Sampling Distribution Research Articles

How promotion mix helps sustain brand sales during market transitions: insights from the pharmaceutical industry

Purpose Pharmaceutical marketers use detailing, sampling and direct-to-consumer advertising (DTCA) to promote a branded prescription drug (brand drug) to health-care professionals and consumers. These promotion mix elements help raise brand awareness, increase prescription likelihood and guard a product market against competing brand drugs. However, the extent to which these elements remain effective when a brand drug goes off patent and its marketing exclusivity expires, allowing low-price generics to enter the market, remains unclear. This study aims to explore the effectiveness of promotion mix elements before and after the market entry of a generic drug. Design/methodology/approach The authors collected and analyzed a panel data set of 41 brand drugs from 7 therapeutic classes between 2007 and 2014 (3,201 observations) using the two-stage control function approach to address potential endogeneity. Findings The effectiveness of promotion mix elements changes significantly, but not in a universally uniform way, during a market transition. Detailing and DTCA are increasingly effective after a brand drug’s generics enter the market. In contrast, sampling is more effective before the market transition. The positive effects of sampling on brand sales are stronger when the price of a brand drug is higher than the average price of its competing brands. Sampling also helps amplify the positive influences of detailing and DTCA on brand sales. Research limitations/implications This study shows that pharmaceutical promotion is not equally effective in the brand drug market with/without generic competitors. Detailing and DTCA are increasingly effective when generic competition intensifies. In contrast, the distribution of free drug samples is less effective after more generic drugs enter the market. Practical implications Incumbent brands’ promotion expenditures often drop dramatically when the expiration of their patents is near, a practice that likely continues after generics enter the market. Taking into consideration these industry norms and their findings, the authors suggest that promotional decisions during a market transition should not overly focus on cutting promotional expenditures across the board. Since a firm’s promotional expenditure tends to be expensive, factoring in information on the effectiveness of each promotion mix element helps marketers make well-informed strategic decisions on resource allocation during the transition from a market without generics to a market with generics. Originality/value Previous studies do not explicitly account for structural changes in the brand drug market over time. However, the expiration of patent and marketing exclusivity marks a transitional period in which a brand drug eventually competes with its generic counterparts. Given the criticality of such structural change, the authors examine brand drugs’ promotion effectiveness in the presence of their generic counterparts.

Raspberry-Like Plasmonic Nanoaggregates with Programmable Hierarchical Structures for Reproducible SERS Detection of Wastewater Pollutants and Biomarkers.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 × 10-10, 1 × 10-16, 3 × 10-8, and 3 × 10-7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.

Boltzmann sampling with quantum annealers via fast Stein correction

Despite the attempts to apply a quantum annealer to Boltzmann sampling, it is still impossible to perform accurate sampling at arbitrary temperatures. Conventional distribution correction methods such as importance sampling and resampling cannot be applied, because the analytical expression of sampling distribution is unknown for a quantum annealer. Stein correction [Q. Liu and J. Lee, in , Proceedings of Machine Learning Research (PMLR, 2017), Vol. 54, pp. 952–961] can correct the samples by weighting without the knowledge of the sampling distribution, but the naive implementation requires the solution of a large-scale quadratic program, hampering usage in practical problems. In this article, a fast and approximate method based on a random feature map and exponentiated gradient updates is developed to compute the sample weights and is used to correct the samples generated by D-Wave quantum annealers. In benchmarking problems, it is observed that the residual error of thermal average calculations is reduced significantly. If combined with our method, quantum annealers may emerge as a viable alternative to long-established Markov chain Monte Carlo methods. Published by the American Physical Society 2024

The EuroFlow PIDOT external quality assurance scheme: enhancing laboratory performance evaluation in immunophenotyping of rare lymphoid immunodeficiencies.

