Simple Process Research Articles

A piezoelectric coded-excitation scanning acoustic transducer for Lamb wave inspections

Abstract Several techniques for Guided Wave (GW) inspections have already been developed. Most of them rely on extensive sensor deployment and damage localization algorithms characterized by significant computational costs. However, there is a growing demand for simpler, more autonomous, and more affordable systems across various fields. In particular, the implementation of wireless, battery-powered systems with a reduced number of sensor nodes and simpler processing would greatly facilitate the transition of this inspection technology on the field. Following this direction, this work presents the design of a novel piezoelectric transducer composed of four different patches, i.e. with only four input/output channels, to scan a given area. The peculiar piezo-load distributions allow the association of different spectral binary sequences for each 6∘ discrete angular step. By evaluating the distance-of-flights (DoFs) of detected peaks, the range coordinates for multiple defects are identified. Meanwhile, the angular information is extracted by demodulating binary sequences of peaks with comparable DoFs across several frequency bands. Since the transducer is designed as an encoder, it is referred to as coded-excitation scanning acoustic transducer (CESAT). More specifically, the Gray code is used to generate spectral patterns to reduce the uncertainty between two adjacent angular steps. A new quantization procedure for the optimal generation of the piezo distributions is also proposed. Ad hoc signal processing algorithms, suitable for embedded applications, were developed to extract multi-target range and angle information. The processing is based on the frequency decomposition of the recorded signal using an FIR filter bank and on dispersion compensation procedures for pulse ‘re-compression’. The transducer encoder behavior is validated through a finite element analysis. Finally, numerical simulations were performed to assess the effectiveness of the CESAT and associated signal processing in multi-defect detection and localization tasks.

Gastric polyps are not created equal: Know your enemy

Gastric polyps are commonly detected during upper gastrointestinal endoscopy. They are most often benign and rarely become malignant. Nevertheless, adequate knowledge, diagnostic modalities, and management strategies should be the endoscopist’s readily available “weapons” to defeat the potentially malignant “enemies”. This article sheds light on the valuable effort by Costa et al to generate a new classification system of gastric polyps as “good”, “bad”, and “ugly”. This comprehensive overview provides clinicians with a simplified decision-making process.

Vialess heterogeneous skin patch for multimodal monitoring and stimulation

System-level wearable electronics require to be flexible to ensure conformal contact with the skin, but they also need to integrate rigid and bulky functional components to achieve system-level functionality. As one of integration methods, folding integration offers simplified processing and enhanced functionality through rigid-soft region separation, but so far, it has mainly been applied to modality of electrical sensing and stimulation. This paper introduces a vialess heterogeneous skin patch with multi modalities that separates the soft region and strain-robust region through folded structure. Our system includes electrical and optical modalities for hemodynamic and cardiovascular monitoring, and a force-electrically driven micropump for drug delivery. Each modality is demonstrated through on-demand drug delivery, flexible waveguide-based PPG monitoring, and ECG and body movement monitoring. Wireless data transmission and real-time measurement validate the feedback operation for multi-modalities. This engineered closed-loop platform offers the possibility for broad applications, including cardiovascular monitoring and chronic disease management.

Quantum dot distributed feedback laser grown on silicon with laterally coupled gratings

Abstract The direct epitaxy of quantum dot (QD) lasers on silicon was considered as holy grail of integrated silicon photonics. With demonstration of various InAs/GaAs QD lasers directly grown on silicon substrates, there are limited research carried on single longitudinal mode lasers on silicon. Here, to avoid any regrowth procedures, distributed feedback (DFB) QD lasers on silicon are fabricated with lateral coupled (LC) gratings via simplified fabrication process. The LC-DFB laser has a maximum laser power of 4.5 mW and threshold current of 80 mA under continuous-wave current mode at room temperature. Under injection current of 110 mA, the side mode suppression ratio (SMSR) reaches 50 dB at room temperature. The RIN of the laser is measured with value of -135dB/Hz with corresponding optical linewidth of 3.5 MHz.

On Demand Copper Electrochemical Deposition on Laser Induced Graphene for Flexible Electronics.

