Absolute Evaluation Research Articles

Investigating Concomitant RAG-2 and LRBA Mutations in SCID and Autoimmunity.

Inborn errors of immunity (IEI) are a large heterogenous group of diseases characterized by immunodeficiency, immune dysregulation, allergy, auto-inflammation and predisposition for malignancies. Most are inherited in an autosomal recessive trait. We studied a patient with severe combined immunodeficiency (SCID) and immune dysregulation who harbored two distinct bi-allelic IEI-associated genetic mutations. Clinical, immunological and genetic data were collected. Genetic investigation included whole exome sequencing on DNA extracted from skin fibroblasts. Family segregation was performed by Sanger sequencing. Immunological evaluation included absolute and functional evaluation of lymphocytes and chimerism analysis post hematopoietic stem cell transplantation (HSCT). Treg subsets, LRBA and CTLA4 expression levels were measured by flow-cytometric analysis. A nineteen-year-old female patient from a consanguine background underwent unconditioned matched sibling related HSCT during infancy due to clinical presentation of SCID with an Omenn phenotype. At that time her underlying genetic defect was not defined. Years after HSCT, severe auto-immune phenomena were noted, including a systemic lupus erythematosus-like syndrome and ophthalmic manifestations. Genetic evaluation revealed bi-allelic homozygous mutations in RAG-2 (c.685C>T, p.Arg229Trp) and a previously undescribed mutation in LRBA (c.3325G>T, p.Asp1109Tyr). LRBA and CTLA4 expression levels were normal, suggesting that the LRBA variant identified in these kindred is unlikely to be pathogenic. Multiple genetic defects causing complex IEIs may be identified in the same individual in highly consanguineous populations. Functional immunological testing is essential for evaluation of novel genetic variants.

Action Research on Collegiate Student-athlete and Student Peer Mentoring Program

PURPOSE The purpose of this study is to explore the effects of the student-athlete and student peer mentoring program as a collegiate class.METHODS The peer mentoring program, conducted at A University in the first semester of the 2023 school year, was evaluated using practical action research (Zuber-Skeritt, 1996).RESULTS In the introduction stage, ‘relative and absolute evaluation’, ‘member ratio’, and ‘definition of professor role’ were categorized as challenge issues. In the progress stage, ‘de-formalized lecture method’, ‘student athlete’s coaching experience’, and ‘student’s experience of football culture’ were discovered as possibilities, while ‘vacancy and absence of mentor-mentee’, ‘limited group activities and limitations of team sports’, and ‘lack of objective evaluation’ required improvement. At the end stage, student-athletes experienced changes in values such as self-identity, football, and human relationships, as well as quantitative and qualitative changes in sports participation.CONCLUSIONS This study confirmed the potential of the peer mentoring program as a collegiate class as well as its practical significance for guaranteeing student-athletes' learning rights and for forming sports culture on collegiate campuses.

직업선택의 자유의 제한과 자격제도

This article begins with a fundamental question about the level of qualifications required by the qualification system and conducts a constitutional review of the unconstitutionality of the quota system selection method in the qualification system. While the qualification system is essentially intended to evaluate qualifications and abilities, the quota system is seen as being determined by a complex combination of social demands and other interests. However, in the 2008 Heonba 110 decision, the Constitutional Court ruled that the quota system (relative evaluation) selection method under the bar examination system did not violate the principle of prohibition of excess, and this article examines the arguments of the existing Constitutional Court decision to discuss the various qualification systems currently in progress. In this regard, the intention was to proceed with a constitutional explanation regarding the quota system. In the main text, the review criteria for freedom of occupational choice are identified as a prerequisite, the German three-stage theory and the Constitutional Court are compared and reviewed, and then the Constitutional Court decision is reviewed. In conclusion, this article believes that the quota selection method contains objective factors that are unrelated to the ability or talent of the person involved. Even if some subjective reasons are acknowledged, it would be necessary to conduct a more rigorous review of the restrictions on occupational choice in the case, at least in the sense that objective reasons are also present. Furthermore, as a result of a new review of the Constitutional Court's argument, we looked at the quota system from the perspective that it violates the principle of prohibition of excess and has the potential to infringe on occupational freedom. In order to reduce the unconstitutionality of the quorum system, it is believed that there is a need to reform the qualification system by at least incorporating the method of absolute evaluation so that it can be objectively recognized as a verification to evaluate ability or qualifications.

