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Many emerging commercial services are based on the sharing or pooling of resources for common use with the aim of reducing costs. Businesses such as delivery-, mobility-, or transport-as-a-service have become standard in many parts of the world, fulfilling on-demand requests for customers in live settings. However, it is known that many of these problems are NP-hard, and therefore both modeling and solving them accurately is a challenge. Here we focus on one such routing problem, the Ride Pooling Problem (RPP), where multiple customers can request on-demand pickups and drop-offs from shared vehicles within a fleet. The combinatorial optimization task is to optimally pool customer requests using the limited set of vehicles, akin to a small-scale flexible bus route. In this work, we propose a quadratic unconstrained binary optimization (QUBO) program and introduce efficient formulation methods for the RPP to be solved using metaheuristics, and specifically emerging quantum optimization algorithms.

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Breathing temporarily pauses during swallowing, and the occurrence of inspiration before and after these pauses may increase the likelihood of aspiration, a serious health problem in older adults. Therefore, the automatic detection of these pauses without constraints is important. We propose methods for measuring respiratory movements during swallowing using millimeter wave radar to detect these pauses. The experiment involved 20 healthy adult participants. The results showed a correlation of 0.71 with the measurement data obtained from a band-type sensor used as a reference, demonstrating the potential to measure chest movements associated with respiration using a non-contact method. Additionally, temporary respiratory pauses caused by swallowing were confirmed by the measured data. Furthermore, using machine learning, the presence of respiring alone was detected with an accuracy of 88.5%, which is higher than that reported in previous studies. Respiring and temporary respiratory pauses caused by swallowing were also detected, with a macro-averaged F1 score of 66.4%. Although there is room for improvement in temporary pause detection, this study demonstrates the potential for measuring respiratory movements during swallowing using millimeter wave radar and a machine learning method.

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Binary matrix factorization is an important tool for dimension reduction for high-dimensional datasets with binary attributes and has been successfully applied in numerous areas. This paper presents a collaborative neurodynamic optimization approach to binary matrix factorization based on the original combinatorial optimization problem formulation and quadratic unconstrained binary optimization problem reformulations. The proposed approach employs multiple discrete Hopfield networks operating concurrently in search of local optima. In addition, a particle swarm optimization rule is used to reinitialize neuronal states iteratively to escape from local minima toward better ones. Experimental results on eight benchmark datasets are elaborated to demonstrate the superior performance of the proposed approach against six baseline algorithms in terms of factorization error. Additionally, the viability of the proposed approach is demonstrated for pattern discovery on three datasets.

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Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

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Protein-ligand docking plays a significant role in structure-based drug discovery. This methodology aims to estimate the binding mode and binding free energy between the drug-targeted protein and candidate chemical compounds, utilizing protein tertiary structure information. Reformulation of this docking as a quadratic unconstrained binary optimization (QUBO) problem to obtain solutions via quantum annealing has been attempted. However, previous studies did not consider the internal degrees of freedom of the compound that is mandatory and essential. In this study, we formulated fragment-based protein-ligand flexible docking, considering the internal degrees of freedom of the compound by focusing on fragments (rigid chemical substructures of compounds) as a QUBO problem. We introduced four factors essential for fragment-based docking in the Hamiltonian: (1) interaction energy between the target protein and each fragment, (2) clashes between fragments, (3) covalent bonds between fragments, and (4) the constraint that each fragment of the compound is selected for a single placement. We also implemented a proof-of-concept system and conducted redocking for the protein-compound complex structure of Aldose reductase (a drug target protein) using SQBM+, which is a simulated quantum annealer. The predicted binding pose reconstructed from the best solution was near-native (RMSD = 1.26 Å), which can be further improved (RMSD = 0.27 Å) using conventional energy minimization. The results indicate the validity of our QUBO problem formulation.

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PURPOSE: Periprosthetic joint infection (PJI) is a major cause of revision surgery after total knee arthroplasty (TKA) and unicondylar knee arthroplasty (UKA). Patient- and hospital-related risk factors need to be assessed to prevent PJI. This study identifies influential factors and differences in infection rates between different implant types. METHODS: Data were obtained from the German Arthroplasty Registry. Septic revisions were calculated using Kaplan-Meier estimates with septic revision surgery as the primary endpoint. Patients with constrained and unconstrained TKA or UKA were analysed using Holm's multiple log-rank test and Cox's proportional hazards ratio. The 300,998 cases of knee arthroplasty analysed included 254,144 (84.4%) unconstrained TKA, 9993 (3.3%) constrained TKA and 36,861 (12.3%) UKA with a maximum follow-up of 7 years. RESULTS: At 1 year, the PJI rate was 0.5% for UKA and 2.8% for TKA, whereas at 7 years, the PJI rate was 4.5% for UKA and 0.9% for TKA (p < 0.0001). The PJI rate significantly increased for constrained TKA compared to unconstrained TKA (p < 0.0001). The PJI rate was 2.0% for constrained TKA and 0.8% for unconstrained TKA at 1 year and 3.1% and 1.4% at 7 years. Implantation of a constrained TKA (hazard ratio [HR] = 2.55), male sex (HR = 1.84), increased Elixhauser score (HR = 1.18-1.56) and implant volume of less than 25 UKA per year (HR = 2.15) were identified as risk factors for revision surgery; an Elixhauser score of 0 (HR = 0.80) was found to be a preventive factor. CONCLUSIONS: Reduced implant volume and constrained knee arthroplasty are associated with a higher risk of PJI. Comorbidities (elevated Elixhauser score), male sex and low UKA implant volume have been identified as risk factors for PJI. Patients who meet these criteria require specific measures to prevent infection. Further research is required on the potential impact of prevention and risk factor modification. LEVEL OF EVIDENCE: Level III.

