Partial Partitions Research Articles

Assessment of the partial partition effect on the control of bioaerosol transmission in a one bed ward

The partition is a boundary that can effectively suppress the spread of bioaerosols. It has been widely used in public places such as restaurants and shopping malls during COVID-19 pandemic. Inspired by this, this study explored the effect of partial partitions in limiting the diffusion of bioaerosols in a typical isolation ward. In bioaerosol experiments, Serratia marcescens was used as bioaerosol releasing material to study the bioaerosol distribution. Simultaneously, the result of bioaerosol sampling was used to validate the numerical model. Furthermore, the effect of five partitions strategies on aerosol diffusion was further explore by Computational fluid dynamics. The flow patterns in the ward and the spatiotemporal distribution of aerosols were obtained and analyzed. The results showed that the best strategy could increase bioaerosol removal effectiveness index from 0.569 to 0.696, which accelerated the bioaerosol removal. Nevertheless, only selecting a reasonable size and installation position, can the partial partition play a positive role in the interruption of aerosol diffusion. Moreover, small-area partial partition can achieve efficient control of bioaerosol diffusion as well.

A clustering-based partially stratified sampling for high-dimensional structural reliability assessment

Assessing structural reliability problem with high-dimensional random inputs is still challenging due to the “curse of dimensionality”. In this paper, this challenge is addressed by extending the Generalized Distribution Reconstruction Method via Characteristic Function Inversion (GDRM-CFI). Specifically, a clustering-based partially stratified sampling method is proposed for selecting high-dimensional points to numerically evaluate the characteristic function (CF) curve of complex high-dimensional problems. An improved number-theoretical method (i-NTM) is used to establish a uniform, efficient point set, ensuring determinism and reducing variability. Subsequently, a partial stratification approach partitions the high-dimensional space into orthogonal two-dimensional subspaces. The fundamental point set is projected into each subspace, and the k-means clustering algorithm identifies centroids within each, acting as representative points. The complete set of representative points from all subspaces formulates the high-dimensional point set. Numerical examples are investigated, which demonstrate the proposed method is effective for high-dimensional structural reliability assessment.

Partial Network Partitioning

We present an extensive study focused on partial network partitioning. Partial network partitions disrupt the communication between some but not all nodes in a cluster. First, we conduct a comprehensive study of system failures caused by this fault in 13 popular systems. Our study reveals that the studied failures are catastrophic (e.g., lead to data loss), easily manifest, and are mainly due to design flaws. Our analysis identifies vulnerabilities in core systems mechanisms including scheduling, membership management, and ZooKeeper-based configuration management. Second, we dissect the design of nine popular systems and identify four principled approaches for tolerating partial partitions. Unfortunately, our analysis shows that implemented fault tolerance techniques are inadequate for modern systems; they either patch a particular mechanism or lead to a complete cluster shutdown, even when alternative network paths exist. Finally, our findings motivate us to build Nifty, a transparent communication layer that masks partial network partitions. Nifty builds an overlay between nodes to detour packets around partial partitions. Nifty provides an approach for applications to optimize their operation during a partial partition. We demonstrate the benefit of this approach through integrating Nifty with VoltDB, HDFS, and Kafka.

Improving Ambulatory Surgery Environments: The Effects on Patient Preoperative Anxiety, Perception, and Noise.

The study aimed to comparatively evaluate three types of preoperative care environment in terms of patient experience outcomes including patient preoperative anxiety, perceived environmental qualities, and noise level. Preoperative anxiety is a major healthcare problem causing delays, complications, dissatisfaction, and rising healthcare costs. The design of preoperative spaces may play an important role in reducing preoperative anxiety and improving outcomes. Anonymous questionnaire surveys were conducted with 228 patients in the three types of preoperative bays that varied in terms of bay size and the amount of hard-wall partitions between bays to compare patient self-reported anxiety and perceived environmental qualities. Sound level measurements were conducted throughout the three preoperative care units. Female patients in the preoperative unit with largest bays and full hard-wall partitions between bays reported significantly lower levels of subjective anxiety (p's = .002, <.001) and higher levels of perceived environmental qualities on privacy, cleanliness, noise, and pleasantness (p's from <.001 to .017) than patients in the units with smaller bays and no or partial hard-wall partitions. Similar but less clear pattern was found among male patients. The lowest average noise levels were recorded in the unit with largest bays and full hard-wall partitions between bays (2.3-6.1 decibels lower than the other units). The design of preoperative care environment may contribute to the better management of preoperative anxiety. Further efforts in research and design are needed to maximize the benefits in clinical, experiential, and financial outcomes.