Absolute Evaluation of the Optical Transfer Function of the High Aperture Infrared Cryogenic Objective

Absolute Evaluation of the Optical Transfer Function of the High Aperture Infrared Cryogenic Objective

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

Ceramic Particle Reinforced Camshaft Lobes: A Performance Evaluation and Comparative Analysis

Ceramic reinforced metal matrix composite (CMMC) cams in engines could improve fuel efficiency and wear resistance compared to traditional steel cams but require absolute evaluation. However, ensuring safe CMMC cam operation demands extensive wear testing, mimicking real-world conditions over longer durations instead of limited lab evaluations. This study is an extension of the previously reported feasibility analysis of the ceramic particle reinforced Al matrix composite labs. The performance of the best selected (Al + 20, 30 vol. % SiC(2µm)) composites for a wear duration of 2.5, 5, 7.5, and 15 h is reported with various combinations of pressures and compared with the reference cam lobes. Results showed that the higher content of ceramic particles improved the wear resistance, however, the influence diminished at larger durations due to surface hardening of the composites. The wear performance of the composite (Al + 30 vol.% SiC) reaches 73% of the conventional cams but it also causes significant wear in the counterface due to initiation of three-body-wear by the dislodged ceramic particles.

A novel zero-force control framework for post-stroke rehabilitation training based on fuzzy-PID method

As the number of people with neurological disorders increases, movement rehabilitation becomes progressively important, especially the active rehabilitation training, which has been demonstrated as a promising solution for improving the neural plasticity. In this paper, we developed a 5-degree-of-freedom rehabilitation robot and proposed a zero-force control framework for active rehabilitation training based on the kinematics and dynamics identification. According to the robot motion characteristics, the fuzzy PID algorithm was designed to further improve the flexibility of the robot. Experiments demonstrated that the proposed control method reduced the Root Mean Square Error and Mean Absolute Error evaluation indexes by more than 15% on average and improves the coefficient of determination ($$ R^{2} $$ ) by 4% compared with the traditional PID algorithm. In order to improve the active participation of the post-stroke rehabilitation training, this paper designed an active rehabilitation training scheme based on gamified scenarios, which further enhanced the efficiency of rehabilitation training by means of visual feedback.

Absolute evaluation of internal and external quantum efficiencies and light extraction efficiency in InGaN single quantum wells by simultaneous photoacoustic and photoluminescence measurements combined with integrating-sphere method

Absolute evaluation of internal and external quantum efficiencies and light extraction efficiency in InGaN single quantum wells by simultaneous photoacoustic and photoluminescence measurements combined with integrating-sphere method

Wild bumblebees use both absolute and relative evaluation when foraging

Abstract Foraging theory assumes that animals assess value based on objective payoffs; however, animals often evaluate rewards comparatively, forming expectations based on recent experience. This form of evaluation may be particularly relevant for nectar foragers such as bumblebees, where individuals can visit thousands of flowers daily that vary in nectar quality. While many animals, including bees, demonstrate reference-based evaluation in experimental contexts, it is unclear whether this occurs in the wild. Here, we asked how daily experience with wildflower nectar influenced wild bumblebees’ reward evaluation. We measured the daily nectar concentration of bee-visited wildflowers (Penstemon spp.), before presenting foragers with conspecific flowers filled with a range of artificial nectar concentrations. We recorded bees’ acceptance of artificial nectar, the probability of subsequent visits to flowers on the same plant, and residence time. While bees had a minimum threshold of nectar acceptability that was unaffected by experience, when there was higher-concentration environmental nectar, they were less likely to accept lower-quality rewards on manipulated plants. Bees also visited more flowers and stayed longer on plants with higher-concentration nectar. This study shows evidence for both absolute and reference-based evaluation in wild bees and points towards differences between bees’ behavior in lab- and wild-foraging contexts.

LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor

Light Field Salient Object Detection (LF SOD) aims to segment the visually distinctive objects out of surroundings. Since light field images provide a multi-focus stack (many focal slices in different depth levels) and an all-focus image for the same scene, they record comprehensive but redundant information. Existing methods exploit the useful cue by long short-term memory with attention mechanism, 3D convolution, and graph learning. However, the importance of intra-slice and inter-slice in the focal stack is not well investigated. In the paper, we propose a learnable weight descriptor to simultaneously exploit different weights in slice, spatial region, and channel dimensions, and therefore propose an LF SOD method based on the learnable descriptor. The method extracts slice features and all-focus features from a weight-shared backbone and another backbone, respectively. A transformer decoder is used to learn the weight descriptor which both emphasizes the importance of each slice (inter-slice) and discriminates the spatial and channel importance of each slice (intra-slice). The learnt descriptor serves as the weight to make slice features attend to important slices, regions, and channels. Furthermore, we propose the hierarchical multi-modal fusion which aggregates high-layer features by modelling the long-range dependency to fully excavate common salient semantics and combines low-layer features by spatial constraint to eliminate the blurring effect of slice features. The experimental result exceeds the state-of-the-art methods at least 25% in terms of mean absolute error evaluation metric. It demonstrates a significant improvement in LF SOD performance via the designed learnable weight descriptor. https://github.com/liuzywen/LFTransNet.

Fintech Key-Phrase: A New Chinese Financial High-Tech Dataset Accelerating Expression-Level Information Retrieval

Expression-level information extraction is a challenging task in natural language processing (NLP), which aims to retrieve crucial semantic information from linguistic documents. However, there is a lack of up-to-date data resources for accelerating expression-level information extraction, particularly in the Chinese financial high technology field. To address this gap, we introduce Fintech Key-Phrase: a human-annotated key-phrase dataset for the Chinese financial high technology domain. This dataset comprises over 12K paragraphs along with annotated domain-specific key-phrases. We extract the publicly released reports, Chinese management’s discussion and analysis (CMD&A), from the renowned Chinese research data services platform (CNRDS) and then filter the reports related to high technology. The high technology key-phrases are annotated following pre-defined philosophy guidelines to ensure annotation quality. In order to better understand the limitations and challenges in the purposed dataset, we conducted comprehensive noise evaluation experiments for the Fintech Key-Phrase, including annotation consistency assessment and absolute annotation quality evaluation. To demonstrate the usefulness of our released Fintech Key-Phrase in retrieving valuable information in the Chinese financial high technology field, we evaluate its significance using several superior information retrieval systems as representative baselines and report corresponding performance statistics. Additionally, we further applied ChatGPT to the text augmentation approach of the Fintech Key-Phrase dataset. Extensive comparative experiments demonstrate that the augmented Fintech Key-Phrase dataset significantly improved the coverage and accuracy of extracting key phrases in the finance and high-tech domains. We believe that this dataset can facilitate scientific research and exploration in the Chinese financial high technology field. We have made the Fintech Key-Phrase dataset and the experimental code of the adopted baselines accessible on Github: https://github.com/albert-jin/Fintech-Key-Phrase. To encourage newcomers to participate in the financial high-tech domain information retrieval research, we have developed a series of tools, including an open website1and corresponding real-time information retrieval APIs.2

Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective.

To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5mm/2.0mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5mm/2.0mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5mm/2.0mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. 3D-ACS-MRI of the knee can be completed in the 160s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.