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PURPOSE: Due to ageing population, the implantation rate of total knee arthroplasties (TKAs) is continuously growing. Aseptic revisions in primary knee arthroplasty are a major cause of revision. The aim of the following study was to determinate the incidence and reasons of aseptic revisions in constrained and unconstrained TKA, as well as in unicondylar knee arthroplasties (UKAs). METHODS: Data collection was performed using the German Arthroplasty Registry. Reasons for aseptic revisions were calculated. Incidence and comparison of aseptic revisions were analysed using Kaplan-Meier estimates. A multiple χ2 test with Holm's method was used to detect group differences in ligament ruptures. RESULTS: Overall, 300,998 cases of knee arthroplasty with 254,144 (84.4%) unconstrained TKA, 9993 (3.3%) constrained TKA and 36,861 (12.3%) UKA were analysed. Aseptic revision rate in UKA was significantly increased compared to unconstrained and constrained TKA (p < 0.0001). In constrained TKA, a 2.0% revision rate for aseptic reasons were reported after 1 year, while in unconstrained TKA 1.1% and in UKA, 2.7% of revisions were identified. After 7 years in constrained TKA 3.3%, in unconstrained TKA 2.8%, and in UKA 7.8% sustained aseptic revision. Ligament instability was the leading cause of aseptic revision accounting for 13.7% in unconstrained TKA. In constrained TKA, 2.8% resulted in a revision due to ligament instability. In the UKA, the most frequent cause of revisions was tibial loosening, accounting for 14.6% of cases, while progression of osteoarthritis accounted for 7.9% of revisions. Ligament instability was observed in 14.1% of males compared to 15.9% of females in unconstrained TKA and in 4.6% in both genders in UKA. CONCLUSION: In patients with UKA, aseptic revision rates are significantly higher compared to unconstrained and constrained TKA. Ligament instability was the leading cause of aseptic revision in unconstrained TKA. In UKA, the most frequent cause of revisions was tibial loosening, while progression of osteoarthritis was the second most frequent cause of revisions. Comparable levels of ligament instability were observed in both sexes. LEVEL OF EVIDENCE: Level III, cohort study.

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BACKGROUND: While first-generation articulated disc prostheses had an ideal positioning schematically as posterior as possible because of their geometrically determined center of rotation, the dogma may change for viscoelastic implants, whose center of rotation is free. Our hypothesis was to assess whether the anteroposterior positioning (APP) of a viscoelastic implant may influence the clinical or radiological outcomes at follow-up. METHODS: Twenty-five patients (mean age 47 years) were evaluated, with an average follow-up of 25.9 months. The primary outcome was the implants' APP on lateral radiographs. APP between 0% and 49% meant anterior centering, 50% perfect centering, and 51% to 100% posterior centering. The cohort was divided into 2 groups: anterior positioning and posterior positioning. Measurements were performed blindly to the functional outcomes. Visual analog scale for neck pain and radicular pain and the Neck Disability Index were assessed. Range of motion was measured at the last follow-up. The C2 to C7 Cobb angle and the spinocranial angle were also measured. RESULTS: The median crude offset from the vertebral endplate center was 0.4 mm (mean: 0.3 mm, Q1: -1.5 mm, Q3: 2 mm; range, -2.9 to 4 mm). The mean overall APP was 49%, 45.2% (95% CI, 43.2%-47.1%) in the anterior group, and 54.1% (95% CI, 51.4%-55.3%) in the posterior group. Fifteen patients were in the group anterior positioning and 10 in the group posterior positioning. The mean spinocranial angle was 79° preoperatively and 74° preoperatively (P = 0.04). Functional outcomes were significantly improved at the last follow-up (P < 10-4). There was no significant correlation between the APP, functional outcomes, and range of motion. CONCLUSION: The APP of the CP-ESP viscoelastic disc arthroplasty does not significantly influence the clinical or radiological outcomes at follow-up. This study suggests that this type of implant tolerates greater variability in its implantation technique.

