Two-phase Strategy Research Articles

Two-Phase Transition Induced Amorphous Metal Phosphides Enabling Rapid, Reversible Alkali-Metal Ion Storage.

Metal phosphides as anode materials for alkali-metal ion batteries have captured considerable interest due to their high theoretical capacities and electronic conductivity. However, they suffer from huge volume expansion and element segregation during repetitive insertion/extraction of guest ions, leading to structure deterioration and rapid capacity decay. Herein, an amorphous Sn0.5Ge0.5P3 was constructed through a two-phase intermediate strategy based on the elemental composition modulation from two crystalline counterparts and applied in alkali-metal ion batteries. Differing from crystalline P-based compounds, the amorphous structure of Sn0.5Ge0.5P3 effectively reduces the volume variation from above 300% to 225% during cycling. The ordered distribution of cations and anions in the short-range ensures the uniform distribution of each element during cycles and thus contributes to durable cycling stability. Moreover, the long-range disordered structure of amorphous material shortens the ion transport distance, which facilitates diffusion kinetics. Benefiting from the aforementioned effects, the amorphous Sn0.5Ge0.5P3 delivers a high Na storage capacity of 1132 mAh g-1 at 0.1 A g-1 over 100 cycles. Even at high current densities of 2 and 10 A g-1, its capacities still reach 666 and 321 mAh g-1, respectively. As an anode for Li storage, the Sn0.5Ge0.5P3 similarly also exhibits better cycling stability and rate performance compared to its crystalline counterparts. Significantly, the two-phase transition strategy is generally applicable to achieving other amorphous metal phosphides such as GeP2. This work would be helpful for constructing high-performance amorphous anode materials for alkali-metal ion batteries.

Synthesis of a Biomimetic Tetracyclic Precursor of Aspochalasins and Formal Synthesis of Trichoderone A.

Aspochalasins are leucine-derived cytochalasins. Their complexity is associated with a high degree of biosynthetic oxidation, herein inspiring a two-phase strategy in total synthesis. We thus describe the synthesis of a putative biomimetic tetracyclic intermediate. The constructive steps are an intramolecular Diels-Alder reaction to install the isoindolone core of cytochalasins, whose branched precursor was obtained from a stereoselective Ireland-Claisen rearrangement performed from a highly unsaturated substrate. This also constitutes a formal synthesis of trichoderone A.

REAL-TIME MODEL PREDICTIVE CONTROL OF BATTERY ENERGY STORAGE ACTIVE AND REACTIVE POWER TO SUPPORT THE DISTRIBUTION NETWORK OPERATION

This paper proposes a model predictive control technique to optimally dispatch of battery energy storage systems (BESS) installed on the medium voltage distribution network to manage the violations in addition to enhancing the power quality and stability. A two-phase strategy is developed to manage the BESS inverter power on the four active/reactive power quadrants. A multiobjective function is formulated in order to optimize the system voltage, power factor and line losses. The uncertainties associated with demand and generation forecasting are considered in the proposed strategy by introducing a real-time operational phase. The network, BESS, and inverter technical constraints are considered, and the proposed strategy is validated by simulating different scenarios on an 11 kV, 53-node distribution network located in Northern Ireland.

Total Synthesis of Stemarene and Betaerene Diterpenoids: Divergent Ring-Formation Strategy and Late-Stage C–H Functionalization

A unified protecting group-free approach to two stemarene and two betaerene diterpenoids through a bioinspired two-phase strategy has been developed, and three of them were obtained for the first t...

A TWO-PHASE MODELING METHOD BASED ON COHESIVE ELEMENTS FOR MASONRY ARCHES

To simulate the non-homogeneity of masonry materials, a two-phase numerical modeling strategy based on Cohesive elements is proposed. In the proposed method, masonry is considered as a two-phase material, which consists of stone blocks and mortar joints. Solid elements are used to simulate the blocks. A nonlinear constitutive model is introduced to describe their failure behavior. Cohesive elements are inserted between adjacent blocks to consider the shear and tensile failure of the mortar. The effectiveness and applicability of the method are verified by comparison of experimental tests and numerical simulation. The influence of sensitive parameters, such as the shear friction coefficient μ and loading position, on the strength of the arch bridge is analyzed by numerical simulation. The results show that the two-phase numerical model of stone masonry based on Cohesive elements avoided the difficulty in continuous numerical methods in simulating the non-homogeneity of masonry materials, and provided important information for the ultimate strength evaluation of stone arch bridges, such as the load displacement curves and failure modes. In addition, the failure mechanism of the main arch was determined by the bending and shear characteristics of the arches, especially the shear fiction coefficient of the mortar joints.

