This paper presents a new model for solving the optimal server location problem in a spatial hypercube queueing model. Unlike deterministic location models, our approach accounts for server availability, varying utilization levels, and dependencies across servers. We prove that the problem is NP-hard and establish lower and upper bounds, as well as asymptotic results, by relating it to special cases of the classical p-Median problem. To address the computational challenge, we propose two Bayesian optimization approaches: (i) a parametric approach based on a sparse Bayesian linear model with second-order interactions, and (ii) a nonparametric approach using a Gaussian process surrogate with the p-Median objective as the prior mean function. We prove that both methods achieve sublinear regret and converge to the optimal solution. Numerical experiments and a case study using real-world data from the St. Paul, Minnesota, emergency response system show that our approaches consistently identify optimal solutions and outperform all baseline methods.

Hydrogels have potential applications in artificial cartilage, tendons, and ligaments, while still facing great challenges in simultaneous improvement of strength, toughness, and fatigue resistance. In this work, strong, tough, and ionically conductive hydrogels are prepared via the macromolecular chain aggregation engineered multiscale reinforcement strategy. The tensile strength, fracture strain, fracture energy and fatigue threshold can reach values as high as 10.21 +/- 0.79 MPa, 1942.84 +/- 162.92%, 71.58 +/- 4.23 kJ/m2, and 1040.12 J/m2, respectively. The hydrogels with ionic conductivity up to 1.45 S/m can be used as piezoresistive sensors for detection of various human body motions. This article provides a strategy for fabricating strong, tough, stretchable, and fatigue-resistant hydrogels with promising applications in flexible and wearable electronics.

The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

Hydrogels are widely used in tissue engineering, soft robots, wearable electronics, etc. However, it remains a great challenge to develop hydrogels possessing simultaneously high strength, large stretchability, great fracture energy, and good fatigue threshold to suit different applications. Herein, a novel solvent-exchange-assisted wet-annealing strategy is proposed to prepare high performance poly(vinyl alcohol) hydrogels by extensively tuning the macromolecular chain movement and optimizing the polymer network. The reinforcing and toughening mechanisms are found to be "macromolecule crystallization and entanglement". These hydrogels have large tensile strengths up to 11.19 ± 0.27 MPa and extremely high fracture strains of 1879 ± 10%. In addition, the fracture energy and fatigue threshold can reach as high as 25.39 ± 6.64 kJ m-2 and ≈1233 J m-2 , respectively. These superb mechanical properties compare favorably to those of other tough hydrogels, organogels, and even natural tendons and synthetic rubbers. This work provides a new and effective method to fabricate superstrong, tough, stretchable, and anti-fatigue hydrogels with potential applications in artificial tendons and ligaments.

This paper presents a new approach to solving the optimal unit location problem in a stochastic emergency service system that takes into account state transitions and unit availabilities. The goal is to minimize the system-wide mean response time, which is formulated as a combinatorial optimization problem. We show that this problem is NP-hard and develop lower and upper bounds for the optimal solution using a special case of the classic p-median problem. To solve the problem, we develop a Bayesian optimization algorithm that we show always converges to the optimal solution with a sublinear regret rate. We evaluate our approach through numerical experiments and a constructed study using real data from the St. Paul, Minnesota emergency response system and show that our model consistently and quickly converges to the optimal solution. We show how the optimal unit locations change as call rates increase and find that solutions obtained using the deterministic p-median model deteriorate as the traffic intensity increases. We also show how our approach can be adapted to optimize other objective functions.

Chemical spills, especially oil spills, are becoming an increasingly serious environmental issue. It remains a challenge to develop green techniques to prepare mechanically robust oil-water separation materials, especially those capable of separating high-viscosity crude oils. Herein, we propose an environmentally friendly emulsion spray-coating method to fabricate durable foam composites with asymmetric wettability for oil-water separation. After the emulsion, composed of acidified carbon nanotubes (ACNTs), polydimethylsiloxane (PDMS) and its curing agent, is sprayed onto melamine foam (MF), water in the emulsion is first evaporated, while PDMS and ACNTs are finally deposited on the foam skeleton. The foam composite exhibits gradient wettability and turns from superhydrophobicity of the top surface (the water contact angle reaches as high as 155.2°) to hydrophilicity of the interior region. The foam composite can be used for the separation of oils with different densities and has a 97% separation efficiency for chloroform. In particular, the photothermal conversion-induced temperature rise can reduce the oil viscosity and complete the high-efficiency cleanup of crude oil. This emulsion spray-coating technique and asymmetric wettability show promise for the green and low-cost fabrication of high-performance oil/water separation materials.

