Modular Technique Research Articles

Identification of Impeding Factors in Utilising Prefabrication during Lifecycle of Construction Projects: An Extensive Literature Review

In recent years, the topic of climate change has been increasingly noticed by the public, and carbon emission reduction is one of the primary targets for various industries worldwide. The construction industry has a profound influence in this field, so it is significant to consider what kind of efforts can be made in building projects. Many scholars agree to promote prefabrication technology for construction, but its application still faces several challenges. By undertaking desk research, this paper explores the motivation and barriers to adopting modular techniques in construction projects under the lifecycle analysis. The preliminary information of the literature review is collated from dozens of peer-reviewed academic papers. Under the whole lifecycle thought, the PEST analysis tools also present the analytical results. This research finds that the top five barriers are the attitudinal resistance to using modular constructions, lack of sufficient modular expertise and practice, increasing costs and risks on supply chain management, insufficient government support and policy making, and high design and planning requirements. Moreover, the lifecycle analysis can divide the collated barriers into each stage, and adequate government support can assist in promoting the prefabrication in building projects in financial, legal, and technical aspects. The current findings can facilitate the broader use of prefabrication in building projects, improving the environmental sustainability of stakeholders. The process of proposed desk research can also be considered a referenced pattern for other related studies. More first-hand data should be collected and evaluated in further research to improve accuracy and adapt to the newest research field and industrial situations.

Programming quadric metasurfaces via infinitesimal origami maps of monohedral hexagonal tessellations: Part II

In Part I of this study, it was shown that all the three known types of monohedral hexagonal tessellations of the plane, those composed of equal irregular hexagons, have just a single deformation mode when tiles are considered as rigid bodies hinged to each other along the edges. A gallery of tessellated plates was simulated numerically to demonstrate the range of achievable deformed shapes. In Part II, the displacement field was first derived and a continuous interpolant for each type of tessellated plate. It turns out that all corresponding metasurfaces are described by quadrics. Afterwards, a parametric analysis was carried out to determine the effect of varying angles and edge lengths on the curvature, and the values of the geometric Poisson ratio of the plates. Finally, a method of fabrication is proposed based on the additive manufacturing of stiff tiles of negligible deformability and flexible connectors. Using this modular technique, it is possible to join together different monohedral tessellated plates able to deform into piece-wise quadrics. The nodal positions in the deformed configuration of the realized plates are measured after enforcing one principal curvature to assume a chosen value. The estimate of the other principal curvature confirms the analytical predictions. The presented tessellated plates permit to realize doubly curved shape-morphing metasurfaces with assorted shapes, which also can feature a certain surface roughness, and they can be employed in all applications demanding high surface accuracy and few actuators or just one.

On the Arithmetic of Generalized Fekete Polynomials

For each prime number p one can associate a Fekete polynomial with coefficients–1 or 1 except the constant term, which is 0. These are classical polynomials that have been studied extensively in the framework of analytic number theory. In a recent paper, we showed that these polynomials also encode interesting arithmetic information. In this article, we define generalized Fekete polynomials associated with quadratic characters whose conductors could be composite numbers. We then investigate the appearance of cyclotomic factors in these generalized Fekete polynomials. Based on this investigation, we introduce a compact version of Fekete polynomials as well as their trace polynomials. We then study the Galois groups of these Fekete polynomials using modular techniques. In particular, we discover some surprising extra symmetries which imply some restrictions on the corresponding Galois groups. Finally, based on both theoretical and numerical data, we propose a precise conjecture on the structure of these Galois groups.

A modular technique of Booth encoding and Vedic multiplier for low-area and high-speed applications

