Experimental design issues associated with classifications of hyperspectral sensing data

This article is organized around two topics: the first one is the methodology of experimental economics, a research program that is becoming increasingly influential in contemporary economic science. The second one is normative methodology, an issue that has been widely debated by philosophers of economics over the last two decades. A methodological discussion of experimental economics could simply aim at describing the methods used by experimental economists in their daily work, without asking questions of efficacy or justification. Another approach, the one that is pursued here, is to take a more direct normative stance: instead of passively observing what economists do, the philosopher steps down in the arena of scientific debate and tries to issue some cautious advice on methodological matters.

Detection and recognition of latent fingerprints play crucial roles in identification and security. However, the separation of sensor, memory, and processor in conventional ex-situ fingerprint recognition system seriously deteriorates the latency of decision-making and inevitably increases the overall computing power. In this work, a photoelectronic reservoir computing (RC) system, consisting of DUV photo-synapses and nonvolatile memristor array, is developed to detect and recognize the latent fingerprint with in-sensor and parallel in-memory computing. Through the Ga-rich design, we achieve amorphous GaOx (a-GaOx) photo-synapses with an enhanced persistent photoconductivity (PPC) effect. The PPC effect, which induces nonlinearly tunable conductivity, renders the a-GaOx photo-synapses an ideal deep ultraviolet (DUV) photoelectronic reservoir, thus mapping the complex input vector into a dimensionality-reduced output vector. Connecting the reservoirs and a memristor array, we further construct an in-sensor RC system for latent fingerprint identification. The system maintains over 90% recognition accuracy for latent fingerprint within 15% stochastic noise level via the proposed dual-feature strategy. This work provides a subversive prototype system of DUV in-sensor RC for highly efficient recognition of latent fingerprints.

Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and “smart applications” of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated “rain event”. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm−1 and 763 cm−1). The integrated intensities of these two peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (“sill”, “range”, and “nugget”) were examined as possible “indicators” of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

In this chapter, a brief review of existing empirical research on environmental correlates of problem drinking is presented. The review shows that environmental factors do relate to the prevalence of drinking problems and also to the way drinking problems are expressed. In the major section of the chapter, however, it is shown that our present knowledge of how environmental and personal factors combine to influence problem drinking is quite limited, perhaps because almost all of the existing empirical research has attempted to account for problem drinking by means of individual variables alone, environmental variables alone, or in terms of linear combinations of individual and environmental variables. It is shown that alternative approaches offer more promise for understanding how individual and environmental factors combine to influence problem drinking; these approaches are aimed at accounting for problem drinking in terms of the mutual interdependence between persons and their environments. Within two hypothetical sets of data, a number of conceptual and methodological issues, problems, and features of these kinds of interactional or transactional approaches are then illustrated. It is shown that although such approaches offer a promise of greater understanding, they also present a set of interrelated problems which run the gamut from measurement, statistical analysis, experimental design, and sampling issues to paradigm issues lying close to the realm of the philosophy of science.

Sustaining tranquility in small urban green parks: A modeling approach to identify noise pollution contributors

The focus of this paper is to improve simulation-based inference through improved denoising of experimental data. Statistical inference of parameters governing a complex physical process from observed data is an important task for several scientific domains. In a likelihood-free inference setup, the inference engine used to make inference from the experimental data can be trained using simulated data. In many scenarios, experimental data is corrupted by noise during the data acquisition process, which is either unaccounted for in the simulator or whose strength might not match with the one set in the simulator. This deteriorates the accuracy of the inference. While advances in denoising can be leveraged to address this challenge, many denoising techniques ignore the fact that experimental data in many scientific domains tends to be irregular/graph-structured. This paper addresses these two challenges by developing a kernel-based learnable graph (KBLG) denoiser, which can be used to denoise experimental graph-structured data. In order to exhibit the efficacy of the developed denoiser in simulation-driven inference problems, this paper considers the problem of inference of the position of an interaction in an astroparticle detector. The available simulated data in this case is the snapshots of the luminous responses of the photomultiplier tube sensors used within the dark matter detection experiment. In experimental situations, these measurements are corrupted by noise generated by secondary optical and electronic processes. The proposed KBLG denoiser is used to denoise the multiple snapshots of the experimental measurements, which are then used for position reconstruction using a multilayer perceptron trained using noiseless simulated data. Numerical results exhibit that the proposed KBLG denoiser outperforms a graph-agnostic denoiser in terms of MLP-based position reconstruction performances for different levels of noise (noise variance).