Ambulance offload delays (AODs) typically occur when emergency medical service (EMS) personnel cannot immediately transfer a patient to a busy hospital’s emergency department. AOD affects both the patient and EMS system as timely patient care cannot be provided, and the ambulance cannot service new calls. In this study, we focus on determining a real-time multi-priority patient transfer policy to reduce delays in patient treatment. We formulated the patient transfer problem with AOD as a Markov decision process based on post-decision states, and developed an approximate dynamic programming approach with domain knowledge to overcome the curse of dimensionality. We obtained a lower bound on the optimal solution by relaxing the future information and imposing a dual feasible penalty. The results of numerical experiments revealed that the transfer policy obtained from our solution method outperformed the benchmark static policy by 38.93%(±6.77%). We applied the proposed method to one year of historical data from St. Paul, Minnesota. The results revealed a potential reduction in the total weighted time by more than 40%. We found that the expected remaining service time and queue length at each hospital have the most significant impact on the system performance. The proposed approach makes an attempt to mitigate delays in patient care with multiple priority levels and increase the efficiency of the overall system with minimal to no cost.

Polymeric organohydrogels as emerging soft materials have attracted tremendous interests because of their promising applications in wearable electronics, soft robotics and so on. However, it remains great challenges for the organohydrogels to achieve simultaneously high strength, toughness, stretchability, transparence, biocom-patibility and temperature tolerance. Here we report a facile one-step mixed solvent-exchange strategy to prepare high performance organohydrogels (or organogels) by regulating macromolecular noncovalent interaction. The obtained poly(vinyl alcohol) gels have highly dense and homogenous structures comprising abundant hydrogen bonds, entanglements and crystalline domains, originating from the strong interaction of polymer-polymer and polymer-solvent-polymer. Theses gels exhibit excellent mechanical properties with a strength (up to 8.39 +/- 0.51 MPa), fracture strain (up to 1127 +/- 123%), toughness (52.63 +/- 1.98 MJ/m(3)) and fracture energy (up to 16.34 +/- 5.6 kJ m(-2)), anti-freezing (-65 degrees C), long-term tolerance (95% of retention coefficient for 14 d), thermal stability (110 degrees C), transparence (over 90%) and biocompatibility.

Three dimensional composite hydrogels have been good candidates for solar driven interfacial evapora-tion, and it has remained a great challenge to develop high performance hydrogel evaporators with excel-lent mechanical properties, high evaporation efficiency and rate, and outstanding durability. Here, we prepare a polyvinyl alcohol (PVA)/acidified carbon nanotubes (ACNTs) composite hydrogel for solar -powered desalination. ACNTs are uniformly dispersed in the hydrogel and forms hydrogel bonding with PVA macromolecules, greatly improving the mechanical properties of the polymer hydrogel. With excel-lent light absorption, heat localization and water transport capabilities, the composite hydrogel evapora-tor possesses a high evaporation rate (up to 3.85 kg.m(-2).h(-1)) and a photothermal conversion efficiency of 87.6%. This hydrogel evaporator can work in a high concentration brine and during cyclic evaporation, exhibiting outstanding salt rejection performance and long-term durability. The high performance ACNTs filled hydrogel shows promising applications in solar desalination. (c) 2022 Elsevier Ltd. All rights reserved.

Here, authors report on composition of a stretchable, mechanically durable and superhydrophilic polyaniline (PANI)/halloysite nanotubes (HNTs) decorated PU nanofiber (PANI/HNTs@PU). The polymer nanofibers are placed as the core and PANI/HNTs makes the shell section. The PANI/HNTs creates a membrane with outstanding light absorption and photothermal conversion performance. The strong solar absorption capability and superhydrophilicity of the PANI/HNTs@PU remain almost unchanged during stretching, abrasion, and ultrasonic washing tests, exhibiting superior surface stability and durability. When the PANI/HNTs@PU is used for the interfacial evaporation, the evaporation rate and efficiency reach as high as 1.61 kg m − 2 h − 1 and 94.7%, respectively. No salt precipitation is observed on the solar absorber surface even under a high salinity or during the long term or cyclic evaporation test. Furthermore, the excellent interfacial evaporation function is maintained when the nanofiber composite is mechanically stretched. The PANI/HNTs@PU based evaporation device shows promising applications in high performance solar desalination.

