Automatic Program Research Articles

Light‐Manipulated Millimeter‐Scale Swimming Robot for Precise Navigation

AbstractMicro‐swimming robots have been increasingly emphasized in the fields of targeted drug delivery and water surface cleaning, which mainly use open external structures or external environments to assist navigation, but there are still salient problems, such as the difficulty of stable capture and poor navigation accuracy. Theoretically, a light‐driven swimming robot can have a symmetrical embedded structure to constrain surface tension gradient forces and thermophoretic forces in a wide range of directions and convert them into precise directional forces. Therefore, a sustained‐capture swimming robot is developed with precise navigation consisting of TiO2/polypyrrole (PPy) composites containing a symmetric inner truncated‐cone hole structure and realized multiple controllable motion patterns. Excitingly, the capture motion of the TiO2/PPy inner truncated‐cone hole structure robot “TPHR” can be accurately controlled by programmable automation, with capture travel speeds of up to ≈23.64 mm s−1. Its excellent kinematic performance stems from the high photothermal conversion efficiency (≈47.83%) of the composite and the synergistic effect between thermo‐capillary and thermo‐convective flows. Furthermore, a patterning precision movement, water surface oil‐cleaning microstructure assembly, etc. are realized, reflecting the engineering application value of TPHR. This structure provides a novel strategy for the development of micro‐swimming robots with stable capture and sustainable motion.

Automated programming approaches to enhance computer-aided translation accuracy

With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today’s fast-paced environment. Computer-assisted translation (CAT) addresses the issue of slow translation speed; however, the quality of CAT translations still requires rigorous evaluation. This study aims to answer the following questions: How do CAT systems that use automated programming fare compared to more conventional methods of human translation when translating English vocabulary? (2) How can CAT systems be improved to handle difficult English words, specialised terminology, and semantic subtleties? The working premise is that CAT systems that use automated programming techniques will outperform traditional methods in terms of translation accuracy. English vocabulary plays a crucial role in translation, as words can have different meanings depending on the context. CAT systems improve their translation accuracy by utilising specific automated programs and building a translation corpus through translation memory technology. This study compares the accuracy of English vocabulary translations produced by CAT based on automatic programming with those produced by traditional manual translation. Experimental results demonstrate that CAT based on automatic programming is 8% more accurate than traditional manual translation when dealing with complex English vocabulary sentences, professional jargon, English acronyms, and semantic nuances. Consequently, compared to conventional human translation, CAT can enhance the accuracy of English vocabulary translation, making it a valuable tool in the translation industry.

Optimizing the Automated Analysis of Inorganic Gunshot Residue Particles by SEM-EDX: From Synthetic Particle Standards to More Time-Efficient Settings for Daily Casework

Gunshot residues deposited on all surfaces in the nearest vicinity of the shooting incident, when revealed, can contribute to the explanation of various aspects of such an incident for forensic purposes. Examinations of gunshot residue, mainly inorganic particles, at forensic laboratories are expected to be reliable and fast. This primarily depends on the performance of the used scanning electron microscope integrated with an energy dispersive X-ray spectrometer and the automatic program searching for particles of defined characteristics. Among the milestones on the pathway towards quality assurance in examinations of gunshot particles, the invention of the synthetic gunshot residue specimen ought to be named. Such a specimen with particles of known chemical content, size, and location is now used for proficiency testing, which is a condition for a forensic laboratory to obtain accreditation in this subject matter. In this publication, the need for optimization of the procedure for the examination of a synthetic specimen, in alignment with the necessary modifications for real gunshot particles, has been addressed. The presented process of validation resulted in two procedures. The first demonstrates the full capacity of the instrument for detecting all particles present in the synthetic specimen, including the 0.5 micrometer particle at the magnification of 250×. The other procedure is the modification of the first, however aiming at 1-micrometer particles or bigger (at the magnification of 120×) and allowing the necessary backscattered signal threshold changes depending on the actual composition of gunshot residue as well as the abundance of light element debris in the case of real gunshot particles.

