Efficient Method Research Articles

Investigating the Respiratory and Energy Metabolism Mechanisms behind ε-Poly-L-lysine Chitosan Coating's Improved Preservation Effectiveness on Tremella fuciformis.

Freshly harvested Tremella fuciformis contains high water content with an unprotected outer surface and exhibits high respiration rates, which renders it prone to moisture and nutrient loss, leading to decay during storage. Our research utilized ε-poly-L-lysine (ε-PL) and chitosan as a composite coating preservative on fresh T. fuciformis. The findings revealed that the ε-PL + chitosan composite coating preservative effectively delayed the development of diseases and reduced weight loss during storage compared to the control group. Furthermore, this treatment significantly decreased the respiration rate of T. fuciformis and the activity of respiratory metabolism-related enzymes, such as alternative oxidase (AOX), cytochrome c oxidase (CCO), succinic dehydrogenase (SDH), 6-phosphogluconate dehydrogenase, and glucose-6-phosphate dehydrogenase (6-PGDH and G-6-PDH). Additionally, the composite coating preservative also delayed the depletion of ATP and ADP and maintained higher levels of the energy charge while preserving low levels of AMP. It also sustained heightened activities of Mg2+-ATPase, Ca2+-ATPase, and H+-ATPase enzymes. These results demonstrate that utilizing the ε-PL + chitosan composite coating preservative can serve as a sufficiently safe and efficient method for prolonging the shelf life of post-harvest fresh T. fuciformis.

Mg2+-promoted high-efficiency DNA conjugation on polydopamine surfaces for aptamer-based ochratoxin A detection

BackgroundSurface immobilization of DNA is the foundation of a broad range of applications in biosensing and specific DNA extraction. Polydopamine (PDA) coatings can serve as intermediate layers to immobilize amino- or thiol-labelled molecules, including DNA, onto various materials through Michael addition and/or Schiff base reactions. However, the conjugation efficiency is limited by electrostatic repulsion between negatively charged DNA and PDA. Recently, it has been reported that polyvalent metal ions (such as Mg2+ and Ca2+) can mediate the adsorption of DNA on PDA surfaces. Inspired by this, in this work we aimed to exploit polyvalent metal ions to facilitate the conjugation of DNA on PDA. ResultsMg2+ was used to promote the conjugation of amino-terminated DNA complementary to ochratoxin A (OTA) aptamer (cDNA-NH2) on PDA-coated magnetic nanoparticles (Fe3O4@PDA). After the reaction, the unlinked cDNA-NH2 adsorbed on Fe3O4@PDA mediated by Mg2+ was removed with EDTA. In the presence of 20 mM Mg2+, the amount of covalently linked cDNA-NH2 increased approximately 11-fold compared to that in the absence of Mg2+. The resulting Fe3O4@PDA@cDNA conjugates exhibited superior hybridization capacity towards OTA aptamers, minimal nonspecific adsorption, and excellent chemical stability. The conjugates combined with fluorophore-labelled aptamers were employed for OTA detection, achieving a limit of detection (LOD) of 2.77 ng mL−1. To demonstrate versatility, this conjugation method was extended to Ca2+-promoted conjugation of cDNA-NH2 on Fe3O4@PDA nanoparticles and Mg2+-promoted conjugation of cDNA-NH2 on PDA-coated 96-well plates. SignificanceThe conjugation efficiency of DNA on PDA was significantly improved with the assistance of polyvalent metal ions (Mg2+ and Ca2+), providing a facile and efficient method for DNA immobilization. Due to the substrate-independent adhesion property of PDA, this method demonstrates versatility in DNA surface modification and holds great potential for applications in target extraction, biosensing, and other fields.

Preparation of functionalized boron nitride nanosheets by one-step method for improving the thermal conductivity of polymers

Boron nitride nanosheets (BNNSs) with high thermal conductivity (TC) have promising applications in the thermal conduction. However, the preparation of BNNSs is cumbersome and BNNSs are prone to aggregation in the polymer matrix, which reduces the efficient enhancement for the TC of BNNSs/polymer composite. Herein, we propose a one-step method for the simultaneous exfoliation and functionalization of BNNSs with polymer latex as an agent. The obtained WPU-BNNSs displayed as few-layers with sizes of 100-500 nm and were mixed with WPU matrix as filler. The results demonstrated that the BNNSs/WPU composites have higher TC than the h-BN/WPU with the same filler content. When the filler content reached 50%, its TC was 251.4% higher than that of pure WPU. The excellent TC arose from the outstanding dispersion uniformity of WPU-BNNSs in the matrix. This study provides an economical and efficient method of fabricating BNNSs-based thermal conductive composite materials.