Multilevel modeling in food science: A case study on heat-induced ascorbic acid degradation kinetics

Characterization of variation of experimental results is achieved by repeating experiments. Frequently, results are averaged before data are analysed but that may not be the best practice because valuable information is then lost. Three other ways to analyze repetitions are: (1) each experiment is analyzed on its own (no pooling of data), (2) all experiments are analyzed together in one go (complete pooling), (3) data are analyzed together while allowing for similarities as well as differences in the result (partial pooling). Multilevel modeling uses partial pooling by partitioning variance over more than one level. Level 1 consists of the measurements themselves; higher levels consist of groups or clusters of measurements (repetitions, experiments at various temperatures, at various pH values, etc.) and parameters are analyzed both at the population and at the group/cluster level.The approach is applied to a case study in which heat-induced isothermal degradation of ascorbic acid was studied with 15 repetitions in an aqueous solution, making it a two-level study. The data were analyzed using averaging and complete pooling, complete pooling without averaging, no-pooling at all, and partial pooling. The kinetic model was established by letting the data decide about the order of the reaction, while this was compared to a model where the order was fixed at 1 (first-order model). Results show that both averaging with complete pooling, as well as complete pooling without averaging, strongly underestimate variation. The no-pooling technique overestimates variation, while partial pooling partitions variation over the levels and thus gives a better impression of the variation involved. The kinetics of ascorbic acid appear to be subject to strong variation when each experiment is considered separately because it is a compound that is very sensitive to all kinds of experimental conditions. With multilevel modeling it appeared to be possible to characterize the uncertainties involved much better than with single level modeling. A Bayesian analysis was performed, in which parameters are allowed to be variable, which is useful because multilevel modeling leads to characterization of variation of parameters. The Bayesian method allows to visualize the posterior distribution of parameters, thereby giving more insight in their behaviour. Also, a Bayesian analysis focuses more strongly on predictive accuracy of models, including multilevel models. The predictive accuracy of 4 models describing the same ascorbic acid data was compared and the multilevel model with reaction order estimated from the data performed by far the best in this regard. The pros and cons of multilevel modeling are discussed and it is concluded that multilevel modeling is to be preferred whenever the data allow to perform such an analysis.

On the infiniteness of a family of APN functions

APN functions play a fundamental role in cryptography against attacks on block ciphers. Several families of quadratic APN functions have been proposed in the recent years, whose construction relies on the existence of specific families of polynomials. A key question connected with such constructions is to determine whether such APN functions exist for infinitely many dimensions or do not.In this paper we consider a family of functions recently introduced by Li et al. in 2021 showing that for any dimension m≥3 there exists an APN function belonging to such a family.Our main result is proved by a combination of different techniques arising from both algebraic varieties over finite fields connected with linearized permutation rational functions and partial vector space partitions, together with investigations on the kernels of linearized polynomials.

Vibro-Acoustic Energy Transmission Analysis of the Acoustic Cavity with Multiple Partial Partitions

The general dynamic characteristics of the acoustic cavity with multiple partial partitions are presented in this thesis. A theoretical model has been developed for predictions, and several configurations are analyzed. To describe the apertures on the interface of subcavities, the virtual air panel assumption is introduced into the improved Fourier series system. The governing equations of the coupling system are derived by using the energy principle. The results obtained with the proposed model are firstly compared with the numerical calculations based on the finite element method (FEM). Subsequently, a configuration made up from a rigid cavity partitioned by a partial steel panel has been specifically built, and the forced responses of the coupling system have been measured for comparison and model validation. The present results are excellent over most of the studied frequency range. Furthermore, the visualizations of the interior sound intensity field of the acoustic cavity with three partial partitions under different frequencies are researched to illustrate the energy transmission paths and vibro-acoustic coupling mechanism of the complicated system. The obtained results are believed to be helpful in the optimal design of the vibro-acoustic coupling system with optimal sound insulation capacity.

