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Connected and automated vehicles (CAVs) are getting a lot of attention these days as their technology becomes more mature and they benefit from the Internet-of-Vehicles (IoV) ecosystem. CAVs attract malicious activities that jeopardize security and safety dimensions. The cybersecurity systems of CAVs detect such activities, collect and analyze related information during and after the activity, and use cyber threat intelligence (CTI) to organize this information. Considering that CTI collected from various malicious activities may share common characteristics, it is critical to provide the cybersecurity stakeholders with quick and automatic ways of analysis and interrelation. This aims to help them perform more accurate and effective forensic investigations. To this end, we present CAVeCTIR, a novel approach that finds similarities between CTI reports that describe malicious activities detected on CAVs. CAVeCTIR uses advanced machine learning techniques and provides a quick, automated, and effective solution for clustering similar malicious activities. We applied CAVeCTIR in a series of experiments investigating almost 3000 malicious activities in simulation, real-world, and hybrid CAV environments, covering seven critical cyber-attack scenarios. The results showed that the DBSCAN algorithm identified seven no-overlapping core clusters characterized by high density. The results indicated that cybersecurity stakeholders could take advantage of CAVeCTIR by adopting the same or similar methods to analyze newly detected malicious activity, speed up the attack attribution process, and perform a more accurate forensics investigation.

This study aimed to assess the perceived emotional intelligence (EI) of nephrology nurse preceptors before and after an educational intervention. Results showed a statistically significant increase in EI scores, with the Median Schute Self-Report Emotional Intelligence Test score rising from 128 to 132 (p = 0.022). This study supports integrating EI training in nephrology preceptor programs because EI preceptors positively influence novice nurses, leading to better outcomes and improved emotional regulation. An EI learning segment should be included in registered nurse nephrology preceptor programs.

INTRODUCTION: One of the tasks performed by the Federal Police is the verification and cross-referencing of data contained in Financial Intelligence Reports (FIRs) produced and forwarded by the Financial Activities Control Council (COAF) - an activity in which, among the police involved, the absence of a standard system of execution.OBJECTIVES: The present work aims to present a study on the form currently in use within the scope of the Federal Police Station regarding the analysis of FIRs from the COAF, then presenting a methodology suggestion, aiming to speed up the process. Regarding open sources, the objective is to carry out a survey of possible complementary repositories not yet used and to expose ways of implementing queries.METHODS: Pointing out the workflow currently used (displaying it in graphic form) as well as identifying the data sources consulted (open sources and closed sources) during the process,CONCLUSION: understanding how the FIR analysis system is currently carried out, identifying possible aspects for improvement, and suggesting a methodology to be used, indicating for this the use of files in a specific format (.CSV), exclusion of queries in similar repositories (System “A”) and, mainly, the automation of part of the procedure (with the use of the RIBOT prototype software).

The expansion of organized crime across national borders has become a key security concern for the international community. In this theoretically and empirically vibrant portrait of a global phenomenon, Jana Arsovska examines some of the most widespread myths about the so-called Albanian Mafia. Based on more than a decade of research, including interviews with victims, offenders, and law enforcement across ten countries, as well as court files and confidential intelligence reports, Decoding Albanian Organized Crime presents a comprehensive overview of the causes, codes of conduct, activities, migration, and structure of Albanian organized crime groups in the Balkans, Western Europe, and the United States. Paying particular attention to the dynamic relationships among culture, politics, and organized crime, the book develops a framework for understanding the global growth of the criminal underworld and provides a model for future comparative research.

The expansion of organized crime across national borders has become a key security concern for the international community. In this theoretically and empirically vibrant portrait of a global phenomenon, Jana Arsovska examines some of the most widespread myths about the so-called Albanian Mafia. Based on more than a decade of research, including interviews with victims, offenders, and law enforcement across ten countries, as well as court files and confidential intelligence reports, Decoding Albanian Organized Crime presents a comprehensive overview of the causes, codes of conduct, activities, migration, and structure of Albanian organized crime groups in the Balkans, Western Europe, and the United States. Paying particular attention to the dynamic relationships among culture, politics, and organized crime, the book develops a framework for understanding the global growth of the criminal underworld and provides a model for future comparative research.

