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Abstract Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of “last-mile” delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes.

PurposeTo comprehensively compare the long-term outcome of the combined topography guided photorefractive keratectomy (TG-PRK) with accelerated corneal cross-linking (ACXL) and ACXL alone in eyes with progressive keratoconus. The analysis focused on the changes in the detailed corneal aberrometric values.MethodsThis single-center, prospective cohort study included 28 patients (30 eyes) of the TG-PRK plus ACXL group and 14 patients (15 eyes) of the ACXL alone group. The mean duration of the follow-up was 44 ± 10.18 months (ranged from 31 to 65 months). The preoperative data and the postoperative measurement data at the last follow-up visit, including demographic data, uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), manifest refraction, corneal topography, pachymetry, aberrometry and densitometry were analyzed.ResultsThe CDVA significantly improved in the TG-PRK plus ACXL group at the last follow-up visit (p = 0.006), while no significant improvement was found in the ACXL alone group (p = 0.432). The maximal keratometry of the anterior corneal surface (Kmax) of both groups significantly decreased at the last follow-up visit (p

This paper presents a novel approach to optimizing the linear layer of block ciphers using the matrix decomposition framework. It is observed that the reduction properties proposed by Xiang et al. (in FSE 2020) need to be improved. To address these limitations, we propose a new reduction framework with a complete reduction algorithm and swapping algorithm. Our approach formulates matrix decomposition as a new framework with an adaptive objective function and converts the problem to a Graph Isomorphism problem (GI problem). Using the new reduction algorithm, we were able to achieve lower XOR counts and depths of quantum implementations under the s-XOR metric. Our results outperform previous works for many linear layers of block ciphers and hash functions; some of them are better than the current g-XOR implementation. For the AES MixColumn operation, we get two implementations with 91 XOR counts and depth 13 of in-place quantum implementation, respectively.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics-Virus Spread model (PeDViS), an individual-based model that combines pedestrian behaviour models with virus spread models incorporating direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark 'at risk' interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to the buildup of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent. This uncertainty should be considered when assessing transmission risks upon different types of human interactions in indoor spaces. We illustrated the multi-dimensionality of indoor SARS-CoV-2 transmission that emerges from the interplay of human behaviour and the spread of respiratory viruses. A modelling strategy that incorporates this in risk assessments can help inform policy makers and citizens on the safe use of indoor spaces with varying inter-human interactions.

People are increasingly getting type II diabetes mellitus (T2DM) due to unhealthy food styles, decreased outdoor activities caused by the COVID-19 pandemic, and unawareness of type II diabetes risk factors. This disease is hidden in the early stages and causes many comorbidities like fatty liver, heart disease, and peripheral artery disease. This study presents several hybrid algorithms to diagnose T2DM in its early stages without requiring expensive and time-consuming medical tests. We first apply feature selection using the Particle Swarm Optimization (PSO) algorithm to reduce the required computations. Meta-heuristics are used in developed hierarchical algorithms to optimize the hyperparameters of machine learning algorithms for classification. A comparative analysis of the algorithms with performance metrics shows Genetic Algorithm-Support Vector Machine (GA-SVM) has the largest area under the Receiver Operating Characteristic (ROC) curve (0.934) and better performance in most metrics (Accuracy of 0.934 and F1-Measure of 0.945) and reasonable metaheuristic computational time. Therefore, the GA-SVM algorithm is recommended for clinical decision support systems. This algorithm diagnoses T2DM at early stages by responding to several questions with about 93% accuracy, which can help patients to survive future complications through lifestyle intervention therapy.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

During the first year of the COVID-19 pandemic, governments only had access to non-pharmaceutical interventions (NPIs) to mitigate the spread of the disease. Various methods have been discussed in the literature for calculating the effectiveness of NPIs. Among these methods, the interrupted time series analysis method is the area of our interest. To study the second wave, we clustered countries based on levels of implemented NPIs, except for the target NPI (X) whose effectiveness wanted to be evaluated. To do so, the COVID-19 Policy Response Tracker data-set gathered by the “Our World in Data” team of Oxford University, and COVID-19 statistical data gathered by the John Hopkins Hospital were used. After clustering, we selected a counterfactual country from the countries that were in the same cluster as the target country, and implemented NPI (X) at its lowest level. Thus, the target country and the counterfactual country were similar in implementation level of other NPIs and only differed in the implementation level of the target NPI (X). Therefore, we can calculate the effectiveness of NPI (X) without being concerned about the impurity of the effectiveness values that might be caused by other NPIs. This allowed us to calculate the effectiveness of NPI (X) using the interrupted time series analysis with the control group. Interrupted time series analysis assesses the effect of different policy-implementation levels by evaluating interruptions caused by policies in trend and level after the policy-implementation date. Before the NPI-implementation date, the implementation levels of NPIs were similar in both selected countries. After this date, the counterfactual country could be treated as a baseline for calculating changes in the trends and levels of COVID-19 cases in the target country. To demonstrate this approach, we used the generalized least square (GLS) method to estimate interrupted time series parameters related to the effectiveness of school closure (the target NPI) in Spain (the target country). The results show that increasing the implementation level of school closure caused a 34% decrease in COVID-19 prevalence in Spain after only 10 days compared to the counterfactual country.

