Search

Stuttering is a speech disorder characterized by difficulties in maintaining fluent speech. Individuals who stutter often have negative attitudes towards their own communication abilities, which may be influenced by high expectations placed on them by their parents. When the demands for fluent speech exceed their capabilities, stuttering episodes can occur, leading to further frustration and avoidance behaviors. Consequently, people who stutter often avoid discussing their experiences and feelings related to stuttering. Stimulating communication is essential during speech therapy, but encouraging communication is far from simple. In an attempt to facilitate communication and foster a more effective relationship between persons who stutter and their parents, we implemented the so-called awareness drawing according to the IARA model. Fourteen children (M=10; F=4; mean age=10.42; SD=1.65) and eight adolescents (M=6; F=2; mean age = 15.25; SD=1.03) diagnosed with stuttering were involved in the study along with their relatives (28 parents; M=10; F=18; mean age=31.14; SD=4.66 years). Results revealed that the awareness drawings can offer valuable insights into experiences and perceptions of stuttering both in children and adolescents and their parents. In particular, the former expressed a desire for acceptance and a wish to be recognized beyond their stuttering, depicting solutions that encompass the involvement of both parents and speech therapists. Therefore, the awareness drawing during the stuttering assessment session, could provide a valuable insight into the emotions, thoughts of person who stutter, as well as the experiences of their parents. This, in turn, can enhance the design of therapeutic intervention, La tartamudez es un trastorno caracterizado por problemas en la fluidez del discurso Las personas que tartamudean suelen tener actitudes negativas hacia sus propias capacidades comunicativas, que pueden estar influidas por las elevadas expectativas puestas en ellos por sus padres y que conducen a frustración y a conductas de evitación. Las personas que tartamudean suelen evitar hablar de sus experiencias y sentimientos relacionados con la tartamudez. Con el propósito de facilitar la comunicación y promover una relación más efectiva entre las personas que tartamudean y sus padres, implementamos el llamado dibujo de concienciación según el modelo IARA. Catorce niños (M=10; F=4; edad media=10,42; SD=1,65) y 8 adolescentes (M=6; F=2; edad media = 15,25; SD=1,03) diagnosticados de tartamudez participaron en este estudio, junto con sus familiares (28 padres; M=10; F=18; edad media=31,14; SD=4,66 años). Los resultados revelaron que los dibujos de concienciación pueden ofrecer información valiosa sobre las experiencias y percepciones de la tartamudez tanto en niños y adolescentes como en sus padres. En particular, los primeros expresaron el deseo de aceptación y de ser reconocidos más allá de su tartamudez, representando soluciones que abarcan la implicación tanto de los padres como de los logopedas. El dibujo de concienciación durante la sesión de evaluación de la tartamudez podría proporcionar una valiosa visión de las emociones, pensamientos de la persona que tartamudea, así como las experiencias de sus padres. Esto, a su vez, puede mejorar el diseño de la intervención terapéutic

Introduction: Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide with limited therapeutic options. The aim of this study was to analyze the value of adding surgery to the first-line treatment in patients with oligometastatic GC (OGC). Methods: This retrospective study included patients with OGC who underwent induction chemotherapy followed by surgery of both primary tumor and synchronous metastasis between April 2012 and April 2022. Endpoints were overall survival (OS) and relapse-free survival (RFS) analyzed by the Kaplan-Meier method. Prognostic factors were assessed with the Cox model. Results: Data from 39 patients were collected. All cases were referred to our multidisciplinary tumor board (MTB) to evaluate the feasibility of radical surgery. After a median follow-up of 33.6 months (mo.), median OS was 26.6 mo. (95% CI 23.8-29.4) and median RFS was 10.6 mo. (95% CI 6.3-14.8). Pathologic response according to the Mandard criteria (TRG 1-3, not reached versus 20.5 mo. for TRG 4-5; HR 0.23, p=0.019), PS ECOG <= 1 (26.7 mo. for PS <= 1 versus 11.2 mo. for PS >1; HR 0.3, p=0.022) and a low metastatic burden (26.7 mo. for single site versus 12.9 mo. for >= 2 sites; HR 0.34, p=0.039) were related to good prognosis. No major intraoperative complications nor surgery-related deaths occurred in our series. Discussion: A sequential strategy of preoperative chemotherapy and radical surgical excision of both primary tumor and metastases was demonstrated to significantly improve OS and RFS. Multidisciplinary evaluation is mandatory to identify patients who could benefit from this strategy.

Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Purpose: This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods: A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results: From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions: Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area., QC 20240514

Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Stuttering is a speech disorder characterized by frequent and significant problems with normal fluency and flow of speech. People who stutter perceive themselves as poor communicators and develop negative speech attitudes. This development of a negative communicative attitude could be related to the expectations and high demands made by their parents about fluency. When these expectations exceed the stuttering individual’s abilities to produce fluent verbal communication, a stuttering episode occurs. This is perceived by the individual as a communicative performance not adequately meeting the requests. Consequently, these individuals often resort to various avoidance and escape behaviors and generally avoid discussing their experiences and feelings related to stuttering. Therefore, stimulating communication is essential during speech therapy sessions to expose experiences otherwise kept hidden both from children/adolescents and their parents. However, encouraging communication is far from simple. In an attempt to facilitate communication and foster a more effective relationship between persons who stutter and their parents, we implemented the so-called awareness drawing according to the IARA model at the opening of the stuttering assessment session. Fourteen children (M=10; F=4; mean age=10.42; SD=1.65) and eight adolescents (M=6; F=2; mean age = 15.25; SD=1.03) diagnosed with stuttering were involved in this study, along with their relatives (28 parents; M=10; F=18; mean age=31.14; SD=4.66 years). The results of the study revealed that the drawings and comments of children and adolescents with stuttering, along with their parents, can offer valuable insights into their experiences and perceptions of stuttering. Children and adolescents expressed a desire for acceptance and a wish to be recognized beyond their stuttering. They depicted solutions that encompass the involvement of both parents and speech therapists. The utilization of awareness drawing can have a subs, La tartamudez es un trastorno caracterizado por problemas en la fluidez del discurso Las personas que tartamudean suelen tener actitudes negativas hacia sus propias capacidades comunicativas, que pueden estar influidas por las elevadas expectativas puestas en ellos por sus padres y que conducen a frustración y a conductas de evitación. Las personas que tartamudean suelen evitar hablar de sus experiencias y sentimientos relacionados con la tartamudez. Con el propósito de facilitar la comunicación y promover una relación más efectiva entre las personas que tartamudean y sus padres, implementamos el llamado dibujo de concienciación según el modelo IARA. Catorce niños (M=10; F=4; edad media=10,42; SD=1,65) y 8 adolescentes (M=6; F=2; edad media = 15,25; SD=1,03) diagnosticados de tartamudez participaron en este estudio, junto con sus familiares (28 padres; M=10; F=18; edad media=31,14; SD=4,66 años). Los resultados revelaron que los dibujos de concienciación pueden ofrecer información valiosa sobre las experiencias y percepciones de la tartamudez tanto en niños y adolescentes como en sus padres. En particular, los primeros expresaron el deseo de aceptación y de ser reconocidos más allá de su tartamudez, representando soluciones que abarcan la implicación tanto de los padres como de los logopedas. El dibujo de concienciación durante la sesión de evaluación de la tartamudez podría proporcionar una valiosa visión de las emociones, pensamientos de la persona que tartamudea, así como las experiencias de sus padres. Esto, a su vez, puede mejorar el diseño de la intervención terapéutica.

