Time-averaged Research Articles

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.

Ultrafast terahertz Stark spectroscopy reveals the excited-state dipole moments of retinal in bacteriorhodopsin

The photoinduced all-trans to 13-cis isomerization of the retinal Schiff base represents the ultrafast first step in the reaction cycle of bacteriorhodopsin (BR). Extensive experimental and theoretical work has addressed excited-state dynamics and isomerization via a conical intersection with the ground state. In conflicting molecular pictures, the excited state potential energy surface has been modeled as a pure S[Formula: see text] state that intersects with the ground state, or in a 3-state picture involving the S[Formula: see text] and S[Formula: see text] states. Here, the photoexcited system passes two crossing regions to return to the ground state. The electric dipole moment of the Schiff base in the S[Formula: see text] and S[Formula: see text] state differs strongly and, thus, its measurement allows for assessing the character of the excited-state potential. We apply the method of ultrafast terahertz (THz) Stark spectroscopy to measure electric dipole changes of wild-type BR and a BR D85T mutant upon electronic excitation. A fully reversible transient broadening and spectral shift of electronic absorption is induced by a picosecond THz field of several megavolts/cm and mapped by a 120-fs optical probe pulse. For both BR variants, we derive a moderate electric dipole change of 5 [Formula: see text] 1 Debye, which is markedly smaller than predicted for a neat S[Formula: see text]-character of the excited state. In contrast, S[Formula: see text]-admixture and temporal averaging of excited-state dynamics over the probe pulse duration gives a dipole change in line with experiment. Our results support a picture of electronic and nuclear dynamics governed by the interaction of S[Formula: see text] and S[Formula: see text] states in a 3-state model.

Kuramoto model subject to subsystem resetting: How resetting a part of the system may synchronize the whole of it.

We introduce and investigate the effects of a new class of stochastic resetting protocol called subsystem resetting, whereby a subset of the system constituents in a many-body interacting system undergoes bare evolution interspersed with simultaneous resets at random times, while the remaining constituents evolve solely under the bare dynamics. Here, by reset is meant a reinitialization of the dynamics from a given state. We pursue our investigation within the ambit of the well-known Kuramoto model of coupled phase-only oscillators of distributed natural frequencies. Here, the reset protocol corresponds to a chosen set of oscillators being reset to a synchronized state at random times. We find that the mean ω_{0} of the natural frequencies plays a defining role in determining the long-time state of the system. For ω_{0}=0, the system reaches a synchronized stationary state at long times, characterized by a time-independent nonzero value of the synchronization order parameter that quantifies macroscopic order in the system. Moreover, we find that resetting even an infinitesimal fraction of the total number of oscillators, in the extreme limit of infinite resetting rate, has the drastic effect of synchronizing the entire system, even in parameter regimes in which the bare evolution does not support global synchrony. By contrast, for ω_{0}≠0, the dynamics allows at long times either a synchronized stationary state or an oscillatory synchronized state, with the latter characterized by an oscillatory behavior as a function of time of the order parameter, with a nonzero time-independent time average. Our results thus imply that the nonreset subsystem always gets synchronized at long times through the act of resetting of the reset subsystem. Our results, analytical using the Ott-Antonsen ansatz as well as those based on numerical simulations, are obtained for two representative oscillator frequency distributions, namely, a Lorentzian and a Gaussian. Given that it is easier to reset a fraction of the system constituents than the entire system, we discuss how subsystem resetting may be employed as an efficient mechanism to control attainment of global synchrony in the Kuramoto system.

Real-time quantification of in vivo cerebrospinal fluid velocity using the functional magnetic resonance imaging inflow effect.

