Scalar Operators Research Articles

Building resilient healthcare distribution networks: Adapting to crises, securing supplies and improving scalability

Healthcare distribution networks are the backbone of effective medical supply chains, ensuring the timely delivery of critical resources to healthcare providers and patients. However, disruptions caused by crises such as pandemics, natural disasters, and geopolitical conflicts expose vulnerabilities in these networks, highlighting the urgent need for resilience, scalability, and security. This paper examines strategies to build robust healthcare distribution networks capable of adapting to crises, securing supply chains, and scaling operations efficiently to meet fluctuating demands. A resilient distribution network incorporates diversified sourcing strategies, regional warehousing, and advanced logistics planning to mitigate risks associated with supply chain disruptions. Digital transformation plays a critical role, with tools such as blockchain for secure and transparent supply tracking, artificial intelligence (AI) for demand forecasting, and Internet of Things (IoT) devices for real-time monitoring of medical supplies. These innovations enhance decision-making and enable rapid responses to emergencies. The study also explores the importance of collaboration among stakeholders, including governments, private organizations, and non-governmental entities, to create integrated systems that ensure the uninterrupted availability of healthcare supplies. Additionally, scalability is addressed through modular distribution models and data-driven optimization of delivery routes, which reduce costs and improve efficiency. This research underscores the need for proactive planning, investment in technology, and policy reforms to build resilient healthcare distribution networks. Such efforts not only safeguard supply chains during crises but also support long-term improvements in healthcare accessibility, efficiency, and patient outcomes.

Utilization of Augmented and Mixed Reality in Training Mining Machines

Augmented reality (AR) and mixed reality (MR) are used in many disciplines, especially education, science, health, safety and engineering. It is based on the visualization and interaction of virtual graphic designs in the natural environment with its own hardware and software. In this study, the effect of augmented and mixed reality applications in the training of open-pit mining machines is examined. In the application called AR Book, a booklet containing machine visuals and technical information was created. In the project developed in the Unity real-time development engine, the database created in the Vuforia AR engine and 3D machine models matched with them were used. An image-target-based augmented reality application was implemented in this booklet using Android devices. In another application, a ground-plane-based application was developed for the mixed reality device MS Hololens 2. With the Hand Interactions feature, machines positioned in the natural environment can be controlled with drag, rotate and scale operations, enlarged to gigantic sizes and examined. The evaluations of the training group regarding the use of AR and MR applications together with traditional education were sought. They were also asked to make comparisons between the mobile device and Hololens 2. There have been highly positive results in integrating imaging technologies into education.

Farmer participation in cooperatives enhances productive services in village collectives: a subjective evaluation approach

IntroductionProvision of agricultural productive services to farmers is crucial for integrating them into the modern agricultural system. However, small-scale farmers often face difficulties in accessing these services. One internationally recognized approach to addressing this issue is the government-led provision of productive services to small-scale farmers. In China, production services are provided through village collectives, which are economic organizations established in townships and villages to manage collective assets, develop resources and economy, and provide services to members. Farmer participation in these services can enhance inclusive service dynamics, improving access to services and promoting rural equity.MethodsFarmers’ subjective evaluations directly reflect their access to collective agricultural productive services. This study utilized a binary logit model to analyze the impact and mechanism of farmer participation in cooperatives on the collective supply of agricultural productive services. The study involved 3,900 farmers from 29 provinces (autonomous regions and municipalities) in China.Results and discussionThis study proposes for the first time a “cooperative+collective” model for the provision of productive services. In China, safeguarding social equity is one of the important objectives of the Government, and safeguarding and supporting the interests of small-scale farmers is crucial to safeguarding social equity. Participation in cooperatives increased farmers’ access to agricultural productive services. The analysis revealed that farmers have significantly increased their level of access to agricultural production services through participation in the “cooperative+collective” model of productive services. Farmers have participation in cooperatives helped integrate and expand farmers’ demand, leading to the continuous operation and expansion of business scale, thereby enhancing the collective supply of agricultural productive services. Furthermore, those income low-income, older farmers derived more benefits from participating in cooperatives in terms of accessing these services. This study offers empirical evidence supporting the effectiveness of collective agricultural services.

