Total Deviation Research Articles

Enhancing optimization accuracy in power systems: Investigating correlation effects on objective function values

This study addresses the critical yet often overlooked aspect of incorporating correlations among input stochastic variables in power system planning and scheduling optimization. While existing literature has extensively focused on uncertainty modelling, there remains a gap in fully assessing the consequences of disregarding correlations on objective function values across different power network sizes. To bridge this gap, we utilize Monte Carlo simulation with Cholesky decomposition, alongside Quasi-Monte Carlo sampling and Latin Hypercube Sampling, to effectively model uncertainty and capture correlation coefficients among input variables, including wind, solar photovoltaic, and load power. The most efficient technique is then integrated into our optimization model, which is applied to small, medium, and large power network models. Our proposed optimization model addresses conflicting objectives using a hybrid NSGAII-MOPSO, aiming to simultaneously minimize total operational cost, power loss, and voltage deviation. By implementing this model on selected power networks and comparing outcomes between cases with independent and correlated variables, we rigorously assess discrepancies in objective function values. We visualize and analyze these errors across systems of varying sizes, shedding light on the impact of neglecting variable correlations. Notably, the maximum discrepancies are observed at $3.26/h, $40.66/h, and $2754.04/h for the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, respectively. Crucially, as the system size increases, so does the magnitude of these differences, underlining the escalating impact of neglecting variable correlations on optimization outcomes. We stress the importance of integrating such considerations into future planning and operational strategies to mitigate errors and enhance decision-making processes.

Improved mountain gazelle optimizer based interactive distributed strategy to solving large scale OPF

This paper focuses on solving the multi-objective optimal power flow of large-scale power systems under critical loading margin stability with accuracy using a novel improved mountain gazelle optimizer (IMGO)-based flexible distributed strategy. Multi-shunt compensator-based flexible alternative current transmission systems (FACTS), such as SVC and STATCOM devices, are integrated at specified locations to exchange reactive power with the network. Several metaheuristic methods can solve the standard OPF related to small and medium test systems. However, by considering large-scale electric systems based on FACTS devices and renewable energy and by considering the operation under loading margin stability, the majority of these techniques fail to achieve a near-global solution because of the high dimension and nonlinearity of the problem to be solved. This study proposes the Multi-Objective OPF-Based Distributed Strategy (MO-OPFDS), a new planning strategy that optimizes individually and simultaneously various objective functions, in particular the total power loss (T∆P), and the total voltage deviation (T∆V). Standard MGO search is enhanced by automatically balancing exploration and exploitation throughout the search. The robustness of the proposed variant was validated on a large electric test system, the IEEE 118-Bus, and on the Algerian Network 114-Bus under normal conditions and at critical loading margin stability. The obtained results compared with several recent techniques clearly confirm the high performance of the proposed method in terms of solution accuracy and convergence behavior.

Robust reverse 1-center problems on trees with interval costs

This paper concerns the problem of reducing the edge lengths of a tree in a minimum cost way such that the 1-center function at a predetermined vertex is at most a given threshold value. Moreover, it is assumed that the costs of reducing edge lengths are not exactly determined, but they are estimated within intervals and their total deviation is limited within a given bound. This problem is called the robust reverse 1-center problem on trees with interval costs. We first reformulate the problem as a Stackelberg game and translate it into a univariate optimization problem. Based on the special properties of the feasible set, we can state the corresponding problem as a parameterized minimum cost flow problem. Then we develop an algorithm that solves the problem in O ( n 2 log ⁡ n ) time due to the convexity of the univariate objective function. We finally discuss the problem of improving the international transportation network in Iran as a real-life application.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

Digital therapeutics using virtual reality-based visual perceptual learning for visual field defects in stroke: A double-blind randomized trial.