In vivo estimation of cerebrospinal fluid (CSF) velocity is crucial for understanding the glymphatic system and its potential role in neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease. Current cardiac or respiratory-gated approaches, such as 4D flow magnetic resonance imaging (MRI), cannot capture CSF movement in real time because of limited temporal resolution and, in addition, deteriorate in accuracy at low fluid velocities. Other techniques like real-time phase-contrast-MRI or time-spatial labeling inversion pulse are not limited by temporal averaging but have limited availability, even in research settings. This study aims to quantify the inflow effect of dynamic CSF motion on functional MRI (fMRI) for in vivo, real-time measurement of CSF flow velocity. We considered linear and nonlinear models of velocity waveforms and empirically fit them to fMRI data from a controlled flow experiment. To assess the utility of this methodology in human data, CSF flow velocities were computed from fMRI data acquired in eight healthy volunteers. Breath-holding regimens were used to amplify CSF flow oscillations. Our experimental flow study revealed that CSF velocity is nonlinearly related to inflow effect-mediated signal increase and well estimated using an extension of a previous nonlinear framework. Using this relationship, we recovered velocity from in vivo fMRI signal, demonstrating the potential of our approach for estimating CSF flow velocity in the human brain. This novel method could serve as an alternative approach to quantifying slow flow velocities in real time, such as CSF flow in the ventricular system, thereby providing valuable insights into the glymphatic system's function and its implications for neurological disorders.

Preparation of Conductive Cellulose Coated with Conductive Polymer and Its Application in the Detection of pH and Characteristic Substances in Sweat.

Rich biological information in sweat provides great potential for health monitoring and management. However, due to the complexity of sweat, the development of environmentally friendly green electronic products is of great significance to the construction of ecological civilization. This study utilized a simple combination of polystyrene sulfonate sodium (PSS) and filter paper (FP) to prepare cellulose materials coated with conductive polymers, developing an electrochemical sensor based on the modified materials. The mechanical and electrochemical properties of the fabricated PSS/FP membrane were optimized by adjusting the feeding dosage of PSS. The realized PSS/FP composite containing 7% PSS displayed good conductivity (9.1 × 10-2 S/m), reducing electric resistance by 99.2% compared with the original FP membrane (6.7 × 10-4 S/m). The stable current of the membrane in simulated sweat under different pH environments is highly correlated with the pH values. Additionally, when the membrane is exposed to simulated sweat with varying ion concentrations, the current signal changes in real time with the concentration variations. The response time averages around 0.3 s.

Statistical insights of polarization speckle via von Mises–Fisher distribution on the Poincaré sphere

Polarization speckles generated via random scattering of light are ubiquitous in natural and engineered systems. They not only manifest intensity fluctuations but also reveal a spatially fluctuating, random polarization distribution. The precise morphology of the polarization speckle pattern serves as a deterministic signature of the light’s state of polarization fluctuation within a scattering medium. Given the inherent randomness of polarization speckle patterns, a statistical approach emerges as the most pragmatic method for their analysis. Stokes parameters, implemented as temporal or spatial averages, are utilized for this purpose. However, within a polarization speckle field featuring a specific spatial average of Stokes parameters, the polarization state exhibits spatial variations across the speckle pattern. These random polarization fluctuations can be effectively modeled using a particular probability density function (PDF), visually represented on the Poincaré sphere. In this work, von Mises–Fisher (vMF) distribution on the Poincaré sphere is extended and applied to demonstrate a statistical insight of polarization speckle fields. A complete theoretical basis is established to investigate the spatial fluctuation of the state of polarization in the polarization speckle using vMF distribution on the Poincaré sphere, including the spatial mean direction, and spatial concentration parameter. Behavior of the marginal vMF distribution on the axes of the Poincaré sphere and its association with the probability density function of the normalized at-the-point Stokes parameters for three different polarization speckles are examined by experiment and simulation. The experimental results are in good agreement with the simulation results and confirm the usefulness of the established theoretical framework for the analysis of the polarization speckles. Characterization of spatial polarization fluctuation offers significant applications, such as in polarimetric analysis and optical sensing, and the same analogy can be used in quantum optics.

The Effect of Intranasal Plus Transcranial Photobiomodulation on Neuromuscular Control in Individuals with Repetitive Head Acceleration Events.