A technique for efficiently multiplying two signed numbers using limited area and high speed is presented in this paper. This work uses both the Booth and Vedic multiplication sutra methodologies to enhance the speed and reduction in the area by using two VLSI architectures of radix encoding techniques—Radix-4 and Radix-8—with the Vedic multiplier. The functionality of the proposed methods is tested using an Artix-7 Field Programmable Gate Array (FPGA-XC7A100T-CSG324) in Xilinx Vivado 2019.1 and ASIC 45 nm technology. Two methods of Booth encoding using Vedic multiplier (Urdhva-Tiryakbhyam sutra) were used to develop, and examine the benefits of rapid computational multiplier. The results of the proposed multiplier for Booth-Vedic-Radix-4 encoding (BVR-4) decrease area by 89% and improve Area-Delay Product (ADP) by 72% for a 16-bit multiplier when subjected to other existing multipliers. The Booth-Vedic-Radix-8 (BVR-8) method shows that there will be an 89% reduction in area and an improvement in ADP by 72% for the 16-bit multiplier. The performance is evaluated regarding area occupancy (i.e., LUTs number) and propagation delay (output time). In terms of resource utilization, the proposed BVR-4 and BVR-8 multipliers outperform all the current designs with a marginal effect on speed and area for narrower bit-width ranges.

АНАЛИЗ ОСВОЕНИЯ МАНУАЛЬНЫХ НАВЫКОВ РЕСТАВРАЦИИ МОЛЯРОВ СТАРШЕКУРСНИКАМИ СТОМАТОЛОГИЧЕСКОГО ФАКУЛЬТЕТА

Subject. The subject of this research is manual skills in molar restoration.
 Objective. The objective of this research is to analyze the process of mastering manual skills in restoration of phantom molar tooth models by senior dental students.
 Methodology. The study was carried out by the Department of Therapeutic Dentistry and Propedeutics of Dental Disease of Ural State Medical University of the Ministry of Health of the Russian Federation together with the Department of Control Systems Modeling, Ural Federal University. The study involved 54 four- and five-year dental students of Ural State Medical University (24 young male adults, 30 young adult women; the average age was 22.25 ± 1.75 years) in a procedure of dental filling of carious cavities in five “phantom” teeth – extracted molars (Black classification was Class I). This research applied the method developed by Prof. Larisa Lomiashvili together with co-authors for molar restoration using modular techniques. To analyze self-assessment of manual skills for molar restoration by senior students the questionnaire was completed. The questionnaire was made up of three blocks including 12 questions (parameters) evaluated using 1 to 10 rating scale survey, the rating being 10 for “fully satisfied”, and 1 for “completely dissatisfied”. The survey questions were divided into the following blocks: understanding of dental anatomical features, professional skills of students, cognitive motivational factors.
 Conclusions. The analysis of assessment scores and survey responses showed that mastering manual skills by senior students during a molar restoration procedure promotes their accuracy and ability to work with precision. The analysis of mastering manual skills for molar restoration by dental senior students using the survey responses showed rather high self-assessments (p ≤ 0.05).

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research.

An agile autonomous car driving assistance using hybrid optimization-based kernel support vector convolutional network

In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. These requirements make autonomous car driving tasks computationally ineffective and sensitive to ecological changes. In recent times, the emergence of deep learning-based approaches in autonomous car driving systems provided promising solutions for complex vision task interpretation, localization, and environmental perception. But, they create distribution mismatch issues and require huge perilous interaction information to offer safe driving with the prediction of future collisions. Therefore, to overcome this issue, a novel Modified Kernel Support vector Convolutional based Hybrid Grasshopper Harris hawk (MKSC-HGH) approach is proposed. The proposed MKSC-HGH approach efficiently forecasts the uncertainties of agile autonomous car driving and guides proper navigation details of the track to avoid the possibility of collision with on-road obstacles and fences. This system inputs camera images captured through sensors, car speed, and manual driving data to analyze and predict safety control and probable collision. The prominent feature representing the ability of Modified Convolutional Neural Network (MCNN) and complex boundary encoding process of Kernel Support Vector Machine (KSVM) with hyperparameter tuning operation of Modified Grasshopper Optimization Algorithm based Improved Harris Hawk Optimization (MGOA-IHHO) algorithm made proposed system applicable to predict future states (ie. visual prediction, speed prediction, and collision prediction) more accurately. The efficiency of the proposed MKSC-HGH approach is investigated using evaluation measures namely completion time, average speed, top speed, and laps time. The simulation outcome shows the superiority of the proposed MKSC-HGH approach in predicting agile autonomous car driving over state of art techniques about task performance and sample efficiency.

