Optimal Strategy Research Articles

Pain Correlates in Endodontic Procedures: A Narrative Review Examining the Causative Factors and Clinical Approaches for Management.

Endodontic treatment, encompassing the diagnosis and management of dental pulp and periapical conditions, is pivotal in preserving dental health. Root canal treatment, a common endodontic procedure, involves the removal, cleaning, and sealing of the tooth's root system to address issues like pain and infections. Patients undergoing endodontic procedures often encounter pain due to the sensitivity of dental pulp nerves to caries, inflammation, infections, or treatment manipulations. The severity of pain is variable and influenced by individual factors such as pain threshold, anxiety levels, gender, and the extent of dental infection. In conclusion, despite advancements in endodontics, pain management remains a significant concern. The narrative review emphasizes the importance of adopting comprehensive clinical management strategies encompassing the entire treatment process. A thorough assessment of the patient's medical history and dental condition is crucial to identify risk factors for increased pain. Effective communication with patients regarding the procedure and potential discomfort aids in managing expectations and reducing anxiety. Continuous professional development is highlighted as essential for dental professionals to stay abreast of the latest advancements, enabling the implementation of optimal pain management strategies and the delivery of enhanced patient care.

The Role of The Marketing Mix Factors in Hospital Selection

Background: The problems faced by Soerojo Hospital is currently decreasing the number of outpatient visits and decreasing customer satisfaction. Marketing mix analysis based on satisfaction surveys which on average are good based on hospital customer perceptions and targeted revenue targets from out-of-pocket sources of 30 percent require the right marketing mix tactics for potential customers in the comunity. This study will help Soerojo Hospital choose dominant factor of the marketing mix in hospital selection for making optimal marketing strategy. The aims of this study was to identify the role of the marketing mix factors on people's decisions in hospital selection. Materials and Methods: This study was conducted with a descriptive, quantitative approach, cross sectional research design, 385 respondents were surveyed with questionnaires and data analysis was carried out with program packages. Results: The components of the marketing mix that occupy the top three positions are the location component with a median of 4.67(1-5), the process component with a median of 4.60 (1-5) and the cost component with a median of 4.50 (1-5). There was a significant difference between the groups who choose and did not choose Soerojo mental hospital in the doctor/staff and promotion (p value <0.05). Conclusion: The location, process and cost components are the components of the marketing mix that most influence the decision to choose a hospital. Suggestions for improvements that can be made by RSJ Prof. Dr. Soerojo Magelang based on the results of this study.

The effect of contextual factors on a health intervention against podoconiosis in Ethiopia

IntroductionUnderstanding contextual factors that influence the implementation and outcome of interventions is crucial to improving them. Outcome and process evaluation studies have to date focused on the successes and limitations of interventions with little consideration of external factors that could either facilitate or impede them. Guided by the Context and Implementation of Complex Intervention (CICI) framework, we evaluated the effect of contextual factors on a health intervention implemented against the neglected tropical disease (NTD) podoconiosis in Ethiopia.MethodWe purposefully selected a health intervention jointly implemented by two NGOs in podoconiosis-endemic districts in Northwestern Ethiopia. We employed an exploratory mixed methods approach, and data were collected between April and July 2022. We first conducted document analysis, observations, 4 focus group discussions, 32 in-depth interviews and 19 key informant interviews. Subsequently, we administered a survey to 369 rural residents, of whom 42 were affected by podoconiosis. The qualitative data were managed with NVivo version 12 software. The quantitative data were analyzed using Stata version 15 software.ResultsMost project activities were implemented as per the initial plan and the intervention reached patients that never had access to treatment services before. Contextual factors (geographical, epidemiological, socio-economic, socio-cultural, ethical, legal and political) and lived experience of patients influenced the implementation and outcome of the intervention. Most of the contextual factors pose constraints on implementation and affected the acceptability and sustainability of project activities. On the other hand, political context such the commitment of the government to rollout national programs and set up NTD structures at district level positively influenced the implementation of the intervention.ConclusionHealth intervention implementors should be aware of the interactive effects of contextual factors wherein interventions are put into practice. Minimizing the constraining effects of these contextual factors while utilizing opportunities presented by them might help implement optimal strategies to improve the lives of individuals affected by podoconiosis and similar NTDs.