PURPOSE: This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. METHODS: A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. RESULTS: From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. CONCLUSIONS: Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Background: Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a novel intraperitoneal drug delivery method of low-dose chemotherapy as a pressurized aerosol in patients affected by peritoneal cancer of primary or secondary origin. We performed a systematic review and meta-analysis with the aim of assessing the feasibility, safety, and efficacy of PIPAC. Methods: A systematic literature search was performed using Medline and Web of Science databases from 1 January 2011, to inception, to 31 December 2021. Data were independently extracted by two authors. The Newcastle-Ottawa Scale was used to assess the quality and risk of bias of studies. Meta-analysis was performed for pathological response, radiological response, PCI variation along treatment, and for patients undergoing three or more PIPAC. Pooled analyses were performed using the Freeman–Tukey double arcsine transformation, and 95% CIs were calculated using Clopper–Pearson exact CIs in all instances. Results: A total of 414 papers on PIPAC were identified, and 53 studies considering 4719 PIPAC procedure in 1990 patients were included for analysis. The non-access rate or inability to perform PIPAC pooled rate was 4% of the procedures performed. The overall proportion of patients who completed 3 or more cycles of PIPAC was 39%. Severe toxicities considering CTCAE 3–4 were 4% (0% to 38.5%). In total, 50 studies evaluated deaths within the first 30 postoperative days. In the included 1936 patients were registered 26 deaths (1.3%). The pooled analysis of all the studies reporting a pathological response was 68% (95% CI 0.61–0.73), with an acceptable heterogeneity (I2 28.41%, p = 0.09). In total, 10 papers reported data regarding the radiological response, with high heterogeneity and a weighted means of 15% (0% to 77.8%). PCI variation along PIPAC cycles were reported in 14 studies. PCI diminished, increased, or remained stable in eight, one and five studies, respectively, with high heterogeneity at pooled an

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training., QC 20231215

Class imbalance (CI) is a well-known problem in data science. Nowadays, it is affecting the data modeling of many of the real-world processes that are being digitized. The manufacturing industry turns out to be highly affected by this problem, especially in fault inspection, prediction or monitoring processes, and in all those processes where the production efficiency is high and the data samples of anomalous events are rare. In this work, we systematically review all the data manipulation, machine learning or deep learning solutions to the CI problem in the manufacturing domain. We also critically evaluate all the different metrics that researchers can compare in order to estimate the improvements carried by their proposed solutions, and we look at the availability of public source code and data-imbalanced datasets that can be used for benchmarking. Finally, we summarize the most applied solutions to the CI problem in manufacturing and we look at future challenges. While posing a reference for the best practices at the time of this review, we challenge researchers to standardize the use of data science algorithms for CI in the manufacturing domain., QC 20231114

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training., QC 20231215

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training., QC 20231215

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training., QC 20231215

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models' effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model's area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

The training of future experts and operators in manufacturing engineering relies on understanding procedural processes that require applied practice. Yet, current manufacturing education and training overwhelmingly continues to depend on traditional pedagogical methods that segregate theoretical studies and practical training. While educational institutes have generally improved theoretical studies, they often lack facilities and labs to properly reproduce the working environments necessary for practice. Even in industrial settings, it is difficult, if not impossible, to halt the actual production lines to train new operators. Recently, applications with extended reality (XR) technologies, such as virtual, augmented, or mixed reality, reached a mature technology readiness level. With this technological advancement, we can envision a transition to a new teaching paradigm that exploits simulated learning environments. Thus, it becomes possible to bridge the gap between theory and practice for both students and industrial trainees. This article presents a systematic literature review of the main applications of XR technologies in manufacturing education, their goals and technology readiness levels, and a comprehensive overview of the development tools and experimental strategies deployed. This review contributes: (1) a state-of-the-art description of current research in XR education for manufacturing systems, and (2) a comprehensive analysis of the technological platforms, the experimental procedures and the analytical methodologies deployed in the body of literature examined. It serves as a guide for setting up and executing experimental designs for evaluating interventions of XR in manufacturing education and training., QC 20231215

Malignant mesothelioma is a rare aggressive tumour of the mesothelium with a propensity to spread locally and, rarely, to distant organs. The latest advances in its diagnosis and treatment have led to an increase in unusual disease presentations. Although a direct invasion of the perineum has been previously described in a men, a malignant mesothelioma spreading to the perianal region was never reported in a women. We presented a rare case of malignant mesothelioma recurrence spreading from the peritoneal cavity to the perineum through the rectovaginal space.