Shaping Costumes From Laces on the Fibonacci Numbers Basis

Thanks to the large-scale development of technological processes in the fashion industry, favorable opportunities are created for experimenting with the textures and textures of suits, as well as the suit models themselves. Lace, being part of the traditional culture of many countries and peoples, is still perceived by most people as an authentic material with which it is impossible to make any changes in order to avoid the destruction of traditions. The authors of the study Zelenova Yu. and Zelenova N. the goal was to improve the technology of creating lace and approaches to designing suits from lace, which will change the attitude towards lace among professionals and admirers of the fashion industry. The objectives of the study included the study and application of mathematical laws in the artistic design of lace costumes, which are always justified in terms of rational production and positive perception by consumers. Successful and popular costume models are always designed based on the mathematical proportions of the golden ratio. In our study, the golden section ‘Fibonacci series’ is used, with the help of which lace modules were developed, from which costumes were created in the Ariadne author’s lace collection (authors Zelenova Yu., Zelenova N., 2017-2019). The modular technique, on the basis of which the Ariadne author’s collection was developed, allows you to create various lace textures and forms of lace suits, achieving an unlimited variety of combinations from a small number of options. Thus, the modular technique for designing lace suits underlies the transformative shaping of lace suits, which is the most promising direction for the development of the use of lace in a suit in the near future.

Experimental performance and numerical prediction of precast prestressed concrete frames endowed with shear panel dampers

The precast prestressed efficiently fabricated frame (PPEFF) represents a rapid assembly system. However, it may possess limited energy-dissipating capacity when applied to high-rise buildings in high seismic-prone zones. To solve this problem, a precast prestressed efficiently fabricated frame with shear panel dampers (PPEFF-SPDs) is proposed in this paper. Along this vein, this study focuses on the effective modular techniques for damper installation as well as the seismic performance of a PPEFF-SPDs. To investigate the seismic performance of the PPEFF-SPDs, a 0.8-scale two-story specimen was tested using hybrid and quasi-static tests. The test results showed that the PPEFF-SPDs designed according to Chinese building codes exhibited reliable seismic performance with the corresponding performance objectives satisfied. The specimen displayed an effective and stable energy-dissipating capacity even when the inter-story drift ratios reached 2%, and the specimen damage conformed to the failure pattern foreseen for strong columns and weak beams. This study demonstrates the feasibility of the proposed PPEFF-SPDs and provides a promising alternative for the PPEFF in high seismic-prone zones.

CHALLENGES OF PRODUCT MODULARIZATION METHODS IN SMES: LESSONS LEARNED FROM A MANUFACTURER OF RIGID INFLATABLE BOATS

AbstractNowadays, Western rigid inflatable boat (RIB) manufacturers are facing increased competition from Asian and Middle East producers and the need to push the modularization of their product architecture. Many modularization techniques have been developed to support this effort, being the Modular Function Deployment a well-established multi-stage modularization technique with applications in several industries. Despite the reported literature, the challenges and learnings from the application of modularization techniques have focused on large organizations and complex product systems. In this paper, a case study is presented with the objective of analysing the MFD process implementation in the context of an SME manufacturer of RIB. A learning framework focused on the project and process perspectives is used to facilitate the systematic extraction of lessons from the experience of the MFD implementation. This paper contributes to the modularization techniques literature by observing a modularization method as a learning process and makes it evident the call for more investigation on the implementation process of modularization methods.

Modular Set of Reagents in Lateral Flow Immunoassay: Application for Antibiotic Neomycin Detection in Honey

A scheme of modular competitive immunochromatography with an analyte-independent test strip and changeable specific immunoreactants has been proposed. Native (detected) and biotinylated antigens interact with specific antibodies during their preincubation in solution, that is, without the immobilization of reagents. After this, the detectable complexes on the test strip are formed by the use of streptavidin (which binds biotin with high affinity), anti-species antibodies, and immunoglobulin-binding streptococcal protein G. The technique was successfully applied for the detection of neomycin in honey. The visual and instrumental detection limits were 0.3 and 0.014 mg/kg, respectively, and the degree of neomycin revealed in honey samples varied from 85% to 113%. The efficiency of the modular technique with the use of the same test strip for different analytes was confirmed for streptomycin detection. The proposed approach excludes the necessity of finding the condition of immobilization for each new specific immunoreactant and transferring the assay to other analytes by a simple choice of concentrations for preincubated specific antibodies and the hapten-biotin conjugate.

