5G-Practical Byzantine Fault Tolerance: An Improved PBFT Consensus Algorithm for the 5G Network

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant consensus algorithm with three ways to improve PBFT. Firstly, 5G-PBFT constructed the reputation model based on node performance and behavior. The model dynamically selected consensus nodes based on the reputation value to ensure the reliability of the consensus node selection. Next, the algorithm selected the primary node using the reputation model and verifiable random function, giving consideration to the reliability of the primary node and the randomness of the selection process. Finally, we take advantage of the low latency feature of the 5G network to omit the submission stage to reduce the communication complexity from ON² to ON, where N denotes the number of nodes. The simulation results show that 5G-PBFT achieves a 26% increase in throughput and a 63.6% reduction in transaction latency compared to the PBFT, demonstrating significant performance improvements

Feel Bad to Discard a Fashion Product: How AI Designers Influence Individuals\u27 Sustainable Consumption

This study explores how AI technology in fashion design influences consumers\u27 sustainable consumption behaviors, focusing on emotional attachment to products. By comparing AI-generated and human-designed fashion items, the study examines how designer type impacts negative emotions about discarding products, mediated by emotional attachment. Results from two experimental studies reveal that designer type significantly affects negative emotions toward discarding human-designed items, but emotional attachment was not influenced by designer type in the first study. This lack of difference may be due to personal characteristics that moderate the effect. The second study found that individuals who perceive AI as human-like form stronger emotional attachments to AI-generated products, which impacts their sustainable consumption behavior. The findings suggest that fostering emotional connections with AI-designed products could encourage more sustainable consumer behaviors in fashion

Security Enhancement in UAV Swarms: A Case Study Using Federated Learning and SHAP Analysis

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging federated learning and FedAvg for weight averaging, combined with SHAP analysis to identify key contributing features. This AI-based system requires less human intervention and is more effective in detecting novel attacks than traditional intrusion detection systems (IDS). Using the IEEE DataPort UAV Attack Dataset, this study aims to develop a robust distributed ML security solution for UAV swarms, significantly advancing the cybersecurity landscape for CPSs

Don\u27t Leave the Good Things in the Rearview! A Field Experiment Examining the Influence of a Positive Work Reflection Intervention on Taxi Drivers\u27 Work Behaviors

As service jobs tend to be demanding and exhausting, it is critical to identify ways that help service employees stay positive and engage in behaviors that represent high quality customer service. Drawing upon affective events theory, this research aims to examine how a positive work reflection intervention influences service employees\u27 work behaviors via positive affect and the role of promotion focus as a personality moderator. We used a between-subjects design to test the effects of the “three good things” positive work reflection intervention in a field experiment. Data were collected from 74 taxi drivers who were randomly assigned into either an intervention condition or a control condition. They rated their positive affect and work behaviors using daily diary surveys for 7 consecutive days, during which participants in the intervention condition completed the “three good things” exercise at the end of each workday. Results showed that participants in the intervention condition reported higher levels of morning positive affect compared to participants in the control condition, but only for those with higher levels of promotion focus. Further, the intervention indirectly increased extra-role service behavior and reduced rule breaking behavior and passive response to entitled customer demands via positive affect for individuals with higher levels of promotion focus. The intervention showed opposite effects for individuals with lower levels of promotion focus. The intervention also directly enhanced employees\u27 active response in handling entitled customer demands. Our findings suggest that a simple exercise like the “three good things” positive work reflection intervention can significantly influence service employees\u27 work behaviors and the importance of considering the alignment between the intervention and individual differences

Debt Decisions: The Effects of Paying for College in the United States with Student Loans

Student loans have increasingly become a burden on individuals. This qualitative analysis explores how structural inequities and financial literacy gaps influence U.S. student loan borrowers’ decisions to finance postsecondary education. The study highlights racial and economic disparities examining participants’ reflections on debt aversion and systemic barriers to equitable access. We asked why participants took out Federal student loans, asked about their decision processes when taking on debt, whether participants took any action to reduce accumulated debt, and if they’d made any discoveries about their decisions to take on student loan debt. After coding and a thematic analysis, five themes and five subthemes emerged. Respondents saw loans as their only option to afford college, using the funds not just for tuition but for living expenses. Student loan debt has significantly impacted their life choices forcing some to delay major milestones like marriage and family, buying a house, and saving for retirement. A discussion including implications for policy, research, and practice as well as future directions is provided

Dominance of Leading Business Schools in Top Journals: Insights for Increasing Institutional Representation

The competitive push for business schools to publish in prestigious journals has resulted in a disproportionate number of papers in prestigious Management and Operations Research/Management Science (OR/MS) journals coming from a select group of institutions. Our analysis shows the Matthew effect of prestigious journals favors established schools with 51.2% of papers in 18 Management ABS 4* journals and 61.3% of papers in 3 OR/MS ABS 4* journals involving authors from the 100 top business schools identified by the University of Texas at Dallas (UTD). Citation patterns are similarly concentrated among papers authored by scholars from UTD-listed business schools, with nearly 80% of citations from 4* Management journals directed to equally rated 4* Management journals (67.8% for 4* OR/MS journals). An initial regression analysis suggested a positive correlation between the percentage of papers in a journal attributed to authors affiliated with those leading business schools and journal citation performance. However, further examination using multi-level regression adjusted for journal prestige, using the ABS and FT50 lists, showed a negative interaction effect on citation rates for papers from these schools in prestigious OR/MS journals. This insightful finding was confirmed by a post-hoc comparison revealing no significant citation advantage in prestigious journals for papers from leading business schools over those from a broader range of institutions. Thus, while leading business schools benefit from disproportionate space in prestigious journals, this does not translate to a citation advantage for the journals themselves, indicating no Matthew effect at the journal level driven by these schools. We argue that our findings show a unique opportunity for prestigious journals and business schools to expand collaborations with institutions in geographies historically underrepresented without a significant impact on the citation performance of those journals. This inclusion would only benefit research excellence, as our results demonstrate convergence in citation rates, citation patterns on external research areas, and topics across both subsets of papers—from leading institutions and those from a broader institutional spectrum—published in prestigious journals, indicating that diversifying contributions does not compromise the performance of these journals

