Programming System Research Articles

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.

Surface PD-1 expression in T cells is suppressed by HNRNPK through an exonic splicing silencer on exon 3.

Immunotherapy targeting programmed cell death 1 (PDCD1 or PD-1) and its ligands has shown remarkable promise and the regulation mechanism of PD-1 expression has received arising attention in recent years. PDCD1 exon 3 encodes the transmembrane domain and the deletion of exon 3 produces a soluble protein isoform of PD-1 (sPD-1), which can enhance immune response by competing with full-length PD-1 protein (flPD-1 or surface PD-1) on T cell surface. However, the mechanism of PDCD1 exon 3 skipping is unclear. The online SpliceAid program and minigene expression system were used to analyze potential splicing factors involved in the splicing event of PDCD1 exon 3. The potential binding motifs of heterogeneous nuclear ribonucleoprotein K (HNRNPK) on exon 3 predicted by SpliceAid were mutated by site-directed mutagenesis technology, which were further verified by pulldown assay. Antisense oligonucleotides (ASOs) targeting the exonic splicing silencer (ESS) on PDCD1 exon 3 were synthesized and screened to suppress the skipping of exon 3. The alternative splicing of PDCD1 exon 3 was analyzed by semiquantitative reverse transcription PCR. Western blot and flow cytometry were performed to detect the surface PD-1 expression in T cells. HNRNPK was screened as a key splicing factor that promoted PDCD1 exon 3 skipping, causing a decrease in flPD-1 expression on T cell membrane and an increase in sPD-1 expression. Mechanically, a key ESS has been identified on exon 3 and can be bound by HNRNPK protein to promote exon 3 skipping. Blocking the interaction between ESS and HNRNPK with an ASO significantly reduced exon 3 skipping. Importantly, HNRNPK can promote exon 3 skipping of mouse Pdcd1 gene as well. Our study revealed a novel evolutionarily conserved regulatory mechanism of PD-1 expression. The splicing factor HNRNPK markedly promoted PDCD1 exon 3 skipping by binding to the ESS on PDCD1 exon 3, resulting in decreased expression of flPD-1 and increased expression of sPD-1 in T cells.

Clingo2DSR - A clingo-based software system for declarative spatial reasoning

ABSTRACT We present a modified ground-and-solve approach based on the clingo Answer Set Programming (ASP) system to perform non-monotonic spatial reasoning tasks, Clingo2DSR. Our system is distinct from previous research integrating ASP with space in that it deals with complex real-world data and numerous time steps. Clingo2DSR is composed of (i) an input language defining spatial entities, functions, and relations; (ii) an external geometry database for performing spatial computations and checking spatial constraints; and (iii) a theory-based solving approach coupling symbolic ASP with external sources for sound and fast model search. We demonstrate our system on three real building models, in the context of architectural design, submitted for analyses and queries where spatial components play an important role.

Моделирование помех в электронном устройстве при воздействии импульсного магнитного поля с использованием искусственной нейронной сети

Modern electronic means are quite sensitive to electromagnetic interference. At the same time, they must function reliably in the existing electromagnetic environment. Lightning discharges and industrial sources make a significant contribution to the formation of the electromagnetic environment around electronic equipment. In this case, pulsed magnetic fields with microsecond parameters most often arise. The most rational approach to ensuring electromagnetic compatibility of electronic means is the most complete accounting and protection from possible phenomena at the design stage. Different methods for modeling the consequences of exposure to electromagnetic interference have their own advantages and disadvantages. To develop a technique and modeling interference in electronic means based on an artificial neural network using the example of the influence of a pulsed magnetic field a the purpose of this work. A practical technique for calculation the magnitude of interference in electronic means using an artificial neural network the paper proposes. All stages of the technique are described: analysis of the main input parameters affecting the amount of interference in the electronic means; the use of a special experimental stand for measuring interference depending on significant input parameters; choosing the structure and parameters of an artificial neural network to modeling interference; choosing a training method for an artificial neural network; choosing a criterion for assessing the quality of training when solving a regression problem; normalization of training data; training an artificial neural network using measured data; modeling the amount of interference in the communication line of an electronic means when exposed to a pulsed magnetic field; assessment of consequences and selection of methods of protection against interference. As an example, we consider the problem of modeling the amount of interference in a communication line inside an electronic means when exposed to a pulsed magnetic field. The magnetic field has parameters recommended by the requirements of the regulatory document on electromagnetic compatibility of devices. In the problem under consideration, an acceptable discrepancy in results is achieved with an acceptable number of neural network training epochs.

