Factory Settings Research Articles

Comparative Effects of Bio-Wastes in Combination with Plant Growth-Promoting Bacteria on Growth and Productivity of Okra

Plant growth-promoting rhizobacteria with multiple growth-promoting traits play a significant role in soil to improve soil health, crop growth and yield. Recent research studies have focused on the integration of organic amendments with plant growth-promoting rhizobacteria (PGPR) to enhance soil fertility and reduce the hazardous effects of chemical fertilizers. This study aims to evaluate the integrated application of biochar, compost, fruit and vegetable waste, and Bacillus subtilis (SMBL 1) to soil in sole application and in combined form. The study comprises eight treatments—four treatments without inoculation and four treatments with SMBL 1 inoculation in a completely randomized design (CRD), under factorial settings with four replications. The results indicate that the integrated treatments significantly improved okra growth and yield compared with sole applications. The integration of SMBL 1 with biochar showed significant improvements in plant height, root length, leaf chlorophyll a and b, leaf relative water content, fruit weight, diameter and length by 29, 29, 50, 53.3, 4.3, 44.7 and 40.4%, respectively, compared with control. Similarly, fruit N, P and K contents were improved by 33, 52.7 and 25.6% and Fe and Zn in shoot were 37.1 and 35.6%, respectively, compared with control. The results of this study reveal that the integration of SMBL 1 with organic amendments is an effective approach to the sustainable production of okra.

The digestible histidine requirement of juvenile yellowtail kingfish Seriola lalandi

The Essential Amino Acid (EAA) requirements of many cultured species remains unknown and therefore aquafeeds used in new industries are often based on the requirements of similar species or those from a similar trophic level. One such example is the formulation of aquafeeds for Australian Yellowtail Kingfish Seriola lalandi (YTK), which are historically based on the published EAA requirements of Japanese Yellowtail Seriola quinqueradiata. The requirements of YTK for sulphur-amino acids as well as taurine have recently been quantified. These studies indicated that cultured YTK may have different EAA requirements to Japanese Yellowtail which has raised doubts about the adequacy of other EAAs in commercial feeds for YTK. Of these, the adequacy of histidine was considered particularly important due to concerns about cataract formation and poor growth. Therefore, the digestible histidine requirement of YTK was quantified using a dose-response design that employed a fishmeal-free basal diet containing 0.61% digestible histidine. Supplemental crystalline L-histidine was added to the basal formulation in 0.5% increments resulting in four additional diets with a digestible histidine content of 0.96, 1.32, 1.61 and 2.0%, respectively. Each dietary treatment was assigned to triplicate saltwater tanks containing 10 × 80 g YTK which were fed to apparent satiation for seven weeks at a temperature of 20 °C. Despite the incremental addition of histidine there were no differences in growth rate, feed intake, condition factor and feed conversion ratio of YTK in response to dietary treatment. In contrast, evaluation of whole carcass revealed histidine deposition rate increased in response to digestible histidine content before declining at the highest dietary level (i.e. >1.61%), suggesting YTK had reached their maximum rate of histidine deposition under our experimental conditions. A factorial analysis of the retention data from the current study was compared to and combined with previous factorial models on histidine utilisation by YTK. The combined model indicated that while the maintenance demand for histidine varied with experimental conditions, the unit cost of histidine deposition in YTK was constant at 1.99. A conservative estimate of the variable digestible histidine requirement for maintenance at temperatures between 15 and 24 °C was found to be 64.2 mg kgBW-0.7d−1. Feed ration models generated from the factorial data sets indicate YTK should be fed diets that contain between 1.6 and 2.0% digestible histidine in order to meet their daily histidine requirement. These amounts are slightly higher than generally recommended for Japanese Yellowtail.

Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

The embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.

Feeding Volatiles of Larval Sparganothis pilleriana (Lepidoptera: Tortricidae) Attract Heterospecific Adults of the European Grapevine Moth.

