Multiple Spaces Research Articles

Potato leaf disease classification using fusion of multiple color spaces with weighted majority voting on deep learning architectures

Potato leaf disease classification using fusion of multiple color spaces with weighted majority voting on deep learning architectures

Space group prediction of complex alloy systems by product-based neural networks

Complex alloy systems exhibit some unique properties, many of which are attributed to ordering phenomena. At the atomic scale, this phenomenon refers to the occupancy probability of particles at the lattice sites. From the perspective of symmetry, it corresponds to different space groups. This study uses several machine learning algorithms to predict some common space groups of complex alloy systems. Under a large data set and a difficult classification task, the relevant models achieved excellent results on the test set. In the traditional support vector machine algorithm model, the prediction can reach the first-class level. Further, through the Product-based Neural Networks method under the wide and deep framework, the bottleneck of the traditional algorithm is broken through, and the prediction ability of the model is further improved. The average Area under curve value of the model can reach 99 %, and the prediction ability of multiple space groups has been improved. This study can not only provide more ideas for cross-scale modeling of complex systems, but the related models can also provide guidance for specific alloy design.

Craniotomy Tumor Removal for Brain Metastases of Hepatocellular Carcinoma : A Case Report

Introduction: Brain metastases (BM) are the most frequent type of brain tumors in adults and are the leading source of neurological issues related to systemic cancers. Case Description: Female, 49 years old, presented with decreased consciousness that occurred since 1 month before admission. Weakness of the right limbs is found. CT scan abdomen with contrast show hepatic segment VIII infarction. histopathological examination of hepatic tissue biopsy with the results of hepatocellular carcinoma MRI brain shows multiple lesions suggestive of brain metastasis. Discussion: Patient was diagnosed with space occupying lesion on the frontotemporal dd brain metastasis dd orbital roof meningioma + multiple space occupying lesion intracranial on the left parietooccipital ec. brain metastasis + hepatocellular segment V, VI, VII, and VIII. Patients underwent craniotomy tumor removal to remove intracranial masses. The patient's postoperative condition showed improvement in the level of consciousness with GCS E4M5V3. Conclusion. Craniotomy tumor removal provides several advantages for managing patients with brain metastases of hepatocellular carcinoma. Keyword: Craniotomy, Brain Metastases, Hepatocellular Carcinoma

Wavelet and Fourier analytic characterizations of pointwise multipliers of Besov spaces [formula omitted] with 0 < p ≤ 1

For any p∈(0,1] and s∈R, the authors prove two types of characterizations of the pointwise multiplier space M(Bp,ps(Rn)) of the Besov space Bp,ps(Rn). One type is based on wavelet analysis and is an extension of a well-known argument of Yves Meyer. The other type works with Fourier analytic terms. As an application of the above two types of characterizations, the authors further obtain a characterization of bounded functions in the uniform space Bp,p,unifs,τ(Rn) via Haar wavelets in the critical index τ=1p−sn.

Foucault and Wittgenstein: Practical Critique and Democratic Politics

This paper aims to explore a set of convergence points between Foucault’s and Wittgenstein’s perspectives on philosophy and language, integrating them into a mutually complementary approach that I term ‘practical critique.’ The concept of ‘practical critique’ is founded on three pillars: the understanding of philosophy and language as critical practices, the public nature of language, and confessional subjectivity. I examine these three areas of convergence across three subsequent sections. In the concluding section, I discuss how this perspective can be fertile for understanding democratic politics today. I argue that all three pillars predominantly support democratic politics over any other political form. To explain that, I engage with the debate on the language of democratic theory and the potential expansion of the understanding of the public sphere. The notion of the public that emerges from this perspective offers an alternative or supplementation to the classical Habermasian view of the public sphere and democratic theory. It is envisioned as an open space of discursive multiplicity and diversity, where practices of exclusion or oppression can be made visible, challenged, and resisted.

Biofilm Formation is Durably Prevented on Pre-Fabricated Antibiotic Cement Spacers Compared to Cobalt Chrome and Polyethylene