ДВА СПОСОБА МОДЕЛИРОВАНИЯ РИСКА ПРОГРЕССИРУЮЩЕГО АТЕРОСКЛЕРОЗА

Background. Relevance Progressive atherosclerosis has attracted researchers for the last decade due to the fact that it is associated with unfavorable clinical outcomes. Application of modern methods of mathematical modeling will allow to identify patients with such extreme risk of cardiovascular events and optimize their management. Purpose of the study. To develop a mathematical model of the risk of progressive atherosclerosis using the free cross-platform visual programming system Orange and to compare it with the method of nonlinear regression model of logistic type implemented in C++ programming language. Materials and Methods. Female and male patients diagnosed with coronary heart disease (CHD) were included in the study, 100 of them had signs of progressive course of atherosclerosis, 102 - its spontaneous course. Risk models were built using the free cross-platform visual programming system Orange, SPSS 22.0 package, and C++ console. Results and conclusion. The author's proposed approaches to assess the risk of progressive atherosclerosis using regression analysis and machine learning have good prognostic accuracy; both methods are recommended by the authors for application in practice.

МАТЕМАТИЧЕСКИЕ МОДЕЛИ И ЧИСЛЕННЫЕ МЕТОДЫ В КАРДИОЛОГИИ: СОБСТВЕННЫЙ ОПЫТ

Вackground. In the era of digital transformation in medicine, which is radically changing the way patient information is stored, processed and used, medical professionals need new tools and approaches that can efficiently process and interpret data for fast and accurate decision making. These methods also enable the creation of personalized treatment approaches, taking into account individual patient characteristics and risks. In this article, the authors share their own experience in applying numerical methods in cardiology. Purpose of the study. To develop methods of effective risk assessment of cardiovascular outcomes in different categories of patients. Characteristics of patients and methods of the study. Three variants of outcome prediction were included in the study. The 1st - assessment of the total risk of one-year cardiovascular outcomes after myocardial infarction with ST-segment elevation, the 2nd - assessment of the risk of postinfarction chronic heart failure, the 3rd – assessment of the risk of progressive course of atherosclerosis. Statistical analysis methods included the free cross-platform visual programming system Orange, the SPSS 22.0 package, and the C programming language console. Results and Conclusion. The conducted study has shown that the application of different approaches: regression analysis, machine learning, Excel programming capabilities, C programming language and console interface allows to develop models of cardiovascular outcomes prediction with high accuracy.

Development and psychometric evaluation of a Total Worker Health® practice scale.

