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The principle purpose of the current study is to hybridize firefly algorithm (FA) as a nature-inspired meta-heuristic developed based on the flashing patterns and biogeography-based optimization (BBO) to present a qualified algorithm in the case of optimization of steel frame (SF) structures. In the proposed meta-heuristic algorithm, FA works as a global search engine while BBO achieves the local search task. The proposed algorithm is termed as a firefly algorithm–biogeography-based optimization (FA–BBO). FA–BBO algorithm was employed for the optimization of benchmark SF problems for the validity in the case of a new algorithm. The numerical outputs demonstrate that the new FA–BBO algorithm presents a better computational performance in the comparison of the current algorithms. No sponsor

Natural disasters threaten the sustainability of electric power supply. This fact highlights the importance of enhancing technological resourcefulness to handle the upcoming events. Once a natural disaster occurs, the most urgent undertaking is to rapidly pinpoint and assess component damages and dispatch the repair crews towards the most critical impaired elements. Practical efforts confirm that utilising a drone, which is an unmanned aerial vehicle, can notably reduce the duration of post‐disaster distribution system damage assessment (DA) and increase the resilience of power systems. This study presents an optimisation model to determine the optimal number and type of drones required for DA. The main goal is to minimise the time duration of the DA mission, which includes scanning the grid and identifying the damaged components. As a matter of fact, the financial resources are limited, so the problem is subject to the available budget constraint. Besides, the technical capacity of purchased drones for inspecting the grid is considered. The optimisation problem is formulated as a mixed‐integer programme with a guaranteed optimum solution. Case studies confirm the applicability of the model and the optimality of the results. As the major conclusion, the best allocation is a compromise between the speed and price of drones.

Having a robust and accurate grid synchronization unit for distributed generators (DGs) has been accentuated due to rapidly growing integration of inverter‐based DGs to the autonomous microgrids. Although most of the synchronization methods are based on the phased locked loops (PLL) technique, digital filters could result in more salient dynamic performance in non‐ideal sinusoidal situations. This paper presents a novel digital filter as a grid synchronization method that overcomes the flaws of conventional PLLs under harmonic distorted and unbalanced cases as well as large delay of window‐based digital filters. For the sake of computational advantages, the proposed method is executed in two distinct offline and online stages. In the offline stage, the second‐order Taylor series expansion approximation of the bandpass oscillatory signal calculates the state‐transition matrices and state vectors. Then, by applying the concept of Kalman filter, the state vectors are updated in the online calculation section much easier and faster compared to all‐online approach. Compared to the existing methods, the proposed method generates a dynamic response with less settling time, more accuracy, and immunity against harmonic distortion, imbalances, and noise.

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix.

The CO2 capture from gas mixtures is of great importance in the industry in terms of improving the quality of natural gas. Proficient adsorbents should reveal high CO2 capacity and also high CO2 se...

Beam-column joints require a high ductility during the unexpected loadings that necessitate theneed for ductile concrete in such unprotected locations. Alternatively, self-compacting concrete (SCC) isa sort of concrete which has generated tremendous interest throughout the last decades in order to reach aductile structural elements during the seismic actions. Specific properties of this type of concrete includehigh performance, high resistance against segregation and needless to internal or external vibration inorder to compact. In the seismic regions, ductility is one of the most important factors in the design ofreinforced concrete (RC) members, especially structural joints flexural performance; it is due to theenhance in the capability of plastic deformability. This paper describes load-displacement behavior ofexperimental and theoretical analysis of four SCC beam-column joints with different percentage of theratio of the reinforcing bars (ρ). In the theoretical phase of this investigation three-dimensional nonlinearfinite element method (FEM) model i.e., Seismostruct was used and the load-deflection diagrams wereplotted to compare the test results with the numerical output. The experimental results and nonlinear FEMmodeling indicate that using SCC as a workable concrete in beam-column joints of reinforced concretestructures has satisfactory performance in terms of ductility and energy dissipations., Yüksek süneklik gerektiren kolon-kiriş düğüm noktaları gibi korunmasız bölgelerde, beklenmedikyüklemeler altında, sünek betona ihtiyaç duyulmaktadır. Alternatif olarak, kendiliğinden yerleşen beton(KYB), deprem etkisi altinda elemanların daha sünek davranış gösteren bir yapıya ulaşmak için sonyıllarda büyük ilgi yaratan bir beton türüdür. Bu tipteki betonların belirgin özellikleri yüksek performans,segregasyona karşı yüksek direnç ve yerleşme için iç veya dış vibrasyona ihtiyaç duyulmamasıdır.Deprem bölgelerinde, süneklik, özellikle eğilmeye maruz kalan yapısal bağlantı noktalarınınperformansında önemli bir faktördür ve bu davranış plastik yerdeğiştirme kapasitesindeki artıştankaynaklanmaktadır. Bu makalede, KYB kullanılan ve farklı donatı oranlarındaki dört adet kolon-kirişdüğüm noktasının yük-yerdeğiştirme davranışının deneysel ve teorik analizleri açıklanmaktadır. Buaraştırmanın teorik aşamasında, üç boyutlu sonlu elemanlar metodu (SEM), Seismostruct doğrusalolmayan yazılımı kullanılarak, deneysel-nümerik sonuçlar karşılaştırılmış ve yük-yerdeğiştirmediyagramları çizdirilmiştir. Deneysel sonuçlar ve doğrusal olmayan SEM modeli, kolon-kiriş düğümnoktalarında işlenebilir beton olarak KYB’un kullanımının, betonarme yapılarda süneklik ve enerji yutmakapasitesi bakımından tatmin edici bir performansa sahip olduğunu göstermiştir.