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Robots in manufacturing alleviate hazardous environmental conditions, reduce the physical/mental stress of the workers, maintain high precision for repetitive movements, reduce errors, speed up production, and minimize production costs. Although robots have pervaded many industrial sectors and domestic environments, the experiments in the food sectors are limited to pick-and-place operations and meat processing while we are assisting new attention in gastronomy. Given the great performances of the robots, there would be many other intriguing applications to explore which could usher the transition to precision food manufacturing. This review wants open thoughts and opinions on the use of robots in different food operations. First, we reviewed the recent advances in common applications - e.g. novel sensors, end-effectors, and robotic cutting. Then, we analyzed the use of robots in other operations such as cleaning, mixing/kneading, dough manipulation, precision dosing/cooking, and additive manufacturing. Finally, the most recent improvements of robotics in gastronomy with their use in restaurants/bars and domestic environments, are examined. The comprehensive analyses and the critical discussion highlighted the needs of further scientific understanding and exploitation activities aimed to fill the gap between the laboratory-scale results and the validation in the relevant environment.

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Our natural behavioral repertoires include coordinated actions of characteristic types. To better understand how neural activity relates to the expression of actions and action switches, we studied macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of transition probabilities between these actions, we found that behavior is organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, encoding of action category is progressively stronger in more dorsal and more caudal prefrontal regions. Together, these results establish a link between selection of units of primate behavior on one hand and neuronal activity in prefrontal regions on the other.

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There is a growing interest to understand the neural underpinnings of high-level sports performance including expertise-related differences in sport-specific skills. Here, we aimed to investigate whether expertise level and task complexity modulate the cortical hemodynamics of table tennis players. 35 right-handed table tennis players (17 experts/18 novices) were recruited and performed two table tennis strokes (forehand and backhand) and a randomized combination of them. Cortical hemodynamics, as a proxy for cortical activity, were recorded using functional near-infrared spectroscopy, and the behavioral performance (i.e., target accuracy) was assessed via video recordings. Expertise- and task-related differences in cortical hemodynamics were analyzed using nonparametric threshold-free cluster enhancement. In all conditions, table tennis experts showed a higher target accuracy than novices. Furthermore, we observed expertise-related differences in widespread clusters compromising brain areas being associated with sensorimotor and multisensory integration. Novices exhibited, in general, higher activation in those areas as compared to experts. We also identified task-related differences in cortical activity including frontal, sensorimotor, and multisensory brain areas. The present findings provide empirical support for the neural efficiency hypothesis since table tennis experts as compared to novices utilized a lower amount of cortical resources to achieve superior behavioral performance. Furthermore, our findings suggest that the task complexity of different table tennis strokes is mirrored in distinct cortical activation patterns. Whether the latter findings can be useful to monitor or tailor sport-specific training interventions necessitates further investigations.

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Recent advances in quantum hardware offer new approaches to solve various optimization problems that can be computationally expensive when classical algorithms are employed. We propose a hybrid quantum-classical algorithm to solve a dynamic asset allocation problem where a target return and a target risk metric (expected shortfall) are specified. We propose an iterative algorithm that treats the target return as a constraint in a Markowitz portfolio optimization model, and dynamically adjusts the target return to satisfy the targeted expected shortfall. The Markowitz optimization is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The use of the expected shortfall risk metric enables the modeling of extreme market events. We compare the results from D-Wave's 2000Q and Advantage quantum annealers using real-world financial data. Both quantum annealers are able to generate portfolios with more than 80% of the return of the classical optimal solutions, while satisfying the expected shortfall. We observe that experiments on assets with higher correlations tend to perform better, which may help to design practical quantum applications in the near term.

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We are in the noisy intermediate-scale quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrations of the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the train dispatching consequences of the arrival of an already delayed train to a given network segment. This problem is computationally hard and needs to be solved almost in real time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, which is compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) does not perform well on those instances, either.

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This paper deals with the problem of establishing a systematic theoretical formulation of variational principles for the continuum gravitational field dynamics of classical General Relativity (GR). In this reference, the existence of multiple Lagrangian functions underlying the Einstein field equations (EFE) but having different physical connotations is pointed out. Given validity of the Principle of Manifest Covariance (PMC), a set of corresponding variational principles can be constructed. These are classified in two categories, respectively, referred to as constrained and unconstrained Lagrangian principles. They differ for the normalization properties required to be satisfied by the variational fields with respect to the analogous conditions holding for the extremal fields. However, it is proved that only the unconstrained framework correctly reproduces EFE as extremal equations. Remarkably, the synchronous variational principle recently discovered belongs to this category. Instead, the constrained class can reproduce the Hilbert-Einstein formulation, although its validity demands unavoidably violation of PMC. In view of the mathematical structure of GR based on tensor representation and its conceptual meaning, it is therefore concluded that the unconstrained variational setting should be regarded as the natural and more fundamental framework for the establishment of the variational theory of EFE and the consequent formulation of consistent Hamiltonian and quantum gravity theories.