70 - Unmeasured Confounding in Meta-Analyses of Observational Studies Evaluating Maternal Periodontitis and Adverse Birth Outcomes

Background: Maternal periodontitis has been consistently linked to adverse birth outcomes (ABOs). However, a causal relationship is not established due to potential unmeasured confounding factors in observational studies and inconclusive results from randomized controlled trials. This study aimed to assess the impact of unmeasured confounding factors in meta-analyses examining associations between maternal periodontitis and preterm birth (PTB) or low birth weight (LBW). Methods: PubMed and MEDLINE, Cochrane, Embase, and Google Scholar were searched for systematic reviews with meta-analyses of observational studies between 2002 and 2023. Systematic reviews with meta-analyses of observational studies were included if either PTB or LBW were the outcome and were published in English. The E-value analogue method was used to evaluate possible unmeasured confounding effects in random effects meta-analyses. Results: 62 original studies from 9 meta-analyses met the inclusion criteria. In relation to the exposure and outcome, the random-effects meta-analysis showed that even with an unmeasured confounder having a relative risk (RR) of 3.00, 80% of cohort studies evaluating PTB and 90% evaluating LBW would have an RR\u3e2.00. Conclusions: Associations between maternal periodontitis and ABOs in observational studies appear robust to unmeasured confounding factors, supporting the hypothesis that maternal periodontitis is causally related to ABOs

MXene-Based Materials for Enhanced Water Quality: Advances in Remediation Strategies

Two-dimensional MXenes are promising candidates for water treatment because of their large surface area (e.g., exceeding 1000 m²/g for certain structures), high electrical conductivity (e.g., \u3e1000 S/m), hydrophilicity, and chemical stability. Their strong sorption selectivity and effective reduction capacity, exemplified by heavy metal adsorption efficiencies exceeding 95 % in several studies, coupled with facile surface modification, make them suitable for removing diverse contaminants. Applications include the removal of heavy metals (e.g., achieving \u3e90 % removal of Pb(II)), dye removal (e.g., demonstrating \u3e80 % removal of methylene blue), and radioactive waste elimination. Furthermore, 3D MXene architecture exhibit enhanced performance in antibacterial activities (e.g., against bacteria), desalination rejection percentage, and photocatalytic degradation of organic contaminants. However, several challenges have remained, which necessitate further investigation into toxicity (e.g., assessing effects on aquatic organisms), scalability, and cost-effectiveness of large-scale production. This review summarizes recent advancements in 3D MXene-based functional materials for wastewater treatment and water remediation, critically analyzing their both potential and limitations

Representative National Survey on Drug Use During the COVID-19 Stay-At-Home Order in the USA

Objectives This study explores the drug use behaviour in the US general population in the early days of the COVID-19 pandemic with a focus on the relationship between sociopsychological factors, mobility restrictions from March to June 2020 and mental health conditions. Design A retrospective anonymous online survey representing a cross-section of the US population in 2020. Setting A qualified panel of 500 000 Qualtrics participants stratified by gender, race, age and geographical region to represent the US population. Participants 3340 participants voluntarily consented to respond. Measures Outcome measure for illicit and non-medical use of prescription drugs based on the National Institute on Drug Abuse-Modified Alcohol, Smoking and Substance Involvement Screening Test Level-2 Substance Use for Adult Questionnaire and predictor measures include self-reports of mobility behaviours, demographics and mental states using psychometrically validated scales. Results χ² tests showed that those who stayed home reported higher odds (p\u3c0.05) of use across all 10 types of drugs. Logistic regression revealed that those with children at home, larger social circles, and pain, depression or trauma had higher odds, but older individuals and women had lower odds (p\u3c0.05) of drug use. Conclusions Mobility restriction was a risk factor for drug use. Demographics and mental health conditions were important covariates, underscoring the need for further research on unintended consequences of infection control policies during national health crises

30 - Fitting nitrogen vacancy magnetometry spectra

Quantum sensors are useful in a variety of applications, ranging from accelerator science to biomedical imaging. Nitrogen vacancy (NV) centers in diamond can be used to perform sensitive measurements of magnetic fields and make excellent sensors due to the compatibility of the diamond crystals with different environments. We use laser light to excite photoluminescence from NV centers and observe the dependence on external magnetic fields via optically detected magnetic resonance (ODMR). The ODMR spectrum presents as a complex series of multiple resonance peaks whose positions and intensities depend on the interaction of the NV centers with the external magnetic field, in particular its direction and magnitude. We developed a computer code to fit the position of the resonance peaks so that we can extract the value of the magnetic field. Each peak is individually fitted, obtaining the parameters allowing us to then manually fit the entire spectrum of peaks. Currently, we are working towards a fitting algorithm that will handle an arbitrary number of peaks simultaneously, allowing us to automatically fit the entire ODMR signal and extract the magnetic field