Semimodularity and the Jordan–Hölder theorem in posets, with applications to partial partitions

Lattice-theoretical generalizations of the Jordan–Holder theorem of group theory give isomorphisms between finite maximal chains with same endpoints. The best one has been given by Czedli and Schmidt (after Gratzer and Nation), and it applies to semimodular lattices and gives a chain isomorphism by iterating up and down the perspectivity relation between intervals $$[x\wedge y,x]$$ and $$[y,x\vee y]$$ where x covers $$x\wedge y$$ and $$x\vee y$$ covers y. In this paper, we extend to arbitrary (and possibly infinite) posets the definitions of standard semimodularity and of the slightly weaker “Birkhoff condition”, following the approach of Ore (Bull Amer Math Soc 49(8):567–568, 1943). Instead of perspectivity, we associate tags to the covering relation, a more flexible approach. We study the finiteness and length constancy of maximal chains under both conditions and obtain Jordan–Holder theorems. Our theory is easily applied to groups, to closure ranges of an arbitrary poset, and also to five new order relations on the set of partial partitions of a set (i.e. partitions of its subsets), which do not constitute lattices.

Well-founded operators for normal hybrid MKNF knowledge bases

AbstractHybrid MKNF knowledge bases have been considered one of the dominant approaches to combining open world ontology languages with closed world rule-based languages. Currently, the only known inference methods are based on the approach of guess-and-verify, while most modern SAT/ASP solvers are built under the DPLL architecture. The central impediment here is that it is not clear what constitutes a constraint propagator, a key component employed in any DPLL-based solver. In this paper, we address this problem by formulating the notion of unfounded sets for non-disjunctive hybrid MKNF knowledge bases, based on which we propose and study two new well-founded operators. We show that by employing a well-founded operator as a constraint propagator, a sound and complete DPLL search engine can be readily defined. We compare our approach with the operator based on the alternating fixpoint construction by Knorr et al. (2011. Artificial Intelligence 175, 9, 1528–1554) and show that, when applied to arbitrary partial partitions, the new well-founded operators not only propagate more truth values but also circumvent the non-converging behavior of the latter. In addition, we study the possibility of simplifying a given hybrid MKNF knowledge base by employing a well-founded operator and show that, out of the two operators proposed in this paper, the weaker one can be applied for this purpose and the stronger one cannot. These observations are useful in implementing a grounder for hybrid MKNF knowledge bases, which can be applied before the computation of MKNF models.

Three-valued semantics for hybrid MKNF knowledge bases revisited

Knorr et al. (2011) [9] formulated a three-valued formalism for the logic of Minimal Knowledge and Negation as Failure (MKNF), and proposed a well-founded semantics for hybrid MKNF knowledge bases. The main results state that if a hybrid MKNF knowledge base has a three-valued MKNF model, its well-founded MKNF model exists, which is unique and can be computed by an alternating fixpoint construction. However, we show that these claims are erroneous. The goal of this paper is two-fold. First, we provide insight into why these claims fail to hold. This leads to a classification of hybrid MKNF knowledge bases into a hierarchy of four classes, of which the smallest one is what works for the well-founded semantics proposed by Knorr et al. After that, we characterize three-valued MKNF models into what we call stable partitions, which are similar to partial stable models in logic programming. The intuitive notion of stable partitions allows us to discover a new proof-theoretic method to determine whether a three-valued MKNF model exists for a given hybrid MKNF knowledge base. We can guess partial partitions and verify their stability by computing alternating fixpoints and by performing a consistency check. This algorithm can be applied to compute all three-valued MKNF models, as well as all two-valued ones originally formulated by Motik and Rosati for query answering. As a result, our work provides a uniform characterization of well-founded, two-valued, and all three-valued MKNF models, in terms of stable partitions and the alternating fixpoint construction.

Orders for Simplifying Partial Partitions

This paper is the third in a series devoted to orders on partial partitions in the framework of image analysis; the first two (with a view to image filtering and segmentation) identified 4 basic operations on blocks involved in such orders: merging, apportioning, creating and inflating blocks. Here, we consider orders where growing a partial partition decreases its support and diminishes the number of blocks. This can be done by a combination of merging (or apportioning) blocks with removing or deflating blocks (the opposite of creating or inflating blocks). We also introduce related operations on blocks: partial apportioning and partial merging. The new orders that we obtain can be useful in relation to skeletonization, image simplification, for processing segmentation markers or to describe the evolution of object boundaries in hierarchies. There are also possible applications in geographic information processing.

Computing Protein-Protein Association Affinity with Hybrid Steered Molecular Dynamics.