Analysis and study of the problems on the wires used in the MEG CDCH and the construction of the new drift chamber

In the MEG II detector, the measurement of the momentum of the charged particle is performed by a high transparency single volume, full stereo cylindrical Drift Chamber (CDCH). It is composed by 9 concentric layers, each consisting of 192 drift cells. The single drift cell is approximately squared, with a 20 μm gold plate tungsten sense wire surrounded by 40 μm/50 μm silver plated aluminum field wires in a ratio of 5:1. During the construction of the first CDCH, we observed the breaking of about hundred cathode wires: 97 of these were 40 μm aluminum wires, while 10 were 50 μm wires. Since the number of broken cathodes is less than 1% of the total, one can expect the influence on the track reconstruction efficiency to be not so dramatic. We verified by means of simulations that the loss of one cathode does not change the cell electric field appreciably. Here we present the results of the analysis of the effects of mechanical stress and chemical corrosion observed on these broken wires. Finally, we show the studies carried out on new wires to overcome the weaknesses found and the process that will be used for the construction of the new drift chamber (CDCH2). It will be built with the same modular technique, as for the previous one, the use of the wiring robot will be optimized to improve some weaker step in the procedure, new wires will be adopted with a 25% thicker diameter, which has very little effects on the resolution and efficiency of the detector. Furthermore these wires are made with a manufacturing process different from that used previously.

Modular technique to construct lightweight CFRP lattice structures

Carbon fiber reinforced polymer (CFRP) lattice structures are ultralight but stiff and strong. A modular technique based on tubular textiles was proposed to design and made CFRP lattice structures with different unit cell topologies. Foam-filling technique was applied to strength the modular lattice structure (MLS). The out-of-plane compression behavior and energy absorption performance of these MLSs were investigated by axial compression experiments. Modular and foam filling techniques remarkably improve the energy absorption performance through changing the crushing mode from local bucking to progressive folding and can increase the specific energy absorption (SEA) at a level of 66%. Meanwhile, MLSs have much greater compression strength than traditional foams and honeycombs.

Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm

Modular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to resource restrictions. On applying modular technique on a redundancy allocation problem, one can increase the fault tolerance to the optimum design of the system, making it very effective in terms of component redundancy. So, modular redundancy should be seen as a proper replacement for the old technique of component level redundancy for improving the reliability, efficiency, and maintainability of a working system. The multi-level redundancy allocation problem is being addressed and solved comprehensively in this study using an advanced genetic algorithm (GA) and a penalty function approach in both crisp and intuitionistic fuzzy settings Finally, numerical examples are solved and sensitivity studies are carried out visually to evaluate the consequences of changing key parameters involved in GA.

Modular Microfluidics: Current Status and Future Prospects.

This review mainly studies the development status, limitations, and future directions of modular microfluidic systems. Microfluidic technology is an important tool platform for scientific research and plays an important role in various fields. With the continuous development of microfluidic applications, conventional monolithic microfluidic chips show more and more limitations. A modular microfluidic system is a system composed of interconnected, independent modular microfluidic chips, which are easy to use, highly customizable, and on-site deployable. In this paper, the current forms of modular microfluidic systems are classified and studied. The popular fabrication techniques for modular blocks, the major application scenarios of modular microfluidics, and the limitations of modular techniques are also discussed. Lastly, this review provides prospects for the future direction of modular microfluidic technologies.

NOx Emissions Prediction With a Brain-Inspired Modular Neural Network in Municipal Solid Waste Incineration Processes

The timely and accurate measurement of nitrogen oxide (NOx) emissions is important for efficient pollution controlling of municipal solid waste incineratio plants. To design an efficient and effective prediction model for NOx concentrations, a brain-inspired modular neural network (BIMNN) is developed in this study. First, a biologically-inspired modularization technique is proposed, in which the topological modularity gives rise to functional modularity. Consequently, different modules correspond to different tasks, improving the network efficiency by performing task decomposition. Subsequently, an adaptive task-oriented radial basis function (ATO-RBF) neural network is applied to construct each module. The ATO-RBF neural network is comprised of a structure self-organizing mechanism and an adaptive second-order learning algorithm, providing basis for generalization ability of BIMNN. Finally, during the testing or application stages, a competitive strategy is utilized to integrate the modules. The proposed prediction methodology is verified using industrial data, and the experimental results demonstrate its outperformance.