The rapid development of flexible electronics necessitates simplified processes that integrate heterogeneous materials and structures. In this study, laser engraving is combined with electrochemical deposition (ECD) to directly fabricate various micro/nano-structured components and flexible electronic circuits. A theoretical framework and simulation model are developed to design the on-demand ECD on laser induced graphene (LIG), enabling the generation of multi-scale copper (Cu) materials with controllable oxidation states. The Cu-LIG composites exhibit high surface quality and reliability, meeting the requirements of flexible circuits. The study fabricates and characterizes multilayer circuits and complex functional devices, including electrochemical sensors, thin-film heaters, and wireless humidity sensors, to showcase the versatility of the LIG-ECD process. This approach can be extended to various polymer and metal deposition processes, paving the way for the development of high-performance flexible electronic devices.

A Reusable Capillary Flow-Driven Microfluidic System for Abscisic Acid Detection Using a Competitive Immunoassay

Point-of-care (PoC) devices offer a promising solution for fast, portable, and easy-to-use diagnostics. These characteristics are particularly relevant in agrifood fields like viticulture where the early detection of plant stresses is crucial to crop yield. Microfluidics, with its low reagent volume requirements, is well-suited for such applications. Self-driven microfluidic devices, which rely on capillary forces for fluid motion, offer an attractive alternative by eliminating the need for external pumps and complex fluid control systems. However, traditional microfluidic prototyping materials like polydimethylsiloxane (PDMS) present challenges due to their hydrophobic nature. This paper presents the development of a reusable, portable, capillary-driven microfluidic platform based on a PDMS-PEG (polyethylene glycol) copolymer designed for the rapid low-cost detection of abscisic acid (ABA), a key biomarker for the onset of ripening of non-climacteric fruits and drought stress in vines. By employing passive fluid transport mechanisms, such as capillary-driven sequential flow, this platform enables precise biological and chemical screenings while maintaining portability and ease of use. A simplified field-ready sample processing method is used to prepare the grapes for analysis.

Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials

This study introduces a novel composite construction material composed of lunar regolith combined with PEEK in dry powder form. The work demonstrates significant advantages over alternative methods, primarily by reducing production power consumption and simplifying the manufacturing process. Building on previous research that explored binder optimization through process simplification and targeting predefined shapes, this work delves deeper into a comparative analysis of high-performance thermoplastics. Among the various options, PEEK demonstrates the most favorable properties. The study investigates key processing parameters and evaluates the effects of vacuum processing and temperature testing on mechanical properties. The research also evaluates the effects of vacuum processing and temperature testing to assess the material’s performance under lunar conditions. Comparative analysis is performed with standard performance of various reinforced and unreinforced concretes and with standard requirements for construction bricks as per ASTM standards. This shows that the composite, with an organic binder content as low as 5 wt%, has great potential. Notably, the improvements achieved through vacuum curing ensure compliance with lunar environmental conditions and alignment with most Earth-based engineering standards. Samples compacted at 7.50 MPa with 10 wt% binder, and tested at room temperature, achieve a compression strength of 16.3 MPa, exceeding that of industrial floor bricks and matching that of building bricks used on Earth. Bending strength (7.4 MPa) aligns with steel fiber-reinforced and high-strength concretes. Vacuum curing further enhances these properties, with an observed increase of +66% in bending strength and +33% in compression strength.

Green Synthesis, Formulation and Test Field of Lymantria monacha L. (Lepidoptera: Lymantriidae) Sex Pheromone in East European Region

Green Synthesis, Formulation and Test Field of Lymantria monacha L. (Lepidoptera: Lymantriidae) Sex Pheromone in East European Region

Label‐Free Impedimetric Determination of Cortisol Using Gold Nanoparticles Functionalized Laser‐Induced Graphene Interdigitated Electrodes

AbstractA gold nanoparticle (AuNP) functionalized laser‐induced graphene interdigitated electrode (LIG‐IDE) is fabricated for the label‐free detection of cortisol. Self‐assembled monolayers of 3‐Mercaptopropionic acid immobilized on the AuNP functionalized LIG‐IDE are activated via carbodiimide chemistry for the binding of the anti‐cortisol antibody. The resulting immunosensor is tested for the specific detection of cortisol over the physiologic range (0.1 pm–100 nm) in synthetic human sweat. Electrochemical impedance spectroscopy is used to measure the change in impedance due to cortisol bonding to the LIG surface. The sensor exhibits a good linear (log(concentration)) fit over the entire concentration range (R2 = 0.9662) and a LOD of 0.0085 nm. The minimal effect of synethtic sweat shows its potential use in real‐world applications. A specificity test against similarly structured molecules demonstrates high affinity toward cortisol. Furthermore, the developed sensor exhibits excellent reproducibility with a 5.93%RSD. Given the simple and facile processability of immunosensors, the developed label‐free AuNP functionalized LIG‐based biosensing platform shows great promise as an effective and low‐cost tool suitable for healthcare monitoring and point‐of‐care diagnostics, and it can be applied as an immunosensor for other affinity‐based biosensors.