A hybrid differential evolution based on gaining‑sharing knowledge algorithm and harris hawks optimization

Differential evolution (DE) is favored by scholars for its simplicity and efficiency, but its ability to balance exploration and exploitation needs to be enhanced. In this paper, a hybrid differential evolution with gaining-sharing knowledge algorithm (GSK) and harris hawks optimization (HHO) is proposed, abbreviated as DEGH. Its main contribution lies are as follows. First, a hybrid mutation operator is constructed in DEGH, in which the two-phase strategy of GSK, the classical mutation operator “rand/1” of DE and the soft besiege rule of HHO are used and improved, forming a double-insurance mechanism for the balance between exploration and exploitation. Second, a novel crossover probability self-adaption strategy is proposed to strengthen the internal relation among mutation, crossover and selection of DE. On this basis, the crossover probability and scaling factor jointly affect the evolution of each individual, thus making the proposed algorithm can better adapt to various optimization problems. In addition, DEGH is compared with eight state-of-the-art DE algorithms on 32 benchmark functions. Experimental results show that the proposed DEGH algorithm is significantly superior to the compared algorithms.

The role of transcriptomic biomarkers of endometrial receptivity in personalized embryo transfer for patients with repeated implantation failure

BackgroundWindow of implantation (WOI) displacement is one of the endometrial origins of embryo implantation failure, especially repeated implantation failure (RIF). An accurate prediction tool for endometrial receptivity (ER) is extraordinarily needed to precisely guide successful embryo implantation. We aimed to establish an RNA-Seq-based endometrial receptivity test (rsERT) tool using transcriptomic biomarkers and to evaluate the benefit of personalized embryo transfer (pET) guided by this tool in patients with RIF.MethodsThis was a two-phase strategy comprising tool establishment with retrospective data and benefit evaluation with a prospective, nonrandomized controlled trial. In the first phase, rsERT was established by sequencing and analyzing the RNA of endometrial tissues from 50 IVF patients with normal WOI timing. In the second phase, 142 patients with RIF were recruited and grouped by patient self-selection (experimental group, n = 56; control group, n = 86). pET guided by rsERT was performed in the experimental group and conventional ET in the control group.ResultsThe rsERT, comprising 175 biomarker genes, showed an average accuracy of 98.4% by using tenfold cross-validation. The intrauterine pregnancy rate (IPR) of the experimental group (50.0%) was significantly improved compared to that (23.7%) of the control group (RR, 2.107; 95% CI 1.159 to 3.830; P = 0.017) when transferring day-3 embryos. Although not significantly different, the IPR of the experimental group (63.6%) was still 20 percentage points higher than that (40.7%) of the control group (RR, 1.562; 95% CI 0.898 to 2.718; P = 0.111) when transferring blastocysts.ConclusionsThe rsERT was developed to accurately predict the WOI period and significantly improve the pregnancy outcomes of patients with RIF, indicating the clinical potential of rsERT-guided pET.Trial registration Chinese Clinical Trial Registry: ChiCTR-DDD-17013375. Registered 14 November 2017, http://www.chictr.org.cn/index.aspx

Grouped Collaborative Representation for Hyperspectral Image Classification Using a Two-Phase Strategy

This letter proposes a two-phase strategy-based grouped collaborative representation classifier (CRC) for hyperspectral image classification. Specifically, a spectral correlation-based CRC (SCCR) is proposed in the first phase, which considers a regularization term that uses the spectral correlations between the test sample and training samples. The representation coefficient vector generated by SCCR is then transformed into class-specific group weights. In the second phase, we integrate group weights and spectral correlations into the CRC and propose a grouped CRC (GCRC). Experimental results obtained from three real hyperspectral data sets demonstrate that the proposed SCCR and GCRC can provide better classification performance over other state-of-the-art representation-based classifiers.

