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Self-Regulation and Motivation in the Context of Academic Probation: A Phenomenological Study
Full text linkThis hermeneutic phenomenological study explored the lived experience of students on academic probation as they related to navigating the complexity of causal factors and their impact on self-regulated learning behaviors and motivation. Participants were students who had persisted at least one semester after being placed on academic probation at a small, private, liberal arts college in Western Canada. Data was collected through individual interviews with students using a protocol that was designed for the study. Interviews were transcribed, reduced to anecdotes, and then analyzed using a hermeneutic approach centered around the processes of epoché and reduction. Through these processes, emerging themes revealed the complexity of experiences faced by students on academic probation
Prohibiting Government Entities from Paying Ransoms in Ransomware Attacks: A Policy Analysis
No full textRansomware attacks have been on the rise over the past decade and pose a substantial threat to government entities. In response to the escalating ransomware epidemic, several states have introduced legislation prohibiting government entities from paying ransoms to cybercriminals. This technical paper conducts a comprehensive analysis of such prohibitions, considering the potential economic, operational, and security implications for government organizations. This analysis begins by examining the devastating impact of ransomware attacks on government entities, including operational disruptions, the loss of critical data, and financial costs. It then evaluates the risks associated with paying ransoms, such as funding criminal enterprises and the lack of guarantee for data recovery. Additionally, the paper explores the legal landscape surrounding ransom payments by highlighting the implications of recent state laws for how government entities should respond to these attacks. Next, the paper assesses the potential benefits of enacting policies that prohibit ransom payments by government entities. These include deterring future ransomware attacks, promoting proactive cybersecurity measures, and avoiding financial contributions to criminal organizations. These benefits are weighed against the challenges and risks posed by such prohibitions, such as the potential for permanent data loss and operational
disruptions in the absence of viable recovery options. This paper offers policy recommendations for lawmakers that will enable them to balance cybersecurity concerns against economic and operational considerations
INVESTIGATION OF DIAZABOROLIDINES AND BOROXINE-AMINE ADDUCTS THROUGH COMPUTATIONAL AND SYNTHETIC METHODS
Full text linkThis study investigates the boron-nitrogen bond formation through condensation reactions between phenylboronic acid and various amine compounds, focusing on the structural and electronic factors governing these interactions. Benzodiazaboroles are important due to their potential applications in various fields, particularly in semiconductors, optical materials, and polymer sensors. The formation of benzodiazaboroles through the condensation of phenylboronic acids with benzene-1,2-diamine is well-established. However, to our knowledge, the synthesis of diazaborolidines from phenylboronic acid (or its esters) and ethylene-based diamines remains unexplored. Here, we investigate the formation and stability of diazaborolidines using different diamines. Initial synthetic attempts established that ethylene-based diamines predominantly form boroxine-amine adducts rather than the expected
diazaborolidines. Thermodynamic calculations using DFT methods at the B3LYP/6- 311++G(d,p) level revealed that benzodiazaborole formation is energetically favored by approximately 6 kcal/mol compared to diazaborolidine formation. The investigation of
boroxine-amine adducts demonstrated that both electronic and steric factors influence adduct stability, with primary and secondary aliphatic amines forming thermodynamically favorable complexes (ΔG values of -2.65 to -3.25 kcal/mol), while sterically hindered amines like N,N'-diphenylethane-1,2-diamine show unfavorable adduct formation (+13.34 kcal/mol). The thermodynamic analysis of competing reaction pathways reveals a significant energetic preference for the formation of boroxine-amine adduct over the diazaborolidine by approximately 9.69 kcal/mol (approximately 7 orders of magnitude difference in the equilibrium constant)
Network Intrusion Detection using Advanced Machine Learning with Data Engineering
Full text linkAs network technologies rapidly advance and organizational operations
increasingly rely on networked systems, cyberattacks have become more sophisticated, posing widespread threats. Consequently, distinguishing malicious network traffic from normal traffic has become a critical task in protecting organizational infrastructures and ensuring the integrity and reliability of networked systems. To address this, numerous network intrusion detection systems have been developed and studied in various network traffic datasets.
In this research, we present a novel approach featuring a transformer-
based two-level classification, where a transformer classification is applied to
each of the two hierarchical attack levels. Specifically, binary classification is
performed to distinguish between normal and attack traffic, followed by multiclass classification to identify specific types of network attacks.
Experimental results on the CICIDS-2017 dataset demonstrate a reduction
in false negatives (that is, the misclassification of attacks as normal traffic), which is a crucial factor for security-sensitive systems. Specifically, it achieved an accuracy of 0.9874 and an F1-score of 0.9924 for binary classification, and an accuracy of 0.9976 and an F1-score of 0.9715 in multiclass classification. Furthermore, comparative analysis with existing approaches highlights the effectiveness of our method in achieving enhanced classification performance for both binary classification and multiclass attack detection.
To enhance the effectiveness of NIDS, we developed an unsupervised
learning framework that combines matrix factorization and standard K-Means
clustering. In this study, we used three matrix factorizations: Singular Value
Decomposition (SVD), Non-negative Matrix Factorization (NNMF), and CUR
Matrix Decomposition, to extract latent information in each of two NIDS benchmark datasets, NSL-KDD and UNSW-NB15 and applied K-Means clustering to the extracted information. For the binary class, our framework achieved an accuracy of 88% for NSL-KDD and 94% for UNSW-NB15. For multi-class, we attained an accuracy of 72% for NSL-KDD and 53% for UNSW-NB15. These results surpass those of existing approaches, underscoring the effectiveness of our proposed unsupervised learning approach in detecting and mitigating cyber threats more accurately.
We also developed a supervised learning framework that uses XGBoost
classifier. In this study, we used nine NIDS benchmark datasets and applied
XGBoost to the extracted information. For the binary class, this framework achieved an accuracy of 99.99% for nine datasets used. For multi-class, we attained an accuracy of 99.99% for eight out of nine datasets. These results surpass those of existing approaches, underscoring the effectiveness of our proposed unsupervised learning approach in detecting and mitigating cyber threats more accurately
How poor sleep and alcohol use are related to classifications of offending trajectories
Full text linkA growing body of literature has demonstrated that poor sleep and alcohol use are positively associated with antisocial behavior during adolescence. However, little research has investigated how these factors are related to trajectories of offending from childhood to adulthood. Previous research has investigated factors that contribute to the development of offending patterns across several classes of offenders (e.g., high, middle, low), generally finding that separate factors are related to membership in each group. A growing body of literature has also investigated the role of alcohol use and poor sleep in the development of these behaviors. However, little attention has been paid to examining whether these factors are related to different patterns of antisocial behavior. It is possible that these factors are differentially associated with different classifications of offending. Specifically, those who report engaging in the most antisocial behaviors may also be more likely to demonstrate high levels of alcohol consumption and poor sleep quality across their life span. Those who report engaging in comparatively lower levels of antisocial behavior may be less likely to report drinking and poor sleep quality. The current dissertation aims to test these hypotheses using latent growth curve modeling to better understand how changes in sleep and alcohol use are related to levels of antisocial behavior across the life span
COMPUTATIONAL ANALYSIS OF THE HYDROLYTIC STABILITY AND DIRECT EXCHANGE OF PHENYLBENZOBOROLES
No full textPhenylbenzoboroles, five-membered rings containing boron and other heteroatoms, exhibit unique hydrolytic stability, contributing to the structural integrity of materials like covalent organic frameworks. This study examines the thermodynamic properties of dioxa-, diaza-, and oxazaboroles using density functional theory (DFT) with the B3LYP functional and 6,311G++(d,p) basis set in the gas phase. Hydrolysis and exchange mechanisms were investigated using synchronous transit quasi-newton (STQN) methods, along with intrinsic reaction coordinate (IRC) calculations to verify that the modeled transition states were connected to the reactants and products.
Using a single water molecule and a four-membered transition state model, hydrolysis energies indicate that dioxaboroles are the most stable, while diazaboroles are more prone to hydrolysis. Intermediate in stability, oxazaboroles, revealed that B-N bond cleavage required less energy compared to B-O cleavage. Adding a second water molecule significantly reduced the hydrolysis barrier for all systems, suggesting a more favorable transition state geometry. This is attributed to reduced ring strain in the six-membered transition state.
Direct exchange reactions using catechol, ortho-phenylenediamine, and ortho-aminophenol were modeled with a four-membered transition state similar to the single water molecule studies. Analysis of the intermediates and energy barriers indicated that the direct exchange reactions are significantly less favorable compared to hydrolysis.
