Flow Technique Research Articles

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting

Reliable forecasting of short-term traffic flow is an essential component of modern intelligent transport systems. However, existing methods fail to deal with the non-linear nature of short-term traffic flow, often making the forecasting unreliable. Herein, we propose a reliable short-term traffic flow forecasting method, termed hybrid extreme learning, that effectively learns the non-linear representation of traffic flow, boosting forecasting reliability. This new algorithm probes the non-linear nature of short-term traffic data by exploiting the artificial bee colony that selects the best-implied layer deviation and input weight matrix to enhance the multi-structural information perception capability. It speeds up the forecasting time by calculating the output weight matrix, which guarantees the real usage of the forecasting method, boosting the time reliability. We extensively evaluate the proposed hybrid extreme learning method on well-known short-term traffic flow forecasting datasets. The experimental results show that our method outperforms existing methods by a large margin in both forecasting accuracy and time, effectively demonstrating the reliability improvement of the proposed method. This reliable method may open the avenue of deep learning techniques in short-term traffic flow forecasting in real scenarios.

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO2 emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.

Broadband Rotational Spectroscopy in Uniform Supersonic Flows: Chirped Pulse/Uniform Flow for Reaction Dynamics and Low Temperature Kinetics.

ConspectusThe study of gas-phase chemical reactions at very low temperatures first became possible with the development and implementation of the CRESU (French acronym for Reaction Kinetics in Uniform Supersonic Flows) technique. CRESU relies on a uniform supersonic flow produced by expansion of a gas through a Laval (convergent-divergent) nozzle to produce a wall-less reactor at temperatures from 10 to 200 K and densities of 1016-1018 cm-3 for the study of low temperature kinetics, with particular application to astrochemistry. In recent years, we have combined uniform flows with revolutionary advances in broadband rotational spectroscopy to yield an instrument that affords near-universal detection for novel applications in photodissociation, reaction dynamics, and kinetics. This combination of uniform supersonic flows with chirped-pulse Fourier-transform microwave spectroscopy (Chirped-Pulse/Uniform Flow, CPUF) permits detection of any species with a modest dipole moment, thermalized to the uniform temperature of the gas flow, with isomer, conformer, and vibrational state specificity. In addition, the use of broadband, high-resolution, and time-dependent (microsecond time scale) micro- and mm-wave spectroscopy makes it an ideal tool for characterizing both transient and stable molecules, as well as studying their spectroscopy and dynamics.In this Account, we review recent advances made using the CPUF technique, including studies of photodissociation, radical-radical reaction dynamics, and low temperature kinetics. These studies highlight both the strength of universal and multiplexed detection and the challenges of coupling it to a high-density collisional environment. Product branching and product evolution as a function of time have been measured for astrochemically relevant systems, relying on the detailed characterization of these flow conditions via experiments and fluid dynamics simulations. In the photodissociation of isoxazole, an unusual heterocyclic molecule with a very low-energy conical intersection, we have identified 7 products in 5 reaction channels and determined the product branching, pointing to both direct and indirect pathways. We have also approached the same system from separated NO and C3H3 reactants to explore a broader range of the potential energy surface, demonstrating the power of multichannel branching measurements for complex radical-radical reactions. We determined the product branching in the C3H2 isomers in the photodissociation of the propargyl radical and identified the importance of a hydrogen atom catalyzed isomerization to the lowest energy cyclic form. This then motivated a study of direct D-H exchange reaction in radicals, in which we demonstrate that it is an important and overlooked pathway for deuterium fractionation in astrochemical environments. Recently, we have shown the measurement of low temperature kinetics inside an extended Laval nozzle, after which a shock-free secondary expansion to low temperature and density affords an ideal environment for detection by rotational spectroscopy. These results highlight the power and potential of the CPUF approach, and future prospects will also be discussed in light of these developments.