The development of External Quality Assessment Schemes (EQAS) for clinical flow cytometry (FCM) is challenging in the context of rare (immunological) diseases. Here, we introduce a novel EQAS monitoring the primary immunodeficiency Orientation Tube (PIDOT), developed by EuroFlow, in both a 'wet' and 'dry' format. This EQAS provides feedback on the quality of individual laboratories (i.e.,accuracy, reproducibility and result interpretation), while eliminating the need for sample distribution. In the wet format, marker staining intensities (MedFIs) within landmark cell populations in PIDOT analysis performed on locally collected healthy control (HC) samples, were compared to EQAS targets. In the dry format, participants analyzed centrally distributed PIDOT flow cytometry data (n=10). We report the results of six EQAS rounds across 20 laboratories in 11 countries. The wet format (212 HC samples) demonstrated consistent technical performance among laboratories (median %rCV on MedFIs=34.5 %; average failure rate 17.3 %) and showed improvement upon repeated participation. The dry format demonstrated effective proficiency of participants in cell count enumeration (range %rCVs 3.1-7.1 % for the major lymphoid subsets), and in identifying lymphoid abnormalities (79.3 % alignment with reference). The PIDOT-EQAS allows laboratories, adhering to the standardized EuroFlow approach, to monitor interlaboratory variations without the need for sample distribution, and provides them educational support to recognize rare clinically relevant immunophenotypic patterns of primary immunodeficiencies (PID). This EQAS contributes to quality improvement of PID diagnostics and can serve as an example for future flow cytometry EQAS in the context of rare diseases.

From outskirts to core: the suppression and activation of radio AGN around galaxy clusters

Abstract To investigate how the radio-identified active galactic nuclei (AGN) fraction varies with cluster-centric radius, we present the projected and de-projected distributions of a large sample of LOFAR-identified radio AGN out to 30R500 around galaxy clusters. The AGN fraction experiences a $\sim 25\%$ increase above the field fraction in the cluster outskirts at around 10R500, a $\sim 20\%$ decrease around ∼0.5R500, and an increase of over three times the field fraction value in the very cluster core. We label these three radial windows the outer, intermediate and inner regions respectively, and investigate how these radial trends might arise due to intrinsic properties of the AGN population. The only difference seen in host galaxy stellar mass is in the inner region, where there is a much higher fraction of massive host galaxies. Analysing AGN radio luminosity, regions with a higher AGN fraction tend to have more radio luminous AGN, and vice versa. We discuss the physical mechanisms that might be responsible for these results with reference to the literature.

Regulate the Risk Factors of Pseudomonas aeruginosa by some of Sigma Factor Genes

In this research, a study was conducted isolating and identification of Pseudomonas aeruginosa bacteria from clinical samples from various pathological conditions such as burns , otitis media , urinary tract infection , wounds, eye infection, blood infection, catheter infection, throat , vaginal infection and surgical. The present study includes 313 samples collected from patients who attend Alsader Medical City and Al- Hakim General Hospital during the period extended From September 2023 to December 2023 ( men and women ) with age groups between (1 -77) years. The identification of bacterial isolates were detected by classical and molecular technique (PCR), were the frequency among males 163 (52.07% ) was more than that in female 150 (47.92). The samples distribution is made according age group , it appears high for the following high (15.97) and (12.77) with group (16-30) years of male and female respectively . The results revealed that 57/313 specimens of the total number of samples are Pseudomonas aeruginosa by identified according to their morphological, physiological and biochemical properties. Also with molecular identification by using four primers wre the results showed 16S rRNA gene was detected in 57/57 (100%) P. aeruginosa isolates , the Las biofilm gene was detected in 43/57 (75.43%) , the algD gene was detected in 41/57 (71.92%) and 41/57 samples show positive results with the oprL gene. Six types of antibiotics were also tested to determine the resistance of the samples to them.

