Set Of Problems Research Articles

Triple-drug therapy with ivermectin, diethylcarbamazine and albendazole for the acceleration of lymphatic filariasis elimination in Kenya: Programmatic implementation and results of the first impact assessment.

The World Health Organization (WHO) endorsed the use of triple-drug mass drug administration (MDA) regimen with ivermectin, diethylcarbamazine (DEC) and albendazole (commonly abbreviated as IDA) to accelerate the elimination of lymphatic filariasis (LF) as a public health problem in settings where onchocerciasis is not co-endemic. The National Programme for Elimination of LF (NPELF) in Kenya was among the first adopters of the IDA-MDA and two annual rounds were provided in 2018 and 2019 to the residents of Lamu County and Jomvu sub-County in the coast region. This study documented the feasibility of successfully delivering the two rounds of IDA-MDA. An operational research study was undertaken to determine efficient sampling strategies, indicators, and the appropriate population groups that could be used for the monitoring and evaluation of LF programs using IDA-MDA for the elimination of the disease as a public health problem. Two cross-sectional surveys were conducted at baseline in 2018 before IDA-MDA and an impact assessment 17 months after the second round of IDA-MDA. The reported epidemiological treatment coverage was at least 80% in all implementation units during each round of IDA-MDA. Blood samples were tested for filarial antigenemia using commercial Filariasis Test Strips (FTS) and any individual found to be positive was tested again at night for the presence of microfilariae in finger prick blood smears using microscopy. The overall prevalence of circulating filarial antigen (CFA) was relatively low at the baseline survey with Jomvu having 1.39% (95% CI: 0.91, 2.11) and Lamu having 0.48% (95% CI: 0.21, 1.13). Significant reduction in CFA prevalence was observed during the impact assessment after two annual rounds of treatment. The overall relative reduction (%) in CFA prevalence following the two rounds of MDA with IDA was significant in both Jomvu (52.45%, Z = -2.46, P < 0.02) and Lamu (52.71%, Z = -1.97, P < 0.05). Heterogeneity, however, was observed in the CFA prevalence reduction between random and purposive clusters, as well as between adult and child populations. The results of the impact assessment survey offered strong evidence that it was safe to stop the IDA-MDA in the two EUs because transmission appears to have been interrupted. It is also important to implement a post-treatment surveillance system which would enable efficient detection of any recrudescence of LF transmission at a sub-evaluation unit level. Our findings show that IDA-MDA may be considered for acceleration of LF elimination in other settings where onchocerciasis is not co-endemic.

Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation. To generate synthetic patient-nurse communication and to automatically annotate for common health problems encountered in home healthcare settings using GPT-4. We also examined whether augmenting real-world patient-nurse communication with synthetic data can improve the performance of machine learning to identify health problems. Secondary data analysis of patient-nurse verbal communication data in home healthcare settings. The data were collected from one of the largest home healthcare organizations in the United States. We used 23 audio recordings of patient-nurse communications from 15 patients. The audio recordings were transcribed verbatim and manually annotated for health problems (e.g., circulation, skin, pain) indicated in the Omaha System Classification scheme. Synthetic data of patient-nurse communication were generated using the in-context learning prompting method, enhanced by chain-of-thought prompting to improve the automatic annotation performance. Machine learning classifiers were applied to three training datasets: real-world communication, synthetic communication, and real-world communication augmented by synthetic communication. Average F1 scores improved from 0.62 to 0.63 after training data were augmented with synthetic communication. The largest increase was observed using the XGBoost classifier where F1 scores improved from 0.61 to 0.64 (about 5% improvement). When trained solely on either real-world communication or synthetic communication, the classifiers showed comparable F1 scores of 0.62-0.61, respectively. Integrating synthetic data improves machine learning classifiers' ability to identify health problems in home healthcare, with performance comparable to training on real-world data alone, highlighting the potential of synthetic data in healthcare analytics. This study demonstrates the clinical relevance of leveraging synthetic patient-nurse communication data to enhance machine learning classifier performances to identify health problems in home healthcare settings, which will contribute to more accurate and efficient problem identification and detection of home healthcare patients with complex health conditions.