We assessed image quality using a practical and time-efficient protocol for intravenous glucose loading and insulin injection prior to administration of 18F-fluorodeoxyglucose (18F-FDG) for PET myocardial viability evaluation in patients with ischemic cardiomyopathy, with and without type 2 diabetes mellitus. Methods: Metabolic preparation period (MPP) or optimal cardiac 18F-FDG uptake was determined from the time of intravenous infusion of 12.5 or 25 gram of 50% dextrose to the time of 18F-FDG injection. Cardiac 18F-FDG image quality was evaluated according to a 5-point scoring system (5=excellent to 1=non-diagnostic) by two independent observers. In cases of disagreement, consensus was achieved in a joint reading. Fifteen patients with ischemic cardiomyopathy, who underwent oral glucose loading and i.v. insulin administration, served as reference for MPP comparisons. Results: 59 consecutive patients (age: 63±10yrs, men n = 48 and women n = 11) underwent rest 99mTc-tetrofosmin SPECT/CT and 18F-FDG PET/CT for the evaluation of myocardial viability. FDG image quality was scored as excellent in 42%, very good in 36%, good in 17%, fair in 3%, and non-diagnostic in 2%. Comparing diabetic and non-diabetic patients, the quality scores were excellent in 29% vs. 76% , very good in 41% vs. 18%, good in 24% vs. 6%, fair in 4% vs. 0% , and non-diagnostic in 2% vs. 0%. The mean (±SD) quality score was 4.12±0.95 and overall it was better in non-diabetic than in diabetic patients (4.71±0.59 vs 3.88±0.96; p

This review article summarizes the clinical applications of established and emerging PET tracers in the evaluation of the 5 most common gynecologic malignancies: endometrial, ovarian, cervical, vaginal, and vulvar cancers. Emphasis is given to 2-deoxy-2-[18F]fluoro-d-glucose as the most widely used and studied tracer, with additional clinical tracers also explored. The common imaging protocols are discussed, including standard dose ranges and uptake times, established roles, as well as the challenges and future directions of these imaging techniques. The key points are emphasized with images from selected cases.

Transactive energy (TE) is a viable framework to tackle the load-generation mismatch in energy systems with high penetration of renewable energy resources (RERs). In this paper, we propose a TE framework for prosumers with heating, ventilation, and air conditioning (HVAC) systems to address real-time power shortage in a residential microgrid. Our framework consists of two phases. First, to mitigate load-generation mismatch, we develop an online appliance scheduling method to determine the optimal operation schedule of each prosumer’s appliances. In particular, we apply receding horizon optimization (RHO) to tackle the load and renewable generation uncertainties and to better match the real-time power consumption of the appliances with the priorly-purchased power from the day-ahead market. Second, in case that there still exists power shortage at the microgrid level, a TE market based on pay-as-market clearing price (MCP) is proposed among prosumers to reduce the power consumption of their HVAC systems. We capture the competition among the participating prosumers as a non-cooperative game and develop an algorithm to achieve the Nash equilibrium, while considering prosumers’ willingness to participate in the TE market. Extensive simulations are performed to demonstrate the efficiency of our proposed TE framework.

Introduction Type 2 diabetes mellitus (T2DM) is the most common type of diabetes. A decrease in the number of pancreatic beta cells is a pathological sign of diabetes, and to date there is no drug treatment that targets damage to these cells. Pancreatic beta cells have a weak antioxidant system and are highly sensitive to oxidative stress reactions that occur within cells. Thioredoxin interacting protein (TXNIP) inhibits thioredoxin, which is part of the intracellular antioxidant system, thereby accelerating oxidative stress and apoptosis of pancreatic beta cells. Verapamil is a non-dihydropyridine calcium channel blocker. The efficacy of this drug to improve beta cell survival and glucose homeostasis by inhibiting TXNIP expression has been demonstrated in in vitro studies. Although several retrospective studies have shown a lower incidence of T2DM with verapamil treatment, no prospective intervention studies have determined the efficacy of this drug in patients with T2DM. Methods The aim of this randomized, double-blind, placebo-controlled study was to evaluate the efficacy and safety of oral verapamil administration in T2DM patients. In this 90-day study, the effects of verapamil on fasting blood sugar (FBS), hemoglobin A1C (HbA1c), and the lipid profile were evaluated and compared with those of the placebo. Results There was a significant decrease in HbA1c (about 0.5%) in the verapamil group at the end of the intervention period. The effects of verapamil on TXNIP gene expression and glucagon-like peptide-1 receptor (GLP1R) mRNA were compared with those of the placebo (at baseline, after 15 and 30 days, and at the end of the study). During the first month of the study, decreased TXNIP gene expression and increased GLP1R mRNA were associated with the administration of verapamil when compared with the placebo, although the differences were not significant. Conclusion Verapamil can lead to better control of T2DM by reducing TXNIP gene expression and increasing beta cell survival and, possibly, by other mechanisms. Clinical Trial Registration IRCT registration no.: IRCT20180417039339N1 (https://www.IRCT.ir).

With the upward trend of the active participation of distributed energy resources (DERs) and grid-edge assets in grid and market operations, there is increased interest in transactive energy (TE) systems that support both peer-to-market and peer-to-peer energy exchanges. Peer-to-market transactions generally involve the market-based provision of grid services by prosumers (owners and operators of grid-edge assets, or their agents). Peer-to-peer transactions include bilateral exchanges of energy and energy derivatives among prosumers. Associated with this trend, there is growing interest in the potential use of blockchain technology to facilitate and enhance the security of transactive exchanges and streamline the associated settlement processes. This article presents a blockchain-based transactive energy BCTE (BCTE) platform design that supports peer-to-peer and peer-to-market transactive exchanges from grid-edge assets. An energy-blockchain-as-a-service (EBaaS) platform implementation is discussed, along with an illustrative use case.