Plants release volatiles in response to caterpillar feeding. These herbivore-induced plant volatiles (HIPVs) attract natural enemies of the herbivores and repel or attract conspecific adult herbivores in a tri-trophic interaction which has been considered to be an indirect plant defense against herbivores. Recently, we demonstrated the attraction of male and female European grapevine moth, Lobesia botrana (Denis & Schiffermüller) (Lepidoptera: Tortricidae) to a blend of phenylacetonitrile and acetic acid, two compounds identified as HIPVs in heterospecific apple-leafroller interactions. The ecological basis of our findings is not clearly understood. Thus, this work was undertaken to investigate HIPVs in the grapevine-leafroller interaction and study the response of heterospecific adults L. botrana, to these volatiles. We collected headspace volatiles emitted from uninfested grapevines and grapevines infested with larvae of a generalist herbivore, the grapevine leafroller moth, Sparganothis pilleriana (Denis & Schiffermüller), and analyzed them using gas chromatography/mass spectrometry. Infested grape leaves released three compounds (phenylacetonitrile, indole, and 2-phenylethanol) not found from uninfested leaves. Nine different blends, comprising a full factorial set of the three compounds with each blend containing acetic acid, were tested in a field-cage trial. Only lures containing phenylacetonitrile caused a significant increase in trap catches compared to the other lures and blank traps. Electroantennographic tests show that L. botrana can detect the compounds. The results confirm our hypothesis that phenylacetonitrile is released during grapevines infestation with herbivores, and attracts adult L. botrana.

Analysis of Port Accidents and Calibration of Heinrich’s Pyramid

Academics and the maritime industry have used the Heinrich Pyramid for decades to justify overall safety theory, risk assessments, and accident prevention strategies. Most use Heinrich’s original severity ratios (1:29:300) for accident causation development in a factory setting. However, to use the Pyramid effectively and mitigate risks/hazards, it must be calibrated to represent specific industry reality. This paper, for the first time, focuses on calibration of Heinrich’s Pyramid to maritime accident data, using databases from the Marine Accident Investigation Branch of the Department for Transport. This research clusters five years (2013–2017) of accident data, using K-Means clustering on categorical variables and severity levels of accidents, similar logic to Heinrich’s analysis. This approach and descriptive statistics provide new ratios between accident severity classifications for casualties with a ship (CS) and occupational accidents (OAs) separately. Results show that the data do not appear to fall into Heinrich’s Pyramid shape and yield a vastly different and lower ratio to that of Heinrich’s. Especially concerning was that Very Serious and Serious accidents occurred at a 1:5 ratio for CS and 4:1 for OA, very different from Heinrich’s 1:29. Although these results calculated a new ratio, it may not represent reality owing to accident reporting requirements under UK law, a lack of an agreed taxonomy of risk and hazard definitions, and likely underreporting of less severe accidents. This is proven because, in 2017, CS data became pyramid shaped, after a decrease in the number of accidents and a 17% increase in near-misses.

Planning Single-Arm Manipulations with N-Arm Robots

Many robotic systems are comprised of two or more arms. Such systems range from dual-arm household manipulators to factory floors populated with a multitude of industrial robotic arms. While the use of multiple arms increases the productivity of the system and extends dramatically its workspace, it also introduces a number of challenges. One such challenge is planning the motion of the arm(s) required to relocate an object from one location to another. This problem is challenging because it requires reasoning over which arms and in which order should manipulate the object, finding a sequence of valid handoff locations between the consecutive arms and finally choosing the grasps that allow for successful handoffs. In this paper, we show how to exploit the characteristics of this problem in order to construct a planner that can solve it effectively. We analyze our approach experimentally on a number of simulated examples ranging from a 2-arm system operating at a table to a 3-arm system working at a bar and to a 4-arm system in a factory setting.

A Great Conversation With Dr. James Corbett

A Great Conversation With Dr. James Corbett

РЕАЛИЗАЦИЯ НЕЛИНЕЙНОГО ПРЕОБРАЗОВАНИЯ ПОДСТАНОВКИ В ВИДЕ ЭЛЕМЕНТОВ СИСТЕМЫ СЧИСЛЕНИЯ РЯДА ФАКТОРИАЛЬНЫХ МНОЖЕСТВ

The paper investigates a nonlinear substitution transformation, presented as an element of the number system of factorial sets. This approach takes into account the characteristic features of substitutions and al-lows you to minimize the amount of memory for storing. Based on the unambiguous numbering approach for substitutions, three methods of storing in memory are considered: in the form of a vector, in the form of values of cyclic shifts of elements of an identical substitution, and in the form of a decimal substitution number. The results of the analysis of the amount of data required for storing the nonlinear substitution transformation for cryptographic primitives are presented. The complexity of translating the transformed substitutions into the standard form of storage by a vector is determined. The obtained results can be ap-plied in software solutions for ensuring information security using cryptographic primitives containing a nonlinear substitution transformation. The proposed approach to writing in the form of substitution trans-formations is recommended for implementation in complexes with a limited amount of memory.