Occupational Safety and Health (OSH) professionals increasingly need to assess and mitigate a broad scope of worker safety, health, and well-being concerns that extend beyond traditional OSH training curricula. Work-related and non-work-related psychosocial hazards, chronic physical and mental health conditions, and changing work arrangements call for an integrative, public health approach to occupational risk management; this approach has been called, "Total Worker Health®" by United States public health authorities (Schulte et al. 2019). OSH professionals need education to ensure that Total Worker Health (TWH) practice approaches are consistently and effectively applied. This study sought to develop a valid, reliable scale that could be used to assess TWH skills gaps that can be addressed with tailored, competency-based curricula. We developed a survey scale to assess professional competency for specific TWH practice approaches described in prior literature (Lee et al. 2016; Punnett et al. 2020). The scale contained 11 statements and used a 5-point scale (not skilled to highly skilled) to rate the level of competency. We pre-tested and refined the statements for face validity then administered the scale with a convenience sample of 258 professionals from mixed OSH disciplines. A total of 210 OSH professionals (31% safety, 17% industrial hygiene, 12% occupational nurses, 11% occupational physicians, 29% other) who completed 50% or more of the questions were included in the analysis. Scale reliability was assessed with a Cronbach's alpha test. Scale validity and structure were assessed with exploratory factor analysis (EFA). The 11-item scale had high internal consistency (Cronbach's α = 0.92). The initial EFA solution suggested 2 factors that explained 65.3% of variance, with one cross-loaded question. A final 10-item, 2-factor scale was developed that accounted for 66.0% of variance with no cross-loaded items (Cronbach's alpha α = 0.91). Factor 1 (6 items) contributed to 55.5% of the variance and captured skills related to TWH program leadership (e.g. leader commitment, integrating program systems, engaging with other program leaders and workers). Factor 2 (4 items) contributed to 10.5% of the variance and captured technical skills related to hazard identification and control (e.g. identifying hazards, designing work to reduce hazards). Internal consistency was very good for both TWH program leadership (Cronbach α = 0.89) and TWH risk assessment and control (Cronbach α = 0.80) subscales. A novel 10-item TWH skill scale to assess specific TWH practice approaches was developed with very good reliability. Factor analysis revealed 2 latent constructs: TWH leadership skills and TWH risk assessment and control skills. This study offers an evidence-based tool to assess competency for specific TWH practices among OSH professionals. The results of this study contribute to the broader research base needed to formalize a TWH competency framework, as advocated by other scholars interested in TWH workforce education (Newman et al. 2020).

Efficiency assessment of a novel automatic mosquito pupae sex separation system in support of area-wide male-based release strategies

This study provides a comparative analysis of two state-of-the-art automatic mosquito pupae sex sorters currently available: the ORINNO and the WOLBAKI Biotech pupae sex separation systems, which both exploit the sexual size dimorphism of pupae. In Aedes aegypti, the WOLBAKI sex sorter and the ORINNO with a sieve mesh size of 1.050 mm achieved sex separation with female contamination rates below 1%, low pupae mortality rates and high male flight capacity. However, in Ae. albopictus, there was more variability, with female contamination rates above the 1% threshold and pupae mortality reaching 27% when using the ORINNO sorter. On the other hand, the WOLBAKI sorter achieved a male pupae recovery of 47.99 ± 8.81% and 50.91 ± 11.77% in Ae. aegypti and Ae. albopictus, respectively, while the ORINNO sorter with a smaller sieve size achieved male pupae recoveries of 38.08 ± 9.69% and 40.16 ± 2.73% in Ae. aegypti and Ae. albopictus, respectively. This study provides valuable information for researchers and practitioners in the field, assisting in the selection of the most suitable system for mosquito control, management and research programs depending on their specific requirements.

Characterization of Asoka (Saraca Indica L.) branch wood for pulp and paper manufacturing

Asoka (Saraca indica L.) branch wood was evaluated for its chemical composition, morphological features, kraft pulping behavior, and physical properties. The Asoka used in this study contained low amounts of cellulose and hemicellulose, but higher amounts of lignin and ash. It was found that the morphological indices of Asoka branch wood were mostly adequate for pulping and papermaking. The pulping of this wood was performed using chemi-mechanical pulping (CMP), conventional kraft pulping, and soda pulping processes under different conditions, i.e., active alkali and anthraquinone (AQ) addition. The results showed that alkaline pupling and CMP provided low screened yield values. Acceptable kappa numbers and reject levels were obtained at 22% active alkali of soda and kraft-AQ pulpings. CMP pulp showed higher brightness and screened yield than that of alkaline pulps. Alkaline pulpings of Asoka branch wood provided slightly different strength properties of paper sheets than those of CMP pulp. Based on the findings, it can be concluded that Asoka branch wood is more suitable as a raw material for mechanical pulping than it is for alkaline pulping.