BackgroundA 2-stage revision remains the standard for managing chronic periprosthetic joint infection. Despite multiple spacer options, whether a particular one better resists biofilm formation remains unclear. Prefabricated polymethylmethacrylate (PMMA) articulating spacers containing antibiotics and a proprietary pore structure were developed to increase antibiotic elution characterized by a rapid burst phase for the initial one to two days and an extended slow-release phase for > 28 days. This in vitro study determined whether biofilm formation is prevented during the initial rapid burst phase and/or the slow-release phase. MethodsS. aureus-Xen36 was incubated in 1.5 mL of Luria-Bertani broth with PMMA discs with the proprietary pore structure either with or without gentamycin and vancomycin or with ‘Hoffman style’ positive-control discs (ultra-high molecular weight polyethylene or cobalt-chrome). Nonadherent bacteria were removed by three phosphate buffered saline rinses every 20 to 24 hours. Planktonic bacterial growth in the culture broth and biofilm formation on the discs were measured by colony forming unit (CFU) counting and resazurin reduction assays. Experiments were repeated > four times. ResultsNo detectable planktonic bacterial growth or biofilm formation occurred in cultures containing PMMA with antibiotics (≤ 15 CFUs/disc), whereas biofilms formed on PMMA without antibiotics, ultra-high molecular weight polyethylene, and cobalt-chrome (1 × 107 to 4 × 108 CFUs/disc, P < 0.0001). Biofilm formation was confirmed by a 100-fold decrease in sensitivity to vancomycin. To determine whether the antibiotic slow-release phase is sufficient to block biofilm formation, PMMA discs with antibiotics were preeluted for 14 days with multiple saline changes prior to bacterial inoculation. After antibiotic elution, still no detectable biofilms formed on PMMA discs with antibiotics (≤ 15 CFUs/disc, P < 0.0001). ConclusionsAntibiotic release during both the initial and slow-release phases prevented biofilm formation on PMMA with the proprietary pore structure. This may translate into improved infection eradication rates clinically.

Multi-sensor possibility PHD filter for space situational awareness

Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness (SSA) applications. In this process, the uncertainties of targets, dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system. The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.

Association Analysis Between Albumin Level and Maxillofacial Space Infection Severity

Early detection and treatment of maxillofacial space infection (MSI) can lead to successful outcomes; however, delayed intervention may result in severe systemic manifestations, posing a potential threat to life. The purpose of this study was to measure the association between albumin (ALB) levels and MSI severity. This was a retrospective cohort study including patients who were diagnosed with MSI at the Second Hospital of Lanzhou University from 2013 to 2023. Patients with unknown etiology, incomplete data, and those with psychiatric disorders and malignant tumors were excluded. Cured subjects are defined as the absence of clinical symptoms. The primary predictor variable was the ALB levels measured at the time of admission. We categorized ALB levels at admission into <30g/L, 30-35g/L, and >35g/L. The outcome variables were MSI severity measured by the length of stay (LOS) and death. LOS >14days was a prolonged length of hospital stay. Covariates included age, sex, involved anatomical spaces, neutrophil proportion and white blood cell counts. Statistical analysis was conducted using Pearson's χ2 test, one-way analysis of variance, independent sample t-test, multivariate logistic regression, Fisher's exact test, Kruskal-Wallis H test, Mann-Whitney U test, and the Gamma test. The P value was set at .05. This study encompassed 201 patients, of whom 123 were male (61.19%) and 78 were female (38.81%). Among these, 190 subjects (94.53%) were cured. The mean LOS was 16.38±18.93days, and 81 subjects (42.63%) had a LOS exceeding 14days. There were 11 deceased patients (5.47%). The neutrophil proportion (χ2=8.31; P<.01), and white blood cell count (χ2=11.14; P<.01) were significantly higher in deceased patients compared to those who were cured, and among the deceased patients, there was a greater percentage of patients with odontogenic infections (χ2=7.48; P=.02). There was no difference in ALB levels among the deceased patients (χ2=3.08 P=.21). A reduction in ALB levels is associated with an increased risk of prolonged LOS (χ2=21.77; P<.001). The relative risk (RR) of LOS extension when the ALB level is between 30 and 35g/L is 1.32 times higher than when the ALB level exceeds 35g/L (RR=1.32, 95% confidence interval (CI)=0.99 to 1.76). Furthermore, when ALB levels fall below 30g/L, the RR increases to 2.19 times higher compared to ALB levels greater than 35g/L (RR=2.19, 95% CI=1.47 to 3.26). Moreover, the LOS was used to measure the severity of MSI. Multivariate regression analysis found ALB levels were negatively associated with LOS. Subjects with ALB levels less than 30g/L had a 2.98 times higher risk (95% CI=1.12 to 7.75; P=.03) than subjects with ALB levels more than 35g/L. The risk factors of MSI including the ALB levels less than 35g/L (odds ratio (OR) (>35:30 to 35:<30)=1.00:1.85:2.98), multiple space infections (OR (1:2-3:4-5:>5)=1.00:0.52:1.92:5.49), and descending necrotizing mediastinitis (OR=4.30). ALB levels less than 35g/L, multiple space infections, descending necrotizing mediastinitis occurrence, and increased LOS are risk factors for increased severity of MSI and may lead to prolonged LOS.

