Simple Machine Research Articles

Sosialisasi Pembuatan Mesin Pirolis Plastik Sederhana sebagai Alat Pengolah Sampah Plastik Menjadi Bahan Bakar Minyak di Desa Lopo Kabupaten Gorontalo

Garbage is something that humans throw away because it can no longer be used. An environment with a buildup of garbage can cause various problems such as hygiene, health, beauty, environmental pollution, and social issues. One of the problems regarding waste is located in Lopo Village-Gorontalo District. Garbage is thrown away and then burned, especially plastic waste. The combustion results can undoubtedly pollute the environment. This happens due to the need for more guidance and community empowerment in recycling waste into something that can be useful. One of the solutions for handling waste into something that can be useful is to convert plastic waste into fuel oil through the socialization of making a simple plastic pyrolysis machine. This socialization activity provides an understanding of waste and its impact on the environment, methods of waste management, and how to make a simple plastic pyrolysis machine. The participants were very enthusiastic about participating in this socialization because they learned how to process plastic waste into fuel and make simple plastic pyrolysis machines that can be products of high economic value

Emergence and collapse of reciprocity in semiautomatic driving coordination experiments with humans

Forms of both simple and complex machine intelligence are increasingly acting within human groups in order to affect collective outcomes. Considering the nature of collective action problems, however, such involvement could paradoxically and unintentionally suppress existing beneficial social norms in humans, such as those involving cooperation. Here, we test theoretical predictions about such an effect using a unique cyber-physical lab experiment where online participants (N = 300 in 150 dyads) drive robotic vehicles remotely in a coordination game. We show that autobraking assistance increases human altruism, such as giving way to others, and that communication helps people to make mutual concessions. On the other hand, autosteering assistance completely inhibits the emergence of reciprocity between people in favor of self-interest maximization. The negative social repercussions persist even after the assistance system is deactivated. Furthermore, adding communication capabilities does not relieve this inhibition of reciprocity because people rarely communicate in the presence of autosteering assistance. Our findings suggest that active safety assistance (a form of simple AI support) can alter the dynamics of social coordination between people, including by affecting the trade-off between individual safety and social reciprocity. The difference between autobraking and autosteering assistance appears to relate to whether the assistive technology supports or replaces human agency in social coordination dilemmas. Humans have developed norms of reciprocity to address collective challenges, but such tacit understandings could break down in situations where machine intelligence is involved in human decision-making without having any normative commitments.

Can we read stones? Quantifying the information loss in flintknapping

One of the methods of analysing the manufacturing process of lithic tools is by “reading” the scars of removals visible on the surface of cores or bifaces. The paper aims to review the limitations of this approach, by answering the question as to how much information from the original knapping process is still preserved in the cores and bifaces. To quantify the information loss we used experimentally knapped pieces, that we then refitted to check which removals are still visible on the surface of the analysed forms. We tested different measures of information loss in the context of removals and sequences of removals, but also the chronological relationship between sequences i.e. the number of linear extensions and missing nearest connections. On average, 39.9% of the original removals and 54.1% of the sequences of removals are preserved on the surface, but only 14.1% of the original relationships between sequences can be observed. Finally, we used simple machine learning to find an algorithm for estimating the loss of the nearest chronological relationships between sequences when no refits are available. Due to the extremely high information loss for the linear order of the knapping process we: a) propose to abandon these elements of the diacritic approach which aim to reconstruct the rhythms and schemes of knapping; b) evaluate the quality of the reconstructed knapping scheme (partial order) prior to further comparative analyses. Here we propose an algorithm to estimate the number of missing connections to test the quality of the knapping process while using diacritic schemes.

