Selectivity and specificity: pros and cons in sensing

Sensing using specific and selective receptors provides two very different but complementary strategies. This Sensor Issues article will discuss the merits and challenges of specific sensors, and selective sensors based on synthetic arrays. We will examine where each has been successfully applied to a sensing challenge, and then look at how a combined approach could take elements of both to provide new sensor platforms

Production lot sizing and scheduling with non-triangular sequence-dependent setup times

[NB some mathematical symbols in this abstract may not be correctly reproduced - please check the full text.] This article considers a production lot sizing and scheduling problem with sequence dependent setup times that are not triangular. Consider, for example, a product p that contaminates some other product r unless either a decontamination occurs as part of a substantial setup time stpr or there is a third product q that can absorb p’s contamination. When setup times are triangular then stpr ≤ stpq + stqr and there is always an optimal lot sequence with at most one lot (AM1L) per product per period. However, product q’s ability to absorb p’s contamination presents a shortcut opportunity and could result in shorter non-triangular setup times such that stpr > stpq +stqr. This implies that it can sometimes be optimal for a shortcut product such as q to be produced in more than one lot within the same period, breaking the AM1L assumption in much research. This article formulates and explains a new optimal model that not only permits multiple lots (ML) per product per period, but also prohibits subtours using a polynomial number of constraints rather than an exponential number. Computational tests demonstrate the effectiveness of the ML model, even in the presence of just one decontaminating shortcut product, and its fast speed of solution compared to the equivalent AM1L model

Integrated capacitated lot sizing and scheduling problems in a flexible flow line

The lot sizing and scheduling problem in a Flexible Flow Line (FFL) has extensive real-world applications in many industries. An FFL consists of several production stages in series with parallel machines at each stage. The decisions to be taken are the determination of production quantities (lots), machine assignments and production sequences (schedules) on each machine at each stage in an FFL. Lot sizing and scheduling problems are closely interrelated. Solving them separately and then coordinating their interdependencies is often ineffective. However due to their complexity, there is a lack of mathematical modelling and solution procedures in the literature to combine and jointly solve them. Up to now most research has been focused on combining lotsizing and scheduling for the single machine configuration, and research on other configurations like FFL is sparse. This thesis presents several mathematical models with practical assumptions and appropriate algorithms, along with experimental test problems, for simultaneously lotsizing and scheduling in FFL. This problem, called the ‘General Lot sizing and Scheduling Problem in a Flexible Flow Line’ (GLSP-FFL). The objective is to satisfy varying demand over a finite planning horizon with minimal inventory, backorder and production setup costs. The problem is complex as any product can be processed on any machine, but these have different processing rates and sequence-dependent setup times & costs. As a result, even finding a feasible solution of large problems in reasonable time is impossible. Therefore the heuristic solution procedure named Adaptive Simulated Annealing (ASA), with four well-designed initial solutions, is designed to solve GLSP-FFL. A further original contribution of this study is to design linear mixed-integer programming (MILP) formulations for this problem, incorporating all necessary features of setup carryovers, setup overlapping, non-triangular setup while allowing multiple lot production per periods, lot splitting and sequencing through ATSP-adaption based on a variety of subtour elimination.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Local field enhancement of nano-structured metallic target irradiated by polarized laser beam

The local field enhancement is studied numerically in samples of metallic nanoparticles (NPs) randomly distributed over a metallic substrate. The sample was assumed to be irradiated by polarized laser beam. Based on dipole-dipole approximation (DDA), the electric field was calculated Two-dimensionally at the irradiated region. The results show that the optimized field enhancement is strongly depends on NPs characteristics, beam polarization and incident angle

Conservative interventions to improve foot progression angle and clinical measures in orthopedic and neurological patients:A systematic review and meta-analysis