Visual field defects (VFDs) represent a debilitating poststroke complication, characterized by unseen parts of the visual field. Visual perceptual learning (VPL), involving repetitive visual training in blind visual fields, may effectively restore visual field sensitivity in cortical blindness. This current multicenter, double-blind, randomized, controlled clinical trial investigated the efficacy and safety of VPL-based digital therapeutics (Nunap Vision [NV]) for treating poststroke VFDs. Stroke outpatients with VFDs (>6 months after stroke onset) were randomized into NV (defective field training) or Nunap Vision-Control (NV-C, central field training) groups. Both interventions provided visual perceptual training, consisting of orientation, rotation, and depth discrimination, through a virtual reality head-mounted display device 5 days a week for 12 weeks. The two groups received VFD assessments using Humphrey visual field (HVF) tests at baseline and 12-week follow-up. The final analysis included those completed the study (NV, n=40; NV-C, n=35). Efficacy measures included improved visual area (sensitivity ≥6dB) and changes in the HVF scores during the 12-week period. With a high compliance rate, NV and NV-C training improved the visual areas in the defective hemifield (>72 degrees2) and the whole field (>108 degrees2), which are clinically meaningful improvements despite no significant between-group differences. According to within-group analyses, mean total deviation scores in the defective hemifield improved after NV training (p=.03) but not after NV-C training (p=.12). The current trial suggests that VPL-based digital therapeutics may induce clinically meaningful visual improvements in patients with poststroke VFDs. Yet, between-group differences in therapeutic efficacy were not found as NV-C training exhibited unexpected improvement comparable to NV training, possibly due to learning transfer effects.

An effective gradient jellyfish search algorithm for optimal reactive power dispatch in electrical networks

AbstractAn effective optimization technique, called gradient jellyfish search optimizer (GJSO), is introduced here to address the optimal reactive power dispatch (ORPD) issue in electric networks. The ORPD problem is a complex non‐linear optimization issue involving integrated variables, aimed at achieving safe and cost‐effective operation of the system by determining optimal values for generator voltage, tap changers of transformers, and reactive power compensation. The performance of the original JSO technique is enhanced by integrating the local escaping operator into the GJSO approach. The effectiveness of the GJSO methodology is evaluated via comparison with two existing methodologies: the original jellyfish search optimizer and the equilibrium optimizer. Jellyfish search optimizer is a meta‐heuristic optimization algorithm inspired by the movement of jellyfish in the water, while equilibrium optimizer draws inspiration from game theory and equilibrium concepts. Simulations were conducted using typical IEEE‐30 bus and IEEE‐57 bus systems to validate the performance of the GJSO methodology. Two versions of the objective function are examined: minimizing line power loss and minimizing total voltage deviations at the buses. The simulation results demonstrated that the GJSO algorithm exhibited superior performance in terms of accuracy and stability compared to the standard jellyfish search optimizer and equilibrium optimizer algorithms.

Research on Dynamic Reactive Power Cost Optimization in Power Systems with DFIG Wind Farms

As the power market system gradually perfects, the increasingly fierce competition not only drives industry development but also brings new challenges. Reactive power optimization is crucial for maintaining stable power grid operation and improving energy efficiency. However, the implementation of plant–grid separation policies has kept optimization costs high, affecting the profit distribution between power generation companies and grid companies. Therefore, researching how to effectively reduce reactive power optimization costs, both technically and strategically, is not only vital for the economic operation of the power system but also key to balancing interests among all parties and promoting the healthy development of the power market. Initially, the study analyzes and compares the characteristic curves of synchronous generators and DFIGs, establishes a reactive power pricing model for generators, and considering the randomness and volatility of wind energy, establishes a DFIG reactive power pricing model. The objective functions aimed to minimize the cost of reactive power purchased by generators, the price of active power network losses, the total deviation of node voltages, and the depreciation costs of discrete variable actions, thereby establishing a dynamic reactive power optimization model for power systems including doubly-fed wind farms. By introducing Logistic chaotic mapping, the CSA is improved by using the highly stochastic characteristics of chaotic systems, which is known as the Chaotic Cuckooing Algorithm. Meanwhile, the basic cuckoo search algorithm was improved in terms of adaptive adjustment strategies and global convergence guidance strategies, resulting in an enhanced cuckoo search algorithm to solve the established dynamic reactive power optimization model, improving global search capability and convergence speed. Finally, using the IEEE 30-bus system as an example and applying the improved chaotic cuckoo search algorithm for solution, simulation results show that the proposed reactive power optimization model and method can reduce reactive power costs and the number of discrete device actions, demonstrating effectiveness and adaptability. When the improved chaotic cuckoo algorithm is applied to optimize the objective function, the optimization result is better than 7.26% compared to the standard cuckoo search algorithm, and it is also improved compared to both the PSO algorithm and the GWO algorithm.

