Deterministic Threshold Research Articles

Research on pollution identification and safety thresholds based on the olfactory effect on a halogenated hydrocarbon contaminated site

To effectively address odor control issues at sites contaminated with halogenated hydrocarbons, it is essential to establish an odor risk prediction system for evaluating potential risks that may impact future planning. This research focuses on a representative halogenated hydrocarbon-contaminated site, examining the spatial and temporal distribution characteristics of key pollutants in soil gas. By analyzing odor contribution rates, the study identifies significant odorants in soil gas, which enables the derivation of both probabilistic and deterministic safety thresholds for soil and groundwater based on olfactory effects. The findings indicate that 1,1-dichloroethylene, vinyl chloride, chloroform, and 1,1-dichloroethane are prevalent throughout the contaminated site, displaying elevated concentration levels and substantially influencing the overall contamination extent. These substances are highlighted as critical pollutants requiring attention. Correlation analysis (P < 0.05) reveals a strong relationship between the concentrations of vinyl chloride, 1,1-dichloroethane, and chloroform with groundwater depth and air temperature. Additionally, the analysis of odor activity values (OAV) identified 1,1-dichloroethene, 1,4-dichlorobenzene, chlorobenzene, chloroform, and vinyl chloride as key olfactory factors at the site. The corresponding probabilistic safety thresholds are 0.68, 1.65, 0.50, 7.87, and 3.72 mg kg−1 for soil, and 9.29, 3.46, and 1.09, 69.55, and 47.01 mg L−1 for groundwater, respectively. Among them, the odor risks of chlorobenzene and 1,1-dichloroethylene warrant more attention than soil contamination risks; regarding 1,4-dichlorobenzene, it is recommended to concurrently consider odor risks during human health risk assessment; as for vinyl chloride and chloroform, their odor risks can be largely eliminated based on human health-oriented pollution management.

Efficient Representation Learning for Generalized Category Discovery

Generalized Category Discovery (GCD) aims to discover and cluster data from previously unseen classes. Numerous studies have delved into GCD; however, they either overlooked the significance of representation learning or naively adapt the contrastive loss from self-supervised learning without further refinements. Therefore, in this paper, we propose a simple yet effective method to address this task. Our first contribution is to design a robust Divide-and-Combine strategy with an improved contrastive loss to generate informative features. Subsequently, in light of the scarce supervised signals, we propose a Neighborhood-based Sampling module based on the analysis of cluster-level alignment and uniformity to better discover novel categories. Ultimately, to boost the model’s ability to learn from unlabeled data, we adopt a fusion training approach utilizing combinational patch features and the deterministic threshold to selectively identify high-confidence samples. We unify these learning processes within a single framework and jointly optimize. Extensive experiments across three general datasets and four fine-grained benchmark datasets show the compelling evidence for the superiority of our proposed method—Efficient Representation Learning for Generalized Category Discovery (ERL4GCD).

Pulsed field ablation with the pentaspline catheter compared with cryoablation for the treatment of paroxysmal atrial fibrillation in the UK NHS: a cost-comparison analysis

ObjectivesPulsed field ablation (PFA) is a promising new ablation modality for the treatment of atrial fibrillation (AF) that has recently become available in the UK National Health Service (NHS). We...

Exact and heuristic approaches for the Modal Shift Incentive Problem

It is widely acknowledged that freight transportation in general, and road freight transportation in particular, contributes significantly to greenhouse gas and pollutant emissions. In response, many countries have implemented incentive schemes to divert freight traffic from roads towards more environmentally friendly transportation modes (e.g., rail, maritime). However, incentive-based policies to promote modal shift are often sporadic and uncoordinated across non-road modes, thus preventing full leverage of dedicated budgets. In this study, we present a strategic problem encountered by national and supranational entities aiming to reduce emissions from the freight transportation sector, referred to as the Modal Shift Incentive Problem (MSIP). The problem involves designing incentive policies in the form of subsidies aimed at reducing the cost of eco-friendly transportation modes, with the aim of minimizing the volume of freight transported via less environmentally friendly modes. These incentive policies must satisfy various constraints, including a limited budget for incentives, equity requirements, and compliance with regulations pertaining to competitiveness and free-market dynamics. To address the MSIP, we propose an original mathematical formulation on the basis of a deterministic threshold modal choice model used to simulate the shipper behavior in selecting the transportation modes. Then, we develop a two-phase solution method, able to determine good solutions with a small computational burden based on the generation of a subset of promising incentive schemes. Finally, we solve real-world instances generated utilizing data derived from a comprehensive case study in Italy. The results on different instance types, in terms of number of shipments, budget, and costs, show the relationship between these factors, the complexity of the MSIP, and the modal shift.

