Reusable Features Research Articles

Unsupported Nanoporous Copper Catalyzed Insertion of Carbenes from α‐Diazo Compounds into Hydrosilanes

The insertion of diazo carbene into Si−H bonds with unsupported nanoporous copper (CuNPore) catalyst has been developed. α‐Aryl diazoesters, diazophosphonates, and unstable aryl diazoalkyls can be used as carbene precursors to exquisitely insert into the Si−H bond of silanes. A variety of α‐silylesters, silylphosphonate, and functionalized arylsilane were obtained in 35−82% yields. The reusability feature of the CuNPore catalyst for this insertion reaction demonstrated its high heterogeneous catalytic activity and practicality.Further, mechanistic studies revealed that the insertion of carbene from diazo compounds into the Si−H bond may include a three‐center concerted transition state on CuNPore.

Thermal Conversion of Coral Waste and its Utilization as Low-Cost Catalyst for Biodiesel Production

This study investigates the thermal conversion of waste coral and its utilization as a heterogeneous catalyst for biodiesel production from soybean oil. In this work, waste coral is calcined at varied temperatures of 800, 900, and 1000°C, and the effect of the calcination temperature on the physicochemical character of the solid is evaluated through Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, and basicity measurement. The results show that the higher temperature facilitates the conversion of CaCO3 of the aragonite and calcite phases in raw waste coral into CaO, achieving a complete conversion at the temperature of 1000°C. Thermal conversion influences the increased surface basicity of the solid, which is associated with the higher activity for biodiesel production. Further studies on the obtained CaO as a catalyst demonstrate the catalyst dosage and the methanol-to-oil ratio as significant factors for fatty acid methyl ester production. The highest yield of 98.7% is achieved after a 3 hours reaction with 8 wt.% catalyst dosage and 9:1 methanol-to-oil ratio. The catalyst exhibits stability with an insignificantly decreased yield until the fifth usage cycle. The optimum conditions and reusability features of the calcined waste coral suggest that waste coral is a favorable CaO catalyst source for biodiesel production.

A reinforcement learning approach to solving very-short term train rescheduling problem for a single-track rail corridor

Railway operations are regularly affected by incidents such as disturbances and disruptions, which cause temporary operational restrictions to the trains in the network. Compared to real-time disturbances and disruptions, sometimes these incidents can be known at a short notice, e.g., 24–48 h beforehand, which is known as the Very-Short-Term-Planning in British rail operations. This paper presents a novel reinforcement learning based approach for rescheduling train services for in a single-track corridor with bi-directional traffic. As an important subfield of machine learning, reinforcement learning offers an alternate strategy for tackling the NP-hard train (re)scheduling problems and shows its advantages in balancing computational efficiency and solution quality. We propose a Q-learning approach with a tiered rewarding strategy and lightweight train representation in state vectors, which enables more efficient learning and knowledge sharing among homogeneous trains. Compared with an existing reinforcement learning approach, our proposed method can find better quality solutions due to its unique representation of state vectors and a novel tiered rewarding/punishing mechanism, overcoming certain disadvantages in existing approaches. Knowledge reusability is another advantage of the proposed approach, as prior knowledge obtained from training one instance can significantly enhance the performance of another, potentially more challenging, instance on the same corridor with substantially reduced computational time and effort on algorithm development. We also discuss the potential applications of the knowledge reusability feature inherent in reinforcement learning algorithms, which we believe will benefit the entire industry in addressing NP-hard problems through data-driven technologies.

Fabrication of Cu3Mo2O9 doped MWCNT nanocomposites as efficient photocatalyst for malachite green dye degradation

In the present work, Cu3Mo2O9@MWCNT was synthesised by a simple co-precipitation method followed by ultrasonication. The synthesised nanocomposite was analysed by spectral and analytical characterization techniques, such as FT-IR, XRD, PL, FESEM, XPS, HRTEM, BET and LCMS. The Cu3Mo2O9@MWCNT nanocomposites achieved photocatalytic degradation efficiency of 96 % for malachite green dye under visible light in 180 min. Furthermore, scavenger tests were carried out to identify the role of reactive species aided in the degradation process. Cu3Mo2O9@MWCNT shows stable photocatalytic performance until the fourth cycle. The constructed nanocomposites exhibited excellent stability and reusability features that were suitable for its practical application.

