Set Of Experiments Research Articles

Effect of End Groups on the Cloud Point Temperature of Aqueous Solutions of Thermoresponsive Polymers: An Inside View by Flory-Huggins Theory.

In an effort to gain insight into the origin of the effects of end groups on the cloud point temperature (Tcp) as a function of the polymer molar mass of thermoresponsive polymers with lower critical solution behavior in dilute aqueous solutions, we use the Flory-Huggins (FH) theory amended for end groups. The theory was applied to available experimental data sets of poly(N-isopropylacrylamide) (PNIPAM), poly(4-vinylbenzyl methoxytris(oxyethylene) ether) (PTEGSt), and poly(α-hydro-ω-(4-vinylbenzyl)tetrakis(oxyethylene) ether) (PHTrEGSt). The theory relates the variations in TcpM,ϕcp for different end groups to the effective FH χ parameter of the end groups and explains the qualitative notion that the influence of the end groups is related to the hydrophobicity/hydrophilicity of the end groups relative to that of the so called intrinsic TcpM,ϕcp response of a polymer without end groups. The limits to the applicability of the FH theory are established, and a set of possible theoretical improvements is considered. The ultimate scrutiny of the simple FH theory and suggested improved theories must await the measurement of truly thermodynamic cloud points; the available cloud points are merely estimations of the thermodynamic cloud point, for which the deviation to the true cloud point cannot be established with sufficient accuracy.

Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.

Limits of ethical non-human subjects research in an applied setting.

Ethical research follows numerous rules and regulations to ensure that all human subjects are protected during the collection and dissemination of research outcomes. Nevertheless, there is often a critical distinction drawn between human subjects research and non-human subjects research (NHSR). The latter can also be described as non-research activities, which typically reduces any oversight even if human subjects are involved. Despite the need to conduct ethical NHSR or non-research activities in an applied setting, there are several ways this determination can be used to circumvent regulatory oversight. In particular, the problem arises because one or more of several key functions become conflated in an applied setting, whereas they would be compartmentalized and independent in controlled or experimental settings. These functions include: 1) ethical oversight; 2) funding; 3) execution; and 4) peer review. The current discussion outlines how NHSR in an applied setting can allow these functions to overlap, and how personnel might stretch the boundaries of ethical conduct even while following existing regulations. As such, the goal is to guide future practices when conducting or reviewing NHSR in an applied setting so that unethical practices do not bias the results.

A novel predictive–reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time

In practical printed circuit boards (PCB) drilling production systems, multiple spindle processing capabilities and uncertainties are commonplace. This paper addresses predictive–reactive scheduling, considering parallel batch processor lot-sizing and scheduling with sequence-dependent setup times in a dynamic environment. We propose a predictive scheduling algorithm named S-AGAIG to balance machine spindle utilization and order tardiness in steps. Multiple splitting solutions are first formed by using a split heuristic algorithm to size the sub-lots of orders. Then the adaptive genetic algorithm with iterated greedy search is applied to select the solutions and scheduling. Furthermore, we present a schedule repair strategy based on sub-lot co-processing considering the impact of critical sub-lots, and construct a scheduling stability metric for rescheduling. In 36 sets of case experiments encompassing diverse load and machine type configurations, S-AGAIG showcases a 21.27% enhancement in average tardiness performance when compared to its closest rival. Across 28 cases involving varying disturbances, the framework demonstrates exceptional robustness.