A Review on Robotics and Automation in the 21st Century: Shaping the Future of Manufacturing, Healthcare, and Service Sectors

A detailed examination of the literature reveals the significant effects that automation and robotics have had on the modern industrial, healthcare, and service sectors. This research explores the acceptability, challenges, and advantages of robots in various industries, tracing their evolution from simple machines to extremely intelligent and complex entities today. The report demonstrates how robots have evolved from conventional manufacturing roles to become essential in Industry 4.0, healthcare, and service sectors, utilizing academic research, real business data, and technical advancements. Robots have greatly increased production efficiency in the industrial sector, decreased error margins, and resulted in cost savings and higher-quality goods. The introduction of networked production systems and smart factories highlights the importance of robotics in contemporary manufacturing techniques. In healthcare, robots are revolutionizing the industry by transforming clinical settings, enhancing patient monitoring and diagnostics, and increasing the precision of minimally invasive surgical operations. This change illustrates the potential for robots to completely transform patient care and the healthcare sector as a whole. The study also examines the growing importance of robots in the service sector, where new technologies are altering the way people interact, shop, and entertain themselves. Automation and robotics are redefining how individuals engage with services, highlighting the dynamic relationship between technology and human interaction. This study underscores the potential of robots to revolutionize multiple industries and emphasizes the necessity of further research and adaptation to fully realize these advantages. Automation and robotics are key drivers of technological progress, offering prospects for ongoing innovation and improved productivity, precision, and overall quality across various sectors.

A STEM activity in design and development of a simple machine using 3D printing and GoGo board

In 21st-century education, numerous research studies have shown that learner-centered teaching is significantly more effective than traditional lectures. A critical aspect of studying physics effectively is developing conceptual understanding through hands-on activities. With today’s technology easily accessible to both students and teachers, hands-on activities aimed at promoting physics understanding and design thinking have become more accessible than in the past. This research aims to create supplementary activities that utilize Makerspace tools such as 3D printers, laser cutters, and GoGo Board learning kits to enhance learning efficiency in the context of simple machines in physics. The primary goal of these activities is to help students apply their knowledge of simple machines, focusing on concepts such as mechanical advantage and machine efficiency while also developing Makerspace tool proficiency. Additionally, students will practice design thinking by creating prototypes of simple machines to accomplish specific tasks. Their tool utilization and design thinking abilities will be evaluated based on the simple machines they create, and mechanical advantage will be measured using a device capable of creating and measuring the tension in a rope designed with a GoGo Board. The process of developing these learning modules will be presented and discussed in detail.

Measuring coating layer shape in arbitrary geometry

Coating processes are typically analyzed on systems with flat substrates, such as the Landau–Levich–Derjaguin configuration in dip coating. However, actual applications often exhibit a much wider variety of geometries. For example, dip coating is also employed as a batch process with three-dimensional substrates. After the batch dip coating process, the coating layer is likely to exhibit irregular geometries near the lower edge of the substrate; the substrate profile is not flat, and the fluid can form overhangs depending on process conditions and rheological properties. These irregularities make it impossible to define layer shape measures, such as average thickness and roughness, in traditional ways. In this study, we propose generalized measures to overcome this issue by using offset distance and curve similarity. Our measures can quantify the shape of the coating layers in arbitrary geometries and are, therefore, robust against irregularities. We applied our measures to analyze the formation of external electrodes on multi-layer ceramic capacitors by batch dip coating. Coating layer profiles during the process were acquired for the analysis using a simple machine vision technique. As a result, differences in the coating layer shapes between fluids with different rheological properties were quantified. The results show that our measures can be used to compare coating qualities in arbitrary geometries for designing optimal process conditions.

Explaining the Chemical Inventory of Orion KL through Machine Learning

The interplay of the chemistry and physics that exists within astrochemically relevant sources can only be fully appreciated if we can gain a holistic understanding of their chemical inventories. Previous work by Lee et al. demonstrated the capabilities of simple regression models to reproduce the abundances of the chemical inventory of the Taurus Molecular Cloud 1 (TMC-1), as well as to provide abundance predictions for new candidate molecules. It remains to be seen, however, to what degree TMC-1 is a “unicorn” in astrochemistry, where the simplicity of its chemistry and physics readily facilitates characterization with simple machine learning models. Here we present an extension in chemical complexity to a heavily studied high-mass star-forming region: the Orion Kleinmann–Low (Orion KL) nebula. Unlike TMC-1, Orion KL is composed of several structurally distinct environments that differ chemically and kinematically, wherein the column densities of molecules between these components can have nonlinear correlations that cause the unexpected appearance or even lack of likely species in various environments. This proof-of-concept study used similar regression models sampled by Lee et al. to accurately reproduce the column densities from the XCLASS fitting program presented by Crockett et al.