CFT constraints on parity-odd interactions with axions and dilatons

We illustrate how the conformal Ward identities in momentum space completely determine the structure of a parity-odd three-point correlator involving currents, energy-momentum tensors, and at least one scalar operator in d=4. Conformal invariance fixes almost all such possible correlators to vanish. The only exceptions are given by the ⟨JJO⟩odd and the ⟨TTO⟩odd which in momentum space are protected by chiral and conformal anomalies. Specifically, one can obtain a nonvanishing solution by considering scalar operators such as O=∇·JA, O=gμνTμν, or their shadow transforms. We comment on the implications of these results that constrain the coupling of axions and dilatons in a conformal phase of the early universe. Published by the American Physical Society 2024

Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits

The adoption of machine learning solutions is rapidly increasing across all parts of society. As the models grow larger, both training and inference of machine learning models is increasingly outsourced, e.g. to cloud service providers. This means that potentially sensitive data is processed on untrusted platforms, which bears inherent data security and privacy risks. In this work, we investigate how to protect distributed machine learning systems, focusing on deep convolutional neural networks. The most common and best-performing mixed MPC approaches are based on HE, secret sharing, and garbled circuits. They commonly suffer from large performance overheads, big accuracy losses, and communication overheads that grow linearly in the depth of the neural network. To improve on these problems, we present Dash, a fast and distributed private convolutional neural network inference scheme secure against malicious attackers. Building on arithmetic garbling gadgets [BMR16] and fancy-garbling [BCM+19], Dash is based purely on arithmetic garbled circuits. We introduce LabelTensors that allow us to leverage the massive parallelity of modern GPUs. Combined with state-of-the-art garbling optimizations, Dash outperforms previous garbling approaches up to a factor of about 100. Furthermore, we introduce an efficient scaling operation over the residues of the Chinese remainder theorem representation to arithmetic garbled circuits, which allows us to garble larger networks and achieve much higher accuracy than previous approaches. Finally, Dash requires only a single communication round per inference step, regardless of the depth of the neural network, and a very small constant online communication volume.

Maps on self-adjoint operators preserving some relations related to commutativity

Abstract In this paper, we give the complete description of maps on self-adjoint bounded operators on Hilbert space which preserve a triadic relation involving the difference of operators and either commutativity or quasi-commutativity in both directions. We show that those maps are implemented by unitary or antiunitary equivalence and possible additive perturbation by a scalar operator.

Holographic thermal correlators and quasinormal modes from semiclassical Virasoro blocks

Motivated by its relevance for thermal correlators in strongly coupled holographic CFTs, we refine and further develop a recent exact analytic approach to black hole perturbation problem, based on the semiclassical Virasoro blocks, or equivalently via AGT relation, the Nekrasov partition functions in the Nekrasov-Shatashvili limit. Focusing on asymptotically AdS5 black hole backgrounds, we derive new universal exact expressions for holographic thermal two-point functions, both for scalar operators and conserved currents. Relatedly, we also obtain exact quantization conditions of the associated quasinormal modes (QNMs). Our expressions for the holographic CFT4 closely resemble the well-known results for 2d thermal CFTs on ℝ1,1. This structural similarity stems from the locality of fusion transformation for Virasoro blocks. We provide numerical checks of our quantization conditions for QNMs. Additionally, we discuss the application of our results to understand specific physical properties of QNMs, including their near-extremal and asymptotic limits. The latter is related to a certain large-momentum regime of semiclassical Virasoro blocks dual to Seiberg-Witten prepotentials.

German schon and noch as scalar additives with a marginality twist

AbstractThis article presents a description of German schon and noch as nontemporal scalar focus operators. Both items operate in a scalar model of sufficiency and signal that the focus value yields a more informative proposition than all alternatives under consideration; that is, they are special cases of scalar additives. Where the two expressions differ is in the complementary perspectives they evoke. Schon relates to higher alternatives. Noch relates to lower alternatives, but brings about an inverse (i.e., antonymically ordered) scalar model. The use of schon and noch as scalar sufficiency operators is traced back to an amalgamation of two other uses of the same items. The descriptive findings contribute to the advancement of our cross-linguistic understanding of scalar focus operators and raise fundamental questions pertaining to the typological and theoretical status of scale reversal phenomena.*