Web-Based Outsourcing Workforce Information System


 Trimegah Cipta Mandiri is a company that operates in the service sector, namely outsourcing or what is known as outsourcing and this company, so far, for marketing still uses telephone, print media and comes directly to the prospective employment company so it cannot find out information about PT. Trimegah Cipta Mandiri is complete and up to date and takes quite a long time and costs quite a lot. Apart from that, when work relations require new potential workers, they experience difficulties, because they do not know directly the data about potential new workers. The purpose of writing this thesis is to design a web-based ordering system at PT. Trimegah Cipta Mandiri, so that it can act as a liaison between PT. Trimegah Cipta Mandiri and work relations without being limited by the location of a region and access times, as well as ease of obtaining information about prospective workers. new work required by work relations. The research methods used included field studies and literature studies. Field studies include interviews and direct observations. Meanwhile, literature research is carried out by looking for materials that are relevant to the problem. Design methods include system analysis in the form of Flow of Documents, system design in the form of Context Diagrams, DFD, ERD, Normalization. The results of the design are applied in the form of web design using the PHP programming language and database management using MySQL. After the system analysis and design has been carried out, it can be seen that PT. Trimegah Cipta Mandiri requires a more effective and efficient method of processing and data collection, so that it is hoped that it will be able to produce information quickly, precisely and accurately which is useful for improving services to work relations.
 

Highly selective ion precipitation flotation for ternary Co–Zn–Mn separation: Stepwise chelation capture of Co and Zn from simulated zinc hydrometallurgy wastewater

Ion precipitation flotation technology was demonstrated to be an efficient method for the separation of valuable metals from low-concentration solution. However, the selective separation of three metals from mixing solution is a great challenge, and highly selective reagents are the key to polymetallic separation. In this work, stepwise separation of Co and Zn from the simulated zinc hydrometallurgy wastewater containing ternary Co–Zn–Mn metals by ion precipitation flotation process was proposed. It's demonstrated that organic reagents of 1-nitroso-2-naphthol (NN) and sodium dimethyldithiocarbamate (SDDC) had excellent selectivity for the capture of Co and Zn to form respective precipitate from wastewaters via the chelation reactions. After precipitation, dodecylpyridinium chloride (DPC) and tetradecyltrimethylammonium bromide (TTAB) were chosen as surfactants for the separation of Co and Zn sediments from the solution via the flotation process. The effects of solution pH, molar ratio, reaction temperature, and reaction time on the selective precipitation efficiencies of Co and Zn as well as the effects of surfactant dosage and flotation gas velocity on the flotation separation efficiencies were systematically investigated. It's demonstrated that the comprehensive recovery rates of Co, Mn, and Zn reach 98%, 90%, and 99%, respectively. After separation, oxidation calcination of the foam products was conducted to prepare high-purity Co3O4 and ZnO nanoparticles in which the organic matters were burnt out with gas emissions. The stepwise chelation capture mechanisms of Co and Zn by highly selective precipitation reagents were minutely discussed. It's demonstrated that the proposed selective stepwise precipitation and flotation method is suitable for recovery of critical metal ions from low-concentration polymetallic wastewaters.

Developing an efficient method for melanoma detection using CNN techniques

BackgroundMore and more genetic and metabolic abnormalities are now known to cause cancer, which is typically deadly. Any bodily part may become infected by cancerous cells, which can be fatal. Skin cancer is one of the most prevalent types of cancer, and its prevalence is rising across the globe. Squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and causes the majority of deaths, are the primary subtypes of skin cancer. Screening for skin cancer is therefore essential.MethodsThe best way to quickly and precisely detect skin cancer is by using deep learning techniques. In this research deep learning techniques like MobileNetv2 and Dense net will be used for detecting or identifying two main kinds of tumors malignant and benign. For this research HAM10000 dataset is considered. This dataset consists of 10,000 skin lesion images and the disease comprises nonmelanocytic and melanocytic tumors. These two techniques can be used for detecting the malignant and benign. All these methods are compared and then a result can be inferred from their performance.ResultsAfter the model evaluation, the accuracy for the MobileNetV2 was 85% and customized CNN was 95%. A web application has been developed with the Python framework that provides a graphical user interface with the best-trained model. The graphical user interface allows the user to enter the patient details and upload the lesion image. The image will be classified with the appropriate trained model which can predict whether the uploaded image is cancerous or non-cancerous. This web application also displays the percentage of cancer affected.ConclusionAs per the comparisons between the two techniques customized CNN gives higher accuracy for the detection of melanoma.