The Analysis of Misconceptions in Simple Airplane Material for 8th Grade Students in SMP Muhammadiyah 3 Gorontalo

This study aims to (1) determine students' conception maps in simple machine material (2) analyze students' misconceptions in simple machine material. This research is a descriptive research. The research subjects were class VIII of SMP Muhamadiyah 3 Gorontalo City with a sample size of 25 students. The/research data were obtained from students' answers through a three-level multiple-choice diagnostic test (Three Tier Multiple Choice) and processed based on the pattern of student answers grouped into categories of understanding the concept, misconception 1, misconception 2, misconception 3 and not understanding the concept. The results showed that category 1 misconception had a percentage of 23%, misconception 2 had a percentage of 17%, and misconception 3 had a percentage of 25%.

Advancing Healthcare Diagnostics: A Study on Gaussian Naive Bayes Classification of Blood Samples

This research paper presents a comprehensive analysis of the Gaussian Naive Bayes (GNB) classifier's application in predicting health conditions from blood samples, underpinned by a handcrafted dataset representative of typical physiological ranges. Through a meticulous 5-fold cross-validation approach, the study assesses the GNB model's performance in terms of accuracy, precision, recall, and F1-score, revealing not only high efficacy but also consistent improvement in predictive capability across successive folds. A detailed confusion matrix provides further insights into the model's classification proficiency. The results affirmatively address the research hypotheses, indicating the GNB classifier's reliability and effectiveness as a diagnostic tool. With the increasing need for rapid and accurate medical diagnostics, the study's findings underscore the potential of even simple machine learning models to augment traditional blood test analyses, thereby offering significant contributions to the field of biomedical informatics. The research lays the groundwork for future explorations into the integration of machine learning in clinical settings, advocating for the verification of these promising results with real-world clinical data and the comparative analysis of various machine learning models. The potential for automated, precise diagnostic processes paves the way for enhanced patient care and resource optimization in healthcare.

Static in bone implants: standard steady-state torque and primary stability in a bioactive kinetic screw

Establishing a standard measurement for drilling and screwing bone implants in different amounts and qualities of bone tissue, in a simple and adequate way to control and predict results, is the gold standard for successful primary stability and better results on long-term osseointegration. So far, the maximum insertion torque (MIT) has been used as the main parameter to achieve success in primary stability and osseointegration, although it has shown conflicting results in the literature for over four decades when predicting standard or minimum values. Basically, the surgeon's experience guides the planning and execution of the surgical procedure, adapted in each case according to his tactile experience, guided by X-ray analysis and the bone and general conditions of the patient. In this work, using a new biomechanical simple machine as a dental implant, a new method will be described mathematically and experimentally, which standardizes the compression and torque in the implant-bone contact, in five different bone densities, during the achievement of mechanical primary stability. The results described the relationship between the MIT, maximum removal torque, and maximum force of static friction between implant-bone and bone-to-bone, achieving a controlled and predictable standard steady-state torque that maintains equilibrium in elastic stress for the primary stability of bone implants, hereby established for an innovative simple machine Bioactive Kinetic Screw.

Machine learning assisted discrimination and detection of antibiotics by using multicolor microfluidic chemiluminescence detection chip