Euclidean wormholes in holographic RG flows

We describe a one-parameter family of Euclidean wormhole solutions with the topology of a compact hyperbolic space times an interval in Einstein gravity minimally coupled to a massless scalar field in AdSd+1 commonly referred to as Einstein-dilaton gravity. These solutions are locally described by the same metric and dilaton profile as the single-boundary Janus domain wall solutions in the same theory which are usually studied in the context of holographic RG flows. The wormholes compute the averaged product of partition functions of CFTs on either boundary deformed by different marginal couplings to the scalar operator dual to the dilaton. We observe that the renormalised volumes of these wormholes increase monotonically with the difference in the marginal couplings on the boundary thereby showing that the pair of CFTs on the boundaries get increasingly decorrelated as the difference in the marginal couplings increases. We use the partition functions of the three-dimensional wormhole solutions to determine the variance of the OPE data of local operators between the marginally deformed 2d CFTs and quantify how the variance decays with the difference in marginal couplings. In addition, a family of wormholes sourced by a thin shell of dust determine how the variance of the matrix elements of the dual line defect decays with the difference in marginal couplings. Applying the GKPW dictionary to wormholes, we compute averages of integrated dilaton correlators treating the wormhole amplitude as a functional of the dilaton sources. We observe that the crossed two-point correlators with a dilaton insertion on either boundary decay monotonically with the difference in marginal couplings consistent with the observation that the CFTs increasingly decorrelate as the difference in marginal couplings grows.

An evolutionary analysis method for small cracks in drive shafts based on cross-scale modeling

Due to the significant scale and complex operating conditions of drive shafts, experimental analysis of small crack evolution becomes challenging. This study presents a cross-scale modeling approach to analyze the evolution of small cracks in drive shafts. Firstly, by employing the sub-model method and the variable-node finite element method, two mesoscale models based on crystal plasticity theory are integrated into a macroscale model, constructing a cross-scale analysis model for small crack evolution. Then, by measuring the heterogeneous deformation degree in the mesoscale models, models for surface small crack initiation and propagation, accounting for heterogeneous deformation, are developed. Besides, energy-based models for internal small crack nucleation and propagation are established. To simulate the initiation and propagation of surface small cracks, the study utilizes a mesoscale variable-node shell model incorporating the Crystal Plasticity Finite Element Method (CPFEM), coupled with the Crystal Plastic Coupling Extended Finite Element Method (CP-XFEM). From the simulation results, a cross-section of the mesoscale solid model is extracted to establish a polycrystalline plasticity mesoscale cross-section model. Then, utilizing the element deletion method in ABAQUS, simulations for internal small crack nucleation and propagation are performed. The findings demonstrate that the cross-scale model effectively reflects the irregular propagation rate of microscopic small cracks. The propagation patterns of surface small cracks are generally consistent, with more significant path deviations observed in models exhibiting significant grain orientation differences. In the latter stages of internal small crack propagation, as the number of failed grains rises, stress concentration in the microstructure intensifies, resulting in a significant acceleration phase in crack propagation and a significant decrease in propagation life.

Modeling a pure qP wave in attenuative transverse isotropic media using a complex-valued fractional viscoacoustic wave equation

Seismic anisotropy, characterized by direction-dependent seismic velocity and attenuation, is a fundamental property of the earth, widely observed in field and laboratory measurements. This anisotropy significantly influences seismic wavefield attributes, such as amplitude, phase, and traveltime. Accurate quantification of anisotropy-related seismic wave propagation is essential for global- and regional-scale studies in seismology, such as imaging and inversion. Because the elastic assumption of the earth requires contending with S waves, which are often weak in our seismic recording, we derive a new general complex-valued spatial fractional Laplacian (SFL) viscoacoustic anisotropic pure quasi-P (qP) wave equation (WE) in attenuative transverse isotropic (A-TI) media under the acoustic assumption and use the fixed-order SFL operator to quantify seismic wave propagation in heterogeneous media. Our WE accurately captures anisotropic effects in seismic velocity and attenuation, even in strongly attenuative and anisotropic environments. It also describes the decoupled energy attenuation and velocity dispersion behaviors of the frequency-independent quality factor Q, beneficial for seismic imaging and inversion. Compared with existing viscoacoustic anisotropic pure qP WEs in A-TI media, our WE simplifies computational complexity and is easy to implement, as it decomposes into a complex-valued scalar operator and two differential operators. The evaluated dispersion curves further confirm the accuracy of our approach. Numerical examples in complex geologic settings demonstrate the new equation’s precision, stability, and efficiency in modeling seismic wave propagation.

