Process In Order Research Articles

New strategy for structure rearrangement of thin-film composite (TFC) polyamide (PA) membranes and offering potential use in both aqueous and organic solvents

The purity of sodium hexafluorophosphate (NaPF6) is crucial for the preparation of electrolytes in manufacturing. We propose a simple process in order to induce membrane structure rearrangement process (SRP) by activating thin-film composite (TFC) membranes with polyamide (PA) structure and explore their potential green recycling application in concentrating sodium-ion batteries (SIBs) electrolyte. The method utilizes a solution containing polar aprotic solvent as the soaking and activating solution to swell and disintegrate the loosely cross-linked PA layer, and a non-solvent solidification solution complete the SRP, enhancing the permeability of the PA layer while maintaining high rejection rates for divalent ions. Subsequently, during the further treatment of the membrane surface in structure rearrangement, tert-butanol (TBA) is introduced into the aqueous solution to reconstruct the surface PA layer, further improving the permeability. The optimal D8010T10 achieved a permeation flux of 24.2 L m−2 h−1·bar−1, with rejection rates for sodium sulfate (Na2SO4), magnesium chloride (MgCl2), and magnesium sulfate (MgSO4) reaching 94.7 %, 98.4 %, and 99.1 % respectively. And the mechanism of SRP with different solvent is analyzed and explained through dissipative particle dynamics (DPD) simulations. This study provides a commercially viable approach for manufacturing high-performance membranes. The D8010T10 membrane achieves more than 90 % separation efficiency for sodium hexafluorophosphate (NaPF6) dissolved in different solvents, providing a potential solution for the concentration and recovery of NaPF6 in lab-scale, verified its potential use in the organic solvent nanofiltration (OSN) and a path way for waste membrane recycling and degrading use in the industry.

Identification of Relevant Parameters for Traceability in the Continuous Mixing Process in Battery Cell Production

The growing demand for lithium‐ion batteries in electric vehicles pose challenges, particularly for the use of critical materials such as cobalt in electrode slurry mixing. Due to regulatory requirements such as the battery passport, an accurate traceability system is mandatory. However, there is no solution for the continuous mixing process, which is a major challenge in electrode production. This article describes an approach to solve this problem. Based on the results of a literature review, a design structure matrix is constructed to analyze parameter influences. The overall result is a list of relevant parameters that should be recorded in the continuous mixing process in order to characterize the mixing process.

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

Pengembangan Aplikasi Catering Pada Rumah Makan Berbasis Mobile

Ordering catering is an activity carried out by customers to buy products in the form of food. At the Tebar Rasa Restaurant, the catering ordering process is carried out by customers by coming directly to the location. Based on the author's interviews with related parties, in the process of ordering catering there are several customers who complain about the ordering process because it takes more time, effort and costs. As a solution to this problem the authors built a Web and Mobile-Based Catering Order Application to assist in the process of ordering catering by customers. In making this application the author uses the android programming base and MySQL as the data storage. This application is made to streamline time, effort and produce accurate information. The results of testing the built application have performance according to user needs. By combining the advantages of the PHP and Java programming languages, this research has the potential to create a more efficient and satisfying food ordering experience for customers.

Sustainable Workforce in a Green Era: Indonesia’s Energy Sector Transition

The purpose of this study is to determine and analyze the effect of renewable energy transition variables and economic viability in the Indonesian employment industry on the balance of business sustainability through social and environmental development as an intervening variable. The subjects of this study are small, medium, and large industry owners in Indonesia, totaling 5,993,566 business units. The variables in this study are independent variables, namely the variables of renewable energy transition and economic viability in the Indonesian labor industry, while the dependent variable is the balance of business sustainability, and the intervening variable is the variable of social and environmental considerations. The results of the data analysis are made through SEM analysis through SMART PLS 4.0 software. From the results of existing research, it is concluded that partially renewable energy transition variables and viability variables in the Indonesian labor industry have a direct effect on the balance of business sustainability and social and environmental consideration variables. Simultaneously, the renewable energy transition variable and the viability variable in the Indonesian labor industry indirectly affect the business sustainability balance variable through social and environmental considerations as intervening variables. With the renewable energy transition process, it will have an impact on people's good social lives because there is no threat of environmental damage or natural disasters, so the environment is safe, the industry creates a technology transfer process, and it increases economic viability in the labor industry in Indonesia in order to create quality human resources. With this transition process, trained human resources are needed and are also able to adapt well to the transition process and technology transfer in order to balance the sustainability of industrial businesses.