Fuzzy seismic fragility analysis of underground structures considering multiple failure criteria

To avoid the deficiency of a single failure criterion and deterministic thresholds of limit states in seismic fragility analysis of underground structures, a method of fuzzy seismic fragility analysis of underground structures considering multiple failure criteria is proposed by combining fuzzy-random theory and copula theory. In this method, probabilistic seismic demand and capacity analyses based on different failure criteria are first performed. Then, the fuzziness of the limit states of different failure criteria is characterized by using membership functions, and the fuzzy seismic fragility of underground structures considering a single failure criterion with different membership functions is further calculated. On this basis, the copula function and the combination of membership functions are optimized to characterize the correlation between multiple failure criteria and establish fuzzy seismic fragility curves considering multiple failure criteria. Taking the Daikai subway station as the background, the effects of different failure criteria and the fuzziness of limit states on the seismic fragility are discussed, and the optimal combination of membership functions that characterizes the fuzziness of limit states under the failure criteria of deformation and strength is also presented. The results show that the failure criteria and fuzziness of limit states have significant effects on the seismic fragility of underground structures, and the combination of trigonometric membership functions is the optimal combination that characterizes the fuzziness of multiple failure criteria. Moreover, the fuzzy seismic fragility of underground structures considering multiple failure criteria is more sensitive to the fuzziness of limit states under frequent earthquakes and basic earthquakes, while the multiple failure criteria and the correlation between different failure criteria have more significant effects on the fuzzy seismic fragility considering multiple failure criteria under rare earthquakes and extremely rare earthquakes.

Probabilistic thresholds for regional rainfall induced landslides

Traditionally, the deterministic rainfall threshold has been widely used for early warning of landslides, which is often developed based on visual inspection of historical rainfall events in a region that triggered landslides. As there is no deterministic relationship between rainfall and the occurrence of landslides, it is not uncommon to observe in practice that a rainfall above the deterministic rainfall threshold may not cause landslides, and vice versa. In this paper, a numerical study is first conducted to gain insights on factors affecting the rainfall threshold for early warning of landslides. Then, a procedure based on the support vector machine is suggested to develop probabilistic rainfall thresholds, where the reliability associated with different rainfall thresholds can be explicitly quantified. Finally, two application examples are introduced to illustrate and validate the suggested method for developing rainfall thresholds probabilistically. The probabilistic rainfall thresholds presented in this study can provide more comprehensive information for issuing warning messages and risk management of landslide hazards.

Cost-aware defense for parallel server systems against reliability and security failures

Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication. Yet, these components remain vulnerable to random malfunctions and malicious attacks, motivating the need for resilient dynamic routing that is both traffic-stabilizing and cost-efficient. In this paper, we consider a parallel server system with dynamic routing subject to reliability and stability failures. For the reliability setting, we consider an infinite-horizon Markov decision process where the system operator strategically activates a protection mechanism upon each job arrival based on traffic state observations. We prove that an optimal deterministic threshold protecting policy exists based on the dynamic programming recursion of the Hamilton–Jacobi–Bellman equation. For the security setting, we extend the model to an infinite-horizon stochastic game where the attacker strategically manipulates routing assignments. We show that both players follow a threshold strategy at every Markov perfect equilibrium. For both failure settings, we also analyze the stability of the traffic queues. Finally, we develop approximate dynamic programming algorithms to compute the optimal/equilibrium policies and present numerical examples/experiments for validation and illustration.

Weather condition and Urban infrastructure for sustainable water supply protection from water meter frost: the case of Seoul

ABSTRACT Wintertime water meter frost is a substantial threat to sustainable water supply. However, little is known about the social and environmental conditions associated with its occurrence. This study identifies characteristics of water meter frost in Seoul, South Korea, by examining natural and anthropogenic conditions, such as local weather and urban infrastructures. The deterministic threshold of seasonal water meter frosts is the number of cold days below −10°C. The wall-type water meter was the first to freeze during a cold surge, whereas the manhole-type water meter froze gradually, which increased after a persistent cold surge of more than three days. In addition, deteriorated buildings (older than 30 years), narrow pipes with diameters of 15–20 mm, and housing types such as townhouses and shopping malls were found to be vulnerable to frost. The critical thresholds proposed in this study are necessary for maintaining a stable water supply in megacities in cold regions, world-wide.

Social contagion induced by uncertain information.