Inverse problem of correlation functions in holography

This paper shows that the bulk metric of a planar/spherically/hyperbolically symmetric asymptotically anti-de Sitter static black brane/hole can be reconstructed from its boundary frequency 2-point correlation functions of two probe scalar operators by solving Gel’fand-Levitan-Marchenko integral equation. Since the frequency correlation function is easily handled in experiments and theories, this paper not only proposes a new method to “measure” the corresponding holographic spacetime for a material that has holographic dual but also provides an approach to experimentally check if a system has holographic dual.

The Impact of Agricultural Socialization Services on the Ecological Protection of Rice Farmland in Jianghan Plain, China

The ecological protection of cultivated land is crucial for advancing high-quality agricultural development. This study analyzes the impact of agricultural socialization services on the ecological conservation of farmland, focusing on the reduction of chemical fertilizers and pesticides among rice farmers in the Jianghan Plain area. Utilizing data from 743 farmer household surveys conducted in 2023, the findings reveal that agricultural socialization services significantly encourage farmers to reduce chemical input usage. For every 1% increase in the level of agricultural social services, the average fertilizer use per hectare will decrease by 14% and the average pesticide use per hectare will decrease by 16.4%. The study identifies scale operation, factor substitution, and alleviation of capital constraints as mediating factors enhancing the efficacy of these services. Furthermore, heterogeneity analysis indicates that these services are more effective in reducing chemical inputs among large-scale and newer-generation farmers compared to their small-scale and older counterparts. Additionally, technology-intensive socialized services exhibit a stronger impact on reducing chemical inputs than labor-intensive services.

Challenges and Solutions in Scaling Operations Globally: A Guide for U.S. Food and BeverageExecutives

Abstract—This paper explores the challenges and solutions associated with scaling operations globally in the food and beverage sector from a U.S. executive’s perspective. It examines why expanding internationally makes sense for companies with successful domestic products, highlighting the advantages of U.S. innovation and lower costs of innovation compared to local markets abroad. The paper outlines a strategic roadmap for global expansion, including test markets, partnerships with local firms, and focusing on countries with positive sentiments toward U.S. food safety standards. A hypothetical case study illustrates the growth process, emphasizing the need for internal buy-in, patience, and a long-term view. Potential pitfalls and strategies for success are discussed, along with competitive analysis and the role of disruption in scaling products. Index Terms—Global Expansion, Food and Beverage Industry, Consumer Packaged Goods (CPG), International Operations, U.S. Innovation, Market Entry Strategy, Competitive Analysis, Disruption

Exploring the lepton flavor violating decay modes b → sμ±τ∓ in SMEFT approach

We perform an analysis of the consequences of various new physics operators on the lepton flavor violating (LFV) decay modes mediated through b→sℓ1ℓ2 transitions. We scrutinize the imprints of the (pseudo)scalar and axial (vector) operators on the exclusive LFV decay channels B(s)→(ϕ,K⁎,K2⁎)ℓ1ℓ2 and Λb→Λℓ1ℓ2, where ℓ1,ℓ2 represent μ or τ. The new physics parameters are constrained by using the upper limits of the branching fractions of the Bs→τμ and B→Kτμ processes, assuming the new physics couplings to be real. We then explore the key observables such as the branching fractions, the forward-backward asymmetries, and the longitudinal polarization fractions of the B→(K⁎,ϕ,K2⁎)τ±μ∓ decays. In addition, we also investigate the impact of the new physics couplings on the baryonic Λb→Λτ±μ∓ decay channels mediated by the b→s quark level transition. With the experimental prospects at LHCb upgrade and Belle II, we also predict the upper limits of the above-discussed observables, which could intrigue the new physics search in these channels.

