Cumberland University

This study examines the convergence of rapid Central Bank Digital Currency adoption and the growing threat that quantum computing poses to existing cryptographic systems. As digital sovereign currencies expand globally, with many economies actively exploring or piloting CBDCs, the vulnerability of classical public key cryptography to quantum attacks creates an urgent need for quantum resistant solutions. The research responds to this challenge by developing an evaluation framework focused on secure and interoperable cross border CBDC systems in a post quantum environment. Using desk based computational analysis and simulation methods suitable for remote researchers, the study evaluates lattice based algorithms standardized by the National Institute of Standards and Technology, specifically ML KEM for key encapsulation and ML DSA for digital signatures. The analysis confirms strong quantum resistance against known quantum algorithms, effectively removing harvest now decrypt later risks for future transactions. Interoperability assessments show that ISO 20022 with cryptographic abstraction is well suited for retail CBDC systems, while bridge based architectures such as BIS mBridge are more effective for wholesale settlements. Performance testing on thousands of simulated transactions indicates that post quantum protocols are feasible for wholesale use but introduce significant computational and bandwidth overheads that limit immediate retail scalability without hardware acceleration. Overall, the findings support a phased migration strategy that combines classical and post quantum cryptography in the near term, guided by cryptographic agility. The study provides practical insights for central banks seeking to balance security, efficiency, and cross border compatibility in an evolving quantum threat landscape.

Autism Spectrum Disorder (ASD) presents significant challenges in both early detection and individualized therapy due to the heterogeneity of symptoms and developmental trajectories. Recent advances in artificial intelligence offer promising avenues to enhance personalized autism care. This research aims to develop robust machine learning models for accurate ASD detection and investigate reinforcement learning (RL)–inspired architectures to facilitate adaptive, personalized therapy recommendations. Utilizing the Autistic Spectrum Disorder Screening Data for Adults dataset, comprising 704 records with behavioral AQ-10 questionnaire scores and demographic variables, two deep learning models were developed: a regularized Deep Q-Network (DQN)–inspired feed-forward neural network and a transformer-based neural network adapted for tabular data. Both models employed rigorous regularization techniques, including layer normalization, dropout, input noise, and L1/L2 penalties, and were trained with the Adam optimizer and early stopping to prevent overfitting. The models were evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The regularized transformer achieved a superior accuracy of 99.81%, while the DQN-inspired model attained 99.53%, with both models demonstrating exceptional AUC-ROC values above 0.99, underscoring their strong discriminative power. Confusion matrix analyses confirmed minimal misclassification, critical for clinical reliability. The transformer’s architecture offers scalability toward session-based therapy personalization, while the DQN model provides a robust decision-making framework compatible with RL environments. These findings position AI-powered models as effective tools for enhancing ASD screening and pave the way for future adaptive, personalized therapeutic interventions incorporating sequential feedback.

Moving toward a Circular Economy (CE) is not straightforward. One of the persistent hurdles lies in keeping track of how products progress through their lifecycles and, more critically, knowing exactly when they reach the End-of-Life (EoL) stage. This stage plays a central role in deciding how materials can be recovered and reused. While machine learning is increasingly explored in the Circular Economy, a significant gap persists in establishing a validated benchmark for the critical End-of-Life (EoL) prediction, complicated by class imbalance and reliance on unstructured diagnostic data. To address this, our work provides a first-of-its-kind, head-to-head comparative analysis of the three leading gradient boosting frameworks— XGBoost, LightGBM, and CatBoost—specifically focusing on maximizing recall for the minority EoL class and leveraging Natural Language Processing (NLP) to incorporate repair descriptions, thereby setting a new technical standard for lifecycle prediction. Publicly available lifecycle datasets formed the basis of the analysis, which moved from data preprocessing to hyperparameter tuning with GridSearchCV. Model performance was then measured using Stratified K-Fold Cross-Validation, and the results were compared across accuracy, precision, recall, and F1-score. Our analysis indicates that XGBoost was the strongest predictor. By implementing SMOTE to address the class imbalance, XGBoost achieved a robust EoL Recall of 85% (F1-score of 87%), demonstrating its strong capability to identify the crucial minority EoL class. These findings confirm that a high-recall, machine-learning approach is viable for optimizing material recovery logistics. From these findings, we draw practical guidance for choosing algorithms suited to lifecycle prediction, and we see clear evidence that machine learning can support better decisions in waste reduction and resource recovery. Even so, the imbalance between classes remains a key challenge, and addressing it will be important for improving the accuracy of future EoL detection efforts.