To establish the comparative effects of conservative interventions on modifying foot progression angle (FPA) in children and adults with orthopaedic and neurological disease was the main aim of the literature review. Pubmed, Embase, Cinahl, and Web of Science were systematically searched for studies evaluating the effects of conservative interventions on correcting the FPA. The study protocol was registered with PROSPERO (CRD42020143512). Two reviewers independently assessed studies for inclusion and quality. Studies that assessed conservative interventions that could have affected the FPA and objectively measured the FPA were included. Within group Mean Differences (MD) and Standardized Mean Differences (SMDs) of the interventions were calculated for the change in FPA and gait performance (walking speed, stride/step length) and clinical condition (pain). Intervention effects on FPA were synthesized via meta-analysis or qualitatively. 41 studies were identified. For patients with knee osteoarthritis gait training interventions (MD = 6.69° and MD = 16.06°) were significantly more effective than mechanical interventions (MD = 0.44°) in modifying the FPA towards in-toeing (p < 0.00001). Increasing or decreasing the FPA significantly improved pain in patients with medial knee OA. Results were inconclusive for the effectiveness of gait training and mechanical devices in patients with neurological diseases. Gait feedback training is more effective than external devices to produce lasting improvements in FPA, reduce pain, and maintain gait performance in patients with medial knee OA. However, in neurological patients, the effects of external devices on improvements in FPA depends on the interaction between patient-specific impairments and the technical properties of the external device

Dijagnostičke vrijednosti proteina akutne faze u iranskoga domaćega goveda invadiranoga praživotinjom Theileria annulata

This study was conducted to assess the pattern of changes and the relative value of acute phase proteins (APP) including haptoglobin (Hp), serum amyloid A (SAA), ceruloplasmin and fi brinogen in Iranian indigenous cattle infected with Theileria annulata. The diseased group comprised 24 Iranian indigenous dairy cattle, 2-3 years old, naturally infected with Theileria annulata. The infected animals were divided into 4 subgroups with different parasitemia rates (<1% and 1-3%). As a control group, 10 uninfected cattle were also sampled. Blood samples were collected and all measurements were made using validated methods. There were significant differences in red blood cells (RBCs), packed cell volume (PCV), hemoglobin (Hb) and concentrations of Hp, SAA, ceruloplasmin and fibrinogen between healthy cattle and those infected with T. annulata with different parasitemia rates (P<0.05). As the parasitemia rate increased in infected cattle, a signifi cant decrease was observed in RBCs, PCV and Hb. In contrast, with the increase in the parasitemia rate, a significant increase in Hp, SAA, ceruloplasmin and fibrinogen was evident. The optimal cut-off point was set by the receiver operating characteristics (ROC) method to >5.68 μg/mL for SAA, >0.09 g/L for Hp, >0.049 g/L for ceruloplasmin and >1.90 g/L for fibrinogen, with corresponding 71.50% sensitivity and 100% specificity for SAA, 83.30% sensitivity and 70% specificity for Hp, 50% sensitivity and 90% specificity for ceruloplasmin and 71.30% sensitivity and 80% specificity for fi brinogen. In conclusion, measuring SAA with the highest sensitivity, specificity and AUC compared to other APPs, can be a suitable indicator of inflammatory reactions in indigenous cattle infected with Theileria annulata.Istraživanje je provedeno s potrebom da se utvrdi dinamika promjena i relativne vrijednosti proteina akutne faze, uključujući haptoglobin (Hp), serumski amiloid A, ceruloplazmin i fibrinogen, u iranskoga domaćega goveda invadiranoga praživotinjom Theileria annulata. Skupina pokusnih životinja sadržavala je ukupno 24 iranska domaća mliječna goveda u dobi od dvije do tri godine invadirana praživotinjom Theileria annulata. Invadirane životinje bile su podijeljene u četiri podskupine s obzirom na različite razine parazitemije (5,68 μg/mL za serumski amiloid A, >0,09 za haptoglobin, >0,049 g/L za ceruloplazmin i >1,90 g/L za fibrinogen. Za serumski je amiloid osjetljivost iznosila 71,50% dok je specifičnost bila 100%. Osjetljivost je za haptoglobin iznosila 83,30% dok je specifičnost iznosila 70%. Za ceruloplazmin je osjetljivost iznosila 50%, a specifičnost 90%. Za fibrinogen je osjetljivost iznosila 71,30% dok je specifičnost iznosila 80%. Zaključno se može reći da mjerenje vrijednosti serumskoga amiloida A može biti prikladan pokazatelj upale uzrokovane praživotinjom Theileria annulata jer se u odnosu na ostale proteine akutne faze odlikuje najvišom razinom osjetljivosti, specifičnosti i AUC

Sister chromatid exchange in peripheral blood lymphocytes as a possible breast cancer risk biomarker: A study of Iranian patients with breast cancer