Optimal Consumption and Investment with Independent Stochastic Labor Income

We develop a new dynamic continuous-time model of optimal consumption and investment to include independent stochastic labor income. We reduce the problem of solving the Bellman equation to a problem of solving an integral equation. We then explicitly characterize the optimal consumption and investment strategy as a function of income-to-wealth ratio. We provide some analytical comparative statics associated with the value function and optimal strategies. We also develop a quite general numerical algorithm for control iteration and solve the Bellman equation as a sequence of solutions to ordinary differential equations. This numerical algorithm can be readily applied to many other optimal consumption and investment problems especially with extra nondiversifiable Brownian risks, resulting in nonlinear Bellman equations. Finally, our numerical analysis illustrates how the presence of stochastic labor income affects the optimal consumption and investment strategy. Funding: A. Bensoussan was supported by the National Science Foundation under grant [DMS-2204795]. S. Park was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea, South Korea [NRF-2022S1A3A2A02089950].

Virtual Power Plant’s Optimal Scheduling Strategy in Day-Ahead and Balancing Markets Considering Reserve Provision Model of Energy Storage System

In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RESs, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract’s requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP’s optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS’s reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP’s trading schedule are analyzed. The results show that the proposed model has practical significance.

Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory

Training in Feed Forward Deep Neural Networks is a memory-intensive operation which is usually performed on GPUs with limited memory capacities. This may force data scientists to limit the depth of the models or the resolution of the input data if data does not fit in the GPU memory. The re-materialization technique, whose idea comes from the checkpointing strategies developed in the Automatic Differentiation literature, allows data scientists to limit the memory requirements related to the storage of intermediate data (activations), at the cost of an increase in the computational cost. This paper introduces a new strategy of re-materialization of activations that significantly reduces memory usage. It consists in selecting which activations are saved and which activations are deleted during the forward phase, and then recomputing the deleted activations when they are needed during the backward phase. We propose an original computation model that combines two types of activation savings: either only storing the layer inputs, or recording the complete history of operations that produced the outputs. This paper focuses on the fully heterogeneous case, where the computation time and the memory requirement of each layer is different. We prove that finding the optimal solution is NP-hard and that classical techniques from Automatic Differentiation literature do not apply. Moreover, the classical assumption of memory persistence of materialized activations, used to simplify the search of optimal solutions, does not hold anymore. Thus, we propose a weak memory persistence property and provide a Dynamic Program to compute the optimal sequence of computations. This algorithm is made available through the Rotor software, a PyTorch plug-in dealing with any network consisting of a sequence of layers, each of them having an arbitrarily complex structure. Through extensive experiments, we show that our implementation consistently outperforms existing re-materialization approaches for a large class of networks, image sizes and batch sizes.

An optimal cooperative relay selection strategy for delay aware image transmission in large scale multi-radio multi-channel multimedia wireless sensor network

High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%.

Imaging of the anterior cruciate ligament and anterolateral rotational instability of the knee joint