A fast multi-objective pelican optimizer for optimal coordination of DGs and DSTATCOM considering risk penetration level of wind

In this study, a new variant, the fast pelican optimizer (FPO), is proposed to improve the performance of the radial distribution network (RDEN). The proposed variant is characterized by creating a dynamic interaction between two phases, exploration and exploitation, during the search process. The modifications introduced within the standard algorithm allow the proposed new variant, namely FPO, to be fast and adaptive to efficiently solve various complex optimization problems. In the first stage, the proposed FPO is newly adapted and applied to solve the optimal locations of various types of distributed generation based renewable sources and multi shunt compensators, namely the DSTATCOM devices based FACTS technology, and in the second stage, the proposed FPO is applied to optimize the active power of DG units in coordination with the reactive power of multi DSTATCOM. Three objective functions, such as the total power losses, the total voltage deviation, and the margin stability, are optimized individually and in coordination to enhance the performances of the practical radial distribution network (RDEN) 33-bus. A deep comparative study in terms of solution quality and convergence accuracy based statistical analysis is elaborated to demonstrate the competitive aspect of the proposed FPO.

Alterations in Corneal Sensitivity, Staining and Biomechanics of Alopecia Areata Patients: Novel Findings in a Case-Control Study.

Background: A higher prevalence of ophthalmological alterations in systemic inflammatory diseases has been demonstrated. Objectives: Our objectives were to determine anterior segment findings and corneal properties in alopecia areata (AA). Methods: This is a case-control study. Severe AA patients (Severity of Alopecia Tool > 50%) and non-AA subjects underwent a general ophthalmological examination, a Pentacam and Corvis scheimpflug technology examination (Oculus Optikgeräte GmbH, Wetzlar, Germany). Visual acuity, refractive error, corneal aesthesiometry, and biomechanical and topographic variables were registered. Results: In total, 25 AA patients (50 eyes; 50.6 ± 8.1 years) and 29 controls (58 eyes; 49.4 ± 8.6 years) were included. AA patients had decreased corneal sensitivity, more corneal staining, and a more advanced cataract (p ≤ 0.004). The anterior topographic flat meridian, mean anterior keratometry, and maximum keratometric point were increased in AA (p ≤ 0.040), while pachymetry values were thinner (p ≤ 0.001). Keratoconus index and Belin/Ambrosio-enhanced ectasia total deviation display were increased (p ≤ 0.007). Two eyes with a topographic diagnosis of keratoconus and four eyes with subclinical keratoconus were detected in AA. Applanation lengths were smaller in AA (p ≤ 0.029). The Corvis Biomechanical Index was increased in AA (p = 0.022). Conclusions: AA patients have reduced corneal sensitivity and increased corneal staining. Topographic and biomechanical parameters are altered, and there could be a higher risk of keratoconus, thus possibly requiring routine ophthalmological examination.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

Correction effect of jet-grouting on high-speed railway subgrade lateral deformation

Viable remediation of the large lateral deformation of high-speed railway (HSR) subgrades, especially in soft soil areas, is still absent. An integrated rectification scheme of high-pressure jet-grouting (HPJG) combined with stress-release techniques was conducted to rectify the large lateral deformation of an operational HSR subgrade in the soft soil area of China, but without any refined design and mechanical analysis before rectification due to the emergency. The objective of the study is to post-evaluate the rectification effects utilising field monitoring data and numerical calculations. The monitoring data showed that the maximum lateral deviation of the subgrade was 69.1 mm, which almost met the expected correction requirements. However, the influence of the excess pore water pressure (EPWP) dissipation on the correction deviation was not considered in the scheme. Therefore, a numerical model was established to further investigate this effect and corresponding mitigation methods. The calculated results revealed that EPWP in the foundation dissipated mostly within 6 months after rectification, and the deformation loss accounted for 30.7% of the total deviation. Prolonging the interval of two-row pile construction can be a plausible approach to mitigate the deviation loss. The findings provide a feasible method for correcting large lateral deformation of HSR subgrades.

Sector-specific Association of Intraocular Pressure Dynamics in Dark-room Prone Testing and Visual Field Defect Progression in Glaucoma

PurposeTo investigate sectoral differences in the relationship between intraocular pressure (IOP) dynamics during dark-room prone testing (DRPT) and visual field (VF) defect progression in primary open-angle glaucoma (POAG) patients. DesignRetrospective, longitudinal study ParticipantsThis retrospective study included 116 eyes of 84 POAG patients who underwent DRPT and had at least five reliable VF tests conducted over a more than two-year follow-up period. We excluded eyes with mean deviation worse than -20 dB or a history of intraocular surgery or laser treatment. MethodsAverage total deviation (TD) was calculated in the superior, central, and inferior sectors of the Humphrey 24-2 or 30-2 program. During DRPT, IOP was measured in the sitting position, and after 60 minutes in the prone position in a dark room, IOP was measured again. The relationship between IOP change during DRPT, IOP after DRPT, and TD slope in each quadrant was analyzed with a linear mixed-effects model, adjusting for other potential confounding factors. Main Outcome MeasuresTD slope in each quadrant, IOP change during DRPT, and IOP after DRPT ResultsIOP after DRPT and IOP change during DRPT were 18.16±3.42 mmHg and 4.92±3.12 mmHg, respectively. Superior TD slope was significantly associated with both IOP after DRPT (β=-0.28, p=0.003) and IOP change during DRPT (β=-0.21, p=0.029), while central (β=-0.05, p=0.595; β=-0.05; p=0.622) and inferior (β=0.05, p=0.611; β=0.01, p=0.938) TD slopes were not. ConclusionDRPT might be a useful test to predict the risk of superior VF defect progression in eyes with POAG.

Effect of auxiliary geometric devices on the accuracy of intraoral scans in full-arch implant-supported rehabilitations: An in vitro study

ObjectivesThe aim of the present in vitro study was to evaluate the effect of a novel auxiliary geometric device (AGD) on the accuracy of full-arch scans captured with 3 different intraoral scanners (IOS). MethodsAn edentulous maxillary model with four internal connection implant replicas was scanned using 3 different IOS: iTero Element 5D (ITERO) (Align Technology, Tempe, AZ, USA), Trios 4 (TRIOS) (3Shape A/S, Copenhagen, Denmark), and Carestream 3700 (CS) (Carestream Dental, Atlanta, USA). Thirty-six scans were taken with each IOS, 18 with the AGD in place, and 18 without the AGD. A digital master model was created using an industrial optical scanner (ATOS compact Scan 5M, GOM GmbH, Braunschweig, Germany). The master and IOS models were aligned using the scan bodies as a reference area. A surface comparison was performed, and deviation labels were exported for each scan body to evaluate the linear and angular deviation. Total body, platform and angular deviations were measured. ResultsThe use of AGD resulted in a statistically significant increase of angular deviation: 0.87° (SD=0.21) in the AGD group versus 0.64° (SD=0.46) in the no AGD group (p-value=0.005). The difference between the AGD and no AGD groups was not statistically significant for total body and platform deviation values (p-value=0.051 and 0.302 respectively).Using AGD, ITERO showed a statistically significant increase in angular deviation (mean difference=-0.46 µm, p-value=0.002) and a decrease in mean platform deviation (mean difference=63.19 µm, p-value<0.001). No statistically significant differences were found for the other IOS. ConclusionsThe use of AGD did not add benefit on CS and TRIOS. On ITERO, there was an improvement in platform deviation, that was outweighed by the worsening of the angular deviation. Clinical SignificanceIn vitro data suggest that intraoral scans can be successfully used in full-arch cases. The use of AGD has no additional benefit on CS and TRIOS. On ITERO there was an improvement in platform deviation that was outweighed by the worsening of the angular deviation. Translational application to clinical practice deserves further investigation, taking into account patient-related and anatomical variables.