According to the importance of management of obstructive sleep apnea syndrome by otolaryngologists, this study was designed to investigate knowledge, attitudes and practice of junior and senior residents of otolaryngology and evaluate the effect of current residency training program on choosing the first lines of treatment. A total of 110 residents of otolaryngology were selected. Our study tools were obstructive sleep apnea knowledge and attitudes (OSAKA and OSAKA-KIDS) questionnaires. The participants were classified as junior and senior. Senior residents had significantly higher total knowledge score for OSAKA based on independent t test (12.73 Vs. 10.52). No significant difference was observed for OSAKA-KIDS (11.31 Vs. 10.69). The most frequent choice for the first line was CPAP (63.8%) and weight loss (41.5%) among junior and senior residents, respectively. Although the knowledge of otolaryngology residents increased during their program, the choice of first line treatment in obstructive sleep apnea was different between junior and senior residents. We found a need for further multidisciplinary education for residents especially in the management of sleep apnea particularly toward CPAP usage and this syndrome in pediatrics.

Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial.

Introduction: Considering the COVID-19 pandemic and its severe effects on life, there is no doubt that an analysis of the components and drivers of abnormal behaviors can facilitate the emergence of moral behaviors. Therefore, we conducted this study in order to investigate the drivers of abnormal behaviors from the perspective of health managers during the COVID-19 pandemic. Methods: This research is a cross-sectional, qualitative, and applied study that was conducted using snowball sampling method and semi-structured interviews with 13 managers at different management levels in so called social security hospitals in Mazandaran province in the first half of 2020. Also, validation of Lincoln and Guba reliability criteria and data analysis were performed by combined summary content analysis method with MAXQDA10 software. Results: Findings of this study show that a number of factors are among the drivers that cause the occurrence of abnormal behaviors in hospitals. These factors could be divided into three types: Structural factors that include inadequate payroll system, job characteristics, organizational misconduct, and managerial weakness; behavioral factors include stress, aggression in the organization, antisocial behavior, and individual psychological disorders; and environmental factors as well as internal and external environment of the organization. Conclusion: Through increasing financial supports and incentives, results of this study help managers reduce stressors, create better conditions for employees, increase the quality of performance and services provided to patients, and improve the health level of the country.

Objective: Review a single institution’s vestibular schwannoma (VS) microsurgery experience to determine (1) correlations between demographics, comorbidities, and/or surgical approach on hospital length of stay (LOS) and discharge disposition and (2) trends in surgical approach over time. Methods: Retrospective case series from a multidisciplinary skull base program at a tertiary care, academic hospital. All adult (>18 years) patients undergoing primary microsurgery for VS between 2008 and 2018 were included. Results: A total of 147 subjects were identified. Surgical approach was split between middle fossa (MF) (16%), retrosigmoid (RS) (35%), and translabyrinthine (TL) (49%) craniotomies. For the 8% of patients had other than routine (OTR) discharge. Mean LOS was significantly longer for patients undergoing RS than either MF or TL. Brainstem compression by the tumor was associated with longer LOS as were diagnoses of chronic obstructive pulmonary disease (COPD) and peripheral vascular disease (PVD). For all discharges, the 40 to 50- and 50 to 60-year-old subgroups had significantly shorter LOS than the 70-years-and-older patients. For the 92% of patients routinely discharged, there was a significantly shorter LOS in the 40 to 50-year-olds compared to the 70-years-and-older patients. There was a significant shift in surgical approach from RS to TL over the study period. Conclusion: Over 90% of VS microsurgery patients were routinely discharged with a median hospital LOS of 3.2 days, both of which are consistent with published data. There is an inverse relationship between age and LOS with patients older than 70 years having significantly longer LOS. Brainstem compression, COPD, PVD, and the RS approach negatively affect LOS. Level of Evidence 4

Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

Power system protection and asset management have drawn the attention of researchers for several decades; but they still suffer from unresolved and challenging technical issues. The situation has been recently exacerbated in the wake of the ever-changing landscape of power systems driven by the growing uncertainty and volatility subsequent to the vast renewable energy integration, more frequent natural extreme events due to climate changes, increasing malicious cyberattacks, and more constrained transmission systems as the result of load growth and limited investments. On the opposite side, the proliferation of advanced measuring devices such as phasor measurement units, emerging electric and non-electric sensors, and Internet of Thing (IoT)-enabled data gathering platforms continually expand/nourish the databases; they hence offer unprecedented opportunities to take the advantage of data-driven techniques. Machine learning (ML) as a principal class of artificial intelligence is the perfect match solution to this need and has newly revoked many researchers’ interests to tackle the problems excluding their exact/detailed models. This paper aims to provide an overview on applications of ML techniques in power system protection and asset management. This paper elaborates on issues pertaining to (1) synchronous generators, (2) power transformers, (3) transmission lines, and (4) special and system-integrity protection schemes. In addition to the opportunities offered by the ML techniques, this paper discourses on the barriers and challenges to the wide-spread application of ML techniques in real-world practices.