Acceptance Sampling Plans based on Percentiles of Exponentiated Inverse Kumaraswamy Distribution

Objectives: To prepare the percentile-based acceptance sampling plans for the Exponentiated Inverse Kumaraswamy Distribution (EIKD) at a specific truncation time to inspect the defective lots corresponding to the desired acceptance level. Methods: The failure probability value is estimated using the cumulative probability function F(.) at time ‘t’ which is converted in terms of the scale parameter σ as 100th percentile using quantile function. The minimum size of the sample, Operating Characteristic (OC) and the minimum ratios are calculated for a required levels of consumer’s as well as producer’s risk. Findings: The percentile-based sampling plans are obtained through the minimal size of the sample ‘n’ under a truncated life test with a target acceptance number c in a manner that the proportion of accepting a lot which is not good (consumer’s risk) would not be more than . These values are calculated at The function of probability of acceptance for variations in the quality of a lot (OC function) L(p) of the sample plan are evaluated for the acceptance values of c=1 and c=5. The minimum ratio values are calculated for the acceptability of the lot with producers’ risk of using the sampling plan. Novelty: The modernity of this study is the designing of the acceptance sampling plans to a non-normal data using an asymmetrical distribution that has all three shape parameters. Also, the monitor of the implementation and suitability of statistical quality control and process control aspects using Exponentiated Inverse Kumaraswamy Distribution when compared to other asymmetrical distributions which has at least one scale parameter. Keywords: Sampling plans, Consumer's risk, Operating characteristics function, Truncated life tests, Producer's risk

ASAM features harm reduction, methadone, OTPs at conference

Not surprisingly, last week'k American Society of Addiction Medicine (ASAM) conference was sold out, with its big focus on moving methadone out of the opioid treatment program (OTP) system and into general office‐based care (preferably by only ASAM‐boarded physicians), and also considerable attention paid to the value of high‐quality treatment with methadone. A track called “Methadone in the Modern Era” included Ruth Potee, M.D., in an impassioned speech focused on the need for more access to methadone, and on the for‐profit OTPs that are out there. That track also featured Yngvild Olsen, M.D., director of the Center for Substance Abuse Treatment at the Substance Abuse and Mental Health Services Administration (SAMHSA) who talked about the other modern (aka, real world) era in which OTP regulations have been completely updated and revised. Aaron Ferguson, who said at a public conference last fall that he owed his life to diverted methadone (see https://onlinelibrary.wiley.com/doi/10.1002/adaw.33908) despite being a patient advocate, also presented at the conference, this time on harm reduction, which is beneficial to keeping people alive. Many OTPs, especially the large chain for‐profit OTPs, have been a target of ASAM members for years; most are also members of the American Association for the Treatment of Opioid Dependence, and some are members of the National Association for Behavioral Healthcare.

Evaluation of Multimedia Teaching Platform for Piano Accompainment Using Logic Pro

The role of education in a country's progress is crucial. It ensures the intellectual and human resources a country needs to grow. There has been a growing consensus in China that gaining a thorough understanding of advanced foreign theories and effective teaching is crucial to keeping up with the country's rapid advancement and growth of its higher education system and the continuous intensifying and improved performance of the reform of education and teaching activities. College and university music programs often include piano teaching (PT) as a required curriculum component. As a result, the job it does in the classroom is crucial to developing modern musical literacy and understanding.. This article shows how to regulate the classroom tempo by utilizing multimedia instructional tools, how to pique students' interest in learning, and how to use multimedia tools to improve the quality of piano instruction. We used a logic programming system to design a piano teaching platform.it is used to analyze the knowledge of piano learners and manipulate their interference. By gathering user behavior from different Internet distribution platforms and evaluating the feature vector by the function space created by the pertinent evaluation metrics, the analysis system is also utilized to assess some instructional techniques' online public thought assessment. The system's accuracy is 96%, indicating a successful teaching assessment. The system's value evaluation paradigm makes it possible to comprehend user behaviors precisely and apply them to other disciplines.