The anterior cruciate ligament (ACL) is essential for the stability of the knee joint and ACL tears are one of the most common sports injuries with ahigh incidence, especially in sports that require rotational movements and abrupt changes in direction. Injuries of the ACL are rarely isolated and are often accompanied by meniscal and other internal knee injuries, which increase the risk of osteoarthritis. The spectrum of ACL injuries includes strains, partial tears and complete tears. Magnetic resonance imaging (MRI) plays apivotal role in the diagnostics as it can accurately depict not only the ACL but also accompanying injuries. Proton density and T2-weighted sequences are particularly suitable for evaluating the ACL, which is usually well visible and assessable in all planes. In addition to depicting fiber disruption as adirect sign and central diagnostic indicator of an ACL tear, there are numerous other direct and indirect signs of an ACL injury in MRI. These include abnormal fiber orientations, signal increases and an anterior subluxation of the tibia relative to the femur. The bone marrow edema patterns often associated with ACL tears are indicative of the underlying injury mechanism. The treatment of ACL tears can be conservative or surgical depending on various factors, such as the patient's activity level and the presence of accompanying injuries. The precise and comprehensive description of ACL injuries by radiology is crucial for optimal treatment planning. Anterolateral rotational instability (ALRI) of the knee joint characterizes acondition of excessive lateral and rotational mobility of the tibia in relation to the femur in the anterolateral knee region. This instability is primarily caused by arupture of the ACL, with the anterolateral ligament (ALL) that was rediscovered about 10 years ago, also being attributed arole in stabilizing the knee. Although ALRI is primarily diagnosed through clinical examinations, MRI is indispensable for detecting injuries to the ACL, ALL, and other internal knee structures, which is essential for developing an optimal treatment strategy.

Investigation of Antibody Pharmacokinetics in the Brain Following Intra-CNS Administration and Development of PBPK Model to Characterize the Data.

Despite the promising potential of direct central nervous system (CNS) antibody administration to enhance brain exposure, there remains a significant gap in understanding the disposition of antibodies following different intra-CNS injection routes. To bridge this knowledge gap, this study quantitatively investigated the brain pharmacokinetics (PK) of antibodies following intra-CNS administration. The microdialysis samples from the striatum (ST), cerebrospinal fluid (CSF) samples through cisterna magna (CM) puncture, plasma, and brain homogenate samples were collected to characterize the pharmacokinetics (PK) profiles of a non-targeting antibody, trastuzumab, following intracerebroventricular (ICV), intracisternal (ICM), and intrastriatal (IST) administration. For a comprehensive analysis, these intra-CNS injection datasets were juxtaposed against our previously acquired intravenous (IV) injection data obtained under analogous experimental conditions. Our findings highlighted that direct CSF injections, either through ICV or ICM, resulted in ~ 5-6-fold higher interstitial fluid (ISF) drug exposure than IV administration. Additionally, the low bioavailability observed following IST administration indicates the existence of a local degradation process for antibody elimination in the brain ISF along with the ISF bulk flow. The study further refined a physiologically based pharmacokinetic (PBPK) model based on new observations by adding the perivascular compartments, oscillated CSF flow, and the nonspecific uptake and degradation of antibodies by brain parenchymal cells. The updated model can well characterize the antibody PK following systemic and intra-CNS administration. Thus, our research offers quantitative insight into antibody brain disposition pathways and paves the way for determining optimal dosing and administration strategies for antibodies targeting CNS disorders.

Fireworks algorithm with elitism-based selection strategy and optimal particle guidance mechanism

With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved Fireworks Algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions.

Comparing the Efficacy of Topical 4% Benzoyl Peroxide Versus Topical 0.1% Adapalene for Treatment of Acne Vulgaris in Skin of Color Population: A South Asian Perspective.