Objective: This proof-of-concept study was to investigate the relationship between photobiomodulation (PBM) and neuromuscular control. Background: The effects of concussion and repetitive head acceleration events (RHAEs) are associated with decreased motor control and balance. Simultaneous intranasal and transcranial PBM (itPBM) is emerging as a possible treatment for cognitive and psychological sequelae of brain injury with evidence of remote effects on other body systems. Methods: In total, 43 (39 male) participants, age 18-69 years (mean, 49.5; SD, 14.45), with a self-reported history of concussive and/or RHAE and complaints of their related effects (e.g., mood dysregulation, impaired cognition, and poor sleep quality), completed baseline and posttreatment motor assessments including clinical reaction time, grip strength, grooved pegboard, and the Mini Balance Evaluation Systems Test (MiniBEST). In the 8-week interim, participants self-administered itPBM treatments by wearing a headset comprising four near-infrared light-emitting diodes (LED) and a near-infrared LED nasal clip. Results: Posttreatment group averages in reaction time, MiniBEST reactive control subscores, and bilateral grip strength significantly improved with effect sizes of g = 0.75, g = 0.63, g = 0.22 (dominant hand), and g = 0.34 (nondominant hand), respectively. Conclusion: This study provides a framework for more robust studies and suggests that itPBM may serve as a noninvasive solution for improved neuromuscular health.

Time‐And‐Space Averaging Applied to Intermittent Multiphase Flow Experiments

AbstractVarious researchers have studied fluctuations in pore‐scale phase occupancy during multiphase flow in porous media using synchrotron‐based X‐ray microcomputed tomography (micro‐CT). However, the impact of these fluctuations on the concept of a representative volume is not yet fully understood. In this study, we performed spatial and temporal averaging of multiphase flow experiments visualized with synchrotron‐based micro‐CT, focusing on oil saturation as the key parameter to determine a representative time‐and‐space average. Our findings revealed that a saturation value representative of both time and space was achieved during fractional flow experiments in drainage mode with fractional flows of 0.8, 0.5, and 0.3. Furthermore, we computed a range of relative permeabilities on the basis of whether momentaneous saturation or time‐and‐space averaged saturation was utilized for direct simulation. Our results highlighted the importance of time‐and‐space averaging in determining a representative relative permeability and indicated that the temporal and spatial scales covered in a typical micro‐CT flow experiment were sufficient to obtain a representative saturation value for sandstone rock under intermittent flow conditions.

Probing the galactic and extragalactic gravitational wave backgrounds with space-based interferometers

We employ the formalism developed in [1] and [2] to study the prospect of detecting an anisotropic Stochastic Gravitational Wave Background (SGWB) with the Laser Interferometer Space Antenna (LISA) alone, and combined with the proposed space-based interferometer Taiji. Previous analyses have been performed in the frequency domain only. Here, we study the detectability of the individual coefficients of the expansion of the SGWB in spherical harmonics, by taking into account the specific motion of the satellites. This requires the use of time-dependent response functions, which we include in our analysis to obtain an optimal estimate of the anisotropic signal. We focus on two applications. Firstly, the reconstruction of the anisotropic galactic signal without assuming any prior knowledge of its spatial distribution. We find that both LISA and LISA with Taiji cannot put tight constraints on the harmonic coefficients for realistic models of the galactic SGWB. We then focus on the discrimination between a galactic signal of known morphology but unknown overall amplitude and an isotropic extragalactic SGWB component of astrophysical origin. In this case, we find that the two surveys can confirm, at a confidence level ≳ 3σ, the existence of both the galactic and extragalactic background if both have amplitudes as predicted in standard models. We also find that, in the LISA-only case, the analysis in the frequency domain (under the assumption of a time average of data taken homogeneously across the year) provides a nearly identical determination of the two amplitudes as compared to the optimal analysis.

Asymptotics of the sticky particles evolution

We study the long-time asymptotic behavior of the Sticky Particles dynamics on the real line. The time average of the Sticky Particles Lagrangian map has a limit which arises as a general property of projections onto closed convex cones in Hilbert spaces. More notably, we prove that the map itself has an asymptotic limit in the case where the sticky particles dynamics is confined to a compact set.

Enhanced Attention Guided Teacher–Student Network for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) has attracted increasing attention due to the convenience and low-cost of acquiring image-level annotations. Most existing WSOD methods follow Multiple Instance Learning (MIL) paradigm to select bounding boxes from proposals based on their classification scores. However, MIL-based WSOD methods often focus on discriminative regions, leading to incomplete and missing instances. To address these issues, we introduce a novel WSOD framework named Enhanced Attention Guided Teacher–Student Network (EATSN). This framework aims to improve detection performance through the guidance of attention map and consistency learning. specifically, we initially train a teacher network using MIL process to generate pseudo ground-truth labels. Subsequently, the weak augmented and strong augmented images are fed into teacher and student models to produce the enhanced attention map. During the training iterations, pseudo labels are utilized to guide the student model, while the teacher model refines its parameters through the Exponential Moving Average(EMA) from the student. Finally we design a proposal selection method that leverages the enhanced attention map and bounding boxes scores to achieve better detection results. Experimental results on benchmark datasets demonstrate that our method achieves comparable performance.

