Search

One approach to address climate change is to replace vapor cooling systems with less harmful technologies. Solid-state refrigeration shows promise because it virtually eliminates the use of greenhouse gases. This paper proposes a concept for a barocaloric regenerator tube bank setup using a solid-state refrigerant, specifically encapsulated pure polydimethylsiloxane rubber. We presented the geometric requirements, operating parameters, and thermal performance of this setup. Given the complexity of simulating the full caloric process, we employed a simple two-dimensional finite element analysis to study the transient thermal and fluid phenomena. We discussed the temperature gradient of the active barocaloric matrix over time in the regenerator. For an isostatic pressure of 130 MPa, the best coefficient of performance for refrigeration achieved was 4.7 using a five-tube arrangement. [ABSTRACT FROM AUTHOR]

Rock fragmentation upon impact during a rockfall event is a very challenging phenomenon to predict. To estimate the likelihood of breakage upon impact, one needs the survival probability of the rock. Recently, a survival probability model was proposed and validated for brittle spheres. The model was later extended to irregular shapes under colinear impact with promising preliminary experimental validation. These two models rely on the mechanical properties of the intact material prior to any impact, and as such, these models are only applicable to the very first impact. However, it is very likely for a rock to sustain some damage during a fall and only fragment after several impacts, in which case the survival probability cannot be predicted by the aforementioned models. This numerical study provides new insight into damage accumulation upon multiple impacts in free fall. The study systematically investigates how the work required to achieve failure in quasistatic compression (referred to as work at failure) is affected by prior impacts. Attention is focused on the work at failure because it is one of the key inputs of the survival probability models developed by one of the authors, and tracking its evolution with the number of impacts could allow one to adjust the survival probability with impact history. The simulations show that the governing mechanism for the loss of strength following multiple impacts is the interaction among different damage zones and that there exists no unique relationship between damage accumulation and work at failure. The paper concludes with a conceptual model based on the findings of this study and statistical considerations that can be used to predict the evolution of critical work with the impact velocity for different impact scenarios, which in turn can be used to predict the evolution of the survival probability of brittle spheres or rocks during a fall. Highlights: Simulations of impact on marble spheres were conducted to investigate the relationships between impact velocity, number and location of impacts and damage accumulation. The loss of strength was quantified in terms of work required to fail the spheres in quasistatic compression post impact, a parameter that is related to the survival probability The interaction of the multiple damage zones plays a key role in the loss of strength post-impact A conceptual model was proposed to estimate the reduction in critical work with normalised impact velocity for different impact scenarios. The reduced critical work can be used to predict the evolution of the survival probability of rocks with multiple impacts along a slope. [ABSTRACT FROM AUTHOR]

In this paper we present an in-depth evaluation of the Business Process Management Notation (BPMN) security extension. The evaluation mechanism is based on a conceptual model, particularly the Method Evaluation Model (MEM). The model is tested by engaging participants from different domains and possessing variable expertise level, related to security requirements and business process management systems. In particular, the BPMN security extension is evaluated for acceptance by end-users. Towards this, we conducted workshops and meetings with participants as part of our experimentation for BPMN extension evaluation. Three groups of participants participated in the evaluation process and are grouped based on their expertise level, including (i) experts, (ii) participants with some knowledge, and (iii) novices. We present an in depth quantitative and statistical analysis for the usability of the proposed framework, which includes parameters such as perceived ease of use, perceived usefulness, and intention to use. An empirical evaluation of results shows that the extended model is easy to use within the security requirements engineering framework, and participants are highly likely to adapt it in their future system development. [ABSTRACT FROM AUTHOR]

Implementing Industry 4.0 is hindered by the absence of a strategic focus on integrating digital technologies with traditional manufacturing. An inadequate Industry 4.0 implementation framework prevents practitioners from successfully transitioning to an Industry 4.0-enabled industrial environment. Thus, to achieve long-term Triple bottom line (TBL) benefits, the Industry 4.0 implementation framework should be carefully planned, designed, and aligned to effectively manage and optimize company resources (digital and physical infrastructure, human resources, technical processes, and technology systems). This paper proposes an Industry 4.0 framework to manage the key implementation issues using soft system methodology (SSM). A symptom map, rich picture, CATWOE for root definition, initial conceptual model, final implementable model, and Industry 4.0 implementation framework were developed from expert perspectives from multiple functional domains. The proposed framework consists of three main components: the implementation management system, which encompasses the technological framework system, recruitment and retention system, and governing system; the Industry 4.0 enabled manufacturing system; and the digital preparedness evaluation system. This paper offers a comprehensive understanding of the implementation issues linked with Industry 4.0 both at the organizational level and functional domain level. [ABSTRACT FROM AUTHOR]