The precision of drill calibration for dynamic navigation

ObjectivesTo quantify the reproducibility of the drill calibration process in dynamic navigation guided placement of dental implants and to identify the human factors that could affect the precision of this process in order to improve the overall implant placement accuracy. MethodsA set of six drills and four implants were calibrated by three operators following the standard calibration process of NaviDent® (ClaroNav Inc.). The reproducibility of the position of each tip of a drill or implant was calculated in relation to the pre-planned implants’ entry and apex positions. Intra- and inter-operator reliabilities were reported. The effects of the drill length and shape on the reproducibility of the calibration process were also investigated. The outcome measures for reproducibility were expressed in terms of variability range, average and maximum deviations from the mean distance. ResultsA satisfactory inter-rater reproducibility was noted. The precision of the calibration of the tip position in terms of variability range was between 0.3 and 3.7 mm. We noted a tendency towards a higher precision of the calibration process with longer drills. More calibration errors were observed when calibrating long zygomatic implants with non-locking adapters than with pointed drills. Flexible long-pointed drills had low calibration precision that was comparable to the non-flexible short-pointed drills. ConclusionThe clinicians should be aware of the calibration error associated with the dynamic navigation placement of dental and zygomatic implants. This should be taken in consideration especially for long implants, short drills, and long drills that have some degree of flexibility. Clinical significanceDynamic navigation procedures are associated with an inherent drill calibration error. The manual stability during the calibration process is crucial in minimising this error. In addition, the clinician must never ignore the prescribed accuracy checking procedures after each calibration process.

Radon Leakage in LSC Vials: Material-dependent Analysis for Utilization and Reutilization

Liquid scintillation counting (LSC) is a method commonly used for natural occurring radionuclides measurements in water, in which radon concentration is measured after placing water samples in LSC vials that contain scintillation cocktails. Due to the low levels of activity that are involved in natural environments, it is required to characterize any radon loss during the measuring process in order to minimize errors and uncertainties. Hence, radon leakage in different types of vials (plastic and plastic lined with Teflon) is studied. This analysis reported that, after one and a half radon half-lifes, plastic and Teflon lined vials, compared with the values expected for no radon leakage, presented deviations in radon concentration over the 7% and 3%, respectively. Additionally, the diffusion of radon through the vials’ wall is checked, as well as if it remains embedded in the polymeric matrix. This study confirmed that radon remains embedded in the polymeric matrix and can diffuse back to the vial, having the plastic vials a greater contribution. The possibility to re-utilize single-use vials is also studied from characterizing the presence of radon depending on the vial’s material.

On the strength-ductility modifications in pure copper after severe plastic deformation

The aim of this work is to investigate experimentally and numerically the modifications of both strength and ductility after processing by severe plastic deformation, using several passes of repetitive corrugation and straightening on sheets made of pure copper. Experimental stress–strain curves are determined before and after processing in order to study the influence of the process on the mechanical properties. The modelling of the mechanisms responsible of the mechanical properties evolution is done through the development of an extended Gurson model including a dislocation-based modelling of hardening. The model developed is implemented into a finite element code and applied to the numerical prediction of the repetitive corrugation and straightening followed by a tensile test. The modifications of strength and ductility predicted numerically are qualitatively in good agreement with the experimental observations.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

Simulations of non-integer upconversion in resonant six-wave scattering

Resonant upconversion through a sixth order relativistic nonlinearity resulting in a unique resonance was recently proposed [Malkin and Fisch, Phys. Rev. E 108, 045208 (2023)]. The high order resonance is a unique non-integer multiple of a driving pump frequency resulting in a frequency upshift by a factor of ≈3.73. We demonstrate the presence, unique requirements, and growth of this mode numerically. Through tuning waves to high amplitude, in a mildly underdense plasma, the six-photon process may grow more than other non-resonant but lower order processes. The growth of the high frequency mode remains below the nonlinear growth regime. However, extending current numerical results to more strongly coupled resonances with longer pulse propagation distances suggests a pathway to significant upconversion.

Seismic Risk Assessment of Built Environment in the Himalayan Foothill City of Dehradun, Uttarakhand

Himalayan foothill city of Dehradun in Uttarakhand state is located in a tectonically active region and is prone to earthquakes. The growth of Dehradun city is analysed using high resolution remote sensing datasets in the current study, with a focus on its vulnerability to seismic risks for single and double storey structures as the city is predominantly made up of low-rise structures. Analysis shows that 10% of urban expansion has occurred in spectral acceleration zones that are vulnerable to double-storey buildings, while 38% of urban expansion has happened in spectral acceleration zones that are vulnerable to single-storey buildings. The proposed land use as per Dehradun Masterplan 2041 is examined with reference to National Earthquake Hazards Reduction Program (NEHRP) for seismic regulating provisions. It is emphasized that the intended land use is incongruous in areas with higher spectral acceleration zones since it comprises structures with higher occupancy and the amenities required during post-earthquake rehabilitation. On the other hand, the open spaces are more appropriate in these locations, which will lessen susceptibility as the influence of seismic forces can be sufficiently attenuated. The present study, thus highlights the significance of including seismic risk information in urban planning processes in order to ensure resilient cities.

