Multiple Examples Research Articles

Global logistics and supply chain integration in the digital era: a focus on China's Belt and Road Initiative

PurposeTo delve into the integration of global logistics and supply chain networks amidst the digital transformation era. This study aims to investigate the potential role of China’s Belt and Road Initiative (BRI) in facilitating the integration of global flows encompassing both tangible goods and intangibles. Additionally, the study seeks to incorporate third-party logistics activities into a comprehensive global logistics and supply chain integration framework.Design/methodology/approachPrior research is synthesised into a global logistics and supply chain integration framework. A case study was undertaken on Yuan Tong (YTO) express group to investigate the framework, employing qualitative data analysis techniques. The study specifically examined the context of the BRI to enhance comprehension of its impact on global supply chains. Information was collected in particular to two types of supply chain flows, the physical flow of goods, and intangible information and cash flows.FindingsThe proposed framework aligns well with the case study, leading to the identification of global logistics and supply chain integration enablers. The results demonstrate a range of ways BRI promotes global logistics and supply chain integration.Research limitations/implicationsThe case study, with multiple examples, focuses on how third-party logistics firms can embrace global logistics and supply chain integration in line with BRI. The case study approach limits generalisation, further applications in different contexts are required to validate the findings.Originality/valueThe framework holds promise for aiding practitioners and researchers in gaining deeper insights into the role of the BRI in global logistics and supply chain integration within the digital era. The identified enablers underscore the importance of emphasising key factors necessary for success in navigating digital transformation within global supply chains.

Message-level Claims Require Message-level Data Analyses: Aligning Claims and Evidence in Communication Research

ABSTRACT Researchers often invoke individual-level correlations (correlations between properties of individuals) as a basis for message-level claims (claims about properties of messages). For example: “People who are more transported by a narrative message are generally also more persuaded by it; individuals’ transportation and persuasion scores are positively correlated. Therefore narratives that are more transporting will be more persuasive than narratives that are less transporting.” But that inference is mistaken. The reasoning mistake is not specific to that example, but rather is a common mistake in communication research. This article explains the reasoning mistake, identifies multiple examples of the mistake, and discusses implications of and remedies for this circumstance.

Logging-While-Drilling Laterolog vs. Electromagnetic Propagation Measurements: Which Is Telling the True Resistivity?

Summary The electromagnetic propagation (EMP) measurement frequently acquired with logging-while-drilling (LWD) tools in high-angle wells is sensitive to geometrical effects that can mask the true formation resistivity. Less commonly used, the LWD laterolog measurement is sometimes perceived as providing data too shallow to give a true formation resistivity (Rt). In this paper, we presents modeling and actual examples to demonstrate that the laterolog can often provide a superior resistivity measurement for formation evaluation to that of the LWD EMP tool. We examine the laterolog and EMP resistivities in several high-angle wells crossing carbonate formations in 8.5-in. and 6.125-in. hole sizes. In the 8.5-in. sections, producers and water injectors (high- and low-resistivity ranges) were evaluated. In the 6.125-in. sections, one reservoir sandwiched between two very high-resistivity layers and another borehole in a highly fractured reservoir were examined. The laterolog data were corrected for invasion using a 1D inversion of the memory data. Structure-based forward modeling was used to examine and explain the differences between the laterolog and EMP resistivity measurements. In the first example in a thick low-resistivity water reservoir, laterolog resistivity and EMP resistivity agree, showing that the two tools provide the same measurement when no geometrical effects are present. In the first part of the second example, a reservoir zone was initially drilled only with the LWD EMP resistivity measurement. The LWD laterolog was run several days later, and the resistivity data read much lower in the relogged section compared with the EMP resistivity. The laterolog 1D inversion was unable to resolve Rt because of the excessively deep invasion that occurred over the course of several days. In the second part of the second example, the laterolog resistivity showed a clear conductive invasion profile. While the deepest laterolog real-time resistivity data indicated lower resistivity than the EMP resistivity, the true resistivity, Rt (invasion-corrected 1D-inverted laterolog resistivity), matched the EMP Rt resistivity. This result validated both measurements and emphasized that the differences were due to invasion. The first two examples demonstrated that when acquired in normal drilling conditions (within 1–2 hours of drilling the section), the laterolog measurements can provide uninvaded formation resistivity even in the presence of invasion. A reservoir in another example was sandwiched between resistive layers that caused difficult-to-explain elevated EMP resistivity readings. Structural modeling reproduced the elevated behavior of the EMP data and explained the differences between resistivity measurements. This result showed that the laterolog is better suited to evaluate resistivity in thin reservoirs where there is a high-resistivity contrast to the adjacent layer. Finally, fractured reservoir examples are presented, which show that both the laterolog and EMP can be affected by the presence of fracture swarms. The examples presented in this paper demonstrate that in high-angle wells, when acquired under normal drilling conditions, invasion-corrected laterolog resistivity is often nearer to Rt than EMP resistivity. In those cases, the laterolog measurement provides data that are better inputs to water saturation calculations. Multiple examples from high-angle wells illustrate the findings.

