Total Processing Time Research Articles

Optimization of the seru production system with demand fluctuation: A Mean-CVaR model

The increasingly diverse and non-deterministic customer demands have given rise to the seru production system. This study aims to address the multi-objective seru formation problem under demand fluctuations, with the mean and the CVaR or standard deviation of total completion time minimized. In particular, two multi-objective stochastic optimization models, i.e., Mean-CVaR and Mean-Std, are constructed to determine the number of seru, the allocation scheme of workers and batches. Then, the Local Search Fast Non-dominated Sorting Genetic Algorithm (NSGA-II-LS) is designed to solve the proposed models. Numerical experiments validate the method’s effectiveness by demonstrating that, compared to the Mean-Std model, the Mean-CVaR model enhances the seru system’s capability and stability in coping with stochastic demand fluctuations through an average reduction of 4% in expected value and an average reduction of 36% in standard deviation. The effects of confidence level, the product batch number, and the standard deviation on the results have been analyzed. The results show that the Mean-CVaR method helps achieve a more stable seru system in terms of reducing standard deviation. Moreover, product batch division is beneficial to the CVaR of total processing time but detrimental to the mean processing time.

Queuing Theory-Based Model for Optimization of Covid-19 Vaccination and Booster Delivery

Although queuing theory is commonly utilized in businesses to analyze and model processes involving waiting lines, the healthcare sector sees a difference from other industries when it comes to optimizing fixed resources under alterable demand conditions. To enhance operational effectiveness and cut down on waiting times, hospital operation managers need to be informed on the state of business processes. A scientific method to reduce systemic inefficiencies and raise patient satisfaction is the queuing theory. The objective of this study is to use queuing theory to optimize COVID-19 vaccination and booster delivery. This study discussed two distinct models, one for bigger MV hubs and the other for smaller GP vaccination clinics. The current study demonstrated how these models may be used to anticipate staffing needs to prevent bottlenecks, predict daily throughput given staff capacity limits, and simulate the queuing process. With respectable face validity, we produced accurate estimates of the distributions of given service times and overall processing times. In the future, this may be improved by carrying out a time-use survey to get empirical data on total processing time, which could be compared to the projected processing time of the model and service times for each station, which would help guide the model's inputs.

Lossy numerical simulation of HRTFs using a linear-logarithmic frequency scale

Acoustic simulations for virtual reality (VR) or augmented reality (AR) require the use of filters designed to model the changes undergone by sound waves emitted from specific points in 3-D space along their path to the subjects' ears, known as head-related transfer functions (HRTFs). Individual HRTFs usually produce more realistic perceptual results, as they account for the specific shape of the listener's body. Numerical simulations in the frequency domain are an attractive option to produce individual HRTFs by computing discrete complex pressure values within regular frequency steps. One of the main disadvantages of this procedure is the high computational power needed, which increases with frequency. Aiming to reduce it, this work explores a new approach that employs a linear-logarithmic hybrid frequency scale. The results obtained for numerical and subjective evaluations (conducted with 23 subjects) suggest that the proposed procedure can produce perceptually equivalent results, even for approximations that save as much as nearly 87% of the original total processing time for the entire audible spectrum.

Smart Autism Spectrum Disorder Learning System Based on Remote Edge Healthcare Clinics and Internet of Medical Things

Autism spectrum disorder (ASD) is a brain disorder causing issues among many young children. For children suffering from ASD, their learning ability is typically slower when compared to normal children. Therefore, many technologies aiming to teach ASD children with optimized learning approaches have emerged. With this motivation, this study presents a smart autism spectrum disorder learning system based on remote edge healthcare clinics and the Internet of Medical Things, the objective of which is to offer an online education and healthcare environment for autistic children. Concave and convex optimization constraints, such as accuracy, learning score, total processing time with deadline, and resource failure, are considered in the proposed system, with a focus on different autism education learning applications (e.g., speaking, reading, writing, and listening), while respecting the system’s quality of service (QoS) requirements. All of the autism applications are executed on smartwatches, mobile devices, and edge healthcare nodes during their training and analysis in the system. This study presents the smartwatch autism spectrum data learning scheme (SM-ASDS), which consists of different offloading approaches, training analyses, and schemes. The SM-ASDS algorithm methodology includes partitioning offloading and deep convolutional neural network (DCNN)- and adaptive long short-term memory (ALSTM)-based schemes, which are used to train autism-related data on different nodes. The simulation results show that SM-ASDS improved the learning score by 30%, accuracy by 98%, and minimized the total processing time by 33%, when compared to baseline methods. Overall, this study presents an education learning system based on smartwatches for autistic patients, which facilitates educational training for autistic patients based on the use of artificial intelligence techniques.