Introduction: Sister chromatid exchanges (SCEs) can be induced by variousgenotoxic treatments, suggesting that SCEs refl ect a DNA repair process and it may be a good index for assessment of genomic instability. However, the occurrence of genetic instability and in particular, of spontaneous SCEs has been strongly linked to cancer. Several chromosomal regions and many genes have been implicated in breast cancer.Materials and Methods: Blood samples were obtained from 31 Iranian breast cancer patients and 11 healthy women. SCE was measured in peripheral blood lymphocytes by adding to Ham’sF10 medium in presence of PHA, BrdU (5-bromo-deoxy Uridine) fl uorochrome Hoechst 33258, exposure to UV light and Giemsa staining. Then, SCE frequencies of patient and control groups were compared by the Mann-Withney U-test.Results: Signifi cantly difference was observed between two groups (

CAE Methodology for Optimization of Automotive NVH Performance through Wheel Structure Modifications

Noise, Vibration and Harshness (NVH) has been considered as one of the biggest challenges in the automotive industry since it is a source of complaints from passengers for decades. A typical automotive wheel has a very important role in optimizing the NVH performance of the vehicle body. An automotive tire is the primary component which is directly in contact with road disturbances. If structural dynamics of the tire is optimized, it can significantly reduce the transmitted noise and vibration to the passenger cabin. Here frequency response analysis is conducted using a developed finite element model of the wheel (tire and rim). The frequency response has been derived using an impulse input force and measuring the acceleration in radial and axial directions. This analysis can give us the resonances and anti-resonances that can be tuned to achieve a desirable performance. Desirable output can be considered as a low noise and vibration inside the automotive cabin to have customer satisfaction

Association between self-efficacy and quality of life in women with breast cancer undergoing chemotherapy

Background: Self-efficacy is known as a factor which influences health behaviors, chronic diseases management and quality of life in patients with cancer. Objective: The aim of this study was to investigate the association of self-efficacy and quality of life in women with breast cancer undergoing chemotherapy. Methods: This cross sectional study was conducted in 100 women with breast cancer referred to Seyed Al-Shohada Hospital, Isfahan in 2015. The study subjects were selected by simple random sampling method. The measurement tools were the Sherer self-efficacy scale and the World Health Organization WHOQOL-BREF quality of life assessment. Data were analyzed using one-way ANOVA and Pearson’s correlation coefficient. Findings: Mean age was 48.25±11.93 years. The mean self-efficacy score and quality of life score were 55.78± 11 and 75.91±15.28, respectively and both of them were average. There was positive significant correlation between self-efficacy and quality of life. There was also significant association between self-efficacy and quality of life domains including physical health, mental health, social relationships and environment. Conclusion: With regards to the results, it seems that activities such as workshops for patients, presence of a psychologist in department of chemotherapy, and providing health facilities can be effective for increasing self-efficacy and quality of life in patients with cance

Comparison of Machine Learning Methods in the Selection of Predictors of Atmospheric-Ocean General Circulation Models

IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied, compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature. Materials and MethodsToday, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and Kling-Gupta Efficiency (KGE) were used.Results and DiscussionBefore using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the algorithms used the important factors including F1, F2, F15, F16, F18, F20 and F26 by more than 50% and the first variable (mean pressure at the ocean surface) was the most important parameter in downscaling of maximum temperature. Also, the highest importance was for P1_v and the lowest value related to P5_u, as 73.2% and 15%, respectively. Violin plots of downscaled maximum temperature in validation step of different algorithms along with the observed maximum temperature in Birjand synoptic station in each of the algorithms showed that the values of the first and third quartiles in the output data of SPSA algorithm compared to other algorithms were closer to the observed data. According to the evaluation criteria, SPSA algorithm has a higher performance than other algorithms in reproducing the maximum monthly temperature values in Birjand synoptic station. Also, based on the volumetric efficiency evaluation criteria and relative Nash-Sutcliffe, GBM algorithm was more successful in selecting predictors than Ridge and LASSO algorithms. It is also observed that SPSA algorithm shows different results than other algorithms. In comparison of mean and variance of downscaled and observed maximum temperature, the results of t-test and F-test showed that SPSA algorithm has higher efficiency than other algorithms in regenerating mean and variance of observed maximum temperature in Birjand synoptic station at the 5% significance level.ConclusionThe data used in this study included large scale atmospheric variables and the maximum observed temperature at Birjand station. The algorithms were used to select important predictors and the performance of these methods in the validation part. According to the results of this study, the highest importance among large-scale variables is related to P1_v and the lowest value is related to P5_u, the values of which were 73.2% and 15%, respectively. The SPSA algorithm also performs better than other algorithms in selecting predictors and consequently the maximum temperature