Seasonal variability of volatile organic compounds (VOCs) at a high-altitude station in the Western Ghats, India: Influence of biogenic, anthropogenic emissions and long-range transport

Seasonal variability of volatile organic compounds (VOCs) was studied using year-long observations (June 2019–May 2020) over a high-altitude (1380m AMSL) forested site in the Western Ghats of India. Isoprene, a well-known biogenic VOC, peaked during pre-monsoon. Temperature stress and higher photo synthetically active radiation (PAR) were the drivers behind isoprene emissions from the surrounding forested regions. The daytime average isoprene mixing ratio was 1.92 ± 1.51 ppb during summer and the night time average was 0.02 ± 0.01 ppb. A strong relationship between isoprene mixing ratio and ambient temperature was derived. Most of the anthropogenic VOCs like acetonitrile, benzene, toluene and xylene were found to be higher during winter. These anthropogenic VOCs mostly exhibited afternoon to evening peaks, which suggests the contribution by traffic emissions along with other combustion sources (biofuels) and vertical updraft of pollutants from the surrounding valley region. The toluene to benzene ratio (T/B) was >1 during the afternoon and evening hours in monsoon, whereas, aged air masses with lower T/B ratios (<0.7) prevailed during other seasons. Polar bi-variate plots reveal that toluene, xylene and tri-methyl benzene (TMB) were mostly emitted at the south-west of the measurement site, irrespective of the season, indicating a dominant local traffic related source throughout the year. The contribution of local emissions and regional background on two selected aromatic VOCs, toluene and xylene was estimated, revealing the dominance of local anthropogenic sources during monsoon and post monsoon. Positive matrix factorization revealed three source factors in all the seasons with one additional factor during winter and pre-monsoon. The first factor was constituted of toluene, benzene, xylene and TMB, and the T/B ratio was ∼1.2, indicating traffic/fossil fuel contribution. A second ‘biomass burning’ factor was dominated by acetonitrile, ethanal, acetone, and benzene in which the T/B ratio was 0.45. Factors one & two were further validated by comparison with chamber experiments conducted using different combustion sources and solvents. The third factor was dominated by isoprene and monoterpenes which were attributed to biogenic emissions. The fourth factor comprised mainly of acetonitrile, ethanal, and acetone indicating aged air masses due to long-range transport with contributions from photochemical production and biomass burning.

#817 Genetic analysis in a group of patients with persistent isolated microscopic hematuria