Agent-based power management in apartment buildings: Tenant preferences and distributed energy resources

Apartment buildings (APBs) consist of multiple residential spaces and common facilities, unlike single-family buildings, and have correspondingly diverse energy demands; however, household preferences and behaviors have received little attention so far. Therefore, bidirectional flows between households and the APB must be considered to manage the APB energy demands by considering dynamic relationships built by households with preferences and behaviors. This study presents a demand-side management system called the unified home energy management system (U-HEMS), which considers distributed energy resources (DERs) for the APB. The U-HEMS was developed by bidirectional flows of management and decision-making processes for households and the APB. In the management process from the household to the APB, the APB power demands were optimized using a two-stage system, whereas in the decision-making process from the APB to the households, the next actions expressed by weighting factors of the household side were determined for the management process based on an agent-based model. The functionality and performance of the U-HEMS were compared with those of conventional approaches for a 100-unit APB, and five case studies were conducted to analyze their power demands considering different aspects: decision-making process, combinations of households’ preferences, reward system, penetration of electric vehicles, and DER capacities. The results reveal that the U-HEMS effectively reduces the overall costs and total peak power demand because a bidirectional model considers the dynamic relationships between households in the APB and DER operations subject to the time-varying pricing policy. Further studies are needed to assess seasonal effects on APB demands.

Common Fixed Point Theorems in multiplicative $\mathfrak{m}$-metric space with Applications to the System of Multiplicative Integral Equations and Numerical Results

This paper presents common fixed point results for a pair of self-mappings in multiplicative $\mathfrak{m}$-metric space. Also, we present the multiplicative partial metric structure as a specific case of a multiplicative m-metric space and demonstrate some common fixed point results. To support our conclusions, we present an illustrative example with discontinuous self-mappings. We also provide numerical iterations to approximate the common fixed point and graphs to visually substantiate our findings. As the consequences of our results, we demonstrate several common fixed point results in $m$-metric space and partial metric space. Our findings generalize various fixed point results from the literature. Furthermore, we employ the results to demonstrate the existence and uniqueness of solutions to a system of multiplicative Fredholm integral equations.

Implications of data topology for deep generative models

Many deep generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs), learn an immersion mapping from a standard normal distribution in a low-dimensional latent space into a higher-dimensional data space. As such, these mappings are only capable of producing simple data topologies, i.e., those equivalent to an immersion of Euclidean space. In this work, we demonstrate the limitations of such latent space generative models when trained on data distributions with non-trivial topologies. We do this by training these models on synthetic image datasets with known topologies (spheres, torii, etc.). We then show how this results in failures of both data generation as well as data interpolation. Next, we compare this behavior to two classes of deep generative models that in principle allow for more complex data topologies. First, we look at chart autoencoders (CAEs), which construct a smooth data manifold from multiple latent space chart mappings. Second, we explore score-based models, e.g., denoising diffusion probabilistic models, which estimate gradients of the data distribution without resorting to an explicit mapping to a latent space. Our results show that these models do demonstrate improved ability over latent space models in modeling data distributions with complex topologies, however, challenges still remain.

The Chemistry of Metal-Organic Frameworks for Multicomponent Gas Separation.

Adsorbents-based gas separation technologies are regarded as the potential energy-efficient alternatives towards current thermal-driven methods, and the study on multi-component gas separation is essential to deepen our understanding of the adsorbents for practical use. Relative to the ideal two-component mixtures, both the adsorption behavior and separation mechanisms are obviously more complex in multiple gas mixtures due to their close or even overlapped sizes and properties. The emergence of metal-organic frameworks with controllable pore size and pore chemistry provides the platform for the tailor-made pore structure to satisfy the harsh requirements of multi-component gas separation. This minireview highlights the recent advance of multi-component gas separation using metal-organic frameworks, including multiple impurities removal and selective molecular capture. Combining with the typical cases of hydrocarbon separations (C2, C4, and C8), the detailed discussion about the developed strategies (e.g. self-adaptive binding sites, multiple binding spaces, synergistic binding sites, synergistic sorbent separation technology, gate-opening effect, size and thermodynamic combine effect) that are adaptive to different scenarios would be provided. The review will conclude with our perspective on the existing barriers and the future direction of this topic.

