Transfer Learning for Inverse Design of Tunable Graphene-Based Metasurfaces

This paper outlines a new approach to designing tunable electromagnetic (EM) graphene-based metasurfaces using convolutional neural networks (CNNs). EM metasurfaces have previously been used to manipulate EM waves by adjusting the local phase of subwavelength elements within the wavelength scale, resulting in a variety of intriguing devices. However, the majority of these devices have only been capable of performing a single function, making it difficult to achieve multiple functionalities in a single design. Graphene, as an active material, offers unique properties, such as tunability, making it an excellent candidate for achieving tunable metasurfaces. The proposed procedure involves using two CNNs to design the passive structure of the graphene metasurfaces and predict the chemical potentials required for tunable responses. The CNNs are trained using transfer learning, which significantly reduced the time required to collect the training dataset. The proposed inverse design methodology demonstrates excellent performance in designing reconfigurable EM metasurfaces, which can be tuned to produce multiple functions, making it highly valuable for various applications. The results indicate that the proposed approach is efficient and accurate and provides a promising method for designing reconfigurable intelligent surfaces for future wireless communication systems

An eleven-year retrospective cross-sectional study on pulmonary alveolar proteinosis

Introduction: Pulmonary alveolar proteinosis (PAP) is a rare disease in the field of pulmonary medicine. The efficacy of whole-lunglavage (WLL) as the treatment of PAP had never been evaluated in the Iranian population. Therefore, there is a real need to investigatethe characteristics of PAP and also to evaluate the efficacy of WLL in this rare disease. The study aimed to investigatedemographic features, clinical presentation and treatment outcomes of the disease in Iranian PAP patients. Material and methods: Data of 45 patients with definite diagnosis of PAP, who had regular follow-ups from March 2004 to March2015 at an Iranian referral respiratory hospital, were collected. Whole-lung lavages (WLL) efficacy was assessed by comparingspirometric, arterial blood gas parameters and six-minute walk test (6MWT) results before and after all lavages. Results: Mean age at diagnosis of disease was 30.33 ± 14.56 years. Four patients (8.8%) reported non-massive hemoptysis and threesubjects (6.6%) had concomitant pulmonary tuberculosis. In 71.1% of cases, transbronchial lung biopsy and bronchoalveolar lavage weresufficient for diagnosis. Spirometric results and arterial blood gas parameters and 6MWD improved significantly after all the lavages. Fourpatients (8.8%) died because of respiratory failure. The only variable capable of predicting treatment failure was the history of hemoptysis. Conclusion: The study revealed sufficiency of WLL as the PAP patients’ treatment. Also hemoptysis was found to be the independentfactor that can predict treatment failure

The Effect of Remote Ischemic Preconditioning on the Incidence of Acute Kidney Injury in Patients Undergoing Coronary Artery Bypass Graft Surgery: A Randomized Controlled Trial

Background: Remote ischemic preconditioning (RIPC) protects other organs from subsequent lethal ischemic injury, but uncertainty remains. We investigated if RIPC could prevent acute kidney injury (AKI) in patients undergoing coronary artery bypass graft (CABG) surgery. Methods: This parallel-group, double-blind, randomized, controlled trial was done on adults undergoing elective or urgent on-pump CABG surgery from 2013 to 2017 in Shiraz, Iran. Patients were allocated to RIPC or control groups through permuted blocking. The patients in the RIPC group received three cycles of 5 min ischemia and 5 min reperfusion in the upper arm after induction of anesthesia. We placed an uninflated cuff on the arm for 30 min in the control group. The study primary endpoint was an incidence of AKI. Secondary endpoints included short-term clinical outcomes. We compared categorical and continuous variables using Pearson χ2 and unpaired t tests, respectively. P<0.05 was considered significant. Results: Of the 180 patients randomized to RIPC (n=90) and control (n=90) groups, 87 patients in the RIPC and 90 patients in the control group were included in the analysis. There was no significant difference in the incidence of AKI between the groups (38 patients [43.7%] in the RIPC group and 41 patients [45.6%] in the control group; relative risk, 0.96; 95% confidence interval, 0.69 to 1.33; P=0.80). No significant differences were seen regarding secondary endpoints such as postoperative liver function, atrial fibrillation, and inpatient mortality. Conclusion: RIPC did not reduce the incidence of AKI, neither did it improve short-term clinical outcomes in patients undergoing on-pump CABG surgery. Trial Registration Number: IRCT2017110537254N1