Distributed numerical and machine learning computations via two-phase execution of aggregated join trees

When numerical and machine learning (ML) computations are expressed relationally, classical query execution strategies (hash-based joins and aggregations) can do a poor job distributing the computation. In this paper, we propose a two-phase execution strategy for numerical computations that are expressed relationally, as aggregated join trees (that is, expressed as a series of relational joins followed by an aggregation). In a pilot run, lineage information is collected; this lineage is used to optimally plan the computation at the level of individual records. Then, the computation is actually executed. We show experimentally that a relational system making use of this two-phase strategy can be an excellent platform for distributed ML computations.

A two-phase virtual machine placement policy for data-intensive applications in cloud

Cloud computing and big data are two technologies whose combination can yield many benefits and challenges. One of the most significant challenges is the traffic produced by data-intensive jobs within a data center. A possible way to manage the produced traffic is optimizing the placement of virtual machines (VM) on hosts. However, placing VMs compactly to reduce the communication cost can negatively impact on other aspects such as host utilization and load balancing. In this paper, we aim to make a balance between optimizing the host utilization and the communication cost while considering load balancing. We investigate the VM placement problem by modeling it as the minimum weight K-vertex-connected induced subgraph. We prove the NP-Hardness of the problem and propose a novel two-phase strategy for placing VMs on hosts. At first phase, in order to balance traffic and workload among racks, we rank all racks using a fuzzy inference system and select the best ones based on a linear programming model. At second phase, we introduce a novel greedy algorithm to assign each VM to a host regarding a proposed communication cost metric. We evaluate our approach using CloudSim simulator whose results show our two-phase strategy is able to make a balance between host utilization and network traffic. It keeps more than 80 percent of the traffic rack local while reduces the average network link saturation to almost 40 percent with a low variance. Besides, the number of used hosts increase linearly by increasing the number of VMs which leads to a higher host utilization.

A combination sampling approach for epidemiologic research in humanitarian settings: a case analysis of a study of depressive disorder prevalence among refugees in Greece

BackgroundUnderstanding the burden of common mental health disorders, such as depressive disorder, is the first step in strengthening prevention and treatment in humanitarian emergencies. However, simple random sampling methods may lead to a high risk of coercion in settings characterized by a lack of distinction between researchers and aid organizations, mistrust, privacy concerns, and the overarching power differential between researchers and populations affected by crises. This case analysis describes a sampling approach developed for a survey study of depressive disorder in a Syrian refugee camp in Greece (n = 135).DiscussionSyrian refugees face an extraordinarily high burden of depressive disorder during the asylum process (43%), necessitating population screening, prevention, and treatment. In order to preserve the informed consent process in this refugee camp setting, the research team developed a two-phase sampling strategy using a map depicting the geographical layout of the housing units within the camp. In the first phase, camp management announced a research study was being undertaken and individuals were invited to volunteer to participate. The participants’ container (housing) numbers were recorded on the map, but were not linked to the survey data. Then, in the second phase, the camp map was used for complementary sampling to reach a sample sufficient for statistical analysis. As a result of the two phases of the sampling exercise, all eligible adults from half the containers in each block were recruited, producing a systematic, age- and sex-representative sample.ConclusionsCombining sampling procedures in humanitarian emergencies can reduce the risk of coerced consent and bias by allowing participants to approach researchers in the first phase, with a second phase of sampling conducted to recruit a systematic sample. This case analysis illuminates the feasibility of a two-phase sampling approach for drawing a quasi-random, representative sample in a refugee camp setting.

Programming framework and infrastructure for self-adaptation and optimized evolution method for microservice systems in cloud–edge environments

Edge computing technologies facilitate the deployment of services on nearby edge servers with a large number of end users and their mobile devices to fulfill personalized demands. Owing to frequent changes in user mobility and demands, service systems deployed in an edge–cloud environment must continuously adapt to ensure that the quality of service (QoS) perceived by the end users is maintained at a stable and satisfactory level. As it is difficult for system operation engineers to manually deal with such frequent and large-scale evolution due to problems of cost and efficiency, self-adaptation of the system is essential. In this paper, we present a programming framework for microservices (EPF4M) and an infrastructure for self-adaptive microservice systems (EI4MS) for the cloud–edge environment based on microservice architecture. Our study follows a “monitoring–analyzing–planning–execution” control loop that empowers the service systems to redeploy the services according to changes in the QoS. A two-phase strategy is adopted to minimize the side effects of the loop on the performance of the service system. A prototype of this framework and infrastructure has been open-sourced and verified through experiments conducted in a real cloud–edge environment. The results demonstrate the usefulness and advantages of our approach.