Research on Dimension Reduction Method for Combustion Chamber Structure Parameters of Wankel Engine Based on Active Subspace

The combustion chamber structure of a rotary engine involves a combination of interacting parameters that are simultaneously constrained by engine size, compression ratio, machining, and strength. It is more difficult to study the weight of the effect of the combustion chamber structure on the engine performance using traditional linear methods, and it is not possible to find the combination of structural parameters that has the greatest effect on the engine performance under the constraints. This makes it impossible to optimize the combustion chamber structure of a rotary engine by focusing on important structural parameters; it can only be optimized based on all structural parameters. In order to solve the above problems, this paper proposes a method of dimensionality reduction for the structural parameters of a combustion chamber based on active subspace and combining a probability box and the EDF (Empirical Distribution Function). This method uses engine performance indexes such as explosion pressure, maximum cylinder temperature, and indicated average effective pressure as the influence proportion analysis targets and quantitatively analyzes the influence proportion of combustion chamber structure parameters on engine performance. Eight main structural parameters with an influence of more than 85% on the engine performance indexes were obtained, on the basis of which three important structural parameters with an influence of more than 45% on the engine performance indexes and three adjustable structural parameters with an influence of less than 15% on the engine performance indexes were determined. This quantitative analysis work provides an optimization direction for the further optimization of the combustion chamber structure in the future.

Fault diagnosis of bearings under variable operating conditions based on domain adaptation

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.

Bias Mitigation and Representation Optimization for Noise-Robust Cross-modal Retrieval

The remarkable progress in cross-modal retrieval relies on accurately-annotated multimedia datasets. In practice, most existing datasets used for training cross-modal retrieval models are automatically collected from the Internet to reduce data collection costs. However, it inevitably contains mismatched pairs, i.e. , noisy correspondences, thus degrading the model performance. Recent advances utilize the predicted similarity distribution of individual samples for noise validation and correction, which easily faces two challenging dilemmas: 1) confirmation bias and 2) unstable performance with increasing noise. In light of the above, we propose a generalized B ias M itigation and R epresentation O ptimization framework (BMRO). Specifically, we propose a Bias Estimator (BE) to estimate the unbiased confidence factor of a sample by contrasting it against its nearest neighbors. Unbiased confidence factor can precisely adjust sample contribution and enhance accurate sample division. This facilitates the Adaptive Representation Optimizer (ARO) in providing tailored optimization strategies for clean and noisy samples. ARO performs contrastive learning between clean samples and generated hard samples, thus promoting the generalizability and robustness of the representation. Besides, it utilizes complementary learning to reduce incorrect guidance from noisy samples. Extensive experiments on five visual-text benchmarks verify that our BMRO can significantly improve the matching accuracy and performance stability against noisy correspondences.

Mixed Poisson Processes with Dropout for Consumer Studies

We adapt the classical mixed Poisson process models for investigation of consumer behaviour in a situation where after a random time we can no longer identify a customer despite the customer remaining in the panel and continuing to perform buying actions. We derive explicit expressions for the distribution of the number of purchases by a random customer observed at a random subinterval for a given interval. For the estimation of parameters in the gamma–Poisson scheme, we use the estimator minimizing the Hellinger distance between the sampling and model distributions, and demonstrate that this method is almost as efficient as the maximum likelihood being much simpler. The results can be used for modelling internet user behaviour where cookies and other user identifiers naturally expire after a random time.

Determinants of short birth interval in Ethiopia: A multilevel analysis based on EDHS 2019, Ethiopia, 2023.