Suicide among American Muslims is understudied, despite recent research highlighting increased suicide attempts among this population. While suicide is forbidden in Islam, formal guidelines for addressing and responding to suicide within Muslim communities did not exist until recently. The Stanford Muslim Mental Health and Islamic Psychology Lab has responded to a number of suicides in Muslim communities across North America and implemented an original model for suicide response and community healing. This approach incorporates Islamic principles and values to create a culturally and religiously congruent response to suicide that can support loss survivors and steer impacted communities toward healing. The Muslim Postvention Community Healing session described in this paper aims to provide a safe space for individuals impacted by suicide to come together and process their emotions, while also using Islamic teachings to guide the healing process. This unique model has the potential to serve as a valuable resource for Muslim communities across North America, and beyond, in addressing and responding to suicide. [ABSTRACT FROM AUTHOR]

Although there are numerous data modeling tools for relational databases, data modeling for NoSQL databases has seen another perspective. These databases (a) do not define any explicit schema, (b) store data in a denormalized manner, and (c) give many structure alternatives. The decision to structure the data always relies on rules of thumb, which do not guarantee an optimal structural solution. Based on this motivation, this paper offers a workload-driven model for the logical schema design of a NoSQL document database. It consists of Model input, Intermediate transformation, and Final schema generation. The proposed model takes the conceptual schema (EER model) and application workload (queries and anticipated data volume) as input and describes a procedure to convert it into a logical model for NoSQL document stores. The conversion process initially converts the application queries into query graphs. The query graphs, along with the anticipated data volume, are used to generate the query labels. The resulting query labels are assigned on the schema graph designed from the EER model. The schema graph and labels are used to transform the EER model into the appropriate logical schema model based on the actions defined for each label. We evaluate the model using a case study in the eCommerce application domain. The experimental evaluation shows the proposed model outperforms the existing conventional, optimized, and query path graphs models in multiple aspects, including query performance, storage space efficiency, aggregate pipeline efficiency, read–write latency, collection-wise performance, scalability, throughput and latency. By effectively addressing the challenges associated with managing the variety and volume of big data through a well-designed schema, our proposed model significantly reduces the time, cost, and effort required for schema development and repair. [ABSTRACT FROM AUTHOR]

Many inequities exist in serving and supporting Black survivors of violent crime. A key question in reducing inequities in care after victimization is whether police first responders and other formal system providers identify the victim as an "offender" and/or someone who is "undeserving" of supports. These labels and associated biases can directly reduce access to supports through a variety of mechanisms that include police withholding information about one's rights as a victim, among other direct and indirect barriers to social and health services. Unaddressed financial, mental, and physical health consequences of victimization contribute to poorer health outcomes later in life. This paper seeks to bring together the extant research on help-seeking, discrimination in criminal legal system functioning, and barriers to victim services by synthesizing these discrete threads into a theoretically and empirically informed conceptual model that captures the range of factors that shape Black Americans' decision to report their victimization to the police and subsequent help-seeking. Qualitative and quantitative data from a purposive sample of 91 Black victims of community violence is used to ground the developing model. The conceptual model can help lay the foundation for research that seeks to remedy the marked mismatch between the prevalence of violent victimization and help-seeking among Black Americans. Research findings can be applied to guide policies and programming to reduce inequities in care for victims of violence. [ABSTRACT FROM AUTHOR]

Ontology is a significant data model for identifying semantic information for Coronavirus disease (COVID-19) discovery. Large-scale biological and general datasets have recently been more readily available for study and to support the development of COVID-19 repositioning, but efficiently utilizing and standard metadata modeling of these datasets still remains challenging. Coronavirus disease is a deadly disease. Researchers have focused on the different aspects of COVID-19, such as studies about COVID-19, symptoms, treatment, prevention, resources, infected cases, patient information, global impact, and related research. Compilation and analysis of metadata by knowledge gathered in various field studies can help in efficient data sharing and better decision-making of data related to COVID-19. However, considering the heterogeneous nature of data sources, it is challenging and innovative to make the COVID-19 information human and machine understandable and to provide answers to user queries at all possible aspects of the COVID-19 pandemic. This paper propose a COVID-19 ontology named as CovidO metadata model that provides a common conceptual model to facilitate interoperability between metadata ontologies from various heterogeneous data sources. The CovidO has the following objectives: (1) The ontology serves as a reference schema for reporting COVID-19 data. (2) It also offer a customized schema of the existing ontologies to build a standard global data model. (3) It covers all the possible aspects/dimensions of COVID-19. (4) the proposed model aims to utilize findability, accessibility, interoperability, and reusability (FAIR) principles by providing common conceptualization metadata and thereby abstracting from heterogeneous structures of existing sources of (static) data. The experiments are conducted over CovidO using OOPS, OntoMetric, and RDF/SPARQL query to evaluate the efficiency of the proposed model. The result shows that the proposed model has a broad scope with fewer pitfalls than existing COVID-19 ontologies. [ABSTRACT FROM AUTHOR]