Late-stage C-H trifluoroacetylation of quinoxaline-2(1H)-ones using masked trifluoroacyl reagents.

A strategy for trifluoroacetylation of quinoxaline-2(1H)-ones has been investigated. This strategy employs masked trifluoroacyl reagents to obtain trifluoroacetylated quinoxaline-2(1H)-ones under metal-, catalyst-, and light-free conditions. This approach is distinguished by its functional group compatibility and tolerance, as well as the simplicity of the experimental process, making it suitable for gram-scale synthesis.

Predictive Analytics for Housing Market Trends and Valuation

Introduction: The demand for housing in major cities is exceptionally high due to the concentration of offices and economic hubs in these areas. The combination of limited available land and increased demand drives house prices upward.Objective: To accommodate this, developers are increasingly constructing residential areas on the outskirts of cities, offering easier access to transportation such as trains and highways. These developers compete by offering competitive pricing, diverse housing options, simplified mortgage processes, and attractive promotions like zero down payments. Buying a house is a significant long-term investment, as property values typically appreciate over time. Therefore, a thorough analysis is crucial when purchasing a home. Several key factors, such as location, land size, building area, and property type, play a role in determining house prices.Method: This study adopts a quantitative approach, which involves systematically investigating phenomena by collecting measurable data and analyzing it through statistical, mathematical, or computational methods. Result: This paper discusses the most effective techniques for data collection, pre-processing, feature extraction, model training, and evaluation. The purpose of this research method is to develop theoretical frameworks related to real-world phenomena. Conclusions: Measurement plays a pivotal role in this quantitative study, as it is central to understanding the data and drawing meaningful conclusions. Finally, we evaluate the current state of research, identifying trends and gaps in the field

Identification of Plant Chromatin Interaction Networks Using IP-MS and co-IP.

Proteins often act in concert to perform their function. Thus, the identification of protein complexes is crucial if we want to understand how they work. In this chapter, we present a highly sensitive protocol for the immunoprecipitation of nuclear chromatin-linked proteins in Arabidopsis thaliana that does not rely on time-consuming nuclei extraction. Interaction partners are identified using mass spectrometry and confirmed by co-immunoprecipitation. To help solubilize chromatin-bound proteins and eliminate nonspecific interactions of proteins binding the same DNA stretch, we include an enzymatic digestion step to remove DNA before immunoprecipitation. Our protocol offers a simplified process using optimized buffers, which facilitates quick and effective immunoprecipitation. The outcome is high-quality eluates that are ideal for identifying proteins through MS.

Sustainable ballistic solutions for recycling silkworm cocoon waste into high-performance bulletproof materials

ABSTRACT This study aims to develop a method for producing ballistic panels meeting NIJ Level IIIA standards by integrating silkworm cocoon waste (SCW) as reinforcement and utilizing bias fabric through cold pressing. Various SCW quantities (100 g designated as P2, 150 g designated as P3, and 225 g designated as P4) alongside 50 g of resin and 230 g of bias fabric were used, comparing six (type I) and three (type II) layer patterns. Panels with SCW exhibit enhanced mechanical properties, particularly in type II layouts, which show superior performance in tensile, impact, and flexural resistance. Ballistic impact tests reveal that only type I-P3, I-P4, II-P3, and II-P4 composite panels effectively stop bullet penetration, with BFS measurements ranging from 10.6–14.6 mm, below the 44 mm NIJ standard limit. Consequently, the study indicates that II-P3 is selected for producing bulletproof armor compliant with NIJ Level IIIA standards, supported by the method's simplicity, cost-effectiveness, and consistent outcomes. The decision to use II-P3 for producing ballistic panels is made due to its simplified processing requirements. Upon calculating the total component cost for panels sized at 150 × 150 mm, it is established that each panel has a cost of $1.5.

