Low Deviation Research Articles

A novel approach for missing data recovery and fault nodes detection in wireless sensor networks

SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.

Revision of ex vivo endodontic biofilm model using computer aided design

ObjectiveMost endodontic diseases are bacterium-mediated inflammatory or necrotic process induced by contaminated dental pulp. Although great advances are being performed to obtain more efficient antibacterial strategies for persistent infections, most studies lack of representative models to test their antibacterial effects and their outcomes cannot be promptly translated to clinical practice. Therefore, this study aimed to refine an ex vivo endodontic biofilm model combining human tooth, computer guided design and 3D printing to obtain a more reproducible and predictable model. MethodsMonoradicular teeth were cut using three different methods: hand-held (HCC), mechanical precision (MPC) and computer aid guided cutting (CGC). Then, blocks were reassembled. The different model preparations were assessed in terms of dimensional tolerance, surface analysis, liquid tightness and Enterococcus faecalis biofilm development for 21 days, which was studied by metabolic assays and confocal microscopy. Then, the proposed model was validated using different commercial disinfecting treatments. ResultsCGC exhibited significantly lower deviation and surface without defects compared to HHC and MPC, leading to superior liquid tightness. Similarly, mature biofilms with high metabolic activity and vitality were observed in all conditions, CGC showing the lowest variation. Regarding the model validation, all antibacterial treatments resulted in the complete eradication of bacteria in the standard 2D model, whereas commercial treatments exhibited varying levels of efficacy in the proposed ex vivo model, from moderately reduction of metabolic activity to complete elimination of biofilm. ConclusionsThe novel guided approach represents a more reliable, standardized, and reproducible model for the evaluation of endodontic disinfecting therapies. Clinical SignificanceDuring antibacterial treatment development, challenging 3D models using teeth substrates to test antibacterial treatments novel guided approach represents a more reliable, standardized, and reproducible model for the evaluation of endodontic disinfecting therapies.

Viscosity of Hydrogen and Methane Blends: Experimental and Modelling Investigations

Understanding of thermophysical and transport properties of H2-NG blends are needed for the gradual introduction of hydrogen into the national gas grid. A capillary tube viscometer was used to measure the viscosity of hydrogen + methane blends (with hydrogen mole fraction = 0, 0.1000, 0.1997, 0.5019, and 1) at temperatures from 213 to 324 K and pressures up to 31 MPa. A total 147 experimental viscosity measurements were made for the three H2 + CH4 blends and compared against the predictions of five different viscosity models: a one-reference corresponding states (Pedersen) model, a two-reference corresponding states (CS2) model, an extended corresponding states (ECS) model, a corresponding states model derived from molecular dynamic simulations of Lennard Jones (LJ) fluids, and a residual entropy scaling (SRES) method. All the model predictions showed a relatively low deviation compared to the measured viscosities. The density required for viscosity model predictions were computed using Multi-Fluid Helmholtz Energy Approximation (MFHEA) equations of state (EoS). To check the experimental procedure and applicability of the viscometer equipment, viscosity validation measurements were carried out for propane, hydrogen, and methane. The measured viscosities of the pure components were in good agreement with the respective viscosity models with AARD of 0.24%, 0.25%, and 0.58% for propane, hydrogen, and methane, respectively.Graphical

Comparison of selective laser melting and stereolithography etching templates for guided endodontics.

With the increasing application of guided endodontics to treat complex root canal treatment, the entire process of root canal treatment has become more precise, reducing damage to tooth structure and improving success rates. However, due to the limitations of the operating space, the use of guided endodontic templates in posterior root canal treatment is less common. This study aims to compare the accuracy and reliability of selective laser melting (SLM) and traditional stereolithography etching (SLA) guided endodontic templates for posterior root canals, providing better treatment strategies for posterior root canal treatment. The teeth were randomly assigned to either SLM or SLA group. Preoperative cone-beam computed tomography (CBCT) and a three-dimensional (3D) scanner were used to establish the 3D root canal system and the accurate occlusal models of the teeth. The virtual access to the canal access was designed using Mimics 19.0 and 3-Matic 11.0. The endodontic access was performed based on either SLM or SLA templates. The accuracy of endodontic preparation was measured in three-dimensions by calculating deviations from planned accesses. The template height and tooth substance loss rates in each group were measured. SLM-guided templates have a low average deviation at the entry point and apical portion of the bur of total posterior teeth (including premolars and molars) and individual molars (P < 0.05). Moreover, there was a significant difference in angular deviations and height of template in total posterior teeth and individual molars (P < 0.05). The mean substance loss rate of the SLA group was slightly greater than that of the SLM group, but the difference was not statistically (P > 0.05). SLM-guided endodontics provides a more predictable and precise location of root canal orifice for the treatment of posterior teeth.