Information and individual activities often spread globally through the network of social ties. While social contagion phenomena have been extensively studied within the framework of threshold models, it is common to make an assumption that may be violated in reality: each individual can observe the neighbors' states without error. Here, we analyze the dynamics of global cascades under uncertainty in an otherwise standard threshold model. Each individual uses statistical inference to estimate the probability distribution of the number of active neighbors when deciding whether to be active, which gives a probabilistic threshold rule. Unlike the deterministic threshold model, the spreading process is generally nonmonotonic, as the inferred distribution of neighbors' states may be updated as a new signal arrives. We find that social contagion may occur as a self-fulfilling event in that misperception may trigger a cascade in regions where cascades would never occur under certainty.

Development of a point-of-care genetic test for effective treatment of ischaemic stroke: an early model-based cost-effectiveness analysis.

Background: People who have experienced a stroke are at high risk of recurrent strokes. Clopidogrel is prescribed to people who have had a non-cardioembolic stroke. There is evidence that clopidogrel is not effective for patients with CYP2C19 loss-of-function alleles. Pharmacogenetic testing is a potential strategy to identify such patients and guide prescription of appropriate antiplatelet treatment. This study aimed to provide an early estimate of the cost-effectiveness of using a point-of-care pharmacogenetic CYP2C19 test in the UK National Health System. Methods: A decision-analytic model comprising a linked decision tree and Markov model were created in R comparing pharmacogenetic testing with current prescribing practice. In the pharmacogenetic testing arm, patients identified to have one of three loss-of-function alleles were prescribed modified-release dipyridamole and aspirin or aspirin alone. Indicative data were sourced from reviews of the literature supported by expert consultation to select the most appropriate value for the input parameters. The healthcare costs (£;2021) and quality adjusted life years resulting from each strategy were estimated and the incremental cost-effectiveness of testing calculated. Deterministic threshold analysis and probabilistic sensitivity analysis (PSA) was conducted to account for uncertainty in the parameter estimates. Results: The pharmacogenetic testing strategy generated 0.107 additional QALYs per patient tested and saved £512. Pharmacogenetic testing dominated current prescribing practice. The results were robust to extreme changes in key input variables. The PSA suggested that there was a 77% chance that pharmacogenetic testing would be cost-effective with a 62% chance it is cost-saving. Conclusions: A point-of-care pharmacogenetic test to guide prescription of clopidogrel for people who have experienced a stroke has the potential to provide a significant health gain by preventing secondary strokes and may save resources in the health system. This early economic analysis has also informed the direction for future research.

Supervised Learning Technique for First Order Multipaths Identification of V2V Scenario

In geometrical localization techniques, the propagated signal’s first-order multipath (FOMP) characteristics are used to calculate the location based on geometrical relationships. Utilizing the characteristics of higher order multipath (HOMP) results in a significant localization error. Therefore, distinguishing between FOMPs and HOMPs is an important task. The previous works used traditional methods based on a deterministic threshold to accomplish this task. Unfortunately, these methods are complicated and insufficiently accurate. This paper proposes an efficient method based on supervised learning to distinguish more accurately between the propagated FOMP and HOMP of millimeter-Wave Vehicle-to-Vehicle communication in an urban scenario. Ray tracing technique based on Shoot and Bounce Ray (SBR) is used to generate the dataset’s features including received power, propagation time, the azimuth angle of arrival (AAOA), and elevation angle of arrival (EAOA). A statistical analysis based on the probability distribution function (PDF) is presented first to study the selected features’ impact on the classification process. Then, six supervised classifiers, namely Decision Tree, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Random Forest, and artificial neural network, are trained and tested, and their performance is compared in terms of HOMP misclassification. The effect of the considered features on the classifiers’ performance is further investigated. Our results showed that all the proposed classifiers provided an acceptable classification performance. The proposed ANN showed the best performance, whereas the NB was the worst. In fact, the HOMP misclassification error varied between 2.3% and 16.7%. The EAOA exhibited the most significant influence on classification performance, while the AAOA was the least.

The role of RNA condensation in reducing gene expression noise.