According to the World Health Organization and Ethiopian Demographic and Health Survey on birth spacing, there should be at least a two-year gap between conception and the first of two children born in quick succession. In poor nations like Ethiopia, resource issues were complex, making it difficult to get statistics for the entire country. However, by examining Ethiopian mini demographic and health survey data, we were able to provide data at the national level. The cross-sectional survey-based study was conducted in several of Ethiopia's administrative cities and nine regions. In the analysis, sampling weight was used to correct the survey's non-proportional sample distribution to strata and areas throughout the survey process and restore representative data. The study's household population was presented and described using descriptive statistics such as weighted frequencies and percentages. The statistically significant factors linked to frequent short birth intervals were found using a multivariable, multilevel logistic regression analysis. Overall, 4306 weighted multigravida mothers nested within 305 enumeration areas were included in the analysis. The respondents' mean (standard deviation) of the birth interval was 42.027(26.69). Higher-educated women had 12% lower odds of having a shorter pregnancy (AOR = 0.88; 95% CI: 0.35, 0.98) than women without higher education. The odds of a short birth interval were 3.04 times greater among women in the age category of 40-49 years at first marriage (AOR = 3.04; 95% CI: 1.08, 8.46) than among women in the age category of 15-19 years. This indicates that older women were most likely to have short birth intervals. In the multilevel logistic regression model, maternal age, maternal educational status, the wealth quintile index, use of contraceptives, duration of breastfeeding, and contextual regions were significantly associated with short birth intervals in Ethiopia.

Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution

This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.

Spatially correlated stellar accretion in the Lupus star-forming region. Evidence for ongoing infall from the interstellar medium

Growing evidence suggests that protoplanetary discs may be influenced by late stage infall from the interstellar medium (ISM). It remains unclear the degree to which infall shapes disc populations at ages $ 1$ Myr. We explored possible spatial correlations between stellar accretion rates in the Lupus star-forming region, which would support the hypothesis that infall can regulate stellar accretion. We considered both the `clustered' stars towards the centre of Lupus 3, and the `distributed' stars that are more sparsely distributed across the Lupus complex. We took the observed accretion rates in the literature and explore spatial correlations. In particular, we tested whether the clustered stars exhibit a radial gradient in normalised accretion rates, and whether the distributed stars have spatially correlated accretion rates. We found statistically significant correlations for both the clustered and distributed samples. The clustered sample exhibits higher accretion rates in the central region, consistent with the expected Bondi-Hoyle-Lyttleton accretion rate. Stars that are spatially closer among the distributed population also exhibit more similar accretion rates. These results cannot be explained by the stellar mass distribution for either sample. Age gradients are disfavoured, though not discounted because normalised disc dust masses are not spatially correlated across the region. Spatially correlated stellar accretion rates within the Lupus star-forming region argue in favour of an environmental influence on stellar accretion, possibly combined with internal processes in the inner disc. Refined age measurements and searches for evidence of infalling material are potential ways to further test this finding.

A clinically feasible algorithm for the parallel detection of glioma-associated copy number variation markers based on shallow whole genome sequencing.

Molecular features are incorporated into the integrated diagnostic system for adult diffuse gliomas. Of these, copy number variation (CNV) markers, including both arm-level (1p/19q codeletion, +7/-10 signature) and gene-level (EGFR gene amplification, CDKN2A/B homozygous deletion) changes, have revolutionized the diagnostic paradigm by updating the subtyping and grading schemes. Shallow whole genome sequencing (sWGS) has been widely used for CNV detection due to its cost-effectiveness and versatility. However, the parallel detection of glioma-associated CNV markers using sWGS has not been optimized in a clinical setting. Herein, we established a model-based approach to classify the CNV status of glioma-associated diagnostic markers with a single test. To enhance its clinical utility, we carried out hypothesis testing model-based analysis through the estimation of copy ratio fluctuation level, which was implemented individually and independently and, thus, avoided the necessity for normal controls. Besides, the customization of required minimal tumor fraction (TF) was evaluated and recommended for each glioma-associated marker to ensure robust classification. As a result, with 1× sequencing depth and 0.05 TF, arm-level CNVs could be reliably detected with at least 99.5% sensitivity and specificity. For EGFR gene amplification and CDKN2A/B homozygous deletion, the corresponding TF limits were 0.15 and 0.45 to ensure the evaluation metrics were both higher than 97%. Furthermore, we applied the algorithm to an independent glioma cohort and observed the expected sample distribution and prognostic stratification patterns. In conclusion, we provide a clinically applicable algorithm to classify the CNV status of glioma-associated markers in parallel.