Sustainability Planning for a Community Network to Increase Participation in Evidence-Based Lifestyle Change Programs: A Mixed-Methods Approach.

Community-based chronic disease prevention programs can have long-term, broad public health benefits. Yet, only 40 to 60% of evidence-based health programs are sustained. Using established frameworks and evidence-based tools to characterize sustainability allows programs to develop structures and processes to leverage resources effectively to sustain effective program activities and systems. This study used a mixed-methods, partner-engaged approach to identify barriers and facilitators to sustaining a community network (the Alliance program) aimed to increase participation in evidence-based lifestyle change programs delivered in the community. Surveys and qualitative interviews were conducted with the Alliance partners based on the Program Sustainability Assessment Tool and Consolidated Framework for Implementation Research. Overall, partners felt Alliance had a high capacity for sustainability. Strategic planning, communication, and partnerships were areas partners prioritized to improve the potential for sustaining the program. Results informed the co-development of a sustainability action plan. This paper furthers our understanding of factors critical for the sustainability of community-based programs for chronic disease prevention and health equity and presents a process for developing action plans to build sustainability capacity.

Trends in medical device company payments in neurosurgery: a nationwide, multidatabase, geospatial analysis.

Industry partnerships help advance the field of neurosurgery. Given the nature of the field and its close relationship with innovation, neurosurgeons frequently partner with the medical device industry to advance technology and improve outcomes. However, this can create important ethical concerns for patients. In this paper, the authors sought to comprehensively study how physician payments from medical device companies have changed and what geographic parameters influence the trends observed over the years. The authors queried and merged several large databases, including Medicare and Medicaid provider usage data and databases from the Open Payments Program, National Plan and Provider Enumeration System, and US Census Bureau. Geospatial analysis was performed using Moran's I and II clustering. Univariate and multivariable analyses were performed using the Mann-Whitney U-test and geospatially weighted multivariable regression for hot spot and cold spot membership. Data for 952 counties across the continental United States were analyzed. Ninety-seven counties constituted geographic hot spots. These hot spots were primarily concentrated in Florida, the New York-Pennsylvania region, central Colorado, and southwestern United States. Independent predictors of hot spot membership included greater unemployment rates, the percentage of White patients, the presence of mobile homes, and the percentage of county Hispanic and Black populations. Company-based differences were examined. The vast majority of Medtronic's payments were in the form of royalties and licensing (86.6%). Royalties and licensing accounted for the majority of payments for DePuy (69.4%), Globus Medical (62%), and NuVasive (77.1%). In contrast, other companies, such as Boston Scientific, opted to pay physicians in the form of ownership and investment interests (42.1%). The impact of the COVID-19 pandemic was also assessed. During the onset of the pandemic in 2020, physician payments fell or remained the same across all regions with the exception of the South Atlantic region. However, it was observed that nearly all regions rebounded, with stark elevations in physician payments immediately in 2021. This analysis demonstrates that there are national hot spots and cold spots of physician payments, and offers some social, economic, and company-dependent predictors that may influence the magnitude of payments. Further analysis is needed to better understand this clinical-commercial partnership in healthcare, specifically within neurosurgical practice.