Abstract Background There is still a lack of data on deep vein thrombosis (DVT) following surgically treated femoral shaft fracture (FSF). The goal of this study was to investigate the characteristics of postoperative DVT and the association between the occurrence of DVT and risk factors in patients undergoing surgical treatment for FSF. Methods This observational retrospective case-control study reviewed 308 patients who received surgical treatment of FSF between January 2016 and October 2020 at a university hospital. Univariate analyses were performed on the data of demographics, comorbidities, laboratory biomarkers, and operation-related indexes. The receiver operating characteristic (ROC) curve analysis, univariate analyses, and multivariate logistic regression analysis were employed to identify the independent risk factors associated with DVT. Results In total, 308 patients with surgically treated FSF were included, among whom 48 (15.6%) patients had postoperative DVTs. The univariate analyses showing significant differences regarding DVT were American Society of Anesthesiologists (ASA) classification, diabetes mellitus, current smoking, aspartate transaminase (AST), and very-low-density lipoprotein (VLDL) level among the 34 factors. According to the ROC results, the optimal cutoff values for intraoperative blood loss, d-dimer, and age were 350 ml, 1.08 μg/ml, and 35 years, respectively. The multivariable model demonstrated 4 significantly independent associations with postoperative DVT, including current smoking, intraoperative blood loss (> 550 ml), age (> 35 years), and d-dimer > 1.09 μg/ml. Conclusion These risk factors as screening tools contribute to risk stratification of the occurrence of thromboembolic events. In addition, our findings would help orthopedic surgeons make a cross-specialty decision and implement targeted precaution measures for patients with FSF.

BackgroundLung metastasis (LM) is an independent risk factor for survival in patients with endometrial cancer (EC).MethodsWe reviewed data on patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The independent predictors of LM in patients with EC were identified using univariate and multivariate logistic regression analyses. A nomogram for predicting LM in patients with EC was developed, and the predictive model was evaluated using calibration and receiver operating characteristic (ROC) curves.ResultsUnivariate and multivariate logistic regression analyses showed that high grade; specific histological type; high tumor and node stages; larger tumor size; and liver, brain, and bone metastases were positively associated with LM risk. A new nomogram was developed by combining these factors to predict LM in patients newly diagnosed with EC. Internal and external verification of the calibration charts showed that the nomogram was well calibrated. The areas under the ROC curves for the training and validation cohorts were 0.924 and 0.913, respectively.ConclusionWe performed a retrospective analysis of 42,073 patients with EC using the SEER database, established a new nomogram for predicting LM based on eight independent risk factors, and visualized the model using a nomogram for the first time.

Summary: Fe3+ is essential for human physiological function. However, excess amounts of Fe3+ are dangerous. Metal-organic frameworks (MOFs) with high porosity, stability, and facile tunability are promising for adsorption and environmental applications. In this study, we show that thiol-functionalized Zr-MOFs can remove Fe3+ with the highest adsorption capacity (481 mg g−1) and fastest kinetics (1.07 g mg−1 h−1) reported so far, to the best of our knowledge. The MOFs, containing a high density of sulfur, are highly selective for Fe3+, rapidly purifying water with excess Fe3+ to a drinkable level. Density functional theory and projected density of states calculations confirm that Fe3+ has the largest adsorption energy on our MOFs compared with the other competitive metals tested. In addition, we find that unpaired d electrons in the Fe3+ contribute to the adsorption. Therefore, the post-functionalized Zr-MOFs with high stability, capacity, kinetics, selectivity, and recyclability are promising adsorbents for the purification of water contaminated with heavy metal ions.

The Covid-19 global pandemic that began in March 2020 was not fully mitigated through governmental Non-Pharmaceutical Interventions (NPIs) and continued to infect people and take lives through 2021. Since many countries were affected by the second, third, and fourth waves of Covid-19, governments extended and strengthened NPIs, but these actions led to citizen protests and fatigue. In this study, we investigate the effect of a lockdown policy on Covid-19 third wave implemented by the province of Ontario, Canada, on April 3rd 2021, followed by a stay-at-home order on April 7th 2021 while free Covid-19 testing and vaccination were in progress. Herein, the effect of both NPIs and vaccination are considered simultaneously. We used the prevalence of Covid-19 cases, tests, and administered vaccines data reported publicly by the Government of Ontario on their website. Because mobility changes can reflect the behaviors and adherence of residents with a stay-at-home order, Covid-19 community mobility data for Ontario provided by Google was also considered. A statistical method called interrupted time series was used to analyze the data. The results indicated that, although vaccinations helped to control the Covid-19 infection rate during this time, the stay-at-home order caused a rate reduction by decreasing the trend of the Covid-19 prevalence by 13 (±0.8962) persons per million daily and the level by 33 (±7.6854) persons per million. Furthermore, the stay-at-home order resulted in approximately a 37% reduction in Covid-19 prevalence one week after the intervention's effective date. Therefore, Ontario's strict lockdown policy, including several NPIs, mitigated the Covid-19 surge during the third wave. The results show that even when vaccination is in progress, strict NPIs such as lockdown is required to control Covid-19 waves, and early re-openings should be avoided. These results may also be useful for other countries that have implemented delayed vaccination schedules.