Introduction Gastric cancer with peritoneal metastasis (GCPM) has an unfavourable prognosis. Cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy (CRS + HIPEC) and pressurized intraperitoneal aerosol chemotherapy (PIPAC) are promising treatment options that have been shown to improve survival. The aim of this study was to assess the impact of different treatments such as systemic chemotherapy, systemic chemotherapy + PIPAC, and CRS + HIPEC in patients with GCPM.Material and methods This single-centre retrospective study included 82 patients with GCPM treated between January 2016 and June 2021. After first-line chemotherapy, depending on disease response and burden, the patients were divided into three treatment groups: chemotherapy alone, chemotherapy + PIPAC, and CRS + HIPEC. The primary outcome was overall survival (OS) from diagnosis, which was compared among the treatment groups.Results Thirty-seven (45.1%) patients were administered systemic chemotherapy alone, 25 (30.4%) received chemotherapy + PIPAC, and 20 (24.4%) underwent CRS + HIPEC. The CRS + HIPEC group had better OS (median 24 months) than the PIPAC group (15 months, p = 0.01) and chemotherapy group (5 months, p = 0.0001). Following CRS + HIPEC, the postoperative grade 3-4 complication rate was 25%, and no postoperative in-hospital deaths occurred. The median disease-free survival (DFS) was 12 months. Multivariate analysis identified peritoneal carcinomatosis index (PCI) > 7 as an independent predictor of worse DFS. No independent predictors of OS were identified.Conclusion Among patients with GCPM, we identified a highly selected population with oligometastatic disease. In this group, CRS + HIPEC provided a significant survival advantage with an acceptable major complication rate compared with other available therapies (systemic chemotherapy alone or in combination with PIPAC).

In the context of Industry 4.0, companies understand the advantages of performing Predictive Maintenance (PdM). However, when moving towards PdM, several considerations must be carefully examined. First, they need to have a sufficient number of production machines and relative fault data to generate maintenance predictions. Second, they need to adopt the right maintenance approach, which, ideally, should self-adapt to the machinery, priorities of the organization, technician skills, but also to be able to deal with uncertainty. Reinforcement learning (RL) is envisioned as a key technique in this regard due to its inherent ability to learn by interacting through trials and errors, but very few RL-based maintenance frameworks have been proposed so far in the literature, or are limited in several respects. This paper proposes a new multi-agent approach that learns a maintenance policy performed by technicians, under the uncertainty of multiple machine failures. This approach comprises RL agents that partially observe the state of each machine to coordinate the decision-making in maintenance scheduling, resulting in the dynamic assignment of maintenance tasks to technicians (with different skills) over a set of machines. Experimental evaluation shows that our RL-based maintenance policy outperforms traditional maintenance policies (incl., corrective and preventive ones) in terms of failure prevention and downtime, improving by ≈75% the overall performance.

In the context of Industry 4.0, companies understand the advantages of performing Predictive Maintenance (PdM). However, when moving towards PdM, several considerations must be carefully examined. First, they need to have a sufficient number of production machines and relative fault data to generate maintenance predictions. Second, they need to adopt the right maintenance approach, which, ideally, should self-adapt to the machinery, priorities of the organization, technician skills, but also to be able to deal with uncertainty. Reinforcement learning (RL) is envisioned as a key technique in this regard due to its inherent ability to learn by interacting through trials and errors, but very few RL-based maintenance frameworks have been proposed so far in the literature, or are limited in several respects. This paper proposes a new multi-agent approach that learns a maintenance policy performed by technicians, under the uncertainty of multiple machine failures. This approach comprises RL agents that partially observe the state of each machine to coordinate the decision-making in maintenance scheduling, resulting in the dynamic assignment of maintenance tasks to technicians (with different skills) over a set of machines. Experimental evaluation shows that our RL-based maintenance policy outperforms traditional maintenance policies (incl., corrective and preventive ones) in terms of failure prevention and downtime, improving by ≈75% the overall performance.

The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation., QC 20221123

Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education., QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications., QC 20220909