Metody oceny potencjału lokalnych strategii i programów w zakresie energii i efektywności energetycznej w społecznościach Ukrainy

There is suggested the Methods for assessing the potential of local strategies and programmes as a practical tool for assessing the ability of local strategies and programmes in the field of energy and energy efficiency. Due to the use of the Methods, local communities and specific energy cities will be able to identify a compliance level of their local strategies or programmes to legislative and regulative acts of both national and regional levels. This assessing the ability of local strategies and programmes will provides ordering a system of strategies and programmes, and will be a significant step to the practice of energy efficiency and energy sustainability. In the paper, we will analyze strategies and programmes of six local communities of six Regions (Oblasts) of Ukraine with the help of the suggested Methods. Due to this approach, we will cover with analysis different regions of Ukraine. The findings will be represented in conclusions. In practice, findings represented in the paper, could be interesting for energy companies, Local Self-Government Authorities, utility companies, academia, and a wide audience interested in issues on energy and energy efficiency. The approbation of the Methods is represented on the example of Ukrainian communities; nevertheless, this is a good example that can be used by territorial units in different countries.

Optimization of chemotherapy regimens using mathematical programming

Cancer is a leading cause of death and a cost burden on healthcare systems worldwide. The mainstay of treatment is chemotherapy which is most often administered empirically. Optimizing the frequency of drug administration would benefit patients by avoiding overtreatment and reducing costs. In this work, the optimization of chemotherapy regimens using mathematical programming techniques is demonstrated by developing a simple mathematical programming model for the administration of a fictitious drug. The question to be answered by the solution of the model is how often the drug should be administered so that the tumor size does not exceed a predefined size and the treatment cost reaches a minimum value. The proposed mathematical programming model is computer-implemented using a well-established mathematical programming system, thus keeping the cost and effort of obtaining the optimization results low. An example is used to demonstrate the superiority of the proposed optimization approach over the mainstay approach.

Reidentification of Participants in Shared Clinical Data Sets: Experimental Study

Background Large curated data sets are required to leverage speech-based tools in health care. These are costly to produce, resulting in increased interest in data sharing. As speech can potentially identify speakers (ie, voiceprints), sharing recordings raises privacy concerns. This is especially relevant when working with patient data protected under the Health Insurance Portability and Accountability Act. Objective We aimed to determine the reidentification risk for speech recordings, without reference to demographics or metadata, in clinical data sets considering both the size of the search space (ie, the number of comparisons that must be considered when reidentifying) and the nature of the speech recording (ie, the type of speech task). Methods Using a state-of-the-art speaker identification model, we modeled an adversarial attack scenario in which an adversary uses a large data set of identified speech (hereafter, the known set) to reidentify as many unknown speakers in a shared data set (hereafter, the unknown set) as possible. We first considered the effect of search space size by attempting reidentification with various sizes of known and unknown sets using VoxCeleb, a data set with recordings of natural, connected speech from >7000 healthy speakers. We then repeated these tests with different types of recordings in each set to examine whether the nature of a speech recording influences reidentification risk. For these tests, we used our clinical data set composed of recordings of elicited speech tasks from 941 speakers. Results We found that the risk was inversely related to the number of comparisons an adversary must consider (ie, the search space), with a positive linear correlation between the number of false acceptances (FAs) and the number of comparisons (r=0.69; P<.001). The true acceptances (TAs) stayed relatively stable, and the ratio between FAs and TAs rose from 0.02 at 1 × 105 comparisons to 1.41 at 6 × 106 comparisons, with a near 1:1 ratio at the midpoint of 3 × 106 comparisons. In effect, risk was high for a small search space but dropped as the search space grew. We also found that the nature of a speech recording influenced reidentification risk, with nonconnected speech (eg, vowel prolongation: FA/TA=98.5; alternating motion rate: FA/TA=8) being harder to identify than connected speech (eg, sentence repetition: FA/TA=0.54) in cross-task conditions. The inverse was mostly true in within-task conditions, with the FA/TA ratio for vowel prolongation and alternating motion rate dropping to 0.39 and 1.17, respectively. Conclusions Our findings suggest that speaker identification models can be used to reidentify participants in specific circumstances, but in practice, the reidentification risk appears small. The variation in risk due to search space size and type of speech task provides actionable recommendations to further increase participant privacy and considerations for policy regarding public release of speech recordings.