A homogenized two-phase computational framework for meso- and macroscale blood flow simulations

Background and ObjectiveOwing to the complexity of physics linked with blood flow and its associated phenomena, appropriate modeling of the multi-constituent rheology of blood is of primary importance. To this effect, various kinds of computational fluid dynamic models have been developed, each with merits and limitations. However, when additional physics like thrombosis and embolization is included within the framework of these models, computationally efficient scalable translation becomes very difficult. Therefore, this paper presents a homogenized two-phase blood flow framework with similar characteristics to a single fluid model but retains the flow resolution of a classical two-fluid model. The presented framework is validated against four different sets of experiments. MethodsThe two-phase model of blood presented here is based on the classical diffusion-flux framework. Diffusion flux models are known to be less computationally expensive than two-fluid multiphase models since the numerical implementation resembles single-phase flow models. Diffusion flux models typically use empirical slip velocity correlations to resolve the motion between phases. However, such correlations do not exist for blood. Therefore, a modified slip velocity equation is proposed, derived rigorously from the two-fluid governing equations. An additional drag law for red blood cells (RBCs) as a function of volume fraction is evaluated using a previously published cell-resolved solver. A new hematocrit-dependent expression for lift force on RBCs is proposed. The final governing equations are discretized and solved using the open-source software OpenFOAM. ResultsThe framework is validated against four sets of experiments: (i) flow through a rectangular microchannel to validate RBC velocity profiles against experimental measurements and compare computed hematocrit distributions against previously reported simulation results (ii) flow through a sudden expansion microchannel for comparing experimentally obtained contours of hematocrit distributions and normalized cell-free region length obtained at different flowrates and inlet hematocrits, (iii) flow through two hyperbolic channels to evaluate model predictions of cell-free layer thickness, and (iv) flow through a microchannel that mimics crevices of a left ventricular assist device to predict hematocrit distributions observed experimentally. The simulation results exhibit good agreement with the results of all four experiments. ConclusionThe computational framework presented in this paper has the advantage of resolving the multiscale physics of blood flow while still leveraging numerical techniques used for solving single-phase flows. Therefore, it becomes an excellent candidate for addressing more complicated problems related to blood flow, such as modeling mechanical entrapment of RBCs within blood clots, predicting thrombus composition, and visualizing clot embolization.

Spectral separation of evoked and spontaneous cortical activity, Part 1: Delta to high gamma band

Spectral analysis of repeatedly evoked potentials (EPs) is challenging since recordings contain a superposition of evoked signals and spontaneous activity. We developed a novel approach, N-interval Fourier Transform Analysis (N-FTA), which allows for reliable separation and simultaneous assessment of triggered and background spectral components. Median nerve stimulation data from a total of eleven volunteers recorded in two labs with different experimental settings were investigated. Consistently, short latency spectral components were mainly contained in the gamma and high gamma bands. In contrast, spontaneous activity displayed a 1/f spectral profile with distinct alpha and beta peaks. Spontaneous power spectral densities (PSDs) obtained for real and sham stimulation were highly comparable. The low frequency background PSD was more than two orders of magnitude above the spectral short latency peaks. Within the 30Hz to 90Hz band, the evoked peaks were -17dB to -4dB below the background suggesting that target band filtered short latency deflection might be extracted using less than 100 trials.SEPs following tibial nerve stimulation (3 subjects) displayed a narrower spectral band at about half the bandwidth as compared to median nerve stimulation. Evoked peaks were between 30Hz and 37Hz at PSD levels being -10dB to -4dB below the background activity. These spectral peaks were related to the short latency response of typical W-morphology.Cortical short latency responses are contained in distinct spectral target bands which are much narrower than the standard bandwidth recommendations for routine recordings. In particular, the high pass corner frequency may be selected about one order of magnitude above the current standard. This might render SEP recordings more robust since it eases the suppression of spontaneous activity and movement artifacts such as eye-blinks. Real-time zero-phase filters are required for translating these findings into improved recording systems.

Reconciling categorization and memory via environmental statistics.

