Discovering a Profile for Protect and Defend: Penetration Testing

The epidemiological transition has led to an increase in life expectancy and to a growing population of chronic patients, often with heart failure. These patients are frequently affected by comorbidities and frailty, which in turn increase the risk of disability and worsening quality of life, requiring an accurate multidimensional assessment (MDA). In this context, MDA is crucial for integrated and holistic management of elderly patients, considering not only the pathology but the patient in his complexity. MDA requires a multidisciplinary team to ensure a comprehensive and integrated assessment of the elderly patient. MDA tools assess various domains of health, using scales and validated tools to explore physical, functional, mental and socio-economic status. MDA is applied in two phases: an initial screening procedure and an in-depth analysis of individual problems for targeted interventions. MDA can be performed in various care settings, including outpatient clinics, hospitals, nursing homes, home care, and rehabilitation centers. Several studies show that MDA improves survival as well as functional and mental status, reducing hospitalization times and the frequency of institutionalization. This ANMCO position paper discusses MDA tools of older adults with chronic heart disease, highlighting the need for a holistic approach to address comorbidities and frailty in a growing population.

Design and Implementation of a RAG-Enhanced LLM Chatbot for Penetration Testing Tasks

The analysis of emerging technologies and their impact on penetration testing is a crucial aspect of staying ahead in the ever-evolving field of cybersecurity. As new technologies continue to emerge, so do new vulnerabilities and attack vectors that malicious actors can exploit. Therefore, it is imperative for penetration testers to stay updated on the latest trends and advancements in technology in order to effectively assess and mitigate risks. We highlighted the benefits of penetration testing such as compliance to industry regulations, vulnerability identification, enhancing the effectiveness of security controls, strengthening cybersecurity defences, among others. The result of our analysis identifies some of the key features and advancements in the analysis of emerging technologies and their implications for penetration testing.

Chapter 12 - Wireless Penetration Testing

There is a flurry of discussion, press and vendors explaining how helpful data science techniques can assist in cyber security defence; however, there is little information available about how to effectively leverage and implement data science techniques within a company’s cyber security defence team. The goal of this paper is to empower security incident response teams (SIRTs) to seamlessly build, deploy and operate ML solutions at scale. Our proposed solution is designed to cover the end-to-end ML workflows. Take-aways include managing and deploying a prediction pipeline, training data, prediction model evaluations and continuously monitoring these deployments to assist in SIRTs’ ability to defend and thwart cyber security attacks. An additional use case of implementing a machine learning (ML) application to predict domain-generating algorithms with the integrated data science pipeline and platform is also discussed and used as a reference.

The cognitive content-specificity hypothesis proposes that depression and anxiety can be discriminated on the basis of unique cognitive profiles. Alternatively, the Tripartite model suggests that, although depression and anxiety share a general distress factor, anhedonia is a characteristic of depression with anxious arousal a characteristic of anxiety. Past research devoted to integrating these two models has been limited in a number of ways. To remedy these limitations, this study attempted to assess the complete Tripartite model and used a multidimensional cognitive assessment tool to handle the heterogeneity of anxious cognitive content. Results on data collected from 411 clients seeking services at a university counseling center suggested that a one-to-one mapping between Tripartite dimensions and cognitive content was possible. Further, variables from each model simultaneously explained unique variance in depression and anxiety ratings.

Background In recent years, a growing number of researchers showed significant interest in psychological and social interventions to manage chronic musculoskeletal (MSK) pain. Cognitive and emotional empathy is an attractive and valuable sociopsychological factor that may provide protection and resilience against chronic MSK pain. However, its effect on outpatients remains underexplored. Objective To compare the empathy ability between chronic MSK pain outpatients and healthy controls and explore the relationship between cognitive/emotional empathy and chronic pain. Methods Patients with chronic MSK pain (n = 22) and healthy controls (n = 26) completed the pain assessment and empathy ability task, utilizing a multidimensional empathy assessment tool with satisfactory reliability and validity (i.e., the Chinese version of the Multifaceted Empathy Test (MET-C)). Results The data indicated that the chronic MSK pain outpatients had impaired cognitive empathy (i.e., lower squared cognitive empathy accuracy: Student's t = −2.119, P = 0.040, and longer task completion time: Student's t = 3.382, P = 0.002) compared to healthy controls, and cognitive empathy was negatively correlated with pain intensity (r = −0.614, P = 0.002). Further, the impaired cognitive empathy was present in identifying positive, but not negative emotions. Conclusion These results indicate that chronic MSK pain is associated with impaired empathy ability. Our studies contribute to offering a potential direction for developing psychosocial interventions to treat chronic MSK pain.

