Objective Risk Assessment Research Articles

Community breast pain clinics can provide safe, quality care for women presenting with breast pain.

Breast pain is not typically a symptom of breast cancer, yet nationally 20% of 2-week wait (2WW) breast referrals are breast pain alone. The East Midlands Breast Pain Pathway improves patient experience and frees capacity in secondary care diagnostic breast clinics, managing women with breast pain only in a community setting. We report the results of implementation of community breast pain clinics (CBPCs) at sites in Derbyshire (catchment population ~1 million), with 12 months follow-up data. 1036 patients were seen at CBPCs between June 2021 and February 2023. The median patient age was 49 (range 16-88) years. 993 patients (95.8%) were discharged from the clinic with breast pain management advice. 43 (4.2%) patients were referred for further assessment at a 2WW breast diagnostic clinic. Objective family history risk assessment identified 124 patients (12.3%) above population risk of breast cancer, who were offered referral to familial cancer services for ongoing management. Seven patients were diagnosed with breast cancer at or within 12 months of CBPC attendance. Five patients were diagnosed through attending the CBPC, one patient was subsequently referred to 2WW clinic with a new symptom and had a mammographically occult tumour and one was diagnosed following a subsequent routine breast screening invitation. Two of the five patients had a personal history of breast cancer which was a stated exclusion criterion for the CBPC. Breast cancer incidence in women with breast pain only and fulfilling CBPC referral criteria was 4.8/1000, confirming that this population is at low risk of developing breast cancer.Patient service satisfaction was high with 99% (n=1022) 'extremely likely or likely' to recommend the service. The results confirm the pathway is the first to demonstrate women can be safely managed with breast pain alone in a community setting with high levels of patient satisfaction.

Dental care staff’s experience with risk assessment of dental erosion: a qualitative study

BackgroundThe risk assessment of dental erosion among children and adolescents is an important aspect of dental care, as dental erosion constitutes a rapidly growing, global problem. Dental professionals rely solely on their own perception, as the current risk assessment process is not completely automatized, which affects the risk assessment reliability.AimTo explore dental professionals’ experiences with risk assessment of dental erosion among children and adolescents.MethodIn-depth interview was used as data collection method. A total of 11 dental professionals were interviewed. The interviews were analyzed using qualitative content analysis.ResultsThe findings were summarized in the categories Professionals’ responsibility, Systematic approach , and Collaboration and communication. Dental staff perceived that their basic knowledge regarding erosion should be improved, and skills development was desired to reduce the knowledge gaps around the risk assessment of dental erosion. They alleged that the systematic approach could be improved by reducing workplace stress, implementing a universal dental erosion index, improving the existing risk assessment software, and automating the risk assessment of the condition. Dental professionals also experienced a need to calibrate and collaborate with each other and with other healthcare professionals to improve patient care.ConclusionDental professionals experienced their basic knowledge of dental erosion and their risk assessment as good, but a more advanced skill development was required. Furthermore, they experienced the risk assessment software as a good tool that should be improved to compile more objective risk assessment. A universal erosion index was also requested.

Predicting Heart Failure Survival with Machine Learning: Assessing My Risk

This study investigates the application of machine learning techniques for heart disease prediction using a comprehensive dataset of 918 patients. The research employs multiple algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks, to develop predictive models based on 11 clinical features. The dataset, compiled from five independent sources, underwent thorough preprocessing and was split into training (70%) and test (30%) sets. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics. Results demonstrate consistently high performance across all models, with the SVM achieving the highest overall performance (accuracy: 88.41%, precision: 89.76%, recall: 90.85%, F1-score: 90.30%, ROC-AUC: 94.97%). Key predictors identified include age, maximum heart rate, and ST depression (Oldpeak). The study's findings have significant implications for clinical practice, offering the potential for rapid, objective heart disease risk assessment. The consistent performance across different model architectures provides flexibility for implementation in various healthcare settings. Limitations include potential data collection variability and gender imbalance in the dataset. Future research directions include developing more sophisticated neural networks, incorporating additional data types, and conducting prospective studies to validate model performance in real-world clinical settings. This research contributes to the growing body of evidence supporting the use of machine learning in medical diagnostics. The developed models enhance early detection and risk stratification of heart disease, potentially improving patient outcomes through timely interventions.