Initial sarcopenia and body composition changes as prognostic factors in cervical cancer patients treated with concurrent chemoradiation: An artificial intelligence-based volumetric study

ObjectivesThis study aimed to investigate the influence of baseline sarcopenia and changes in body composition on survival during cervical cancer treatment. MethodsPatients diagnosed with stage IB1-IVB cervical cancer who underwent primary concurrent chemoradiation therapy (CCRT) between 2002 and 2022 were included. The exclusion criteria were prior radical hysterectomy, lack of pretreatment computed tomography (CT) imaging, or significant comorbidities. An artificial intelligence-based automatic segmentation program assessed body composition by analyzing CT images, defining L3 sarcopenia (L3 skeletal muscle index [SMI] <39cm2/m2) and volumetric sarcopenia (volumetric SMI <180.4 cm3/m3). Comparative and multivariate analyses identified the prognostic factors. The impact of body component changes during CCRT was explored. ResultsAmong 347 patients, there were 125 recurrences and 59 deaths (median follow-up, 50.5 months). Seven patients were excluded from the volumetric sarcopenia analysis because of incomplete baseline CT data, and 175 patients were included in the analysis of body composition changes. Patients with L3 sarcopenia had a lower 5-year progression-free survival (PFS) rate (55.6% vs. 66.2%, p = 0.027), while those with volumetric sarcopenia showed a poorer 5-year overall survival rate (76.5% vs. 85.1%, p = 0.036). Patients with total fat loss during CCRT had a worse 5-year PFS rate than those with total fat gain (61.9% vs. 73.8%, p = 0.029). Multivariate analyses revealed that total fat loss (adjusted hazard ratio [aHR], 2.172; 95% confidence interval [CI], 1.066–4.424; p = 0.033) was a significant factor for recurrence, whereas L3 sarcopenia was not. Volumetric sarcopenia increased the risk of death by 1.75-fold (aHR, 1.750; 95% CI, 1.012–3.025; p = 0.045). ConclusionsAmong patients with cervical cancer undergoing CCRT, initial volumetric sarcopenia and fat loss during treatment are survival risk factors. These findings suggest the potential importance of personalized supportive care, including tailored nutrition and exercise interventions.

AUTOMATIC PATH PROGRAMMING FOR THE MANUFACTURING OF SHEET METAL COMPONENTS USING INCREMENTAL FORMING TECHNOLOGY

Asymmetric Incremental Sheet Forming (AISF) is an unconventional forming technology that involves localized and progressive plastic deformation of a sheet using a tool, typically a punch, following a continuous and numerically controlled path. Due to its specific characteristics and advantages, such as flexibility and relatively low implementation cost, it is considered a technology with great potential for competitively producing small series of sheet metal components. However, there are still some challenges, such as the lack of specific CAM tools for this technology, which has hindered its industrialization. Although the process may appear simple, with a 3D path sufficient to transform the original flat sheet into the final part, it has been observed that the manufacturing of real designs requires extensive knowledge of the technology to choose the appropriate path based on the target geometry. In this regard, this study presents a new solution developed by Tecnalia that incorporates expert process knowledge and enables automatic programming of Incremental Forming paths. Specifically, the solution allows programming paths aimed at manufacturing different geometric features commonly found in sheet metal components. This development has been validated through the manufacturing of several parts in a robotic cell implemented at Southeast University in Nanjing.

Framework for Implementation of Building Automation Control Programs for Industrial Heating and Cooling Systems

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns.

A study into the fluid power control characteristics of a hydrostatic transmission for mobile machinery

In response to improve the quality and level of hydrostatic transmission (HST) products for modern mobile machineries, the closed-loop transfer functions of a HST are established by mathematical modeling, a comprehensive testing equipment is developed employing a proportional relief valve for automatic program loading and fuzzy-PID oil temperature control, the testing equipment is capable of conducting various performance tests for various models of HST integrated pump-motor products, including factory and type tests, especially continuous impact and endurance performance tests under high temperature and high-speed conditions. Comprehensive performance test of the above test sample is carried out using the developed equipment, that the test results agree well with the previous simulation results verifies the correctness of the established theoretical analysis model. The obtained theory and equipment from this work provide theoretical and experimental technical support for improving design and development of high-end fluid components for modern mobile machinery HST.

ANALISIS KINERJA DINAS MATERIEL DAN SENJATA TNI ANGKATAN LAUT MELALUI PENDEKATAN BUDAYA ORGANISASI DAN KOMPETENSI YANG DIMODERASI AUTOMATIC ADJUSTMENT