Biomolecular condensates have been shown to play a fundamental role in localizing biochemistry in a cell. RNA is a common constituent with proteins and can determine biophysical properties. Functions of biomolecular condensates are varied including activating, inhibiting, and localizing reactions. Recently it has been proposed that condensates can buffer noise to diminish cell to cell variability. Here we introduce a phenomenological model in which phase separation of mRNAs into RNP droplets can regulate the abundance of the protein these mRNA encode and markedly decreases expression noise. We place particular emphasis on how this mechanism can facilitate efficient transcription by reducing noise even in the limit of infrequent bursts of transcription, by exploiting the physics of concentration dependent, deterministic phase separation threshold. We investigate two biologically relevant scenarios in which phase separation acts to either “buffer” noise by storing mRNA in inert droplets, or “filter” phase separated mRNAs by accelerating their decay, and quantify expression noise as a function of kinetic parameters. In either case the most efficient expression occurs when bursts produce mRNAs close to the phase separation threshold, which we find to be consistent with observations of a RNP-droplet forming cyclin in multinucleate Ashbya gossypii cells. We finally consider the contribution of noise in the phase separation threshold, and exhibit that protein copy number noise can be efficiently suppressed under modest assumptions on the magnitude and time scale of phase separation threshold fluctuations.

On the firefighter problem with spreading vaccination for maximizing the number of saved nodes: the IP model and LP rounding algorithms.

When an infectious disease spreads, how to quickly vaccinate with a limited budget per time step to reduce the impact of the virus is very important. Specifically, vaccination will be carried out in every time step, and vaccinated nodes will no longer be infected. Meanwhile, the protection from vaccination can spread to the neighbors of a vaccinated node. Our goal is to efficiently find optimal and approximation solutions to our problem with various algorithms. In this paper, we first design an integer linear program to solve this problem. We then propose approximation algorithms of (1) Linear programming (LP) deterministic threshold rounding, (2) LP dependent randomized rounding, and (3) LP independent randomized rounding. We prove that the LP independent randomized rounding algorithm has a high probability of finding a feasible solution that gives an approximation ratio of , where a small constant between 0 and 1 reduces the lower bound on the feasibility probability. We also provide experimental results for three different rounding algorithms to show that they perform numerically well in terms of approximation ratios. These analytical and numerical studies allow each individual to adopt the most appropriate approximation algorithm to efficiently resolve the vaccination problem when her reliance on commercial optimization solvers is costly.

A Novel Deterministic Threshold Proxy Re-Encryption Scheme From Lattices

Aiming at the problem that it is difficult to flexibly realize, the sharing and efficient search of encrypted data in large data-bases, this paper proposes a deterministic threshold proxy re-encryption scheme under the auxiliary input model. This scheme uses Shamir's secret sharing technology to achieve threshold control, uses homomorphic signature technology to verify the legitimacy of ciphertext, and applies deterministic algorithms to solve the search problem in large databases, while ensuring the user's control over their own data, and proves its security can reach indistinguishable semantic security (PRIV1-INDr) under the standard model. Compared with other schemes, this scheme not only shortens the length of the ciphertext and improves the decryption efficiency, but it also has anti-auxiliary input, robustness, and multi-hop characteristics and can better meet actual needs.

An exact method for influence maximization based on deterministic linear threshold model

An exact method for influence maximization based on deterministic linear threshold model

Recursive Estimation of a Failure Probability for a Lipschitz Function

Let g:Ω=[0,1] d →ℝ denote a Lipschitz function that can be evaluated at each point, but at the price of a heavy computational time. Let X stand for a random variable with values in Ω such that one is able to simulate, at least approximately, according to the restriction of the law of X to any subset of Ω. For example, thanks to Markov chain Monte Carlo techniques, this is always possible when X admits a density that is known up to a normalizing constant. In this context, given a deterministic threshold T such that the failure probability p:=ℙ(g(X)>T) may be very low, our goal is to estimate the latter with a minimal number of calls to g. In this aim, building on Cohen et al. [9], we propose a recursive and (in a certain sens) optimal algorithm that selects on the fly areas of interest and estimates their respective probabilities.

Economic threshold analysis of delivering a task-sharing treatment for common mental disorders at scale: the Friendship Bench, Zimbabwe

BackgroundTask-sharing treatment approaches offer a pragmatic approach to treating common mental disorders in low-income and middle-income countries (LMICs). The Friendship Bench (FB), developed in Zimbabwe with increasing adoption in other...

Distributed Detection and Mitigation of Biasing Attacks Over Multi-Agent Networks

This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper bound on the noise support, we define the thresholds on the residuals in a probabilistic sense. After detecting and isolating the attacked agent, a system-digraph-based mitigation strategy is proposed to replace the attacked measurement with a new observationally-equivalent one to recover potential observability loss. We adopt a graph-theoretic method to classify the agents based on their measurements, to distinguish between the agents recovering the system rank-deficiency and the ones recovering output-connectivity of the system digraph. The attack detection/mitigation strategy is specifically described for each type, which is of polynomial-order complexity for large-scale applications. Illustrative simulations support our theoretical results.