On the Local Fourier Uniformity Problem for Small Sets

On the Local Fourier Uniformity Problem for Small Sets

Resistance to Targeted Inhibitors of the PI3K/AKT/mTOR Pathway in Advanced Oestrogen-Receptor-Positive Breast Cancer.

The PI3K/AKT/mTOR signalling pathway is one of the most frequently activated pathways in breast cancer and also plays a central role in the regulation of several physiologic functions. There are major efforts ongoing to exploit precision medicine by developing inhibitors that target the three kinases (PI3K, AKT, and mTOR). Although multiple compounds have been developed, at present, there are just three inhibitors approved to target this pathway in patients with advanced ER-positive, HER2-negative breast cancer: everolimus (mTOR inhibitor), alpelisib (PIK3CA inhibitor), and capivasertib (AKT inhibitor). Like most targeted cancer drugs, resistance poses a major problem in the clinical setting and is a factor that has frequently limited the overall efficacy of these agents. Drug resistance can be categorised into intrinsic or acquired resistance depending on the timeframe it has developed within. Whereas intrinsic resistance exists prior to a specific treatment, acquired resistance is induced by a therapy. The majority of patients with ER-positive, HER2-negative advanced breast cancer will likely be offered an inhibitor of the PI3K/AKT/mTOR pathway at some point in their cancer journey, with the options available depending on the approval criteria in place and the cancer's mutation status. Within this large cohort of patients, it is likely that most will develop resistance at some point, which makes this an area of interest and an unmet need at present. Herein, we review the common mechanisms of resistance to agents that target the PI3K/AKT/mTOR signalling pathway, elaborate on current management approaches, and discuss ongoing clinical trials attempting to mitigate this significant issue. We highlight the need for additional studies into AKT1 inhibitor resistance in particular.

Missing data in amortized simulation-based neural posterior estimation.

Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.

Minimum connected dominating set and backbone of a random graph

We study the minimum dominating set problem as a representative combinatorial optimization challenge with a global topological constraint. The requirement that the backbone induced by the vertices of a dominating set should be a connected subgraph makes the problem rather nontrivial to investigate by statistical physics methods. Here, we convert this global connectivity constraint into a set of local vertex constraints and build a spin glass model with only five coarse-grained vertex states. We derive a set of coarse-grained belief-propagation equations and obtain theoretical predictions of the relative sizes of the minimum dominating sets for regular random and Erdös–Rényi random graph ensembles. We also implement an efficient message-passing algorithm to construct close-to-minimum connected dominating sets and backbone subgraphs for single random graph instances. Our theoretical strategy may also be applicable to some other global topological constraints.

Investment–consumption optimization with transaction cost and learning about return predictability

In this paper, we investigate an investment–consumption optimization problem in continuous-time settings, where the expected rate of return from a risky asset is predictable with an observable factor and an unobservable factor. Based on observable information, a decision-maker learns about the unobservable factor while making investment–consumption decisions. Both factors are supposed to follow a mean-reverting process. Also, we relax the assumption of perfect liquidity of the risky asset through incorporating proportional transaction costs incurred in trading the risky asset. In such way, a form of friction posing liquidity risk to the investor is examined. Dynamic programming principle coupled with an Hamilton–Jacobi–Bellman (HJB) equation are adopted to discuss the problem. Applying an asymptotic method with small transaction costs being taken as a perturbation parameter, we determine the frictional value function by solving the first and second corrector equations. For the numerical implementation of the proposed approach, a Monte-Carlo-simulation-based approximation algorithm is adopted to solve the second corrector equation. Finally, numerical examples and their economic interpretations are discussed.

