Efficient Analysis Research Articles

Facile Preparation of Magnetic COF-on-COF for Rapid Adsorption and Determination of Sulforaphane from Cruciferous Vegetables

Sulforaphane (SFN) is a natural isothiocyanate compound widely abundant in cruciferous vegetables with multiple bioactive functions. However, traditional analytical methods for the extraction and determination of SFN are cumbersome, time-consuming, and low sensitivity with large amounts of organic solvents. Herein, novel magnetic COF-on-COFs (MB-COFs) were fabricated using Fe3O4 as a magnetic core and COFs-1 grown with COFs-2 as a shell, and they were used as efficient adsorbents of magnetic dispersive solid-phase extraction for rapid quantification of SFN in cruciferous vegetables by combining with HPLC-MS/MS. At the optimal ratio of COFs-1 to COFs-2, MB-COFs had a spherical cluster-like structure and a rough surface, with a sufficient magnetic response for rapid magnetic separation (1 min). Due to the introduction of Fe3O4 and COFs-2, MB-COFs exhibited outstanding extraction efficiencies for SFN (92.5–97.3%), which was about 18–72% higher than that of the bare COFs. Moreover, MB-COFs showed good adsorption capacity (Qm of 18.0 mg/g), rapid adsorption (5 min) and desorption (30 s) to SFN, and favorable reusability (≥7 cycles) by virtue of their unique hierarchical porous structure. The adsorption kinetic data were well fitted by the pseudo-second-order, Ritchie-second-order, intra-particle diffusion, and Elovich models, while the adsorption isotherm data were highly consistent with the Langmuir, Temkin, and Redlich–Peterson models. Finally, under the optimized conditions, the developed method showed a wide linear range (0.001–0.5 mg/L), high sensitivity (limits of quantification of 0.18–0.31 μg/L), satisfactory recoveries (82.2–96.2%) and precisions (1.8–7.9%), and a negligible matrix effect (0.82–0.97). Compared to previous methods, the proposed method is faster and more sensitive and significantly reduces the use of organic solvents, which can achieve the efficient detection of large-scale samples in practical scenarios. This work reveals the high practical potential of MB-COFs as adsorbents for efficient extraction and sensitive analysis of SFN in cruciferous vegetables.

Large-Scale Data Processing and Machine Learning Analysis Model Based on Distributed Algorithm

The model of large-scale data processing and ML(machine learning) analysis based on DA(distributed algorithm) is a powerful computing method, which aims at processing huge data sets and performing efficient ML analysis. In this paper, a cluster topology driver module based on gradient switching and aggregate communication is designed, and its core goal is to adapt the distributed system to various underlying network topologies. By designing decentralized gradient exchange algorithm and aggregate communication framework, the parallel transmission ability of multi-interface network can be fully exerted, thus improving the model synchronization efficiency of ML task. The experimental results show that the cluster topology driver module can provide better performance than the existing methods in terms of training convergence, cluster scalability and communication overhead. Large-scale data processing and ML analysis model based on DA is widely used in processing massive data and realizing complex analysis tasks.

Efficiency analysis of productive waqf management at Jogokariyan Mosque Yogyakarta

Waqf plays an important role in promoting the welfare of society, especially in overcoming poverty and social inequality in Indonesia, a Muslim-majority country. This study aims to analyze the production efficiency of productive waqf management by Nazir in Jogokariyan Mosque Yogyakarta and identify problems in the management. The research method uses a quantitative approach with Data Envelopment Analysis (DEA) and Banxia Frontier Analyst software. Primary data comes from the Jogokariyan Mosque's finances managed by Nazhir waqf. The results showed that DMU 1 (lodging) achieved 100% efficiency in 2017, 2018, and 2019. DMU 2 (rice fields) achieved 100% efficiency from 2017 to 2023. Efforts to improve the efficiency of DMU 1 involve successively reducing the input variables of employee salaries (10.42%, 14.30%) in 2020-2021, and employee salary costs and operating costs in 2022 (14.19%, 4.35%). However, revenue and profit outputs need to be improved (27.94%, 32.45%) in 2020, (26.27%, 30.70%) in 2021, and (25.71%, 30.45%) in 2022. DMU 2 does not require efficiency improvement as it reached 100% from 2017 to 2022.