ВИЯВЛЕННЯ ТА ОЦІНЮВАННЯ КІБЕРАТАК В ІНФОРМАЦІЙНИХ МЕРЕЖАХ ІЗ ВИПАДКОВИМ МОМЕНТОМ ПОЯВИ

Detection, prevention or significant impediment to cyberattacks (CA) is one of the central areas of cybersecurity in information networks. Determining the generalized requirements for cyber protection of information networks from cyberattacks on information and assessing the degree of their security are quite difficult tasks. There are real possibilities for unforeseen situations as a result of cyberattacks, leading to the loss of information or to its loss and loss of information network. The concept of cybersecurity of the information network on the basis of threats from cyberattacks should determine the necessary tools, methods and procedures for detection and evaluation of cyberattacks in networks. Therefore, for the effective functioning of information networks in modern conditions and means of their protection, as well as for the reliable detection and evaluation of cyberattacks, it is necessary to develop new approaches and methods, their implementation. Some results related to joint sequential detection and evaluation were obtained in [3]. As follows from [3], in the general case, is not possible to find a constructive solution even in the two-alternative problem. Losses in the process of recognition (detection) and evaluation of a cyberattack depend on both errors in its detection and inaccuracy of evaluation, which will not provide adequate response, and there is a problem of joint development and evaluation [1, 2]. Therefore, we will try to solve this problem of multi-alternative sequential detection and evaluation of a cyberattack with a random moment of its occurrence. Consistent detection and evaluation procedures are generally more efficient than inconsistent ones. Therefore, it is important and urgent to find optimal, consistent or close to them procedures that will increase the cybersecurity of information. The purpose of the article is to find optimal, consistent or close to them procedures that will increase the cybersecurity of information. Based on the probabilistic assessment, the optimal, sequential or close to them procedures are revealed in the work, which allow to increase the cybersecurity of information. The problem to build the optimal N-truncated sequential procedure for joint detection of cyberattacks (CA) and to estimate the moment of its occurrence in the loss function was solved. Statistics related to the average volume of the forecast (UOP) are analyzed. A general view of the optimal procedure for sequential detectionevaluation of a cyberattacks with an unknown moment of occurrence during these losses is proposed. Thus, if the optimal procedure is based on comparing one-dimensional statistics Ln c with a deterministic threshold for solving the problem of detection without estimating the moment of cyberattacks or for solving the problem of detection and evaluation separately, then the optimal areas for stopping and continuation of observations are determined in three-dimensional space for solving these problems.

The Scientific and Clinical Case for Reviewing Diagnostic Radiopharmaceutical Extravasation Long-Standing Assumptions.

Background: The patient benefit from a diagnostic nuclear medicine procedure far outweighs the associated radiation risk. This benefit/risk ratio assumes a properly administered radiopharmaceutical. However, a significant diagnostic radiopharmaceutical extravasation can confound the procedure in many ways. We identified three current extravasation hypotheses espoused by medical societies, advisory committees, and hundreds of individual members of the nuclear medicine community: diagnostic extravasations do not cause harm, do not result in high absorbed dose to tissue, and require complex dosimetry methods that are not readily available in nuclear medicine centers. We tested these hypotheses against a framework of current knowledge, recent developments, and original research. We conducted a literature review, searched regulatory databases, examined five clinical cases of extravasated patients, and performed dosimetry on those extravasations to test these globally accepted hypotheses.Results: A literature review found 58 peer-reviewed documents suggesting patient harm. Adverse event/vigilance report database reviews for extravasations were conducted and revealed 38 adverse events which listed diagnostic radiopharmaceutical extravasation as a factor, despite a regulatory exemption for required reporting. In our own case material, assessment of care was evaluated for five extravasated patients who underwent repeat imaging. Findings reflected results of literature review and included mis- or non-identification of lesions, underestimation of Standardized Uptake Values (SUVs) by 19–73%, classification of scans as non-diagnostic, and the need to repeat imaging with the associated additional radiation exposure, inconvenience, or delays in care. Dosimetry was performed for the same five cases of diagnostic radiopharmaceutical extravasation. Absorbed doses to 5 cm3 of tissue were between 1.1 and 8.7 Gy, and shallow dose equivalent for 10 cm2 of skin was as high as 4.2 Sv.Conclusions: Our findings suggest that significant extravasations can or have caused patient harm and can irradiate patients' tissue with doses that exceed medical event reporting limits and deterministic effect thresholds. Therefore, diagnostic radiopharmaceutical injections should be monitored, and dosimetry of extravasated tissue should be performed in certain cases where thresholds are thought to have been exceeded. Process improvement efforts should be implemented to reduce the frequency of extravasation in nuclear medicine.