A Noise-Oriented and Redundancy-Aware Instance Selection Framework

Fine-tuning transformer-based deep learning models is currently at the forefront of natural language processing (NLP) and information retrieval (IR) tasks. However, fine-tuning these transformers for specific tasks, especially when dealing with ever-expanding volumes of data, constant retraining requirements, and budget constraints, can be computationally and financially costly, requiring substantial energy consumption and contributing to carbon dioxide emissions. This article focuses on advancing the state-of-the-art (SOTA) on instance selection (IS) – a range of document filtering techniques designed to select the most representative documents for the sake of training. The objective is to either maintain or enhance classification effectiveness while reducing the overall training (fine-tuning) total processing time. In our prior research, we introduced the E2SC framework, a redundancy-oriented IS method focused on transformers and large datasets – currently the state-of-the-art in IS. Nonetheless, important research questions remained unanswered in our previous work, mostly due to E2SC's sole emphasis on redundancy. In this article, we take our research a step further by proposing biO-IS – an extended bi - ob jective i nstance s election solution, a novel IS framework aimed at simultaneously removing redundant and noisy instances from the training. biO-IS estimates redundancy based on scalable, fast, and calibrated weak classifiers and captures noise with the support of a new entropy-based step. We also propose a novel iterative process to estimate near-optimum reduction rates for both steps. Our extended solution is able to reduce the training sets by 41% on average (up to 60%) while maintaining the effectiveness in all tested datasets, with speedup gains of 1.67 on average (up to 2.46x). No other baseline, not even our previous SOTA solution, was capable of achieving results with this level of quality, considering the tradeoff among training reduction, effectiveness, and speedup. To ensure reproducibility, our documentation, code, and datasets can be accessed on GitHub – https://github.com/waashk/bio-is .

Rapid determination of phenylalanine and tyrosine in volumetrically collected dried blood spots using fully automated processing and analysis by capillary electrophoresis

An off-the-shelf Agilent 7100 capillary electrophoresis (CE) instrument was employed for the automated processing and analysis of dried blood spots (DBSs) collected by Capitainer®B volumetric devices. Solutions for DBS elutions were transferred directly into CE vials through a separation capillary by the application of an auxiliary nitrogen gas connected to the external pressure line of the CE instrument. This allowed for liquid handling at pressures up to 15 bar and enabled the use of a single capillary for rapid DBS processing and efficient CE separations. The resulting DBS eluates were at-line injected into a short capillary end, which served for improved instrumental simplicity and short CE analysis times. The current set-up necessitated neither hardware nor software adjustments of the CE instrument, except for the connection of a gas cylinder to an in-built connector. The novel features presented in this study (DBSs with exact blood volumes, high external pressures, and short-end injections) were used for the automated determination of clinically relevant markers, phenylalanine (Phe) and tyrosine (Tyr), in DBS samples. Sensitive and selective Phe and Tyr quantification was achieved by CE-UV in 375 mM formic acid and 0.01 % (v/v) Tween 20 (pH 2.09) as a background electrolyte. The total processing and analysis times per one DBS were <1.5 and 4.5 min, respectively, in a sequence of 36 DBSs, and resulted in a sample throughput of >10 DBSs per hour. The intra- and inter-day repeatability values were better than 5.9 and 1.1 % RSD for peak areas and migration times, respectively, and calibration curves were linear in the 20–3000 μM (Phe) and 20–250 μM (Tyr) range (R2 ≥ 0.9973). The limits of detection were ≤2 μM and enabled the determination of endogenous Phe and Tyr concentrations as well as elevated Phe concentrations and Phe/Tyr ratios, which are the typical markers for neonatal phenylketonuria screening.