A novel model for real-time risk evaluation of vehicle–pedestrian interactions at intersections

Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle–pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle–pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: ’driving risk index’ and ’driving risk gradient,’ facilitates the delineation of driving spaces for identifying safety–critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle–pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle–pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle–pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle–pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle–pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.

Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.

Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an LSTM autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.

Pre-existing autoimmune disease as a risk factor for immune-related adverse events in cancer patients receiving immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have been widely used as standard therapies for various cancers. However, in 20-30% of cases, ICIs can lead to immune-related adverse events (irAEs), which sometimes require discontinuation of treatment. Due to the increased risk of irAEs, patients with pre-existing autoimmune diseases (AI) are often advised against receiving ICIs. However, there has not been sufficient objective risk assessment for AI. In our study, we conducted logistic regression analysis to assess the risk of irAEs by analyzing 478 cases that received anti-PD-(L)1 Ab and/or anti-CTLA4 Ab at our hospital between April 3, 2017, and May 24, 2022. Among these cases, 28 (5.9%) had pre-existing AI. We selected several independent factors for analysis: gender, age, performance status (PS), cancer type, type of ICI, type of combined anti-cancer agents, best overall response, and pre-existing AI. The adjusted odds ratio (OR) of AI for irAE occurrence was 2.52 [95% CI: 1.08-5.86] (p = 0.033), and the adjusted OR of AI for ICI discontinuation due to irAE was 3.32 [1.41-7.78] (p = 0.006). Patients with pre-existing AI experienced a significantly shorter irAE-free survival time compared to those without AI (median irAE-free survival: 5.7 months [95% CI: 3.5-7.8] vs 10.4 months [95% CI: 7.9-12.9], respectively, p = 0.035). Frequently observed irAEs in full ICI cohort, such as dermatologic issues (7.5%), pneumonitis (7.1%), hepatitis (4.6%), and hypothyroidism (4.2%), were often accompanied by pre-existing AI. Furthermore, pre-existing AI flared up in 6 cases (37.5% in AI-positive irAE-positive cases). The activity of AI was not related to the occurrence of irAEs. Grade 3 or higher irAEs were observed in 6 out of 20 (30.0%) cases in AI-accompanied patients complicated with irAEs. Although having a complicated AI increases the risk of irAEs, it may not necessarily be a contraindication for ICI treatment if closely monitored. (292<300 characters).

A comparison of subjective and objective caries risk assessment methods

Background: Dental caries is a serious public health problem significantly affecting oral health. Though there have been many advancements in treating dental caries, complete prevention of dental caries is still beyond reach. Several risk factors are associated with caries formation and progression. Previous studies have been conducted on dental caries and the efficiency of diagnosis using subjective and objective methodologies. Hence, this study was conducted to assess the contradictions in results of subjective and objective interpretations. Methods: A descriptive study was carried out in the clinical setting of rural areas near Chengalpattu. Using convenience sampling, 285 samples were collected according to inclusion and exclusion criteria within the age range of 7–20 years. The subjects were selected from the Outpatient Department of Karpaga Vinayaka Institute of Dental Sciences. American Dental Association (ADA) questionnaires were used for subjective caries risk assessment (CRA), unstimulated salivary samples were collected to measure salivary pH for objective CRA, and DMFT (decayed, missing, filled tooth) and def indices were measured for reference. After data collection, statistical analysis was performed using SPSS version 22. Chi-square and Pearson correlation tests were performed to find the statistical differences, and a correlation was found between subjective and objective assessment results. Results: In this study, the correlation between objective risk assessment based on salivary pH and actual caries status was slightly better (r=0.159) than other risk assessment methods (r=0.050). Moreover, a negative correlation was found between subjective and objective CRA (r=- 0.062). Conclusion: The study findings show a negative correlation between subjective and objective assessment. Objective CRA using salivary pH was positively correlated with actual caries status.