Black hole singularity from OPE

Eternal asymptotically AdS black holes are dual to thermofield double states in the boundary CFT. It has long been known that black hole singularities have certain signatures in boundary thermal two-point functions related to null geodesics bouncing off the singularities (bouncing geodesics). In this paper we shed light on the manifestations of black hole singularities in the dual CFT. We decompose the boundary CFT correlator of scalar operators using the Operator Product Expansion (OPE) and focus on the contributions from the identity, the stress tensor, and its products. We show that this part of the correlator develops singularities precisely at the points that are connected by bulk bouncing geodesics. Black hole singularities are thus encoded in the analytic behavior of the boundary correlators determined by multiple stress tensor exchanges. Furthermore, we show that in the limit where the conformal dimension of the operators is large, the sum of multi-stress-tensor contributions develops a branch point singularity as predicted by the geodesic analysis. We also argue that the appearance of complexified geodesics, which play an important role in computing the full correlator, is related to the contributions of the double-trace operators in the boundary CFT.

Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification

Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).

AUTOMAP: Inferring Rank-Polymorphic Function Applications with Integer Linear Programming

Dynamically typed array languages such as Python, APL, and Matlab lift scalar operations to arrays and replicate scalars to fit applications. We present a mechanism for automatically inferring map and replicate operations in a statically-typed language in a way that resembles the programming experience of a dynamically-typed language while preserving the static typing guarantees. Our type system---which supports parametric polymorphism, higher-order functions, and top-level let-generalization---makes use of integer linear programming in order to find the minimum number of operations needed to elaborate to a well-typed program. We argue that the inference system provides useful and unsurprising guarantees to the programmer. We demonstrate important theoretical properties of the mechanism and report on the implementation of the mechanism in the statically-typed array programming language Futhark.

Efficient pure qP-wave modeling and reverse time migration in tilted transversely isotropic media calculated by a finite-difference approach

Subsurface anisotropy is commonly induced by shale layers, aligned cracks, and fine bedding and has a significant impact on seismic wave propagation. Ignoring anisotropic effects during seismic migration degrades image quality. Therefore, we derive a pure qP-wave equation with high accuracy for modeling seismic wave propagation in tilted transversely isotropic (TTI) media. However, the derived pure qP-wave equation requires a computationally expensive spectral-based method for performing numerical simulations. This is unsuitable for large-scale industrial applications, particularly 3D applications. For numerical efficiency, we first decompose the newly derived wave equation into some fractional differential operators and spatial derivatives. The spatial derivatives can be directly solved by conventional finite-difference (FD) approaches. Then, we use an asymptotic approximation to find an equivalent form of fractional differential operators, obtaining scalar operators that we can discretize with the FD method. Numerical tests show that our TTI pure qP-wave equation with an FD discretization can produce accurate and highly efficient wavefield simulations in TTI media. We also use our TTI pure qP-wave equation with an FD discretization as a forward engine to implement TTI reverse time migration (RTM). Synthetic examples and a field data test demonstrate that our TTI RTM can effectively correct the anisotropic effects, providing high-quality imaging results while maintaining good computational efficiency.

Enhancing SME Competitiveness Through Coopetition Networks: A Service-Dominant Logic Approach

In the era of rapid globalization and digital transformation, small and medium-sized enterprises (SMEs) in the manufacturing sector encounter significant challenges in scaling operations, enhancing operational efficiency, and fostering innovation. This study focuses on manufacturing SMEs, where coopetition– a strategic blend of competition and cooperation– addresses these challenges. Despite its potential to drive innovation and value creation, coopetition networks often struggle to succeed, particularly under the pressures of advancing technology and the need for digital collaboration. To address these issues, this research develops a comprehensive framework that integrates Service-Dominant (S-D) Logic with technology-enabled coopetition networks. Utilizing the Design Science methodology, the study presents a framework that underscores the crucial role of technology in fostering value co-creation within SME networks. This framework is designed to enable sustainable and effective engagement in coopetition, significantly mitigating the risk of network failure by aligning technological innovation with coopetition principles. The proposed framework was evaluated through a targeted survey of managers from SMEs in the Portuguese stone manufacturing sector. The survey results con firmed the practical applicability and potential to promote robust and sustainable coopetition strategies. By integrating theoretical insights with practical applications, this research offers a roadmap for SMEs to effectively manage the complexities of coopetition within the digital supply chain environment.