These findings provide insight into the relative stability of phenylbenzoboroles derivatives, with implications in dynamic covalent chemistry and materials design
How Gratitude and Social Support Influence the Link Between Exposure to Negative Parenting Practices and Adverse Outcomes
No full textThe current positive psychology literature has highlighted gratitude as a potential avenue for addressing societal concerns (e.g., delinquency, psychopathology; Bono et al., 2017; Cregg & Cheavens, 2021). In addition, research has found social support as effective in mitigating negative outcomes, including mental health problems and delinquent behavior (e.g., Sampson & Laub, 2005; Wang et al., 2018). However, no known research is available regarding the protective nature of gratitude or social support in the context of exposure to negative parenting practices (e.g., parental rejection, physical/corporal punishment) and subsequent maladaptive outcomes, such as delinquent behavior and psychopathology. The current study evaluated gratitude and social support as potential protective factors in these relationships. Specifically, participants completed measures assessing gratitude, perceived social support, exposure to negative parenting practices, and maladaptive outcomes to further understand both direct associations and potential moderating effects. Notably, the study’s main findings revealed that gratitude moderated the relationship between parental rejection and psychopathology, highlighting the value of promoting gratitude in resilience-based frameworks aimed at reducing mental health symptoms following exposure to negative parenting practices. Parental rejection was also significantly associated with higher levels of both delinquent behavior and psychopathology, suggesting the potential negative impact of early caregiver experiences. Additionally, post-hoc testing indicated that individuals who reported experiencing corporal punishment during childhood, compared to those who did not, showed significantly higher levels of delinquent behavior and perceived parental rejection. Exploratory regression analyses also provided further information regarding associations among study variables and maladaptive outcomes, contributing to a broader understanding of these relationships
Evaluating Stallion Sperm Quality Post-Thaw: Effects of Selection Methods on Concentration, Motility, and Mitochondrial Activity
No full textThe commercial use of frozen-thawed equine semen for foal production is a well-established clinical practice. However, to address fertility issues in some stallions, sperm selection techniques are essential for improving semen quality. This study aimed to evaluate the quality of post-thaw sperm processed using either a density gradient (DG; EquiPure™; NidaCon International AB, Sweden) or a microfluidic device (MD; VetMotl, Inc., Gaithersburg, MD, USA) compared to a control (CON) that was unprocessed. Frozen-thawed semen from 6 stallions was analyzed with two replicates per stallion. Thawed straws were diluted with extender (1:2.5; Botusemen® Gold, Botupharma USA, Phoenix, AZ, USA) and aliquoted into their respective selection methods for processing. The thawed sperm were assessed for concentration (CONC; 10⁶/ml), total motility (TMOT), progressive motility (PMOT), and active mitochondria measured by double-stain fluorescence assay (H33342/Rhodamin123) using computer assisted sperm analysis (CASA; AndroVision®, Minitube USA, Inc., Verona, WI, USA). Statistical analysis was performed using the mixed model of SAS V9.4, with stallion included as a random effect to account for variability between individual stallions. Statistical significance was set at P < 0.05, and trends noted at P < 0.10. A significant effect on mean CONC was observed (P= 0.0004). MD exhibited a significantly lower mean CONC (70.92 x 106/mL) compared to both the control (350.05 x 106/mL) and DG (266.39 x 106/mL). There was no difference in TMOT between processing methods (P = 0.1118). Interestingly, an overall difference was observed for PMOT (P=0.0038). The CON had a lower mean percentage of PMOT (38.5%) compared to both DG (49.5%) and MD (56.7 %). However, no difference in PMOT was observed between DG and MD (P = 0.1226). Furthermore, there was a difference in active mitochondria (P = 0.0003). The MD had a greater mean percentage of sperm with active mitochondria (40.5%) compared to both the DG (15.8%; P < 0.0001) and the CON (23%; P = 0.0012). Overall, this study highlights the differential impact of sperm selection methods on the quality of post-thaw equine spermatozoa. While MD showed advantages in terms of active mitochondria, it did not outperform DG in PMOT. These findings suggest that both methods have distinct benefits and may be utilized based on specific criteria for improving semen quality in equine reproductive practices
The Effects of Implicit Racial Bias in Law Enforcement: Implications for Racial Bias Screening of Police Candidates
No full textImplicit bias in law enforcement significantly undermines police performance, distorts departmental culture, and erodes public trust. This white paper delves into these impacts and explores the implications of screening police candidates for implicit racial bias to ensure impartiality and fairness in policing. Implicit bias, unconscious attitudes, or stereotypes that influence behavior can lead to discriminatory practices even among well-intentioned officers. Addressing this issue is crucial. The adverse effects of implicit racial bias on police performance are profound. Research shows that biased decision-making influences critical aspects of policing, such as disproportionately high rates of traffic stops, searches, arrests, and the use of force in minority communities. These biased practices not only undermine the effectiveness of law enforcement but also perpetuate systemic inequalities, leading to unjust treatment and adverse outcomes. Implicit bias also distorts police department culture by normalizing discriminatory attitudes and behaviors. Public trust, a cornerstone of effective policing, is severely compromised by implicit bias. Policy reforms at various levels are essential to effectively combat implicit bias in law enforcement. These reforms should encompass changes in hiring practices, implementing accountability measures, and initiatives to foster positive community engagement. To address implicit racial bias, police agencies should implement comprehensive policies that include a component to screen for implicit racial bias during the hiring process and provide ongoing training and support.LEMI
Artificial Intelligence in Policing
No full textThe traditional model of policing is no longer sustainable in the current technological environment of 21st-century police officers. Today’s police officers must be more effective and efficient due to the national climate facing the police industry. Police departments are facing significant challenges from staffing shortages, increased crime, and police reform. To overcome these challenges, police departments should incorporate artificial intelligence into their processes. The infusion of artificial intelligence into current software systems utilized by police will greatly enhance its abilities in the areas of data management, analytics, data mining, and predictive policing. Artificial intelligence will also allow departments to automate several of their positions and supplement any shortages and staffing issues. The contextual data collected will allow police departments to reduce crime by providing analytical data that is actionable. On this basis, the integration of artificial intelligence into policing does show to have a significant impact on crime reduction, reducing the impact of staffing shortages, and enhancing the intelligence processing of data.LEMI