The effects of variability in catch effort on the precision of statistical population reconstruction

Statistical population reconstruction (SPR) models have emerged as a robust and versatile framework for estimating the demographic dynamics of harvested wildlife populations using commonly collected age‐at‐harvest and catch‐effort data. Although numerous studies have suggested that higher interannual variability in catch effort may improve the accuracy and precision of reconstructed estimates, particularly in the absence of auxiliary data on annual abundance or survival, the extent and magnitude of these effects has not been explored. We examined the influence of catch‐effort variability, as measured by the ratio between years of highest and lowest effort, on the relative absolute deviation of reconstructed estimates of population abundance, as well as on the actual percent coverage and width of the corresponding confidence intervals. We used a Monte Carlo simulation to generate catch‐effort data with different levels of variability for populations experiencing a wide range of demographic and harvest conditions. For similar amounts of age‐at‐harvest data, using catch‐effort data with higher interannual variability resulted in reconstructed estimates of annual abundance that had significantly lower deviations from reality, better coverage, and narrower confidence intervals (as measured by the margin of error). These improvements were consistent and linear at low to medium levels of catch‐effort variability, but leveled off and became substantially less pronounced at higher levels. We found that the inclusion of auxiliary data largely mediated this relationship, although higher catch‐effort variability still resulted in more accurate and precise estimates of annual abundance even when these data were included. Our research highlights the need to include a thorough investigation of the available catch‐effort data alongside the established practices of assessing the number of years of available data, the average number of animals harvested each year, and the availability of auxiliary data from radio‐telemetry studies or other sources.

The Effect of Varying the Light Spectrum of a Scene on the Localisation of Photogrammetric Features

In modern digital photogrammetry, an image is usually registered via a digital matrix with an array of colour filters. From the registration of the image until feature points are detected on the image, the image is subjected to a series of calculations, i.e., demosaicing and conversion to greyscale, among others. These algorithms respond differently to the varying light spectrum of the scene, which consequently results in the feature location changing. In this study, the effect of scene illumination on the localisation of a feature in an image is presented. The demosaicing and greyscale conversion algorithms that produce the largest and smallest deviation of the feature from the reference point were assessed. Twelve different illumination settings from polychromatic light to monochromatic light were developed and performed, and five different demosaicing algorithms and five different methods of converting a colour image to greyscale were analysed. A total of 300 different cases were examined. As the study shows, the lowest deviation in the polychromatic light domain was achieved for light with a colour temperature of 5600 K and 5000 K, while in the monochromatic light domain, it was achieved for light with a green colour. Demosaicing methods have a significant effect on the localisation of a feature, and so the smallest feature deviation was achieved for smooth hue-type demosaicing, while for greyscale conversion, it was achieved for the mean type. Demosaicing and greyscale conversion methods for monochrome light had no effect. The article discusses the problem and concludes with recommendations and suggestions in the area of illuminating the scene with artificial light and the application of the algorithms, in order to achieve the highest accuracy using photogrammetric methods.