This research examines the vulnerabilities and resilience mechanisms of Software-Defined Networking (SDN) and cloud networks, with a specific focus on controller failures and security attacks. The study leverages both simulated and real-world data to assess how these vulnerabilities impact network performance metrics including downtime, packet loss, latency, and throughput. A significant observation from the study is that the nature and impact of network disruptions vary significantly depending on the type of failure or attack, highlighting the need for tailored resilience strategies. Machine learning techniques, notably Support Vector Machines (SVMs), are employed to classify these disruptions with high accuracy, suggesting a promising direction for proactive network management. The research proposes a novel framework that combines the dynamic control capabilities of SDN with machine learning and automation to improve the networks’ fault tolerance and recovery mechanisms. The effectiveness of this framework is demonstrated through enhanced resilience and reduced performance degradation during network disruptions. This study contributes to the field by outlining a scalable and efficient approach to mitigating vulnerabilities in SDN and cloud networks, thereby enhancing overall network stability and reliability.

Jungian Counseling and Play Therapy is both an introduction to Jung’s theory and a practical guide to Jungian-informed practice. Readers journey through the development of the mental health crisis of the digital age (which Jung foresaw) and are presented with solutions he suggested that are still being met with resistance, despite compelling facts. This book not only advocates for a more widespread integration of Jungian ideas into clinical practice, but also for greater acknowledgement of the integrity, creativity, and intersecting identities of clients, professional counselors, and play therapists. Integrating historical theory with contemporary research, this book helps students to weave creative techniques into their online and in-person clinical work. This is an ideal text for a psychodynamic theory or methods class, or to support counseling students and supervisors becoming interested in, or familiar with, the work of Carl Jung.

We report the use of clickable monoacylglycerol (MAG) analogs as probes for the labeling of glycerolipids during lipid metabolism. Incorporation of azide tags onto the glycerol region was pursued to develop probes that would label glycerolipids, in which the click tag would not be removed through processes including acyl chain and headgroup remodeling. Analysis of clickable MAG probes containing acyl chains of different length resulted in widely variable cell imaging and cytotoxicity profiles. Based on these results, we focused on a probe bearing a short acyl chain (C4 -MAG-N3 ) that was found to infiltrate natural lipid biosynthetic pathways to produce click-tagged versions of both neutral and phospholipid products. Alternatively, strategic blocking of the glycerol sn-3 position in probe C4 -MEG-N3 served to deactivate phospholipid tagging and focus labeling on neutral lipids. This work shows that lipid metabolic labeling profiles can be tuned based on probe structures and provides valuable tools for evaluating alterations to lipid metabolism in cells.

<p>SUMMARY:</p>\n\n<p>The people of Ukraine, a country of 44 million, have suffered a widespread Russian Federation-led military invasion beginning 24 February, 2022, resulting in increasing loss of life, destruction of infrastructure, and deterioration of services. The conflict has kicked off a humanitarian crisis that is rapidly devolving into a catastrophe, with threats of massive numbers of internally displaced persons (IDP) and refugees, significant civilian deaths from indiscriminate aerial and rocket bombardment of urban areas by Russian military forces, and long term political and economic instability. In the short term, individual mental and physical trauma, and dismantling of communities and families may become widespread even in areas that see limited military impacts in the coming days. The rapidly changing environment, uncertainty in outcome and resolution of the conflict, and military threats will make provision of humanitarian relief difficult, necessitating a robust near-term stabilization of the security situation. To this end, we recommend immediate establishment of a North Atlantic Treaty Organization (NATO)-led No Fly Zone (NFZ), humanitarian corridors for relief and movement displaced persons, and long term humanitarian commitment to the people of Ukraine.</p>\n\n<p>RECOMMENDATIONS:</p>\n\n<p><strong>We describe the humanitarian and military rationale for establishing a NATO-led No Fly Zone to prevent Russian air attacks inside Ukraine, and speed an end to the conflict. </strong>The ultimate goal of US/NATO direct actions described in this commentary is complete withdrawal of Russian-backed forces from Ukraine and repatriation of Ukrainian territory. Only a resolution where Russian Federation withdraws its forces will facilitate longer term regional security. Moreover, continuation of economic sanctions, appropriate reparations, and referral of evidence to the International Criminal Court (ICC) for prosecution of war crimes against civilians are recommended. This commentary will be used as a resource and reference material for political and humanitarian decision-makers in the United States and other countries.</p>