How people represent categories and how those representations change over time is a basic question about human cognition. Previous research has demonstrated that people categorize objects by comparing them to category prototypes in early stages of learning but consider the individual exemplars within each category in later stages. However, these results do not seem consistent with findings in the memory literature showing that it becomes increasingly easier to access representations of general knowledge than representations of specific items over time. Why would one rely more on exemplar-based representations in later stages of categorization when it is more difficult to access these exemplars in memory? To reconcile these incongruities, our study proposed that previous findings on categorization are a result of human participants adapting to a specific experimental environment, in which the probability of encountering an object stays uniform over time. In a more realistic environment, however, one would be less likely to encounter the same object if a long time has passed. Confirming our hypothesis, we demonstrated that under environmental statistics identical to typical categorization experiments the advantage of exemplar-based categorization over prototype-based categorization increases over time, replicating previous research in categorization. In contrast, under realistic environmental statistics simulated by our experiments the advantage of exemplar-based categorization over prototype-based categorization decreases over time. A second set of experiments replicated our results, while additionally demonstrating that human categorization is sensitive to the category structure presented to the participants. These results provide converging evidence that human categorization adapts appropriately to environmental statistics.

Effects of Laser Defocusing on Bead Geometry in Coaxial Titanium Wire-Based Laser Metal Deposition.

Coaxial wire-based laser metal deposition is a versatile and efficient additive process that can achieve a high deposition rate in the manufacturing of complex structures. In this paper, a three-beam coaxial wire system is studied, with particular attention to the effects of deposition height and laser defocusing on the resulting bead geometry. As the deposition standoff distance changes, so does the workpiece illumination proportion, which describes the ratio of energy going directly into the feedstock wire and into the substrate. Single titanium beads are deposited at varying defocus levels and deposition rates and the bead aspect ratio is measured and analyzed. Over the experimental settings, the defocusing level and deposition rate were found to have a significant effect on the resulting bead aspect ratio. As the defocusing level is increased away from the beam convergence plane, the spot size increases and the deposited track is wider and flatter. Process parameters can be used to tune the deposited material to a desired aspect ratio. In coaxial wire deposition, defocusing provides an adjustment mechanism to the distribution of heat between the wire and substrate and has an important impact on the resulting deposit.

Toxicological Assessments of Agrochemicals in Stingless Bees in Brazil: a Systematic Review.

The growing concern with the decline of pollinators worldwide is centered on honey bees, due to their wide distribution, economic, and ecological importance. This type of concern remained less evident for stingless bees, which are widely distributed in the Neotropics, until recently. Since exposure to agrochemicals has been identified as one of the potential threats to bees, the present systematic review compiled information from toxicological evaluations in stingless bees in Brazil, home to a considerable portion of the existing species. This systematic review was performed considering species, research institutions, scientific journals, metrics, experimental set ups, and agrochemicals. The first article in this topic was published in 2010. Since then, 93 scientific papers were published, which showed that there are few species of stingless bees used for toxicological evaluations and Brazilian institutions lead these evaluations. Only 1.5% of the stingless bees' species that occur in Brazil were assessed through chronic exposure in the larval stage. The Universidade Federal de Viçosa (UFV) is responsible for 37% of the total publications. The main route of exposure was acute, using adults in laboratory conditions. The main group of agrochemicals studied were insecticides, in particular the neonicotinoids. The current results reveal the advances achieved and point out the gaps that still need to be filled considering toxicological evaluations in stingless bees.

Potassium Solubilizers, Aspergillus sp. and Trichoderma sp. Promote Growth in Tomato


 
 
 Potassium (K) is the third most significant plant macronutrient that plays a crucial role in growth and development of plants. K is abundant in soil, but 90-98% of K exists as insoluble minerals and only 1-2% becomes available to the plants. Certain microbes, K solubilizing microbes (KSMs), can solubilize unavailable forms of K and making it available to plants. The objective of the current study was to evaluate the growth promotion ability of two previously (2019) isolated KSMs, Aspergillus sp. and Trichoderma sp in-vitro and under greenhouse conditions using feldspar as the K source. Khandeparkar‘s selection ratio calculated based on K solubilizing assay on Alexandrov medium showed that Aspergillus (1.72+0.16) has a higher and faster K solubilization ability than Trichoderma (1.0+0.16). Effect of KSMs was assayed in a Tomato seed germination assay carried out in Petri plates with KSMs treated and non-treated soils. Both Aspergillus (58%) and Trichoderma (72%) showed enhanced percentage germination of tomato seeds under in vitro conditions compared to that of controls (Aspergillus sp.: 24%, 14%, 22.7%, 51.3%, 39.3%, Trichoderma sp.: 22%, 12%, 13%, 59%, 51%) which was statistically significant (Aspergillus, P=0.026; Trichoderma, P=0.003) at 0.05 confidence level. Seedling length (P=0.102, P=0.028) and root length (P=0.453, P=0.002) of tomato were enhanced when raised in non-autoclaved natural soil than autoclaved natural soil inoculated with Aspergillus and Trichoderma respectively. The greenhouse experiment was carried out with same experimental set up as an in vitro assay. No significant difference was observed in shoot length, number of leaves and length of leaves of tomato plants planted in soil treated with Aspergillus (P=0.093, P=0.906, P=0.260) and Trichoderma (P=0.334, P=0.239, P=0.243) respectively in comparison to controls during the vegetative stage (2 months after sawing). However, flowering was started four days early in plants planted in non-autoclaved soil treated with Aspergillus sp. suggesting that the impact of Aspergillus sp. starts to become visible in the reproductive stage. The results suggest that K solubilizing ability of Aspergillus sp. and Trichoderma sp. seem to show a positive effect on germination ability of tomato, however, only Aspergillus sp. seems to have a positive effect on the later stages of plant growth where K is mainly needed for flower and fruit development. Therefore, Aspergillus sp. can be a potential candidate to be used in up scaling the natural K fertilizers such as feldspar after intensive future experimentation.
 Keywords: Plant Growth Promoting Microbes (PGPMs), Growth promotion, K deficiency
 
 

Q-space imaging based on Gaussian radial basis function with Laplace regularization.

To introduce the diffusion signal characteristics presented by spherical harmonics (SH) basis into the q-space imaging method based on Gaussian radial basis function (GRBF) to robustly reconstruct ensemble average diffusion propagator (EAP) in diffusion MRI (dMRI). We introduced the Laplacian regularization of the signal into the dMRI imaging method based on GRBF, and derived the relevant indicators of microstructure imaging and the orientation distribution function (ODF) providing fiber bundle direction information based on EAP. In addition, this method is combined with a multi-compartment model to calculate the diameter of fiber bundle axons. The evaluation of the results included qualitative comparisons and quantitative assessments of the signal fitting. The results show that the proposed method achieves the more significant accuracy improvement in reconstructing signal. Meanwhile, ODFs estimated by the proposed method show the sharper profiles and less spurious peaks, even under the sparse and noisy conditions. In the 36 sets of axon diameter estimation experiments, 34 and 30 sets of results showed that the proposed method reduced the mean and SD of axon diameter estimates, respectively. Moreover, compared with the current state-of-the-art method, the mean and SD of axon diameter estimated by the proposed method are mostly lower, with 32 and 29 of 36 groups. The proposed method outperforms the GRBF regarding signal fitting and the estimation of the EAP and ODF with multi-shell sparse samples. Moreover, it shows the potential to recover important features of microstructures with less uncertainty by using proposed method together with multi-compartment models.

Modeling investigation of wave-induced longshore current distribution patterns on barred beaches