Research on wire icing simulation technology considering the weights of meteorological elements

Wire icing seriously threatens the safety and reliability of power systems. Strengthening the simulation study of icing thickness is crucial for disaster prevention and mitigation, as well as the adjustment of power system operation strategies. The meteorological elements, including air temperature, precipitation and wind speed, are the key factors affecting wire icing. In this study, the meteorological elements related to icing thickness are obtained by numerical models, and the relationship between these elements and icing thickness observations is established to develop a model for simulating icing thickness. The model is applied to study typical icing in Yunnan Province, China. The results indicate that the deviation of the simulated icing thickness is about 2 mm, smaller than that from the traditional model. Batch experiments demonstrate that the new model developed in this research is applicable to the vast majority of 238 observation stations in Yunnan Province for icing thickness simulation, and the method can yield lower simulation deviations.

After Surgically Induced Remission, Ileal and Colonic Mucosa-Associated Microbiota Predicts Crohn’s Disease Recurrence

Investigating the tissue-associated microbiota after surgically induced remission may help to understand the mechanisms initiating intestinal inflammation in Crohn's disease. Patients with Crohn's disease undergoing ileocolic resection were prospectively recruited in 6 academic centers. Biopsy samples from the neoterminal ileum, colon, and rectosigmoid were obtained from colonoscopies performed after surgery. Microbial DNA was extracted for 16S rRNA gene sequencing. Microbial diversity and taxonomic differential relative abundance were analyzed. A random forest model was applied to analyze the performance of clinical and microbial features to predict recurrence. A Rutgeerts score ≥i2 was deemed as endoscopic recurrence. A total of 349 postoperative colonoscopies and 944 biopsy samples from 262 patients with Crohn's disease were analyzed. Ileal inflammation accounted for most of the explained variance of the ileal and colonic mucosa-associated microbiota. Samples obtained from 97 patients who were in surgically induced remission at first postoperative colonoscopy who went on to develop endoscopic recurrence at second colonoscopy showed lower diversity and microbial deviations when compared with patients who remained in endoscopic remission. Depletion of genus Anaerostipes and increase of several genera from class Gammaproteobacteria at the 3 biopsy sites increase the risk of further recurrence. Gut microbiome was able to predict future recurrence better than clinical features. Ileal and colonic mucosa-associated microbiome deviations precede development of new-onset ileal inflammation after surgically induced remission and show good predictive performance for future recurrence. These findings suggest that targeted microbial modulation is a plausible modality to prevent postoperative Crohn's disease recurrence.

Paper 52: Association of Mechanical Axis with Osteochondritis Dissecans of the Femoral Condyle

Objectives: Osteochondritis dissecans (OCD) is a condition characterized by abnormal subchondral bone with disturbance of overlying articular cartilage. Pathophysiology for the etiology and underlying mechanism of OCD lesion development is not fully understood. The purpose of this study was to determine the association of lower extremity weightbearing axis with femoral condyle OCD lesion location, and if this association was affected by certain patient and OCD lesion characteristics such as lesion stability, skeletal maturity, and the presence of the OCD lesion on the medial versus lateral femoral condyle. Methods: This was a retrospective case series at a single, large academic institution. The primary outcome was the association of femoral condyle OCD lesion location with lower extremity weightbearing axis as categorized by Cahill and Berg zones of the knee. Secondary outcomes included sub-group analyses (treatment, skeletal maturity, and lesion stability) and contribution of femoral and tibial mechanical axis to lower extremity weightbearing axis deviations (mechanical lateral distal femoral angle [mLDFA] and medial proximal tibial angle [MPTA]). For unilateral OCD lesions, mechanical axis was compared between affected and unaffected lower extremities. Results: Eighty-six patients and 95 knees with an OCD lesion of the femoral condyle were identified, with 9 patients (11%) having bilateral OCD lesions. There were 74% male patients with average age of 21 years old. 65% of knees had closed distal femoral physes, 82% of OCD lesions were unstable on MRI imaging, and 85% underwent a surgical procedure. Seventy-five percent of OCD lesions were located in Cahill and Berg zone 2 at the lateral aspect of the medial femoral condyle. Lower extremity mechanical axis was located in the same zone as the OCD lesion in 48% of cases and within the same knee compartment in 56% of cases. Over 90% of OCD lesions were within ±1 zone of the weightbearing axis. There were no significant differences in the association of mechanical axis and OCD lesion location in subgroup analysis of medial versus lateral femoral condyle OCD lesions, skeletal maturity, stability of the lesion, or treatment group. For patients with varus alignment and medial femoral condyle OCD lesions, the mean mLDFA was 89.9° and MPTA was 85.4°, whereas for valgus alignment and lateral femoral condyle lesions, the mLDFA was 84.8° and MPTA was 88.8°. Both the mLDFA and MPTA were significantly different when comparing medial to lateral femoral condyle OCD lesions (p &lt;0.001 and &lt;0.001, respectively). Patients with unilateral medial femoral condyle OCD lesions were more likely to have varus alignment of the affected extremity compared to the contralateral extremity (59% vs. 36%, p = 0.01). Conclusions: This study demonstrated a relationship between lower extremity mechanical weightbearing axis and femoral condyle OCD location. Patients with unilateral medial femoral condyle OCD lesions are more likely to have varus alignment in the affected extremity. Varus alignment associated with medial femoral condyle lesions had similar varus contributions from both femoral and tibial alignment whereas valgus alignment in the setting of lateral femoral condyle lesions appear to have predominantly femoral contributions. There were no significant differences in association with mechanical axis among medial versus lateral femoral condyle lesions, OCD lesion stability, or distal femoral physeal status. More work is required to better understand the relationship between mechanical axis and femoral condyle OCD lesion pathophysiology with the long-term goal of identifying patients at risk for progression of lesion instability and developing potential interventions to preserve articular cartilage.