DESIGN AND DEVELOPMENT OF AN ANDROID-BASED FERTILIZER ORDERING APPLICATION IN THE BANGKALA REGION

In the Bangkala Region of Jeneponto Regency, farmers continue to depend on the conventional system for the fertilizer ordering process. This system results in inefficiencies and consumes the time of both sellers and buyers. We require an Android-based fertilizer ordering system that integrates directly with the server. The objective of this study is to develop an Android-based application for ordering fertilizer in the Bangkala region. This application was developed using the Research and Development (R&D) methodology and the waterfall development model. The results of this research have yielded a product in the form of an Android-based fertilizer ordering application, which will be utilized by farmers in the Bangkala Region, Jeneponto Regency. The system was tested using the ISO 25010 standard across four aspects: functional suitability, portability, usability, and performance efficiency. Functional suitability testing indicates that this application is "suitable" for use. Portability testing revealed that this fertilizer ordering application system runs well on several versions of Android. The usability testing results of the application have been categorized as "Very Good". Furthermore, performance efficiency testing shows that the CPU usage is at 4%, memory usage at 156.4 Mb, and the application network activity is at 80 Kb/s. Based on this data, the application has been well-received by users and can serve as a streamlined platform for ordering fertilizers for farmers.

QR Based Food Ordering System including Sentimental Analysis

Abstract: Ordering food directly has never been easier for the customers. This all-in-one QR Menu solution is for simplifying the food ordering process for customers and streamlining operations for businesses in the food service industry. The system offers the capability to upgrade for online food delivery through various digital channels, including websites and social media platforms, with a backend CRM facilitating the management of special customer requests. The primary target is quick-service restaurants, kiosks, takeout establishments, and food delivery services and the platform eliminates the need for customers to download additional mobile applications. By easily integrating with any POS system and supporting credit card payments, it provides a proper solution for online ordering and payment processing. Also, this platform combines sentiment analysis to understand customer preferences and easily make personalized menu suggestions and ranking of food items. This feature helps in enhancing the overall customer experience by offering specific recommendations based on individual tastes and preferences. Also, the system has user-friendly management tools for QR code generation and customization, ensuring compatibility with a wide range of QR code readers and scanning devices. The in-built QR code generator provides unique QR codes. As a cloudbased solution, it offers scalability and easy management of online menus, and allows businesses to list their offerings on various marketplaces. This system has unique capabilities in providing a seamless and integrated solution for online food ordering, payment processing, and customer engagement in the food service industry.

Artificial Intelligence for Decision Making in the Supply Chain

Artificial intelligence (AI) is a technology that has the potential to significantly impact organizational decision-making. In supply chain management (SCM), data analysis and organizational decision-mak- ing play a crucial role in providing insights into various aspects of the supply chain, such as relevant goods, inventory levels, order processing times, delivery times, and transportation costs. By analyzing data, companies can identify inefficiencies in their supply chain and take corrective actions to improve performance. The SC system is a customer-oriented and integrated system that determines planning, administration, and management processes for internal and external material and related flows, for- mulating the optimal factor of added value. In this study, we aim to identify hazardous products by analyzing product identification information at customs checkpoints and locations where vendible items are delivered to customers. We have identified several commodity groups and substances that are potentially harmful and dangerous to human health.

Fabrication and characteristics of Li2TiO3 pebbles manufactured by using powder injection molding (PIM) process

The solid type of tritium breeder materials is usually used in a pebble form because of the high packing factor, efficiency of tritium extraction, and advantage of thermal conduction between the structural materials. The breeder pebbles are required a good sphericity and uniform microstructure from the viewpoint of a packing factor and a release of bred tritium. Therefore, in this study, the lithium metatitanate (Li2TiO3) pebbles have been fabricated by powder injection molding process in order to secure a high sphericity of pebbles. In addition, the physical, mechanical, and thermal properties of the pebbles have been investigated. The Li2TiO3 green pebbles have been fabricated by using the powder injection molding process (PIM process). The diameter and sphericity of green pebbles were about 1.000 mm and 1.001, respectively. Then, the green pebbles were sintered at 1000 °C for 3 hr in air atmosphere. The diameter of sintered pebbles is about 0.95 mm. The volume shrinkage ratio was about 16 % during the sintering process. The uniform microstructure with open pores on the inside and outside of the sintered pebbles was observed.