Rainbow-inspired multicolor lateral flow immunoassay using multibranched platinum–rhodium-cobalt nanoparticles

Traditional lateral flow immunoassay (LFIA) using single-color labels has limitations in indistinct color change and restriction to the single-mode readout, which can hardly meet the demands of high-performance detection. Inspired by the multiple colors of the rainbow, we outline a multicolor LFIA using multibranched trimetallic platinum–rhodium-cobalt nanoparticles (PRCNPs) as signal labels to initiate multicolor conversion for diverse and multicolor readouts. The intrinsic properties of PRCNPs including their trimetallic composition, multibranched morphology, and strong absorption in the visible spectrum, confer advantages in catalytic activity, antibody utilization, and signal readout. Through the synergistic integration of the catalytic PRCNPs with the etching of gold nanorods, PRCNPs-LFIA enables multicolor readouts in a rainbow-like manner. PRCNPs-LFIA allows for qualitative analysis of acetamiprid, a widely-used neonicotinoid insecticide, at 2 ng mL−1. It also provides semi-quantitative analysis based on the discrimination of at least 10 colors. Furthermore, quantitative analysis can be conducted across multiple color spaces within the range of 0.01-10 ng mL−1, achieving a lowest detection result of 0.006 ng mL−1. The recoveries of acetamiprid in food samples detected by PRCNPs-LFIA in grayscale and multiple color space modes are 87.35%-103.59%, 85.56%-114.33%, 86.14%-114.77%, and 87.81%-112.01%, respectively, with all coefficients of variation below 15%. These results demonstrate the feasibility and accuracy of PRCNPs-LFIA in practical applications. Overall, this study illustrates the potential of multifunctional PRCNPs in the development of multicolor LFIA and broadens the biosensing strategies for point-of-care testing.

Droplet dynamics in a two-dimensional rarefied gas under Kawasaki dynamics

This is the second in a series of three papers in which we study a lattice gas subject to Kawasaki conservative dynamics at inverse temperature β>0 in a large finite box Λβ⊂Z2 whose volume depends on β. Each pair of neighboring particles has a negative binding energy−U<0, while each particle has a positive activation energyΔ>0. The initial configuration is drawn from the grand-canonical ensemble restricted to the set of configurations where all the droplets are subcritical. Our goal is to describe, in the metastable regime Δ∈(U,2U) and in the limit as β→∞, how and when the system nucleates, i.e., grows a supercritical droplet somewhere in Λβ. In the first paper we showed that subcritical droplets behave as quasi-random walks. In the present paper we use the results in the first paper to analyze how subcritical droplets form and dissolve on multiple space–time scales when the volume is moderately large, namely, |Λβ|=eΘβ with Δ<Θ<2Δ−U. In the third paper we consider the setting where the volume is very large, namely, |Λβ|=eΘβ with Δ<Θ<Γ−(2Δ−U), where Γ is the energy of the critical droplet in the local model, i.e., when Λβ has a fixed volume not depending on β and particles can be created and annihilated at the boundary, and use the results in the first two papers to identify the nucleation time. We will see that in a very large volume critical droplets appear more or less independently in boxes of moderate volume, a phenomenon referred to as homogeneous nucleation. Since Kawasaki dynamics is conservative, i.e., particles move around and interact but are preserved, we need to control non-local effects in the way droplets are formed and dissolved. This is done via a deductive approach: the tube of typical trajectories leading to nucleation is described via a series of events, whose complements have negligible probability, on which the evolution of the gas can be captured by a coarse-grained Markov chain on a space of droplets, which we refer to as droplet dynamics.

Hierarchical optimization by spatial-temporal indictor in multi-scale decision pyramid for constrained large-scale multi-objective problems

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not so efficient when handling constrained large-scale multi-objective problems (CLSMOPs). To overcome CLSMOPs, a hierarchical optimization, based on the spatial–temporal indictor in the decision pyramid (HOSTI-DP), is proposed to determine the dynamic trade-off between feasible and infeasible solutions in the effectively shrunken searching area. The decision pyramid consists of multiple decision spaces with a set of descending numbers of variables; it is initially constructed to speed up the solution convergence by the layer-by-layer dimensionality reduction and increase the population diversity based on the accumulation of these layers. The spatial–temporal indictor considers both the optimization stage (time) and the hierarchy structure of the decision pyramid (space) and it is designed to restrain the prematurely feasible solutions and find the promising infeasible solutions. Finally, the hierarchical optimization strategy, which applies the spatial–temporal indicator to the evolutionary algorithms in every layer of the decision pyramid and dynamically arranging the proportion among them, is designed for comprehensive balance of objectives, constraints, and computational complexity. Nine representative and state-of-the-art CMOEAs have been compared with the HOSTI-DP to demonstrate its effectiveness through comparative experiments on CLSMOPs with equality and inequality constraints and 1000 decision variables. Experimental results show that HOSTI-DP can significantly improve the performance of these CMOEAs.