The fabrication of multicolor chemiluminescence (CL) sensing chip for the discrimination and detection of multianalytes remains a great challenge. Herein, machine learning assisted multicolor microfluidic CL detection chip for the identification and concentration prediction of antibiotics was presented. Firstly, a three-channel microfluidic CL detection chip was fabricated. The three detection zones of the microfluidic detection chip were modified with CL catalyst Co(II) and different CL reagents including luminol, luminol mixed with fluorescein, and luminol mixed with phloxine B, respectively. Strong blue, green and pink-purple colored light emissions can be generated from the three detection zones in the presence of H2O2 solution. The three multicolor CL emissions show different degrees of reduce in intensity and change in color in the presence of different antibiotics, including diethylstilbestro (DES), metronidazole (MNZ), kanamycin (KAN), isoniazide (INH), and ceftiofur sodium (CS), resulting in distinct fingerprint-like response patterns. The red (R), green (G), blue (B) and gray scale values of the three multicolor light emissions were extracted and ten characteristic sensing parameters were chosen to obtain multicolor CL response database. Then, machine learning assisted data analysis were carried out. The five antibiotics can be facilely classified by using principal component analysis (PCA) and hierarchical clustering analysis (HCA), and further quantified by using deep neural networks (DNN) algorithm. Good results were obtained for identification of binary antibiotic mixtures, spiked antibiotics in water samples, and unknown antibiotic samples. Satisfied results were obtained for concentration prediction of antibiotics. This work provides a simple machine learning assisted and multicolor microfluidic CL detection chip based CL sensing strategy for discrimination and quantitative detection of multiple analytes.

Effect of problem-based learning on students’ problem-solving ability to learn physics

A learner’s success is supported by the ability to understand real-world problems. This study aimed to examine the effect of problem-based learning on problem-solving ability in the teaching and learning of physics. The study was guided by socio-constructivism theory. A quasi-experiment was conducted with 829 Senior-2 Physics students (age 13–15) from eight (8) selected lower secondary schools in Sheema District, Uganda. Schools were assigned to treatment and control groups using a purposive random sampling technique. Students’ problem-solving ability was measured by conducting a problem-solving ability test in each group before and after studying a chapter on simple machines in physics. Repeated measures analysis of variances was applied for data analysis. The study’s findings showed a significant improvement in students’ problem-solving ability with simple machines in the treatment group compared to the control group (p < .001). Therefore, educators are encouraged to embrace learner-centred methods that enhance students’ problem-solving abilities to help them compete in the world of work.

A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.

Thrombocytopenia is a known prognostic factor in sepsis, yet the relationship between platelet-related genes and sepsis outcomes remains elusive. We developed a machine learning (ML) model based on platelet-related genes to predict poor prognosis in sepsis. The model underwent rigorous evaluation on six diverse platforms, ensuring reliable and versatile findings. A retrospective analysis of platelet data from 365 sepsis patients confirmed the predictive role of platelet count in prognosis. We employed COX analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine (SVM) techniques to identify platelet-related genes from the GSE65682 dataset. Subsequently, these genes were trained and validated on six distinct platforms comprising 719 patients, and compared against the Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ-Failure Assessment (SOFA) score. A PLT count <100×109/L independently increased the risk of death in sepsis patients (OR = 2.523; 95% CI: 1.084-5.872). The ML model, based on five platelet-related genes, demonstrated impressive area under the curve (AUC) values ranging from 0.5 to 0.795 across various validation platforms. On the GPL6947 platform, our ML model outperformed the APACHE II score with an AUC of 0.795 compared to 0.761. Additionally, by incorporating age, the model's performance was further improved to an AUC of 0.812. On the GPL4133 platform, the initial AUC of the machine learning model based on five platelet-related genes was 0.5. However, after including age, the AUC increased to 0.583. In comparison, the AUC of the APACHE II score was 0.604, and the AUC of the SOFA score was 0.542. Our findings highlight the broad applicability of this ML model, based on platelet-related genes, in facilitating early treatment decisions for sepsis patients with poor outcomes. Our study paves the way for advancements in personalized medicine and improved patient care.

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category's cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

Selection for Size in Molecular Self-Assembly Drives the DeNovo Evolution of a Molecular Machine.