Since the introduction of the concept of learning curves in manufacturing, many articles have been applying the model to study learning phenomena. In assembly, several studies present a learning curve when an operator is trained over a new assembly task; however, when comparisons are made between learning curves corresponding to different training methods, unaware researchers can show misleading results. Often, these studies neglect either or both the stochastic nature of the learning curves produced by several operators under experimental conditions, and the high correlation of the experimental samples collected from each operator that constitute one learning curve. Furthermore, recent studies are testing newer technologies, such as assembly animations or augmented reality, to provide assembly aid, but they fail to observe deeper implications on how these digital training methods truly influence the learning curves of the operators. This article proposes a novel statistical study of the influence of expert video aid on the learning curves in terms of assembly time by means of functional analysis of variance (FANOVA). This method is better suited to compare learning curves than common analysis of variance (ANOVA), due to correlated data, or graphical comparisons, due to the stochastic nature of the aggregated learning curves. The results show that two main effects of the expert video aid influence the learning curves: one in the transient and another in the steady state of the learning curve. The transient effect of the expert video aid, where the statistical tests suffer from a high variance in the data, appears to be a reduction in terms of assembly time for the first assemblies: the operators seem to benefit from the expert video aid. As soon as the steady state is reached, a slower and statistically significant effect appears to favor the learning processes of the operators who do not receive any training aid. Since the steady state of the learning curves repres, Not duplicate with DiVA 1588621QC 20220531

The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation., QC 20221123

Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education., QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

Background: Cytoreductive surgery (CRS) is a technically demanding procedure, and there is considerable debate about its safe application. This study investigated the learning curve for CRS and the clinical outcomes of consecutive patients treated by a single surgeon at a single institution. Methods: We collected 251 consecutive patients who underwent CRS for peritoneal metastases by a single surgeon at Fondazione Policlinico Universitario A. Gemelli IRCCS, between January 2016 and December 2020. The learning curve was estimated using the cumulative summation analysis (CUSUM) for operative time (OT). Risk-adjusted CUSUM (RA-CUSUM) charts were developed using a composite variable (surgical failure), defined as the occurrence of at least one of the following events: major postoperative complications (Clavien-Dindo grade >= 3), blood loss >= 500 mL, incomplete cytoreduction. Three learning phases were thus derived from the RA-CUSUM analysis, and were compared in terms of perioperative outcomes. Results: CUSUM-OT showed that the operation time improved significantly after the 161(th) case. RA-CUSUM analysis allowed to break the CRS learning curve into three different phases: phase 1, "the learning phase " (cases 1-99), phase 2 "the experienced phase " (cases 100-188), and phase 3, "the mastership phase " (cases 189-251). The rate of major postoperative complications decreased significantly over the three phases (p = 0.019). Operative time decreased significantly as well (p = 0.031) and was significantly shorter in phase 3 with respect to the other two phases (phase 3 vs phase 2: 420 min vs 500 min, p = 0.017; phase 3 vs phase 1: 420 min vs 503 min, p = 0.021). Blood loss consistently decreased throughout the three phases (p = 0.001). The rate of incomplete cytoreduction was significantly lower in phase 3 than in phase 2 (4.8% vs 14.6%, p = 0.043). Conclusion: The CRS failure rate stabilized after the first 99 cases, and the complete surgical proficiency was achiev

Objectives Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a novel drug administration method with promising efficacy for the treatment of peritoneal metastases (PM). This study aimed to evaluate the prognostic value of an immunonutritional assessment on the feasibility, safety, and survival in this setting.Methods Data of PM patients undergoing PIPAC between September 2018 and May 2020 were prospectively recorded. A CT scan-derived body composition assessment was performed for each patient.Results Fifty-one patients were enrolled, of which 30 (58%) underwent multiple PIPAC cycles, with a pathological response rate of 55%. Prognostic nutritional index (PNI) and neutrophil-to-lymphocytes predicted completion of more than one PIPAC cycle, with a cut off of 36.5 and 4.8 respectively. Muscle attenuation and body fat tissues were associated with pathological response. At multivariate Cox regression analysis, only the presence of a low PNI (HR 2.41, 95% CI 1.08-5.46) was significantly associated with a worse OS.Conclusions A pretreatment immunonutritional assessment may provide valuable information for PIPAC patients' selection and survival, while body composition parameters are able to predict pathological response. Further larger studies are needed to validate the role of these biomarkers in tailoring the treatment and monitoring PM patients undergoing PIPAC.