IB MYP 학교 교육적 실행 양상과 과제

Objectives This study aimed to obtain educational implications for the settlement of IB public education by identifying the educational practice patterns and perceptions of teachers applying the program in IB MYP schools introduced into public education. Methods To this end, data were collected through group focus group interviews, class observations, and teachers' council visits with 16 IB MYP candidate schools and world school teachers (as of ΄21) in A region that are introducing the IB program in public education. Analyzed and organized. Results As a result, teachers' curriculum literacy improved in the process of understanding the national curriculum for the application of the IB program, and professionalism, autonomy, and responsibility increased in the composition of classes and assessments. Although the IB evaluation system was cited as the biggest factor in improving class instruction, under the current situation, they felt the burden of operating dual evaluation systems such as nice evaluation and summative evaluation, relative evaluation and absolute evaluation, and short answer and multiple choice evaluation. Most of the teachers said that systematic IB education contributes greatly to the development of teacher professionalism and the strengthening of students' competence, but it is very important to secure the research time necessary for teachers' class design and preparation. Teachers said that the ‘evaluation system’, ‘school quality management system’, and ‘structure that induces collaboration’ were attractive in IB education, while lack of research time, sense of responsibility and pressure for IB program application classes and evaluation, and individual feedback The large number of students managed and the burden as a leading teacher appeared as job stress. Teachers thought that the IB program was a very good educational device, but it should be accompanied by training support, work reduction, and changes in the form of entrance exams to secure teacher expertise. Conclusions Therefore, in this study, in order for IB education to stably settle in public education, the feasibility of introducing IB education and the formation of strong consensus among members on the educational effects, securing teacher research time, supporting IB teacher network establishment, and developing IB school teachers and student growth. Necessities such as development of measuring tools, improvement of teacher training system, accumulation of data on introductory evaluation method, and exploration of the IB program applied curriculum education implementation process were suggested.

To eat, or not to eat: a phantom decoy affects information-gathering behavior by a free-ranging mammalian herbivore.

When foraging, making appropriate food choices is crucial to an animal's fitness. Classic foraging ecology theories assume animals choose food of greatest benefit based on their absolute value across multiple dimensions. Consequently, poorer options are considered irrelevant alternatives that should not influence decision-making among better options. But heuristic studies demonstrate that irrelevant alternatives (termed decoys) can influence the decisions of some animals, indicating they use a relative rather than absolute evaluation system. Our aim was to test whether a decoy influenced the decision-making process-that is, information-gathering and food choice-of a free-ranging mammalian herbivore. We tested swamp wallabies, Wallabia bicolor, comparing their behavior toward, and choice of, two available food options over time in the absence or presence of the decoy. We used a phantom decoy-unavailable option-and ran two trials in different locations and seasons. Binary preferences (decoy absent) for the two available food options differed between trials. Irrespective of this difference, across both trials the presence of the decoy resulted in animals more likely to overtly investigate available food options. But, the decoy only shifted food choice, weakly, in one trial. Our results indicate that the decoy influenced the information-gathering behavior during decision-making, providing the first evidence that decoys can affect decision-making process of free-ranging mammalian herbivores in an ecologically realistic context. It is premature to say these findings confirm the use of relative evaluation systems. Whether the foraging outcome is more strongly affected by other decoys, food dimensions, or ecological contexts, is yet to be determined.