A generalized prediction model for complete performance investigation of pump operated as turbine in microhydro applications using novel approach for improving sustainability

Centrifugal pumps operated in reverse mode (popularly called pump as turbines (PATs)) are well-established as green energy converters, mainly used in microhydro applications due to their lower capital cost. However, the appropriate selection of the pump for reverse-mode application is a critical issue due to the unavailability of its characteristic curve in turbine mode. Therefore, various researchers have proposed models to predict PAT parameters from pump characteristics based on diverse approaches. Still, researchers are striving to develop a prediction model, which can predict the head and flow rates for the PAT with less than ±20 % deviation for both. This work proposed a novel approach to predict the characteristics of PAT with the integration of ensemble regression modelling for Best Efficiency Point (BEP) and from it, by polynomial equations complete characteristics curve. The data from in-house experiments and open literature are used to develop the proposed model excluding the data of four pumps, which are typically used for validation of it. The predicted parameters of PAT from pump characteristics show good agreement for four selected PATs with <10 % maximum deviation. Compared to the literature prediction models, it shows a lower deviation for the same four PATs. A case study based on the field data is carried out to justify the applicability of the proposed model. By using this model selected pump when operated in PAT will produce higher power and generate more revenue compared to literature models, resulting in the economical utilization of resources and improving sustainability. Though the savings are lower, it will be significant for a large number of PAT applications.

Hydrate-based gas separation model considering hydrate structure transformation

The study introduces a pioneering model for hydrate-based gas separation technology, which enhances the prediction method for the coexistence of multiple structure hydrates. The calculated results demonstrate strong agreement with 96 sets of experimental data, exhibiting a lower average relative deviation of 8.00 % compared to the traditional model’s average relative deviation of 60.00 %. Furthermore, it offers a comprehensive discussion on the composition of hydrate structures in (H2 + CH4 + C2H6 + C3H8) during gas–water-hydrate equilibrium, providing a more precise depiction of hydrate structure evolution. Additional gas-hydrate equilibrium experiments were conducted for the (H2 + CH4 + C2H6 + C3H8) system to optimize the model parameters. The fitting goodness of the hydrate structure transformation formula (niI-Si formula) is enhanced from 0.9162 to 0.9816.