Decidability bounds for Presburger arithmetic extended by sine

We consider Presburger arithmetic extended by the sine function, call this extension sine-Presburger arithmetic (sin-PA), and systematically study decision problems for sets of sentences in sin-PA. In particular, we detail a decision algorithm for existential sin-PA sentences under assumption of Schanuel's conjecture. This procedure reduces decisions to the theory of the ordered additive group of real numbers extended by sine, which is decidable under Schanuel's conjecture. On the other hand, we prove that four alternating quantifier blocks suffice for undecidability of sin-PA sentences. To do so, we explicitly interpret the weak monadic second-order theory of the grid, which is undecidable, in sin-PA.

Dual hesitant fuzzy set in multi-objective transportation problems in time sequence frame work

Decision makers may have different information in different situations when making decisions about the same attributes, depending on the circumstances. A time-sequential framework is necessary for navigating these kinds of circumstances. We established the notion of a Time-sequential dual hesitant fuzzy set (TS-DHFS) to illustrate the significance of time sequence in decision-making with the aid of a dual hesitant fuzzy set (DHFS), one of the important tools to handle the uncertainty embedded in the real world. The proposed set describes the hesitant situations more accurately by inducing a time sequence framework. We defined the fundamental operations, two score function for ranking and a series distance measures for the proposed set. For the pragmatic application of our proposed set, we devised the mathematical model in three different types of Time-sequential dual hesitant fuzzy multi-objective transportation problems (TS-DHF-MOTPs). The proposed three models are classified based on the nature of the parameters such as transportation cost (TC), transportation time (TT), availability, and demand. The goal of the proposed transportation systems is TC and TT simultaneously in a single framework. We established an approach utilizing one of the ranking functions that have been proposed, followed by the fuzzy programming (FP) and weighted sum technique (WST), to optimize the constructed models. Additionally, numerical computations are also carried out to emphasize the efficiency and benefits of MOTP under the TS-DHF environment.

Dynamic microservice placement in multi-tier Fog networks

Fog computing extends Cloud-like infrastructure to the Edge, advancing Industrial Internet-of-Things (IIoT) applications in which parts of business-oriented enterprise systems, decoupled via fine-grained microservices, can be deployed in Fog networks in proximity to sensor devices. It boosts the responsiveness to IIoT events significantly where timely decision-making and actions are required, particularly for problematic trends and anomalies. However, microservice placement is a non-trivial problem in IIoT settings, given the dynamic occurrence of different IIoT events and variable resource needs of microservices against resource-constrained, heterogeneous, and distributed Fog devices. The cost of matching and deploying microservices in real-time, highly dynamic, and contentious settings can easily exceed the benefit of just-in-time Edge deployments. Current studies largely focus on managing the microservice placement from Cloud servers and offloading to the Cloud when Fog devices are resource-deficient. Yet, there is a lack of research focusing on efficient Fog resource use and managing the placement at the edge to improve the responsiveness. The complexity increases when multiple inter-dependent microservices with similar priorities are required to be placed on the same Fog devices. To mitigate this, we develop a tiered framework for dynamic microservice placement, in which costly resource decision-making for microservices is dedicated to Master Fog devices and Citizen Fog devices are exclusively assigned to executing microservice requests. Firstly, we implement a priority-based algorithm in each Master Fog device to identify the high-priority Edge-required microservices. Secondly, we sort the Fog devices according to their resource availability and place the microservices based on their priority and dependencies. Thirdly, we implement the strategies for microservice placement, scaling and request escalation in each Master Fog, managing a small number of Citizen Fog devices. These exempt the Citizen Fog devices from common resource and communication management responsibilities and enable them to allocate more resources to Edge-required microservices with a higher number of instances. Finally, we implement and evaluate our framework in a simulated Fog environment. The results outperform state-of-the-art frameworks in terms of efficient resource utilisation, resulting in reduced Cloud dependency by one-third compared to other approaches and average application execution time by 65–70%.