Influence of Cow Dung Extract Complemented with Nutrient Solution on the Growth Performance of Lettuce (Lactuca sativa L)

Limited studies have focused on plant growth performance using organic-based solutions complemented with mineral elements in a hydroponic system. Thus, this study aimed to investigate the growth performance of lettuce (Lactuca sativa L.) as influenced by cow dung extract combined with a hydroponic nutrient solution. Treatments considered as four different levels of aerated cow dung extracts (C), viz., C1 = 50 g l-1, C2 =100 g l-1, C3 =150 g l-1 and C4 =200 g l-1 and four strengths of standard nutrient solution (S), viz, S1 = 30% of standard nutrient solution, S2 = 40% of standard nutrient solution, S3 = 50% of standard nutrient solution and S4 = 60% of standard nutrient solution. The experiment was carried out using a deep flow technique in a semi-greenhouse. Various growth and physiological parameters were measured in this experiment. The obtained data were subjected to statistical analysis with 4 replicates by analysis of variance (ANOVA) using SPSS. In the case of growth parameters, the tallest plant (23.54 cm), maximum number of leaves per plant (17.01) broadest leaf (13.32 cm), and the highest fresh weight (112.05 g/plant) were recorded from C3 while the lowest in C1. For hydroponic nutrient solution, the tallest plant (23.13 cm), the maximum number of leaves per plant (16.66), the widest leaf breath (14.17 cm), and the highest fresh weight (116.0 g/plant) were recorded from S4 while the lowest in S1. On the other hand, physiological traits viz. leaf area, net assimilation ratio, and relative growth rate were statistically higher in C3 and lowest in C1. In the nutrient solution, all physiological parameters were highest in S4 and the lowest in S1. In the case of the interaction effect, the highest fresh weight and almost all the parameters were found best in C3S4 and the lowest in C1S1. Therefore, the analysis showed that in terms of growth promotion properties hydroponic nutrient solution along with cow dung extract had a substantial impact and C3S4 was the most preferable treatment combination. Based on these findings, in a hydroponic system, an inorganic nutrient solution combined with organic liquid fertilizer derived from cow dung extract (as an alternative nutrient source) requires further improvements to achieve optimal growth and yield.

Implicit EXP-RBF techniques for modeling unsaturated flow through soils with water uptake by plant roots

Modeling unsaturated flow through soils with water uptake by plan root has many applications in agriculture and water resources management. In this study, our aim is to develop efficient numerical techniques for solving the Richards equation with a sink term due to plant root water uptake. The Feddes model is used for water absorption by plant roots, and the van-Genuchten model is employed for capillary pressure. We introduce a numerical approach that combines the localized exponential radial basis function (EXP-RBF) method for space and the second-order backward differentiation formula (BDF2) for temporal discretization. The localized RBF methods eliminate the need for mesh generation and avoid ill-conditioning problems. This approach yields a sparse matrix for the global system, optimizing memory usage and computational time. The proposed implicit EXP-RBF techniques have advantages in terms of accuracy and computational efficiency thanks to the use of BDF2 and the localized RBF method. Modified Picards iteration method for the mixed form of the Richards equation is employed to linearize the system. Various numerical experiments are conducted to validate the proposed numerical model of infiltration with plant root water absorption. The obtained results conclusively demonstrate the effectiveness of the proposed numerical model in accurately predicting soil moisture dynamics under water uptake by plant roots. The proposed numerical techniques can be incorporated in the numerical models where unsaturated flows and water uptake by plant roots are involved such as in hydrology, agriculture, and water management.

TEDR: A spatiotemporal attention radar extrapolation network constrained by optical flow and distribution correction

In recent years, deep learning has been widely applied to meteorological radar extrapolation due to the shortcomings of traditional optical flow methods in predicting the genesis and dissipation of radar echoes. However, it still faces challenges in addressing issues of clarity and overall intensity attenuation caused by uncertainty. This study implemented a dual-path spatiotemporal attention network that integrates optical flow techniques by employing intra-frame static attention and inter-frame dynamic attention, which could simulate motion fields and the overall intensity distribution of radar echoes separately. Our approach effectively resolve the issues of systematic intensity attenuation and clarity degradation introduced by deep learning methods. Through the comparisons of key metrics such as MSE, SSIM, CSI20, CSI30, and CSI40, the results demonstrated significant improvements over traditional approaches, particularly in CSI30 and CSI40, where the metrics improved by more than 35 %.

Risk factor of pickleball injury: systematic review and meta-analysis

Background and purpose Purpose: to identify and evaluate critically the evidence pertaining to pickleball injury risk factors. Material and methods The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework was followed for conducting the systematic review. Google Scholar, PubMed, and Scopus article searches The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow technique is used during the data collection phase of the literature review. The following factors are taken into consideration while looking for research journals: (1) variables that match the needs of the writing; (2) consistent data collection techniques; (3) measurable and reliable research findings; and (4) clearly and precisely stated data analysis. Researchers used the Google Scholar, PubMed, and Scopus databases to search for literature on the subject of risk factors for pickleball injuries. Google Scholar, PubMed, and Scopus were chosen because of their ease of access and eligibility to get complete and in-depth articles. In addition, researchers are looking for open-access articles so that they can access them for free. Keywords used include pickleball, injury, risk, and factor. In the initial search, 417 articles were found, consisting of 406 articles from Google Scholar, 9 articles from Scopus, and 2 articles from PubMed. After going through the selection process, 392 articles were automatically deleted, and 25 potentially relevant articles were obtained to go through the next selection process. Of the 25 articles, 5 that did not meet the inclusion criteria were removed, and 20 articles were found. Of the 20 articles reviewed through research titles and abstracts, 10 were deleted, and the remaining 10 were worth analyzing. Of the 10 articles analyzed by researchers, six were found to be reviewed. The deletion of 4 articles was due to 2 articles not being able to explain risk factors for injury to pickleball, 1 article being a systematic literature review, and 1 article not explaining injury to pickleball. Result Age, gender, technique, equipment, a lack of warm-up and stretching, past injuries, overuse, and court surface were found in six articles to be risk factors for pickleball injuries. Conclusion Excessive use and a lack of stretching and warming up are two things that require attention out of all the risk factors for injury that have occurred in the past. The greatest risk factor for injuries to recur years later is prior injuries. To reduce the likelihood of more injuries, the best therapy must be given. A pickleball player must be aware of the volume, intensity, and rest intervals of their activities as these can be risk factors for injury. Prior to participating in sports, especially pickleball, it's important to take into account the lack of a warm-up and stretching routine. Despite the lack of particular study on this topic, it is crucial to lower the risk of harm.

N-Acetylcysteine Mitigates Oxidative Stress Induced by Transplanting Lettuce Seedlings into a DFT Hydroponic System

Oxidative stress results from an imbalance between the production and accumulation of reactive oxygen species (ROS), which can impede plant growth under various environmental stresses. While waterlogging is a well-known inducer of oxidative stress, the effects of oxidative stress on plant roots grown using the deep flow technique (DFT) hydroponic system remain poorly understood. In this study, we demonstrate that N-acetylcysteine (NAC) enhances the growth of lettuce seedlings transplanted into a DFT system. NAC application significantly improved both shoot and root growth, with the most pronounced effects observed at a concentration of 0.3 mM. Moreover, NAC mitigated the accumulation of hydrogen peroxide in roots following transplantation. It also reduced a temporary increase in lipid peroxidation and total phenolic content in both roots and shoots. These results suggest that NAC functions as an antioxidant, alleviating oxidative stress by scavenging hydrogen peroxide in the roots. Importantly, NAC’s protective effects may extend to other hydroponically grown crops, offering broader potential for reducing oxidative stress across various cultivation systems.

Construction of on-site DNA pre-treatment device and rapid electrochemical biosensor set for Escherichia coli detection in milk

Restaurants and cafeterias are constantly exposed to bacterial contamination. In such environments, it is highly needed the rapid bacteria detection system with simple pre-treatment device. To solve this problem, this study presented the pre-treatment device and Rapid electrochemical (REC) biosensor set for detecting Escherichia coli (E. coli) gene in milk sample. The pre-treatment device is consisted of tetraethyl orthosilicate (TEOS)-coated bead that operated with sonication method, and it can effectively remove the E. coli residues and extract E. coli DNA well. By analyzing the sequences of various foodborne pathogens, we selected the highly variable region sequences of E. coli DNA to use as the bioprobe for REC biosensor. The selected thiol-modified bioprobe was immobilized onto the fabricated biosensor electrode. For detecting the extracted E. coli gene fragment rapidly, the alternating current electrothermal flow (ACEF) technique was introduced to REC-biosensor. ACEF technique minimize the target-bioprobe binding time to 10 mins. Using the extracted E. coli DNA fragments with the pre-treatment device, the proposed REC-biosensor shows the limit of detection (LOD) in milk samples was 9.235 × 10-5 ng/μL, and the target detection accuracy was 92.5 %, with a maximal error rate up to 6.730 %. Moreover, the biosensor demonstrated high selectivity in electrochemical performance evaluation (Matrix effect = 7.425 %). The developed pre-treatment device provides simple and quick preprocessing time (within 20 min) of E. coli, and REC biosensor provides the rapid detection of E. coli (within 10 min). In conclusion, the proposed pre-treatment device and biosensor set can be easily applied to other types of bacteria, virus or germs in the near future.