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

Background: Pregnancy-specific vitamin reference ranges are currently not available for maternal vitamin management during pregnancy. This study aimed to propose pregnancy-specific vitamin reference ranges and to investigate the factors influencing vitamin levels during pregnancy.Methods: A cross-sectional study that included pregnant women from 17 cities in 4 provinces in western China was conducted from 2017 to 2019. A total of 119,286 subjects were enrolled in the study. Serum vitamin A, vitamin D, and vitamin E levels were measured. A multivariable linear regression model and restricted cubic spline function were used to analyze the factors related to vitamin levels.Results: The reference ranges for vitamin A, D, and E levels were 0.22–0.62 mg/L, 5–43 ng/mL, and 7.4–23.5 mg/L, respectively. A linear relationship was found between vitamin E level and age (β = 0.004; 95% confidence interval [CI], 0.0037–0.0042; p < 0.001), and a nonlinear relationship was found between vitamin D (p nonlinear = 0.033) and vitamin A levels and age (p nonlinear < 0.001). Season, gestational trimester, and regions were related to the levels of the three vitamins in the multivariable models (p < 0.05).Conclusions: The lower limit of vitamin A during pregnancy was the same as the reference value currently used for the general population. The reference ranges of vitamins D and E during pregnancy were lower and higher, respectively, than the currently used criteria for the general population. Vitamin A, D, and E levels differed according to age, season, gestational trimester, and region.

Metro-bikeshare integration, an important way of improving the efficiency of public transportation, has grown rapidly during the last decades in many countries. However, most previous analysis of metro-bikeshare transfer trips were based on limited sample size and the number of recognized metro-bikeshare trips were not sufficient. The primary objective of this study is to derive a method to recognize metro-bikeshare transfer trips. The two data sources are provided by Nanjing Metro Company and Nanjing Public Bicycle Company over the same period from 9–29 March 2016. The identifying method includes three steps: (1) Matching Card Pairs (2) Filtering Card Pairs and (3) Identifying Card Pairs. The case study indicates that the Support Vector Classification (SVC) performs best with a high prediction accuracy of 95.9% using seamless smartcards. The identifying method is then used to recognize the transfer trips from other types of cards, resulting in 17,022 valid metro-bikeshare transfer trips made by 2948 travelers. Finally, travel patterns extracted from the two groups of identified transfer trips are analyzed comparatively. The method proposed presents new opportunities for analyzing metro-bikeshare transfer trip characteristics.

Nowadays, there is a need for tools to support city planners in assessing the performance of cycling infrastructure and managing bicycles and mixed flows. Microscopic and macroscopic bicycle traffic models can be used to fulfill this need. However, fundamental knowledge on individual cyclist interaction behavior (which should underpin these models) is hardly available in literature. Detailed bicycle traffic data are necessary if we want to gain insight into cyclist interaction behavior and develop sound behavioral theories and models. Laboratory experiments have been proven to be one of the most effective ways to collect detailed traffic data. For this reason, a controlled experiment aimed to investigate cyclist interaction behavior has been carried out at Delft University of Technology. This paper describes the experimental design, the resulting microscopic bicycle trajectories, and some preliminary results regarding one of the most common interaction situations: the bidirectional interaction. The preliminary results reveal how and to what extent cyclists interact in bidirectional cycling. It is found that cyclists perform a clearly-visible evading (collision avoidance) maneuver when they have face-to-face encounters. During these maneuvers, changes in speed and displacements in the lateral direction are observed. Cyclists start to deviate from their original path when they are around 30 m from each other, and they strongly prefer passing on the right-hand side. Moreover, the expectation of gender differences in cycling behavior reported in the literature is confirmed: our results show that women generally cycle more slowly than men and deviate more from their intended paths in face-to-face encounters. More observations will be available in the next stage of data analysis. These findings can be used to formulate improved microscopic bicycle traffic models for infrastructure design and policy development.

The rational design and development of the low-cost and effective electrocatalysts toward oxygen evolution reaction (OER) are essential in the storage and conversion of clean and renewable energy sources. Herein, a ternary copper-cobalt sulfide nanosheets electrocatalysts (denoted as CuCoS/CC) for electrochemical water oxidation has been synthesized on carbon cloth (CC) via the sulfuration of CuCo-based precursors. The obtained CuCoS/CC reveals excellent electrocatalytic performance toward OER in 1.0 M KOH. It exhibits a particularly low overpotential of 276 mV at current density of 10 mA cm−2, and a small Tafel slope (58 mV decade−1), which is superior to the current commercialized noble-metal electrocatalysts, such as IrO2. Benefiting from the synergistic effect of Cu and Co atoms and sulfidation, electrons transport and ions diffusion are significantly enhanced with the increase of active sites, thus the kinetic process of OER reaction is boosted. Our studies will serve as guidelines in the innovative design of non-noble metal electrocatalysts and their application in electrochemical water splitting