Preference for working from home – subjective perceptions of COVID-19 matter more than objective information on occupational exposure to contagion

We investigate how subjective and objective assessment of COVID-19 risks affect preferences toward working from home (WFH) and whether informing workers about the level of exposure to contagion in their occupation affects these preferences. In the summer of 2021, we conducted a discrete choice experiment combined with an information provision experiment with more than 11 000 workers in Poland. Estimating willingness to pay for WFH, we find that, on average, workers' are willing to give up 3.2%, 95% CI [2.8%; 3.6%] of earnings for such an option. The subjective assessment of COVID-19 risk matters as workers who perceive COVID-19 as a threat are willing to sacrifice a higher share of earnings for WFH than those who do not (4.1%, vs. 1.3% [p<0.00]). However, the preferences toward WFH differ to a smaller extent between workers in occupations with high or low exposure to COVID-19 [3.8% vs. 2.7%, p=0.01]. Informing workers about occupational exposure to contagion generally does not affect preferences toward WFH.

A Novel Global Probabilistic Fuzzy System for Occupa-tional Risk Assessment (GPFSORA)

Occupational risk assessment is the process of estimating the magnitude of risks that cannot be avoided. Then, the corresponding assessment is carried out, using comparative tables with different evaluation methods. Current risk assessment techniques enable the individual assessment of each potential risk, but there is no method to globally assess potential risks in an organization. The motivation of this research was to develop an objective and quantitative risk assessment system through a diffuse probabilistic model integrating stochastic and non-stochastic uncertainty. To this effect, an empirical collective record was used, whose attribute of interest was the occurrence of different accident types over a period of 52 weeks. Here, each of the collectives represented a linguistic input variable. In the probabilistic fuzzification stage, the frequentist probability of the occurrence of accidents was determined. One of our most important contributions to probabilistic fuzzy systems lies in our classification of language labels based on the linguistic projection of frequentist probabilities via a projection membership function determined by experts. The use of the total probability theorem in the implication stage is also proposed. The output of the system determines the type of risk, its evaluation, and the probability of its occurrence, vital factors to be considered in prevention work. The system’s stages are explicitly described and applied to real data corresponding to construction materials distribution company. One of the relevant conclusions of this research is that the integration of stochastic and imprecise uncertainty allows for a more reliable risk assessment system.

Simulated impact of lift car sizes on transport of critical care patients: Informing the design of the New Dunedin Hospital.

The New Dunedin Hospital (NDH) is New Zealand's largest health infrastructure build. Here we describe the use of a simple simulation-based hospital design exercise to inform the appropriate lift car size for critical care intrahospital transfers in the NDH. The intensive care unit (ICU) user group tested a series of entries and exits of simulated complex patient transfers in mocked-up lift cars of three different dimensions. Time taken to enter and exit the lift were recorded, reflecting the relative difficulty of transfer. Qualitative assessments were made of ease and perceived safety of transfer. These simulations demonstrated that recommended standard patient lift cars, often proposed for critical care transfers, could not physically accommodate all complex ICU transfers. A size of 1800 mm wide (W) × 3000 mm deep (D) had the physical capacity to permit all simulated ICU transfers, but with staff and patient risk. As lift car size increased to 2200 mm W × 3300 mm D, the simulation demonstrated reduced transfer times, smoother entry and exit, improved access to the head end of the bed, and reduced risk of disconnection or dislodgement of lines and airway support. The resultant clinical recommendations for the dimensions of a critical care lift car surpass current international health architecture guidelines and may help to inform future updates. The NDH project benefited from an objective assessment of risk, in language familiar to clinicians and healthcare architects. The outcome was an upsizing of the two ICU-capable lifts.