Introduction Acne vulgaris is one of the most common skin problems encountered in the dermatology department. It is a chronic, inflammatory disease of the pilosebaceous unit, clinically presenting with comedones, papules, pustules, nodules, and cysts. With its particularly high prevalence in the younger population, it has significant adverse sequelae on patient's quality of life. At present, due to an enhanced understanding of the pathogenesis of acne, various therapeutic modalities are available. The current management strategies generally follow a systematic treatment escalation based on disease severity and treatment response. However meticulous choice of appropriate anti-acne medicine for the acne type is the key to the management plan. Starting with mild to moderate types of acne as per the Leeds photometric grading scale, the most useful topical agents include topical retinoids, benzoyl peroxide, and topical antibiotics while systemic therapies such as oral antibiotics or isotretinoin are generally reserved for moderate to severe acne treatment. The skin of color (SOC) population is a relatively neglected group concerning the optimum and safe management strategies in different dermatological conditions and acne is no different, where there remains a need for comparing the available topical modalities for appropriate drug selection in the treatment of mild to moderate acne in SOC population. Objective The objective of this study was tocompare the efficacy of topical 4% benzoyl peroxide versus topical 0.1% adapalenein the treatment of acne vulgaris in the SOC population. Methods The participants were divided into two groups, groups A and B. A total of 64 patients of both genders, with acne vulgaris (duration > three months) were included in the study. In group A, 32 patients were administered topical 0.1% adapalene whereas, in group B, 32 patients were given topical 4% benzoyl peroxide. Both medicines were applied at night daily. Patients were called for follow-up after 12 weeks. In both groups, the final efficacy evaluation was done using the Global Acne Grading System (GAGS) scoreafter 12 weeks of treatment period. Results In group A, the age ranged from 15 to 40 years with a mean age of 25.781±3.93 years while the duration of complaint was 5.843±1.27 months. GAGS score was 25.281±2.65 and mean BMI was 23.092±3.51 kg/m2. In group B, the mean age was 25.187± 4.06 years, the duration of complaint was 7.375±2.25 months, the GAGS score was 23.906± 2.60 while the mean BMI was 21.485±3.88 kg/m2. Efficacy in group A was noted in 25 (78.1%) patients as compared to 24 (75%) patients in group B (p =0.768). Conclusion The present study showed that the safety and efficacy of 0.1% adapalenethe traditional drug 4% benzoyl peroxide in the SOC population was comparable.

Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks

Severe and fatal crashes involving large trucks result in significant social and economic losses for human society. Unfortunately, the notably low proportion of severe and fatal injury crashes involving large trucks creates an imbalance in crash data. Models trained on imbalanced crash data are likely to produce erroneous results. Therefore, there is a need to explore novel sampling approaches for imbalanced crash data, and it is crucial to determine the appropriate combination of a machine learning model, sampling approach, and ratio. This study introduces a novel cluster-based under-sampling technique, utilizing the k-prototypes clustering algorithm. After initial cluster-based under-sampling, the consolidated cluster-based under-sampled data set was further resampled using three different sampling approaches (i.e., adaptive synthetic sampling (ADASYN), NearMiss-2, and the synthetic minority oversampling technique + Tomek links (SMOTETomek)). Later, four machine learning models (logistic regression (LR), random forest (RF), gradient-boosted decision trees (GBDT), and the multi-layer perceptron (MLP) neural network) were trained and evaluated using the geometric mean (G-Mean) and area under the receiver operating characteristic curve (AUC) scores. The findings suggest that cluster-based under-sampling coupled with the investigated sampling approaches improve the performance of the machine learning models developed on crash data significantly. In addition, the GBDT model combined with ADASYN or SMOTETomek is likely to yield better predictions than any model combined with NearMiss-2. Regarding changes in sampling ratios, increasing the sampling ratio with ADASYN and SMOTETomek is likely to improve the performance of models up to a certain level, whereas with NearMiss-2, performance is likely to drop significantly beyond a specific point. These findings provide valuable insights for selecting optimal strategies for treating the class imbalance issue in crash data.

Chalk Talks for the Clinical Setting: Evaluation of a Medical Education Workshop for Fellows.

Chalk talks are effective teaching tools in the clinical setting. However, data on optimal strategies for teaching medical educators how to develop and deliver them are limited. We designed and implemented two 50-minute workshops to help subspecialty fellows across GME create and deliver a chalk talk. The first workshop comprised a demonstration of an effective chalk talk and a discussion of best practices for creating chalk talks; the second was a practice session where fellows presented their chalk talks and received feedback from faculty and peers. We evaluated pre- and postworkshop confidence in the ability to create and deliver a chalk talk and develop learning objectives. Secondary outcomes were faculty and peer evaluations of the chalk talks. Eighteen of 33 participants (54% response rate) completed both pre- and postsession surveys. Fellows reported improved confidence in their ability to create a chalk talk (22% vs. 83%, p < .001), deliver a chalk talk (17% vs. 83%, p < .001), and develop well-written learning objectives (11% vs. 83%, p < .001). After the workshop, participants were more likely to correctly identify a chalk talk that made use of an advanced organizer (67% vs. 89%, p < .05). Thirty-eight faculty and peers completed feedback evaluations of participants' chalk talks; most rated fellows' chalk talks highly in domains of content, delivery, design, learning objectives, and engagement. The incorporation of these workshop within a course on medical education can effectively develop clinical teaching skills among subspecialty fellows in GME.