Target assignment for multiple stages of weapons systems using a deep Q-learning network and a modified artificial bee colony method

Multistage Weapon Target Assignment (MWTA) in defense systems is a critical process that focuses on minimizing deviations and inaccuracies during target assignment across stages like initialization, tracking, and counterattack. The research problem lies in efficiently assigning targets to weapon systems while accounting for distractions and tracking accuracy constraints. Existing approaches often face challenges related to computational complexity, real-time engagement limitations, and scalability issues, leading to inaccuracies in various stages within the system's perceived radius. This paper proposes a novel State-based Modified Artificial Bee Colony Method (SMABCM) that integrates Q-learning to generate adaptive target assignment decisions. SMABCM aims to maximize assignment accuracy by prioritizing low deviation rewards, enabling precise one-to-one target tracking and reducing the population of the artificial bee colony. The Q-learning network dynamically defines generation and reduction states based on deviations to minimize distracting targets and enhance tracking precision. Experimental results demonstrate SMABCM's superior performance over existing approaches in target precision comparison, hit probability, increase in distracting target detection, reduced deviation factor, and computation time. As the speed increases, the proposed SMABCM improves target accuracy by 9.54 %, hit probability by 9.17 %, distracting target detection by 12.35 %, while reducing deviation by 7.39 % and computation time by 10.11 %. This approach addresses complexities in multistage weapon systems through dynamic strategies, contributing to proactive defense against evolving security threats.

Evaluation of a Semi-Automated Wound-Halving Algorithm for Split-Wound Design Studies: A Step towards Enhanced Wound-Healing Assessment.

Background: Chronic leg ulcers present a global challenge in healthcare, necessitating precise wound measurement for effective treatment evaluation. This study is the first to validate the "split-wound design" approach for wound studies using objective measures. We further improved this relatively new approach and combined it with a semi-automated wound measurement algorithm. Method: The algorithm is capable of plotting an objective halving line that is calculated by splitting the bounding box of the wound surface along the longest side. To evaluate this algorithm, we compared the accuracy of the subjective wound halving of manual operators of different backgrounds with the algorithm-generated halving line and the ground truth, in two separate rounds. Results: The median absolute deviation (MAD) from the ground truth of the manual wound halving was 2% and 3% in the first and second round, respectively. On the other hand, the algorithm-generated halving line showed a significantly lower deviation from the ground truth (MAD = 0.3%, p < 0.001). Conclusions: The data suggest that this wound-halving algorithm is suitable and reliable for conducting wound studies. This innovative combination of a semi-automated algorithm paired with a unique study design offers several advantages, including reduced patient recruitment needs, accelerated study planning, and cost savings, thereby expediting evidence generation in the field of wound care. Our findings highlight a promising path forward for improving wound research and clinical practice.

Design of a sustainable supply chain network of biomass renewable energy in the case of disruption

Non-renewable energy sources, including fossil fuels, are a type of energy whose consumption rate far exceeds its natural production rate. Therefore, non-renewable resources will be exhausted if alternative energy is not fully developed, leading to an energy crisis in the near future. In this paper, a mathematical model has been proposed for the design of the biomass supply chain of field residues that includes several fields where residue is transferred to hubs after collecting the residue in the hub, the residue is transferred to reactors. In reactors, the residue is converted into gas, which is transferred to condenser and transformers, converted into electricity and sent to demand points through the network. In this paper, the criteria of stability and disturbance were considered, which have been less discussed in related research, and the purpose of the proposed model was to maximize the profit from the sale of energy, including the selling price minus the costs. Genetic algorithm (GA) and simulated annealing (SA) algorithm have been used to solve the model. Then, to prove the complexity of the problem, different and random examples have been presented in different dimensions of the problem. Also, the efficiency of the algorithm in small and large dimensions was proved by comparing GA and SA due to the low deviation of the solutions and the methods used have provided acceptable results suitable for all decision-makers. Also, the effectiveness of the algorithm in small and large dimensions is proven by comparing the genetic algorithm and simulated annealing, and the genetic algorithm's values are better, considering the deviation of 2.9%.and have provided solution methods suitable for all decision makers.