Assessment of psychosocial risks at work in supervisors of clothing stores during the pandemic context

The main objective of this paper is to demonstrate how to carry out a structured, sustained and objective assessment of psychosocial risks at work. Several studies in the literature on this topic are presented as risk assessments, when in fact they reflect the results from worker consultation procedures and/or surveys about the psychosocial work climate. As relevant as these studies are, many are not professional risk assessments. This assessment obeys a set of assumptions that must be strictly respected and performed by duly qualified professionals, as national and European legislation determine. Using a practical case with supervisors of clothing stores evaluated during the pandemic context, it is explained and applied an approach to perform this kind of risk assessment. The data presented show the effectiveness of the methodology applied. Regarding the results obtained in the practical case mobilized, a positive psychosocial work environment, even taking into account the pandemic context, with most risk factors showing acceptable or moderate incidences. It is possible to conclude that the pandemic period, despite creating contextual constraints, was not consider a specific risk factor in these stores and the supervisors felt safe in their workplace, because there was a robust system of preventive measures implemented. However, it is important to underline that exist also dissatisfaction with working conditions in some segments of supervisors, as well as a high level of risk of professional exhaustion, interpersonal conflicts at work and interference of work with personal/family life. For this reason, intervention measures were proposed to the company.

Optimizing surgical approaches for anterior communicating artery aneurysms: Development and internal validation of a novel surgical scoring system.

The objective was to provide comprehensive preoperative information on both the aneurysm orientation and the side and method of surgical approach for optimal preoperative information and safe clipping using 3D imaging modalities. This was achieved by making an objective risk assessment on the surgical side/method and evaluating its effectiveness with internal validation. Radiologic data of 61 ACoA aneurysm patients between 2012 and 2020 were retrospectively analyzed. A scoring system based on five criteria; ACoA aneurysm dome orientation, A1 symmetry/control, perforating artery control, A2 trace orientation, and A2 fork symmetry was developed. The system is designed to align with the most common surgical approaches in ACoA aneurysm surgery. The patients were categorized into three groups based on the scoring results to determine the most appropriate surgical method. Group I was recommended, Group II was less recommended, and Group III was least recommended. Internal validation was performed to assess the system's effectiveness. Outcomes and complication rates were statistically evaluated. When the scoring system was utilized, the mean score difference between the first group and the other groups was 2.71 and 4.62, respectively. There was a homogeneous distribution among the groups in terms of age, sex, WFNS, and Fisher scores. Complication occurred in three patients in Group I and nine patients each in Group II and Group III. The further the deviation from the first option, the higher the complication rate (p = 0.016), and a significant cause-effect relationship was identified (p = 0.021). The ROC curve established a cut-off value of 12.5 points for complications and outcomes. Our study introduces a new scoring system for ACoA aneurysms, enhancing the use of 3D CTA in daily practice and providing internal validation for the proposed approach. By evaluating objective criteria, this scoring system helps predict surgical risks, prevent complications, and supports personalized evaluation and selection of the surgical approach based on objective criteria.

Novel Prediction Score for Arterial-Esophageal Fistula in Patients with Esophageal Cancer Bleeding: A Multicenter Study.

To develop and internally validate a novel prediction score to predict the occurrence of arterial-esophageal fistula (AEF) in esophageal cancer bleeding. This retrospective cohort study enrolled patients with esophageal cancer bleeding in the emergency department. The primary outcome was the diagnosis of AEF. The patients were randomly divided into a derivation group and a validation group. In the derivation stage, a predictive model was developed using logistic regression analysis. Subsequently, internal validation of the model was conducted in the validation cohort during the validation stage to assess its discrimination ability. A total of 257 patients were enrolled in this study. All participants were randomized to a derivation cohort (n = 155) and a validation cohort (n = 102). AEF occurred in 22 patients (14.2%) in the derivation group and 14 patients (13.7%) in the validation group. A predictive model (HEARTS-Score) comprising five variables (hematemesis, active bleeding, serum creatinine level >1.2 mg/dL, prothrombin time >13 s, and previous stent implantation) was established. The HEARTS-Score demonstrated a high discriminative ability in both the derivation and validation cohorts, with c-statistics of 0.90 (95% CI 0.82-0.98) and 0.82 (95% CI 0.72-0.92), respectively. By employing this novel prediction score, clinicians can make more objective risk assessments, optimizing diagnostic strategies and tailoring treatment approaches.