Optimizing the Use of Flexible Retort Pouches for Production of Ready to Eat Cobia (Rachycentron canadum) Fish Chunks in Curry Medium

Cobia fish chunks in curry medium, a famous North Eastern Indian dish, was developed using Cobia (Rachycentron canadum) fish chunks prepared in a traditional culinary style. The present study was aimed to optimize F0 values for Cobia fish chunks in curry medium using flexible retortable pouches. The product underwent thermal processing at two distinct lethality levels and heat penetration rates, demonstrating equivalent cooling and heating lag effects. The fh values were 15 and 18.5 minutes for the product processed at F0 values of 9.29 and 8.42 minutes, respectively. The total process times were 39.79 and 38.80 minutes, respectively, for the F0 values of 8.42 and 9.29 minutes. Cook values for the processed product were 86.60 minutes for the F0 value of 8.42 minutes and 92.57 minutes for the F0 value of 9.29 minutes. Based on sensory characteristics, the product processed at an F0 value of 8.42 minutes was rated better over a storage period of 90 days.

Heat Penetration Characteristics and Quality Evaluation of Pangasius (Pangasius hypophthalmus) Chunks in Masala Using Retort Pouches

A South Indian dish was developed using Pangasius fish (Pangasius hypophthalmus) fillet chunks in masalas as a medium. The aim of the current study was to optimize the various F₀ values for Pangasius fillet chunks packed in masala, using flexible, retortable pouches. After being processed at different lethality levels, the product showed a similar heating and cooling lag factor. For F₀ values of 7.37 and 8.12 minutes, the total process time were 26.94 and 31.37 minutes, respectively. The fh values for products processed at F₀ values of 7.37 and 8.12 minutes were 13 and 16.5 minutes, respectively. The cook value obtained for the thermally processed product was 64.70 minutes for F₀ 7.37 minutes and 65.87 minutes for F₀ 8.12 minutes. Over a storage duration of up to 90 days, all instrumental texture indicators exhibited a declining pattern. The product processed at Fo value of 7.37 and 8.12 minutes were achieved commercial sterility. The product processed at F₀ 7.37 minutes was considered more favourable based on organoleptic assessment.

Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm

The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

Comparative Analysis Scoring System Auto Essay (simple-o) Based Algoritma Generalized Latent Semantic Analysis (GLSA) Laplacian Eigenmaps Embedding (LEM) and Hybrid Indexing

This research discusses a comparison of two algorithms for an automatic essay assessment system (Simple-O), namely generalized latent semantic analysis (GLSA), Laplacian Eigenmaps embedding (LEM) and hybrid indexing. The two algorithms are compared to find out how the two algorithms work, processing speed, and assessment results. Comparison of how it works is done by comparing the pseudocode of each algorithm. The processing speed is calculated to find out a faster algorithm for assessing essays. The GLSA hybrid indexing algorithm is a development of the LEM algorithm. The fundamental difference between the two algorithms is in the treatment of nouns and words other than nouns. This research used a sample of eight questions completed by 48 students (384 data). From the research results, GLSA LEM has a total processing time of 46.51454 seconds, which is faster than GLSA hybrid indexing. Meanwhile, the average processing time for GLSA LEM and GLSA hybrid indexing to assess one answer is 6-6.6 seconds. The assessment results from GLSA LEM and GLSA hybrid indexing have the highest similarity level of 95.83% and the lowest 16.67%. Of the eight questions tested, five of them had a similarity level of more than 83.33%.