Identification of Novel Serological Autoantibodies in Takayasu Arteritis Patients Using HuProt Arrays

AbstractTo identify novel autoantibodies of Takayasu arteritis (TAK) using HuProt array-based approach, a two-phase approach was adopted. In Phase I, serum samples collected from 40 TAK patients, 15 autoimmune disease patients, and 20 healthy subjects were screened to identify TAK-specific autoantibodies using human protein (HuProt) arrays. In phase II, the identified candidate autoantibodies were validated with TAK-focused arrays using an additional cohort comprised of 109 TAK patients, 110 autoimmune disease patients, and 96 healthy subjects. Subsequently, the TAK-specific autoantibodies validated in phase II were further confirmed using western blot analysis. We identified and validated eight autoantibodies as potential TAK-specific diagnostic biomarkers, including anti-SPATA7, -QDPR, -SLC25A2, -PRH2, -DIXDC1, -IL17RB, -ZFAND4, and -NOLC1 antibodies, with AUC of 0.803, 0.801, 0.780, 0.696, 0.695, 0.678, 0.635, and 0.613, respectively. SPATA7 could distinguish TAK from healthy and disease controls with 73.4% sensitivity at 85.4% specificity, while QDPR showed 71.6% sensitivity at 86.4% specificity. SLC25A22 showed the highest sensitivity of 80.7%, but at lower specificity of 67.0%. In addition, PRH2, IL17RB, and NOLC1 showed good specificities of 88.3%, 85.9%, and 86.9%, respectively, but at lower sensitivities (<50%). Finally, DIXDC1 and ZFAND4 showed moderate performance as compared with the other autoantibodies. Using a decision tree model, we could reach a specificity of 94.2% with AUC of 0.843, a significantly improved performance as compared with that by each individual biomarker. The performances of three autoantibodies, namely anti-SPATA7, -QDPR, and -PRH2, were successfully confirmed with western blot analysis. Using this two-phase strategy, we identified and validated eight novel autoantibodies as TAK–specific biomarker candidates, three of which could be readily adopted in a clinical setting.

Optimisation approach to solve the truck loading and delivery problem at long haul distances with heterogeneous products and fleet

This paper proposes a sequential optimisation approach for addressing a complex real world problem of dispatch planning and freight loading for a set of highly irregular products with a heterogeneous fleet of trucks. The approach focuses on the case of goods with 'low-density values', highly varied with large travel distances. The proposed approach is based on a two-phase strategy: the first optimises the space assignment process inside trucks to each type of product. It is achieved by minimising long-haul transportation costs as a function of the fleet size and capacity, considering a set of predefined feasible and efficient loading solutions or patterns. The second phase minimises the number of visits per truck, assuming a fleet with fixed size and capacities for each type of product, which is determined in the first stage. The approach was successfully applied to a rolled steel company in Colombia, whose results show that the proposed model efficiently addresses the analysed problem, which is reflected in reasonable solution times and costs from a practical implementation perspective.

Two-Phase Bidirectional Dual-Relay Selection Strategy for Wireless Relay Networks

In this article, we introduce a new bi-directional dual-relay selection strategy with its bit error rate (BER) performance analysis. During the first step of the proposed strategy, two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER, based on the suggested algorithm which checks if the selected relays using the max-min criterion are the best ones. In the second step, the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals, leading to a significant improvement in the achievable coding and diversity gain. To further improve the overall system performance, the selected relay-nodes apply also a digital network coding scheme. Furthermore, this paper discusses the analytical approximation of the BER performance of the proposed strategy, where we prove that the analytical results match almost perfectly the simulated ones. Finally, our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.

Image defogging algorithm using a two-phase feature extraction strategy

Image defogging algorithm using a two-phase feature extraction strategy

Promising Aging in Community Models in the U.S.: Village, Naturally Occurring Retirement Community (NORC), Cohousing, and University-Based Retirement Community (UBRC).