Classification of Temperature and Humidity in Green Open Spaces by Implementing Internet of Things (IoT) using Mamdani Fuzzy Logic

The condition of green open spaces plays a pivotal role in influencing the comfort and well-being of users engaged in various activities within these environments. Hydrological and microclimate factors, particularly temperature and humidity, hold significant sway over the ecological balance in these areas. This study addresses the need for a tool to assess air temperature and humidity, facilitating public access to critical environmental data that impacts the psychological well-being of living organisms. In pursuit of this objective, a comprehensive monitoring system harnessing the power of the IoT was developed. This system integrates DHT22 and MQ-9 sensors to monitor temperature, humidity, and CO2 gas levels within multiple green open spaces. Data from these sensors is transmitted to a central database with minimal time delays: 2 seconds from the sensors to the server, 1 second from the server to the application, and 3 seconds from the hardware to the application. Impressively, this data transmission achieved a 100% success rate. To assess the comfort levels of these green open spaces, the system employs fuzzy Mamdani logic. It processes data obtained from the DHT22 sensor, using predefined temperature and humidity value thresholds. This innovative monitoring system provides valuable insights into the environmental conditions of green open spaces, contributing to the enhancement of the overall quality and suitability of these areas for various activities.

Consensus local graph for multiple kernel clustering

Due to the ability to represent the relationship among data, graph has received extensive attention in clustering field. As illustrated by existing studies, learning graph in kernel space is an effective way to capture the structure of data. Among kernel-based methods, multiple kernel learning (MKL) is increasingly popular since it can automatically utilize the complementary information contained in base kernels. Although many existing MKL-based graph learning algorithms have promising learning abilities, we observe that they (1) put too much attention on learning the consensus kernel which probably contains lots of redundant information and loses the diversity of base kernels; (2) ignore the local structure of the representations in multiple kernel spaces. To eliminate the bad effect of these issues, we propose a novel graph learning method, termed consensus local graph based on MKL (CLGMKL), for clustering. In CLGMKL, a low-rank kernel matrix is used to extract the discriminative information of each base kernel and a local graph is constructed to capture the local structure contained in each new kernel. Then CLGMKL combines these local graphs in a self-weighed way. Since the above processes are associated with each other, we jointly optimize them to obtain the overall optimality. An effective learning scheme with proved convergence is developed to optimize the combined objective function. Finally, extensive experiments on some popular datasets are conducted to test the effectiveness of the presented method. As illustrated, CLGMKL is more competitive than the state-of-the-art algorithms.

The world without, the world within and the space between: Amanda Dalton’s adaptation of Nosferatu for BBC Radio 3

Made for BBC Radio 3, The Midnight Cry of the Deathbird (2012) is an adaptation of F. W. Murnau’s German expressionist silent film Nosferatu (1922), which serves as both a target text (of Bram Stoker’s novel Dracula, 1897) and a source text (of The Midnight Cry). Thus, complex layers of adaptation and audience expectations are evoked through medium-specific qualities, such as the ability of radio to represent the immaterial or disembodied and multiple spaces at the same time, including a between-world along inside/outside worlds. Radio can exist almost entirely in the mind, effortlessly navigating between outer and inner dimensions. This article explores questions of narration, embodiment/non-corporeality and inner space. Using Scott McCloud’s concept of radio as a ‘mono-sensory medium’, it centres primarily on the characters of the Nosferatu (a bodiless essence of contagion) and Roger, a friendly Everyman who serves as the listener’s radio guide.

A map without guarantees: Stuart Hall and Palestinian geographies

Israel has imposed different forms of settler colonialism across the map of historic Palestine, but there is no inevitability about future outcomes Thinking about space as an entry point, this article traverses multiple spaces within historic Palestine to explore how Israeli settler colonialism unfolds as a set of contingent and differentiated relations, practices and processes. It uses a theoretical diary, photographs, and countermaps to bring into sharp relief both the sedimented nature of settler colonial domination and its vulnerability to Palestinian practices of resistance. Moving from the refugee camp in the West Bank to the village and the city in the territories Israel occupied in 1948, the article draws on Stuart Hall’s approach to ‘conjunctural analysis’ to demonstrate how there exist multiple Palestinian geographies; how such geographies stand in relations of domination and subordination vis-??-vis one another and the Israeli state; and how such geographies remain prone to rupture and transformation. The article insists on a conjunctural reading that refutes teleological understandings of settler colonialism: there are no guarantees that its dominant modes of dispossession and elimination will succeed.

EEG Motor Imagery Classification: Tangent Space with Gate-Generated Weight Classifier.

Individuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain-computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.