Computing protein-protein association affinities is one of the fundamental challenges in computational biophysics/biochemistry. The overwhelming amount of statistics in the phase space of very high dimensions cannot be sufficiently sampled even with today's high-performance computing power. In this article, we extend a potential of mean force (PMF)-based approach, the hybrid steered molecular dynamics (hSMD) approach we developed for ligand-protein binding, to protein-protein association problems. For a protein complex consisting of two protomers, P1 and P2, we choose m (≥3) segments of P1 whose m centers of mass are to be steered in a chosen direction and n (≥3) segments of P2 whose n centers of mass are to be steered in the opposite direction. The coordinates of these m + n centers constitute a phase space of 3(m + n) dimensions (3(m + n)D). All other degrees of freedom of the proteins, ligands, solvents, and solutes are freely subject to the stochastic dynamics of the all-atom model system. Conducting SMD along a line in this phase space, we obtain the 3(m + n)D PMF difference between two chosen states: one single state in the associated state ensemble and one single state in the dissociated state ensemble. This PMF difference is the first of four contributors to the protein-protein association energy. The second contributor is the 3(m + n - 1)D partial partition in the associated state accounting for the rotations and fluctuations of the (m + n - 1) centers while fixing one of the m + n centers of the P1-P2 complex. The two other contributors are the 3(m - 1)D partial partition of P1 and the 3(n - 1)D partial partition of P2 accounting for the rotations and fluctuations of their m - 1 or n - 1 centers while fixing one of the m/n centers of P1/P2 in the dissociated state. Each of these three partial partitions can be factored exactly into a 6D partial partition in multiplication with a remaining factor accounting for the small fluctuations while fixing three of the centers of P1, P2, or the P1-P2 complex, respectively. These small fluctuations can be well-approximated as Gaussian, and every 6D partition can be reduced in an exact manner to three problems of 1D sampling, counting the rotations and fluctuations around one of the centers as being fixed. We implement this hSMD approach to the Ras-RalGDS complex, choosing three centers on RalGDS and three on Ras (m = n = 3). At a computing cost of about 71.6 wall-clock hours using 400 computing cores in parallel, we obtained the association energy, -9.2 ± 1.9 kcal/mol on the basis of CHARMM 36 parameters, which well agrees with the experimental data, -8.4 ± 0.2 kcal/mol.

Orders on Partial Partitions Based on Block Apportioning

Some problems in image analysis require considering an order on space partitions: in image filtering the partition of image zones must grow, while in image segmentation one maximizes the partition into image objects.In the classical refinement order, the growth of a partition is achieved through the merging of blocks. We generalize this operation into block apportioning: some blocks in a partial partition may be split, and their parts are merged with some remaining blocks; this includes the possibility of a block being merged with another without being split. We study this operation in detail; we obtain then the apportioning order, which extends the refinement order.For partial partitions, the standard order combines the merging of blocks with the growth of existing blocks and the creation of new blocks. Combining these operations with block apportioning leads to the apportioning-inflating and extended orders; the latter contains the standard order. Our analysis rests on a study of Serra’s building order on partial partitions, which intervenes in the above new orders.Except the building order, all these orders are graded, with a compound grading for the apportioning-inflating and extended orders; this gives the number of elementary operations involved in a growth of a partial partition.

An Investigation of Natural Convection Heat Transfer in A Cubic Enclosure with Oriented Partial Partitions at Different Angles

Natural convection heat transfer in an enclosure provided with inclined partitions to the two adiabatic sides, heated from the bottom with uniform heat flux and cooled from the top at constant temperature is studied experimentally and numerically in this work. The inclined partitions is well covered with an insulated material, so that, it can be assumed as parts of the adiabatic walls that places on. The governing parameter, Rayleigh number, is fixed in this work within 2.6x1011, so that the effect of inclination angles of the two side’s partitions can be investigated. The inclination angles of the two baffles range as (0o ≤ and ≥ 150o). In numerical solution the effect of turbulence is modelled using (k-ε) model. Some applications need to use the enclosed fluid layers as insulation, so that one purpose of this work deals with improve the insulating properties of fluid layers. The experimental and numerical works are done in 36 runs, grouped into 6 collections. Each collection with 6 runs done under a fixed inclination angle of one baffle and change the second baffle inclination angle to investigate the enclosure flow field and heat transfer. The result shows that a multi cells forms when the two baffles aboard to each other’s, which is a reason to make a separation between a cold, and hot circulation cells that forms in the enclosure and act as insulator. It is also conclude that for all cases, the long insulated baffle of any inclination angle causes a reduction to the heat exchange inside the enclosure due to the damping cause to the flow field. The less average Nusselt number occurs when the two angles are equals, and the worst case is (θ=β=90o).