Novel Multidimensional Pain Assessment Tool Is a Feasible, Valid, and Enjoyable Approach to Communication of Pain Symptoms in Pediatric Sickle Cell Disease

The 3D/3D+ multidimensional geriatric assessment tool provides an optimal model of emergency care for patients aged 75 and over who attend the Emergency Department (ED). The baseline, or static, component (3D) stratifies the degree of frailty prior to the acute illness, while the current, or dynamic, component (3D+) assesses the multidimensional impact caused by the acute illness and helps to guide the choice of care facility for patients upon their discharge from the ED. The objective of this study was to evaluate the prognostic value of the 3D/3D+ to predict short- and long-term adverse outcomes in ED patients aged 75 years and older. Multivariable logistic regression models were used to identify the predictors of mortality 30 days after 3D/3D+ assessment. Two hundred and seventy-eight patients (59.7% women) with a median age of 86 years (interquartile range: 83-90) were analyzed. According to the baseline component (3D), 83.1% (95% CI: 78.2-87.3) presented some degree of frailty. The current component (3D+) presented alterations in 60.1% (95% CI: 54.1-65.9). The choice of care facility at ED discharge indicated by the 3D/3D+ was considered appropriate in 96.4% (95% CI: 93.0-98.0). Thirty-day all-cause mortality was 19.4%. Delirium and functional decline were the dimensions on the 3D/3D+ that were independently associated with 30-day mortality. These two dimensions had an area under receiver operating characteristic of 0.80 (95% CI: 0.73-0.86) for predicting 30-day mortality. The 3D/3D+ tool enhances the provision of comprehensive care by ED professionals, guides them in the choice of patients' discharge destination, and has a prognostic validity that serves to establish future therapeutic objectives.

Measuring Acute Pain Over Time in the Critically Ill Using the Multidimensional Objective Pain Assessment Tool (MOPAT)

• Frailty subtypes were assessed using the Kihon Checklist in older adults. • Housebound status independently predicted future fractures in a 9-year cohort. • A single KCL item on going out weekly identified high fracture risk. • KCL may serve as a simple screening tool for fracture prevention in the community. • Findings support including social frailty in fracture risk assessment models. This study aims to determine whether frailty, assessed using the simple multidimensional frailty assessment tool, the Kihon Checklist (KCL), is associated with future fractures and, if so, to identify which frailty subtypes exhibit the strongest association with fracture incidence. This study recruited local residents who visited government-organized examination sites for specific health check-ups for older adults, conducted over a three-day period each year and 468 community-dwelling older adults from 2014 to 2023 in Hino Town, Tottori Prefecture, Japan. Characteristics, physical structure and performance, and frailty were assessed at the baseline assessment. Frailty was assessed using KCL and categorized into six subtypes: lower motor function, malnutritional, lower oral function, housebound, reduced cognitive function, and possible depression. Self-Reported incidence fracture was determined through annual follow-up surveys. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate the association between frailty subtypes and fracture incidence. A total of 352 participants were followed for fracture incidence over a mean period of 6.9 years. During the follow-up period, 41 participants experienced at least one fracture, resulting in an incidence rate of 1.69 per 100 person-years. Both lower motor function and housebound were significantly associated with fracture incidence. However, after adjusting for previous fall history, and lower bone mass, housebound (HR = 2.436, 95 % CI: 1.125–5.271) remained significantly associated with fracture incidence. Unlike previous studies emphasizing musculoskeletal frailty, our findings highlight housebound status as a factor associated with fractures. Fracture prevention strategies should integrate social engagement interventions alongside conventional musculoskeletal management.

This study aimed to develop a Self-Active Aging Index (S-AAI) for the rural community of Thailand using the World Health Organization (WHO) framework, and score it according to age and gender. Overall, 1,098 elderly people were randomly selected. The self-reported questionnaires were categorized into three segments: health, participation, and security according to the WHO framework. An exploratory factor analysis was used to determine appropriate components. The S-AAI comprised 28 indicators and 9 factors: (1) mental/subjective health; (2) physical health; (3) health behavior and chronic disease; (4) vision and hearing; (5) oral health; (6) social participation; (7) stability in life; (8) financial stability; and (9) secure living. The overall S-AAI for all components was 0.65, with the index inversely proportional to age, but with no gender differences. The S-AAI is potentially Thailand's first multi-dimensional interactive aging assessment tool with a unique cultural context for rural areas. Although this tool is valid, it requires reliability testing.

