A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem

Flexible job shop scheduling problem is a key factor of using efficiently in production systems. This paper attempts to simultaneously optimize three objectives including minimization of the make span, total workload and maximum workload of jobs. Since the multi objective flexible job shop scheduling problem is strongly NP-Hard, an integrated heuristic approach has been used to solve it. The proposed approach was based on a floating search procedure that has used some heuristic algorithms. Within floating search procedure utilize local heuristic algorithms; it makes the considered problem into two sections including assigning and sequencing sub problem. First of all search is done upon assignment space achieving an acceptable solution and then search would continue on sequencing space based on a heuristic algorithm. This paper has used a multi-objective approach for producing Pareto solution. Thus proposed approach was adapted on NSGA II algorithm and evaluated Pareto-archives. The elements and parameters of the proposed algorithms were adjusted upon preliminary experiments. Finally, computational results were used to analyze efficiency of the proposed algorithm and this results showed that the proposed algorithm capable to produce efficient solutions

Effectiveness of Three-Dimensionally Printed Models in Anatomy Education for Medical Students and Resident Physicians: Systematic Review and Meta-Analysis

© 2020 American College of Radiology Introduction: Despite a surge in the use of three-dimensional printing (3DP) in medical education, a comprehensive evaluation of randomized trials in its effectiveness is lacking. Radiologic studies play an integral role in affording educators the ability to create customized realistic anatomic models. This systematic review and meta-analysis sought to assess the effect of 3DP versus traditional 2-D methods for anatomy education. Methods: PubMed, Scopus, Cochrane Library, ERIC, and IEEE Xplore were queried to identify randomized controlled trials that quantitatively investigated anatomy education via postintervention assessments of medical students or resident physicians who were exposed to 3DP versus traditional methods. Criteria for the meta-analysis required that studies additionally included a pre-intervention assessment. Results: A total of 804 articles were reviewed, identifying 8 and 7 studies for systematic reviews of medical students and resident physicians, respectively, of which 4 and 7 were included in the meta-analyses. 3DP models were associated with higher anatomy examination scores for medical students (P \u3c .0001), but for resident physicians were statistically not significant (P = .53). Discussion: The 3DP models are shown to positively impact medical students especially given their limited fund of knowledge in anatomy. It is postulated that the lack of a statistically significant result for the resident physicians was multifactorial, in part because of the small test group sizes introducing noise and nonrepresentative samples, as well as relative simplicity of the 3DP models used with resident physicians, which were below their level of training. More trials are required to evaluate the usefulness of highly customized 3DP models

Traumatic lumbar spine fractures: Transverse process fractures dominate.

PURPOSE: With motor vehicle collisions (MVC) predominating as a source of trauma now, we sought to 1) reassess the types of traumatic lumbar spine fractures, 2) highlight the coincidence of transverse process fractures (TPF) with visceral injuries and 3) emphasize the difference in management between compression fracture (CF) and TPF. METHODS: We retrospectively reviewed the reports of lumbar spine and abdominopelvic CT scans from 2017 and 2018 to classify the types of spine fractures, their mechanism of injury, treatment and coexistence of abdominopelvic injuries. RESULTS: 2.2% of patients had posttraumatic lumbar spine fractures (113/5229), including 58 patients (51.3%) with isolated TPF and 42 (37.2%) with isolated CF; 13 patients had mixed types. TPF accounted for 70% of all fractures (195/277) as opposed to 24% for CF (67/277). MVC was responsible for 60.3% (35/58) of TPF but falls accounted for 73.8% (31/42) of CF. The odds ratio of having isolated TPF from MVC was 4.1[1.8-9.0] versus CF after a fall from standing was 4.5[2.0-10.5]. Of patients with both visceral injuries and lumbar spine fractures, 75% (27/36) had isolated TPF (odds ratio of visceral injury with TPF was 4.4[1.8-10.7]). No TPF were treated with an intervention, however 77% (40/52) of CF were addressed surgically or with braces. CONCLUSION: TPF are the most common lumbar spine fractures and are often associated with MVC. There is a high association between TPF and abdominopelvic visceral injury requiring radiologists\u27 attentiveness even though the TPF is not directly addressed

Detection of Biochemically Recurrent Prostate Cancer with [¹⁸F]DCFPyL PET/CT: An Updated Systematic Review and Meta-Analysis with a Focus on Correlations with Serum Prostate-Specific Antigen Parameters

[18F]DCFPyL is increasingly used for prostate-specific membrane antigen (PSMA) mediated imaging of men with biochemically recurrent prostate cancer (BRPCa). In this meta-analysis, which is updated with the addition of multiple new studies, including the definitive phase III CONDOR trial, we discuss the detection efficiency of [18F]DCFPyL in BRPCa patients. PubMed was searched on 29 September 2022. Studies evaluating the diagnostic performance of [18F]DCFPyL among patients with BRPCa were included. The overall pooled detection rate with a 95% confidence interval (95% CI) was calculated among all included studies and stratified among patients with PSA ≥ 2 vs. 18F]DCFPyL in men with BRPCa has trended down since a previous meta-analysis, which may reflect increasingly stringent inclusion criteria for studies over time

Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET

Abstract Background Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa. Methods This was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [18F]DCFPyL PET/CT scans were assigned to PSMA reporting and data system (PSMA-RADS) categories and randomly partitioned into training, validation, and test sets. The framework extracted image features, radiomic features, and tissue type information from a cropped PET image slice containing a lesion and performed PSMA-RADS and PCa classification. Performance was evaluated by assessing the area under the receiver operating characteristic curve (AUROC). A t-distributed stochastic neighbor embedding (t-SNE) analysis was performed. Confidence and probability scores were measured. Statistical significance was determined using a two-tailed t test. Results PSMA PET scans from 267 men with PCa had 3794 lesions assigned to PSMA-RADS categories. The framework yielded AUROC values of 0.87 and 0.90 for lesion-level and patient-level PSMA-RADS classification, respectively, on the test set. The framework yielded AUROC values of 0.92 and 0.85 for lesion-level and patient-level PCa classification, respectively, on the test set. A t-SNE analysis revealed learned relationships between the PSMA-RADS categories and disease findings. Mean confidence scores reflected the expected accuracy and were significantly higher for correct predictions than for incorrect predictions (P < 0.05). Measured probability scores reflected the likelihood of PCa consistent with the PSMA-RADS framework. Conclusion The framework provided lesion-level and patient-level PSMA-RADS and PCa classification on PSMA PET images. The framework was interpretable and provided confidence and probability scores that may assist physicians in making more informed clinical decisions