A comprehensive study for a classification scheme for dwellings in Brazil

ABNT NBR 15575 was published in 2013 and establishes acoustic insulation criteria for residential buildings in Brazil. This standard presents three levels of performance - minimum (mandatory), intermediate, and superior - through an informative annex that classifies the acoustic performance for each requirement. Even after ten years of publication, the application of the standard is still not fully consolidated, although its application is mandatory under Brazilian law. This article proposes a classification of the entire acoustic system of dwellings, intending to enable the general public to understand acoustic performance quickly and intuitively, thus encouraging its application. The proposed classification scheme is based on ISO/TS 19488:2021, which presents harmonized criteria for different acoustic requirements. A large database of field measurements of building systems, typical of Brazil and obtained in different regions, was analyzed to verify the distribution of samples using this new scheme. An A/B/C/D scale is proposed, and results are presented for airborne sound insulation/impact between dwellings, airborne sound insulation for facades, and criteria for equipment sound levels in commercial buildings.

Enhanced physics-informed neural networks without labeled data for weakly and fully coupled transient thermomechanical analysis

In this paper, a series of enhanced physics-informed neural networks (PINN) models without labeled data is proposed to solve the weakly and fully coupled thermomechanical problems. In these models, to better predict the thermal and mechanical responses, PINNs consisting of different deep neural networks (DNN) representing temperature, displacement, and stress are specifically constructed. Furthermore, to elevate the accuracy and avoid possible training failure, several advanced algorithms are developed to ensure the effectiveness of imposing boundary conditions, refining sampling distributions, and enhancing training strategy. A notable aspect of the enhanced PINNs is their independence from expensive, labeled data, relying solely on the temporal and spatial information embedded within the sampling points. The effectiveness and accuracy of the enhanced PINNs are validated through extensive numerical examples, including heat conduction and both weakly and fully coupled thermomechanical problems. The comparation between original PINN and enhanced PINN illustrates the necessity of involving these enhanced methods. The results demonstrate the significant potential of PINN methodologies in engineering areas involving complex thermomechanical processes.

The Impact of Ambiet Factors on Consumer Purchase Decisions (KKV Offline Store)

The rise of globalization and the increasing dominance of information technology have ‎transformed the paradigm for business success. Infrastructure is no longer the sole foundation for ‎entrepreneurs. Instead, the economic landscape now places greater emphasis on intangible assets ‎such as intellectual capital. This includes elements like market perception, interpersonal ‎relationships, company ethos, brand reputation, patents, trustworthiness, strategic vision, and ‎specialized expertise. In this evolving economic environment, these intangible factors play a ‎pivotal role in determining the competitiveness and prosperity of businesses. This research uses ‎quantitative research methods and approaches. Data is collected through random sample ‎distribution of questionnaires to respondents. The respondents of this study are offline store ‎KKV visitors. In this study, ambient temperature are predictors that have significant correlations ‎and ‎influence, contributing to 43.8 percent of variance in customers purchase decisions.‎

Pengaruh Kemampuan Digital dan Kualitas Layanan Elektronik Terhadap Minat Beli Melalui E-Commerce

Currently, the world is experiencing rapid changes with the advent of the Fourth Industrial Revolution. Digital technology is rapidly developing worldwide, including in Indonesia. Technological advancements have permeated all human activities, including the business world, through innovations in electronic commerce, commonly known as e-commerce. This research uses descriptive quantitative research methods and approaches. The study employs Partial Least Squares-SEM as a tool to analyze data obtained from distributed questionnaires. Data was collected through random sampling distribution of questionnaires to respondents, specifically students in Bogor who have shopped on e-commerce platforms. The results of this study indicate that the variable of digital capability has a positive and significant effect on purchase intention, as evidenced by an Original Sample value of 0.152 and a P-value of 0.027 < 0.05. Additionally, the quality of electronic services has a positive and significant effect on purchase intention, as seen from an Original Sample value of 0.640 and a P-value of 0.000 < 0.05.