There is a renewed interest in taking phenomenology seriously in consciousness research, contemporary psychiatry, and neurocomputation. The neurophenomenology research program, pioneered by Varela, rigorously examines subjective experience using first-person methodologies, inspired by phenomenology and contemplative practices. This review explores recent advancements in neurophenomenological approaches, particularly their application to meditation practices and potential clinical research translations. First, we examine innovative multidimensional phenomenological assessment tools designed to capture subtle, dynamic shifts in experiential content and structures of consciousness during meditation. These experience sampling approaches enable shedding new light on the mechanisms and dynamic trajectories of meditation practice and retreat. Second, we highlight how empirical studies in neurophenomenology leverage the expertise of experienced meditators to deconstruct aversive and self-related processes, providing detailed first-person reports that guide researchers in identifying novel behavioral and neurodynamic markers associated with pain regulation, self-dissolution, and acceptance of mortality. Finally, we discuss a recent framework, deep computational neurophenomenology, that updates the theoretical ambitions of neurophenomenology to naturalize phenomenology. This framework uses the formalism of deep parametric active inference, where parametric depth refers to a property of generative models that can form beliefs about the parameters of their own modeling process. Collectively, these methodological innovations, centered around rigorous first-person investigation, highlight the potential of epistemologically beneficial mutual constraints among phenomenological, computational, and neurophysiological domains. This could contribute to an integrated understanding of the biological basis of mental illness, its treatment, and its tight connections to the lived experience of the patient.

The study purpose was three-fold: finding a point of entry for primary prevention of child neglect, assessing for child neglect risk level for both first-time mothers and their partners, and individuals opinions about establishing and administering a multi-dimensional family assessment tool prior to birth. This study utilized a non-experimental, cross-sectional design to survey 250 first-time expectant mothers and their partners, attending Lamaze Classes at a public hospital in Lexington, Kentucky. Participants completed a 97-item questionnaire comprised of eleven different measures identifying individual personal (Attachment Theory) and situational characteristics (Ecological Theory). Results indicated that this particular population was predominately White Caucasian first-time expectant mothers and partners, the partners were at greater risk of child neglect than first-time mothers. First-time mothers were at low risk of child neglect but at higher risk of being unable to recognize situations indicative of child neglect. Preoccupied first-time mothers were more likely to recognize child neglect than fearful mothers. Participants with history of depression were better able to recognize scenarios of child neglect. Partners were less likely to possess knowledge addressing child development issues at ages 3 and 6 months. Partners, who were parented in a permissive manner exhibited more problems in identifying incidences of child neglect. The minority and non-married partners were more at-risk of child neglect. Participants were not particularly favorable regarding passage of a law mandating the assessment of child neglect prior to birth, even though they indicated they would participate if a law was enacted. Of the 22 predictor variables, three (opinions about babies, race, and physical health) and two (race and marital status) significantly contributed to the predictive model for risk of child neglect for the first-time expectant mothers and their partners respectively. One variable (history of depression) and two (history of depression and parented father permissive) significantly contributed to the predictive model for awareness regarding recognition of child neglect for mothers and partners respectively. This research study identified some of the predictive factors that should be considered in designing and initiating preventive services to address child neglect.

ObjectiveTo describe the overall sleep health of the Catalan population using data from the 2015 Catalan Health Survey and to compare the performance of two sleep health indicators: sleep duration and a 5-dimension sleep scale (SATED).MethodsMultistage probability sampling representative of the non-institutionalized population aged 15 or more years, stratified by age, gender and municipality size, was used, excluding nightshift-workers. A total of 4385 surveys were included in the analyses. Associations between sleep health and the number of reported chronic diseases were assessed using non-parametric smoothed splines. Differences in the predictive ability of age-adjusted logistic regression models of self-rated health status were assessed. Multinomial logistic regression models were used to assess SATED determinants.ResultsOverall mean (SD) sleep duration was 7.18 (1.16) hours; and SATED score 7.91 (2.17) (range 0–10), lower (worse) scores were associated with increasing age and female sex. Alertness and efficiency were the most frequently impaired dimensions across age groups. SATED performed better than sleep duration when assessing self-rated health status (area under the curve = 0.856 vs. 0.798; p-value <0.001), and had a linear relationship with the number of reported chronic diseases, while the sleep duration relationship was u-shaped.ConclusionsSleep health in Catalonia is associated with age and gender. SATED has some advantaged compared to sleep duration assessment, as it relates linearly to health indicators, has a stronger association with self-rated health status, and provides a more comprehensive assessment of sleep health. Therefore, the inclusion of multi-dimensional sleep health assessment tools in national surveys should be considered.