Placement Error Research Articles

Machine Learning-Based Edge Placement Error Analysis and Optimization: A Systematic Review

As the semiconductor manufacturing process is moving towards the 3 nm node, there is a crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the integrated circuit (IC) devices. EPE is the most important metric that quantify the fidelity of fabricated patterns in multi-patterning processes, and it is the combination of overlay errors and critical dimension (CD) errors. Recent advances in machine learning have enabled many new possibilities to improve the performance and efficiency of EPE optimization techniques. In this paper, we conducted a survey of recent research work that applied machine learning/ deep learning techniques for the purposes of enhancing virtual overlay metrology, reducing overlay error, and improving mask optimization methods for EPE reduction. Thorough discussions about the objectives, datasets, input features, models, key findings, and limitations are provided. In general, the results of the review work show a great potential of machine learning techniques in aiding the improvement of EPE in the field of semiconductor manufacturing.

Nonlinear Finite-Element Analysis for Structural Investigation and Preservation of Reinforced Hybrid Thin Tile–Concrete Domes of the Historic School of Ballet Classrooms in Havana, Cuba

The striking tiled domes of the Ballet School of the Instituto Superior de Arte (ISA) in Havana are internationally acclaimed Cuban architectural icons. However, due to their deceptive tiled appearance and their only recently rediscovered hybrid tile–concrete constructive system, the domes have eluded the structural study and characterization which increasingly is demanded not only because of their unique morphology and monumental status, but also because of their visible damage. This work defined a suitable numerical analysis method for diagnosing these hybrid structures by applying both linear elastic and nonlinear, using the concrete damaged plasticity constitutive model, numerical analyses. A site investigation campaign provided materials and geometric data that informed the model creation and damage documentation that validated the analyses results. A new synthesis of architectural and technical sources covering the development and spread of the hybrid structural system allowed the ISA domes to be placed, for the first time, in their proper structural context and, consequently, for their conception and design to be understood more fully. The analyses, construction records, and site tests indicated a rebar placement error as the primary structural damage cause, and the contextual analysis informed a discussion of its possible sources, both technical and managerial. Because the investigation indicated significant risk of irreversible damage or failure without timely intervention, preliminary intervention explorations were conducted and, based on the holistic understanding of the structures, the technically, architecturally, and culturally optimal solution was identified. The study concluded that the homogeneous solids model, coupled with the concrete damaged plasticity mechanical model, yields realistic and insightful results illuminating the structural behavior, efficiency, condition or status, and collapse mode of the domes, providing necessary insight for the preservation planning for the Ballet School domes, which also will inform the study and understanding of other structures of similar composition and significance.

Alpha dose modeling in diffusing alpha-emitters radiation therapy. Part II: Lattice studies.

Diffusing alpha-emitters radiation therapy ("DaRT") is a new method, presently in clinical trials, which allows treating solid tumors by alpha particles. DaRT relies on interstitial seeds carrying μCi-level 224 Ra activity on their surface, which release a chain of short-lived alpha emitters that spread throughout the tumor volume primarily by diffusion. Alpha dose calculations in DaRT are based on describing the transport of alpha emitting atoms, requiring new modelingtechniques. A previous study introduced a simplified framework, the "diffusion-leakage (DL) model," for DaRT alpha dose calculations, and employed it to a point source, as a basic building block of arbitrary configurations of line sources. The aim of this work, which is divided into two parts, is to extend the model to realistic seed geometries (in Part I), and to employ single-seed calculations to study the properties of DaRT seed lattices (Part II). Such calculations can serve as a pragmatic guide for treatment planning in future clinicaltrials. We employ the superposition of single-seed solutions, developed in Part I, to study the alpha dose in DaRT seed lattices and investigate the sensitivity of the required seed activity and spacing to changes in the DL model parameters and to seed placementerrors. We show that the rapid fall-off of the dose, which guarantees sparing healthy tissue already 2-3 mm away from the tumor, strongly favors a hexagonal, rather than square, seed placement pattern. Realistic variations in the seed manufacturing parameters (224 Ra activity and emission rate of its daughters) are shown to have a negligible effect on the required lattice spacing. On the other hand, tumor parameters (i.e., diffusion lengths and 212 Pb leakage probability), as well as seed placement errors, have a significanteffect. In most cases, hexagonal lattice spacing on the scale of ∼3.5-4.5 mm using seeds carrying a few μCi/cm 224 Ra will enable overcoming realistic uncertainties in measured tumor environment parameters, as well as seed placement errors, and result in therapeutically relevant alpha dose levels.