Aims A complete metabolic response (CMR) on early post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a positive prognostic factor for cervical cancer patients treated with definitive chemoradiation, but long-term outcomes of this group of patients are unknown. Patterns of failure and risk subgroups are identified. Materials and methods Patients who received curative-intent chemoradiation from 1998 to 2018 for International Federation of Gynecology and Obstetrics (FIGO) stage IB1–IVA cervical cancer and had a CMR on post-treatment FDG-PET within 5 months of treatment completion were included. Cox proportional hazards models determined factors associated with locoregional and distant failure. Kaplan–Meier estimates of freedom from any recurrence (FFR) of patient subgroups were compared with Log-rank tests. Results There were 402 patients with a CMR after chemoradiation on FDG-PET. Initial T stage was T1 (38%)/T2 (40%)/T3 (20%)/T4 (2%); initial FDG-avid nodal status was no nodes (50%)/pelvic lymph nodes (40%)/pelvic and para-aortic lymph nodes (10%). After a median follow-up of 6 years, 109 (27%) recurred. The pattern of recurrence was locoregional (27%), distant (61%) or both (12%). No factors were associated with locoregional failure. Distant recurrence was more likely in patients with T3–4 lesions (hazard ratio = 2.4, 95% confidence interval 1.5–3.8) and involvement of pelvic (hazard ratio = 1.6, 95% confidence interval 1.0–2.7) or para-aortic lymph nodes (hazard ratio = 2.7, 95% confidence interval 1.4–5.0) at diagnosis. The 5-year FFR rates for T1–2 patients with no nodes, pelvic nodes alone or para-aortic nodes at diagnosis were 85, 76 and 62%, respectively (P = 0.04, none versus para-aortic nodes). The 5-year FFR for T3–4 patients with no nodes, pelvic nodes alone or para-aortic nodes at diagnosis were 68, 56 and 25%, respectively (P = 0.09, none versus para-aortic nodes). Conclusions T3–4 tumours and para-aortic nodal involvement at diagnosis are poor prognostic factors, even after a CMR following chemoradiation.

Prevention of ischemic stroke relies on the use of antithrombotic medications comprising antiplatelet agents and anticoagulation. Stroke risk is particularly high in patients with cardiovascular disease. This review will focus on the role of antithrombotic therapies in the context of different types of cardiovascular disease. We will discuss oral antiplatelet medications and both IV and parental anticoagulants. Different kinds of cardiovascular disease contribute to stroke via distinct pathophysiological mechanisms, and the optimal treatment for each varies accordingly. We will explore the mechanism of stroke and evidence for antithrombotic therapy in the following conditions: atrial fibrillation, prosthetic heart values (mechanical and bioprosthetic), aortic arch atherosclerosis, congestive heart failure (CHF), endocarditis (infective and nonbacterial thrombotic endocarditis), patent foramen ovale (PFO), left ventricular assist devices (LVAD), and extracorporeal membrane oxygenation (ECMO). While robust data exist for antithrombotic use in conditions such as atrial fibrillation, optimal treatment in many situations remains under active investigation.

Purpose: Chemoradiotherapy for locally advanced cervical cancer fails in over a third of patients. Biomarkers with therapeutic implications are therefore needed. We investigated the relationship between an established prognostic marker, maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography, and the inflammatory and immune state of cervical cancers. Experimental Design: An SUVmax most prognostic for freedom from progression (FFP) was identified and compared with known prognostic clinical variables in a cohort of 318 patients treated with definitive radiation with prospectively collected clinical data. Gene set enrichment analysis (GSEA) and CIBERSORT of whole-transcriptome data from 68 patients were used to identify biological pathways and immune cell subpopulations associated with high SUVmax. IHC using a tissue microarray (TMA, N = 82) was used to validate the CIBERSORT findings. The impact of macrophages on cervical cancer glucose metabolism was investigated in coculture experiments. Results: SUVmax Conclusions: SUVmax is a prognostic marker in cervical cancer that is associated with activation of inflammatory pathways and tumor infiltration of myeloid-derived immune cells, particularly macrophages. Macrophages contribute to changes in cervical cancer glucose metabolism. See related commentary by Williamson et al., p. 4136

Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computer vision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.

Aim Assessment of knowledge, attitude, and practice of healthcare providers regarding Zika virus, provides information on their ability to prevent its transmission, and emphasizes the necessity of implementing educational programs if their knowledge, attitude, and practice are low. Therefore, the present study aimed to investigate the knowledge, attitude, and practice of the staff of comprehensive health services centers affiliated with Tehran University of Medical Sciences regarding Zika Virus. Methods This cross-sectional study was conducted on 354 staffs of comprehensive health service centers. The researcher-made questionnaire consisted of two sections. The first section of the questionnaire was about demographic and professional characteristics of the participants and the second section was assessment of knowledge, attitude, and practice regarding Zika virus. Data analysis was performed by SPSS 16 software using descriptive and analytical statistics. Results In this study, 70.9% of the participants had poor knowledge, 24.1% had moderate knowledge, and only 5% had good knowledge about Zika virus. Regarding attitude, 10.6% of the participants had negative attitude, 45.9% had neutral attitude, and 43.5% had positive attitude toward Zika virus. Moreover, 40.6% of the participants had poor practice, 24.7% had moderate practice, and 34.7% had good practice in Zika virus. Conclusion The knowledge level in participants about Zika virus was unacceptable and their attitude and practice level was relatively acceptable. Therefore, it is necessary to design and implement educational interventions aimed at promoting knowledge, attitude, and practice of the staff of comprehensive health services centers regarding Zika Virus.

A 49-year-old woman with a history of diabetes mellitus (DM), and hypothyroidism referred to the emergency ward complaining of shortness of breath which had lasted for three weeks. Due to inspiratory and expiratory stridor in the clinical examination, a tracheal lesion was proposed for her. In computed tomography (CT) scan (without contrast) of the neck and chest, a lesion resembling a malignant tracheal tumor was observed spreading around the cervical trachea. Subsequently, the patient's respiratory distress worsened and she underwent tracheostomy under general anesthesia. During tracheostomy, a white to creamy lesion that resembled necrosis with extensive granulation was seen in cervical trachea, and a biopsy was taken. Histopathological reports showed evidence of acute and chronic inflammation, in necrotic background, along with aseptate fungi which confirmed mucormycosis. Initially, intravenous liposomal amphotericin-B was selected as an antifungal drug which was discontinued due to drug-induced acute renal failure. Posaconazole suspension was replaced as an antifungal drug. After about six weeks, the patient was discharged from the hospital in good general condition. Contrary to few previous studies on mucormycosis of the trachea and lower airways, tracheal disease was limited in our patient; therefore, we avoided debridement of the conflict site and tried to control the disease by controlling the underlying disease (DM), and antifungal therapy. Finally, the desired result was achieved. It should be noted that all patients who have been reviewed in the previous published studies have had a wider conflict sites compared to our patient. Therefore, due to the lack of standard treatment for this disease, our therapeutic approach in this study can be considered as an option in limited and localized cases.