Barriers to Entrepreneurial Intention: A Comprehensive Literature Review

This literature review investigates the barriers affecting entrepreneurial intention, a foundational aspect of business creation influenced by psychological, social, and environmental factors. The study identifies major barriers, such as low self-efficacy, lack of social support, negative attitudes toward entrepreneurship, limited perceived behavioral control, environmental uncertainty, and fear of failure. These factors, documented across diverse contexts, highlight both individual and contextual challenges that deter entrepreneurial motivation. This review aims to clarify these barriers by synthesizing findings from prior research and presenting a framework to inform targeted interventions that can effectively foster entrepreneurial intentions. Emphasis is placed on the role of entrepreneurial education, supportive social structures, and simplified regulatory processes in overcoming these obstacles, thereby supporting potential entrepreneurs in diverse environments. Keyword: Entrepreneurial Intention, Self-Efficacy, Social Support, Perceived Behavioral Control, Fear of Failure, Entrepreneurial Barriers, Literature Review.

Research on fault component extraction and fault type identification of rotating machinery based on MDSM and a novel convolutional neural network

Abstract Given the complexity and difficulty in extracting and recognizing multi-axis mechanical fault components, a method for fault extraction and identification based on the Multi-Axis Displacement Superposition Method (MDSM) and a Novel Convolutional Neural Network (NCNN) is proposed. In the proposed MDSM method, first, correlation analysis is used to determine the operational status of the mechanical system and to identify the location of faults in the multi-axis rotating mechanical system. Secondly, a simplified initial point selection process is introduced to segment the collected fault component. Subsequently, a signal superposition method with position offset correction is employed to perform position correction and superposition operations on the segmented signals, enhancing the accuracy of the fault signal. Finally, the front end of the superimposed signals is extracted as the fault component, completing the separation and extraction of the fault components. For the extracted fault signals, an NCNN is designed for fault-type identification. NCNN improves computational efficiency and effectively completes fault feature identification through a lightweight network architecture and a nonlinear learning rate scheduling strategy. The results of the experiment show that the proposed method can accurately determine the fault occurrence location, extract the fault components, and achieve high-accuracy fault type identification.

Exploring the impact of CO2 sequestration on plastic properties, mechanical performance, and microstructure of concrete

In view of global warming, carbon sequestration techniques are being employed across the globe to minimize the damaging effects of greenhouse gases on the environment. Studies have revealed that adding CO2 during the mixing or curing stage of concrete enhances its mechanical properties and long-term durability. This study aims to examine the effect of CO2 addition during the mixing stage on the plastic, mechanical and microstructural properties of concrete. Various CO2 dosages, ranging from 0.1 to 1%, were injected during mixing to analyze the plastic and hardened properties of concrete. CO2 primarily reacts with calcium hydroxide in concrete to form calcium carbonate (CaCO3), thereby densifying its microstructure and improving its compressive strength by 10–20%. An optimum strength of up to 20% was achieved with 0.75% dosage. Additionally, the results show a 5–10% improvement in flexural and split tensile strength with CO2 addition over the control mix, with 0.75% dosage yielding optimal strength. Semi-adiabatic calorimetry test on early hydrating concrete shows a 14% peak temperature rise at 0.75% CO2 dosage compared to control concrete, indicating enhanced early-age strength development. Thermal Pyrolysis tests, microscopy and infrared spectroscopy indicated the presence of CaCO3, thereby confirming the carbonation process. However, CO2 dosages above 0.5% by weight of cement resulted in a drop in the workability of concrete in the plastic stage. This research attempts to create a simplified CO2 sequestration process in concrete, develop a predictive model to estimate the compressive strength and utilize material characterization techniques to identify the mineralization process.HighlightsInjecting CO2 into fresh concrete improves its strength and lowers its environmental impact.A multiple linear regression model predicts the compressive strength of CO2-injected concrete.Semi-adiabatic calorimetry shows that CO2 injection speeds up early hydration.Thermal Pyrolysis detects mass loss stages linked to dehydration and calcite breakdown.Microscopy and spectroscopy indicate CaCO3 crystal formation in CO2-sequestered concrete.