Predicting Soil Quality Index with a Deep Regression Approach

ABSTRACT The detection, preservation and enhancement of soil quality is essential for ecosystem managers to offer the appropriate support after discovering factors that are linked to soil’s functional capability. In this study, soil quality indicators were combined to establish a soil quality index (SQI) by conducting an efficiency analysis for using Convolutional Neural Network (CNN), Gradient Boosting Methods (GBM) and Decision Tree (DT) approaches. For this purpose, soil’s intrinsic parameters, namely indicators, are utilized in case of fitting a deeper regression model. The experiments were carried out with analysis results of 545 soil samples obtained from previously studies conducted on different areas such as watershed, delta, pasture and farmland. The performance of each model is validated by the reported data. Compared to gradient boosting models, the proposed CNN model with mean absolute loss function achieves a lower error rate of 2.71 in Mean Absolute Percentage Error (MAPE) and best average correlation coefficient 0.90 in R2, which proves generalizability of CNN based regression. The findings show that CNNs may be utilized as an effective tool as giving a better prediction accuracy level and reliability in terms the establishing correlations and providing a conceptual framework for modeling of soil quality.

Analysis of production efficiency of cloned transgenic Yucatan miniature pigs according to recipient breeds with embryo transfer conditions

The purpose of this study was to compare the efficiency of the production of cloned transgenic Yucatan miniature pigs (YMPs) using two recipient breeds, i.e., YMPs and domestic pigs (DPs), under various embryo transfer conditions. We initially assessed the in vitro developmental competence of embryos obtained via somatic cell nuclear transfer (SCNT) from three different transgenic donor cells. No difference was observed among the three groups regarding developmental competence. Furthermore, the cloning efficiency remained consistent among the three groups after the transfer of the SCNT embryos to each surrogate mother. Subsequently, to compare the efficiency of the production of cloned transgenic YMPs between the two recipient breeds using varying parameters, including ovulation status (preovulation and postovulation), duration of in vitro culture (IVC) (incubated within 24 h and 24–48 h), and the number of transferred SCNT embryos (less than and more than 300), we assessed the pregnancy rates, delivery rates, mean offspring counts, and cloning efficiency. Regarding the ovulation status, YMPs exhibited higher pregnancy rates, delivery rates, and cloning efficiency compared with DPs in both statuses. Moreover, the pregnancy rates, delivery rates, and cloning efficiency were affected by the ovulation status in DPs, but not in YMPs. The comparison of IVC duration between groups revealed that YMPs had higher pregnancy rates vs. DPs in both conditions. SCNT embryos cultured for 24–48 h in YMPs yielded higher delivery rates and cloning efficiency compared with those cultured for less than 24 h in DPs. Finally, the analysis based on the number of transferred SCNT embryos showed that both the pregnancy and delivery rates were higher in YMPs vs. DPs. However, the highest average number of offspring was obtained when more than 300 SCNT embryos were transferred into DPs, whereas the cloning efficiency was higher in YMPs vs. DPs. Our results suggest that YMPs are more suitable recipients than are DPs under various conditions for the production of cloned transgenic YMPs.

Foliar application of diethyl aminoethyl hexanoate (DA‐6) alleviated summer bentgrass decline and heat damage to creeping bentgrass