Metal assisted chemical etching derived reusable and scalable production of large-area Ag nanowire-based flexible transparent electrodes for electrochromic Zn-ion battery

To advance polydimethylsiloxane (PDMS)-supported silver nanowires (Ag NWs) based flexible and transparent electrodes (AgNWs/PDMS), innovative patterning techniques are essential for enabling large-area fabrication with cost-effective reusability features. Here, we introduce a metal assisted chemical etching (MACE) protocol for patterning Si wafers, allowing the repetitive fabrication of large-sized AgNWs/PDMS electrodes with controlled penetration depth for the first time. To the best of our knowledge, MACE technology has not previously been employed for fabricating AgNWs/PDMS electrodes. Through the careful selection of etchant, etching time, and suitably doped Si wafers (n-type and p-type), the resulting AgNWs/PDMS electrodes offer favorable optical, electrical and flexiblity characteristics. The electrodes deliver a sheet resistance of 18 Ω‧sq−1 at 88 % transmittance (550 nm) while retaining 93.18 % transmittance with only a 7 Ω‧sq−1 resistance increase after 10,000 bending cycles (3 mm). The penetration depth control offered by this method ensures impressive mechanical durability without additional post-processing. Moreover, the etched Si wafers can be reused multiple times, reducing overall costs. The sizes of AgNWs/PDMS electrodes produced using this method depend entirely on the Si wafer size, allowing scalability by employing larger wafers. As a proof-of-concept, we also demonstrate the fabrication of a robust, flexible, electrochromic zinc ion battery utilizing the AgNWs/PDMS electrodes developed in this study.

Fabrication of zeolite-supported cobalt ferrite (CoFe2O4/ H-zsm-5) catalyst for the synthesis of 4-(9-(4-substituted)-1,2,3,4,5,6,7,8-octahydro-3,3,6,6-tetramethyl-1,8-dioxoacridin-10(9H)-yl)benzoic acid derivatives in semi aqueous condition

A cobalt ferrite (CoFe2O4/H-ZSM-5) catalyst supported by zeolite has been studied as a multicomponent reaction for making dioxoacridin-10(9H)-ylbenzoic acid derivatives in semi-aqueous conditions. This reaction offers a cost-effective and simple operational strategy and provides different substrates. Scope and various functional groups are well tolerated under optimized reaction conditions. Firstly, the catalyst was characterized using FT-IR, XRD, BET, XPS, TGA, and SEM techniques. The study determined the optimal operating conditions for achieving an efficient synthesis of target organic compounds. The optimized conditions for the synthesis of model compounds (4a) with a yield of 96 % were achieved through a reaction time of 40 min, a catalyst amount of 10 mol%, a temperature of 90 °C, and a solvent of 6/4 of the ethanol and water mixture. All of the characterization measurements confirmed that the catalyst's stability remained unaltered even after four consecutive cycles of the synthesis of model compounds. Furthermore, the reusability feature cuts costs and lessens environmental effects, making it an appealing option for practical applications. The findings emphasize this approach's enormous potential for sustainable chemical synthesis, opening the path for future advances in the sector. In addition, an experiment was set up to look into the thermodynamic and kinetic properties of compound (4a) when it was heated to between 343.15 and 373.15 K with the help of a catalyst. The current investigation has shown that the reaction favors thermodynamics rather than kinetic control.

Exploring Temporal Feature Correlation for Efficient and Stable Video Semantic Segmentation

This paper tackles the problem of efficient and stable video semantic segmentation. While stability has been under-explored, prevalent work in efficient video semantic segmentation uses the keyframe paradigm. They efficiently process videos by only recomputing the low-level features and reusing high-level features computed at selected keyframes. In addition, the reused features stabilize the predictions across frames, thereby improving video consistency. However, dynamic scenes in the video can easily lead to misalignments between reused and recomputed features, which hampers performance. Moreover, relying on feature reuse to improve prediction consistency is brittle; an erroneous alignment of the features can easily lead to unstable predictions. Therefore, the keyframe paradigm exhibits a dilemma between stability and performance. We address this efficiency and stability challenge using a novel yet simple Temporal Feature Correlation (TFC) module. It uses the cosine similarity between two frames’ low-level features to inform the semantic label’s consistency across frames. Specifically, we selectively reuse label-consistent features across frames through linear interpolation and update others through sparse multi-scale deformable attention. As a result, we no longer directly reuse features to improve stability and thus effectively solve feature misalignment. This work provides a significant step towards efficient and stable video semantic segmentation. On the VSPW dataset, our method significantly improves the prediction consistency of image-based methods while being as fast and accurate.