The functioning of machines typically requires a concerted action of their parts. This requirement also holds for molecular motors that drive vital cellular processes and imposes constraints on their conformational changes as well as the rates at which they occur. It remains unclear whether, during evolution, features required for functional molecular machines can emerge simultaneously or require sequential adaptation to different selection pressures. We address this question by theoretically analyzing the evolution of filament treadmilling. This process refers to the self-assembly of linear polymers that grow and shrink at equal rates at their opposite ends. It constitutes a simple biological molecular machine that is involved in bacterial cell division and requires that several conditions are met. In our simulation framework, treadmilling emerges as a consequence of selecting for a target average polymer length. We discuss why other forms of assembly dynamics, which also reach the imposed target length, do not emerge in our simulations. Our work shows that complex molecular functions can evolve denovo under selection for a single physical feature.

Research on a Machine–Tractor Unit for Strip-Till Technology

One of the most modern technologies for growing row crops is strip-till. Currently, it occupies an intermediate position between conventional tillage and no-till technologies. Special complex and expensive machines are used to implement strip-till technology practically. To avoid this, a combined unit is proposed, including a tractor and two simple machines: a front-disc harrow and a chisel plough mounted behind the tractor. As experimental studies have shown, this unit makes implementing the strip-till one-pass technology possible. In this case, the oscillations process in the soil-loosening depth of strips is low-frequency since at least 95% of this statistical parameter variance is concentrated in the frequency range of 0–16.8 s−1 or 0–2.7 Hz, and its maximum falls at a frequency of 0.4 Hz. The soil-loosening depth in the strips can deviate from the mean value by ±2 cm once per 7.1 m of the combined unit’s path. With a mean speed of its movement of 2.1 m·s−1, the release frequency of the mean value of the soil-loosening depth exceeding ±2 cm is only 0.29 s or 0.05 Hz. Not less than 95% of the loosened strips’ non-straightness oscillations variance is in the frequency range of 0–0.25 m−1, and the value of the variance itself is small and amounts to 1.08 cm2. Proceeding from this, the non-straightness of the loosened strips by the combined unit can be considered satisfactory since its indicators meet the requirements for the non-straightness of row crops in terms of variance and frequency oscillations.

A machine learning algorithm to predict a culprit lesion after out of hospital cardiac arrest

Abstract Background Between 50-90% of patients suffering from out-of-hospital cardiac arrest (OHCA) have a primary cardiac aetiology of arrest with the presence of a culprit coronary lesion. However, recent trials have failed to show a benefit from an early invasive angiographic approach following OHCA. Identification of patients with high likelihood of a culprit lesion may enable selection of patients likely to benefit from direct conveyance to cardiac arrest centres and for an early invasive approach. However, electrocardiogram (ECG) following return of spontaneous circulation, the current gold-standard, is a poor predictor of presence of a culprit lesion. Purpose We aimed to develop a machine learning algorithm to predict the presence of a culprit lesion in patients with OHCA. Methods We used a retrospective cohort of 398 patients admitted with primary cardiac aetiology OHCA to a tertiary cardiology centre in the United Kingdom, between May 2012 and December 2017. The primary outcome was the presence of a culprit coronary artery lesion, for which a Gradient Boosting model was optimised to predict. The algorithm was then validated in two independent European cohorts comprising 568 patients (346 and 222 patients respectively). Results The development cohort consisted of 398 patients (median age 64.3 years [IQR 53.3-75.4], 74.6% male), and the two validation cohorts consisted of 346 patients (median age 63.0 years [IQR 55.0-73.0], 81.5% male) and 222 patients (median age 63.0 years [IQR 52.0-74.0], 77.0% male). Of all patients, 85.6% had a witnessed arrest, 69.6% received bystander cardiopulmonary resuscitation, and 77.3% had an initial shockable rhythm. A culprit lesion was observed in 67.4% patients receiving early coronary angiography in the development cohort, and 67.9% and 61.1% in the two validation cohorts respectively. A Gradient Boosting model yielded an algorithm, named KOCAR Culprit Predictor, incorporating nine variables including age, a localising feature on ECG (≥2mm of ST change in contiguous leads), regional wall motion abnormality, history of vascular disease and initial shockable rhythm. This model had an AUC of 0.89 in the development and 0.83/0.81 in the validation cohorts with good calibration (Figure 1), and outperformed the current gold-standard ECG alone (AUC 0.69/0.67/0/67). Conclusions A novel simple machine learning derived algorithm (Figure 2) combining nine variables which are readily available to clinicians can be applied to patients with OHCA to predict a culprit coronary artery lesion with high accuracy. The algorithm, KOCAR Culprit Predictor, outperforms the current gold-standard ECG alone.Figure 1Figure 2

Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN

PurposePercutaneous coronary intervention (PCI) is a common treatment modality for coronary artery disease. Accurate prediction of patients at risk for complications and hospital readmission after PCI could improve the overall clinical management. We aimed to develop and validate predictive models to predict any cardiac event within a year post PCI procedure.MethodsThis is a retrospective cohort study utilizing data from the National Cardiovascular Disease (NCVD)-PCI registry. The data collected (N = 28,007) were split into training set (n = 24,409) and testing set (n = 3598). Four predictive models (logistic regression [LR], random forest method, support vector machine [SVM], and artificial neural network) were developed and validated. The outcome on risk prediction were compared.ResultsThe demographic and clinical features of patients in the training and testing cohorts were similar. Patients had mean age ± standard deviation of 58.15 ± 10.13 years at admission with a male majority (82.66%). In over half of the procedures (50.61%), patients had chronic stable angina. Within 1 year of follow up mortality, target vessel revascularization (TVR), and composite event of mortality and TVR were 3.92%, 9.48%, and 12.98% respectively. LR was the best model in predicting mortality event within 1-year post-PCI (AUC: 0.820). SVM had the highest discrimination power for both TVR event (AUC: 0.720) and composite event of mortality and TVR (AUC: 0.720).ConclusionsThis study successfully identified optimal prediction models with the good discriminatory ability for mortality outcome and good discrimination ability for TVR and composite event of mortality and TVR with a simple machine learning framework.

Boosting learning achievement in physics among Ugandan form-2 students: effect of problem-based learning

Enabling students to learn smoothly at an early stage of learning is a paramount effort that African education should takeoff. This study established the effect of problem-based learning (PBL) on students’ learning achievement. The participants were selected from form-2 of lower secondary schools in Sheema District, Western Uganda. Participants were randomly assigned to the treatment class with PBL instruction and the control class with content-based learning (CBL). A quantitative approach and a quasi-experimental design were used. Thus, the pretest-posttest non-equivalent quasi-experimental design was applied. A learning achievement test in simple machines was used as a data collection tool. The test was validated by experts and piloted with a split-half reliability (r = 0.87). Data was analysed in MS Excel and SPSS. A repeated measures analysis of variance revealed a very high statistically significant difference (p < .001) after learning in both classes. The same significance was found between PBL and CBL in favour of PBL. Classical test theory was used to present item difficulty and discrimination index. Teachers were recommended to adopt these methods with more practical hands-on activities to boost their skills and conceptual understanding.

Numerical investigation and modeling of sweep effects on inlet flow field of axial compressor cascades

Swept blades are widely utilized in transonic compressors/fans and provide high load, high through-flow, high efficiency, and adequate stall margin. However, there is limited quantitative research on the mechanism of the effect of swept blades on the flow field, resulting in a lack of direct quantitative guidance for the design and analysis of swept blades in fans/compressors. To better understand this mechanism, this study employs a reduced-dimensional force equilibrium method to analyze more than 1500 swept cascades data. Results verify that circumferential fluctuation terms are responsible for inducing radial migration in the inlet airflow field of the swept blade, resulting in variations in the incidence angle and consequently leading to changes in the characteristics of the swept blade. Thus, a combination of simple functions and machine learning is utilized to model the circumferential fluctuation terms and quantify the sweep mechanism. The prediction accuracy of the model is high, with coefficient of determination greater than 0.95 on the test set. When the model is applied in a meridional flow analysis program, the calculation accuracy of the program for the incidence angle is improved by 0.4° and 0.6° at the design and off-design conditions respectively, compensating for the program's original deficiencies. Meanwhile, the model can also provide quantitative guidance for the design of swept blades, thereby reducing the number of design iterations and improving design efficiency.