A) with a green event setup (Scenario B). Results: In terms of resource performance, the optimized configuration lowered energy use by 4,200 to 2,750 kWh (34.5%), carbon footprinting by 3,250 to 1,780 kg CO₂e (45.2%; from 3.25 to 1.78 kg CO₂e per participant), water use by 30% and solid waste by 37.1%. There was an increase in the share of renewable energy sources from 15% to 40%, the share of materials with recyclable/reusable potential from 22% to 65%, the Circularity Index from 0.35 to 0.68 (+94.3%) and the Sustainability Index Score from 0.47 to 0.82 (+74.5%). Travel interventions can be credited for 48.97% of reductions estimated by the source-wise planner and 81.6% according to the ablation study; this result is consistent with the maximum elasticity coefficient (+0.82) for the transportation category. Conclusion: The proposed framework offers a data-based approach for supporting resource-efficient, sustainable and low-carbon oriented decisions about organizing academic events.
Background: Healthcare Industrial Internet of Things (IIoT) application majorly focuses on clinical decision support, remote healthcare services, intelligent medical data management and patient monitoring to save the life of patients. The existing techniques of anomaly detection have issues of temporal misalignment, noisy physiological signals and poor handling of missing values. Objectives: To design an adaptive healthcare IIoT framework for accurately identifying the anomalies, predicting data degradation risks, improving the data quality and ensures efficient healthcare analytics within the intelligent edge-cloud. Proposed Methodology: Initially, the healthcare data were collected and processed using Synthetic Quality Degradation Injection where, missing values, timestamp misalignment, Gaussian noise, sensor drift, packet loss, and outliers were introduced to simulate IIoT sensing and communication failures. Adaptive Context-Aware Data Ingestion (ACADI) prioritized and synchronized data, while Dynamic Residual Quality Normalization (DRQN) performed missing-value estimation, denoising, error correction, normalization, and outlier removal. These pre-processed data were given to the Temporal Multi-Scale Healthcare Feature Fusion (TMHFF) for extracting and fusing the short-term, long-term, and frequency-domain healthcare features. Then, the fused features are fed into the Reinforced Adaptive Hybrid Long Short-Term Memory Autoencoder (RAHLA) model for detecting the anomalies with the help of Bidirectional LSTM, Sparse Autoencoder Reconstruction, and Reinforcement Learning-based threshold optimization. Predictive Evolutionary Quality Estimator (PEQE) evaluates the quality degradation risks of healthcare data and Self-Healing Intelligent Remediation Engine (SHIRE) reconstructs the degraded healthcare streams. Finally, Meta-Adaptive Feedback Optimization (MAFO) optimized model parameters, and Latency-Aware Adaptive Edge Orchestration (LAAEO) enabled efficient latency aware cloud-edge task orchestration. Result: The proposed framework achieved an Accuracy value of 98.37%, Precision of 99.17% and Latency of 1.79s, which describes better enhancement of healthcare data quality and the performance of anomaly detection. Conclusion: The proposed framework provides scalable, reliable and intelligent healthcare IIoT data quality management for healthcare analytics and monitoring applications.
Characterizing clay soils in Diwaniyah, Iraq, is important because differences in mineralogy, salt content, and chemical composition can significantly affect the swelling tendency, strength, compressibility, and overall performance of foundation and subgrade soils for pavements. In this context, this research focuses on the study of chemical properties of the local clay soils along with physical and mineralogical properties. 16 samples of clay soils were collected from four sites (S1-S4) within a depth range of 0-2.0 m. The laboratory tests that were conducted included particle size distribution, Atterberg limits, pH, electrical conductivity, sulfate, chloride, calcium carbonate, organic matter content, soluble salts, major oxides, mineralogical composition, compaction, unconfined compressive strength (UCS), cohesion, friction angle, California Bearing Ratio (CBR), free swell ratio, and compression index. Soil compositions revealed a mean clay percentage of 59.32% and plasticity index of 28.15%, implying moderate-to-highly plastic behavior of soils. Mean pH and conductivity were 7.86 and 6.10 dS/m, respectively, but the high concentration of soluble salts varied chemically. The dominant mineral was quartz at 27.75%, and montmorillonite averaged 18.50%. Results from engineering tests showed a mean value of 179.50 kPa UCS, 5.60% CBR, 43.25% free swell, and 0.32 compression index. Soil composition S3 performed best in terms of engineering performance while S2 exhibited higher salinity and swelling potential along with inferior strength properties. From the above discussion, it is clear that soil chemistry and mineralogy are interrelated with engineering properties of soils, which could be applied in foundation design, paving construction, drainage, and stabilization of soils in Diwaniyah.
In resource-constrained, conflict-affected primary healthcare settings, communication hurdles persist despite the fact that efficient communication between healthcare personnel and patients is essential to attaining the best possible healthcare results. The purpose of this study was to evaluate the communication skills, behaviors, and perceived obstacles of primary healthcare workers in Al-Ramadi, Iraq. Between September and December of 2024, nine primary healthcare facilities in Al-Ramadi city participated in a descriptive cross-sectional study. Non-probability convenient sampling was used to enroll 92 healthcare professionals. A structured questionnaire addressing sociodemographic, communication styles, and perceived barriers was used to gather the data, which were then descriptively analyzed using chi-square tests of association. Most participants were under 40 years (77%) and married (65%). Good knowledge was found for eye contact (82%) and open-ended questioning (73%), while gaps existed in responding to emotions (27%), the biopsychosocial approach (34%), and SPIKES protocol (54%). Practice was strong for undivided attention (95%) but weaker for privacy (71%) and shared decision-making (70%). A significant knowledge–practice gap was found for empathy, where practice (73%) exceeded conceptual understanding (39%) (χ² = 21.25, p < 0.001). The most common barriers were patients' diverse social backgrounds (82%) and time limitations (77%). Primary healthcare workers in Al-Ramadi demonstrated fair to moderate communication knowledge and practice, with specific, addressable gaps. To improve the quality of physician-patient communication, specific training programs for the development of empathy and good emotional communication are required, along with institutional initiatives targeted at resolving time constraints.