How do methodological choices influence estimation of river metabolism?

AbstractRiver metabolism modeled from diurnal dissolved oxygen (DO) has become a widely used metric of ecosystem function, yet many papers provide insufficient methodological detail for replication. Only 79% of 43 sampled papers published from 2015 to 2019 mention calibration, 44% describe sensor placement, and 34% did not describe estimation approaches such that the study could be replicated. Given spatial heterogeneity in rivers influences metabolism, and measurement sensitivities vary with sensor model, it is important to have appropriately detailed information in reported methods along with a fundamental understanding of how river heterogeneity might influence metabolism. We deployed 2–8 sensors at 92 steppe river reaches to characterize site heterogeneity, evaluating how sensor placement and type, deployment length, drift correction, data source, local vs. remotely sensed data, and calibration can affect metabolism estimates. Estimates of gross primary production (GPP) and ecosystem respiration (ER) were inconsistent and unpredictable depending on deployment location within a river reach; GPP and ER rates varied up to 131% and 69%, respectively, across a river width and up to two orders of magnitude within a reach. DO sensor brands vary in precision and accuracy; we found even when operated within stated performance range, estimates of GPP and ER could vary by 82% and 198%, respectively, if not calibrated beyond factory setting, as determined using field data from a sample site. Inaccuracies from sensor drift over weeklong deployments led to an average 48% ER overestimation, and 2% GPP overestimation comparing uncorrected with corrected field data. We suggest best practices for more comparable, precise, representative, and accurate methods.

A holistic approach for suppression of COVID-19 spread in workplaces and universities.

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the "Community-Workplace" model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

UAV Thrust Model Identification Using Spectrogram Analysis

This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal.

Influence of geometric parameters of screw nozzles of a twin screw extrusion press on the oil output

The relevance of the study is due to the search for rational geometric parameters of screw nozzles and the identification of the influence of the compression ratio of twin screw extrusion presses on the oil compression process. An important criterion in this paper is the indicator of the value of oil yield to obtain economic profit. From the analysis of optimisation methods in similar studies, the following variable geometric parameters of screw nozzles were selected for twin screw extruders: axial pitch, channel width between turns, rig width of the turn, and nozzle length. Two sets of experimental working bodies with modified geometric parameters were manufactured based on theoretical calculations and computer modelling to improve the working bodies of the EK 75/1200 twin screw extrusion press. Their theoretical compression ratio is determined, which is 5.50 and 4.33, respectively. It is also defined for the basic set of working bodies, which was recommended by the manufacturer, which was 4.69. It is established that the general nature of changes in the free volume along the length of the screw shaft is accompanied by an uneven decrease from 40 to 80% toward the press cake output. From the reviewed scientific literature, it was identified that the nature of changes in the free volume of turns along the length of the screw shaft characterises the correctness of its design. However, after analysing the results of the work performed, it was determined that the selection of rational geometric parameters of the working bodies should be considered in conjunction with other structural parameters, which will further intensify the oil compression process. It is experimentally confirmed that the oil yield depends on the degree of compression due to the geometric parameters of the screw nozzles. The oil yield increased by 0.9% when using a set of working bodies of set 1 when compared with the basic set of working bodies from the manufacturer’s factory (set 2) in terms of volume of processed raw materials per unit will bring additional profit. It was determined that with the production volume of 50 tons of oil with an improved set of working bodies (set 1), the economic effect amounted to UAH 19,250

Random Optimization of the Green Closed Chain Supply Chain of Perishable Products

The nature of the market has changed in many products so that manufacturers are increasingly forced to reconsider their supply chain strategies. In particular, perishable products can mentioned that require better and more accurate planning in terms of inventory and distribution in the supply chain. This paper presents a mathematical model for designing a supply chain network for perishable products. The supply chain includes a set of factories, warehouses, customers and collection centers. The objectives of this mathematical model are to reduce the total cost of the supply chain and reduce environmental pollution. In order to evaluate the performance of the mathematical model, three scenarios have been considered. In the first scenario, the reduction of product prices and in the second scenario, the stability of product prices and in the third scenario, the increase of product prices is considered. Then these scenarios are probably included in the mathematical model. The analysis performed in the study of the effect of each scenario shows that the increase in product prices has a more severe impact on environmental pollution. There is also always a conflict between costs and the environmental impact of the supply chain.