Optimization Problems for Assessing the Relative Importance of Elements in the Analytic Hierarchy Process

Analytic hierarchy process (AHP) was proposed by American scientist Thomas Saati. It is a closed logical structure that, with the help of hierarchies, priorities and simple mathematical rules, provides analysis of complex problems in all their diversity. Analytic hierarchy process is successfully used for solving practical problems and quite often leads to optimal answers for planning, forecasting and other problems. It can also be applied to system analysis of the using multimedia factors for increasing the effectiveness of patients’ rehabilitation at various stages of disease.The article proposes two optimization problems for coefficients analysis in the matrix of pairwise comparisons of elements for analytic hierarchy process, with criterion is minimization of the matrix consistency index. The first problem is related to finding integer coefficients in the scale of T. Saati subject to insignificance of components deviations of the vector of priorities of elements rounded to two digits from the given ones. The second problem is related to correction of the specified pairwise comparisons matrix coefficients subject to upper bound on total deviation of the matrix coefficients from the specified ones and subject to deviation of the rounded components of the element priorities vector from the specified components.Calculations of the optimal coefficients in both optimization problems for the example of T. Saati related to study of lighting intensity and the law of the inverse square of the distance to the light source are given. An example of the second optimization problem application for correcting matrix of pairwise comparisons, which was built by a rehabilitation doctor to assess the importance of four elements of the multimedia environment when including them in the rehabilitation program, is considered.The material of the article is presented in three sections. Section 1 describes T. Saati’s example from the study of light intensity and the law of the inverse square of the distance to the light source. Section 2 describes the first optimization problem and provides calculations of the optimal coefficients for the pairwise comparisons matrix for the example of T. Saati. Section 3 describes the second optimization problem and gives examples of its application to bring the already existing pairwise comparisons matrix to the pairwise comparisons matrix with better consistency index both for the experiment of T. Saati and for the analysis of using of multimedia factors for improving efficiency of patients’ rehabilitation.

Assessment of Oral Masticatory Muscle Activity With Different Chewing Gums: A Cross-Sectional Study Based on Electromyogram Analysis.

Facial muscles, particularly those involved in mastication, play a pivotal role in the chewing process. Despite their influence on chewing, these muscles undergo alterations during mastication. Examining the relationship between chewed substances and muscle activity can provide insights into various pathological processes and aid in the development of therapeutic chewing techniques. This study aimed to evaluate the impact of different commercially available chewing gums on the activity of key masticatory muscles. Twenty-two participants were recruited for the study. They were instructed to chew four commercially available gums: group 1 comprised sugar gum with a strong flavor; group 2 included gum containing sorbitol; group 3 consisted of gum containing xylitol; and group 4 provided sugar gum with a mild flavor. Electromyogram (EMG) recordings were utilized to assess muscle activity. Various aspects of muscle activity, including chewing time, maximum muscle potential, and coordination between different muscles, were evaluated. Data tabulation and analysis were performed using IBM SPSS software version 23.0 (IBM Corp., Armonk, NY). Analysis revealed that in terms of temporalis symmetry, group 2 exhibited the highest mean deviation, while for masseter symmetry, group 3 demonstrated the highest mean deviation. The total deviation for the temporalis and masseter muscles was 72.16% and 65.55%, respectively, indicating greater symmetry in the temporalis muscle. Additionally, group 3 displayed the highest mean deviation in both left and right-sided synergic activity of the muscles. The total deviation for the right and left sides was 64.34% and 65.67%, respectively. The findings suggest that sugar-free chewing gums elicit increased muscle activity compared to sugar-containing chewing gums. Furthermore, the utilization of calorie-free chewing gums with a firm texture was associated with better-coordinated muscle activity. These results provide valuable insights into the effects of different chewing gums on masticatory muscle function and coordination, which may have implications for therapeutic interventions and oral health management.