IT Risk Management: Towards a System for Enhancing Objectivity in Asset Valuation That Engenders a Security Culture

In today’s technology-centric business environment, where organizations encounter numerous cyber threats, effective IT risk management is crucial. An objective risk assessment—based on information relating to business requirements, human elements, and the security culture within an organisation—can provide a sound basis for informed decision making, effective risk prioritisation, and the implementation of suitable security measures. This paper focuses on asset valuation, supply chain risk, and enhanced objectivity—via a “segregation of duties” approach—to extend and apply the capabilities of an established security culture framework. The resultant system design aims at mitigating subjectivity in IT risk assessments, thereby diminishing personal biases and presumptions to provide a more transparent and accurate understanding of the real risks involved. Survey responses from 16 practitioners working in the private and public sectors confirmed the validity of the approach but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate. This research contributes to the literature on IT and cyber risk management and provides new perspectives on the need to improve objectivity in asset valuation and risk assessment.

Risk aggregation considering probabilistic and consequential interactions: A general formulation with computational cost handling.

Complicated interaction between risk events is the critical obstacle preventing accurate risk aggregation, which is an important issue in risk management. Recent research integrates interaction into risk aggregation with different perspectives and lacks a comprehensive discussion of this issue, making the risk aggregation process not universal for diverse cases, especially in subjective risk assessment contexts. Therefore, this article proposes a theoretically convincing risk aggregation method embedding different types of interaction to support decision analysis more effectively. The main contributions of this article are as follows: (a) more in-depth and stricter definitions, measures, and graphical descriptions of different types of interaction are developed to ensure the accuracy of risk aggregation; (b) a formal risk aggregation approach that could apply in both objective and subjective risk assessment contexts while elegantly embedding risk interactions is proposed; (c) the additivity of risks and risk sets in the risk aggregation process is discussed in detail and the conditions for additivity are clarified; (d) the quasi-two/three-additive measures, which approximately obtain the aggregate risk value within sufficient reliability, are proposed to greatly reduce the computational cost. To examine the applicability of the proposed general risk aggregation method, a case study is finally presented to show the complete risk aggregation process and its application in the decision-making stage.

Genomic Landscape of Ccus Compared to MDS Indicates a Potential Applicability of the IPSS-M