The Indonesian Navy's weapons and electronics materials service has a very strategic function in the Indonesian Navy. one of its functions is to carry out planning and continuous evaluation of activities in the field of electronics and weapons including ammunition material, navigation, electronic instruments, drones, simulators, sensors, controls and weapons, sensors, controls and weapons. The type of research used in this research is qualitative. The location of this research is the Indonesian Navy's weapons and electronics materials service, Subdiharsewaco Disharkap Koarmada I, Subdiharsewaco Disharkap Koarmada II, Subdiharsewaco Disharkap Koarmada III, Disharsewaco Disharkap Kolinlamil, Subdisren Disharmat Kormar. The analysis tool used is NVIVO 12. The results of the research are that the planning and budget proposed by Dissenlekal are in accordance with Dissenlekal's needs, but with the automatic adjustment, planning cannot reach 100%. The organizational culture of the Indonesian Navy's weapons and electronics materiel service reflects a good, positive, supportive and inclusive work environment. The competency of personnel in this organization is very good, with in-depth knowledge and high skills. The performance of personnel in this organization is very good, with high quality and quantity of work, consistent punctuality, and strong occupational safety awareness and compliance. Organizational culture and competence influence organizational performance. The Indonesian Navy's weapons and electronics materials service has strong adaptability in dealing with budget changes resulting from the automatic adjustment program. The existence of automatic adjustments affects organizational performance but is not significant. With a good organizational culture and competence, good performance will result despite automatic adjustments.

Automated Repair of Smart Contract Vulnerabilities: A Systematic Literature Review

The substantial value held by smart contracts (SCs) makes them an enticing target for malicious attacks. The process of fixing vulnerabilities in SCs is intricate, primarily due to the immutability of blockchain technology. This research paper introduces a systematic literature review (SLR) that evaluates rectification systems designed to patch vulnerabilities in SCs. Following the guidelines set forth by the PRISMA statement, this SLR meticulously reviews a total of 31 papers. In this context, we classify recently published SC automated repair frameworks based on their methodologies for automatic program repair (APR), rewriting strategies, and tools for vulnerability detection. We argue that automated patching enhances the reliability and adoption of SCs, thereby allowing developers to promptly address identified vulnerabilities. Furthermore, existing automated repair tools are capable of addressing only a restricted range of vulnerabilities, and in some cases, patches may not be effective in preventing the targeted vulnerabilities. Another key point that should be taken into account is the simplicity of the patch and the gas consumption of the modified program. Alternatively, large language models (LLMs) have opened new avenues for automatic patch generation, and their performance can be improved by innovative methodologies.

The Trailer of the ACM 2030 Roadmap for Software Engineering

The landscape of software engineering has dramatically changed. The recent advances in AI, the new opportunities of quantum computing, and the new challenges of sustainability and cyber security upset the software engineering research prospective. The 2030 SE-Roadmap special issue of ACM TOSEM Transactions on Software Engineering and Methodology gives a 360° view of the research challenges of the 30ties with a thorough editorial, four roadmap papers from the ACM TOSEM editorial board, and over 30 peer-reviewed papers from the research community. This paper previews the main content of the 2030 roadmap special issue with a report from the 2030 Software Engineering Roadmap workshop, co-located with ACM SIGSOFT FSE Foundations of Software engineering on July 15th and 16th, 2024 in Porto de Galinhas, Brazil, that spotlighted the software engineering research horizon to feed ideas into the ACM TOSEM special issue. The paper discusses the new challenges to software engineering that emerged in the SE2030 workshop: AI for software engineering, software engineering by and for humans, sustainable software engineering, automatic programming, cyber security, validation and verification, and quantum software engineering.

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

Application and Impact of Automation in Crimping Processes

Abstract This paper investigates the Subassembly Process Automation (SPA) program’s application and impact on industrial manufacturing processes, particularly underscoring its efficacy in refining production accuracy and efficiency. It explores SPA’s systematic advancements and the incorporation of meticulous quality control strategies, focusing on how it streamlines operations, boosts efficiency, and ensures adherence to industry norms. The SPA program integrates automation for comprehensive process oversight, including job initiation, ongoing production, and critically, scrap reduction, thereby markedly diminishing waste and upholding rigorous quality standards. Offering an indepth evaluation of SPA’s capabilities, the paper illustrates its role in enhancing production flow and supporting environmental sustainability through effective scrap management. By analysing its implementation in crimping operations, from equipment preparation to production execution, the research highlights SPA’s essential contribution to reducing errors, maximizing resource efficiency, and maintaining exceptional manufacturing quality.