대학 일반재정지원사업의 성과기반 재정배분에 대한 국제비교 연구

The purpose of this study is to find ways to improve the performance-based financial allocation of general financial support programs for universities in Korea. To this end, an international comparative research method was used to obtain implications based on the cases of the United States and Finland, which introduced performance-based systems in university financial allocation before Korea. The implications for policy improvement based on the results of international comparative analysis are as follows. First, the performance-based financial allocation system for universities is operated based on absolute evaluation based on quantitative indicators, but it is necessary to apply a method of adding monitoring-based qualitative evaluation only for subgroups that do not meet the standard value. Second, it is necessary to add indicators related to graduation or employment as 'output' indicators as performance indicators for teaching, a core function of universities, but supplement equity indicators to reflect the achievements of universities' practical efforts. Third, it is necessary to review the application method of differentiated common performance indicators by type considering the characteristics of universities. Specifically, two approaches were proposed: ‘applying differential weights’ and ‘applying differentiated performance indicators’. Fourth, a more rational improvement plan for 'policy indicators' directly related to higher education policy goals was proposed. Fifth, when selecting the autonomous performance indicators, it is necessary to present quantitative indicators from which universities can select within a certain range rather than individual qualitative indicators. Lastly, consulting and evaluation of autonomous performance indicators should be set at a certain periodic cycle.

Comparison of emulation evaluation methods of precast concrete moment resistance structures

Despite widely acknowledged advantages of the precast concrete (PC) structures, limited cases exist in which PC structures have been used to construct middle-to-high-rise structures. To construct PC structures, technologies for various tasks (e.g., transfer mechanisms, assembly, lifting, structural integrity) are required. In particular, the emulation related to the seismic design should be clearly evaluated, that is, whether PC structures have structural performance and serviceability equivalent to monolithic structures. Currently two methods are used to evaluate the emulation of the PC structures: relative and absolute methods. Both methods have advantages and disadvantages. The results of the two methods are not always consistent. Emulation results differed depending on the material and structural characteristics. In this study, ten PC beam–column joint specimens were tested, and their structural performance was evaluated using both the relative and absolute methods of emulation evaluation. Furthermore, the most suitable method was selected by analyzing the test results of 160 reinforced concrete beam–column joint specimens. Experimental and analytical results showed that it was desirable to evaluate the emulation of PC frames on relative evaluation criteria. The method under absolute conditions was too strict for special moment resistance frames. The absolute emulation evaluation criteria should reflect the type of frames, and the material and sectional characteristics of specimens because the strength reduction ratio, energy dissipation ratio, and stiffness of the specimen vary depending on the cross-sectional and material characteristics.

ОЦЕНКА ПРЕДИКТОРНОЙ ВОЗМОЖНОСТИ ИСКУССТВЕННО ИНТЕЛЛЕКТУАЛЬНЫХ МЕТОДОВ В ПРОГНОЗИРОВАНИИ КАРДИОВАСКУЛЯРНЫХ СОБЫТИЙ

Introduction. Predicting the risk of cardiovascular diseases and adverse outcomes are a promising direction in medicine. Using machine learning methods to process large databases to identify new predictors and establish more complex and deeper interactions between them creates other predictive capabilities in a personalized approach to risk assessment. The new approach to improving forecasting is the ability to use artificial intelligence techniques by creating a combination of sophisticated mathematical models and algorithms under certain conditions of computational power and improved database quality. Aim. To estimate the accuracy and reliability of models based on machine learning for solving problems of cardiovascular disease risk prediction. Materials and Methods. Data from a one-stage anonymous survey of volunteers in Primorsky Krai were collected in a multicenter observational study «Epidemiology of Cardiovascular Diseases in the Regions of the Russian Federation». A total of 2 131 participants took part in the study. The main and additional predictors of cardiovascular diseases were selected for further analysis and inclusion in prediction models. Models were created and evaluated using statistical and neural network libraries of the Python programming language. The quality of the models was determined by calculating the total area under the receiver operating characteristic curve. Results and discussion. Compared to the established risk prediction algorithm by calculating the area under the receiver operating characteristic curve, machine-learning algorithms improved the prediction: random forest +1.7%, logistic regression +3.2%, neural networks +3.6%. The algorithm with the highest performance (neural networks) predicted 320 cases (sensitivity 70.2%) and 1217 non-cases (specificity 74.7%), correctly predicting 248 (+7.6%) more patients who developed cardiovascular disease compared to the established absolute cardiovascular risk calculation scale Systematic Coronary Risk Evaluation. Conclusion. The use of neural networks with multilayer perceptron, as one of the methods of layer-by-layer construction of machine learning algorithm, is the best option for creating a prognostic model and currently provides the most reliable results.