Aging in Community (AIC) is the preferred way to age. This systematic review identified promising AIC models in the U.S. and analyzed model characteristics and push-pull factors from older adults’ perspectives. Push factors are those driving older adults to leave, while pull factors attract them to stay in a community. We conducted a two-phase search strategy using eight databases. Phase I identified promising AIC models and Phase II expanded each specific model identified. Fifty-two of 244 screened articles met the criteria and were analyzed. We identified four promising AIC models with the potential to achieve person-environment (P-E) fit, including village, naturally occurring retirement community (NORC), cohousing, and university-based retirement community (UBRC). Each has a unique way of helping older adults with their aging needs. Similar and unique push-pull factors of each AIC model were discussed. Analyses showed that pull factors were mostly program factors while push factors were often individual circumstances. Continued research is needed to address the challenges of recruiting minority older adults and those of lower socio-economic status, meeting older adults’ diverse and dynamic needs, and conducting comparative studies to share lessons learned across the globe.

Control Plane Reflection Attacks and Defenses in Software-Defined Networks

Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query, which may not suffice the huge need of dynamic control plane applications. In this paper, we systematically study the interactions between the control plane applications and the data plane switches, and present two new attacks, namely Control Plane Reflection Attacks, to exploit the limited processing capability of SDN-enabled hardware switches. The reflection attacks adopt direct and indirect data plane events to force the control plane to issue massive expensive downlink messages towards SDN switches. Moreover, we propose a two-phase probing-triggering attack strategy, which makes the reflection attacks much more efficient and powerful. Experiments on a testbed with 3 different physical OpenFlow switches demonstrate that the attacks can lead to catastrophic results such as hurting the establishment of new flows and even disruption of connection between SDN controller and switches. To mitigate such attacks, we present several countermeasures from different perspectives. In particular, we propose a novel, systematical defense framework, SwitchGuard, to detect anomalies of downlink messages and prioritize these messages based on a novel monitoring granularity, i.e., host-application pair (HAP). Implementations and evaluations demonstrate that SwitchGuard can effectively reduce the latency for legitimate hosts and applications under the control plane reflection attacks with only minor overheads.

Symbol error probability optimisation in three‐phase bidirectional amplify‐and‐forward relaying systems

This study deals with the error probability optimisation problem in three-phase bidirectional amplify and forward relaying systems. Different from the common two-phase strategy, in the three-phase counterpart, the relay power is split into two different terminals in terms of data error probability. With such a three-phase division, the authors present a unified optimisation algorithm based on the exact symbol error probability (SEP) and its upper bound expression. In addition, by virtue of the dual decomposition algorithm, the authors derive the optimal power allocation and demonstrate its uniqueness at both terminals and the relay node. Moreover, the unified optimisation algorithm can be reduced to some special cases, such as the maximum minimisation problem and unbalanced link problem. The proposed results reveal that the proposed algorithm has a significant impact on SEP reduction.

Service skill improvement for home robots: Autonomous generation of action sequence based on reinforcement learning

It still remains a challenge for robots to obtain knowledge automatically for performing home services. In the human learning process, natural languages act as an outline in guiding human beings complete tasks. From this point, a conditional generation method transforming textual manipulation instructions into action sequences is proposed, to provide home robots with knowledge automatically and improve the service skills finally. Due to the limited learning ability of the generation model on understanding complex semantic information, we present a two-phase conditional generation strategy in which the action space is reduced at the syntax level before generating action sequences semantically. For representing action sequences effectively, functional labels (FLs) are designed according to the requirements of performing home services, to identify six relationships about objects and actions. In action sequence generation, reinforcement learning is employed to guide the action sequence generation by introducing hierarchical rewards related to a priori knowledge, semantic similarity, and action logic. Based on statistic learning, a priori knowledge is constructed by modeling the relationship about object co-occurrence, action collaboration, and action–object correlation. The semantic similarity with Semantic Role Labeling enables the similarity evaluation between textual sentences (inputs) and produced sequences (outputs). And action logic, represented by the verb sequence in instructions, guides the production of action sequences logically. Experimental results demonstrate that the proposed method can produce competitive action sequences from textual instructions, and produced action sequences can be applied to robot for performing services.