Nutrition Temperature and TDS Control System with Fuzzy Logic on Pak Choy Hydroponics (Brassica rapa subsp. chinensis)

Hydroponics is a method of cultivation that does not use soil as a medium, allowing it to be applied in limited spaces such as urban households. One of the vegetable plants that can be grown using hydroponics is pak choy (Brassica rapa subsp. chinensis). To produce healthy pak choy plants that can efficiently absorb nutrients in a hydroponic system, several factors need to be considered, such as the level of Total Dissolved Solids (TDS) in the nutrient solution, nutrient solution temperature, and air humidity in the hydroponic environment. The ideal nutrient solution temperature for hydroponic plants falls within the range of 25-27°C. In this system, a monitoring and control system will be designed to optimize the growth of pak choy plants in a Deep Flow Technique (DFT) hydroponic system. In this system, the nutrient solution temperature will be controlled with a set point of 25°C using an on/off control for a peltier device. To maintain the TDS level at a set point of 1200 ppm in the nutrient solution, fuzzy logic control will be employed, generating timer-based control signals for the nutrient pump A, nutrient pump B, and water pump. The monitoring system will be displayed on an Internet of Things (IoT) dashboard platform, such as ThingSpeak.

Absolute electron impact ionization cross-sections for CF4: Three dimensional recoil-ion imaging combined with the relative flow technique.

Here we present measurements of dissociative and non-dissociative cross-sections for the electron impact of the CF4 molecule. The present experiments are based on a Recoil Ion Momentum Spectrometer (RIMS), a standard gas mixing setup for CF4, and a reference gas. The measurements were carried out at several electron energies up to 1keV, covering the energy range of previous experiments. We apply the relative flow technique (RFT) to convert the relative cross-sections measured by the RIMS into absolute values. Using the combination of RIMS and RFT, ion collection and calibration errors were minimized. The results were compared with theoretical and experimental studies available in the literature. Previous electron impact experiments present relative cross-sections or use correction terms for the absolute cross-sections due to losses of energetic ions. We elucidate the differences between the new measurement method and the existing ones in the literature and explain why the present method can be considered reliable. Furthermore, we show how reducing correction terms affects the results.

A new method for the prediction of vibration responses of thin plates coupled with discontinuous connections

Due to the limitation caused by the assumption of ideal continuous line junctions between plates or the high computation cost, the existing methods cannot provide reliable prediction solutions of vibration responses of coupled plates with real connections, such as bolts and corner irons. To this end, a discontinuous energy finite element analysis (dEFEA) method with high accuracy is proposed to predict the vibration responses of discontinuously connected plates, which can revert the real connection. This method developes the coupling loss factors (CLFs) on the steady-state energy flow technique to characterize vibration transmission between plates with different connections. In addition, a discontinuous energy finite element equation of coupled plates is established to obtain the vibration responses of those. The efficacy of the dEFEA method is demonstrated by experiments and the method can improve the prediction accuracy of vibration, which is verified by the comparison between discontinuous and ideal continuous connections. To sum up, vibration transmission of connected structures varies greatly depending on the way of connection and cannot be simplified to a line connection.

Fast and efficient chromium(VI) extraction by colloidal Mg/Al layered double hydroxide nanoparticles