A three-dimensional coupled wave-current model was employed to investigate the wave-induced longshore currents on barred beaches. The modeling system was developed by coupling the finite-volume community ocean model (FVCOM) and the simulating waves nearshore (SWAN) model to fully describe wave-current interactions in the nearshore region. The three-dimensional radiation stress, surface rollers and wave-enhanced turbulent mixing were incorporated as the major wave effects on currents. The model was first evaluated against data collected from multiple sets of laboratory experiments. Diagnostic simulations demonstrated that the contributions of the wave rollers and wave-induced mixing, in addition to the radiation stress, were important for ensuring accurate calculations. Then, the longshore current patterns on barred beaches were investigated through simulations of a series of test cases under controlled conditions. The cross-shore distribution of wave-induced longshore currents, which predominantly occurred within the barred beach surf zone, usually exhibited bimodal characteristics. The maximum peak longshore current velocity was positively correlated with the incident wave height and period. As the bar height and width increased, the first velocity peak of the longshore current strengthened, while the second peak weakened. In most cases, the maximum longshore current velocity on a barred beach coincided with the first peak, which could occur anywhere from the bar-trough region to the bar-crest region, depending on the wave and bar heights. This finding could be used to clarify the cross-shore location discrepancies in the maximum peak longshore current velocity reported in previously published studies.

Neurophysiological correlates of interpersonal discrepancy and social adjustment in an interactive decision-making task in dyads.

The pursuit of convergence and the social behavioral adjustment of conformity are fundamental cooperative behaviors that help people adjust their mental frameworks to reach a common goal. However, while social psychology has extensively studied conformity by its influence context, there is still plenty to investigate about the neural cognitive mechanisms involved in this behavior. We proposed a paradigm with two phases, a pre-activation phase to enhance cooperative tendencies and, later, a social decision-making phase in which dyads had to make a perceptual estimation in three consecutive trials and could converge in their decisions without an explicit request or reward to do so. In Study 1, 80 participants were divided in two conditions. In one condition participants did the pre-activation phase alone, while in the other condition the two participants did it with their partners and could interact freely. In Study 2, we registered the electroencephalographical (EEG) activity of 36 participants in the social decision-making phase. Study 1 showed behavioral evidence of higher spontaneous convergence in participants who interacted in the pre-activation phase. Event related Potentials (ERP) recorded in Study 2 revealed signal differences in response divergence in different time intervals. Time-frequency analysis showed theta, alpha, and beta evidence related to cognitive control, attention, and reward processing associated with social convergence. Current results support the spontaneous convergence of behavior in dyads, with increased behavioral adjustment in those participants who have previously cooperated. In addition, neurophysiological components were associated with discrepancy levels between participants, and supported the validity of the experimental paradigm to study spontaneous social behavioral adaptation in experimental settings.

A patch distribution-based active learning method for multiple instance Alzheimer's disease diagnosis

Medical data, particularly the complex brain imaging structures, acquisition presents significant difficulties and high diagnostic expenses, resulting in a scarcity of the trainable samples in the real-world scenarios. To overcome this limitation, we present an active learning-based sampling strategy that selects the most informative samples from the unlabeled candidate sample pool for expert annotation, leading to high classification performance with a reduced number of training samples. This study adopts a patch-level perspective and introduces a multi-instance learning framework for Alzheimer's Disease diagnosis. Initially, a patch pre-selection module is designed to identify pathology-prone regions while excluding background areas and irrelevant information. Subsequently, an inner-patch local attention mechanism block and an outer-patch global attention mechanism block are developed to enhance the extraction of discriminative local and global information by the network model. Finally, an active learning sampling strategy is devised to minimize the costs associated with data acquisition and expert annotation in medical domain. The effectiveness of the proposed network framework and active learning strategy was validated through four sets of control experiments on the ADNI dataset.

NEPTUNE : A Comprehensive Framework for Managing Serverless Functions at the Edge