Out-of-Plane Needle Placements Using 3D Augmented Reality Protractor on Smartphone: An Experimental Phantom Study.

To evaluate the accuracy of needle placement using a three-dimensional (3D) augmented reality (AR) protractor on smartphones (AR Puncture). An AR protractor that can be rotated in three directions against the CT plane with angle guidance lines for smartphones was developed. The protractor center can be adjusted to an entry point by manually moving the smartphone with the protractor center fixed at the center of the screen (Fix-On-Screen) or by image tracking with a printed QR code placed at an entry point (QR-Tracking). Needle placement was performed by viewing a target line in the tangent direction with the Bull's eye method. The needle placement errors placed by four operators in six out-of-plane directions in a phantom using a smartphone (iPhone XR, Apple, Cupertino, CA, USA) were compared with two registration methods. No significant difference in the average needle placement error was observed between the Fix-On-Screen and QR-Tracking methods (5.6 ± 1.7mm vs. 6.1 ± 2.9mm, p = 0.475). The average procedural time of the Fix-On-Screen method was shorter than that of the QR-Tracking method (71.0 ± 23.9s vs. 98.4 ± 59.5s, p = 0.042). The accuracies of out-of-plane needle placements using the 3D AR protractor with the two registration methods were equally high, with short procedure times. In clinical use, the Fix-On-Screen registration method would be more convenient because no additional markers are required.

Curvilinear mask optimization with refined generative adversarial nets

Inverse lithography technology (ILT) can optimize the mask to gain the best process window and image quality when the design dimension shrinks. However, as a pixel level correction method, ILT is very time-consuming. In order to make the ILT method useful in real mask fabrication, the runtime of ILT-based optical proximity correction mask must evidently decrease while keeping the good lithographic metric performance. Our study proposes a framework to obtain the curvilinear ILT mask with generative adversarial network (GAN). It is subsequently refined with the traditional ILT to exclude unexpected outliers generated by the GAN method. We design conditional GAN, reverse GAN (RGAN), and high discretion GAN (HDGAN) to generate curvilinear ILT mask. Their runtime and the performance are compared. Compared with the CILT method, the speed of GAN type methods with the afterward refinement is increased by an order of magnitude. The RGAN has a better performance in edge placement error and process variation band evaluation, and HDGAN has a better performance in the mask error enhancement factor evaluation. The designed RGAN and HDGAN are promising in actual application to generate the curvilinear mask. They can evidently decrease the runtime and have better lithographic metric performance.

A New Program to Estimate the Parameters of Preston’s Equation, a General Formula for Describing the Egg Shape of Birds

Preston’s equation is a general model describing the egg shape of birds. The parameters of Preston’s equation are usually estimated after re-expressing it as the Todd-Smart equation and scaling the egg’s actual length to two. This method assumes that the straight line through the two points on an egg’s profile separated by the maximum distance (i.e., the longest axis of an egg’s profile) is the mid-line. It hypothesizes that the photographed egg’s profile is perfectly bilaterally symmetrical, which seldom holds true because of photographic errors and placement errors. The existing parameter estimation method for Preston’s equation considers an angle of deviation for the longest axis of an egg’s profile from the mid-line, which decreases prediction errors to a certain degree. Nevertheless, this method cannot provide an accurate estimate of the coordinates of the egg’s center, and it leads to sub-optimal parameter estimation. Thus, it is better to account for the possible asymmetry between the two sides of an egg’s profile along its mid-line when fitting egg-shape data. In this paper, we propose a method based on the optimization algorithm (optimPE) to fit egg-shape data and better estimate the parameters of Preston’s equation by automatically searching for the optimal mid-line of an egg’s profile and testing its validity using profiles of 59 bird eggs spanning a wide range of existing egg shapes. We further compared this method with the existing one based on multiple linear regression (lmPE). This study demonstrated the ability of the optimPE method to estimate numerical values of the parameters of Preston’s equation and provide the theoretical egg length (i.e., the distance between two ends of the mid-line of an egg’s profile) and the egg’s maximum breadth. This provides a valuable approach for comparing egg shapes among conspecifics or across different species, or even different classes (e.g., birds and reptiles), in future investigations.

Artificial intelligence-assisted diagnosis of hematologic diseases based on bone marrow smears using deep neural networks

Background and objectivesThe morphological examination of bone marrow (BM) cells is essential in both diagnosing and treating various hematologic diseases. However, it is still done manually with a heavy workload. An artificial intelligence-assisted diagnosis support system of BM cells is highly required to reduce the workloads of examiners and improve the reproducibility of the results. MethodsIn this paper, we proposed an artificial intelligence-assisted diagnosis support system of morphological examination based on bone marrow smears including cells detection, classification and prediction of leukemia types. For cell detection, we trained the novel YOLOX-s model to locate cells precisely and obtain single cell images. For cell classification, we regarded it as a fine- grained classification task and proposed a novel architecture called MLFL-Net utilizing multi-level features. Furthermore, we predicted the leukemia types on a dataset including 40 normal people (BM transplantation donors) and 40 patients of different kinds of acute leukemia according to the World Health Organization (WHO) standard. ResultsWe constructed a large-scale data set of 11,788 fully-annotated micrographs from 728 smears and 131,300 expert-annotated single cell images. With the data set, the detection model achieved 0.9797 AUC and 4.33% box placement error. For cell classification, the total accuracy of our proposed MLFL-Net reached 89.53% which outperformed all the other related models in identifying cell categories. In the meantime, we took acute leukemia as an example to explore the leukemia types prediction procedure of hematological disease. It generated the same diagnostic prediction as the experts gave for 92.5 percent of the cohort. ConclusionThis Artificial Intelligence-assisted system can be implemented to aid in clinical decision making and accelerate diagnosis. The method will contribute to promote the intelligence and modernization of BM cytomorphology, which has vital significance of the development of the medical career.

Salwa Cars Structure Testing Standards Regulation Formula Society of Automotive Engineers

This research was to find out the results of the bending and torsional stiffness testing of the SALWA car according to Formula SAE 2021 regulations. The research method was by designing through Solidworks 2014 software. Testing of test specimens referred to Formula SAE 2021 regulations. The front impact test results produced max Von Mises 1046.921 MPA with a displacement of 8,540 mm; the rear impact test resulted in a max Von Mises of 222,529 MPa with a max displacement of 6.85 mm; and the side impact test resulted in max Von Mises Stress of 104.15 MPa with a max displacement of 9,116 mm. In the front torsional test, the max Von Misses Stress reached 135,266 MPa with a total max displacement of 4,844 mm. The rear torsional test had a max Von Mises Stress value of 147,144 MPa with a displacement of 5.51 mm. The feasibility test still found connection and construction placement errors, and there was a low Factor of Safety value. In conclusion, the construction of the SALWA Electric Car prototype frame is not in accordance with Formula SAE regulations.

Parametric Investigation of Vibratory Responses for Reduced-Order Models of Rotating Bladed Disks

Attempting to predict the physical response of a complex system under realistic conditions is extremely challenging. In this work, a parametric reduced-order model (PROM) of a bladed disk is briefly presented that allows for the quick and efficient modeling of rotational speed effects and mistuning effects in bladed disks. This computational model is tuned to match experimental data. The tuned PROM is then investigated through a parametric study. Three internal parameters investigated are i) forcing magnitude, ii) mistuning value magnitude, and iii) damping magnitude, and four external parameters are iv) forcing location, v) forcing radius, vi) probe placement error, and vii) arrival time error. These parameters are common in the structural analysis of turbomachines. Each internal parameter is increased and decreased by 10, 20, and 50%, while external parameters are changed by varying amounts due to geometric and practical limitations. The model is time integrated to simulate the conditions present in the experimental test rig. The maximum blade amplitudes are then compared to the baseline case and analyzed. The damping, followed by the forcing, is shown to cause the greatest impact on the final system response. This indicates a greater need to accurately measure the damping compared to other parameters.

A Method of Intraoperative Registration Verification to Prevent Accuracy Errors in Robot-Assisted Stereotactic Electroencephalography Electrode Placement

Robotic-assisted stereotactic electroencephalography (sEEG) electrode placement is increasingly common at specialized epilepsy centers. High accuracy and low complication rates are essential to realizing the benefits of sEEG surgery. The aim of this study was to describe for the first time in the literature a method for a stereotactic registration checkpoint to verify intraoperative accuracy during robotic-assisted sEEG and to report our institutional experience with this technique. All cases performed with this technique since the adoption of robotic-assisted sEEG at our institution were retrospectively reviewed. In 4 of 111 consecutive sEEG operations, use of the checkpoint detected an intraoperative registration error, which was addressed before completion of sEEG electrode placement. The use of a registration checkpoint in robotic-assisted sEEG surgery is a simple technique that can prevent electrode misplacement and improve the safety profile of this procedure.

Integrated consensus genetic map and genomic scaffold re-ordering of oil palm (Elaeis guineensis) genome

A high-quality reference genome is an important resource that can help decipher the genetic basis of traits in combination with linkage or association analyses. The publicly available oil palm draft genome sequence of AVROS pisifera (EG5) accounts for 1.535 Gb of the 1.8 Gb oil palm genome. However, the assemblies are fragmented, and the earlier assembly only had 43% of the sequences placed on pseudo-chromosomes. By integrating a number of SNP and SSR-based genetic maps, a consensus map (AM_EG5.1), comprising of 828.243 Mb genomic scaffolds anchored to 16 pseudo-chromosomes, was generated. This accounted for 54% of the genome assembly, which is a significant improvement to the original assembly. The total length of N50 scaffolds anchored to the pseudo-chromosomes increased by ∼18% compared to the previous assembly. A total of 139 quantitative trait loci for agronomically important quantitative traits, sourced from literature, were successfully mapped on the new pseudo-chromosomes. The improved assembly could also be used as a reference to identify potential errors in placement of specific markers in the linkage groups of the genetic maps used to assemble the consensus map. The 3422 unique markers from five genetic maps, anchored to the pseudo-chromosomes of AM_EG5.1, are an important resource that can be used preferentially to either construct new maps or fill gaps in existing genetic maps. Synteny analysis further revealed that the AM_EG5.1 had high collinearity with the date palm genome cultivar 'Barhee BC4' and shared most of its segmental duplications. This improved chromosomal-level genome is a valuable resource for genetic research in oil palm.

Can optical proximity correction solution be learned? The learning limit and a general learning framework

BackgroundOptical proximity correction (OPC) is an indispensable technology that has been propelling the advancement of computational lithography technology. To tightly control edge placement error (EPE) and maintain lithography process window, the demands on OPC computational resources and OPC turnaround time are growing rapidly with alarming acceleration. To tame the trend, machine learning technologies have been explored; however, an in-depth discussion on OPC solution learning limit is still lacking.AimWe aim to present an in-depth discussion on OPC solution learning limit and propose a general machine learning OPC framework that can be extended to curvilinear mask OPC technology.ApproachIn this study, we first investigate the machine learning OPC learning limit by examining noninverse lithography technology (non-ILT) OPC solution space characteristics inherited from edge segmentation and control point setting rules and then propose a general machine learning OPC framework that can take full advantage of deep convolution neural network (DCNN) learning capability while being able to preserve mask data high resolution.ResultsWith this machine learning OPC framework, we have achieved models with average absolute model error <1 nm for 14-nm metal layer. With single GPU, the average time for machine learning OPC models to produce results of 3840 nm × 3840 nm area is 8.74 ms for single channel input model and 12.65 ms for six channels input model.ConclusionsFor non-ILT OPC solution, there is an intrinsic learning limit inherited from edge segmentation rules. Machine learning OPC models should be content with learning low order OPC solutions. This intrinsic learning limit of non-ILT OPC solution may diminish for ILT OPC solution when the constraint on degrees of freedom of OPC solution is lifted. The machine learning OPC framework we proposed is general and extendable to curvilinear OPC technology.

How Early Can We Predict the Need for VPI Surgery?

This retrospective study reviewed documentation of children with moderate-to-severe VPI at age 3 years who did not undergo VPI surgery until age 5 years. Based on the national cleft register, 959 patients with syndromic and nonsyndromic CP were treated by the craniofacial team at Helsinki University Hospital, Finland between 2000 and 2014. Eighty-six patients fulfilled the study inclusion criteria. The speech pathologist evaluated velopharyngeal function at age 3, 5, and 8 years. Of the 86 children presenting with moderate-to-severe VPI at age 3 years, 94% still had moderate-to-severe VPI at age 5 years, even though speech therapy was offered to 77%. Of those whose velopharyngeal function improved by age 5 years, function regressed to incompetent over time. Overall, 93% underwent VPI surgery and 82% underwent VPI surgery between ages 5 and 8 years. Only 23% at age 8 years still had moderate-to-severe VPI. Speech therapy alone did not improve VPI speech characteristics. Moderate-to-severe VPI did not improve from 3 to 5 years or improved but subsequently relapsed. This suggests that the decision for VPI surgery can be made for children aged 3 years with moderate-to-severe VPI.

Process window improvement for fin cut layer in self-aligned double-patterning process based on backscattered electron imaging

BackgroundThe self-aligned double-patterning (SADP) process is being used extensively to overcome the lithographic resolution limit in the manufacture of integrated circuits. One use case is fin definition in a fin field-effect transistor. Fin cut layers are applied to modify the fins to the requirements of the device designs.AimThe traditional secondary electron (SE) imaging exhibits a disadvantage in the process controlling the fin cut layers, and fin damage defects were observed. This work aims to improve the monitoring and controlling capabilities for the process quality of fin cut layers.ApproachA specially designed fin cut process flow and a backscattered electron (BSE) imaging technique are applied to check the process quality. The patterns formed through the fin cut etch and the fin structures are identified and measured simultaneously in one BSE image.ResultsBy measuring the edge-to-edge distance, pitch walking (PW) of fins, and overlay (OV), the root cause of the fin damage is revealed. The linear fitting model and third-order fitting model are applied to reduce the edge placement error (EPE). The edge distance protecting the “at risk” fin is enlarged from 5.6 to 11.6 nm. The range of the distance is reduced from 11.6 to 8.1 nm, and the improvement in standard deviation is about 33%.ConclusionsThis work shows the capability of the BSE imaging technique in the characterization of fin cut layers and the potential in process window improvement restricted to fin damage defects.

An Experimental 1-km Warn-on-Forecast System for Hazardous Weather Events

Abstract An experimental Warn-on-Forecast System (WoFS) ensemble data assimilation (DA) and prediction system at 1-km grid spacing is developed and tested using two landfalling tropical cyclone (TC) events, one springtime severe thunderstorm event, and one summertime flash flood event. To evaluate the impact of DA at 1-km grid spacing, two experiments are conducted. One experiment, namely, the WoFS-1km, generates 3-h ensemble forecasts from the 1-km WoFS analyses while another experiment, namely, the Downscaled-1km, generates 3-h ensemble forecasts from downscaled 3-km analyses. With 1-km DA, the two landfalling TC events and the summertime event show some improvement in predicting high reflectivity, while the springtime event performs worse. Meanwhile, WoFS-1km is slightly better at predicting heavier precipitation (&gt;20 mm h−1) with lower bias. However, heavy precipitation spatial placement error is only mitigated in one TC event and the summertime event with 1-km DA but is neutral or worse in the other two events. Object-based verification for rotation objects indicates that WoFS-1km performs better in one of the TC events, but worse in the springtime event with lower probability of detection and higher false alarm ratio due to fewer strong rotation objects being generated. The forecast skill of WoFS-1km for the springtime event is degraded mainly because the convective cores do not sufficiently develop as the forecast advances. The conditional benefits from 1-km DA in this study highlights the need for evaluation of a larger sample of convective storm cases and further development of the system.

Assessment of 3D Models Placement Methods in Augmented Reality

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

The Classification of Axial Deformity in Patients with Basilar Invagination.

To summarize the variation types of the axis in patients with basilar invagination (BI), then propose a classification scheme of the axis deformity. From December 2013 to September 2020, 92 patients (male 42, female 50) who were diagnosed with BI were studied retrospectively. Based on the imaging data of CT, the width and height of the axis pedicle and the sagittal diameter of the lateral mass were measured in each patient. According to the development of axis pedicle and lateral mass, the types of axis variation were summarized, and then the classification scheme of axis deformity was put forward. All cases were analyzed and axis deformities were divided into four types. Type I: the axis is basically normal (53 cases, 57.6%). Type II: axis lateral mass is dysplasia (eight cases, 8.7%), which includes two subtypes: type IIA, the axis unilateral lateral mass is dysplasia (three cases); type IIB, the axis bilateral lateral masses are all dysplasia (five cases). Type III: axis pedicle is dysplasia (11 cases, 12%), which is subdivided into two subtypes: type IIIA, the axis unilateral pedicle is dysplasia (six cases); type IIIB, the axis bilateral pedicles are all dysplasia (five cases). Type IV: axis pedicle and lateral mass are all dysplasia (20 cases, 21.7%), this type contains the following four subtypes: type IVA, the unilateral axis pedicle and unilateral lateral mass (contralateral or ipsilateral) are all hypoplasia (four cases); type IVB, the unilateral axis pedicle and bilateral lateral masses are all hypoplasia (five cases); type IVC, the bilateral axis pedicles and unilateral lateral mass are all dysplasia (seven cases); type IVD, the bilateral axis pedicles and bilateral lateral masses are all dysplasia (four cases). The left and right abnormal lateral mass sagittal diameter (Type II) was (7.23 ± 1.39) mm and (5.96 ± 1.37) mm, respectively, the left and right abnormal pedicle width (Type III) was (2.61 ± 1.01) mm and (3.23 ± 0.66) mm, respectively, left and right abnormal pedicle height (Type III) was (5.43 ± 2.19) mm and (4.92 ± 1.76) mm, respectively. Moreover, the classification scheme has good repeatability and credibility. The classification about axis deformity could provide personalized guidance for axis screw placement in the BI and other upper cervical surgery, and axis screw placement errors would be effectively avoided.

Optimisation and validation of a co-manipulated robot for brachytherapy procedure.

Low-dose rate brachytherapy is the referent treatment for early-stage prostate cancer and consists in manually inserting radioactive seeds within the organ to destroy tumorous cells. This treatment is inaccurate leading to side effects. Researchers developed robots to improve this technique. Despite ameliorating accuracy, they cannot be clinically used because of size and acceptability. Therefore, a 6-DOFparallel and co-manipulated robot is proposed to meet these requirements. To fulfil the application requirements, a compact design was modelled. The robot's optimal dimensions were defined by establishing kinematics and implementing genetic algorithm. The robot's relevance was evaluated by measuring workspace and needle placement errors. The robot fits into a cube of 300×300×300mm3 and provides a free-singularity workspace of 55×55×150mm3 with a possible end-effector rotation of 15° and a needle placement error <3mm. The results are promising and prove that our robot fulfils the application requirements and presents a beneficial alternative to the manual procedure.

How Much Angular Offset is Acceptable to Achieve Minimum Contact for a Successful First Metatarsophalangeal Joint Fusion?

First metatarsophalangeal joint (MTPJ) fusion is a surgery to treat severe joint arthritis and pain following failure of conservative treatment. A first MTPJ fusion can be performed open or via a percutaneous approach with joint fixation achieved using various techniques including dorsal plate and screw fixation. Variability in the angulation of the guide wire used for reaming during joint resurfacing and the plate angulation can compromise the amount of bony contact needed for successful fusion. The objective of the current study was to utilize a 3-dimensional computational, geometric model to quantitatively analyze the effects of guide wire placement and subsequent bone resurfacing on the achievement of bony contact criteria using 3 dorsiflexion plates in joint fixation. A superposed ideal hemisphere model of the joint interface following resurfacing of the articulating surfaces was the basis for computational analysis. A custom-written code quantified angular offset limits for selected conditions. The angular offset from the central long axis of each bone represents initial guide wire placement error, impacting subsequent fixation and union.For 60% bony contact achieved in the joint interface for 0°, 5°, and 10° dorsiflexion plates angular offset limits for both the sagittal and transverse planes were 21.6°, 20.3°, and 19.0°, respectively. These analyses provide surgical guidance by quantifying the influence of dorsiflexion plate angles for ambulation and guide wire placement on the success of a first MTPJ fusion. The computational model presented can be readily adapted for varying joint geometries and may facilitate the development of a surgical guide prototype.

An Investigation of Common Stress Placement Errors in English Word Roots and their Suffixed Derivatives by Arabic-Speaking EFL Learners

Suffixation influences lexical stress in one of three ways. A suffix attached to a word root/stem may take the stress itself, or it may cause it to move from where it was in the uninflected stem to another syllable, or it may keep stress as is. Stress misassignment is very common in the speech of Arabic-speaking English as a foreign language (EFL) learners. This study was conducted to investigate how Arabic-speaking EFL learners at Prince Sattam bin Abdulaziz University (PSAU) in Saudi Arabia assign lexical stress in word roots/stems and their suffixed derivatives to find out whether stress in suffixed words presented a greater challenge to the learners than stress in the stem; the study also aimed to examine whether errors in stress assignment were caused by first language(L1) interference. 112 students from PSAU were randomly chosen to pronounce 80 suffixed words as well as their roots/stems. The pronunciations were recorded, and the recordings were analyzed by the three researchers and three raters. The study concluded that suffixed words do not present a more significant stress assignment challenge than uninflected stems, the type of syllable attached to a word is not as important as the number of syllables, syllable weight and structure in a word, and that L1 influence may be one of the causes but not the only cause of lexical stress misassignment.