Due to the constraints associated with diffusion and mixing time, traditional kinetic and thermodynamic approaches were inadequate for probing the true mechanism of interaction between chromate and Layered Double Hydroxide (LDH). To circumvent these limitations, colloidal suspensions of Mg/Al-NO3 LDH, characterized by a positively charged surface (approximately +50 mV) in ultrapure water and a mean average diameter of 140 nm, allowing the formation of stable suspensions for days, were swiftly mixed with Cr(VI) suspensions at both pH = 4 and 9 using a stopped flow technique. This rapid mixing, accomplished in <5 milliseconds, enabled the examination of the initial stages of interaction between the toxic anion and the host compound. Two distinct steps in the adsorption process were identified: a very fast step (completed in <5 ms), representing up to 80% of the measured variation, and a slower step lasting up to 100 s. The fast step assumed to be driven by electrostatic interaction (ζ ∼ +50 mV) with the surface, and sites close to the surface are easily accessible to the chromate anions. The slower step corresponded to a diffusion process close or inside the particles. Chromate extraction efficiency was investigated through ultrafiltration tests, varying the LDH and chromate amounts, indicating that 2 nitrate ions are exchanged for 1 chromate, regardless of the pH considered, and a total exchange can be fulfilled with 0.1 g L−1 of LDH within the explored concentration range.

Pengembangan Inovasi Smart Aeroponik sebagai Upaya Efisiensi Urban Farming Produktif Ramah Energi

This article explores the development and application of smart aeroponic innovation as an effort to improve efficiency in energy-friendly urban farming. Smart aeroponics, which utilizes sensor technology and automatic control systems, is designed to maximize plant growth while minimizing energy and water use. In this community service project, we implemented a smart aeroponic system in several urban locations and trained local communities in its operation and maintenance. Evaluation of the results showed that this system can increase energy efficiency by up to 70% when compared to hydroponics with the Deep Flow Technique (DFT) method or each can save 29.02 kWh for mustard greens cultivation. And 41.00 kWh for lettuce cultivation. The potential of this technology is an effort to improve the efficiency of urban agriculture, but also provides significant ecological and economic benefits for urban communities, so that it can be a sustainable model for future agricultural development.

Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning

Visual anomaly detection plays a crucial role in manufacturing to ensure product quality by identifying image patterns that deviate from the expected ones. Existing methods that rely on distribution estimation struggle with the complexity of real-world images, resulting in complex and inefficient procedures. This study leverages normalizing flow techniques to address the cold start anomaly detection problem, where no prior examples of anomalies are available during the training phase. In such scenarios, models must learn exclusively from defect-free images and still accurately identify anomalies. We propose a novel unsupervised multi-scale and multi-semantic normalizing flow model, enhanced with an ensemble of neural networks, to detect anomalies based on their feature distributions. Our model estimates the likelihood of non-defective features, identifying anomalies as out-of-distribution values. Extensive experiments on three state-of-the-art anomaly detection datasets demonstrate that our proposal achieves superior AUROC performance and improves computational efficiency compared to existing approaches. Furthermore, we validate the robustness and adaptability of our proposal through low-shot training experiments using only 20% of available training data, highlighting its potential as an efficient solution for cold start anomaly detection.

MR Lymphangiography: Congenital Lymphatic Flow Disorders.

Congenital lymphatic flow disorders collectively refer to a heterogeneous group of diseases that manifest as chylothorax, chylous ascites, intestinal lymphangiectasia, protein-losing enteropathy, and peripheral extremity or genital lymphedema, all in the absence of identifiable injury to the lymphatic system. We have only recently begun to understand congenital lymphatic flow disorders through the ability to image lymph flow dynamically. Intranodal dynamic contrast-enhanced magnetic resonance lymphangiography (DCMRL) is a crucial technique for imaging lymphatic flow in pediatric patients with congenital lymphatic flow disorders. However, as lymphatic imaging is still a nascent discipline with many uncertainties regarding optimal imaging and treatment, effective patient management requires a comprehensive understanding of imaging techniques, disease pathophysiology, and multidisciplinary treatment approaches. Above all, a fundamental understanding of the physiological lymphatic flow of the central conducting lymphatics is essential for the correct interpretation of DCMRL images. This knowledge helps to avoid unnecessary examinations, erroneous diagnoses, and potentially harmful treatment approaches. This review provides an overview of the methods, advantages, and precautions for interpreting the DCMRL examination, a state-of-the-art lymphatic system imaging technique, and shares various case studies.