Numerical Investigation of Natural Convection in Air Filled Cubical Enclosure with Hot Wavy Surface and Partial Partitions

Natural convection in cubical enclosure with hot surface geometry and partial partitions has been analysed. The geometry is a cube with wavy hot surface (three undulations) and three partitions. The investigation has been performed for different partitions lengths and Rayleigh number while the Prandtl number kept constant. This problem is solved by using the partial differential equations which are the equation of mass, momentum, and energy. The results obtained show that the hot wall geometry with partitions affects the flow and the heat transfer rate in the cavity. It has been found also that the mean Nusselt number decreases compared with the heat transfer in the undulated cubical cavity without partitions.

Effect of Partial Partitions on Natural Convection in Air Filled Cubical Enclosure with Hot Wavy Surface

Natural convection in cubical enclosure with hot surface geometry and partial partitions has been analyzed. The geometry is a cube with wavy hot surface (three undulations) and three partitions. The investigation has been performed for different partitions lengths and Rayleigh number while the Prandtl number kept constant. This problem is solved by using the partial differential equations which are the equation of mass, momentum, and energy. The results obtained show that the hot wall geometry with partitions affects the flow and the heat transfer rate in the cavity. It has been found also that the mean Nusselt number decreases compared with the heat transfer in the undulated cubical cavity without partitions.

Experimental investigation of natural convection in an enclosure with partial partitions at different angles

Natural convection heat transfer in a partially partitioned enclosure has been investigated experimentally using Mach-Zehnder Interferometry technique. The top and bottom of the enclosure are insulated while one of the vertical walls is heated isothermally. The partitions are made of wood fiber and are attached to the heated wall with angles changing from 30? to 150? in different experiments. The length of each partition is equal to the width of the enclosure, therefore dividing the enclosure to isolated cells only at 90?. At other angles the cells are interconnected near the cold wall. Rayleigh number based on the enclosure width is changed from 3500 to 32000. Results for the local and the average Nusselt numbers at the heated wall of the enclosure are presented and discussed for various partition angles and Rayleigh numbers. It is found that, at each Rayleigh number, there exists an optimum inclination angle which minimizes the average Nusselt number.

Vibroacoustic modeling of ventilation window system with internal partial partitions

Domestic buildings exposed to traffic and environmental noise has always been a major concern. Among various noise control measures, ventilation windows offer appealing features by simultaneously allowing good sound attenuation and air ventilation. However, the modeling of such systems is challenging in that the simulation tools should cope with the complexity of the system; to reach reasonably high frequency range; and to offer the flexibility needed for system optimization. In the present study, the sound transmission through a double-glazed ventilation window is investigated using the patch transfer function method along with a handy treatment of the air aperture and micro-perforated elements. Numerical results show the sound insulation performance can be improved with internal partial partitions, which can be either solid or micro-perforated.

Closures on partial partitions from closures on sets

Abstract Jordens and Sturm investigated the link between closure systems on sets and closure systems on partitions. We extend that study to the wider framework of partial partitions, and highlight better the relation between these two families of closure systems. Then we consider the construction of a closure operator on partial partitions by the iterated application a set operator to the blocks of a partial partition; the resulting closure system fits into our framework.

Ordering Partial Partitions for Image Segmentation and Filtering: Merging, Creating and Inflating Blocks

The refinement order on partitions corresponds to the operation of merging blocks in a partition, which is relevant to image segmentation and filtering methods. Its mathematical extension to partial partitions, that we call standard order, involves several operations, not only merging, but also creating new blocks or inflating existing ones, which are equally relevant to image segmentation and filtering techniques. These three operations correspond to three basic partial orders on partial partitions, the merging, inclusion and inflating orders. There are three possible combinations of these three basic orders, one of them is the standard order, the other two are the merging-inflating and inclusion-inflating orders. We study these orders in detail, giving in particular their minimal and maximal elements, covering relations and height functions. We interpret hierarchies of partitions and partial partitions in terms of an adjunction between (partial) partitions (possibly with connected blocks) and scalars. This gives a lattice-theoretical interpretation of edge saliency, hence a typology for the edges in partial partitions. The use of hierarchies in image filtering, in particular with component trees, is also discussed. Finally, we briefly mention further orders on partial partitions that can be useful for image segmentation.