Utilizing superheated steam to inactivate Enterococcus faecium NRRL B-2354 on various material surfaces used in food and produce industries

Traditional sanitation in dry food and produce manufacturing plants is challenging as residual moisture can harbor microbial contamination. Superheated steam (SHS) is produced when water is heated to temperatures (125–400 °C) beyond the boiling point. SHS does not lead to condensation on surfaces. There are limited studies available on the sanitation efficacy of SHS on different surfaces. This study was conducted to understand the sanitation efficacy of SHS to inactivate Enterococcus faecium NRRL B-2354 on different surfaces (stainless steel, concrete, plywood, leather, polytetrafluoroethylene (PTFE), silicon rubber, cotton, and cardboard). The surfaces were either spot inoculated or coated with a representative food matrix (baby formula) and treated with SHS at 150 °C for a total process time (come-up time + treatment time) of 3 min. Spot-inoculated surfaces showed a higher microbial inactivation (range: 9.2 ± 0.9 to 10.7 ± 0.7 log reduction/coupon) whereas surfaces with inoculum coated with baby formula had lower microbial inactivation (range: 7.8 ± 0.4 to 9.6 ± 0.4 log reduction/coupon). The effects of surface characteristics like thermal inertia, surface roughness, and hydrophobicity of microbial inactivation were studied. Prolonged usage of SHS (150 °C for 5 min once a week over 8 weeks) did not alter surface characteristics, particularly roughness of stainless steel, silicone rubber and plywood.

Fast and accurate short-read alignment with hybrid hash-tree data structure

Rapidly increasing the amount of short-read data generated by NGSs (new-generation sequencers) calls for the development of fast and accurate read alignment programs. The programs based on the hash table (BLAST) and Burrows-Wheeler transform (bwa-mem) are used, and the latter is known to give superior performance. We here present a new algorithm, a hybrid of hash table and suffix tree, which we designed to speed up the alignment of short reads against large reference sequences such as the human genome. The total turnaround time for processing one human genome sample (read depth of 30) is just 31 min with our system while that was more than 25 h with bwa-mem/gatk. The time for the aligner alone is 28 min for our system but around 2 h for bwa-mem. Our new algorithm is 4.4 times faster than bwa-mem while achieving similar accuracy. Variant calling and other downstream analyses after the alignment can be done with open-source tools such as SAMtools and Genome Analysis Toolkit (gatk) packages, as well as our own fast variant caller, which is well parallelized and much faster than gatk.

Workflow improvement and financial gain after integration of high-throughput sample processing system with flow cytometer in a high-volume pathology laboratory: Results from a prospective comparative study using Lean principles

BackgroundHighly efficient clinical laboratories are essential for monitoring many human illnesses. Ampath Laboratory Services, the largest pathology lab in South Africa, analyzes large numbers of peripheral blood samples for CD4 levels yearly. ObjectiveTo assess productivity and quality of a newer integrated automated solution, the BD FACSDuet™ Sample Preparation System/BD FACSLyric™ Flow Cytometer using conventional assessment methods and Lean concepts. Materials and methodsThis prospective study compared the performance of the BD FACS™ Sample Preparation Assistant [SPA] III and BD FACSCanto™ II Flow Cytometer with the newly introduced integrated system (BD FACSDuet™/BD FACSLyric™). They were validated for accuracy, precision, and external quality assessment. Process mapping and Lean assessment helped identify steps leading to waste. An economic model was developed to characterize workflow and economic impact associated with total daily hands-on time, processing time, and reworks. ResultsStrong linear correlation was present between both systems. Precision and accuracy studies revealed that all coefficient of variation (CV)% values were below 20% of allowable limits. External proficiency assessments were within limits. The fully automated workflow of BD FACSDuet™/BD FACSLyric™ permitted better consistency with significantly shorter processing time and batch processing and reduced operator interventions. Lean assessment identified defects with motion, over-processing, waiting, and non-utilized talent. Significant reductions in hands-on and total daily processing time that could increase daily specimen testing efficiency and fewer reworks were associated with the BD FACSDuet™/BD FACSLyric™. Lean improvements translated to significant economic savings associated with operator costs and unnecessary reagent consumption. ConclusionBD FACSDuet™/BD FACSLyric™ is an accurate, reliable, and cost-effective fully automated system for high-volume flow cytometry labs that perform T-cell enumeration using a single-platform and single-tube approach.