Research on efficiency optimal torque distribution of stator module combined permanent magnet synchronous machine

The large permanent magnet synchronous machine (PMSM) usually adopts a modular technique to facilitate manufacture and maintenance. The rated power of large mechanical equipment is selected according to the limit capacity, and the motor efficiency is low at a low load. A kind of unwinding stator module combined permanent magnet synchronous machine (SMCPMSM) is proposed. Each module is mechanically and electrically decoupled. It can operate independently. The number of input modules can be flexibly selected according to the torque requirements in direct drive SMCPMSM to realise the optimum efficiency control of the ball mill. The work presents the loss model of the SMCPMSM and analyzes the torque distribution ratio of the modules. The mathematical model of torque distribution with optimum efficiency is established by using the genetic algorithm. The optimum efficiency control under different loads is determined. The motor performance of four operation modes is simulated by the finite element. An 80 kW prototype is developed, and its experiments are carried out. The experimental results verify the validity of the proposed method.

Application of Modular Design in Upcycling Solid Leather Waste: A Sustainable Development Approach from Ethiopia

Fast changes in fashion trends have stimulated customers’ buying behavior; this has increased the demand for leather products. Fulfilling this demand requires unsustainable practices of overproduction and utilization of resources leading to increased waste generation. By far, the highest percentage of waste generated in the leather product industry is from the cutting section. Since leather scraps retain most of the chemicals added in the processing stage, disposal of this waste in landfills contributes to a higher degree of environmental pollution. This paper introduces modular design techniques for the upcycling of solid leather wastes created at the cutting stage. This contributes to creating a more sustainable approach in leather product development through the reuse of waste material, reducing required resources, and avoiding waste disposal to the environment. Primary data gathered from export-oriented leather goods manufacturing industries in Ethiopia identified a percentage of waste created at the cutting stage. Further, the physical property characterization of leather scraps was carried out to determine the appropriate modular design that can reuse the highest percentage of solid waste. The project concludes that most of the solid scraps disposed of in the leather product industry are in both areas and quality useable as raw material for the manufacturing of marketable sustainable leather products.

Robotic-assisted surgery of rectal cancer-Technique, limitations and results

The number of oncological robotic-assisted rectal cancer resections is rapidly increasing in Germany and worldwide; however, the indications, technique and potential limitations of this surgical technique are still discussed. The standardized modular surgical technique, the results in our clinic and the currently published evidence are presented. The procedure should be divided into seven modules in terms of standardization and teaching. After the learning curve there are principally no limitations or contraindications. The robotic-assisted approach is superior to open surgery in the following points: blood loss, lymph node harvest, negative circumferential resection margin (CRM), complication rate and length of hospital stay. In comparison to conventional laparoscopy the conversion rate and postoperative sexual and bladder function disorders are decreased. The operating time is longer. Robotic-assisted rectal cancer resection is firmly established and standardized. The technique is superior to open surgery and conventional laparoscopy in some important aspects and is developing into the standard for this disease.

Integrating Knowledge Compilation with Reinforcement Learning for Routes

Sequential multiagent decision-making under partial observability and uncertainty poses several challenges. Although multiagent reinforcement learning (MARL) approaches have increased the scalability, addressing combinatorial domains is still challenging as random exploration by agents is unlikely to generate useful reward signals. We address cooperative multiagent pathfinding under uncertainty and partial observability where agents move from their respective sources to destinations while also satisfying constraints (e.g., visiting landmarks). Our main contributions include: (1) compiling domain knowledge such as underlying graph connectivity and domain constraints into propositional logic based decision diagrams, (2) developing modular techniques to integrate such knowledge with deep MARL algorithms, and (3) developing fast algorithms to query the compiled knowledge for accelerated episode simulation in RL. Empirically, our approach can tractably represent various types of domain constraints, and outperforms previous MARL approaches significantly both in terms of sample complexity and solution quality on a number of instances.