Facile Transfer Hydrogenation of N-Heteroarenes and Nitroarenes Using Magnetically Recoverable Pd@SPIONs Catalyst.

Catalysts with active, selective, and reusable features are desirable for sustainable development. The present investigation involved the synthesis and characterization of bear-surfaced ultrasmall Pd particles (<1 nm) loaded onto the surface of magnetic nanoparticles (8-10 nm). The amount of Pd loading onto the surface of magnetite is recorded as 2.8 wt %. The characterization process covered the utilization of scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), transmission electron microscopy (TEM), inductively coupled plasma (ICP), and X-ray photoelectron spectroscopy (XPS) methods. The Pd@Fe3O4 catalyst has shown remarkable efficacy in the hydrogenation of quinoline, resulting in the production of >99% N-ring hydrogenated (py-THQ) product. Additionally, the catalyst facilitated the conversion of nitroarenes into their corresponding aniline derivatives, where hydrogen was achieved by H2O molecules with the aid of tetrahydroxydiboron (THDB) as an equilibrium supportive at 80 °C in 1 h. The high efficiency of a transfer hydrogenation catalyst is closely related to the metal-support synergistic effect. The broader scope of functional group tolerance is evaluated. The potential mechanism underlying the hydrogenation process has been elucidated through the utilization of isotopic labeling investigations. The application of the heterocyclic compound hydrogenation reaction is extended to formulate the medicinally important tubular polymerization inhibitor drug synthesis. The investigation of the recyclability of Pd@Fe3O4 has been conducted.

Multi-scale Convolutional Feature Fusion Network Based on Attention Mechanism for IoT Traffic Classification

AbstractThe Internet of Things (IoT) has been extensively utilized in domains such as smart homes, healthcare, and other industries. With the exponential growth of Internet of Things (IoT) devices, they have become prime targets for malicious cyber-attacks. Effective classification of IoT traffic is, therefore, imperative to enable robust intrusion detection systems. However, IoT traffic data contain intricate spatial relationships and topological information, which traditional methods for traffic identification lack the capability to fully extract features and capture crucial characteristics. We propose a multi-scale convolutional feature fusion network augmented with a Convolutional Block Attention Module (MCF-CBAM) for accurate IoT traffic classification. The network incorporates three critical innovations: (1) Parallel convolution extracts multi-scale spatial features from traffic data. The 1 × 1 convolution operation reduces the amount of parameters and calculations of the network, thereby improving work efficiency. (2) The attention module suppresses less informative features while highlighting the most discriminative ones, enabling focused learning on decisive features. (3) Cross-scale connections with channel jumps reuse features from prior layers to enhance generalization. We evaluate the method extensively on three widely adopted public datasets. Quantitative results demonstrate MCF-CBAM establishes new state-of-the-art performance benchmarks for IoT traffic classification, surpassing existing methods by a significant margin. Qualitative visualizations of the learned attention weights provide intuitive insights into how the network automatically discovers the most decisive spatial features for identification. With its strong empirical performance and interpretable attention mechanisms, this work presents a promising deep learning solution to augment real-world IoT intrusion detection systems against growing cybersecurity threats.