Research on Typical Fault Diagnosis of Gear based on GHM Multiwavelet Transform and MCKD Algorithm

Background: Gear is the key part of the transmission part of mechanical equipment, which has a high failure rate under complex working conditions or long-term operation, directly affecting the reliability of mechanical equipment. To realize online monitoring of gears and predict gear faults, whether the diagnosis of typical gear faults is accurate and timely is a key link, so it is necessary to study the diagnosis of typical gear faults. Objective: This paper aims to propose a method that can effectively diagnose typical faults of gears and is easy to program, which is beneficial to realize online monitoring of rotating machinery equipment faults. Method: A typical gear fault diagnosis method based on GHM multiwavelet transform and maximum correlation kurtosis deconvolution algorithm (MCKD) is proposed in this paper. The measured vibration signal is decomposed into components with multiple scale spaces and frequency bands through the GHM multiwavelet transform. Then, the MCKD algorithm is run to suppress the noise of GHM multiwavelet transform coefficients, and the periodic components containing fault information are retained and reconstructed. The typical faults such as gear pitting and broken teeth are tested. Results: GHM multiwavelet transform combined with MCKD algorithm can accurately locate the frequency band containing fault information from the vibration signal with multiple modulations, double sideband, and multi-frequency interference and effectively extract the fault frequency and its frequency multiplication. The root means a square error of the extracted fault frequency is 0.08. This method has a good noise suppression effect and high accuracy in extracting fault frequency. Conclusion: The method proposed in this paper is helpful in realizing automatic programming of gear fault diagnosis and is of great significance in realizing real-time monitoring and online diagnosis of rotating machinery equipment faults. More related patents will appear in the future.

Dynamical Sphere Regrouping Particle Swarm Optimization Programming: An Automatic Programming Algorithm Avoiding Premature Convergence

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO.

Updates on Clinical Language Sampling Practices: A Survey of Speech-Language Pathologists Practicing in the United States.

Language sample analysis (LSA) provides many benefits for assessing, identifying therapy goals, and monitoring the progress of children with language disorders. Despite these widely recognized advantages, previous surveys suggest the declining use of LSA by speech-language pathologists (SLPs). This study aimed to provide updates on clinical LSA use following the recent introduction of two new LSA protocols, namely, the Sampling Utterances and Grammatical Analysis Revised (SUGAR) protocol and the Computerized Language Analysis KIDEVAL program. Survey data from SLPs practicing in the United States (N = 337) were used to examine rates of LSA use, methods, and protocols. Factors predicting LSA use and reported facilitators and barriers were also examined. Results indicated that 60% of SLPs used LSA in the past year. LSA skill level, training, and serving preschool or elementary school children predicted LSA use, whereas workplace, caseload, and years of experience were not significant predictors. Most SLPs reported using self-designed LSA protocols (62%), followed by Systematic Analysis of Language Transcripts (23%) and SUGAR (12%) protocols. SLPs who did not use LSA reported limited time (74%), limited resources (59%), and limited expertise (41%) as barriers and identified additional training on LSA computer programs (52%) and access to automatic speech recognition programs (49%) as facilitators to their adoption of LSA. Reported rates of LSA use and methods were consistent with previous survey findings. This study's findings highlight the ongoing needs for more extensive preprofessional training in LSA.

417 Impact of virtual fence technology on yearling steer behavior and performance

Abstract Virtual fencing (VF) has the capacity to transform the landscape of livestock management within extensive rangeland systems. The objective of this study was to evaluate the effect of Vence (Merck &amp; Co., Inc., Rahway, NJ) VF technology on yearling steer behavior and performance. Steers were allocated into one of two grazing systems [continuous grazing (CG) or rotational grazing (RG) managed using VF] across three different stocking rates (light, moderate, and heavy, 0.32, 0.40, and 0.72 animal unit months, respectively) for three grazing seasons. Based on initial body weight, steers were randomly blocked into six pastures (346 ± 39, 273 ± 34, and 319 ± 29 kg, respectively) at the South Dakota State University Cottonwood Field Station (n = 127, 135, and 127, in 2021, 2022 and 2023, respectively). Steers assigned to the CG treatment had free access to the entire pasture for the duration of the grazing season. Steers assigned to the RG treatment were rotated among virtual ‘paddocks’ for the duration of the grazing season. The days spent grazing in each paddock for the RG treatment were determined based on bi-weekly clip plots for biomass estimation. The VF collars were used to track steer location at 5-min intervals; only steers assigned to the RG treatment were managed with VF boundaries and exposed to auditory and electrical cues from the collars. Raw data was downloaded from Vence Herd Manager through an automatic programming interface and cleaned by removing messages that failed to transmit correctly or were outside the bounds of study site pastures. Animal behavior metrics of daily distance traveled (DDT), daily grazing time, daily resting time, and daily walking time were calculated for each individual animal and averaged by pasture using Program R. Steer weights were collected using SmartScales (C-Lock Inc, Rapid City, SD). Average daily gain (ADG) was calculated using a linear model to develop a regression equation for each individual animal using weight as the dependent variable and day of trial as the independent variable. The slope of the regression line was used to estimate ADG. Individual DDT, behavior, and performance metrics were analyzed using a mixed model analysis of variance (ANOVA) to compare the impact of grazing treatment and stocking rate (fixed effects) with year as the random effect. Treatment did not influence (P &amp;gt; 0.05) grazing, walking, or resting behavior. Similarly, no differences were detected in DDT between treatment groups or across different stocking rates. Furthermore, there were no differences (P &amp;gt; 0.05) in ADG across treatment groups. Implementation of VF had no impact on animal behavior or performance, justifying its use in grazing settings; however, financial and cattle finishing aspects still need to be evaluated to determine its overall benefits and costs.