Designing a low-cost wireless sensor network for particulate matter monitoring: Implementation, calibration, and field-test

Poor air quality can provoke severe impacts on health, necessitating environmental monitoring of atmospheric particulate matter (PM) to assess potential threats to human well-being. However, traditional continuous air quality monitoring systems are often costly and time-consuming in data treatment. Lately, there is a growing trend towards the use of low-cost wireless PM sensors, providing more detailed information than standard systems. This paper presents a system designed to measure air quality, specifically, a wireless sensor network composed of a distributed sensor network linked to a cloud system. The proposed system can efficiently measure air quality as it is cost-effective, small-sized, and consumes little power. Sensor nodes based on low-power long range (LoRa) motes transmit field measurement data to the cloud via a gateway, and a cloud computing system is implemented to store, monitor, process, and visualise the data. Advanced techniques were included in our cloud for data processing and analysis to optimise the detection of PM. Laboratory and field tests in the historic Riotinto mine validate the system's viability, offering real-time air quality information for nearby populations. Once calibrated, sensors demonstrate high accuracy, presenting mean error of −0.3% and low deviation (R2 = 0.96) when compared to regulatory systems for both low (<10 μgPM10/m3) and hazardous concentrations (300 μgPM10/m3), which makes them perfect as early warning systems for atmospheric pollution in mining.

Wavefront rotator with near-zero mean polarization change

A K-mirror is a device that rotates the wavefront of an incident optical field. It has recently gained prominence over the Dove prism, another commonly used wavefront rotator, due to the fact that while a K-mirror has several controls for adjusting the internal reflections, a Dove prism is made of a single glass element with no additional control. Thus, one can obtain much lower angular deviations of transmitting wavefronts using a K-mirror than with a Dove prism. However, the accompanying polarization changes in the transmitted field due to rotation persist even in the commercially available K-mirrors. A recent theoretical work [Appl. Opt. 61, 8302 (2022)APOPAI0003-693510.1364/AO.472543] shows that it is possible to optimize the base angle of a K-mirror for a given refractive index such that the accompanying polarization changes are minimum. In contrast, we show in this paper that by optimizing the refractive index it is possible to design a K-mirror at any given base angle and with any given value for the mean polarization change, including near-zero values. Furthermore, we experimentally demonstrate a K-mirror with an order-of-magnitude lower mean polarization change than that of the commercially available K-mirrors. This can have important practical implications for OAM-based applications that require precise wavefront rotation control.

Navigated augmented reality through a head-mounted display leads to low deviation between planned, intra- and postoperative parameters during glenoid component placement of reverse shoulder arthroplasty: a proof-of-concept case series

BackgroundNavigated augmented reality (AR) through a head-mounted display (HMD) has led to accurate glenoid component placement in reverse shoulder arthroplasty (RSA) in an in-vitro setting. The purpose of this study is to evaluate the deviation between planned, intra-, and postoperative inclination, retroversion, entry point and depth of the glenoid component placement during RSA, assisted by navigated AR through a HMD, in a surgical setting. MethodsA prospective, multicenter study was conducted. All consecutive patients undergoing RSA in two institutions, between August 2021 and January 2023, were considered potentially eligible for inclusion in the study. Inclusion criteria were: age >18 years, surgery assisted by AR through a HMD, and postoperative computed tomography (CT) scans at six weeks. All participants agreed to participate in the study and an informed consent was provided in all cases. Preoperative CT scans were undertaken for all cases and used for three-dimensional (3D) planning. Intra-operatively, glenoid preparation and component placement were assisted by a navigated AR system through a HMD in all patients. Intraoperative parameters were recorded by the system. A postoperative CT scan was undertaken at 6 weeks, and 3D reconstruction was used for obtaining postoperative parameters. The deviation between planned, intra-, and postoperative inclination, retroversion, entry point, and depth of the glenoid component placement was calculated. Outliers were defined as >5° for inclination and retroversion and >5 mm for entry point. Results17 patients (9 females, 12 right shoulders) with a mean age of 72.8±9.1 years old (range, 47.0 to 82.0) met inclusion criteria. The mean deviation between intra- and postoperative measurements was 1.5°±1.0° (range, 0.0° to 3.0°) for inclination, 2.8°±1.5° (range, 1.0° to 4.5°) for retroversion, 1.8±1.0 mm (range, 0.7mm to 3.0mm) for entry point, and 1.9±1.9 mm (range, 0.0mm to 4.5mm) for depth. The mean deviation between planned and postoperative values was 2.5°±3.2° (range, 0.0° to 11.0°) for inclination, 3.4°±4.6° (range, 0.0° to 18.0°) for retroversion, 2.0±2.5 mm (range, 0.0° to 9.7°) for entry point, and 1.3±1.6 mm (range, 1.3mm to 4.5mm) for depth. There were no outliers between intra- and postoperative values and there were three outliers between planned and postoperative values. The mean time (minutes:seconds) for the tracker unit placement and the scapula registration was 03:02 (range, 01:48 to 04:26) and 08:16 (range, 02:09 to 17:58), respectively. ConclusionThe use of a navigated AR system through a HMD in RSA led to low deviations between planned, intra-operative and postoperative parameters for glenoid component placement.