AbstractCreeping bentgrass (Agrostis stolonifera) is sensitive to supra‐optimal temperature, which makes summer bentgrass decline (SBD) a main conundrum troubling turf manager. Diethyl aminoethyl hexanoate (DA‐6) is an artificial and bioactive plant growth regulator affecting tolerance to various abiotic stresses. It is commercially available for field application due to multiple advantages such as low cost, environmentally friendly compound, and sustainable effect. However, the positive effect and mechanism of DA‐6‐mediated heat tolerance in cool‐season grass species and SBD are not yet been explored. Objectives of the study were to screen the optimal dose of DA‐6 for improvement in thermotolerance and to investigate roles of DA‐6 in regulating photosynthesis, osmotic balance, and antioxidant homeostasis in two creeping bentgrass cultivars (heat‐tolerant 13 M and heat‐sensitive Seaside II) in controlled heat condition. Summer heat tolerance induced by the DA‐6 was further evaluated for 2 years under field conditions in a subtropical zone. Results demonstrated that the optimal dose of DA‐6 was screened as 0.1 mM based on analyses of chlorophyll content, photochemical efficiency, and cell membrane stability under heat stress. Foliar application of DA‐6 significantly alleviated heat‐induced leaf senescence by improving photosynthetic performance, accumulation of sugars (sucrose, fructose, and glucose) for osmotic homeostasis, and total antioxidant capacity for reactive oxygen species scavenging in the two cultivars. A 2‐year field test was further carried out to demonstrate that turf quality of the two cultivars was significantly improved after being treated by foliar DA‐6 during hot summer months in 2020 and 2021. Current findings suggested that DA‐6‐mediated thermo‐resistant mechanism was associated with photosynthesis, osmotic adjustment, and antioxidant in cool‐season grass species. Foliar application of DA‐6 could be a cheap, effective, and environmentally friendly strategy to alleviate SBD.

Special Issue on “Efficiency and Productivity Analysis of Public Services in Practice”

Special Issue on “Efficiency and Productivity Analysis of Public Services in Practice”

Simulation of Screening Performance of Linear Vibrating Screen for Sand Sorting Based on Discrete Element Method

Screening technology has been widely used in the grading of granular materials, in order to improve the screening efficiency and screening quality of sand particles, a linear vibrating screening device was designed. Analyzing the motion state of the vibrating screen, it is found that the factors affecting the screening efficiency and screening quality, mainly include: vibration frequency, amplitude and screen surface inclination angle. Taking sand grains as the research object, the Hertz-Mindlin with JKR contact model was chosen to analyze the influence of individual factors on the screening performance, and the discrete element method was applied to simulate and analyze the screening process of the material, and the screening efficiency of the material was used as a measure to obtain the vibration parameter set of the optimal screening performance. It is found that the best vibration parameter set of linear vibrating screen is: vibration frequency f=25Hz, amplitude A=4mm, screen surface inclination angle φ=4^°, at this time the screening efficiency can reach 95.77%. This study proves that the discrete element method has good reliability for the screening of particles, and it can be used to carry out efficiency analysis in the pre-production stage of the screening device, and provide corresponding reference for the design and parameter optimization of the vibrating screen.

A Study on the Operational Efficiency of National Supercomputer Resources Based on DEA Model

As a measure to improve the operational efficiency of the Korean supercomputer joint utilization resources, an efficiency analysis using DEA was conducted to derive the improvement level of the input and output factors of the operation plans of seven specialized centers. The factors that constitute the objective function of efficiency were identified through the operation plans submitted by the specialized centers, and DEA analyzed the improvement factors and levels of inefficient specialized centers through the CCR and BCC models, and then conducted an additional super-efficiency analysis to compare the relative efficiency levels among the efficiency groups. As a result of the analysis, the inefficiency of specialized centers in the meteorological/climate/environmental fields was identified from the reference group, and the need for improvement and the level of improvement of input and output variables were identified. In addition, the authors suggested ways to improve the establishment of resources and operational targets, such as a joint utilization ratio to secure financial efficiency at the government level in establishing a joint utilization resource.