Jedenastoletnie, retrospektywne badanie przekrojowe dotyczące proteinozy pęcherzyków płucnych

WSTĘP: Proteinoza pęcherzyków płucnych (PAP, pulmonary alveolar proteinosis) to rzadka choroba płuc. W populacji irańskiej nie analizowano nigdy skuteczności płukania całego płuca (WLL, whole-lung lavage) jako leczenia PAP. Z tego powodu oceniono charakterystykę PAP, a także skuteczność WLL w tej rzadkiej chorobie. Niniejsze badanie miało na celu analizę cech demograficznych, obrazu klinicznego i wyników leczenie tej choroby u irańskich pacjentów z PAP. MATERIAŁ I METODY: Zgromadzono dane 45 pacjentów z pewnym rozpoznaniem PAP, regularnie obserwowanych od marca 2004 do marca 2015 roku w irańskim szpitalu referencyjnym. Skuteczność WLL oceniano, porównując parametry spirometryczne, gazometrii krwi tętniczej oraz testu 6-minutowego marszu (6MWT, 6 minute walk test) przed i po wykonaniu wszystkich zabiegów płukania płuca. WYNIKI: Średni wiek w momencie rozpoznania choroby wynosił 30,33 ± 14,56 roku. U czterech pacjentów (8,8%) występowało niemasywne krwioplucie, u trzech (6,6%) współistniała gruźlica płuc. Biopsja przezoskrzelowa płuca i płukanie oskrzelowo- -pęcherzykowe w 71,1% przypadków były wystarczającymi badaniami do postawienia rozpoznania. Wyniki spirometryczne, parametry gazometrii krwi tętniczej i 6MWD poprawiły się znamiennie po przeprowadzeniu wszystkich zabiegów płukania płuca. Czterech pacjentów (8,8%) zmarło z powodu niewydolności oddechowej. Jedyną zmienną mogącą przewidzieć niepowodzenie leczenia było występowanie krwioplucia w wywiadzie chorobowym. WNIOSKI: W badaniu wykazano, że WLL jest leczeniem skutecznym u pacjentów z PAP. Krwioplucie było niezależnym czynnikiem predykcyjnym niepowodzenia leczenia.WSTĘP: Proteinoza pęcherzyków płucnych (PAP, pulmonary alveolar proteinosis) to rzadka choroba płuc. W populacji irańskiej nie analizowano nigdy skuteczności płukania całego płuca (WLL, whole-lung lavage) jako leczenia PAP. Z tego powodu oceniono charakterystykę PAP, a także skuteczność WLL w tej rzadkiej chorobie. Niniejsze badanie miało na celu analizę cech demograficznych, obrazu klinicznego i wyników leczenie tej choroby u irańskich pacjentów z PAP. MATERIAŁ I METODY: Zgromadzono dane 45 pacjentów z pewnym rozpoznaniem PAP, regularnie obserwowanych od marca 2004 do marca 2015 roku w irańskim szpitalu referencyjnym. Skuteczność WLL oceniano, porównując parametry spirometryczne, gazometrii krwi tętniczej oraz testu 6-minutowego marszu (6MWT, 6 minute walk test) przed i po wykonaniu wszystkich zabiegów płukania płuca. WYNIKI: Średni wiek w momencie rozpoznania choroby wynosił 30,33 ± 14,56 roku. U czterech pacjentów (8,8%) występowało niemasywne krwioplucie, u trzech (6,6%) współistniała gruźlica płuc. Biopsja przezoskrzelowa płuca i płukanie oskrzelowo- -pęcherzykowe w 71,1% przypadków były wystarczającymi badaniami do postawienia rozpoznania. Wyniki spirometryczne, parametry gazometrii krwi tętniczej i 6MWD poprawiły się znamiennie po przeprowadzeniu wszystkich zabiegów płukania płuca. Czterech pacjentów (8,8%) zmarło z powodu niewydolności oddechowej. Jedyną zmienną mogącą przewidzieć niepowodzenie leczenia było występowanie krwioplucia w wywiadzie chorobowym. WNIOSKI: W badaniu wykazano, że WLL jest leczeniem skutecznym u pacjentów z PAP. Krwioplucie było niezależnym czynnikiem predykcyjnym niepowodzenia leczenia

Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models

Method development for determination of migrated phthalate acid esters from polyethylene terephthalate (PET) packaging into traditional Iranian drinking beverage (Doogh) samples: a novel approach of MSPE-GC/MS technique

In the current study, a novel magnetic solid phase extraction (MSPE) technique combined with a gas chromatography/mass spectroscopy (GC/MS) was developed to determine the phthalate ester content of bottled Doogh samples. Doogh is a yogurtbased drinking beverage, which is frequently consumed in Middle East and Balkans. It is produced by stirring yogurt in Chern separation machine and consists of substances such as water, yogurt, and salt in addition to aqueous extracts of native herbs. The magnetic multi-walled carbon nanotubes (MWCNT-Fe3O4) were used as adsorbents of phthalate acid esters (PAEs) due to a superior adsorption capability of hydrophobic compounds. In this context, the quantity of the extractable migrated phthalate esters (dibutyl phthalate (DBP), dimethyl phthalate (DMP), butyl benzyl phthalate (BBP), diethyl phthalate (DEP), di-N-octyl phthalate (DNOP), and bis (2-ethylhexyl) phthalate (DEHP)) from polyethylene terephthalate (PET) bottles into Doogh samples was measured. The correlation between the concentration of migrated PAEs and some factors such as the type of Doogh (gaseous and without gas), difference in brand (five brands), volume (1500 and 300 mL), and the storage time also was investigated. The migration level into Doogh samples was increased by incorporating of gas as well as increasing the volume of PET bottles. Also, with elaborating of storage time, the migration of some phthalates such as DEHP (the mean from 2419.85 ng L−1 in the first week to 2716.15 ng L−1 in the second month), DEP, and total phthalate was increased. However, no significant difference in concentrations of migrated phthalate esters among different examined brands was noted. Finally, the concentration of migrated PAEs from bottle into all the examined Doogh samples was below the defined standards by EPA; 6 μg/L for DEHP in drinking water