Abstract Background and Aims Autosomal dominant Alport syndrome (ADAS) is a genetic disease caused by pathogenic variants in COL4A3 and COL4A4 genes. ADAS is the main cause of isolated persistent microscopic hematuria (PMH). However, other diseases like IgA nephropathy (IgAN) can present with PMH. Establishing a precise genetic diagnosis in this type of patients would avoid invasive studies like kidney biopsies. In this study we performed a comprehensive genetic test in a cohort of patients with isolated PMH to evaluate the frequency of ADAS or other genetic abnormalities and compare the clinical characteristics of patients with or without ADAS Method Fifty-two patients with PMH were included in the study. PMH was defined by the presence of 3 or more dysmorphic red blood cells per field in at least 5 consecutive urine sediments and a time-averaged proteinuria &amp;lt;1 gr/day. Average proteinuria was determined for every 6 months of follow-up and time-averaged (TA) proteinuria represented an average of the mean proteinuria measurements in each 6-month period. A comprehensive genetic study using whole exome next-generation sequencing was performed. One hundred and one genes related to familial hematuria, focal segmental glomerulosclerosis and cystic kidney diseases were analyzed. Patients with known secondary causes of hematuria were excluded. The baseline clinical characteristics of patients with and without ADAS were compared. Patients with pathogenic variants in COL4A5 gene were excluded of this analysis. Results Thirty-one (60%) patients were diagnosed with ADAS (16 patients with pathogenic variants in COL4A3, 15 in COL4A4), 3 (6%) patients (2 women, 1 man) presented pathogenic variant in COL4A5 gene, and the genetic study was negative in the remaining 18 patients (variants of unknown significance in COL4A3 or COL4A4 were found in 3 of the latter). No other genetic abnormalities were detected in the panel of 101 genes. Table 1 shows the baseline clinical characteristics of patients with and without ADAS. Patients with ADAS presented a higher TA proteinuria [0.23 (0.11-0.6) vs 0.08 (0.05-0.16), p = 0.01], and a more frequent familial history of isolated microscopic hematuria (76% vs 40%, p = 0.01) chronic kidney disease (CKD) (44% vs 0%, p = 0.01) or end stage kidney disease (32% vs 0%, p = 0.02). In the multivariate analysis, only a family history of microscopic hematuria was associated with ADAS: OR 5.62 (1.25-25.49, p = 0.02). Conclusion ADAS is the most common cause of PMH. A genetic study should be the first diagnostic step in order to obtain a precise cause of the disease and avoid invasive procedures. A family history of microscopic hematuria and of CKD increases the likelihood of ADAS prior to the performance of genetic test.

Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising

In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

High-Frequency and High-Current Transmission Techniques for Multiple Earth Electrical Characteristic Measurement Systems Based on Adaptive Impedance Matching through Phase Comparison.

With the increase in groundwater exploration, underground mineral resource exploration, and non-destructive investigation of cultural relics, high-resolution earth electrical characteristic measurement has emerged as a mainstream technique owing to its advantageous non-destructive detection capability. To enhance the transmission power of the high-frequency transmitter in high-resolution multiple earth electrical characteristic measurement systems (MECS), this study proposes a high-frequency, high-current transmission technique based on adaptive impedance matching and implemented through the integration of resonant capacitors, a controllable reactor, high-frequency transformers, and corresponding control circuits. A high-current precisely controllable reactor with a 94% inductance variation range was designed and combined with resonant capacitors to reduce circuit impedance. Additionally, high-frequency transformers were employed to further increase the transmission voltage. A prototype was developed and tested, demonstrating an increase in transmission current at frequencies between 10 and 120 kHz with a peak active power of 200 W. Under the same transmission voltage, compared to the transmission circuit without impedance matching, the transmission current increased to a maximum of 16.7 times (average of 10.8 times), whereas compared to the transmission circuit using only traditional impedance matching, the transmission current increased by a maximum of 10.0 times (average of 4.2 times), effectively improving the exploration resolution.

Estimating indoor airborne concentrations of SARS-CoV-2 RNA using quantitative filter forensics

This investigation used portable air cleaners (PACs) and quantitative filter forensics (QFF) to assess week-long average airborne concentrations of SARS-CoV-2 RNA in homes occupied with COVID-symptomatic individuals, classrooms, and dining locations throughout Toronto, Canada. PACs were deployed for one week each, and dust from filters was collected via vacuuming. SARS-CoV-2 RNA from filter dust was quantified using reverse transcription-polymerase chain reaction (RT-PCR). RNA quantities and PAC metadata were used to estimate airborne concentrations of SARS-CoV-2 RNA. The highest concentrations of RNA were found in isolation rooms (median concentration = 8.68 RNA copies/m3). Classrooms had lower concentrations during summer than in fall and winter (median concentration of sampling weeks when RNA was present = 0.02 RNA copies/m3 and 0.11 RNA copies/m3, respectively), which may be attributed to differences in classroom occupancy among other factors. Limitations include unknown recovery efficiency of RNA from filters and the dynamics of concentrations due to the temporal averaging of QFF. Our results are consistent with previous research highlighting the effectiveness of isolation in preventing distribution of high concentrations of SARS-CoV-2 RNA throughout a home. Overall, QFF is a beneficial tool for environmental sampling of respiratory airborne viral RNA such as SARS-CoV-2 RNA, that can be implemented for long-term sampling in mixed-occupancy environments.