Hearing about the selection of one’s journal by a reputable indexing system pleases every editor. Journals with an international editorial members selected from prolific researchers have a higher chance of being selected by a prestigious indexing system. Geographic distribution of authors’ affiliations is another important factor. Considering a minimum of internationally acceptable editorial standards would also influence the decision of the indexing systems to select a journal—publishing few quality articles is much better than publishing a bunch of poorly designed badly reported ones. The key to success is having a group of dedicated staff with good communication skills.

In recent decades, self-compacting concrete (SCC) has gradually gained popularity based on its unique workability without any internal and external vibration. SCC development must ensure an acceptable level of balance between deformability and stability by following the characteristics of materials and the mix proportions. In the case of the structural performance of reinforced concrete (RC) structures throughout the earthquake, beam-column connections behavior is one of the most common causes of failures. On the other hand, T-shaped beam-column connections on the top floor have a critical condition based on the different types of live loads. As an alternative, SCC with specific properties includes workability and resilience against segregation, which is a great solution to improving the behavior of RC connections in a seismic area. In this paper, experimentally and numerically, investigation of six T-shaped SCC beam-column connections through different reinforcing bars ratio (ρ) are presented to better understand their behavior during the gravitational load like heavy snow, particularly in terms of failure mechanisms and cracking performance. In the experimental stage, firstly, the mechanical specifications of SCC have been examined in terms of slump flow, V funnel, J ring, and L box tests. Secondly, the load-displacement performance of T-shaped beam-column connections at the top floor has been reported in terms of bonding behavior, ductility, and energy dissipation capacity. The displacement ductility of the experimental results are decreased from 12.4 to 3.5 while the ratio of the reinforcing bars was increased from %15 to %66 of the balanced section, and the energy dissipations capacity was increased by increasing the reinforcing ratios until %56 of the balanced section then started to decrease. In the theoretical part of the study, a three-dimensional nonlinear finite element method (FEM) model (ABAQUS) was employed along with the load-deflection curves were reported to compare the experimental results with the numerical output. The test results demonstrate, employing SCC as a type of concrete that has enough workability in RC connections had successful fulfillment in terms of ductility, load-carrying capacity, and energy dissipation.

The main purpose of this study is to respond to an important question in the field of structural engineering: is the seismic collapse capacity of optimally designed concentrically steel braced frames acceptable or not? The present work includes two phases: performance-based design optimization and seismic collapse safety assessment. In the first phase, three nature-inspired metaheuristic algorithms, namely improved fireworks algorithm, center of mass optimization, and enhanced colliding-bodies optimization are employed to carry out the optimization task. In the second phase, seismic collapse capacity of the optimally designed concentrically steel braced frames is evaluated by performing incremental dynamic analysis and generating fragility curves. Two design examples of 5- and 10-story concentrically steel braced frames with two different topologies of braces are presented. The numerical results indicate that the center of mass optimization algorithm outperforms the other algorithms. However, all of the optimal designs found by all algorithms are of acceptable seismic collapse safety. No sponsor

This manuscript describes the use of ultrasound elastography, with the exception of liver applications, and represents an update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography.Diese Arbeit beschreibt den Einsatz der Ultraschall-Elastografie mit Ausnahme der Leberanwendungen und ist eine Aktualisierung der Leitlinien und Empfehlungen der EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) von 2013 zum klinischen Einsatz der Elastografie.

The aim of this article is to study the Sassanid armor types and their construction methods based on Sassanid rock reliefs, surviving fragments, armor depicted on Sassanid dishes and stone statues in Iran. The article explains the construction methods of different armor types used by the Sassanids during their rule and how Sassanids influenced the development of armor technology and were influenced by other cultures. Different types of armor used by Sassanids such as mail armor, scale armor, laminated armor (vambraces and greaves), and lamellar armor and their origins are discussed in the article. Hand, arm and leg protection as well as different helmet types used by Sassanids are also analyzed. These include simple bandhelm type, four-part bandhelm type, spangenhelm (strap helmets) type and lamellar helmet type. Different types of shields used by Sassanids are also discussed in the article. Next to archaeological finds, stone and rock reliefs as well as Sassanid iconography are analyzed to describe different types of armor used by Sassanids.

High penetration of renewable distributed generations is one of the most current challenges of electric distribution networks due to resources uncertainties, and hosting capacity limitation of the networks. The challenges may be more critical when uncertainties are highly correlated. The effect of correlated uncertainties of wind speed and load on the distributed generation hosting capacity of distribution networks is evaluated. A combination of point estimation method, and inverse Nataf transformation is proposed for correlated uncertainties modelling. The efficiency of some ANM schemes on the hosting capacity improvement is also studied in the presence of correlated uncertainties. To do so, an optimisation framework with the objective of maximising the installed capacity of distributed generation subject to network operational constraints such as voltage deviation is proposed. The proposed optimisation problem is formulated in a mixed‐integer quadratically constrained program form, and is solved via CPLEX solver. The proposed method is implemented on the 33‐bus standard test system. The results demonstrate the significant effect of the correlation between uncertainties on the distributed generation hosting capacity of network.

The electricity network is a very large-scale and widely distributed system that is highly interdependent with other large infrastructure systems (e.g., natural gas, telecommunication, water and wastewater, and transportation). Power grids are being stressed by severe loading conditions caused by the rising electrification of infrastructure (e.g., in creasing the use of electric vehicles) as well as the overall growth in world population and electricity usage. Any prolonged electricity interruptions can have a widespread effect on other systems, including disruptions in Internet and security equipment, traffic and street lights, pumping stations and public transportation, home appliances and tall building elevators, large industrial equipment and hospital devices, water supplies and natural gas delivery networks, and so on.