The effects of E-beam irradiation and microwave combination treatment on the morphology, structure and physicochemical properties of red adzuki bean starch

This study investigated the effects of microwave (MW, 300W, 900W), electron beam irradiation (EBI, 8, 24 KGy), and their combination (MW-EBI, EBI-MW) on the morphology, structure and physicochemical properties of red adzuki bean starch. MW or EBI treatments had little effect on starch morphology, but high-frequency MW and high doses of EBI combined modification resulted in starch granule cracking. Both EBI and MW treatments retained the C-type crystalline structure of the starch but reduced the relative crystallinity, Mw, and short-range ordering. Besides, starch's short chain (A, B1) content, pasting temperature, solubility, and RDS content were significantly increased. In contrast, long chain (B2, B3), SDS + RS content, ΔH, SP, and RDS content were decreased considerably after MW and EBI treatments. Furthermore, the effect of MW and EBI combined modification on starch was higher than the single modification, and the MW-EBI modification was more effective than EBI-MW. Therefore, this study provides a green and efficient method to modify starch, which helps to broaden the scope of starch application.

Reinforced photogenerated electron convergence on graphdiyne(g-CnH2n-2) “as an active site and substrate” for enhance photocatalytic hydrogen production

Graphdiyne(GDY), as a carbon-based catalyst with tunable electronic structure, has a significant impact on the advancement of sustainable photocatalytic technology. Its tunable electronic structure can effectively regulate the activity and selectivity of photocatalytic reaction. In this paper, GDY was prepared by a new method of ball milling HEB-TMS, and CoWO4 nanoparticles were uniformly loaded on the surface of GDY to construct a Type-II heterojunction. GDY provides a dispersed and effective substrate for CoWO4, forming a closer contact interface. Based on GDY's unique electronic structure and the formation of Type-II heterojunction, GDY/CoWO4 enhances the separation of photoexcited electrons and holes, thus increasing the electron cloud density on GDY. As an electron acceptor and active site for hydrogen production, GDY promotes the proton reduction reaction. In addition, the charge transfer process of GDY/CW-15 was demonstrated by in-situ XPS and UPS, and the corresponding hydrogen production mechanism was proposed. This study provides a facile and efficient method for preparing GDY and a new idea for heterojunction photocatalyst based on GDY substrate.

Radioactive seed localization is a safe and effective tool for breast cancer surgery: an evaluation of over 25,000 cases.

Radioactive seed localization (RSL) provides a precise and efficient method for removing non-palpable breast lesions. It has proven to be a valuable addition to breast surgery, improving perioperative logistics and patient satisfaction. This retrospective review examines the lessons learned from a high-volume cancer center's RSL program after 10 years of practice and over 25 000 cases. We provide an updated model for assessing the patient's radiation dose from RSL seed implantation and demonstrate the safety of RSL to staff members. Additionally, we emphasize the importance of various aspects of presurgical evaluation, surgical techniques, post-surgical management, and regulatory compliance for a successful RSL program. Notably, the program has reduced radiation exposure for patients and medical staff.