Preparation and application of the thermo-/pH−/ ion-sensitive semi-IPN hydrogel based on chitosan

Chitosan based hydrogels with multiple stimulus responses have broad application prospects in many fields. Considering the advantages of semi interpenetrating network (IPN) technology and the special temperature and ion responsiveness of polymers containing zwitterionic groups, a semi-IPN hydrogel was prepared through in situ free radical polymerization of N,N-dimethyl acrylamide and [2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) ammonium hydroxide with polyethylene glycol dimethacrylate as a crosslinker and carboxymethyl chitosan as filler. The gel mass fraction and swelling ratio were measured, and the preparation conditions were optimized. The result indicated that the hydrogel possessed a unique thermo-/pH−/ ion-sensitive behavior. The swelling ratio increased with the increase of temperature and ion concentration, and showed a decreasing trend with the increase in pH. In addition, the hydrogel was stable when the stimuli changed. Adsorption behavior of the hydrogel to Eosin Y (EY) was systematically investigated. The adsorption process can be described well by the pseudo-second-order kinetic model and Langmuir isotherm model, indicating that it was a chemical adsorption. The experiments indicated that the hydrogel exhibited good antifouling and reusability features. Therefore, the semi-IPN hydrogel with antifouling properties and thermo-/pH−/ion-sensitivity can be easily manufactured is expected to find applications in water treatment fields.

Semi-White-Box Strategy: Enhancing Data Efficiency and Interpretability of Convolutional Neural Networks in Image Processing

Data-hunger is a persistent challenge in machine learning, particularly in the field of image processing based on convolutional neural networks (CNNs). This study systematically investigates the factors contributing to data-hunger in machine-learning-based image-processing algorithms. The results revealed that the proliferation of model parameters, the lack of interpretability, and the complexity of model structure are significant factors influencing data-hunger. Based on these findings, this paper introduces a novel semi-white-box neural network model construction strategy. This approach effectively reduces the number of model parameters while enhancing the interpretability of model components. It accomplishes this by constraining uninterpretable processes within the model and leveraging prior knowledge of image processing for model. Rather than relying on a single all-in-one model, a semi-white-box model is composed of multiple smaller models, each responsible for extracting fundamental semantic features. The final output is derived from these features and prior knowledge. The proposed strategy holds the potential to substantially decrease data requirements under specific data source conditions while improving the interpretability of model components. Validation experiments are conducted on well-established datasets, including MNIST, Fashion MNIST, CIFAR, and generated data. The results demonstrate the superiority of the semi-white-box strategy over the traditional all-in-one approach in terms of accuracy when trained with equivalent data volumes. Impressively, on the tested datasets, a simplified semi-white-box model achieves performance close to that of ResNet while utilizing a small number of parameters. Furthermore, the semi-white-box strategy offers improved interpretability and parameter reusability features that are challenging to achieve with the all-in-one approach. In conclusion, this paper contributes to mitigating data-hunger challenges in machine-learning-based image processing through the introduction of a novel semi-white-box model construction strategy, backed by empirical evidence of its effectiveness.

An occluded cherry tomato recognition model based on improved YOLOv7.

The typical occlusion of cherry tomatoes in the natural environment is one of the most critical factors affecting the accurate picking of cherry tomato picking robots. To recognize occluded cherry tomatoes accurately and efficiently using deep convolutional neural networks, a new occluded cherry tomato recognition model DSP-YOLOv7-CA is proposed. Firstly, images of cherry tomatoes with different degrees of occlusion are acquired, four occlusion areas and four occlusion methods are defined, and a cherry tomato dataset (TOSL) is constructed. Then, based on YOLOv7, the convolution module of the original residual edges was replaced with null residual edges, depth-separable convolutional layers were added, and jump connections were added to reuse feature information. Then, a depth-separable convolutional layer is added to the SPPF module with fewer parameters to replace the original SPPCSPC module to solve the problem of loss of small target information by different pooled residual layers. Finally, a coordinate attention mechanism (CA) layer is introduced at the critical position of the enhanced feature extraction network to strengthen the attention to the occluded cherry tomato. The experimental results show that the DSP-YOLOv7-CA model outperforms other target detection models, with an average detection accuracy (mAP) of 98.86%, and the number of model parameters is reduced from 37.62MB to 33.71MB, which is better on the actual detection of cherry tomatoes with less than 95% occlusion. Relatively average results were obtained on detecting cherry tomatoes with a shade level higher than 95%, but such cherry tomatoes were not targeted for picking. The DSP-YOLOv7-CA model can accurately recognize the occluded cherry tomatoes in the natural environment, providing an effective solution for accurately picking cherry tomato picking robots.

Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches

Artificial intelligence (AI) developments enable human capability to deliver the same outcome at a lower cost. This research performs a high-level matching between AI solutions and challenges within the port area by developing a novel academic approach. This way, the matching is carried out more structured than when one (manager, developer, challenge owner, etc.) fulfils it based on their opinion without following any structured approach. Therefore, the study defines first a comprehensive typology of port stakeholders' challenges, which can be solved via AI solutions. This typology presents challenges, including their main issues, widespread impact, and potential solutions. A state-of-the-art review of AI solutions that can address these challenges is carried out in parallel. Secondly, this review clearly distinguishes between AI solutions based on their technology and functionality. Thirdly, this research selects an appropriate AI solution for addressing each identified challenge in the port operation by upgrading the Gale–Shapley algorithm. Finally, it shows that the most critical presented AI solutions in this study use various machine learning (ML) techniques. Besides, concerning the AI solution's reusability feature and the result of high-level matching, this research shows that the implementation phase effort can be drastically reduced by using the recently developed matching algorithm.

Processable oligoaniline-functionalized polyamide for electrochromic capacitive windows featuring energy recovery and reuse

Electrochromic capacitive windows (ECW) combining energy storage and electrochromism have attracted considerable interest because of their unique energy-saving and energy-recovery features. Therefore, it is highly desirable to explore an electrode material with excellent electrochromic and energy storage properties simultaneously. Herein, we synthesize an electroactive polyamide containing pentaaniline segments (EPA) in the main chain via oxidative coupling polymerization, which showcases electrochemical optical modulation and high capacitance synchronously. Attributed to non-acidic electrolyte and rational matching with V2O5 ion-storage material, the as-fabricated ECW exhibits good dual-band optical modulation performance with high optical contrast of 56.57% at 650 nm (Visible region) and 53.29% at 1250 nm (NIR region), fast optical switching response (<5 s), as well as effective regulation of indoor temperature. The resultant ECW also possesses practical energy storage properties, along with a wide voltage window of 2 V, a large capacitance of 47.26 mF/cm2, and outstanding electrochemical cycling durability. In its coloring state, the energy stored in ECW could be utilized to drive small-scale electronic equipment, indicating its energy recovery and reuse feature. In conclusion, the ECW in this work functions as a color-switching smart window and a rechargeable supercapacitor, which exhibits great promise for development in green buildings.

Dual-Loading of Fe3O4 and Pd Nanoparticles on g-C3N4 Nanosheets Toward a Magnetic Nanoplatform with Enhanced Peroxidase-like Activity for Loading Various Enzymes for Visual Detection of Small Molecules.

Enzyme mimics now play a significant role in biochemistry. Especially, peroxidase mimics have been widely used for developing colorimetric sensors of blood glucose. The peroxidase mimics previously reported could not be recycled for reusing and may generate scattering to cause unwanted optical interference when it was used for fabricating colorimetric sensors. We herein prepared a broad-applicable and reusable magnetic enzyme-loading nanoplatform with enhanced peroxidase-like activity by simultaneously loading Fe3O4 nanoparticles (Fe3O4NPs) and palladium nanoparticles (PdNPs) on graphitic carbon nitride (g-C3N4) nanosheets (Fe3O4NPs/PdNPs/g-C3N4). The prepared Fe3O4NPs/PdNPs/g-C3N4 possesses stable and enhanced peroxidase-like activity and good enzyme-loading capacity and can be used to load various natural enzymes to form highly-efficient and stable double-active nanozyme for fabricating colorimetric sensors for the visual detection of small molecules. Especially, the magnetic feature facilitates the magnetic separation of Fe3O4NPs/PdNPs/g-C3N4 from sample solution, which is in favor of recycling and eliminating the optical interference caused by nanozyme in colorimetric sensors. The prepared Fe3O4NPs/PdNPs/g-C3N4 has been successfully used to load glucose oxidase (GOx) and cholesterol oxidase (Chox) to form magnetic peroxidase-GOx and peroxidase-Chox double-active nanozymes, which can be used to fabricate colorimetric methods for the detection of glucose and cholesterol, respectively, with a visual detection limit of 15 μM and a spectrometry detection limit of 1.0 μM. With the developed glucose and cholesterol detection methods, we have successfully detected glucose and cholesterol in serum with a recovery of 98-104% and a RSD (n = 5) < 5%. With high peroxidase-like activity, good stability, reusable features, and broad applicability of loading enzyme, the developed magnetic Fe3O4NPs/PdNPs/g-C3N4 provided a promising approach for fabricating cost-effective, sensitive, and simple colorimetric sensors for the visual detection of various small molecules.