Machine‐learning‐assisted intelligent synthesis of UiO‐66(Ce): Balancing the trade‐off between structural defects and thermal stability for efficient hydrogenation of Dicyclopentadiene

AbstractMetal‐organic frameworks (MOFs), renowned for structural diversity and design flexibility, exhibit potential in catalysis. However, the pursuit of higher catalytic activity through defects often compromises stability, requiring a delicate balance. Traditional trial‐and‐error method for optimizing synthesis parameters within the complex chemical space is inefficient. Herein, taking the typical MOF UiO‐66(Ce) as an illustrative example, a closed loop workflow is built, which integrates machine learning (ML)‐assissted prediction, multi‐objective optimization (MOO) and experimental preparation to synergistically optimize the defect content and thermal stability of UiO‐66(Ce) for efficient hydrogenation of dicyclopentadiene (DCPD). An automatic data extraction program ensures data accuracy, establishing a high‐quality database. ML is employed to explore the intricate synthesis‐structure‐property correlations, enabling precise delineation of pure‐phase subspace and accurate predictions of properties. After two iterations, MOO model identifies optimal protocols for high defect content (&gt;40%) and thermal stability (&gt;300°C). The optimized UiO‐66(Ce) exhibits superior catalytic performance in hydrogenation of DCPD, validating the precision and reliability of our methodology. This ML‐assisted approach offers a valuable paradigm for solving the trade‐off riddle in materials field.

2258: Feasibility of using an automatic segmentation program in the treatment of metastatic spine lesions.

2258: Feasibility of using an automatic segmentation program in the treatment of metastatic spine lesions.

Auto-WoodSDA: A scalable end-to-end automation framework to perform probabilistic seismic risk and recovery assessment of new residential woodframe buildings

The seismic performance of residential woodframe buildings has a widespread and lasting impact on the socioeconomic vitality of a community. To enable efficient seismic risk and resilience assessment, a comprehensive framework that is rooted in performance-based earthquake engineering (PBEE) methodology is proposed. The framework is formulated as a computational workflow which is operationalized as an automation program dubbed “Auto-WoodSDA”. The end-to-end platform is developed to perform four major tasks: (1) generate code-compliant seismic design, (2) create numerical models, (3) perform nonlinear analyses, and (4) assess earthquake-induced financial losses and functional recovery time. Given the user inputs, Auto-WoodSDA sequentially executes the four tasks without intermediate human intervention to produce seismic performance metrics such as the expected annual loss. It is presented as a ready-to-implement platform that is computationally efficient and scalable in performing large-scale seismic risk and resilience assessment. An example building from the existing literature is used to demonstrate the key features of the automation framework and verify the results. The results underscore the ability of the AutoWoodSDA computational workflow to automate the generation of reliable new code-compliant woodframe building designs, perform nonlinear analysis, and assess the earthquake-induced impacts using PBEE principles.

ARC WELDING ROBOTIC FLEXIBLE CELL SIMULATION USING ROBOGUIDE SOFTWARE

The objective of the work is the design and simulation of a flexible manufacturing structure with two robots, integrated in a welding application, using the Roboguide software. FANUC ROBOGUIDE is a robot simulator that simulates both the movement of the robots and the commands from the designed application, significantly reducing the time needed to create new motion configurations.The possibility to import the CAD models of the components, of the fixing devices and of the final effectors of the robots integrated into the designed application, allows the creation and evaluation of various structures. Automatic program generation simplifies manual programming operations. ROBOGUIDE allows pre-programming of robots before they are installed in a cell, as well as visualization and confirmation of robot trajectories before downloading the programs to the real robot.