A Eulerian Monte Carlo method for the numerical solution of the multivariate population balance equation

Statistical descriptions of particulate phases and granular media based on the notion of a property distribution are particularly advantageous for dense, many-particle dispersions since they support the direct evaluation of Eulerian property statistics, permit a kinematically simple treatment of particle collisions and feature a model complexity that is independent of the number of physical particles. On the minus side, the property distribution is governed by a high-dimensional population balance equation (PBE) whose moment reduction is accompanied by a challenging closure problem and engenders intricate dynamical nonlinearities. Circumventing both the curse of dimensionality and the moment closure problem, we present a Eulerian Monte Carlo (EMC) solution method that harnesses a representation of the property distribution in terms of an ensemble of property samples and is based on a statistical enforcement of the spatially and temporally discretized PBE. After application of a kinetic finite volume scheme, the PBE is subjected to a fractional steps decomposition that separates spatial transport from transport in property space. Conceptually, spatial transport is emulated by assembling the ensemble in one spatial grid cell from samples drawn from the ensembles in neighbouring cells at the previous time point. In this regard, we propose a two-level sampling scheme that is sensitive to large-scale features of the marginal velocity distribution and effectively reduces the instantaneous sampling error. The property transport step, by contrast, is based on the method of characteristics and involves the propagation of individual samples as property characteristics in time. While the sampling error afflicting the property statistics needs to be controlled using a top-level time or ensemble averaging, the EMC method remains well-conditioned for an arbitrary number of samples, is easy to implement and involves a computational expense that scales only linearly with the number of particle properties. Based on two benchmark examples, we analyze the spatial and statistical convergence properties of the EMC method and demonstrate its capacity to capture multiple crossings of particle trajectories. A third example involves the two-way coupled transport of size-polydispersed inertial particles in an inhomogeneous flow field.

Triple Attention Mechanism with YOLOv5s for Fish Detection

Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual maturation. A variety of complex factors makes it difficult to extract effective features, which results in less-than-good model performance. This paper proposes a fish detection method that combines a triple attention mechanism with a You Only Look Once (TAM-YOLO)model. In order to enhance the speed of model training, the process of data encapsulation incorporates positive sample matching. An exponential moving average (EMA) is incorporated into the training process to make the model more robust, and coordinate attention (CA) and a convolutional block attention module are integrated into the YOLOv5s backbone to enhance the feature extraction of channels and spatial locations. The extracted feature maps are input to the PANet path aggregation network, and the underlying information is stacked with the feature maps. The method improves the detection accuracy of underwater blurred and distorted fish images. Experimental results show that the proposed TAM-YOLO model outperforms YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, and SSD, with a mAP value of 95.88%, thus providing a new strategy for fish detection.

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection.

In response to the high breakage rate of pigeon eggs and the significant labor costs associated with egg-producing pigeon farming, this study proposes an improved YOLOv8-PG (real versus fake pigeon egg detection) model based on YOLOv8n. Specifically, the Bottleneck in the C2f module of the YOLOv8n backbone network and neck network are replaced with Fasternet-EMA Block and Fasternet Block, respectively. The Fasternet Block is designed based on PConv (Partial Convolution) to reduce model parameter count and computational load efficiently. Furthermore, the incorporation of the EMA (Efficient Multi-scale Attention) mechanism helps mitigate interference from complex environments on pigeon-egg feature-extraction capabilities. Additionally, Dysample, an ultra-lightweight and effective upsampler, is introduced into the neck network to further enhance performance with lower computational overhead. Finally, the EXPMA (exponential moving average) concept is employed to optimize the SlideLoss and propose the EMASlideLoss classification loss function, addressing the issue of imbalanced data samples and enhancing the model's robustness. The experimental results showed that the F1-score, mAP50-95, and mAP75 of YOLOv8-PG increased by 0.76%, 1.56%, and 4.45%, respectively, compared with the baseline YOLOv8n model. Moreover, the model's parameter count and computational load are reduced by 24.69% and 22.89%, respectively. Compared to detection models such as Faster R-CNN, YOLOv5s, YOLOv7, and YOLOv8s, YOLOv8-PG exhibits superior performance. Additionally, the reduction in parameter count and computational load contributes to lowering the model deployment costs and facilitates its implementation on mobile robotic platforms.

Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.