Daubenton’s bats maintain stereotypical echolocation behaviour and a lombard response during target interception in light

Most bats hunt insects on the wing at night using echolocation as their primary sensory modality, but nevertheless maintain complex eye anatomy and functional vision. This raises the question of how and when insectivorous bats use vision during their largely nocturnal lifestyle. Here, we test the hypothesis that the small insectivorous bat, Myotis daubentonii, relies less on echolocation, or dispenses with it entirely, as visual cues become available during challenging acoustic noise conditions. We trained five wild-caught bats to land on a spherical target in both silence and when exposed to broad-band noise to decrease echo detectability, while light conditions were manipulated in both spectrum and intensity. We show that during noise exposure, the bats were almost three times more likely to use multiple attempts to solve the task compared to in silent controls. Furthermore, the bats exhibited a Lombard response of 0.18 dB/dBnoise and decreased call intervals earlier in their flight during masking noise exposures compared to in silent controls. Importantly, however, these adjustments in movement and echolocation behaviour did not differ between light and dark control treatments showing that small insectivorous bats maintain the same echolocation behaviour when provided with visual cues under challenging conditions for echolocation. We therefore conclude that bat echolocation is a hard-wired sensory system with stereotyped compensation strategies to both target range and masking noise (i.e. Lombard response) irrespective of light conditions. In contrast, the adjustments of call intervals and movement strategies during noise exposure varied substantially between individuals indicating a degree of flexibility that likely requires higher order processing and perhaps vocal learning.

Role of amygdala astrocytes in different phases of contextual fear memory

Growing evidence indicates a critical role of astrocytes in learning and memory. However, little is known about the role of basolateral amygdala complex (BLA-C) astrocytes in contextual fear conditioning (CFC), a paradigm relevant to understand and generate treatments for fear- and anxiety-related disorders. To get insights on the involvement of BLA-C astrocytes in fear memory, fluorocitrate (FLC), a reversible astroglial metabolic inhibitor, was applied at critical moments of the memory processing in order to target the acquisition, consolidation, retrieval and reconsolidation process of the fear memory. Adult Wistar male rats were bilaterally cannulated in BLA-C. Ten days later they were infused with different doses of FLC (0.5 or 1 nmol/0.5 µl) or saline before or after CFC and before or after retrieval. FLC impaired fear memory expression when administered before and shortly after CFC, but not one hour later. Infusion of FLC prior and after retrieval did not affect the memory. Our findings suggest that BLA-C astrocytes are critically involved in the acquisition/early consolidation of fear memory but not in the retrieval and reconsolidation. Furthermore, the extinction process was presumably not affected (considering that peri-retrieval administration could also affect this process).

ТЕОРЕТИЧЕСКИЕ АСПЕКТЫ АВТОМАТИЗАЦИИ ПРОИЗВОДСТВЕННО-ХОЗЯЙСТВЕННОЙ ДЕЯТЕЛЬНОСТИ НА ПРЕДПРИЯТИЯХ ПОЛИГРАФИИ

Automation of production and economic activities at printing enterprises involves the use of advanced digital solutions or technological equipment with automatic configuration and process control functions. The key benefits of automation include speeding up order processing, increasing the accuracy of calculations, optimizing the utilization of process equipment, reducing operating costs, and increasing the efficiency of planning and managing production processes. Digital solutions enable monitoring of financial and operational flows, leading to increased productivity and profitability. The object of this research is production management systems, automated information systems, as well as methods and technologies used to optimize work processes at the stages of production of printed products. The purpose of the study is to summarize the theoretical foundations for improving the efficiency of production and economic activities in printing enterprises by using automation capabilities. Research methods: analysis of literature and scientific sources on existing theories and practices of automation in printing, graphic method, deduction and induction, systematization. The scientific novelty of the study lies in the generalization of modern approaches to automation of production, technological, economic and managerial aspects of the activities of printing enterprises, as well as in a brief overview of currently proposed digital solutions in this area.

Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of neural network models in product form design analysis is a complex process, in which the type of network has to be determined, as well as the structure of the network layers and the neurons in them; the connection coefficients, inputs and outputs have to be explored; and the data have to be collected. In this paper, an impulsive discrete fractional neural network modeling approach is introduced for product design analysis. The proposed model extends and complements several existing integer-order neural network models to the generalized impulsive discrete fractional-order setting, which is a more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate impulsive controllers are designed, and practical Mittag-Leffler stability criteria are proposed. The Lyapunov function strategy is applied in providing the stability criteria and their efficiency is demonstrated via examples and a discussion. The established examples also illustrate the role of impulsive controllers in stabilizing the behavior of the neuronal states. The proposed modeling approach and the stability results are applicable to numerous industrial design tasks in which multi-agent systems are implemented.

Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0.

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool.