A comparison of dislocation risk between dual mobility and traditional constructs used in proximal femoral replacement.

Background: Proximal femoral replacement (PFR) is a reconstruction technique after tumor resection or for revision of failed total hip arthroplasty (THA). However, despite acceptable long-term oncologic and functional outcomes, extensive soft tissue or bone loss increases the risk for prosthetic instability. Instability may depend on the construct chosen for reconstruction, with current options including bipolar, constrained, or dual mobility implants. Clinical studies comparing patient outcomes after PFR with these three different constructs are limited. Methods: This study retrospectively examined a single tertiary academic institution's experience with PFR over a fifteen-year period. The medical records of patients who underwent PFR for indications such as tumor and failed THA with bone loss were reviewed. Patients were stratified into cohorts based on use of bipolar, constrained, or dual mobility implants. Patient demographics, disease characteristics, perioperative data, and data on prosthetic dislocations were recorded. ANOVA and chi-square testing was performed for continuous and categorical variables, respectively. The threshold for statistical significance was set to p < 0.05. Results: 106 patients were identified who underwent PFR. 46 underwent PFR with bipolar prosthesis (follow-up: 20 ± 24.57 months), 42 with constrained liner (follow-up: 30.45 ± 35.32 months), and 18 with dual mobility (follow-up: 15.38 ± 15.67 months). Only BMI (p = 0.036) and smoking history (P = 0.002) differed between groups. Dislocations occurred in 4 (8.7 %) patients who underwent reconstruction with bipolar prosthesis, compared to 8 (19.0 %) with constrained liner, and 3 (16.7 %) patients with dual mobility. Mean time to dislocation was significantly longer in dual mobility patients (P = 0.009). There were no differences in instances of early dislocation between groups (P = 00.238). Conclusion: While study numbers are low, mean time to dislocation was significantly longer with dual mobility. Additional large-scale longitudinal studies are needed to fully elucidate the differences in outcomes amongst these three treatments.

Effects of helmet usage on moped riders' injury severity in moped-vehicle crashes: Insights from partially temporal constrained random parameters bivariate probit models.

Mopeds are small and move unpredictably, making them difficult for other drivers to perceive. This lack of visibility, coupled with the minimal protection that mopeds provide, can lead to serious crashes, particularly when the rider is not wearing a helmet. This paper explores the association between helmet usage and injury severity among moped riders involved in collisions with other vehicles. A series of joint bivariate probit models are employed, with injury severity and helmet usage serving as dependent variables. Data on two-vehicle moped crashes in Florida from 2019 to 2021 are collected and categorized into three periods: before, during, and after the COVID-19 pandemic. Crash involvement ratios are calculated to examine the safety risk elements of moped riders in various categories, while significant temporal shifts are also explored. The correlated joint random parameters bivariate probit models with heterogeneity in means demonstrate their superiority in capturing interactive unobserved heterogeneity, revealing how various variables significantly affect injury outcomes and helmet usage. Temporal instability related to the COVID-19 pandemic is validated through likelihood ratio tests, out-of-sample predictions, and calculations of marginal effects. Additionally, several parameters are noted to remain temporally stable across multiple periods, prompting the development of a partially temporally constrained modeling approach to provide insights from a long-term perspective. Specifically, it is found that male moped riders are less likely to wear helmets and are negatively associated with injury/fatality rates. Moped riders on two-lane roads are also less likely to wear helmets. Furthermore, moped riders face a lower risk of injury or fatality during daylight conditions, while angle crashes consistently lead to a higher risk of injuries and fatalities across the three periods. These findings provide valuable insights into helmet usage and injury severity among moped riders and offer guidance for developing countermeasures to protect them.

Estimating exercisality on urban trails using physical exercise trajectory data and network-constrained approach.

Green exercise is a key aspect of urban vitality, supporting the hypothesis that increased physical exercise boosts urban vitality. Although research on urban vitality considers green space a crucial aspect, existing studies have concentrated on external functioning from the perspective of special systems, often overlooking the unique internal functioning associated with exercisers. This study proposed an original conceptual framework of exercisality, which is composed of four dimensions: density, diversity, time continuity and energy expenditure. Considering urban trails are publicly accessible and linear-type green infrastructure for residents to conduct and maintain regular and habitual green exercise, we have developed an innovative quantitative approach to estimate and validate exercisality on urban trails (EUT), by utilizing physical exercise trajectory data from the Keep APP across central Beijing in 2022. The hot spots of EUT were identified through the innovative method of local indicators of network-constrained clusters. It is argued that this new index of EUT which is scale independence when applied to exercise trajectory big data, generates data driven evidence to support human well-being.

PCLOS based fractional-order sliding mode stochastic path following control for underactuated marine vehicles with multiple disturbances and constraints.

This paper investigates the stochastic path following control of underactuated marine vehicles (UMVs) subject to multiple disturbances and constraints. Firstly, the complex marine environment in which UMVs navigate typically contains stochastic components, thus the multiple disturbances are categorized as slow-varying deterministic disturbances and stochastic disturbances. Secondly, a position-constrained line-of-sight (PCLOS) based fractional-order sliding mode stochastic (FSMS) control strategy is established to achieve path following control of UMVs. A PCLOS guidance law based on universal barrier Lyapunov function is proposed to ensure that the position errors remain within the constraint ranges, which is versatile for systems with symmetric constraints or without constraints. An FSMS controller based on fractional-order theory and sliding mode control is designed to improve the dynamic response speed of the system and effectively attenuate chattering phenomenon. A stochastic disturbance observer is developed to estimate the slow-varying deterministic disturbances in the stochastic system, and auxiliary dynamic compensators are used to mitigate the impact of input constraints. Lastly, theoretical analysis indicates that the closed-loop system is stable and the position constraint requirements are satisfied. Comparative simulations illustrate the effectiveness of the proposed control strategy.

Minimal perception: enabling autonomy in resource-constrained robots.

The rapidly increasing capabilities of autonomous mobile robots promise to make them ubiquitous in the coming decade. These robots will continue to enhance efficiency and safety in novel applications such as disaster management, environmental monitoring, bridge inspection, and agricultural inspection. To operate autonomously without constant human intervention, even in remote or hazardous areas, robots must sense, process, and interpret environmental data using only onboard sensing and computation. This capability is made possible by advancements in perception algorithms, allowing these robots to rely primarily on their perception capabilities for navigation tasks. However, tiny robot autonomy is hindered mainly by sensors, memory, and computing due to size, area, weight, and power constraints. The bottleneck in these robots lies in the real-time perception in resource-constrained robots. To enable autonomy in robots of sizes that are less than 100 mm in body length, we draw inspiration from tiny organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimal sensor and neural system. This work aims to provide insights into designing a compact and efficient minimal perception framework for tiny autonomous robots from higher cognitive to lower sensor levels.

Rational Design of Diatomic Active Sites for Elucidating Oxygen Evolution Reaction Performance Trends.

Diatomic catalysts, especially those with heteronuclear active sites, have recently attracted significant attention for their advantages over single-atom catalysts in reactions with relatively high energy barrier, e.g. oxygen evolution reaction. Rational design and synthesis of heteronuclear diatomic catalysts are of immense significance but have so far been plagued by the lack of a definitive correlation between structure and catalytic properties. Here, we report macrocyclic precursor constrained strategy to fabricate series of transition metal (MT, Ni, Co, Fe, Mn, or Cu)-noble (MN, Ir or Ru) centers in carbon material. One notable performance trend is observed in the order of Cu-MN < Mn-MN < Fe-MN < MN < Co-MN < Ni-MN. However, the pathway has been not altered, still following the traditional adsorption reaction mechanism. The effect of the MT atoms on the performances could possibly originate from the distinct adsorption/desorption behaviors of key intermediates (i.e. *OH, *O and/or *OOH), strongly implying that ΔG*OOH-ΔG*OH could be used as the performance descriptor. We believe that our work provides useful strategy for synthesis of diatomic active sites with sole coordination configuration and varied composition, and in-depth insight to their catalytic mechanism, which could be used for further optimization of diatomic catalysts towards oxygen electrocatalysis.

Mid-term Survivorship and Functional Outcomes of Metaphyseal Sleeves for Addressing Anderson Orthopaedic Research Institute (AORI) Type 2 and 3 Bone Defects in Revision Total Knee Arthroplasty.

Introduction Revision Total Knee Arthroplasty (RTKA) is a complex procedure challenged by significant bone loss, necessitating effective restoration techniques. This study investigates the clinical outcomes and complications of metaphyseal sleeves in RTKA with severe metaphyseal bone loss, aiming to evaluate their efficacy over a minimum four-year follow-up. Methods This was a retrospective observational study on 29 patients who underwent RTKA with Anderson Orthopaedic Research Institute (AORI) type II or III bone defects using porous coated tibial and/or femoral metaphyseal sleeves from December 2016 and January 2019. Data collection included demographic information, etiology for revision, and functional outcomes assessed by the Knee Society Score (KSS) and Oxford Knee Score (OKS). Statistical analysis and Kaplan-Meier survival analysis were performed. Results The cohort comprised patients with a mean age of 62.6 years (SD=7.8), predominantly female (N=21, 71.4%). The primary indication for RTKA was aseptic loosening (N=18, 62.1%) followed by Prosthetic Joint Infection (PJI). Significant improvements were noted in the range of motion. Both KSS (Pre-op:57.97 vs. Post-op:73.59) and OKS (Pre-op:15.86 vs. Post-op:30.66) showed highly significant improvement (p<0.0001). Radiographic assessments indicated stable component position and signs of osseous integration without any osteolysis. No sleeve-related complications were observed. Survival analysis demonstrated a high cumulative survival probability over the study period. Conclusion Metaphyseal sleeves offer a viable solution for managing severe bone loss in RTKA, providing stable fixation, restoring joint line kinematics, and facilitating stress distribution to the metaphyseal region. This study corroborates the effectiveness of metaphyseal sleeves in challenging revision scenarios, aligning with previous research.

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model.

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1). BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-kcat (TUST-kcat) module predicts the kcat values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

Parameter Optimization for Laser Peen Forming on 6005A-T6 Aluminum Alloy Plates to Enhance the Constrained Deformation of Integral Stiffened Plates.

Multiscale parameter optimization for laser peen forming (LPF) on 6005A-T6 aluminum alloy plates was conducted through a combination of simulation and experimentation. By obtaining the optimal parameter, this study aims to explore the constrained deformation and forming laws of the integral stiffened plates. Detailed descriptions were provided regarding the dynamic response process and transient behavior of aluminum alloy plates under ultrahigh strain rates, along with an in-depth analysis of the stress evolution. The results reveal that laser beam diameter and laser beam energy can achieve large range forming, while the number of tracks facilitates the precise deformation adjustment. During the 12-track LPF process, there is an overall upward trend in deformation values accompanied by a dynamic increase in the bend curvature. After static relaxation, the deformation value recovers to 55.2% of the final bending curvature. The chord direction scanning of stiffened plates exhibits a larger bending curvature, indicating its greater forming capacity for large-sized single unfolding direction formation; whereas, the unfolding direction scanning of stiffened plates excels in achieving efficient integrated two-way forming.

Conventional single articulation constrained liners in revision hip arthroplasty: risk factors for failure and their combinations.

BACKGROUND: Constrained liners (CLs) have been used in revision total hip arthroplasty (rTHA) with varying results. Relatively few studies have identified specific risk factors for failure. This study aimed to assess implant survivorship and complication rates, identify risk factors for constraint-related complications, and assess the effect of multiple factors present in a single case. METHODS: We conducted a retrospective analysis of 101 rTHAs for various aseptic indications and as second-stage procedures for periprosthetic joint infection (PJI) utilising 2 models of conventional single-articulation CLs. We excluded 8 cases in which the liners were removed early due to PJI and assessed the risk factors for constraint-related complications in the remaining 93 cases. The mean follow-up duration for complication-free cases was 6.5 years (range 4.7-10.5 years). RESULTS: The incidences of dislocation of a prosthetic head and loosening of the acetabular component were 19.8% and 5.0%, respectively. We also observed 8 cases where the locking ring of the liner was dislodged without dislocation (1 case required re-revision). The presence of factors related to impingement (cup retention, smaller internal diameter CLs, signs of probable impingement from the femoral side) was associated with higher rates of constraint-related complications. The presence of factors related to soft-tissue stabilisers did not increase the rate of complications. The simultaneous presence of multiple impingement-related risk factors resulted in worse outcomes. CONCLUSIONS: CLs may be less effective for treating or preventing instability related to impingement. CLs should be used with caution or avoided when multiple impingement-related risk factors are present.

A model of mechanical loading of the lungs including gravity and a balancing heterogeneous pleural pressure.

Recent years have seen the development of multiple in silico lung models, notably with the aim of improving patient care for pulmonary diseases. These models vary in complexity and typically only consider the implementation of pleural pressure, a depression that keeps the lungs inflated. Gravity, often considered negligible compared to pleural pressure, has been largely overlooked, also due to the complexity of formulating physiological boundary conditions to counterbalance it. However, gravity is known to affect pulmonary functions, such as ventilation. In this study, we incorporated gravity into a recent lung poromechanical model. To do so, in addition to the gravitational body force, we proposed novel boundary conditions consisting in a heterogeneous pleural pressure field constrained to counterbalance gravity to reach global equilibrium of applied forces. We assessed the impact of gravity on the global and local behavior of the model, including the pressure-volume response and porosity field. Our findings reveal that gravity, despite being small, influences lung response. Specifically, the inclusion of gravity in our model led to the emergence of heterogeneities in deformation and stress distribution, compatible with in vivo imaging data. This could provide valuable insights for predicting the progression of certain pulmonary diseases by correlating areas subjected to higher deformation and stresses with disease evolution patterns.

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model.

Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.

Active constrained motion control for a robot-assisted endoscope manipulator in pediatric minimal access surgery.

Robot-assisted laparoscopic surgery has three main system requirements: safety, simplicity, and intuitiveness. However, accidental movement of the endoscope due to body fatigue and misunderstanding of the verbal orders between the surgeon and assistant will contribute to highly unexpected tool-tissue interactions, particularly in pediatric minimal access surgery with restricted working space. This study introduces a compact, lightweight endoscope manipulator with a mechanical remote-center-motion function. Using a custom-designed human-machine interface, the surgeon can intuitively control the movement of the endoscope manipulator over their view. In addition, an active constrained motion control algorithm is proposed to generate a forbidden-region constraint for avoiding collisions between the endoscope tip and surrounding organs in a pediatric abdominal cavity with restricted space. Simulations and experiments demonstrate the performance of the proposed compact endoscope manipulator and the active constrained surface tracking control scheme.

ZnI2-Mediated cis-Glycosylations of Various Constrained Glycosyl Donors: Recent Advances in cis-Selective Glycosylations.

An efficient and versatile glycosylation methodology is crucial for the systematic synthesis of oligosaccharides and glycoconjugates. A direct intermolecular and an indirect intramolecular methodology have been developed, and the former can be applied to the synthesis of medium-to-long-chain glycans like that of nucleotides and peptides. The development of a generally applicable approach for the stereoselective construction of glycosidic bonds remains a major challenge, especially for the synthesis of 1,2-cis glycosides such as ß-mannosides, ß-L-rhamnosides, and ß-D-arabinofuranosides with equatorial glycosidic bonds as well as α-D-glucosides with axial ones. This review introduces the direct formation of cis-glycosides using ZnI2-mediated cis-glycosylations of various constrained glycosyl donors, as well as the recent advances in the development of stereoselective cis-glycosylations.

A fast solution to the lasso problem with equality constraints.

The equality-constrained lasso problem augments the standard lasso by imposing additional structure on regression coefficients. Despite the broad utilities of the equality-constrained lasso, existing algorithms are typically computationally inefficient and only applicable to linear and logistic models. In this paper, we devise a fast solution to the equality-constrained lasso problem with a two-stage algorithm: first obtaining candidate covariate subsets of increasing size from unconstrained lasso problems and then leveraging an efficient combined alternating direction method of multipliers/Newton-Raphson algorithm. Our proposed algorithm leads to substantial speedups in getting the solution path of the constrained lasso and can be easily adapted to generalized linear models and Cox proportional hazards models. We conduct extensive simulation studies to demonstrate the computational advantage of the proposed method over existing solvers. To further show the unique utility of our method, we consider two real-world data examples: a microbiome regression analysis and a myeloma survival analysis; neither example could be solved by naively fitting the constrained lasso problem on the full predictor set.

Multiscale homogenized constrained mixture model of the bio-chemo-mechanics of soft tissue growth and remodeling.

Constrained mixture models have successfully simulated many cases of growth and remodeling in soft biological tissues. So far, extensions of these models have been proposed to include either intracellular signaling or chemo-mechanical coupling on the organ-scale. However, no version of constrained mixture models currently exists that includes both aspects. Here, we propose such a version that resolves cellular signal processing by a set of logic-gated ordinary differential equations and captures chemo-mechanical interactions between cells by coupling a reaction-diffusion equation with the equations of nonlinear continuum mechanics. To demonstrate the potential of the model, we present 2 case studies within vascular solid mechanics: (i) the influence of angiotensin II on aortic growth and remodeling and (ii) the effect of communication between endothelial and intramural arterial cells via nitric oxide and endothelin-1.

A quadratically constrained mixed-integer non-linear programming model for multiple sink distributions.

Rising traffic congestion and fuel costs pose significant challenges for supply chains with numerous retailers. This paper addresses these challenges by optimizing transportation routes for processed tomatoes within a long-haul and intercity distribution network. We use the heterogeneous capacitated vehicle routing problem framework to create a new quadratically constrained mixed-integer non-linear programming model that aims to meet demand at multiple destinations while minimizing transportation costs. Our model incorporates real-time data and route optimization strategies that consider traffic conditions based on freight time and route diversions for expedited deliveries. It aims to devise an optimal transportation schedule that minimizes fuel, operational, and maintenance costs while ensuring efficient delivery of tomato paste. By applying this model to a real-world case study, we estimate a significant 27.59% reduction in transportation costs, dropping them from GH¢20,270 ($1,638.91) to GH¢14,676 ($1,186.61) on average. This highlights the effectiveness of our strategy in lowering costs while maintaining smooth and efficient deliveries.

Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms.

Uncertainty in biology refers to situations in which information is imperfect or unknown. Variability, on the other hand, is measured by the frequency distribution of observed data. Biological variability adds to the uncertainty. The Constrained Disorder Principle (CDP) defines all systems in the universe by their inherent variability. According to the CDP, systems exhibit a degree of variability necessary for their proper function, allowing them to adapt to changes in their environments. Per the CDP, while variability differs from uncertainty, it can be viewed as a regulated mechanism for efficient functionality rather than uncertainty. This paper explores the various aspects of un-certainties in biology. It focuses on using CDP-based platforms for refining fuzzy algorithms to address some of the challenges associated with biological and medical uncertainties. Developing a fuzzy decision tree that considers the natural variability of systems can help minimize uncertainty. This method can reveal previously unidentified classes, reduce the number of unknowns, improve the accuracy of modeling results, and generate algorithm outputs that are more biologically and clinically relevant.

Recognition and Scoring Physical Exercises via Temporal and Relative Analysis of Skeleton Nodes Extracted from the Kinect Sensor.

Human activity recognition is known as the backbone of the development of interactive systems, such as computer games. This process is usually performed by either vision-based or depth sensors. So far, various solutions have been developed for this purpose; however, all the challenges of this process have not been completely resolved. In this paper, a solution based on pattern recognition has been developed for labeling and scoring physical exercises performed in front of the Kinect sensor. Extracting the features from human skeletal joints and then generating relative descriptors among them is the first step of our method. This has led to quantification of the meaningful relationships between different parts of the skeletal joints during exercise performance. In this method, the discriminating descriptors of each exercise motion are used to identify the adaptive kernels of the Constrained Energy Minimization method as a target detector operator. The results indicated an accuracy of 95.9% in the labeling process of physical exercise motions. Scoring the exercise motions was the second step after the labeling process, in which a geometric method was used to interpolate numerical quantities extracted from descriptor vectors to transform into semantic scores. The results demonstrated the scoring process coincided with the scores derived by the sports coach by a 99.5 grade in the R2 index.

A novel framework for automated warehouse layout generation.

Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive optimal layouts within given spatial parameters, adhering to all functional requirements. The feasibility of the generated layouts is verified based on criteria such as item accessibility, required minimum clearances, and aisle connectivity. A scoring function is then used to evaluate the feasible layouts considering the number of storage locations, access points, and accessibility costs. We demonstrate our method's ability to produce feasible, optimal layouts for a variety of warehouse dimensions and shapes, diverse door placements, and interconnections. This approach, currently being prepared for deployment, will enable human designers to rapidly explore and confirm options, facilitating the selection of the most appropriate layout for their use-case.