Introduction Up to one-fifth of patients with colorectal cancer will develop peritoneal metastases, frequently without other districts' involvement. Despite the recent unsuccesses of hyperthermic intraperitoneal chemotherapy (HIPEC) for colorectal cancer peritoneal metastases treatment, the rationale in the prophylactic setting remains strong. Several clinical and pharmacokinetic data suggest that the efficacy of intraperitoneal chemotherapy is highest when the disease is microscopic. However, robust evidence demonstrating whether the addition of HIPEC for high-risk colorectal cancers offers better control of local recurrence is lacking. Methods and analysis This is a multicentre randomised phase 3 trial comparing prophylactic surgery plus HIPEC CO2 with mitomycin, over standard surgical excision in patients with colorectal cancer at high risk of peritoneal carcinomatosis; 388 patients will be included in this study. The primary objective is to compare the efficacy of prophylactic surgery (radical colorectal resection, omentectomy, appendectomy, round ligament of the liver resection and bilateral adnexectomy) plus HIPEC CO2 with mitomycin and standard surgery in terms of local recurrence-free survival. The main secondary endpoints are disease-free survival (DFS), overall survival (OS) and safety. The primary endpoint will be described with a cumulative incidence function and will be analysed with Grey test to take account of the competing risks. DFS and OS will be described with the Kaplan-Meier method. Ethics and dissemination This trial has been evaluated by the Italian Medicines Agency, local ethics committees and will be submitted to the Ministry of Health to notify the start of the trial according to the regulation of trials on devices with CE mark/certification. The results will be submitted for presentation at academic meetings and for publication in a peer-reviewed journal, whatever the findings. Trial registration number NCT03914820.

Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education., QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation., QC 20221123

The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation., QC 20221123

Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education., QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

Ovarian cancer is the eighth most common neoplasm in women with a high mortality rate mainly due to a marked propensity for peritoneal spread directly at diagnosis, as well as tumor recurrence after radical surgical treatment. Treatments for peritoneal metastases have to be designed from a patient’s perspective and focus on meaningful measures of benefit. Hyperthermic intraperitoneal chemotherapy (HIPEC), a strategy combining maximal cytoreductive surgery with regional chemotherapy, has been proposed to treat advanced ovarian cancer. Preliminary results to date have shown promising results, with improved survival outcomes and tumor regression. As knowledge about the disease process increases, practice guidelines will continue to evolve. In this review, we have reported a broad overview of advanced ovarian cancer management, and an update of the current evidence. The future perspectives of the Italian Society of Surgical Oncology (SICO) are discussed conclusively.

Despite the recent increase in Virtual Reality (VR) technologies employed for training manufacturing operators on industrial robotic tasks, the impact of VR methods compared to traditional ones is still unclear. This paper presents an experimental comparison of the two training approaches, with novice operators performing the same manufacturing tasks with a VR robot and with a real robot. The hardware selected is an ABB IRB 120 industrial robot, a HTC Vive head mounted display to operate it, besides a corresponding VR model developed in Unity. Twenty-four students performed two actions — drawing and “pick and place” -– in tasks with increasing difficulty, with both the VR model and the real robot. Completion time and task pass rate are adopted to estimate the learning efficiency, while a questionnaire evaluates the users’ satisfaction. The results show that students using VR overall need less elapsed time to complete all tasks, and they record a higher pass rate. The questionnaire answers show that 83% of participants find the VR model helpful in familiarizing with the real robot, and 75% are in favor of using the virtual tool for training novice operators. Users also report that moving the real robot is more complex than the virtual one; adjusting the speed is harder and the possibility of causing damage is worrisome, whereas the VR robot feels safer to operate and easier to drive. The majority of students are satisfied with the design of the tasks, and feel content with the experience. The main finding is that learning from a VR model allows to master driving a real robot quickly and easily. VR training is more useful than conventional methods because it reduces the learning time, allows for training without hindering production, lowers the risk perception, and improves safety for operators and industrial equipment., QC 20221116