Resonant Acoustic Vibration-Assisted Cathode Stripping for Efficient Recycling of Spent Li-Ion Batteries

Abstract The emerging field of direct recycling for spent Li-ion batteries offers significant advantages, such as reduced energy expenses and minimized secondary pollution, when compared to traditional pyrometallurgical and hydrometallurgical methods. This is due to its direct utilization of the spent cathodes as raw materials. However, efficient harvesting of spent cathode particles remains a challenge. To address this issue, this technical brief is the first to incorporate resonant acoustic vibration technology (RAV) as an efficient method for stripping cathode powders from spent Li-ion batteries. Our findings indicate that RAV-based techniques can achieve a stripping efficiency of up to 92%. A comparative analysis with conventional stripping methods, such as magnetic stirring, sonication, and curling–uncurling, reveals that RAV coupled with heat treatment provides unparalleled scalability and efficiency, eliminating the need for post-processing. Furthermore, the resulting cathode powders retain their original polycrystalline particle structures, with no impurities, such as carbon black or small aluminum fragments detected. These findings highlight the promise of RAV technology for large-scale recovery of electrode powders and its potential role in direct recycling of spent Li-ion batteries.

Facile In Situ Difluoromethylation of Phenols and Thiols Using Ethyl Bromodifluoroacetate and K2CO3

AbstractHerein, we report an efficient method to access difluoromethyl ethers of thiols and phenols using ethyl bromodifluoroacetate and K2CO3. This method demonstrates chemoselective difluoromethylation of thiols and phenols in the presence of amines. The current method also discloses the synthesis of bis(aryloxy)fluoromethane compounds which are least reported in the literature. Mechanistic investigations revealed that the reaction proceeds through a nucleophilic substitution pathway. We strongly believe this protocol would offer an efficient alternative to earlier photocatalyzed or radical-mediated difluoromethylation methods and it has a great potential in the scale-up of pharmaceutical and agrochemical intermediates that possess difluoromethyl group.

Boosting the Electrocatalytic Water Splitting Performance Using Hydrophilic Metal-Organic Framework.

In this study, we employed a rapid and efficient microwave method to synthesize Metal-Organic Framework (MOF-303), which was subsequently embedded onto Palladium/Carbon (Pd/C) electrodes. The resulting hybrid material, Pd/C@MOF-303, was thoroughly characterized, and its performance in the Hydrogen Evolution Reaction (HER) was systematically investigated. The Pd/C@MOF-303 composite exhibited remarkable improvements in HER performance compared to the unmodified Pd/C electrode. At a benchmark current density of 10 mA cm-2, the overpotentials for Pd/C and Pd/C@MOF-303 were measured at 185 mV and 175 mV, respectively. This reduction in overpotential highlights the superior catalytic activity of the Pd/C@MOF-303 hybrid material in facilitating the HER. Furthermore, the Pd/C@MOF-303 electrode demonstrated enhanced HER activity, increased mass activity, and excellent charge transfer rates compared to its unmodified counterpart, Pd/C. The findings underscore the significance of the hydrophilic MOF-303 in tailoring the surface characteristics of electrocatalysts, thereby offering insights into the design principles for advanced materials with superior performance in electrochemical applications.

Using the sulfide-oxidizing bacterium Geobacillus thermodenitrificans to restrict H2S release during chicken manure composting

Hydrogen sulfide (H2S) is a toxic gas massively released during chicken manure composting. Diminishing its release requires efficient and low cost methods. In recent years, heterotrophic bacteria capable of rapid H2S oxidation have been discovered but their applications in environmental improvement are rarely reported. Herein, we investigated H2S oxidation activity of a heterotrophic thermophilic bacterium Geobacillus thermodenitrificans DSM465, which contains a H2S oxidation pathway composed by sulfide:quinone oxidoreductase (SQR) and persulfide dioxygenase (PDO). This strain rapidly oxidized H2S to sulfane sulfur and thiosulfate. The oxidation rate reached 5.73 μmol min−1·g−1 of cell dry weight. We used G. thermodenitrificans DSM465 to restrict H2S release during chicken manure composting. The H2S emission during composting process reduced by 27.5% and sulfate content in the final compost increased by 34.4%. In addition, this strain prolonged the high temperature phase by 7 days. Thus, using G. thermodenitrificans DSM465 to control H2S release was an efficient and economic method. This study provided a new strategy for making waste composting environmental friendly and shed light on perspective applications of heterotrophic H2S oxidation bacteria in environmental improvements.

A facile, regio and diastereoselective synthesis of functionalized azetidin-2-ones: Cu(I) mediated Kinugasa reaction of functionalized nitrones with acetylenic alcohols

The manuscript presents an efficient method for the synthesis of various cis-azetidin-2-ones with high regio- and diastereoselectivity. This approach utilizes Kinugasa reaction involving diverse functionalized nitrones, including diaryl nitrones and α,β-unsaturated nitrones, in conjunction with acetylenic alcohols. Copper (I) serves as a catalyst to facilitate the transformation.