Applications that are constrained by low-latency requirements can hardly be executed on cloud infrastructures, given the high network delay required to reach remote servers. Multi-access Edge Computing (MEC) is the reference architecture for executing applications on nodes that are located close to users (i.e., at the edge of the network). This way, the network overhead is reduced but new challenges emerge. The resources available on edge nodes are limited, workloads fluctuate since users can rapidly change location, and complex tasks are becoming widespread (e.g., machine learning inference). To address these issues, this article presents NEPTUNE , a serverless-based framework that automates the management of large-scale MEC infrastructures. In particular, NEPTUNE provides (i) the placement of serverless functions on MEC nodes according to users’ location, (ii) the resolution of resource contention scenarios by avoiding that single nodes be saturated, and (iii) the dynamic allocation of CPUs and GPUs to meet foreseen execution times. To assess NEPTUNE , we built a prototype based on K3S, an edge-dedicated version of Kubernetes, and executed a comprehensive set of experiments. Results show that NEPTUNE obtains a significant reduction in terms of response time, network overhead, and resource consumption compared with five state-of-the-art solutions.

Engineered T cells secreting anti-BCMA T cell engagers control multiple myeloma and promote immune memory in vivo.

Multiple myeloma is the second most common hematological malignancy in adults and remains an incurable disease. B cell maturation antigen (BCMA)-directed immunotherapy, including T cells bearing chimeric antigen receptors (CARs) and systemically injected bispecific T cell engagers (TCEs), has shown remarkable clinical activity, and several products have received market approval. However, despite promising results, most patients eventually become refractory and relapse, highlighting the need for alternative strategies. Engineered T cells secreting TCE antibodies (STAb) represent a promising strategy that combines the advantages of adoptive cell therapies and bispecific antibodies. Here, we undertook a comprehensive preclinical study comparing the therapeutic potential of T cells either expressing second-generation anti-BCMA CARs (CAR-T) or secreting BCMAxCD3 TCEs (STAb-T) in a T cell-limiting experimental setting mimicking the conditions found in patients with relapsed/refractory multiple myeloma. STAb-T cells recruited T cell activity at extremely low effector-to-target ratios and were resistant to inhibition mediated by soluble BCMA released from the cell surface, resulting in enhanced cytotoxic responses and prevention of immune escape of multiple myeloma cells in vitro. These advantages led to robust expansion and persistence of STAb-T cells in vivo, generating long-lived memory BCMA-specific responses that could control multiple myeloma progression in xenograft models, outperforming traditional CAR-T cells. These promising preclinical results encourage clinical testing of the BCMA-STAb-T cell approach in relapsed/refractory multiple myeloma.

Grey-Taguchi analysis and experimental assessment of 1 GPa HSLA steel treated by quenching and tempering

The effect of quenching and tempering (QT) process on the mechanical properties of the experimental high-strength low-alloy (HSLA) steel was analyzed by Grey-Taguchi method, thus achieving the optimum combination of parameters based on the appropriate nine sets of experiments on an orthogonal array. The grey relational analysis (GRA) reveals that the quenching temperature T1 has the greatest effect on each response variable, followed by the tempering temperature T2, while the quenching time t1 and tempering time t2 have similar and the least effect. Experimental assessment of microstructure evolution was performed by multi-scale characterizations and in-situ investigation combined with modeling, focusing on martensite transfomation kinetics that controlled by the quenching process. Results indicate that the prior austenite grain (PAG) size and the substructure of martensite are significantly refined, as the quenching temperature decreased from 950 to 850 °C. The PAG refinement leads to an increase in driving force for martensite transformation initiation, thus shifting the martensite transformation temperature to lower levels for a higher degree of undercooling, consistent with the experimental and modeling results. The refined microstructure obtained at low quenching temperature contributes to strength improvement, and the various carbides precipitated during tempering process offset the tempering softening of the steels. In general, the value increase of process parameters (T1, T2, t1 and t2) leads to a decrease of strength property, but an increase of ductility and toughness. Based on the theoretical and experimental basis, a 1 GPa grade HSLA steel with improved comprehensive mechanical properties can be produced.

Got the Munchies for an Egg Sandwich? The Effects of Cannabis on Bowel Motility and Beyond.