An asymptotically optimal solution for the minimization of the variation of job completion times in two-stage proportionate no-wait flow shops

We propose an asymptotically optimal solution for the two-stage no-wait proportionate flow shop scheduling problem with the objective of minimizing the total absolute deviation of job completion times. The proposed solution is within 1% of the optimum for problems with more than 20 jobs. We also show that the problem is solved optimally in constant time for the special case where the two smallest jobs have equal processing times. Our results provide answers to most of the outstanding research questions for this problem.

Ectasia Detection by Anterior Segment Optical Coherence Tomography in Scheimpflug Tomographically Normal Keratoconus Fellow Eyes.

The purpose of this study was to determine whether the Scheimpflug tomographically normal fellow eyes of keratoconus patients are also classified as normal using an ectasia screening algorithm based on anterior segment optical coherence tomography (AS-OCT). This monocentric cross-sectional study included 22 very asymmetric ectasia patients with tomographically significant keratoconus in 1 eye and normal Scheimpflug tomography in the fellow eye. Twenty-two eyes of 22 healthy subjects served as a control group. We performed corneal tomography using Pentacam AXL (Oculus, Wetzlar, Germany) and used Belin/Ambrósio Enhanced Ectasia total deviation index as well as Belin ABCD keratoconus classification to identify Scheimpflug tomographically normal eyes. We also performed AS-OCT using Anterion (Heidelberg Engineering, Heidelberg, Germany) and analyzed for the presence of ectasia using the Screening Corneal Objective Risk of Ectasia (SCORE) algorithm, with positive values indicating ectasia suspect tomography. The SCORE value was positive in 9.1% (n = 2) of the healthy eyes, in 45.5% (n = 10) of the Scheimpflug tomographically normal eyes of keratoconus patients and in all eyes (n = 22) with tomographically significant keratoconus. The Scheimpflug tomographically normal eyes of keratoconus patients had higher SCORE values compared with healthy controls ( P < 0.001). The median (interquartile range) SCORE value was -1.7 (1.3) in healthy controls, -0.5 (2.2) in Scheimpflug tomographically normal eyes of keratoconus patients, and 11.1 (11.0) in tomographically significant keratoconus eyes. The Scheimpflug tomographically normal fellow eyes of keratoconus patients had higher AS-OCT-based SCORE values than healthy controls, with positive SCORE values found in 46% of the eyes, indicating early tomographic ectatic changes.

What are relevant predictors of physical activity in older adults with lower limb loss (LLL)? Results of a retrospective analysis.

People with lower limb loss (LLL) have reduced physical activity (PA). There is evidence of physical and psychosocial predictors of PA in older adults with limb loss. However, these 2 areas (physical/psychosocial) have not been evaluated in the same analysis. To describe and identify predictors of PA in individuals with LLL. Cross-sectional study. Secondary analysis of data from a multisite Canadian randomized control trial involving community-dwelling prosthetic ambulators with unilateral transtibial or transfemoral amputation (N = 72). The dependent variable was the Physical Activity Scale for the Elderly. Potential predictors were four step square test, 2-minute walk test, Short Physical Performance Battery, Life Space Assessment, walking while talking test, and Activities-specific Balance Confidence scale. Seventy-two community-dwelling lower limb prosthesis users were enrolled. The sample included 62 male participants (86%), and 58 participants (81%) had transtibial amputation. The average age of participants was 65 (8.9) years, and for 49 participants (70%), the amputation was over 24 months ago. The total mean Standard Deviation (SD) Physical Activity Scale for the Elderly score was 153.2 (88.3), with scores of 148.1 (11.4) and 184.5 (24.7) for male and female participants, respectively. Regression analysis identified Life Space Assessment (β = 1.15, p = 0.007) and Short Physical Performance Battery (β = 3.51, p = 0.043) as statistically significant predictors accounting for 25% of the variance in PA. Community mobility and physical performance are the most meaningful predictors of PA. Future research should examine additional factors (e.g., environment, motivation). Understanding the predictors for PA after LLL would improve clinical practice as clinicians would have increased knowledge to modify and improve training.

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.