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Sleep-related problems are widespread. Numerous devices for sleep monitoring are increasingly available, including smartwatches, sleep monitoring rings, etc. These devices accumulate and analyze a substantial quantity of physiological data. In this study, we develop a smart air-mattress system that can effectively aid health measurements. The proposed system adopts an air-mattress system to detect subtle changes in pressure and thereby collect micro physiological signals, including ballistocardiography (BCG) and breathing signals. The system uses ultrasonic signals to detect the subject's turning movements. To increase the signal recognition accuracy, the BCG signal is processed effectively to reduce noise interference engendered by the body movement during sleep and is processed using regression analysis for heart rate and breathing rate estimation. Accordingly, the proposed system is unconstrained and can be used to collect micro BCG signals, breathing signals, heart rate signals, and turning movements for the long-term health-care.

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Many NP-hard combinatorial optimization (CO) problems can be cast as a quadratic unconstrained binary optimization model, which maps naturally to an Ising model. The final spin configuration in the Ising model can adiabatically arrive at a solution to a Hamiltonian, given a known set of interactions between spins. We enhance two photonic Ising machines (PIMs) and compare their performance against classical (Gurobi) and quantum (D-Wave) solvers. The temporal multiplexed coherent Ising machine (TMCIM) uses the bistable response of an electro-optic modulator to mimic the spin up and down states. We compare TMCIM performance on Max-cut problems. A spatial photonic Ising machine (SPIM) convolves the wavefront of a coherent laser beam with the pixel distribution of a spatial light modulator to adiabatically achieve a minimum energy configuration, and solve a number partitioning problem (NPP). Our computational results on Max-cut indicate that classical solvers are still a better choice, while our NPP results show that SPIM is better as the problem size increases. In both cases, connectivity in Ising hardware is crucial for performance. Our results also highlight the importance of better understanding which CO problems are most likely to benefit from which type of PIM. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.

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This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

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INTRODUCTION: The instantaneous center of rotation (iCOR) of a motion segment has been shown to correlate with its total range of motion (ROM). Importantly, a correlation of the correct placement of cervical total disc replacement (cTDR) to preserve a physiological iCOR has been previously identified. However, changes of these parameters and the corresponding clinical relevance have hardly been analyzed. This study assesses the radiological and clinical correlation of iCOR and ROM following cTDR. MATERIALS/METHODS: A retrospective multi-center observational study was conducted and radiological as well as clinical parameters were evaluated preoperatively and 1 year after cTDR with an unconstrained device. Radiographic parameters including flexion/extension X-rays (flex/ex), ROM, iCOR and the implant position in anterior-posterior direction (IP ap), as well as corresponding clinical parameters [(Neck Disability Index (NDI) and the visual analogue scale (VAS)] were assessed. RESULTS: 57 index segments of 53 patients treated with cTDR were analyzed. Pre- and post-operative ROM showed no significant changes (8.0° vs. 10.9°; p > 0.05). Significant correlations between iCOR and IP (Pearson's R: 0.6; p < 0.01) as well as between ROM and IP ap (Pearson's R: - 0.3; p = 0.04) were identified. NDI and VAS improved significantly (p < 0.01). A significant correlation between NDI and IP ap after 12 months (Pearson's R: - 0.39; p < 0.01) was found. CONCLUSION: Implantation of the tested prosthesis maintains the ROM and results in a physiological iCOR. The exact position of the device correlates with the clinical outcome and emphasize the importance of implant design and precise implant positioning.

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Sleep-related breathing disorder (SRBD) is a sleep disease with high incidence and many complications. However, patients are often unaware of their sickness. Therefore, SRBD harms health seriously. At present, home SRBD monitoring equipment is a popular research topic to help people get aware of their health conditions. This article fully compares recent state-of-art research results about home SRBD monitors to clarify the advantages and limitations of various sensing techniques. Furthermore, the direction of future research and commercialization is pointed out. According to the system design, novel home SRBD monitors can be divided into two types: wearable and unconstrained. The two types of monitors have their own advantages and disadvantages. The wearable devices are simple and portable, but they are not comfortable and durable enough. Meanwhile, the unconstrained devices are more unobtrusive and comfortable, but the supporting algorithms are complex to develop. At present, researches are mainly focused on system design and performance evaluation, while high performance algorithm and large-scale clinical trial need further research. This article can help researchers understand state-of-art research progresses on SRBD monitoring quickly and comprehensively and inspire their research and innovation ideas. Additionally, this article also summarizes the existing commercial sleep respiratory monitors, so as to promote the commercialization of novel home SRBD monitors that are still under research.

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