An assembly accuracy analysis approach of mechanical assembly involving parallel and serial connections considering form defects and local surface deformations

Tolerance analysis including tolerance model representation and assembly deviation propagation, is a key approach for predicting and controlling the assembly accuracy of mechanical product. In the current researches, the Non-Gaussian skin model shapes (NSMS) is a valid approach to represent tolerance model of parts considering form defects. However, there is a lack of valid methods to analyze the deviation propagation of mechanical assembly involving parallel and serial connections due to without considering the effect of local surface deformations between mating surfaces (LSDMS) in assembly process, which may affect the assembly accuracy analysis. In addition, the calculation of relative positioning deviation between mating surfaces (RPDMS) of parallel connection has not been combined with practical assembly process closely, which may lead to the loss of accuracy. To address above problems, this paper proposes an assembly accuracy analysis approach of mechanical assembly involving parallel and serial connections considering form defects and LSDMS. Firstly, an integrated Jacobian-NSMS model is presented based on NSMS and Unified Jacobian-Torsor model, which combines the advantages of efficient deviation propagation analysis and high-fidelity representation of tolerance. Secondly, based on the registration method and the difference surface method for solving RPDMS of serial connections, the RPDMS of parallel connection is obtained by performing algebraic operations combining with practical assembly process, by which the assembly involving parallel and serial connections is transformed into a new serial assembly, then the assembly deviation for mechanical assembly involving parallel and serial connections considering form defects and LSDMS can be calculated with the integrated Jacobian-NSMS model. Finally, a case study is presented to verify the proposed method, the result shows that there is different effect on assembly deviation between considering and without considering form defects and LSDMS, which indicates that the form defects and LSDMS have non-ignorable effect on assembly accuracy analysis of mechanical assembly involving parallel and serial connections. In addition, the result shows there is different effect on assembly deviation between considering and without considering practical assembly process in parallel connection. It is expected that the proposed method can help improve the assembly accuracy of mechanical assembly involving parallel and serial connections.

Energy efficiency analysis and optimization of heat exchange network under the goal of “double carbon”: a case for production process of isopropyl acetate

ABSTRACT In order to response to the “double carbon” strategy for reducing emissions, chemical production processes were optimized to lower the amount of utility work and equipment investment expenses with increasing the system’s capacity for heat recovery. A sensitivity analysis and the energy efficiency analysis with pinch technique were performed on the distillation and purification of the 30 kt/a isopropyl acetate (IPAC) production process by using process simulation software of Aspen Plus. The IPAC refining tower optimization results show that the purity of the refined IPAC could be reached 99.9% at circumstances of 44 theoretical plates, 19 feed plates, and 0.755 reflux ratio. According to the optimized energy consumption data from Aspen Energy Analyzer (AEA), the cold and heat logistics matching was performed. It can be seen that the heat exchange network was tuned to maximize energy recovery by reducing the amount of utility work. The optimized cold and heat utility usage were 734.69 and 727.81 kW, which meaning that compared with original process, the cold and heat utility usage energy can be save with 10.0%, respectively. The optimized results provide a certain theoretical basis and solution for improving energy saving and reducing investment costs.

Unveiling the potential of concentric tubular solar stills (CTSS) through comprehensive thermodynamic modeling and analysis

ABSTRACT The present research work delves into the thermodynamic modeling and analysis of concentric tubular solar stills (CTSS) using a rigorous mathematical approach. The primary objective is to gain a comprehensive understanding of the system’s behavior by formulating and analyzing various crucial parameters. These parameters include the basin water temperature, inner and outer glass cover temperatures, heat transfer coefficients, heat loss coefficients, thermal efficiency, exergy efficiency, hourly yield, and cumulative yield. The study is specifically conducted under the environmental conditions of Nagpur city (21.1458° N, 79.0882° E), India, in the month of May 2022. By selecting this location, the present study ensures that their findings are contextually relevant and applicable to the local conditions. To facilitate the computation of results and generate informative visualizations, MATLAB code has been developed. This code allows for efficient analysis, processing, and presentation of the obtained data, enabling a comprehensive interpretation of the research findings. Results of the study indicate that hourly yield, considering only bottom losses, is 5.071 k g / m 2 and 3.012 k g / m 2 when water and air are used as cooling mediums, respectively. However, when all losses are taken into account, the hourly yield decreases to 4.575 k g / m 2 and 2.555 k g / m 2 for water and air as cooling mediums, respectively. The percentage error when water and air are used as the cooling medium by considering bottom losses is 1.05 and 3.06, while considering all losses, the error is 10.73 and 18.26.