A simulated annealing metaheuristic approach to hybrid flow shop scheduling problem

This study investigates a complex hybrid flow shop scheduling problem prevalent in the industrial sector, characterized by dedicated machines, availability dates, and delivery times. The primary objective is to minimize the total completion time (makespan) in a two-stage workshop setting. We conducted a comprehensive literature review, revealing a scarcity of research on this specific configuration, and employed the Simulated Annealing metaheuristic as our main resolution method. Special emphasis was placed on the meticulous parameterization and configuration of this metaheuristic, crucial for navigating the complexity of the problem.Our findings demonstrate the remarkable effectiveness of the Simulated Annealing method, particularly in achieving low deviation from the lower bound in larger problem sizes and specific instance classes. This consistency highlights the method’s robustness and suitability for complex scheduling scenarios. The study also reveals varying degrees of problem solvability across different instance classes, with computation times generally reasonable except in more challenging scenarios.

Digital Evaluation of Trueness and Precision of Modern Impression Materials in Implant- Retained Mandibular Overdentures.

The purpose of this in vitro study was to evaluate the dimensional accuracy, trueness, and precision of vinyl siloxane ether (VSXE) and polyvinylsiloxane (PVS) impression materials using different impression techniques. A three-dimensional (3D) printed mandibular model with implants and metal rods served as the reference model. Impressions were taken in custom trays, resulting in four groups: PVS-closed-tray, VSXE-closed-tray, PVS-open-tray, and VSXE-open-tray. The reference model and impressions were scanned and analyzed using 3D analysis software to assess the trueness and precision within each group. There was significant difference in trueness between the groups, with PVS closed tray showing a higher deviation than VSXE-closed-tray and PVS-open-tray. VSXE-open-tray had the lowest deviation, which was statistically significant. In terms of precision, PVS-closed-tray showed the highest deviation, while no significant differences were found among the other groups. VSXE impression material with an open tray technique consistently demonstrated the highest levels of accuracy and precision. Conversely, PVS impression material with a closed tray technique yielded less favorable results. Better understanding of trueness and precision of new impression materials with new impression techniques will increase their clinical effectiveness.

Ready-to-print alginate inks: The effect of different divalent cations on physico-chemical properties of 3D printable alginate hydrogels

Studies about the use of different divalent cations to produce 3D printed scaffolds are almost limited to their application as secondary crosslinking agents after the printing of alginate. For this reason, this research aims to demonstrate the possibility to develop alginate hydrogel-inks for 3D-printing application, exploiting the ionotropic gelation in a preprint step, by paying attention to the role of divalent cations on hydrogel-inks properties. The investigation of transversal relaxation time highlighted differences among inks (barium-ink 90.04 ms, calcium-ink 84.33 ms, and zinc-ink 75.05 ms) suggesting a potential influence of different cations. If all the inks showed a shear thinning behaviour with similar flowability index (0.153±0.018), they were characterised by different consistency index (from 2420 to 574 Pa•s), extrudability and homogeneity, parameters that influence the printing setup. In fact, to reach the same flowability and thus low deviation in the layer width for all inks, a variation in printing pressure and speed was necessary. Overall, it can be deduced that alginate inks preparation following a preprint crosslinking approach could be a valid method to overcome the alginate printability issues underlining the possibility to select the crosslinking cation according to the technological properties wanted for the final matrix.