Purpose: This study presents a management methodology for comprehensively evaluating teaching performance by integrating statistical analysis of quantitative data, sentiment mining from text, and artificial intelligence tools. The objective is to provide academic managers with a robust and efficient diagnostic system that enables the continuous improvement of educational quality through the systematic identification of faculty strengths and areas for improvement, thereby facilitating the decision-making process in academic management.Design/methodology/approach: The research adopts an Action Research approach, developing and implementing the EvalúaPro application using MATLAB® App Designer. Student evaluations from the 2425-1 (September-December 2024), 2425-2 (January-April 2025) and 2425-3 (April-July 2025)  academic periods were analyzed, which included quantitative (Likert scale questions) and qualitative (open-ended comments) components. For the 2425-1 period, 362 evaluations were analyzed, corresponding to 30 sections of 21 courses taught by 20 faculty members. For the 2425-2 period, 338 evaluations from 33 sections of 24 courses taught by 24 faculty members were processed, and for the 2425-3 period, 447 evaluations were analyzed, corresponding to 31 sections of 24 courses taught by 23 faculty members. All participants belonged to a department within the engineering faculty. Teaching competencies were strategically categorized into Soft Skills (Effective Communication, Interpersonal Skills, Time Management, and Organization) and Technical/Professional Skills (Content Mastery, Teaching Methodology). The qualitative analysis implemented the VADER algorithm for sentiment mining, while descriptive statistics were used for the quantitative analysis. Validation included tests with department heads to assess the application's effectiveness as a management tool.Findings: The methodology proved highly effective for the managerial diagnosis of teaching performance, facilitating the identification of patterns at both individual and departmental levels. In the validation with department heads, 87.5% \"agreed\" or \"strongly agreed\" that the information presented by the prototype facilitates decision-making regarding faculty support, monitoring, and evaluation (37.5% \"strongly agree,\" 50% \"agree,\" 6.3% \"neither agree nor disagree,\" 6.3% \"strongly disagree\"). Regarding the generated improvement plan, 93.8% of department heads \"agreed\" or \"strongly agreed\" that it accelerates feedback to faculty (43.8% \"strongly agree,\" 50% \"agree,\" 6.3% \"strongly disagree\"). Concerning its utility for diagnosis and decision-making for continuous improvement, 87.5% expressed they \"agreed\" or \"strongly agreed\" (62.5% \"strongly agree,\" 25% \"agree,\" 12.5% \"neither agree nor disagree\"). The system generated personalized improvement plans for faculty with scores below 3.0 and departmental strategies when more than 25% of professors showed similar areas for improvement. Furthermore, the system translates its integrated data analysis into a predictive tool, automatically alerting managers to signs of student dissatisfaction and thereby facilitating preemptive support measures.Research limitations/implications: The main limitations include adapting the VADER algorithm for the specific academic context and requiring constant feedback to refine the artificial intelligence algorithms. Further research is required to validate the effectiveness of the automatically generated improvement plans in subsequent academic periods and their impact on improving teaching performance.Practical implications: The methodology significantly reduces academic managers' time analyzing teaching evaluations, enabling faster and more specific feedback. The system facilitates identifying specific training needs that institutional resources, such as the Teaching Center, can address, thereby improving the efficiency of academic human resource management.Social implications: Implementing this methodology enhances the analysis of educational evaluation, ensuring that student opinions are systematically considered for continuous institutional improvement, which can potentially reduce student attrition and enhance the overall educational experience.Originality/value: This methodology represents an innovation that improves educational management by integrating advanced data analysis tools with structured managerial processes. The holistic approach, which combines statistical analysis, text mining, and artificial intelligence for faculty evaluation, offers significant value to educational institutions seeking to implement evidence-based continuous improvement systems. The strategic categorization of skills and the automatic generation of personalized improvement plans constitute an original contribution to educational management.

DIA Periodic Intelligence Summary is prepared by the U.S. Defense Intelligence Agency to provide the [U.S. Joint Chiefs of Staff; U.S. Armed Forces. Unified and Specified Commands; U.S. Armed Forces; Government agencies] with periodic Intelligence estimates regarding actual or simulated foreign Emergency situations; Defense Intelligence Notice is a brief Intelligence Report produced by the U.S. Defense Intelligence Agency to report the facts and significance of a single event; Spot Intelligence Report is prepared by the U.S. Defense Intelligence Agency to provide information regarding significant events to the [U.S. Joint Chiefs of Staff; U.S. Department of Defense. National Military Intelligence Center; U.S. Armed Forces. Unified and Specified Commands; Government agencies]; Spot Intelligence Report is a narrative Intelligence Report designed to provide facts and Intelligence analysis of a variety of significant events; U.S. Defense Intelligence Agency prepares the Defense Intelligence Notice to provide the [U.S. Joint Chiefs of Staff; U.S. Armed Forces; U.S. Armed Forces. Unified and Specified Commands; Government agencies] with Current intelligence regarding significant events; Daily Intelligence Summary is prepared by U.S. Defense Intelligence Agency to provide the [U.S. Joint Chiefs of Staff; U.S. Department of Defense. National Military Intelligence Center; U.S. Armed Forces. Unified and Specified Commands; Government agencies] with a daily analysis of [Military actions; Military exercises] and a summary of Intelligence produced during the preceding 24 hours; Daily Intelligence Summary is an Intelligence Report which provides Intelligence analysis of hostile [Military actions; Counterintelligence] against the U.S.; DIA Periodic Intelligence Summary is an Intelligence Report which provides Current intelligence on a variety of significant events and conditions

Este estudio tiene como objetivo analizar la relación entre la inteligencia emocional (IE) autoinformada por los hijos y la IE percibida sobre sus padres con la percepción del clima familiar. El marco teórico que lo sustenta es el Modelo de habilidad de Mayer y Salovey (1997) y las medidas de autoinforme relacionadas con éste. La muestra la componen 156 hijos (71 varones y 85 mujeres). La escala para evaluar la IE fue la TMMS-24 (Fernández-Berrocal, Extremera y Ramos, 2004) y una adaptación de ésta, la PTMMS-24 para evaluar la percepción de los hijos sobre la IE de sus padres en cada uno de los factores, Atención, Claridad y Reparación. El clima familiar percibido fue evaluado con la escala FES (Moos, Moos y Trickett, 1995). Se encontraron relaciones significativas entre la percepción de la IE de los padres y el clima familiar percibido por los hijos. Los análisis de regresión estratificados por bloques de cada subescala del clima familiar, muestran cómo tanto la IE auroinformada como la IE percibida son buenos predictores de factores como la expresividad en el clima familiar.

Intelligence reports state that the first \"rollout\" Launch bases in China (People's Republic) for CSS-3 Missiles are near Operational readiness