Multi-objective distributed reentrant permutation flow shop scheduling with sequence-dependent setup time

The distributed reentrant permutation flow shop (DRPFS) is a combination of the reentrant flow shop problem and distributed scheduling. The DRPFS is a NP-hard problem that consists of two subproblems: (1) assigning a set of jobs to a set of available factories and (2) determining the operation sequence of jobs in each factory. This paper is the first study to consider the inclusion of sequence-dependent setup time in the DRPFS. The industrial applications of flow shop indicate that the machine setup time to process a job may depend on the previously processed jobs. Particularly, in DRPFS, the effect of sequence-dependent setup time is intensified due to its reentrant characteristic. An improved version of the multi-objective adaptive large neighborhood search (MOALNS) is proposed as a solution method for the sequence-dependent DRPFS with the aim to minimize the makespan, production cost, and tardiness. The proposed algorithm enhances the standard MOALNS by embedding an improved solution acceptance and non-dominated set updating criteria to assist the algorithm in finding the near-optimal Pareto front of the factory allocation and scheduling problems. To address the multiple objectives and the issue of non-uniform setup time, a new set of destroy and repair heuristics are developed. Further, the numerical experiments demonstrate the efficiency of IMOALNS in finding high-quality solutions in a relatively short time.

Influence of air staging strategies on flue gas sensible heat losses and gaseous emissions of a wood pellet boiler: An experimental study

An effective air staging strategy is of great importance for achieving low emissions and sensible heat losses through flue gas extraction. In cases where a commercially available system needs to be optimised, often the only viable measure is the modification of process parameters. In this work, the aim is to (1) Discover a combination of the most suitable process parameters based on a multi-criteria decision-making method and (2) To unveil relevant correlations between the two process parameters under study (PA/SA ratio and excess air), emissions and combustion temperatures. A modified commercial small-scale hot water wood pellet boiler was installed into a laboratory heating system. Nine different cases have been addressed within a parametric study, differing in the PA/SA ratio and overall excess air. Emissions, temperatures inside the combustion chamber and the flow rate of air entering the combustion chamber were measured. A low PA/SA ratio of 0.53 (54.7% reduction from factory settings), combined with a low O2 concentration in the flue gases of 5.26% (39.8% reduction from factory settings), and the elimination of infiltration air resulted in a simultaneous reduction of NOx and CO emissions by 14.4% and 93.9% respectively and a flue gas sensible heat loss reduction of 31.6%.

Conceptualising Transfer of Wicked Industry 4.0 Opportunities Through Learning Factories

Industry 4.0 (I4.0) implies significant wicked opportunities for manufacturing. However, manufacturing companies lack competencies to innovate their processes to transfer these wicked opportunities quickly and thoroughly. Process innovation can support the utilisation of a new or significantly improved method for production or distribution, with the intention to decrease unit costs of production or distribution, to increase quality, or to produce or distribute new or significantly improved products. However, transfer of I4.0 related methods often requires significant changes in the manufacture's technology, software, tasks, structures, and employees' competencies. To advance the development of employee's competencies to transfer wicked opportunities of I4.0, the concept of Learning Factories can help employers in transfer, learning, and training. A Learning Factory that facilitates transfer, learning and training can be organised and facilitated as either a personal or collective in situ experimental learning process that networks industry participants, faculty, and students. Learning Factories offers opportunities to improve the learning and transfer processes; however a stronger conceptual frame for the innovation of learning and transfer processes could aid the development of Learning Factories. Consequently, this study unites and synthesises existing research on transfer to advance theory building, to strengthen process innovation in Learning Factory settings. Based on the findings, the initial work of an operational model for knowledge transfer of I4.0 wicked opportunities in Learning Factories is discussed.

Multi-modal User Intent Classification Under the Scenario of Smart Factory (Student Abstract)

Question-answering systems are becoming increasingly popular in Natural Language Processing, especially when applied in smart factory settings. A common practice in designing those systems is through intent classification. However, in a multiple-stage task commonly seen in those settings, relying solely on intent classification may lead to erroneous answers, as questions rising from different work stages may share the same intent but have different contexts and therefore require different answers. To address this problem, we designed an interactive dialogue system that utilizes contextual information to assist intent classification in a multiple-stage task. Specifically, our system incorporates user’s utterances with real-time video feed to better situate users’ questions and analyze their intent.