The fourth industrial revolution is based on a few technological advancements that promise an industrial transformation based on achieving sharing and circular economies. Selecting and applying these advancements correctly, i.e., following relevant value drivers, is a key to the success of manufacturing firms. This results in an increasing body of knowledge from academy and practitioners in the domain of the adoption of digital technology in industry. Given the breadth of the topic, the literature deals with both a vast amount of promising technologies and related existing and prospect industrial application. This work focuses on the contributions in the production sub-domain of assembly systems and technology. In detail, relevant high-impact scientific and engineering works have been identified and analyzed with the purpose of highlighting the innovation patterns in term of the prominent technological advancement (push) and related application (pull). The results of the present study show that the most relevant areas of research are: (1) the Industrial Internet of Things, (2) Augmented and Virtual Reality as assistance to the assembly and applied to the training of operators, and (3) the horizontal and vertical system integration through Digital Twins (DT) and Cyber Physical Systems (CPS). The prominent value drivers are the improvement of resources and processes as well as asset utilization and labor. Moreover, this work represents a first step towards a unitary framework to synchronize different research efforts in the domain of assembly and support the envisaged green industrial transformation., QC 20221123

Learning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education., QC 20221123Part of proceedings: ISBN 978-3-031-18325-6

The alluvial valleys have historically been a preferable location for human settlements, for exploiting the proximity to transportation routes and waterways. However, the position of a settlement with respect to the valley can sensibly affect the degree of environmental risk with respect to natural phenomena such as floods, landslides, and earthquakes. In particular, alluvial valley deposits affect seismic site response by changing the frequency content, duration and amplitude of ground motion. Therefore, it is of the greatest importance for earthquake engineering to understand the relevant mechanisms and to quantify the most significant effects. Indeed, in this way it is possible to improve the prediction of seismic actions for the design of new constructions and the assessment of existing ones. At present, even the most advanced technical standards allow to take into account in a simplified way only the effects of topographic and stratigraphic amplification, while in the presence of valleys it is in principle necessary to carry out complex and time-consuming 2D or 3D numerical analyses. In this study, the results obtained from extensive parametric two-dimensional analyses of the seismic response of shallow alluvial valleys are reported, synthesized, and discussed. They allowed to highlight the influence of the most significant parameters and to define a valley amplification factor, VAF, which expresses the average ratio between the response spectrum all along the valley surface and that predictable by one-dimensional seismic response analyses at the valley centre. Analytical relationships are formulated for expressing the amplification factor as a function of the geometrical and mechanical properties of the valleys. Simplified charts are generated from them, which can be used for a quick estimate of the ground motion amplification. In addition, two different engineering approaches are proposed, in order to update the technical codes for considering valley effects

Industrial processes are mainly based on procedural knowledge that must be continually elicited from experienced operators and learned by novice operators. In the context of Industry 4.0, machines already play a key role in knowledge transfer; however, new models and methods based on the artificial intelligence advances of the past few years need to be developed and applied. The future of human-machine collaboration is not limited to physical applications, but it has the potential to harness both the strength of human skills, experience and the computational power provided by the surrounding machines for truly adaptive industrial processes. The winning recipe is a balance between letting humans exploit their inherent experience and letting machines integrate the missing skills to preserve production standards. This work introduces a procedural knowledge model to be used for the design of industrial and scientific adaptive processes and it paves the way to transforming human-machine collaboration into an efficient solution to make industrial and scientific processes resilient to a constantly changing world., Industriella processer baseras huvudsakligen på den procedurella kunskapen som fortlöpande måste tas fram och anpassas av erfarna operatörer och läras in av nybörjare. Inom ramen för Industri 4.0 spelar maskiner redan en nyckelroll i kunskapsöverföring; dock behöver nya modeller och metoder utvecklas och användas, som baseras på de senaste årens framsteg inom artificiell intelligens. Framtiden för samarbete mellan människa och maskin är inte begränsad till fysiska applikationer, utan den har potential att utnyttja såväl styrkan i mänsklig kompetens och erfarenhet som den beräkningskraft som de omgivande maskinerna tillhandahåller, för att åstadkomma verkligt anpassningsbara industriella processer. Det vinnande receptet är att hitta en balans mellan att låta människor utnyttja sina egna erfarenheter och att låta maskiner tillhandahålla de saknade färdigheterna för att kunna följa produktionsstandarder. I detta arbete introduceras en procedurell kunskapsmodell som kan användas för utformning av industriella och vetenskapliga, anpassningsbara processer och banar väg för att omvandla samarbete mellan människor och maskiner till effektiva lösningar för att göra industriella och vetenskapliga processer följsamma i en ständigt föränderlig värld.

Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP., QC 20210914