USE OF NATURAL WASTE CARRIER IN ENZYME IMMOBILIZATION: CATALASE IMMOBILIZATION ONTO EGGSHELL MEMBRANE

The immobilization process improves the stability properties of enzymes and reduces production costs in industrial processes due to its reuse feature. In this study, catalase was immobilized onto eggshell membrane (ESM), which is a natural carrier macromolecule, via adsorption and cross-linking methods. Experimental parameters such as enzyme concentration, ESM amount, cross-linking agent concentration and adsorption time were determined. Then, optimization studies were performed for both free catalase and catalase immobilized ESM. It was observed that ESM improved the stability properties of catalase enzyme such as optimum temperature, thermal stability and optimum pH after the immobilization. Especially in alkaline conditions, catalase immobilized ESM showed higher activity than free enzyme. The catalase immobilized ESM was able to maintain more than 50% of its activity even after repeated 25 uses. In addition, characterization studies of ESM and catalase immobilized ESM were performed using Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM).

Brain tumour segmentation based on an improved U-Net

BackgroundAutomatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours.MethodsTo solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding–decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics.ResultsThe performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother.ConclusionsOur algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.

Removal of Non-Steroidal Anti-Inflammatory Drugs from Drinking Water Sources by GO-SWCNT Buckypapers.

Pharmaceutical products such as antibiotics, analgesics, steroids, and non-steroidal anti-inflammatory drugs (NSAIDs) are new emerging pollutants, often present in wastewater, potentially able to contaminate drinking water resources. Adsorption is considered the cheapest and most effective technique for the removal of pollutants from water, and, recently, membranes obtained by wet filtration method of SWCNT aqueous solutions (SWCNT buckypapers, SWCNT BPs) have been proposed as self-standing porous adsorbents. In this paper, the ability of graphene oxide/single-walled carbon nanotube composite membranes (GO-SWCNT BPs) to remove some important NSAIDs, namely Diclofenac, Ketoprofen, and Naproxen, was investigated at different pH conditions (pH 4, 6, and 8), graphene oxide amount (0, 20, 40, 60, and 75 wt.%), and initial NSAIDs concentration (1, 10, and 50 ppm). For the same experimental conditions, the adsorption capacities were found to strongly depend on the graphene oxide content. The best results were obtained for 75 wt.% graphene oxide with an adsorption capacity of 118 ± 2 mg g-1 for Diclofenac, 116 ± 2 mg g-1 for Ketoprofen, and 126 ± 3 mg g-1 for Naproxen at pH 4. Overall, the reported data suggest that GO-SWCNT BPs can represent a promising tool for a cheap and fast removal of NSAIDs from drinking water resources, with easy recovery and reusability features.

Forseti: Dynamic chunk-level reshaping for data processing on heterogeneous clusters

Data-intensive computing frameworks typically split job workload into fixed-size chunks, allowing them to be processed as parallel tasks on distributed machines. Ideally, when the machines are homogeneous and have identical speed, chunks of equal size would finish processing at the same time. However, such determinism in processing time cannot be guaranteed in practice. Diverging processing times can result from various sources such as system dynamics, machine heterogeneity, and variable network conditions. Such variation, together with dynamics and uncertainty during task processing, can lead to significant performance degradation at job level, due to long tails in job completion time resulted from residual chunk workload and stragglers.In this paper, we propose Forseti, a novel processing scheme that is able to reshape data chunk size on the fly with respect to heterogeneous machines and a dynamic execution environment. Forseti mitigates residual workload and stragglers to achieve significant improvement in performance. We note that Forseti is a fully online scheme and does not require any a priori knowledge of the machine configuration nor job statistics. Instead, it infers such information and adjusts data chunk sizes at runtime, making the solution robust even in environments with high volatility. In its implementation, Forseti also exploits a virtual machine reuse feature to avoid task start-up and initialization cost associated with launching new tasks. We prototype Forseti on a real-world cluster and evaluate its performance using several realistic benchmarks. The results show that Forseti outperforms a number of baselines, including default Hadoop by up to 68% and SkewTune by up to 50% in terms of average job completion time.