One-shot screening: Utilization of a two-dimensional convolutional neural network for automatic detection of left ventricular hypertrophy using electrocardiograms

Background and ObjectiveLeft ventricular hypertrophy (LVH) can impair ejection function and elevate the risk of heart failure. Therefore, early detection through screening is crucial. This study aimed to propose a novel method to enhance LVH detection using 12-lead electrocardiogram (ECG) waveforms with a two-dimensional (2D) convolutional neural network (CNN). MethodsUtilizing 42,127 pairs of ECG-transthoracic echocardiogram data, we pre-processed raw data into single-shot images derived from each ECG lead and conducted lead selection to optimize LVH diagnosis. Our proposed one-shot screening method, implemented during pre-processing, enables the superimposition of waveform source data of any length onto a single-frame image, thereby addressing the limitations of the one-dimensional (1D) approach. We developed a deep learning model with a 2D-CNN structure and machine learning models for LVH detection. To assess our method, we also compared our results with conventional ECG criteria and those of a prior study that used a 1D-CNN approach, utilizing the same dataset from the University of Tokyo Hospital for LVH diagnosis. ResultsFor LVH detection, the average area under the receiver operating characteristic curve (AUROC) was 0.916 for the 2D-CNN model, which was significantly higher than that obtained using logistic regression and random forest methods, as well as the two conventional ECG criteria (AUROC of 0.766, 0.790, 0.599, and 0.622, respectively). Incorporating additional metadata, such as ECG measurement data, further improved the average AUROC to 0.921. The model's performance remained stable across two different annotation criteria and demonstrated significant superiority over the performance of the 1D-CNN model used in a previous study (AUROC of 0.807). ConclusionsThis study introduces a robust and computationally efficient method that outperforms 1D-CNN models utilized in previous studies for LVH detection. Our method can transform waveforms of any length into fixed-size images and leverage the selected lead of the ECG, ensuring adaptability in environments with limited computational resources. The proposed method holds promise for integration into clinical practice as a tool for early diagnosis, potentially enhancing patient outcomes by facilitating earlier treatment and management.

Large-eddy simulation-based reconstruction of turbulence in a neutral boundary layer using spectral-tensor regularization

We propose an efficient method to reconstruct the turbulent flow field in a neutrally stratified atmospheric boundary layer using large-eddy simulation (LES) and a series of lidar measurements. The reconstruction is formulated as a strong four-dimensional variational data assimilation problem, which involves optimizing two competing terms that contribute in the objective functional. The first term is a likelihood term, while the second contains the initial background distribution of turbulent velocity fluctuations and works as a regularization term. However, computing and storing the full background covariance tensor in turbulent flows is time consuming and resource intensive. In the current work, we investigate the possibility of replacing the complex background tensor by simple analytical approximations based on spectral tensors such as the Hunt–Graham–Wilson (HGW) model (Boundary-Layer Meteorol., vol. 85, 1997, pp. 35–52) or the Mann model (J. Fluid Mech., vol. 273, 1994, pp. 141–168). Afterwards, the problem is solved using a quasi-Newton algorithm and preconditioned to enhance the convergence rate. We test the method using virtual lidar measurements collected on a fine reference LES. Results show a super-linear convergence rate of the optimization algorithm to a local minimum and very good agreement between virtual lidar measurements and reconstruction in the scanning region. Furthermore, we demonstrate that incorporating the Saffman energy spectrum ( $E(k) \sim k^2$ where E is the energy spectrum and k is the magnitude of the wavenumber vector) at low wavenumbers into the Mann spectral tensor yields a longer streamwise correlation length, resulting in reduced reconstruction error when compared with the Batchelor spectrum ( $E(k) \sim k^4$ ). Finally, we observe that using the HGW model or Mann model with a Saffman spectrum yields similar results.

Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy

Efficient structural reliability analysis method is crucial to solving reliability analysis of complex structural problems. High-computational cost and low-failure probability problems greatly limit the efficiency in structural reliability analysis problems, causing the safety and reliability of the structure to be questioned. In this work, a highly efficient structural reliability analysis method coupling active Kriging algorithm with conjugate first order reliability method (AK-CFORM) is proposed. Specifically, the resample strategy is considered to reduce the number of samples evaluated in each active learning process; the uniform sampling is used to better balance global and local optimal problems; the conjugate map is used to improve the robustness of analytical first order reliability method; and the approximate numerical differential formula is proposed to solve the problems of non-convergence when solving the gradient of the Kriging surrogate model. Finally, three numerical cases and four engineering cases are used to illustrate the effectiveness and robustness of the proposed method. The results show that the proposed AK-CFORM has greater advantages in the number of calling system response and surrogate model with robust and accurate performance.

Effects of water washing and KOH activation for upgrading microalgal torrefied biochar

Torrefaction is an effective pathway for microalgal solid biofuel upgrading, and alkali metal activation is also an efficient method to enhance fuel properties. This study explores the comparison of torrefaction alone and KOH activation combined with torrefaction to determine a better operation for biochar production from the microalga Nannochloropsis Oceanica. The results indicate that the HHV ranges of KOH-activated biochar and unactivated biochar are 25.611–32.792 MJ·kg−1 and 25.024–26.389 MJ·kg−1, respectively. Furthermore, KOH-activated biochar is better than unactivated biochar, with less residue, broader pyrolysis and combustion temperature ranges, higher elemental carbon, and less combined carbon. Moreover, KOH-activated biochar is close to the unactivated one from the viewpoint of expense calculation and life cycle assessment and thus possesses a better comprehensive performance. Overall, KOH activation is an efficient method for upgrading microalgal solid biofuel. The results are conducive to exploring further modification of microalgal solid biofuel production with better properties, thus leading to a greener and more efficient approach for upgrading fuel performance.

An efficient hybrid adaptive large neighborhood search method for the capacitated team orienteering problem

This paper focuses on a NP-Hard vehicle routing problem with profits, and presents a hybrid heuristic solution approach for the Capacitated Team Orienteering Problem (CTOP), which is a variant of the well-known team orienteering problem. In the CTOP, given a set of homogeneous fleet and a set of clients to each is associated a deterministic profit and volume demand, one maximizes the collected profits for serving customers by operating a set of routes while respecting vehicles’ limited time and volume capacities. Our proposed method to solve the CTOP is a Hybrid Adaptive Large Neighborhood Search (HALNS) heuristic. Several removal and insertions operators with different node selection strategies and local search procedures are developed to generate solutions in the first step. In the second step, solutions’ routes are passed to a set packing problem which is solved using an exact branch-and-cut procedure in order to obtain the best solution. Compared to all the state-of-the-art methods, extensive computational experiments show that our method outperforms in terms of solution quality and/or computational times when tested on the small and large-scale benchmark CTOP instances respectively proposed by Archetti et al. (2009) and Tarantilis et al. (2023). Furthermore, our hybrid method improves 10 of the best known solutions for large-scale benchmark instances.

Efficient machine learning-assisted failure analysis method for circuit-level defect prediction

Integral to the success of transistor advancements is the accurate use of failure analysis (FA) which benefits in fine-tuning and optimization of the fabrication processes. However, the chip makers face several FA challenges as device sizes, structure, and material complexities scale dramatically. To sustain manufacturability, one can accelerate defect identification at all steps of the chip processing and design. On the other hand, as technologies scale below the nanometer nodes, devices are more sensitive to unavoidable process-induced variability. Therefore, metallic defects and process-induced variability need to be treated concurrently in the context of chip scaling, while failure diagnostic methods to decouple the effects should be developed. Indeed, the locating a defective component from thousands of circuits in a microchip in the presence of variability is a tedious task. This work shows how the SPICE circuit simulations coupled with machine learning based-physical modeling should be effectively used to tackle such a problem for a 6T-SRAM bit cell. An automatic bridge defect recognition system for such a circuit is devised by training a predictive model on simulation data. For feature descriptors of the model, the symmetry of the circuit and a fundamental material property are leveraged: metals (semiconductors) have a positive (negative) temperature coefficient of resistance up to a certain voltage range. Then, this work successfully demonstrates that how a defective circuit is identified along with its defective component's position with approximately 99.5 % accuracy. This proposed solution should greatly help to accelerate the production process of the integrated circuits.