Silicon heterojunction back-contact solar cells by laser patterning.

Back-contact silicon solar cells, valued for their aesthetic appeal because they have no grid lines on the sunny side, find applications in buildings, vehicles and aircraft and enable self-power generation without compromising appearance1-3. Patterning techniques arrange contacts on the shaded side of the silicon wafer, which offers benefits for light incidence as well. However, the patterning process complicates production and results in power loss. We employed lasers to streamline the fabrication of back-contact solar cells and enhance the power-conversion efficiency. Using this approach, we produced a silicon solar cell that exceeded 27% efficiency. Hydrogenated amorphous silicon layers were deposited onto the wafer for surface passivation and to collect light-generated carriers. A dense passivating contact, which differs from conventional technology practice, was developed. Pulsed picosecond lasers operating at different wavelengths were used to create the back-contact patterns. The approach developed is a streamlined process for producing high-performance back-contact silicon solar cells, with a total effective processing time of about one-third that of the emerging mainstream technology. To meet the terawatt demand, we developed indium-less cells at 26.5% efficiency and precious silver-free cells at 26.2% efficiency. Thus, the integration of solar solutions into buildings and transportation is poised to expand with these technological advances.

A heuristic method for multi-objective hybrid flow shop scheduling problem with parent-child relationships and space constraints

ABSTRACT In modern ship manufacturing, a ship is divided into hundreds of blocks in the design stage, and the productivity of the block manufacturing process is vital to ship delivery. This paper investigates the production scheduling problem of ship blocks with the parent-child relationship; this relationship is characterised by the fact that sibling jobs at the same BOM layer must be processed on adjacent machines at their assembly stages. We formulate this problem as a multi-objective hybrid flow shop scheduling problem: the first objective is to minimise the total deviation from the target time of the jobs and the second one is to minimise the total processing time. To solve this problem, we propose a heuristic method based on the multi-objective genetic algorithm, in which an adjacent machine priority method is first proposed to generate feasible solutions that satisfy the space constraints. Then, we improve the individual quality in crossover and mutation of the algorithm via tabu search. We verify the performance of our algorithm by test instances generated from real manufacturing data of a shipbuilding company. Results show that the proposed method outperforms the existing algorithms when identifying the Pareto optimal solutions, especially for large-sized problems.

An improved intelligent optimization algorithm for small-batch order production scheduling

Although the critical path method (CPM) is effective for the integrated scheduling of small-batch orders, its overemphasis on vertical process relationships and neglect of horizontal parallel relationships have imposed limitations on scheduling, often leading to suboptimal outcomes in terms of the total product completion time. This study introduces an innovative algorithm designed to overcome these limitations and further optimize the total processing time of products. We propose a strategy of "exchanging adjacent processes on the same device", which operates based on the scheduling results of the CPM. By swapping adjacent and interchangeable processes within the constraints of the problem, this algorithm generates multiple new scheduling schemes, effectively expanding the solution space. This expansion enables the discovery of optimized solutions that leverage "horizontal parallel relationships", which is crucial for reducing the "total processing time of products". Finally, the effectiveness of the proposed algorithm is verified through experiments.

Meminimasi Waste pada Distribution Center Area Non Food dengan Pendekatan Lean Warehousing di Perusahaan Retail PT XYZ

PT. XYZ is a minimarket retail company that sells various needs of the community. PT One of the DCs is located in the city of Batam, this DC still has some waste that occurs in its warehousing activities so research needs to be carried out to eliminate this waste. To eliminate problems, an approach was taken using lean warehousing with the tools used being Value Stream Mapping (VSM), Process Activity Mapping (PAM), Gemba shikumi, 5 whys, Lean matrix 2 in the activity mapping process (Process Activity Mapping/PAM) in a warehouse which identified 18 Non-Value Added activities and after processing it with Gemba Shikumi, 4 critical wastes were obtained, consisting of errors in handover evidence, re-inputting data, errors in picking goods, and looking for replacement goods due to incorrect picking. The 4 (four) wastes were given proposals for improving employee training, restructuring the issuing flow, making product labels, internal audits and receiving automation systems. These improvement recommendations are estimated to reduce warehousing process time by 6720 seconds or the equivalent of 1.87 hours. This figure is quite significant because it represents a time reduction of 19.83% from the previous total warehousing process time which reached 33,887 seconds or 9.42 hours.

Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA heterogeneous computation.

Because conventional low-light cameras used in single-molecule localization microscopy (SMLM) do not have the ability to distinguish colors, it is often necessary to employ a dedicated optical system and/or a complicated image analysis procedure to realize multi-color SMLM. Recently, researchers explored the potential of a new kind of low-light camera called colorimetry camera as an alternative detector in multi-color SMLM, and achieved two-color SMLM under a simple optical system, with a comparable cross-talk to the best reported values. However, extracting images from all color channels is a necessary but lengthy process in colorimetry camera-based SMLM (called CC-STORM), because this process requires the sequential traversal of a massive number of pixels. By taking advantage of the parallelism and pipeline characteristics of FPGA, in this paper, we report an updated multi-color SMLM method called HCC-STORM, which integrated the data processing tasks in CC-STORM into a home-built CPU-GPU-FPGA heterogeneous computing platform. We show that, without scarifying the original performance of CC-STORM, the execution speed of HCC-STORM was increased by approximately three times. Actually, in HCC-STORM, the total data processing time for each raw image with 1024 × 1024 pixels was 26.9 ms. This improvement enabled real-time data processing for a field of view of 1024 × 1024 pixels and an exposure time of 30 ms (a typical exposure time in CC-STORM). Furthermore, to reduce the difficulty of deploying algorithms into the heterogeneous computing platform, we also report the necessary interfaces for four commonly used high-level programming languages, including C/C++, Python, Java, and Matlab. This study not only pushes forward the mature of CC-STORM, but also presents a powerful computing platform for tasks with heavy computation load.

A Novel Low-Complexity and Parallel Algorithm for DCT IV Transform and Its GPU Implementation

This study proposes a novel factorization method for the DCT IV algorithm that allows for breaking it into four or eight sections that can be run in parallel. Moreover, the arithmetic complexity has been significantly reduced. Based on the proposed new algorithm for DCT IV, the speed performance has been improved substantially. The performance of this algorithm was verified using two different GPU systems produced by the NVIDIA company. The experimental results show that the novel proposed DCT algorithm achieves an impressive reduction in the total processing time. The proposed method is very efficient, improving the algorithm speed by more than 4-times—that was expected by segmenting the DCT algorithm into four sections running in parallel. The speed improvements are about five-times higher—at least 5.41 on Jetson AGX Xavier, and 10.11 on Jetson Orin Nano—if we compare with the classical implementation (based on a sequential approach) of DCT IV. Using a parallel formulation with eight sections running in parallel, the improvement in speed performance is even higher, at least 8.08-times on Jetson AGX Xavier and 11.81-times on Jetson Orin Nano.

Non-invasive estimation of the powder size distribution from a single speckle image

Non-invasive characterization of powders may take one of two approaches: imaging and counting individual particles; or relying on scattered light to estimate the particle size distribution (PSD) of the ensemble. The former approach runs into practical difficulties, as the system must conform to the working distance and other restrictions of the imaging optics. The latter approach requires an inverse map from the speckle autocorrelation to the particle sizes. The principle relies on the pupil function determining the basic sidelobe shape, whereas the particle size spread modulates the sidelobe intensity. We recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network, trained efficiently through a physics-informed semi-generative approach. In this work, we eliminate one of the most time-consuming steps of our previous method by engineering the pupil function. By judiciously blocking portions of the pupil, we sacrifice some photons but in return we achieve much enhanced sidelobes and, hence, higher sensitivity to the change of the size distribution. The result is a 60 × reduction in total acquisition and processing time, or 0.25 seconds per frame in our implementation. Almost real-time operation in our system is not only more appealing toward rapid industrial adoption, it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying, blending, and other chemical and pharmaceutical manufacturing processes.