Preliminary Findings of Usability Studies on an Ontology-Aware Taxon-by-Character Matrix Editor

Taxonomic treatments start with the creation of taxon-by-character matrices. Systematics authors recognized data ambiguity issues in published phenotypic characters and are willing to adopt an ontology-aware authoring tool (Cui et al. 2022). To promote interoperable and reusable taxonomic treatments, we have developed two research prototypes: a web-based application, Character Recorder (http://chrecorder.lusites.xyz/login), to faciliate the use and addition of ontology terms by Carex systematist authors while building their matrices, and a mobile application, Conflict Resolver (Android, https://tinyurl.com/5cfatrz8), to identify potential conflicts among the terms added by the authors and facilitate the resolution of the conflicts. We have completed two usability studies on Character Recorder. a web-based application, Character Recorder (http://chrecorder.lusites.xyz/login), to faciliate the use and addition of ontology terms by Carex systematist authors while building their matrices, and a mobile application, Conflict Resolver (Android, https://tinyurl.com/5cfatrz8), to identify potential conflicts among the terms added by the authors and facilitate the resolution of the conflicts. We have completed two usability studies on Character Recorder. In the one-hour Student Usabiilty Study, 16 third-year biology students with a general introduction to Carex used Character Recorder and Excel to record a set of 11 given characters for two samples (shape of sheath summits = U-shaped/U shaped). In the three-day Expert Usability Study, 7 established Carex systematists and 1 graduate student with expert-level knowledge used Character Recorder to record characters for 1 sample each of Carex canesens and Carex rostrata as they would in their professional life, using real mounted specimens, microscope, reticles, and rulers. Experts activities were not timed but they spent roughly 1.5 days on recording the characters and the rest of time discussing features and improvements. Features of Character Recorder have been reported in 2021 TDWG meeting and we included here only a few figures to highlight its interoperability and reusability features at the time of the usability studies (Fig. 1, Fig. 2, and Fig. 3). The Carex Ontology accompanying Character Recorder was created by extracting terms from Carex treatments of Flora of China and Flora of North America using Explorer of Taxon Concept (Cui et al. 2016) with subsequent manual edits. The design principle of Character Recorder is to encourage standardization and also leave the authors the freedom to do their work. While it took students an average of 6 minutes to recover all the given characters using Microsoft® Excel®, as opposed to 11 minutes using Character Recorder, the total number of unique meaning-bearing words used in their characters was 116 with Excel versus 30 with Character Recorder, showing the power of the latter in reducing synonyms and spelling variations. All students reported that they learned to use Character Recorder quickly and some even thought their use was as fast or faster than using Excel. All preferred Character Recorder to Excel for teaching students to record character data. Nearly all of the students found Character Recorder was more useful for recording clear and consistent data and all students agreed that participating in this study raised their awareness of data variation issues. The expert group consisted of 3, 2, 1, 3 experts in age ranges 20-49, 50-59, 60-69, and &amp;gt;69, respectively. They each recorded over 100 characters for two or more samples. Detailed analysis of their characters is pending, but we have noticed color characters have more variations than other characters (Fig. 4). All experts reported that they learned to use Character Recorder quickly, and 6 out of 8 believed they would not need a tutorial the next time they used it. One out of 8 experts somewhat disliked the feature of reusing others' values ("Use This" in Fig. 2) as it may undermine the objectivity and independence of an author. All experts used Recommended Set of Characters and they liked the term suggestion and illustration features shown in Figs 2, 3. All experts would recommend that their colleagues try Character Recorder and recommended that it be further developed and integrated into every taxonomist's toolbox. Student and expert responses to the National Aeronautics and Space Administration Task Load Index (NASA-TLX, Hart and Staveland 1988) are summarized in Fig. 5, which suggests that, while Character Recorder may incur in a slightly higher cost, the performance it supports outweighs its cost, especially for students. Every piece of the software prototypes and associated resources are open for anyone to access or further develop. We thank all student and expert participants and US National Science Foundation for their support in this research. We thank Harris &amp; Harris and Presses de l'Université Laval for the permissions to use their phenotype illustrations in Character Recorder.