Abstract: Rapid scaling of cloud services and data-intensive applications has increased the strategic importance of forecasting cloud compute demand and network traffic with statistical rigour. While machine learning methods have gained attention, econometric models remain essential for identifying structural drivers, quantifying elasticities, and generating policy-relevant counterfactuals. This study investigates a comparative econometric framework for modelling cloud and network demand using R, Stata, SPSS, and EViews, focusing on methodological transparency and cross-tool reproducibility. The analytical design integrates time-series and dynamic regression approaches, including ARIMA, ARDL, and VAR families, and evaluates their performance under realistic macro-technology and pricing conditions. The research employs realistic, domain-plausible monthly indices reflecting publicly observable patterns of cloud market growth, digital adoption shifts, electricity-price cycles, and the accelerating share of AI-related workloads. Demand indices spanning 2019–2023 are linked with explanatory variables that capture digital adoption, cloud price trends, energy-cost pressures, and monetary conditions. Estimation results indicate strong demand persistence, positive sensitivity to adoption and AI workload share, and negative elasticity with respect to cloud price indices. A cross-tool accuracy assessment suggests that R and EViews provide marginal advantages in flexible multi-model workflows, Stata offers stable ARDL inference, and SPSS provides competitive univariate baselines. These findings confirm the continuing value of econometric modelling as a governance-grade forecasting instrument for capacity planning and sustainable infrastructure strategy. Keywords—Cloud demand forecasting, network traffic, econometrics, ARDL, VAR, ARIMA, R, Stata, SPSS, EViews, capacity planning.

Canalization is a form of network robustness found in genetic regulatory networks that results in a reduction of the variation of phenotypic expression relative to the complexity of the genome. Recently, it was discovered that canalization can evolve in a complex network through a self-organization of node (gene) behavior resulting from a competition of a network's nodes that selects for a diversity of behavior [K.E. Bassler, C. Lee, and Y. Lee, Phys. Rev. Lett. 93, 038101 (2004)]. Previously, this mechanism for the evolution of canalization has been studied only in deterministic model systems. This paper considers the effects of stochastic noise in the signals exchanged between nodes on the self-organized evolution of canalization. We find that small levels of stochastic noise increase the amount of canalization produced. At higher levels of noise, the amount of canalization produced levels off and reaches a maximum value, before it reduces at large levels of noise.

Mechanistic modelling in pharmaceutical product and process development: A review of distributed and discrete approaches

Two-stage logistic regression model

Fecal particle size (FPS) as quantified by wet sieving analysis is a measure of chewing efficiency relevant for the understanding of physiological adaptations and constraints in herbivores. FPS has not been investigated systematically in frugivores, and important methodological problems remain. In particular, food items that are not chewed may skew estimates of FPS. We address such methodological issues and also assess the influence of diet type and age on FPS in wild chimpanzees. About 130 fecal samples of 38 individual chimpanzees (aged from 1.3 to ∼55 years) from the Kanyawara community of Kibale National Park (Uganda) were collected during three fruit seasons and analyzed using standardized wet sieves (pores from 16 to 0.025 mm). The effects of using different sieve series and excluding large seeds were investigated. We also assessed the relationship between FPS and sex, age, and fruit season. The treatment of seeds during the sieving process had a large influence on the results. FPS was not influenced by chimpanzee sex or age, but was smaller during a fig season (0.88 ± 0.31 mm) than during two drupe-fruit seasons (1.68 ± 0.37 mm) (0.025-4 mm sieves, excluding seeds). The absence of an age effect on FPS suggests that dental senescence might be less critical in chimpanzees, or in frugivores in general, than in folivorous herbivores. To increase the value of FPS studies for understanding frugivore and hominoid dietary evolution we propose modifications to prior herbivore protocols.

Recent work uses the change in equity value surrounding an announcement of a change in financial policy as an explanatory variable in regressions that examine whether changes in financial policy convey information about firm performance. We explore the methodological and econometric issues of this approach and show that using the change in equity value as an explanatory variable can severely bias ordinary least squares estimates. We demonstrate the effect of this bias on standard statistical tests and conclude that the bias has a significant impact on the power of these tests. We propose an estimator to partially correct the biases.

In recent years, quantum computing has been applied in optimizing different challenging systems in the domains of science and engineering. This work reflects the identification of the input nonlinear output error (I-NOE) model by using the Quantum-based avian navigation optimizer algorithm (QANA). QANA is an evolutionary method inspired by the precision navigation of migratory birds. The assessment of QANA is achieved on various iterations, population, and noise levels. The complexity, statistical, and convergence investigation of QANA with arithmetic optimization algorithm (AOA), coati optimization algorithm (COA), grey wolf optimizer (GWO), particle swarm optimization (PSO), synergistic swarm optimization algorithm (SSOA), and velocity pausing particle swarm optimization (VPPSO) approves the robustness of QANA for I-NOE model identification.

This paper presents a statistical approach, originally developed for mapping disease risk, to ecological regression analysis in the presence of spatial autocorrelated extra-Poisson variation. An insight into the effect of allowing for spatial autocorrelation on the relationship between disease rates and explanatory variables is given. Examples based on cancer frequency in Scotland and Sardinia are used to illustrate the interpretation of regression coefficient and further methodological issues.

Methodological Issues in the Estimation of Current Account Imbalances