Development of a quality management monitoring system for social and pedagogical educational programs

This paper underscores the pressing need to address educational challenges in Kazakhstan. The government's educational system development policy seeks to comprehensively modernize education across all levels emphasizing innovative teaching methods and tools. A crucial responsibility that necessitates the development of effective procedures and methods is ensuring the efficacy of educational programmes. This research employs both theoretical methods (such as analysis, generalization and classification) and empirical methods (including surveys and mathematical statistics) to investigate this topic. The statistical significance of the results can be determined by applying the student's t-test after the survey data has undergone mathematical processing. This rigorous approach adds credibility to the findings and enhances the reliability of the research. This paper posits that internal monitoring of educational programs yields significant insights into educational quality. This approach evaluates indicators related to student satisfaction and learning outcomes, recognizing that program quality is dynamic and influenced by evolving educational needs, teacher competence and material and technical infrastructure. In a nutshell, educational program monitoring assumes a pivotal role in enhancing the quality of education in Kazakhstan. This monitoring system contributes to continuous efforts to modernize and align the education system with changing demands and standards by evaluating learning results and student satisfaction. This provides valuable information for programme enhancement.

Effectiveness of designing a knowledge-based artificial intelligence chatbot system into a nursing training program: A quasi-experimental design

BackgroundChatbots have gained popularity in the healthcare industry due to their ability to provide prompt and accurate responses to a wide range of inquiries. This has been particularly beneficial for nurses who often require quick access to information and may have questions related to patient care. PurposeTo evaluate the effectiveness of designing a knowledge-based artificial intelligence chatbot system for a nursing training program. MethodsThe study utilized a Quasi-experimental design and collected data from a purposive sample of 73 nurses using Google Forms. The tools used in the study included 1) a structured questionnaire for nurses (a) Demographic data (b) Nurses' knowledge regarding chatbots, 2) Nurses' knowledge about artificial intelligence, 3) Nurses' perception of the application of chatbots in nursing, and 4) Nurses' opinions about the use of nursing chatbots or traditional methods of education. ResultsThere was a highly statistically significant improvement in nurses' knowledge-based chatbot systems post-intervention (p = 0.001). ConclusionIntegrating an artificial intelligence chatbot system into a nursing training program provides nurses with easy access to reliable and evidence-based knowledge. The chatbot offers immediate answers, explanations, and up-to-date resources, empowering nurses to make informed decisions, stay updated, and served as a communication platform connecting nurses through a common language and enhance their practice. RecommendationIncorporating a knowledge-based chatbot system into a nursing training program. Furthermore, further research should be conducted to explore the long-term impact of chatbot systems on nursing practices and education.

PRogramAR: Augmented Reality End-User Robot Programming

The field of end-user robot programming seeks to develop methods that empower non-expert programmers to task and modify robot operations. In doing so, researchers may enhance robot flexibility and broaden the scope of robot deployments into the real world. We introduce PRogramAR (Programming Robots using Augmented Reality), a novel end-user robot programming system that combines the intuitive visual feedback of augmented reality (AR) with the simplistic and responsive paradigm of trigger-action programming (TAP) to facilitate human-robot collaboration. Through PRogramAR, users are able to rapidly author task rules and desired reactive robot behaviors, while specifying task constraints and observing program feedback contextualized directly in the real world. PRogramAR provides feedback by simulating the robot’s intended behavior and providing instant evaluation of TAP rule executability to help end users better understand and debug their programs during development. In a system validation, 17 end users ranging from ages 18 to 83 used PRogramAR to program a robot to assist them in completing three collaborative tasks. Our results demonstrate how merging the benefits of AR and TAP using elements from prior robot programming research into a single novel system can successfully enhance the robot programming process for non-expert users.