This paper provides a theoretical overview of how the concept of driver readiness can be objectively measured, using controlled experimental data. First, a literature review regarding the concept of driver readiness is provided. Then, it highlights challenges for a standardized readiness estimation model. A conceptual readiness estimation model is presented, and a methodology is proposed for defining readiness thresholds for use by Driver State Monitoring (DSM) systems. The paper then explores how this model can be used to estimate readiness thresholds. A proof of concept for the model application is presented, using previously collected experimental involving SAE Level 2 automation. This paper contributes to the state of the art in DSM-development, by providing a methodology for estimating driver readiness, while considering variabilities across individual drivers. The model also allows readiness thresholds to be defined with data from driving simulator experiments, without relying on subjective assessment of readiness as its ground truth. [ABSTRACT FROM AUTHOR]

In the business process lifecycle, models can be approached from two perspectives: on the one hand, models are used to create systems in the design phase, and on the other hand, systems in use produce (event) logs that are used to discover the models representing the structure of the systems. These discovered models can be the starting point of a new cycle of analysis, redesign, implementation, etc. Therefore, proper logging of implemented processes in line with system design is a critical element for process discovery. Recently, the consideration of the integration of data and process aspects has seen a surge in interest in both the model-for-design domain as in the automated-model-discovery domain. However, it seems that these domains use different conceptualizations of data/object-aware systems. A definition of how the captured event logs are related to the structure of the global system they are extracted from or are trying to discover is still missing. Especially the concept of an event needs to be aligned, as this is the main concept that the domains have in common. This paper investigates the concepts and terminology used in the different phases of the business process lifecycle: the design phase, the implementation phase (including the implementation of logging) and the discovery phase. The paper contains an extensive running example that is used to illustrate five misalignment issues. The main contribution of this paper is a meta-model that presents a unified terminology for modelling both domains and is demonstrated using the running example. The paper also shows how the concepts of iDOCEM relate to the concepts of a conceptual modelling approach and several event logging formats. iDOCEM is validated with the implementation of a log generator for the running case, demonstrating the feasibility of generating DOCEL-compliant logs from an application. [ABSTRACT FROM AUTHOR]

This guest editorial presents the papers contributing to the 15th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modelling (PoEM 2022). The best papers were selected for invitation for revision and significant expansion. Five papers were finally accepted in the special section. Collectively, these papers provide an excellent representation of the state of the art of Enterprise Modelling in both research and practice. [ABSTRACT FROM AUTHOR]

Hate crimes and racist incidents are occurring with alarming and increasing frequency on college and university campuses. As colleges work to reduce racist incidents on campus, there is still a need to prepare students to respond to racism when it occurs. When students are prepared to respond to racist incidents, they tend to have better mental health. We draw on prior literature to examine whether learning about critical race theory (CRT) and receiving CRT-informed mentoring—as embedded in an undergraduate research experience program—related to four-year college students’ self-assessed ability to respond to racism. Specifically, we propose and test a conceptual model to examine the benefits of participating in an undergraduate research experience program that required students to learn about CRT and receiving CRT-informed mentorship. Consistent with prior literature on undergraduate research experience programs, we found that the intervention positively related to students’ sense of self-efficacy and science identity, but it did not have an independent, direct relationship with self-assessed ability to respond to racism. However, the intervention indirectly supports students’ preparedness to respond to racism through its positive relationship with self-efficacy. As state legislatures around the country work to ban CRT, this paper offers empirical evidence that embedding CRT in university programs can support student development in multiple related ways. [ABSTRACT FROM AUTHOR]

Treatment systems, such as intensive outpatient programs, partial hospitalization programs, residential programs, and psychiatric hospitals, can be some of the most challenging settings in which clinical social workers practice. Two primary factors contribute to the challenges experienced by clinicians in these settings. First, patients who require this level of care usually present with comorbid diagnoses and other complex underlying problems that make their conditions challenging to treat. Second, system enactments are prevalent in these treatment settings. System enactments are complex, confounding, and divisive multi-person clinical encounters that manifest through the convergence of patients’ complex psychopathology, individual staff vulnerabilities, intra-staff dynamics, and the organizational dynamics of the clinical system in which all are embedded (Morey, C. M. (2022). Clinical Social Work Journal, 50(2), 170–182). Effective clinical practice in these settings requires knowledge and skills based on conceptual frameworks developed explicitly for treating complex patients in the multi-dimensional context of a treatment system. This paper introduces a conceptual model, referred to as a system enactment framework, which delineates four steps to guide clinicians in working through divisive and other challenging dynamics in the context of system enactments. A pathway to optimize clinical practice is illustrated by the application of this framework to clinical case material. When recognized and engaged, these enactments have significant clinical utility in systems treatment. This approach aims to enhance patient outcomes by repairing alliance ruptures with patients, restoring intra-staff functioning, and re-establishing equanimity within treatment systems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a graphical method for modeling and reasoning with linguistic expressions. The former represents a graph called a conceptual graph, and the latter involves graph transformations. In our conceptual graphs, nodes represent linguistic concepts and edges links between these concepts. This model facilitates reasonining with linguistic concepts by making direct consequences easy to infer. [ABSTRACT FROM AUTHOR]