The sensitivity of radiobiological models in carbon ion radiotherapy (CIRT) and its consequences on the clinical treatment plan: Differences between LEM and MKM models.

Carbon ion radiotherapy (CIRT) relies on relative biological effectiveness (RBE)-weighted dose calculations. Japanese clinics predominantly use the microdosimetric kinetic model (MKM), while European centers utilize the local effect model (LEM). Despite both models estimating RBE-distributions in tissue, their physical and mathematical assumptions differ, leading to significant disparities in RBE-weighted doses. Several European clinics adopted Japanese treatment schedules, necessitating adjustments in dose prescriptions and organ at risk (OAR) constraints. In the context of these two clinically used standards for RBE-weighted dose estimation, the objective of this study was to highlight specific scenarios for which the translations between models diverge, as shortcomings between them can influence clinical decisions. Our aim was to discuss planning strategies minimizing those discrepancies, ultimately striving for more accurate and robust treatments. Evaluations were conducted in a virtual water phantom and patient CT-geometry, optimizing LEM RBE-weighted dose first and recomputing MKM thereafter. Dose-averaged linear energy transfer (LETd) distributions were also assessed. Results demonstrate how various parameters influence LEM/MKM translation. Similar LEM-dose distributions lead to markedly different MKM-dose distributions and variations in LETd. Generally, a homogeneous LEM RBE-weighted dose aligns with lower MKM values in most of the target volume. Nevertheless, paradoxical MKM hotspots may emerge (at the end of the range), potentially influencing clinical outcomes. Therefore, translation between models requires great caution. Understanding the relationship between these two clinical standards enables combining European and Japanese based experiences. The implementation of optimal planning strategies ensures the safety and acceptability of the clinical plan for both models and therefore enhances plan robustness from the RBE-weighted dose and LETd distribution point of view. This study emphasizes the importance of optimal planning strategies and the need for comprehensive CIRT plan quality assessment tools. In situations where simultaneous LEM and MKM computation capabilities are lacking, it can provide guidance in plan design, ultimately contributing to enhanced CIRT outcomes.

Personalized decision support system for tailoring IgA nephropathy treatment strategies

BackgroundThe ongoing debate surrounding the use of immunosuppressive treatments for IgA nephropathy (IgAN) underscores the demand for personalized and effective strategies. MethodsAnalyzed data from 807 IgAN patients over 5+ years using three methods: Random Forest with molecular biomarkers, network biomarkers with graph engineering, and an auto-encoder model. All models were trained using identical demographic, clinical, and pathological data, employing an 80–20 split for training and testing purposes. ResultsIn the comprehensive assessment of IgAN prognosis, the Random Forest model, employing molecular biomarkers, demonstrated strong performance metrics (AUC = 0.83, sensitivity = 0.51, specificity = 0.96). However, traditional graph feature engineering on patient-specific networks outperformed these results with an AUC of 0.90, sensitivity of 0.64, and specificity of 0.94. The Auto-encoder model showed the best accuracy (AUC = 0.91, sensitivity = 0.46, specificity = 0.96). The findings highlighted the superior predictive capabilities of network biomarkers over molecular biomarkers for adverse renal outcome prediction in IgAN. Consequently, we integrated Auto-encoder-derived Network Biomarkers with Random Forest Models to enhance prognostic precision in diverse IgAN treatment scenarios. The prediction for the prognosis of patients receiving supportive care, glucocorticoid therapy, and immunosuppressant treatment yielded AUC values of 0.95, 0.96, and 1, respectively, indicating high specificity. Drawing from these insights, we pioneered the development of an innovative decision support model for IgAN treatment. This model demonstrated the ability to make medical decisions comparable to those by experienced nephrologists, enabling the customization of personalized disease management strategies. ConclusionOur system accurately predicted IgAN prognosis and evaluated various treatment efficacies, aiding physicians in devising optimal therapeutic strategies for patients.

Comparison of prognostic outcomes between endoscopic submucosal dissection and surgical treatment for early gastric cancer: a retrospective cohort study

Background and aimThe optimal management strategy for early gastric cancer (EGC) a topic of contention. This study aims to compare the prognostic outcomes of endoscopic submucosal dissection (ESD) and surgical treatment in patients diagnosed with EGC.MethodsIn thisretrospective cohort study, we analyzed data from539 patients diagnosed with EGC between January 2012 and December 2020 from two centers. We compared Clinicopathological features, procedure-related complications, recurrence rate, overall survival, and disease specific survival between the 262 patients who underwent ESD and the 277 patients who underwent surgical treatment. ESD procedures were conducted using a dual knife by experienced endoscopists, while surgical treatments included laparoscopic or open gastrectomy. Regular ollow-up examinations were conducted post-treatment.ResultsThe two groups exhibited comparable baseline characteristics. Multivariable Cox regression analysis identified vascular invasion as a risk factor for worse recurrence-free survival (RFS), and overall survival (OS) in patients with early gastric cancer. The ESD group experienced fewer overall postoperative complications compared to the surgical treatment group. Kaplan-Meier curves demonstrated no significant differences in recurrence rate or overall survival between the two groups.ConclusionsBoth ESD and surgical treatment emerged as safe and effective approaches for managing EGC. The choice of treatment should be tailored to individual patient factors. ESD can be considered an alternative treatment option for selected patients who are not suitable candidates for surgery. Further studies are warranted to determine the long-term outcomes of ESD and surgical treatment for EGC.

Adaptive fuzzy-evidential classification based on association rule mining

As one of the most promising classification approaches, association classification (AC) integrates data classification and association discovery techniques for generating a compact set of classification association rules. Recently, the fuzzy set and evidence theories are successively applied into AC in order to improve the classification performance in terms of accuracy and interpretability. However, from the perspectives of applicability and universality, there still exists two important issues in the current AC framework. On one hand, several key parameters, such as the number of fused rules in classification and the minimum support threshold in association discovery, are difficult to be accurately predefined in practice. On the other hand, the fixed grid-based fuzzy partition is not benefit to adapt for those datasets with large number of features. In this paper, an association rule-based adaptive fuzzy-evidential classification framework (AR-AFEC) is developed for overcoming the above limitations. To do so, an optimal rule fusion strategy and a dynamic minimum support threshold setting scheme are proposed for adaptively learning the parameters during classification and association mining respectively. In addition, an entropy-based trapezoidal fuzzy partition technique is proposed to adaptively obtain the fuzzy sets defined on each continuous feature domain. Experiments on 26 benchmark datasets and a human activity recognition application demonstrate that the proposal can achieve better accuracy than some state-of-the-art rule-based classification approaches, using less rules with more general structure.

Enhancing urban system resilience to earthquake disasters: Impact of interdependence and resource allocation

During the post-disaster recovery process of the urban system (US), it is critical to understand the interdependencies of critical infrastructure systems (CISs) and strategically allocate resources among them. However, due to the complexity of the problem and the limitations of the perspective, the existing research usually ignores the implicit impact of interdependence and resource allocation on urban resilience. To bridge this gap, this study establishes a multilayer network-based methodological framework to characterize various types of interdependencies between different CISs and integrate the US as a complex “system of systems”. Then, the system functionality of the US under different resource allocation strategies is quantified and optimized by resilience metrics. This proposed framework was demonstrated in a virtual US including a transportation subsystem (TS), an electric power supply subsystem (EPSS), and a community subsystem (CS) under catastrophic earthquakes. The sensitivity of urban resilience to interdependencies is investigated, and the corresponding results reveal that urban resilience is most sensitive to the interdependence between TS and EPSS. In particular, when there exists strong interdependence between the TS and EPSS, the optimal resource allocation strategy to maximize urban resilience is assigning resource allocation coefficients of 0.1, 0.8, and 0.1 for the TS, EPSS, and CS, respectively. These results can be effectively applied in future planning and investment in urban resilience.