This article presents the development, results, validation, and implications of the TRansformative Outcomes and PrOcesses Scale (TROPOS), a 30-item, exploratory instrument to assess transformative learning (TL) among participants in educational programs. Core findings include a reliable (α = .884) and internally valid instrument with moderate correlation between transformative processes and outcomes ( r = .593). This study reflects the importance of critical reflection’s association with TL ( r = .541) but also places potential constraints on its prominence as a central component of TL, raising subtle questions into additional moderating or mediating constructs impacting TL.

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Nanoparticle Synthesis to Application: a Nanobiotechnology Lab Course for Biomedical Engineering Introduction The investigation of living systems using nanoscale technologies has evolved into a new field of research, bionanotechnology. Bionanotechnology is the development of novel technology at the nanoscale level that is used to interface with biological systems. This highly interdisciplinary area integrates knowledge from traditional fields of study such as engineering, chemistry, physics, biology, material science and medicine. The advent of bionanotechnology is changing the way traditional nanotechnology courses are taught. These courses are shifting from pure nanoparticle (NP) synthesis and characterization to include the interaction of NPs in biological systems. Laboratories provide students the opportunity to apply new knowledge in a simulated situation and enable the student to achieve learning objectives not easily obtained in the classroom. Fiesel and Rosa identify several general learning objectives for laboratory courses including instrumentation, modeling, experimentation, data analysis, communication, and teamwork.1 To achieve these learning objectives, practical hands-on application of concepts from a course and technical skills must be practiced. Additionally, Edward surveyed engineering students and found the students believe laboratory courses are essential to gaining a full understanding of course material.2 A bionanotechnology course, when housed within an engineering discipline, benefits from a lab by the active application of ideas and concepts presented in the lecture. Laboratory courses also appeal to the kinesthetic, self- many engineering students.2 This bionanotechnology laboratory course has been developed to accompany a three hour bionanotechnology lecture course that uses the textbook Nanotechnology in Biology and Medicine: Methods, Devices, and Applications by Tuan Vo-Dinh.3 The biomedical engineering (BME) course is designed for 4th year undergraduates as a one hour BME elective (3 contact hours). The objective of the laboratory is to engage students in nanoparticle development from synthesis to final application, similar to a research laboratory setting. The laboratory exercises are presented as two modules, the first being the synthesis of gold and silver NPs, and the second being the functionalization of quantum dot (QD) NPs with biological molecules such as folic acid. During each module, interaction of these NPs with living systems is evaluated and lab activities are correlated to the lectures. The learning outcomes of these labs are listed and learning outcomes in Table 1. The first module addresses NP toxicity, specifically the toxicity of gold and silver NP when interacting with cells. The toxicity of NPs has been a highly debated area of research.4-6 The biological effects of nanoparticles depend on composition, surface charge, size, concentration, cell type, and other factors. In this module, students synthesized gold and silver NPs prepared by sodium borohydride precipitation and characterized the particles by optical spectroscopy and dynamic light scattering (DLS).7, 8 The students conducted in vitro NP toxicology studies on a human cell line and quantified the viability of the cells by fluorescence flow cytometry. The pre- laboratory preparation included readings pertaining to NP toxicity.6 During this module, in