The use of medicinal cannabis has a long history dating back thousands of years. Recent discoveries have shed light on its mechanism of action with the identification of cannabinoid receptors and endocannabinoids, which make up the body's endocannabinoid system. Cannabinoid receptors, particularly the cannabinoid 1 and 2 receptors, play a crucial role in modulating the gut-brain axis and serve as potential therapeutic targets for gastrointestinal motility and inflammatory disorders. With increasing legalization of cannabis and a rising number of users, understanding the effects of cannabis on gut motility is essential for nuclear medicine providers. Although tetrahydrocannabinol, the principal psychoactive constituent of cannabis, may decrease gut motility in experimental settings, it appears to paradoxically improve symptoms in gastroparesis. Treatment effects are difficult to measure given the large number of variables that could significantly alter outcomes, such as cannabinoid type, potency, and route of intake. Another consideration is the highly personalized gut microbiome, which directly interacts with the endocannabinoid system. Further research is required to delineate these multifaceted, complex cannabinoid interactions. The goal of this article is to explore the knowns and unknowns of the impact of cannabis on the alimentary system.

Performance evaluation of an occupant metabolic rate estimation algorithm using activity classification and object detection models

To create a comfortable indoor environment, the metabolic rate (MET), which affects the thermal sensation of occupants, needs to be reflected in real-time. Recently, methods employing computer vision techniques classify activities based on the pose of the body in images. However, these methods face challenges in determining the MET depending on the objects used, even with the same pose. Therefore, the objective of this study is to develop a MET estimation algorithm that can estimate various METs by integrating a pose-based activity classification model and an object detection model. To achieve this, an object detection model capable of detecting and classifying six regularly used objects indoors was developed, and a performance evaluation was conducted. The MET estimation algorithm was assessed through the implementation of a thermal control system, validating its applicability in experimental settings. As a result, the object detection model exhibited a real-time classification accuracy of 89%. Additionally, when evaluating the mode value over 15-s intervals, it demonstrated a classification accuracy of 100%. The algorithm exhibited a real-time estimation accuracy of 83% for the six METs and examining the mode value for 15-s intervals, it demonstrated a classification accuracy of 99%. This study thus confirmed the control capability of the proposed MET estimation algorithm and its potential for the estimation of various METs. The developed method can be used for the real-time estimation of occupant thermal comfort in indoor comfort-based control systems, contributing to the realization of a comfortable environment for occupants that protects their well-being.

Study of helminth eggs (Ascaris suum) inactivation by anaerobic digestion and electrochemical treatment

Background The use of insufficiently treated wastewater or Faecal sludge in agriculture raises concerns because of the pathogen content. Helminth eggs (HE) are one of the most crucial pathogens for ensuring public health and safety. Widely used disinfection treatment methods do not guarantee the complete inactivation of helminth eggs. The current study evaluated the effectiveness of anaerobic digestion and electrochemical process on helminth (Ascaris suum) egg inactivation. Methods Lab-scale biochemical methane potential (BMP) assay was conducted by spiking A. suum eggs in a serum bottle. Total solid (TS), volatile solid (VS), pH, biogas production and its composition, and volatile fatty acids (VFA) were analyzed along with A. suum inactivation every third day for the initial 15 days and fifth day for 45 days. In the second set of experiments, a hypochlorite (4700 ppm) solution was generated by electrolysis of aqueous NaCl solution in a membrane-less electrochemical cell. The hypochlorite was diluted (940, 470, 235, and 156ppm) in wastewater, spiked with A. suum eggs and then examined for inactivation at regular intervals. Results The results of the anaerobic digestion treatment documented 98% inactivation of A. suum eggs (0.15 eggs/mL) in 35 days and remained at 0.14 eggs/mL until day 45. Correlation analysis revealed a positive relationship between non-viable eggs and pH and a negative relationship with all the other parameters. Electrochemical treatment achieved 10% inactivation at 940 ppm concentration in 24h. Conclusions This study revealed that the inactivation of A. suum eggs by anaerobic digestion or electrochemical treatment is a combined effect of more than one parameter.