A vertebrobasilar junction aneurysm successfully treated with a combination of surgical clipping and flow diverter placement based on the results of computational fluid dynamics analysis: illustrative case.

The treatment of vertebrobasilar junction (VBJ) aneurysms is challenging. Although flow diverters (FDs) are a possible treatment option, geometrical conditions hinder intervention. VBJ aneurysms possess dual inflow vessels from the bilateral vertebral arteries (VAs), one of which is ideally occluded prior to FD treatment. However, it remains unclear which VA should be occluded. A 75-year-old male with a growing VBJ complex aneurysm exhibiting invagination toward the brainstem and causing perifocal edema required intervention. Preoperative computational fluid dynamics (CFD) analysis demonstrated that left VA occlusion would result in more stagnant flow and less impingement of flow than right VA occlusion. According to the simulated strategy, surgical clipping of the left VA just proximal to the aneurysm was performed, followed by FD placement from the basilar artery trunk to the right VA. The patient demonstrated tolerance of the VA occlusion, and follow-up computed tomography angiography at 18 months after FD treatment confirmed the disappearance of the aneurysm. Preoperative flow dynamics simulations using CFD analysis can reveal an optimal treatment strategy involving a hybrid surgery that combines FD placement and direct surgical occlusion for a VBJ complex aneurysm.

Clinical efficacy of clopidogrel and ticagrelor in patients undergoing off-pump coronary artery bypass grafting: a retrospective cohort study.

Ticagrelor is reportedly more effective than clopidogrel in preventing atherothrombotic events in patients with percutaneous coronary intervention. However, the optimal antiplatelet therapy strategy after off-pump coronary artery bypass grafting (OPCABG) is yet to be established. This study was performed using the prospectively-maintained database at our institution. Patients who underwent OPCABG were divided into the clopidogrel and the ticagrelor groups. Propensity score matching analysis was performed between the two groups. The clinical outcome was the occurrence of major adverse cardiovascular event (MACE), defined as a composite of vascular death, myocardial infarction, or stroke 1 year after surgery. In total, 545 patients completed the entire follow-up assessment. After propensity score matching, 232 patients each were included in the clopidogrel and ticagrelor groups. The primary outcome occurred in 7.8% and 4.3% of patients in the clopidogrel and ticagrelor groups, respectively (P=0.113). CYP2C19 variants (*2, *3, and *17) did not impact the clinical outcomes, regardless of the use of clopidogrel or ticagrelor. The rates of MACE were significantly lower in patients carrying the ABCB1 C3435T CT/TT genotypes in the ticagrelor group than in those carrying the ABCB1 C3435T CC genotype in the clopidogrel group (1.4% vs. 9.1%, adjusted P=0.030), as well as those carrying the ABCB1 C3435T CC genotype in the ticagrelor group (1.4% vs. 8.9%, adjusted P=0.036). The ABCB1 C3435T CC genotype was significantly associated with the incidence of 1-year MACE (HR=1.558, 95% CI 1.109-2.188, P=0.011). Patients who experienced severe perioperative bleeding exhibited a significantly higher incidence of MACE than those who did not experience severe perioperative bleeding (14.0% vs. 4.9%, adjusted P=0.007). There was no significant difference in the 1-year MACE between patients receiving clopidogrel and those receiving ticagrelor after OPCABG. Notably, The ABCB1 C3435T CC genotype was related to a higher risk of MACE.