Microgrids are finding a prominent role in the emerging electricity landscape characterized by increasing distributed energy resource (DER) penetration and active consumer engagement. They are designed and deployed for different purposes and objectives. While one microgrid may place a lot of emphasis on the quality and availability of the electric power it produces, another may primarily focus on the operation efficiency or environmental benefits. To fulfill different objectives and deliver the benefits they are designed for, appropriate microgrid management systems (MMSs) are needed. This article develops a classification for different microgrids with a comprehensive technology framework for delivering their benefits. It concludes with a number of actual microgrid deployments to illustrate and reinforce the described concepts.

Inactivated four-factor prothrombin complex concentrate (I4F-PCC, Kcentra®) has become an important agent for the urgent or emergent reversal of bleeding associated with vitamin K antagonists such as warfarin. There is recognized inter-institutional variability with the use of I4F-PCC, especially as it relates to dosing practices. We sought to characterize variations in I4F-PCC dosing practices and their impact on patient outcomes and describe overall real-world clinical practice surrounding I4F-PCC utilization in the context of the management of warfarin-related intracranial hemorrhage (ICH). This is a multicenter retrospective pragmatic registry study of adult patients admitted at a participating study site between January 1, 2014, and December 31, 2015, who received I4F-PCC for reversal of warfarin-related ICH. Practices around warfarin-related ICH reversal in context of I4F-PCC utilization are described, including repeat I4F-PCC dosing, adjunctive reversal agents, and dose rounding policies (i.e., rounding doses to nearest vial size vs preparing exact/unrounded doses). All research was approved by local human investigation committees at each institution. Seventeen institutions contributed data on 528 patients to this registry. These institutions were primarily urban centers (74%), located in the southeast USA (47%), with Level 1 Trauma designation (79%), and with Comprehensive Stroke Center designation (74%). Most patients included in the study had sustained a non-traumatic ICH (68%), had a median admission GCS of 14 (IQR 7–15), and were receiving warfarin for atrial fibrillation (57.4%). There was substantial time latency between baseline INR and I4F-PCC (median 2.4 h, IQR 1.4–4.5 h). Most patients received adjunctive reversal agents, including vitamin K (89.5%) and fresh frozen plasma (FFP) (31.9%). A smaller proportion (6.0%) of patients received repeat I4F-PCC dosing. The median ICU length of stay (LOS) was 3 days (IQR 2–7 days), median hospital LOS was 6 days (IQR 3–12 days), and overall mortality rate was 28.8%. For institutions rounding doses to the nearest vial size, the first post-I4F-PCC dose INR was statistically but not clinically significantly lower than for institutions without vial size dose rounding, with comparable degrees of INR reduction from baseline. No differences were observed between dose rounding cohorts in adverse effects, ICU or hospital LOS, modified Rankin score at discharge, or mortality rates. Most patients received single doses of I4F-PCC, with adjunctive reversal agents and rounding doses to vial size. The time difference from baseline INR to factor product administration is a potential opportunity for process improvement in the management of warfarin-related ICH.

Introduction: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy for those in cardiopulmonary failure, including post-cardiac arrest. Despite a high volume of ECMO patients using anti-seizure medication, there is a paucity of data concerning the dosing, levels, and clinical scenarios for their use. Case Report: We present three cases of ECMO patients post-PEA arrest who were on valproic acid (VPA) for treatment of seizure and/or myoclonus. The total and free levels of VPA are reported. Discussion: The trough levels are consistent throughout therapy, suggesting VPA is not significantly removed by the ECMO circuitry. Although the total serum levels remained below the toxic range, the free level was elevated in two patients. These patients did not develop signs of toxicity. Conclusion: VPA may be an effective anti-seizure medication in ECMO patients. Free VPA levels should be more readily available to better quantify efficacy or toxicity, especially in ECMO patients.

In this paper, a modified linear-minimum-mean-square-error (LMMSE)-based estimator is presented to reduce speckle noise in ultrasound (US) medical images. In order to significantly improve the performance of the LMMSE estimator, we consider the data redundancy feature which naturally exists in the US images. Since the classical LMMSE method, due to the use of local statistics, cannot perform well in areas where the intensity variation is high, we exploit the similarity between pixels to resolve this problem. In this regards, by using characteristics of the second order local statistics and Pearson distance, an optimum set of similar pixels is selected to be used in the proposed LMMSE-based estimator. Therefore, a good balance between maintaining small details and reducing speckle noise in different regions of US images can be achieved. Quantitative and qualitative results on synthetic and real US data demonstrate that the proposed method yields competitive results in despeckling process compared to the state-of-the-art methods.

Breast cancer as the most common cancer worldwide is influenced by genetic and physiological factors. Based on some evidence indicating the role of estrogen receptor 1 gene (ESR1) in breast cancer development, in this study, the association of three common variations in ESR1 gene with breast cancer and density in an Iranian population was evaluated. In a case–control study, 400 blood samples were collected for DNA extraction and genotyping. Breast density was assessed using mammography. ESR1 rs6915267 (G/A), rs2077647 (C/T) and rs1801132 (C/G) were genotyped using ARMS-PCR method. PHASE program was used to estimate the haplotypes frequencies. Our data analysis showed rs6915267 GA genotype in the heterozygous (GA) as well as co-dominant models was associated with lower mammographic density. None of the three variations were associated with the breast cancer risk. Haplotype analysis indicated G-T-C haplotype of rs6915267, rs2077647 and rs1801132 [OR = 0.54, 95% CI (0.31–0.92), p = 0.025] and G-T/G-T diplotype of rs6915267-rs2077647 [OR = 0.38, 95% CI (0.17–0.86), p = 0.019] were associated with a decreased risk of breast cancer. ESR1 may affect density of the breast and its haplotypes may modulate breast cancer risk.