Essays on Tactical and Operational Problems in Healthcare

One of the essential challenges in healthcare operations management is to efficiently utilize the expensive resources needed in the healthcare system, while maintaining or increasing the quality of care. Optimization methods can be used to increase the supply of healthcare services, to minimize the cost of the system, and maximize the quality of care by minimizing patients\u27 waiting times, minimizing travel needs, maximizing health outcomes and maximizing access to services. In this dissertation, we study some of the important tactical and operational problems in healthcare, and propose plans to efficiently improve the current healthcare systems by applying optimization methods. In chapter 1 of this dissertation, we develop a novel scheduling model called ``postponement model\u27\u27 to reduce the indirect waiting time of higher priority outpatients in a diagnostic clinic. In diagnostic clinics, the arrivals mostly arise from three sources: inpatients, emergency patients, and outpatients. Emergency patients are seen as soon as they arrive and inpatients receive appointments within 24 hours. However, outpatient appointments are scheduled within a longer time horizon based on appointment availability. Currently, most diagnostic clinics save a proportion of their capacity for inpatients and emergency patients, and offer the earliest remaining appointments to the outpatients on a first-come-first-serve basis. This capacity allocation and scheduling mechanism may lead to unused inpatient capacity. Furthermore, there is no prioritization in scheduling of outpatients whose medical needs may be at different urgency levels. We model the appointment scheduling problem as a two-stage stochastic integer program. In the first stage we compute the proportion of capacity that is allocated to emergency patients and inpatients. In the second stage the decisions regarding scheduling of outpatients are taken. Outpatient appointments are not necessarily scheduled immediately upon patients\u27 arrivals and may be postponed to observe more requests. This postponement strategy enables the scheduler to observe more of the demand and schedule outpatient appointments considering the patient priorities. We solve the problem using Sample Average Approximation (SAA) and a decomposition based branch and bound algorithm. The results show that using the postponement acceptance patients with higher priority receive sooner appointments compared to the no-postponement scheduling policy used in current practice. Meanwhile, the utilization of the system is increased. In chapter 2, we study a dynamic model for Tuberculosis (TB) screening of healthcare personnel. Healthcare employees take TB diagnostic tests regularly as part of efforts to prevent TB outbreaks in hospitals. A simple strategy that is mostly used in countries with low rate of TB infections is annual screening of all employees. There are currently two TB diagnostic tests on the market: skin test and blood test. The blood test is more expensive than the skin test, however it is more accurate. In this study, we propose an alternative testing scheme where testing frequency and test type for different groups of employees is dependent on their infection risk and the cost of time lost due to testing. We develop a discrete time infinite horizon Markov Decision Process (MDP) model which determines the optimal time between the tests for different groups of employees. Another outcome of our model is the type of the TB diagnostic test administered for each employee group. Classification of employees into groups is done based on the characteristics that affect the probability of getting infected with TB (e.g., job type and work location) and employee salary levels. The objective of our model is to minimize the total cost of the healthcare facility which depends on the type of the tests administered, employees\u27 lost time, and the number of false-positive or false-negative results in each group tested. Due to the curse of dimensionality, we use Approximate Dynamic Programming (ADP) to estimate the value function. Then, we use column generation to solve the ADP-based linear program associated with the proposed MDP model. The results provide screening policies that determine which test should be allocated to each group of employee in different states of the system. By investigating the results, we also estimate the frequency of the test for each group. Comparison of the screening policies obtained using our model with the current annual screening policy show that the screening costs can be reduced by half while achieving the same the overall infection rate among the healthcare personnel. In chapter 3, we propose a dynamic model for scheduling of healthcare workers during an infectious disease outbreak, with a specific focus on the ongoing Coronavirus Disease 2019 (COVID-19) pandemic. Healthcare workers play an important role during a pandemic to control the infection spread in the general population. On the other hand, they are at high risk of getting infected because of being in direct contact with patients. Thus, taking operational measures to limit the exposure of healthcare workers to infectious patients is critical for the safety of the healthcare workers and their patients. Emerging literature indicates that creating teams of healthcare workers and scheduling or isolating these teams in coordination might be beneficial during the COVID-19 pandemic. In this study, we build a MDP model to determine the optimal policy for scheduling such worker teams. The objective of the model is to maximize the expected total discounted number of working employees while taking the possibility of infection, and thus quarantine, for workers who are scheduled to work into account. The optimal policy specifies which teams of workers should work and which teams should isolate dependent on the system state. This problem is difficult to solve due to the large size of the state space of the MDP. Thus, we use state space reduction techniques to decrease the number of states. Using the data on number of infections in the state of South Carolina, we obtain optimal scheduling policies under different infection probabilities for the general population. We also consider additional scenarios to understand the effect of changing model parameters on the state space reduction results and the approximate optimal policy. The results show that strategic benching of healthcare worker teams can significantly improve the total discounted workable physician days compared to only segregating workers into teams