Development of high-gain switched-capacitor based bi-directional converter for electric vehicle applications

High efficiency, high voltage transfer ratio (VTR), and low input ripple current is required in any bidirectional DC-DC converter (BDC) that plays a major role in interfacing batteries in applications like dc microgrids and electric vehicles (EVs). For meeting these requirements, a switched capacitor-based BDC is proposed to interface the battery with a propulsion system via DC Link. It has a simple circuit with only a set of switching operations, High VTR, and lesser ripple current on the low voltage (LV) side are advantages of the proposed High Gain Switched-Capacitor Bi-directional DC-DC Converter (SC-BDC) making it appropriate for use in EVs. The steady-state analysis, design consideration of passive components, loss and efficiency analysis are presented. Finally, the proposed High Gain SC-BDC is compared with few of the existing BDC in the literature. The feasibility of the converter was demonstrated by simulating a 200 W converter and validating results produced in a MATLAB environment.

Microfluidic SERS chip for quantitative detection of weak surficial affinity molecules

Microfluidic SERS chip has become increasingly attractive in sensitive chemical and biomolecular analysis. However, microfluidic SERS chip is substantially limited when detecting weakly adsorbed molecules in fluids, let alone achieving quantitative analysis that is vital for biochemical applications. Herein, a microfluidic SERS chip that can achieve quantitative analysis of weak affinity molecules is developed. By “direct-laser-writing”, silver aggregates inherently modified with citrate are facilely fabricated in microfluidic channel. We demonstrate that the surficial citrate can not only “detain” analytes on plasmonic surface by multi-hydrogen bonding, but also serve as a stable internal-standard, which well solves the two challenges. It shows that the relative standard deviation of melamine SERS intensity at 701 cm−1 can be effectively reduced from about 20% to less than 10% by normalization using the citrate band at 1370 cm−1. Quantification of melamine in aqueous and milk samples down to 10−7 M are achieved, respectively. And the recoveries are in the range 95%-115%. Moreover, the detection of a kind of hypoglycemic drug (rosiglitazone) is also performed. This microfluidic SERS chip capable of robust quantification of weak affinity molecules would certainly extend the application scenarios of microfluidic SERS, especially for low dose, high efficiency and real time liquid analysis.

Green Approaches for Efficient Functional Group Analysis of Organic Compounds

Green Approaches for Efficient Functional Group Analysis of Organic Compounds

Reliability assessment of freight wagon passing through railway turnouts using adaptive Kriging surrogate model

ABSTRACT Railway turnout (RT) is a crucial component of railway infrastructure that consists of several components. Assessing the derailment probability of freight wagons passing through the turnout is crucial for quantifying failure risks and optimizing the performance of the freight wagon-turnout system (FWTS). However, existing assessment methods often require extensive model evaluations and impose substantial computational costs. To address this issue, an efficient reliability analysis method is established for assessing the derailment risk at RTs. Firstly, a dynamic model is developed to capture the wheel-rail dynamic interaction and the numerical model is validated by field tests. Secondly, to reduce the computational cost in the reliability analysis, an efficient adaptive Kriging method based on an error stopping criteria and a learning function is adopted to estimate the failure probabilities under multiple failure modes of wheel derailments. Based on the efficient learning function and convergence criterion, accurate failure probability results can be obtained with a small number of multibody and finite element coupled dynamic simulations. Furthermore, the prediction accuracy of the proposed method in capturing random characteristics for FWTS is evaluated. Finally, the influence of the evolution of rail wear on the failure probability is further discussed.