Background: The 5 th edition of the WHO classification newly included myeloid precursor lesions introducing CCUS as an entity. CCUS is defined as clonal hematopoiesis (CH) in the presence of unexplained, persistent cytopenia requiring detection of either somatic mutation in certain CH associated genes or clonal chromosomal abnormalities. Aim: Characterize a large CCUS cohort with respect to cytogenetics and molecular genetics and compare the findings to an MDS cohort to evaluate differences in the genomic landscape. Methods: The CCUS cohort comprised 222 cases (median age: 76 y; female: 33%), the MDS cohort 698 cases (median age: 73 y; female: 43%). The diagnoses were established following WHO 2022. All samples were analyzed by cytomorphology, cytogenetics, targeted NGS panel (median coverage 1500x) and whole genome (median coverage 100x) sequencing. Mutation (MUT) status of 59 genes associated with myeloid malignancies were analyzed in detail (54 CH associated; 5 relevant for IPSS-M). Results: Significant differences in age, gender, BM blasts, blood parameters and number and types of cytopenias were detected. CCUS patients (pts) were older, had more WBC, less PLT, harbored a higher HB, less BM blasts and were even more predominantly male compared to MDS pts (all p&amp;lt;0.05). Within the CCUS cohort 29 pts (13%) showed cytogenetic abnormalities (CA) only (thereof 69% loss of chromosome Y (Y-loss)), while the remaining 193 pts (87%) showed at least one MUT (VAF ≥2%) in any of the 54 CH associated genes (thereof 84% MUT only; 16% MUT+CA). Regarding cytogenetics 162 CCUS pts (73%) harbored normal karyotypes while CA were detected in 27% (MDS: 43%; p&amp;lt;0.001) with Y-loss being most frequent (39/60; 65%). Y-loss was more frequently found in CCUS than in MDS (17% vs. 5%; p&amp;lt;0.001), while complex karyotypes and del(5q) were more frequent in MDS (11% and 16% vs. 1% and 0%; both p&amp;lt;0.001). Based on WGS data additional pts with Y-loss (n=7; confirmed by FISH) and recurrent CN-LOH (4q: n=4; 7q: n=5) were observed. With respect to somatic MUTs, 356 MUTs were found within the entire CCUS cohort (in 28 CH genes, 3 non-CH genes). Overall, CCUS pts showed less MUTs (median: 1 [0-6]) compared to MDS (median: 2 [0-12]; p&amp;lt;0.001). CCUS pts most frequently harbored MUTs in DNMT3A (32%), TET2 (28%) and ASXL1 (14%) followed by MUTs in splicing genes and TP53 (Fig. 1A). MUTs in DNMT3A and PPM1D were more frequent in CCUS than MDS while MUTs in ASXL1, TP53, SF3B1, STAG2, RUNX1, NRAS, CUX1 were less frequent in CCUS (each p&amp;lt;0.05). Notably, biallelic TP53 MUTs were less frequent in CCUS compared to MDS ( TP53bi of mutated: 14% vs. 55% p=0.008). Within the 15 most frequently mutated genes in CCUS, MUTs in 6 genes ( DNMT3A, SF3B1, TP53, CBL, STAG2, PPM1D) showed a median VAF &amp;lt;10%. Nine of the top 15 genes showed significantly lower median VAFs than in MDS ( DNMT3A, TET2, ASXL1, SRSF2, U2AF1, SF3B1, TP53, PPM1D, IDH2; each p&amp;lt;0.05). Thus, the frequency and VAF of ASXL1, TP53, SF3B1 MUTs were significantly lower in CCUS than in MDS, while DNMT3A and PPM1D MUTs were significantly more often detected in CCUS but at a lower VAF than in MDS. Combining cytogenetics and mutational analysis we calculated the IPSS-M, a patient-specific risk score for MDS resulting in six risk categories, and detected a clear skewing towards low risk categories in CCUS (Fig. 1B). The frequency of categories very low (VL) and low (L) were significantly higher in CCUS than in MDS (30% vs. 15%; 50% vs. 40%; both p&amp;lt;0.013), while high and very high categories were more rare (1% vs. 13%; 1% vs. 14%; both p&amp;lt;0.001). Although CCUS follow-up data were short (median: 1.5 y), no differences in overall survival were observed between CCUS VL+L and MDS VL+L, while within CCUS survival differed between VL+L and the remaining cases ( p=0.05). Conclusions: Our data confirm the comparable mutational spectrum between CCUS and MDS but clearly show major differences in the frequency and VAF of distinct gene mutations. These biological differences hint towards different subgroups within CCUS with cases closer to MDS than others. A combined risk score for MDS and CCUS would reflect this continuous spectrum and has the potential to derive an objective risk assessment irrespective of observer-dependent grading of dysplasia. Our study indicates plausible short term results for CCUS patients stratified according to the IPSS-M. However, the definite applicability of the IPSS-M needs to be confirmed in larger CCUS studies with longer follow-up.

Analysis of Spacecraft Materials Discrimination Using Color Indices for Remote Sensing for Space Situational Awareness

The increasing number of space missions have resulted in an augmented density of artificial objects positioned in orbital domains near Earth. Knowing the material composition of the resident space object can be of value in object identification and risk assessment using remote sensing techniques. To perform this task, it is increasingly imperative to optically characterize spacecraft materials to identify unique material-specific spectroscopic markers. In this work, a variety of materials frequently utilized by the aerospace industry in spacecraft design and construction were analyzed using reflectance spectroscopy. The collected data provide a spectral characterization baseline for modern-day and historical spacecraft materials. The color index was computed for standard astronomical and suggested theoretical filter passbands and compared to each other in their capability to discriminate materials belonging to different classes. The color index was calculated from reflectance spectra of common spacecraft materials in their pristine, as-received conditions that fell under different family groupings. Different color-index combinations were studied using color-color diagrams. Visual and quantitative analysis of the color-color diagrams were used to evaluate the possibility of discriminating materials from one another by means of optical measurements. Results of the analysis show that polyimide and photovoltaic materials are most easy to discriminate from all other materials via color indices using different filter passbands evaluated in this study.