Objective This paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness. Population studied 5 313 965 Veterans who at least had two primary care visits between 2008 and 2016. Study design The dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set. Principal findings Separate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4- to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77. Conclusion In predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes. Implications for practice Policies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH.

Energy systems, in general, and power systems, in particular, are undergoing a changing landscape in terms of generation and demand portfolios. These transitions, although offering new economic and labor market opportunities, bring forth a suite of unprecedented technical implications and challenges in the interim. According to experts, our planet's population will reach 9.3 billion in the next four decades. A fundamental and granular outlook on global energy systems (prior to the COVID-19 outbreak) predicted that electricity consumption will double by 2050, with electrified transportation and higher living standards in buildings being among the principal components of this increase.

Alcohol withdrawal syndrome (AWS) can range from mild jittery movements, nausea, sweating to more severe symptoms such as seizure and death. Severe AWS can worsen cognitive function, increase hospital length of stay, and in-hospital mortality and morbidity. Due to a lack of reliable history of present illness in many patients with neurological injury as well as similarities in clinical presentation of AWS and some commonly encountered neurological syndromes, the true incidence of AWS in neurocritical care patients remains unknown. This review discusses challenges in the assessment and treatment of AWS in patients with neurological injury, including the utility of different scoring systems such as the Clinical Institute Withdrawal Assessment and the Minnesota Detoxification Scale as well as the reliability of admission alcohol levels in predicting AWS. Treatment strategies such as symptom-based versus fixed dose benzodiazepine therapy and alternative agents such as baclofen, carbamazepine, dexmedetomidine, gabapentin, phenobarbital, ketamine, propofol, and valproic acid are also discussed. Finally, a treatment algorithm considering the neurocritical care patient is proposed to help guide therapy in this setting.

The clinician could have CNB in mind for thyroid nodules when FNA results were nondiagnostic. Our study would suggest CNB a safe and efficient method for investigating thyroid nodules. Usefulness of preoperative tissue sampling and pathology diagnoses in thyroid tumors were accepted worldwide. We investigate the role of Core needle biopsy (CNB) in the thyroid nodules lesions when FNA results are nondiagnostic. We conducted a cross-sectional study to evaluate twenty-six CNBs results of suspicious malignant thyroid nodules with nondiagnostic repeated fine needle aspiration. 25 from 26 CNBs were diagnostic. Twenty-one needle biopsy reports were papillary thyroid carcinoma, three CNB samples diagnosed medullary thyroid carcinoma and one of them had anaplastic results. All diagnostic needle biopsies results were compatible with final pathology. Our study would suggest CNB a safe and efficient method for investigating thyroid nodules while repeated FNA yielded nondiagnostic results.

We present a 4.4-MHz resonant accelerometer based on the electrostatic softening effect in a bulk acoustic wave (BAW) resonator, fabricated using a metal-less AlN-HARPSS process with combined piezoelectric and capacitive transduction. BAW accelerometers can be used in high-g and high-dynamic range applications with a very wide bandwidth. The fabrication process supports both piezoelectric and nanogap capacitive transduction in a micro/nanoelectromechanical device, primarily applicable to stiff high-frequency devices that use a nanoscale (

This investigation focuses on servoing a robot's tool with respect to a smooth workpiece surface by making use of the surface's characteristic local differential properties. A novel formulation for 6 degree-of-freedom (DoF) textureless visual servoing based on these properties is proposed, which extends an existing 3-DoF scheme. Our approach naturally combines the geometric tools of computer-aided design and machining (CAD/CAM) theory with the manipulator control tools of visual servoing synergistically to achieve full 6-DoF pose control. A novel family of observed feature sets and their associated interaction matrices are presented. A geometric condition on the surface shape is derived under which local asymptotic stability for 6-DoF is guaranteed. Validation of the proposed method is performed in simulation and experiment using an articulated desktop robot equipped with only a monocular camera and 16 laser pointers.

Recessive dystrophic epidermolysis bullosa (RDEB) is a genodermatosis that leads to skin fragility and chronic wound formation. Patients with RDEB are at risk for cutaneous squamous cell carcinoma (SCC) which is a major cause of morbidity and mortality in these patients. No standard of care exists for the treatment of SCC in this patient population and therapy is based on anecdotal reports and expert opinion. We report a 32-year-old man with RDEB with previously localized SCC who later developed metastatic SCC. He was started on cemiplimab (an immune checkpoint inhibitor) 350 mg IV every 3 weeks. An objective radiological response was noted within 3 cycles. On 14 months follow-up, there was a durable response to treatment clinically and on imaging, without immune-related adverse events. To our knowledge, this is the first case report describing safe administration of immune checkpoint inhibitors in a patient with RDEB with objective and durable response of metastatic SCC. Larger case series and controlled clinical trials are needed to further investigate these medications in the RDEB population, given their high burden of aggressive and often lethal SCC.

Energy management systems (EMS) are viable techniques to reduce the customers billing cost as well as enhancing the grid reliability and efficiency. Uncertainty and information privacy of users are serious concerns in designing EMS of a residential nanogrid with renewable energy resources. This paper addresses a decentralized energy management algorithm for day-ahead scheduling of residential nanogrids using the concept of Mean Field (MF). The aim of each user is to find its optimal demand strategy by minimizing an objective function consisting of energy consumption cost, battery degradation cost, and cost of user discomfort while meeting a suite of constraints. The strategy of a user affects the objective functions of other users through the electricity price; such interaction among the users is hence modeled as a game problem. Each user, without any information exchange with other users, sends its electricity demand to the utility company. The utility company broadcasts the aggregated demand as a common information of the grid (MF term) to all users. The robustness of the algorithm in presence of uncertainties in decision making of the clients and non-residential load prediction error is analytically verified. In particular, it is shown that the algorithm converges to $\epsilon$ -Nash equilibrium point of the game and $\epsilon$ uniformly converges to its minimum value as the population size of the users goes to infinity. Simulation results show the advantages of the proposed method.