We model the facility location problem in an emergency service system as an optimization problem in which the objective is to minimize the system-wide mean response time, which requires exponential complexity to solve. We show that this problem is NP-hard and develop lower and upper bounds for the optimal solution from a special case of the classical p-median problem. We propose a Bayesian optimization solution to this problem that includes searching within feasible trust regions and adaptive swapping strategies. We show that our algorithm always converges to a globally optimal solution with a regret bound guarantee. Our algorithm consistently outperforms the p-median solution in numerical experiments and quickly converges to the optimal solution. We also apply our method to solve the optimal ambulance location problem in St. Paul, Minnesota, using one year of real data. We show that our method converges to the optimal solution very quickly. Our method can be applied to solve the optimal unit locations in the emergency service systems of the largest cities.

Metal-organic frameworks (MOFs) are one of the most advanced crystal materials assembled by organic ligands as linkers and metal ions as center ions, which can be used as excellent materials for batteries and supercapacitors due to their high adjustable pore sizes, controllable structures, and specific surface areas. Carbon-based functional materials (e.g., graphene, reduced graphene oxide and carbon nanotubes) have excellent electrochemical properties, thermal stability and electrical conductivity. The controllable integration of MOFs and carbon-based functional materials can further enhance the electrochemical stability and electrical conductivity of pristine MOFs. The assembled MOF/carbon-based functional materials composites possess superior rate, cycling properties and high reversibility, enabling the obtained MOF/carbon-based functional materials composites to be applied in wider fields. This review summarizes the recent research progress of MOF/carbon-based functional materials composites in the field of batteries and supercapacitors. In addition, the difficulties and challenges encountered by MOF/carbon-based functional materials composites are put forward. Finally, the future development of MOF/carbon-based functional material composites in electrochemical energy storage devices is prospected.

Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.

Ambulance offload delay (AOD) occurs when emergency medical services (EMS) transferring a patient to a busy hospital emergency department (ED). It is a crucial bottleneck for patient cares on both patient and ambulance sides that delays the transfer of a patient from an ambulance to the emergency department, which will negatively influence the care quality and efficiency. This paper formulates the AOD problem as a Markov decision process (MDP) and develops an approximate dynamic programming (ADP) approach to overcome the curse of dimensionality. We formulate the problem based on the post-decision states and derive two approximate dynamic programming solution algorithms using linear regression and neural network frameworks. The numerical result shows that the transfer policy obtained from our solution approach outperforms the myopic policy, which always transfers patients to the closest hospital. Our findings suggest that our approach can effectively alleviate the AOD problem, improve health care quality, and improve the overall system efficiency.

The optimization of resource is crucial for the operation of public cloud systems such as Microsoft Azure, as well as servers dedicated to the workloads of large customers such as Microsoft 365. Those optimization tasks often need to take unknown parameters into consideration and can be formulated as Prediction+Optimization problems. This paper proposes a new Prediction+Optimization method named Correlation-Aware Heuristic Search (CAHS) that is capable of accounting for the uncertainty in unknown parameters and delivering effective solutions to difficult optimization problems. We apply this method to solving the predictive virtual machine (VM) provisioning (PreVMP) problem, where the VM provisioning plans are optimized based on the predicted demands of different VM types, to ensure rapid provisions upon customers' requests and to pursue high resource utilization. Unlike the current state-of-the-art PreVMP approaches that assume independence among the demands for different VM types, CAHS incorporates demand correlation when conducting prediction and optimization in a novel and effective way. Our experiments on two public benchmarks and one industrial benchmark demonstrate that CAHS can achieve better performance than its nine state-of-the-art competitors. CAHS has been successfully deployed in Microsoft Azure and significantly improved its performance. The main ideas of CAHS have also been leveraged to improve the efficiency and the reliability of the cloud services provided by Microsoft 365.