Assessing soil microbial biodiversity as affected by grazing and woody vegetation cover in a temperate savannah

AbstractAlthough it is well established that land use and management affects soil microbial biodiversity, with consequences for soil‐mediated ecosystem services, knowledge on human‐induced variability in soil bacterial and fungal communities is more abundant for cropping than for livestock systems. In particular, grazing management affects vegetation cover and light availability to lower vegetation layers, particularly in wooded grazing ecosystems. Our objective was to evaluate the use of T‐RFLP analysis to characterise soil microbial community patterns. We worked in a grazed temperate savannah in Entre Ríos (Argentina) and focused on two farms with different grazing management, resulting in different vegetation cover. At each farm, we sampled soils from four conditions forming a two shrub cover conditions (open and close) by two light availability conditions (high and low) factorial set with three replicates. T‐RFLP data for bacteria and fungi were used to characterise biodiversity patterns (diversity indexes, relative abundance of operational taxonomic units and community structure). Analyses were performed, between and within farms, using nonmetric multidimensional scaling analysis, non‐parametric multivariate analysis of variance and network analysis. Variation patterns of soil microbial communities were detected using T‐RFLP analysis in savannah soils from these two livestock farms. Farm, light and shrub conditions were statistically significant factors shaping variation patterns. For bacterial communities, light availability and shrub cover affected biodiversity and community structure respectively, but this impact was detected only for the farm with lower grazing intensity. For fungal communities, light availability was the factor shaping community structure, also with slightly higher impact on the farm with lower grazing intensity. These results suggest that in temperate woodlands lower grazing intensity, associated with higher vegetation cover, increases bacterial biodiversity while it decreases fungal community structure. Soil bacterial and fungal communities, showed contrasting patterns, and appear as sensitive indicators to monitor grazing management effects. We showed that T‐RFLP analysis is a cost‐effective methodology (compared to Illumina sequencing) that would be useful to disentangle impact of grazing intensity and vegetation cover in grazed temperate savannah.

ReLy: Machine Learning for Ultra-Reliable, Low-Latency Messaging in Industrial Robots

Robotic factory floors are transforming the manufacturing sector by delivering an unprecedented boost to productivity. However, such a paradigm raises questions on safety and coordination, especially when in the presence of unexpected events. Time-critical communication messages for such industrial robots mandate the requirement of ultra-reliable low-latency communication (URLLC). Classical WiFi-connected industrial robots often suffer from the traditional dense network problems prevalent in production WiFi networks, where transmission of an emergency notification packet is “best effort,” devoid of time guarantees. In this work, we propose a machine-learning-based framework called ReLy that intelligently embeds the time-critical messages in the preamble of outgoing frames at the transmitter. These messages are inferred from the channel state information variations at the receiver. As ReLy is implemented entirely at the physical layer, the transmitter is able to deliver information within 5 ms latency and ultra-high reliability of 99 percent. We experimentally demonstrate the feasibility of achieving URLLC with moving robots in a busy workshop with a number of other peer robots, equipment, desks, and robotic arms, as expected in a typical factory setting.

Characterization of a Kanomax® fast condensation particle counter in the sub-10 nm range

A butanol-based fast condensation particle counter (Fast CPC, Model 3650, Kanomax Inc., Japan) was characterized in this study with three different types of monodisperse particles, including positively and negatively charged nickel-chromium (Ni/Cr) particles, positively charged tetraheptyl ammonium bromide (THABr) and tetrabutyl ammonium bromide (TBABr) particles, and positively charged polyethylene glycol (PEG) particles. The PEG particles were characterized with varied numbers of charges, whereby the charge number effect on particle activation was investigated. The performance of the Fast CPC was compared with a TSI® 3775 CPC and a TSI® 3776 CPC, respectively, under both factory temperature settings and boosted temperature settings, the latter of which corresponded to the brink of butanol homogeneous nucleation. Our results show that size-resolved Ni/Cr particles were detected by the Fast CPC with comparable detection efficiencies regardless of the charge polarity. When the charge number on a PEG particle increased from 1 to 3, an increasing tendency of the detection efficiency was observed, hinting that multiple charges on a particle will facilitate its activation. Particle activation was affected by the particle chemical composition as well, which is suggested by the higher activation efficiencies of PEG particles than those of Ni/Cr particles. The effects of multiple charges and chemical composition on particle activation were both more significant under factory temperature settings, but became less pronounced under boosted temperature settings. In addition to a quicker response time, the Fast CPC was capable of detecting sub-3 nm particles under boosted temperature settings, with the detection efficiency higher than that of the 3775 CPC but lower than that of the 3776 CPC, and can be used as a good candidate for the measurement of rapidly varying concentrations of small particles.