Introducing ViDA3, An Earthquake Early Warning Algorithm for Offshore Hypocenter Determination Using Onshore Seismic Networks

ABSTRACT The objective functions adopted by earthquake early warning (EEW) location algorithms are inadequate for out-of-network earthquakes. As a result, the real-time locations of these earthquakes are often erroneous. The consequences of mislocating out-of-network earthquakes are that their magnitudes are miscalculated, and the loci of their shaking predictions map are shifted. Given that the largest earthquakes occur in subduction settings, improving real-time out-of-network earthquake location is of great importance. In this study, the Virtual Dynamically Assembled Array Algorithm (ViDA3) is introduced, which addresses the location issue of offshore and off-network earthquakes. The guiding principle underlying the new EEW location algorithm is that standard seismic networks may be viewed as a collection of medium-sized seismic arrays, with each array consisting of three or more network stations. The potential of array seismology for EEW against out-of-network earthquakes stems primarily from the slowness vector, which points at the direction of the epicentral region. Thus, this region may be constrained merely by intersecting two or more such vectors. In addition, the length of the slowness vector depends on the hypocentral distance and depth and is thus vital for addressing an acute problem in a subduction setting—discriminating between upper crust and deep slab earthquakes. Furthermore, when the slowness of the P phase is known, the slowness of the S phase is deduced, and the S-phase arrival is searched for using the shift-and-sum practice. What makes ViDA3 so attractive is that, in locations where a real-time network is already in place, these added values may be achieved without extra hardware or substantial budget requirements. We present the result of ViDA3 real-time operation on a shallow earthquake offshore Vancouver Island and the result of its replay on a deep slab earthquake in northern Chile. ViDA3 performance is further assessed using a dataset of seismograms from the Mendocino Triple Junction area. It is concluded that ViDA3 location scheme outperforms currently available EEW location algorithms for out-of-network earthquakes.

Coercive and Noncoercive Mixed Generalized Complementarity Problems

Impressed with the very recent developments of noncoercive complementarity problems and the use of recession sets in complementarity problems, here, we discuss mixed generalized complementarity problems in Hausdorff topological vector spaces. We used the Tikhonov regularization procedure, as well as arguments from the recession analysis, to establish the existence of solutions for mixed generalized complementarity problems without coercivity assumptions in Banach spaces.

A novel local search approach with connected dominating degree-based incremental neighborhood evaluation for the minimum 2-connected dominating set problem

A novel local search approach with connected dominating degree-based incremental neighborhood evaluation for the minimum 2-connected dominating set problem

Classifying subset feedback vertex set for H-free graphs

In the Feedback Vertex Set problem, we aim to find a small set S of vertices in a graph intersecting every cycle. The Subset Feedback Vertex Set problem requires S to intersect only those cycles that include a vertex of some specified set T. We also consider the Weighted Subset Feedback Vertex Set problem, where each vertex u has weight w(u)>0 and we ask that S has small weight. By combining known NP-hardness results with new polynomial-time results we prove full complexity dichotomies for Subset Feedback Vertex Set and Weighted Subset Feedback Vertex Set for H-free graphs, that is, graphs that do not contain a graph H as an induced subgraph.

A new portmanteau test for predictive regression models with possible embedded endogeneity

In the widely used predictive regression model, any possible serial correlation in innovations leads to estimation bias and statistical inference distortions. Hence, it is important to pretest the existence of such serial correlation. Nevertheless, in the presence of embedded endogeneity, which is a common problem in the predictive regression setting, traditional serial correlation tests such as Box–Pierce (BP) and Ljung–Box (LB) tests are found to perform poorly. Motivated by this, we develop a new portmanteau test in this article as a pretest for serial correlation in predictive regression under possible embedded endogeneity. This test is based on the sample splitting idea and the jackknife empirical likelihood method. The asymptotic distribution of the proposed test has been derived, and the Monte Carlo simulations confirm good finite sample performances. As an illustration, we apply our proposed test in pretesting the serial correlation in predictive regression, where financial variables are used to predict the excess return of S&amp;P 500.