Speech action patterns of Indonesian at multi-language campus

This study aims to describe the types and functions of speech acts constructed by Indonesian Language and Literature Education students at the Teaching and Education Faculty, Universitas Jambi, who have multilingual backgrounds. This research is a qualitative descriptive study. Data were collected from 30 informants. Data was collected using free listening techniques in various settings on the campus, as well as recordings and field notes. Data were analyzed using the flow technique from Miles and Huberman through the steps of data reduction, data presentation, and drawing conclusions. The data validation technique used theory and method triangulation techniques. The results of the research can be seen in events of students of Indonesian Language and Literature Education, Teaching and Education Faculty, Universitas Jambi, which is multilingual, including speech acts; (1) commission, (2) assertive, (3) declarative, (4) expressive, and (5) directive. Likewise, the function of the speech act that is revealed is a function; (1) competitive, (2) fun, (3) cooperative, (4) contradictory. The students’ multilingual background in uncovering the types and functions of speech acts consists of speakers of the language; (1) Bugis, (2) Batak, (3) Malay Jambi, (4) Javanese, (5) Palembang, (6) Kerinci, and (7) Minangkabau. The speech patterns built by speakers of different mother tongues can be seen and the cultural background of each ethnicity is very influential. Their cultural different background brings about different styles of language. The language culture of Bugis, Batak, and Palembang speakers feels more assertive, and vulgar and shows a slightly harsh tone. Whereas, those with a cultural background in Minangkabau, Kerinci, Javanes, and Jambi Malay languages feel more relaxed and softer. This can be seen both in each type of speech act, as well as in terms of the expression of the function of the speech act. However, when they are faced with the reality of being multi-language, they are forced to use Bahasa Indonesia, and in turn, they become a reflection of patterns of speech acts in Bahasa Indonesia as well as a profile of Indonesian sociolinguistics.

Establishment of the rapid electrochemical-SELEX monitoring system by using ACEF technique and its application to dengue virus aptasensor fabrication

Aptamers, which consist of single-stranded DNA or RNA, possess considerable potential as biomolecular recognition elements. The systematic evolution of ligands by exponential enrichment (SELEX) serves as a general method for synthesizing aptamers. However, the conventional SELEX process for monitoring aptamer-target binding is time-consuming and labor-intensive, highlighting the need for rapid monitoring and selection methods. To address this problem, this study introduces a novel approach called rapid electrochemical-SELEX (RE-SELEX) monitoring method, which significantly reduces the target-aptamer binding time from 3 h to 10 min. This process is achieved by employing an alternating current electrothermal flow (ACEF) technique. The Au micro gap-type electrode was used to RE-SELEX monitoring device. As a proof-concept of this method, the dengue virus (DENV) aptamers were synthesized. For confirming the feasibility of this DENV aptamer, we analyzed target binding of produced aptamers test including gel electrophoresis, dissociation constant analysis. Then, two aptamers were selected for constructing the rapid electrochemical DENV aptasensor. The RE biosensor was achieved by ACEF technique that reduce the target-aptamer binding within 10 min. The fabricated device can detect 37 fM of the target dengue envelope protein in diluted human serum well. Thus, the proposed method provides a reliable SELEX-monitoring method for the rapid synthesis of aptamers using a simple electrochemical platform.

The pulse of Yarlung Tsangpo River: Ultra-high-resolution insights into the flow and sediment

Traditional monitoring techniques of flow and sediment are inadequate for the sparsely populated, the rivers in the Qinghai-Tibetan Plateau region which are experiencing amplified climate change. Inspired by the Euchiloglanis fish, this study developed a unique self-contained hydrodynamic observation platform to withstand the high velocities of mountain rivers typically encountered in mountain rivers. The platform, equipped with sensors, was successfully deployed in the Yarlung Tsangpo River, collecting ultra-high-resolution data on flow and sediment over a period of nearly two months. Performance assessments showed that the median value of discharge per unit width measured and suspended sediment concentration by this system were basically consistent with those daily measurements from the hydrological station. Meanwhile, the superiority of the system was reflected in the more significant daily average change, especially at the flooding period. The data unveiled significant minute-level hydrological fluctuations and a complex interplay of factors affecting the river’s flow and sediment transport. A wavelet coherence analysis revealed asynchrony changes in short-period flow and sediment variables. Persistent short-period and coherent long-period signals are significantly displayed during flood events. The platform promises future applications, such as automated benthic observations and replacing traditional ’lead-line’ methods. Despite acknowledging system limitations of underwater attitude, burying tendency, and real-time data transmission capabilities, the study highlighted the crucial role of this system in understanding and managing river dynamics, particularly amid escalating environmental threats.