Improving the operational efficiency of control centers for emergency events by using GIS technologies

The object of the study is the functioning of control centers during emergencies. Ukraine has faced large-scale emergency events caused by Russian aggression. The experience of crisis management in emergency events showed the need to find new solutions and improve the activities of emergency management centers at the State Emergency Service of Ukraine and GIS technologies in decision-making support systems. The curent paper has formulated a conceptual model of the work of emergency management centers at the Emergency Service of Ukraine; based on it, their main functions were formed. Identification of risks for objects that carry potential danger was carried out and a block diagram of object risk assessment was developed to improve the work of emergency control centers at the State Emergency Service of Ukraine. An approximate list of software and online resources for the operation of emergency management centers at Emergency Service of Ukraine is provided, which makes it possible to summarize the risks that are present in the given territory, isolate them, and pay special attention to prevent possible consequences from them. Based on the computer software MARPLOT, CAMEO, and ALOHA, an example of response to an emergency event was described. In this case, the weather conditions, physico-chemical parameters of the dangerous substance, and the radii of the danger zones of its distribution and the number of people who need to be evacuated were taken into account. In practice, having this knowledge, a head of the emergency management center at the State Emergency Service of Ukraine will be able to effectively manage people without exposing them to danger, as well as conduct timely evacuation. The use of these programs makes it possible to reduce the time of hazard assessment and visualize them, which creates advantages over the existing methodology of the State Emergency Service of Ukraine in terms of speed of hazard assessment. The results have made it possible to solve the problem of increasing the efficiency of the emergency management centers at the Emergency Service of Ukraine and to create the prerequisites for building a geo-information portal that could combine various methods and techniques on one platform

Detection of moving objects in multi-complex environments using selective attention networks (SANet)

Object detection studies aim to solve safety problems at industrial sites; however, improving object detection performance and ensuring real-time capabilities simultaneously remains challenging in multi-complex industrial environments. This paper proposes an unconditionally protected detector to address this problem. The detector's backbone network uses a residual block with a bottleneck structure to reduce computation and enhance real-time performance. Additionally, a selective attention network enhances object detection by extracting important features based on the morphological characteristics of the feature map in the neck structure. The method includes a whole-body estimation to locate individuals obstructed by obstacles, thereby preventing collisions with vehicles and avoiding hazardous situations in restricted areas. The proposed method enables real-time object detection and risk assessment for nearby objects, enhancing safety for industrial vehicle drivers and workers. Moreover, it can lead to further improvements in object detection performance in research fields such as autonomous driving and AI CCTV utilizing cameras.

İç Denetim Planlamasında Çok Kriterli Karar Verme ile Objektif Şube Seçimi

In recent years, expectations from internal audit have changed with globalization, diversification of risks, technological innovations and developments, international standards and legal regulations. Within the framework of both legal regulations and international internal audit standards, it has emerged that internal audit activities should integrate a risk-based perspective into internal audit processes rather than the traditional perspective. While this situation affects all internal audit units operating in different sectors, it has affected the internal audit units of banks, which are mandatory units for banks in Turkey, relatively more. The first stage of the risk-based perspective in internal audit is the planning of the areas to be audited through the risk assessments carried out objectively by the internal audit units. However, there are some difficulties as there is no method for objective risk assessment in practice. For this purpose, in this study, an application has been proposed on how a bank's internal audit unit can objectively prioritize the branches that should be audited, based on an imaginary example. In the proposed application, criteria for the branches to be prioritized in the annual internal audit plan were defined and scored, their weights were calculated with the standard deviation method, and the prioritization rankings for the branches were carried out using the ARAS method.