Re-Evaluation Method by Index Finger Position in the Face Area Using Face Part Position Criterion for Sign Language Recognition.

Several researchers have proposed systems with high recognition rates for sign language recognition. Recently, there has also been an increase in research that uses multiple recognition methods and further fuses their results to improve recognition rates. The most recent of these studies, skeleton aware multi-modal SLR (SAM-SLR), achieved a recognition rate of 98.00% on the RGB video of the Turkish Sign Language dataset AUTSL. We investigated the unrecognized parts of this dataset and found that some signs where the fingers touch parts of the face were not correctly recognized. The proposed method is as follows: First, those with slight differences in top-1 and top-2 evaluation values in the SAM-SLR recognition results are extracted and re-evaluated. Then, we created heatmaps of the coordinates of the index finger in one-handed sign language in the face region of the recognition result in the top-1 to top-3 training data of the candidates based on the face part criteria, respectively. In addition, we extracted four index finger positions from the test data where the index finger stayed longer and obtained the product of the heatmap values of these positions. The highest value among them was used as the result of the re-evaluation. Finally, three evaluation methods were used: the absolute and relative evaluation with two heatmaps and an evaluation method integrating the absolute and relative evaluation results. As a result of applying the proposed method to the SAM-SLR and the previously proposed model, respectively, the best method achieved 98.24% for the highest recognition rate, an improvement of 0.30 points.

Parametric and General Evaluation of Mathematical Models Used for Critical Diameter Determination in Cyclone Separators

Cyclone separators are used in multi-phase flows for phase separation or classification. Pressure drop and separation efficiency are essential parameters for determining the performance of a cyclone separator, and critical diameter is an important parameter related to cyclone separation efficiency. In this study, we investigate the critical diameter prediction ability of various mathematical models; in particular, approximately 400 experimental data available in the literature, reflecting different geometries and operating parameters, are used to investigate the critical diameter prediction ability of the selected models. These data are calculated using ten mathematical models selected among the most widely used models which operate based on different principles, and the associated critical diameter values are determined. These calculated values are then compared with those obtained in the experimental studies. As a result, there are many parameters affecting the performance of cyclone separators. The fact that some of these parameters are not used in the models can lower the prediction accuracy of the model, depending on the context. For this reason, the parameters for which the models give better results are analyzed in detail. According to these results, users can choose a model more appropriately. In addition, an absolute evaluation is provided, demonstrating that the models provide important ideas regarding cyclone performance.

Regularised Semi-parametric Composite Likelihood Intensity Modelling of a Swedish Spatial Ambulance Call Point Pattern

Motivated by the development of optimal dispatching strategies for prehospital resources, we model the spatial distribution of ambulance call events in the Swedish municipality Skellefteå during 2014–2018 in order to identify important spatial covariates and discern hotspot regions. Our large-scale multivariate data point pattern of call events consists of spatial locations and marks containing the associated priority levels and sex labels. The covariates used are related to road network coverage, population density, and socio-economic status. For each marginal point pattern, we model the associated intensity function by means of a log-linear function of the covariates and their interaction terms, in combination with lasso-like elastic-net regularized composite/Poisson process likelihood estimation. This enables variable selection and collinearity adjustment as well as reduction of variance inflation from overfitting and bias from underfitting. To incorporate mobility adjustment, reflecting people’s movement patterns, we also include a nonparametric (kernel) intensity estimate as an additional covariate. The kernel intensity estimation performed here exploits a new heuristic bandwidth selection algorithm. We discover that hotspot regions occur along dense parts of the road network. A mean absolute error evaluation of the fitted model indicates that it is suitable for designing prehospital resource dispatching strategies. Supplementary materials accompanying this paper appear online.