Performance and Efficiency Exploration of Hardware Polynomial Multipliers for Post-Quantum Lattice-Based Cryptosystems

The significant effort in the research and design of large-scale quantum computers has spurred a transition to post-quantum cryptographic primitives worldwide. The post-quantum cryptographic primitive standardization effort led by the US NIST has recently selected the asymmetric encryption primitive Kyber as its candidate for standardization and indicated NTRU, as a valid alternative if intellectual property issues are not solved. Finally, a more conservative alternative to NTRU, NTRUPrime was also considered as an alternate candidate, due to its design choices that remove the possibility for a large set of attacks preemptively. All the aforementioned asymmetric primitives provide good performances, and are prime choices to provide IoT devices with post-quantum confidentiality services. In this work, we present a comprehensive exploration of hardware designs for the computation of polynomial multiplications, the workhorse operation in all the aforementioned cryptosystems, with a thorough analysis of performance, compactness and efficiency. The presented designs cope with the differences in the arithmetics of polynomial rings employed by distinct cryptosystems, benefiting from configurations and optimizations that are applicable at synthesis time and/or run time. In this context, we target a use case scenario where long-term key pairs are used, such as the ones for VPNs (e.g., over IPSec), secure shell protocols and instant messaging applications. Our high-performance design variants exhibit figures of latency comparable to the ones needed for the execution of the symmetric cryptographic primitives also included in the Post-Quantum schemes. Notably, the performance figures of the designs proposed for NTRU and NTRU Prime surpass the ones described in the related literature.

Performance Analysis of Employees at the Regional Secretariat of Tomohon City

This study undertakes a comprehensive exploration of employee performance within the regional secretariat of Tomohon city. Employing a descriptive qualitative approach, the research involves five informants, utilizing observation, interviews, and documentation for data collection. The analysis follows a structured process of data reduction, presentation, and conclusion verification. The findings reveal suboptimal performance in carrying out main duties and functions by employees, as evidenced in the analysis of effectiveness and efficiency in staffing, knowledge, skills, and discipline within the general section of the Tomohon city regional secretariat. Notably, while some tasks are executed effectively and efficiently, others display shortcomings, impacting overall performance. The study also identifies knowledge gaps among employees, affecting the secretariat’s functional execution. Coordination issues and non-compliance with government regulations are evident in skills-related sub-focus areas. Additionally, a deficiency in employee initiative is observed, as some employees hesitate to discuss work-related problems or demonstrate proactive engagement. This study sheds light on critical areas requiring attention for enhancing employee performance in the regional secretariat of Tomohon city.

An efficient seismic analysis technique for PCSG assembly using sub-structuring method and homogenization method

This study significantly reduced the seismic analysis time of PCSG assembly by introducing a reduced model using homogenization and sub-structuring methods. The homogenization method was applied to the primary and secondary micro-channel sheets, and the sub-structuring method was applied to the PCSG module sets. Modal analysis and frequency response analysis were then performed to validate the accuracy of the reduced model. The analysis results were compared with the full model and it was confirmed that the reduced model provided almost the same analysis results as the full model. To verify the computational efficiency of the reduced model, the computational time was then compared with the full model, and it was confirmed that the modal analysis time was reduced by 3.42 times and the frequency response analysis time was reduced by 4.59 times.

Peringatan Dini Banjir Berbasis Internet Of Things (IOT) dan Telegram

Flooding is a natural event that can have serious impacts on ecosystems, the economy and human life. In efforts to mitigate and control flood risks, early warning plays an important role in providing quick information to the public and the authorities. In this context, the application of the Internet of Things (IoT) provides an innovative solution by utilizing the latest technology to create an efficient and accurate flood early warning system. This research is intended to create a prototype river water level monitoring system using NodeMCU and the Telegram application programming interface as a first step in anticipating flooding. The main objective is to provide assistance to supervisory officers in monitoring river water levels in real time. This research method includes the development and integration of sensors connected to an IoT network to accurately detect changes in water level. The system also implements efficient data analysis algorithms to determine whether conditions require early warning. Telegram was chosen as communication platform to convey warning messages to the public. The IoT device is connected to a buzzer which will provide a sound warning when the water level reaches a threshold. The research results obtained show that water level conditions in the range of 2cm-50cm are categorized as Alert 1 status, so the buzzer (Alarm) is On. In the 51cm-99cm range, the status is Alert 2, and in the 100cm-400cm range the status is Alert 3 with the buzzer Off. Apart from that, via the Telegram bot the status of Alert 3, Alert 2, and Alert 1 will be informed