Background: Preemptive analgesia is one of the techniques to manage postoperative pain, which increases patient satisfaction and decreases the duration of hospitalization. The present study aimed to evaluate and compare the pain relief achieved by preoperative intravenous ketorolac and oral pregabalin in patients undergoing surgery for mandibular fractures. Methods: In the present clinical trial, 60 patients with unilateral fractures of the mandible were randomly assigned to two groups. In group A, intravenous injections of ketorolac 30 mg and in group B, pregabalin 150 mg capsules were administrated one hour preoperatively. The severity of pain was determined using a visual analog scale (VAS) up to 24 hours postoperatively. Finally, the total doses of an opioid analgesic (pethidine) prescribed for each patient in mg during the first 24 hours and the time for the request of the first analgesic dose in minutes were recorded for each patient. Then, their means were compared between the two groups. Results: Maximum pain severity was experienced immediately after surgery, which decreased gradually during the 24-hour postoperative period (P < 0.0001). The mean severity of pain immediately after regaining consciousness and the mean pain score during the 24-hour postoperative period were lower in the pregabalin group than in the ketorolac group (P < 0.0001). In the ketorolac group, a slightly higher dose of the opioid was administered; however, the difference was not significant (P > 0.05). Conclusions: The oral administration of pregabalin 150 mg one hour preoperatively was more effective than the intravenous administration of ketorolac 30 mg in relieving postoperative pain.

This nationwide study of business communication instructors examined course delivery, course outlook, topics and depth of coverage, social media and technology coverage, diversity coverage, critical thinking, and accessibility. The outlook for the course appears positive and promising, and instructors continue to add content to the course. An important finding is that business communication instructors’ level of confidence in technology significantly affects how they cover technology-mediated communication. Therefore, we suggest professional associations and higher education institutions should provide more opportunities for voluntary training in these newer communication technologies. Further research is needed about the strain placed on business communication instructors.

Background: Hamartomas of the breast are rare benign tumors. Pre- and also postoperative differentiation from other benign or even malignant tumors is challenging. Case Presentation: A 36-year-old female presented with a giant tumor of the left breast. The patient had suffered from an early breast cancer of the contralateral right breast the year before, which was treated with breast-conserving therapy, radiation, and endocrine therapy ever since. The hamartoma was classified as BI-RADS 2 in mammography and BI-RADS 4 in ultrasound. On clinical examination, a tumor of nearly 15 cm in size led to an abstruse deformity of the breast and the nipple-areola complex. We found an indolent, grand bulging tumor with an elastic texture directly beneath the skin. A biopsy that had been performed before was compatible with the suspected hamartoma. Because of the remaining diagnostic uncertainties after contralateral breast cancer and the progressive malformation of the left breast, a tumor extirpation utilizing a reduction mammaplasty was performed without complications. Subsequent genetic analyses excluded a loss of PTEN in this patient. Conclusion: We presented the rare case of a 36-year-old woman with a history of breast cancer and a 700-g breast hamartoma. The preoperative and even the postoperative specification of a hamartoma remains challenging, and associations with genetic alterations should be considered.

Power utilities leverage demand response (DR) events to effectively reduce the peak load at critical times with excessive power demand. DR programs are generally categorized as manual or automated from the automation perspective. The opportunities for automated DR in the residential sector have emerged with the integration of smart and connected loads. For example, smart appliances with deferrable loads can be scheduled to shift their load without consumers’ direct involvement, given that many consumers might not engage sufficiently to participate in the manual DR. However, it has been shown that unjustified load shifting from many consumers in peak times could result in high off-peak demand. Therefore, it is essential for utilities to identify and target consumers for participation according to efficacy criteria. To address this issue, in this letter, we propose a data-driven method for the selection of consumers according to their potential for demand reduction in a DR program. The proposed method characterizes the frequency, consistency, and the peak time usage of deferrable loads across several subsequent days. By measuring the impact on peak-load shaving, we evaluated our approach on a subset of electricity dataset from the Pecan Street Dataport. The findings demonstrate the efficacy of the proposed method in selecting consumers with deferrable loads based on their potential for demand reduction in the future events.

One of the challenges of ocular drug delivery systems is sustainable delivery of the intended drug to the posterior segment of the eye. Periocular routes of drug delivery are a promising method to bridge this challenge. The purpose of this study is to investigate the recent advances and potentials of subconjunctival route and its pros and cons in comparison with other ocular drug delivery routes. In this literature review, the comprehensive search of publications was performed in MEDLINE/PubMed indicating investigations on subconjunctival drug delivery systems using relevant keywords. Based on the resultant detailed criteria to choose an appropriate ocular drug delivery route, it is revealed that most of these routes are either highly invasive and/or provide low bioavailability of drug to the target tissue. Nevertheless, subconjunctival drug delivery could be considered as one of the less invasive and easily accessible routes for delivering various drugs to both anterior and posterior segments of the eye. However, most of such researches are at the stage of animal study in their pipelines. Periocular route of drug delivery is one of the most efficient routes for delivering the drugs to both anterior and posterior segments of the eye. Subconjunctival sustained drug delivery is highly effective and less invasive compared to other periocular routes. This makes subconjunctival implants and injections one of the most proper ways of treating various ranges of ocular diseases and disorders, e.g., diabetic retinopathy, dry eye syndrome, glaucoma, etc.