Deep Reinforcement Learning-Based Process Control in Biodiesel Production

The control of complex industrial processes has been a forefront research topic. Biodiesel production, as a typical complex industrial reaction process, exhibits multivariable coupling, nonlinearity, and high latency, making it challenging for traditional control methods to achieve real-time control under varying operating conditions. To address this issue, this paper proposes a control method based on the twin delayed deep deterministic policy gradient (TD3) algorithm, which dynamically adjusts process parameters to achieve the adaptive optimization control of complex processes. A simplified biodiesel production process model was established to simulate the actual production process. Subsequently, a controller based on the TD3 algorithm was designed and implemented for this model. To evaluate the performance of the proposed controller, comparative experiments were conducted with traditional controllers and deep deterministic policy gradient (DDPG) controllers. The effects of different control strategies were analyzed through simulation experiments. The results demonstrate that the proposed controller achieves the objectives while exhibiting a lower overshoot and shorter settling time and fully validates the effectiveness of the proposed control strategy in terms of both the dynamic and steady-state performance in the production process.

Enzymatic Synthesis and Its Use in Cladribine Production

Enzymatic synthesis is an alternative to chemical synthesis and provides new possibilities in the preparation of pharmaceutically active substances. The aim is to minimize the inefficiency of the chemical method of preparation and increase its effectiveness. Despite advances, biotechnological processes in nucleoside synthesis using catalysis by nucleoside phosphorylases are not actively applied industrially. The development of an enzymatic synthesis route could bring benefits in terms of improved efficiency, process simplicity, and minimization of organic solvent consumption and thus environmental impact. The use of enzymatic synthesis in the production of cladribine provides a simple and rapid way of producing this active substance in high purity without the formation of undesirable by-products.

The Impact of Selected Macroeconomic Variables on Agricultural Sector Performance in Nigeria

The study investigates the impact of selected macroeconomic variables on agricultural sector performance in Nigeria using time series data spanning from 1990 to 2021. The research adopts an ex-post facto design and utilizes data obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin. Advanced statistical tools, including Auto Regressive Distributed Lag (ARDL) models, Descriptive Statistics, Augmented Dickey-Fuller Unit Root Tests, Breusch-Godfrey Serial Correlation Lagrange Multiplier (LM) Tests, and the Cumulative Sum of Recursive Residuals (CUSUM) Squares Test, were employed. The findings reveal that credit to the private sector has a positive but insignificant impact on agricultural sector performance. Broad money supply, monetary policy rate, and inflation rate exhibit negative and insignificant impacts, while banks' lending rates show a positive and significant impact. Jointly, the selected macroeconomic variables have a weak but significant impact on agricultural sector performance. The results highlight that while macroeconomic variables collectively influence the sector, the lending rate plays a more critical role in agricultural growth. Based on these findings, the study recommends improving access to agricultural credit through simplified loan processes and specialized financing products, enhancing monetary policy transmission to reduce financial intermediation frictions, and fostering a stable macroeconomic environment through effective inflation control and exchange rate management. Additionally, monetary policy adjustments should consider their potential effects on different sectors, including agriculture, to ensure sustainable credit growth and investment. Stabilizing inflation through sound fiscal and monetary policies is also emphasized to create a conducive environment for agricultural producers.

Trait-focused low-cost and stereo-based 3D plant modeling for phenotyping via deep neural detection

Abstract. Phenotyping, the measurement of plants physical traits, plays a pivotal role in advancing sustainable agricultural practices. Therefore, developing efficient, low-cost, means to generate such measures is vital. Though image based 2D-driven methods are commonly applied for that purpose due to their processing simplicity, it is clear that only 3D information can offer the necessary plant details. Notwithstanding, the generation of such data is challenged by the requirement to acquire a large set of images or the use of active sensors, which exhibit sensitivity to illumination and require lengthy acquisition campaigns. Consequently, 3D plant phenotyping is presently limited to controlled laboratory conditions and is hardly applied in actual growth setups. To address this shortcoming, this paper argues that by focusing on relevant plant traits, modeling can be simplified and the need for detailed plant reconstruction can be relieved. Accordingly, only a minimal set of images, specifically a stereo pair, can suffice for the reconstruction, thereby providing a low-cost sensing solution. To facilitate the reconstruction, we adapt an anchor-free detection deep neural network and integrate low- and high-level features to accurately detect our plant traits of interest. As the paper demonstrates our adapted network facilitates a robust 3D reconstruction of the entities of interest. Performance analysis demonstrates how our detection is reliable and accurate compared to standard anchor-free frameworks, translating to accurate reconstruction, as we validate against 3D plant scans.