Distributionally robust disaster relief planning under the Wasserstein set

We study a two-stage natural disaster management problem modeled as a stochastic program, where the first stage consists of a facility location problem, deciding where to open facilities and pre-allocate resources such as medical and food kits, and the second stage is a fixed-charge transportation problem, routing resources to affected areas after observing a disaster. Our model has binary variables present in both stages. Due to the lack of data, classical stochastic programming approaches may be ill-suited, and we propose a two-stage distributionally robust formulation with a Wasserstein ambiguity set, where we consider distributions consistent with historical data and a tunable parameter to control the level of risk aversion. We develop a tailored column-and-constraint generation (CCG) algorithm to solve an extensive reformulation, where scenarios are iteratively generated. We handle the presence of binary variables in the second stage by leveraging the structure of our support set and second-stage problem, and provide conditions under which the optimal value of the latter is concave with respect to the intensity of the disaster, leading to an efficient scenario generation procedure. We also show that our results extend to the case where the second stage is a fixed-charge network flow problem. We perform extensive computational experiments demonstrating the computational advantage of our method over classical CCG implementations on synthetic instances, and illustrate the benefits of our approach on a popular case study from the literature of hurricane threats on the Gulf of Mexico in the United States.

Leaf sector covers with applications on circle graphs

Damian and Pemmaraju (2002) [8] introduced leaf sector covers for circle graphs and presented an O(n2)-time algorithm to find this data structure. They subsequently employed it as an algorithmic tool, leading to the development of an 8-approximation algorithm for the dominating set problem, a (2+ε)-approximation scheme for the dominating set problem, and a (3+ε)-approximation scheme for the total dominating set problem.In this paper, we have improved the time complexity from O(n2) to O(n+m) for finding a leaf sector cover. Furthermore, we employed leaf sector covers as a powerful algorithmic tool to design a 10-approximation algorithm for the total dominating set problem and a 12-approximation algorithm for the paired-dominating set problem. Moreover, we also proposed a (2+ε)-approximation scheme for the total dominating set problem and a (4+ε)-approximation scheme for the paired-dominating set problem. The results presented above are currently the best algorithms for circle graphs.

Optimal distributed control for a Cahn-Hilliard-Darcy system with mass sources, unmatched viscosities and singular potential

We study a Cahn-Hilliard-Darcy system with mass sources, which can be considered as a basic, though simplified, diffuse interface model for the evolution of tumor growth. This system is equipped with an impermeability condition for the averaged velocity u as well as homogeneous Neumann boundary conditions for the phase function ϕ and the chemical potential µ. The source term in the convective Cahn-Hilliard equation contains a control R that can be thought as a drug or a nutrient in applications. Our goal is to study a distributed optimal control problem in the two-dimensional setting with a cost functional of tracking-type. In the physically relevant case with unmatched viscosities for the binary fluid mixtures and a singular potential, we first prove the existence and uniqueness of a global strong solution with ϕ being strictly separated from the pure phases ±1. This well-posedness result enables us to characterize the control-to-state mapping S: R→ϕ. Then we obtain the existence of an optimal control, the Frechet differentiability of S and first-order necessary optimality conditions expressed through a suitable variational inequality for the adjoint variables. Finally, we prove the differentiability of the control-to-costate operator and establish a second-order sufficient condition for the strict local optimality.

Quantum-Informed Recursive Optimization Algorithms

We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component (e.g., locality) and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We showcase the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow circuits of the quantum approximate optimization algorithm, solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a template for a broader class of hybrid quantum-classical algorithms for combinatorial optimization. Published by the American Physical Society 2024

A focus on perpetrators of intimate partner violence in mental health settings is urgently needed.

A national study in the UK has shown that perpetration of intimate partner violence is common for men and women attending mental health settings. People who perpetrated intimate partner violence were more likely to have experienced intimate partner violence, particularly for women. Perpetrators who were men were more likely to also perpetrate non-partner violence against family, friends or strangers. Mental health clinicians require training in identification, risk assessment and response, including referrals to behavioural programmes. More research is required to inform such responses; however, the need to address this common hidden problem in mental health settings is urgent.