Brave the Wind and the Waves: Discovering Robust and Generalizable Graph Lottery Tickets.

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate GNN inference on large-scale graphs by jointly pruning the graph structure and the model weights. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios, which are also long-standing and critical problems in deep learning ideology. In real-world scenarios, the distribution of unseen test data is typically diverse. We attribute the failures on out-of-distribution (OOD) data to the incapability of discerning causal patterns, which remain stable amidst distribution shifts. In traditional spase graph learning, the model performance deteriorates dramatically as the graph/network sparsity exceeds a certain high level. Worse still, the pruned GNNs are hard to generalize to unseen graph data due to limited training set at hand. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs. Concretely, we reactivate a fraction of weights/edges by instantaneous gradient information at each pruning point. After sufficient pruning, we conduct environmental interventions to extrapolate potential test distribution. Finally, we perform last several rounds of model averages to further improve generalization. We provide multiple examples and theoretical analyses that underpin the universality and reliability of our proposal. Further, RGLT has been experimentally verified across various independent identically distributed (IID) and out-of-distribution (OOD) graph benchmarks.

Numerical solutions to one dimensional linear Volterra-Fredholm integral equations based on LS-SVM model

This paper introduces a novel numerical approach using least squares support vector machine (LS-SVM) to handle one dimensional linear Volterra-Fredholm integral equations. This method can also handle weak singular kernel Fredholm/Volterra integral equations and linear systems of Volterra-Fredholm integral equations. By minimizing errors and utilizing least squares support vector machines, the approach derives continuous closed-form solutions to the origin problem. Ultimately, the method offers an efficient numerical approximation for solving Volterra-Fredholm integral equations through the resolution of linear equations. The paper includes multiple numerical examples that demonstrate the effectiveness and superiority of the proposed method.

Sample-analysis based adversarial attack with saliency map

With the widespread application of deep learning, the vulnerability of neural networks has attracted considerable attention, raising reliability and security concerns. Therefore, research on the robustness of neural networks has become increasingly critical. In this paper, we propose a novel sample-analysis based robustness evaluation method that overcomes the drawbacks of existing techniques, such as solving difficulty, single strategy, and loose radius. Our algorithm comprises two parts: robustness evaluation and adversarial attacks. Specifically, we introduce formal definitions of multiple sample types and a general solution to the problem of adversarial samples. We formulate a disturbance model-based description of adversarial samples in the adversarial attack algorithm and utilize saliency map to solve them. Our experimental results demonstrate that our adversarial attack algorithm not only achieves a high attack success rate in a relatively small disturbance range but also generates multiple adversarial examples for each clean example. Our algorithm can evaluate the robustness of complex datasets and models, overcome the lack of a single strategy in solving adversarial examples, and provide a more accurate radius of robustness.

From ICU Syndromes to ICU Subphenotypes: Consensus Report and Recommendations For Developing Precision Medicine in ICU.

Critical care uses syndromic definitions to describe patient groups for clinical practice and research. There is growing recognition that a "precision medicine" approach is required and that integrated biologic and physiologic data identify reproducible subpopulations that may respond differently to treatment. This article reviews the current state of the field and considers how to successfully transition to a precision medicine approach. In order to impact clinical care, identified subpopulations must do more than differentiate prognosis. They must differentiate response to treatment, ideally by defining subgroups with distinct functional or pathobiological mechanisms (endotypes). There are now multiple examples of reproducible subpopulations of sepsis, acute respiratory distress syndrome, and acute kidney or brain injury described using clinical, physiological, and/or biological data. Many of these subpopulations have demonstrated the potential to define differential treatment response, largely in retrospective studies, and that the same treatment-responsive subpopulations may cross multiple clinical syndromes (treatable traits). To bring about a change in clinical practice, a precision medicine approach must be evaluated in prospective clinical studies requiring novel adaptive trial designs. Several such studies are underway but there are multiple challenges to be tackled. Such subpopulations must be readily identifiable and be applicable to all critically ill populations around the world. Subdividing clinical syndromes into subpopulations will require large patient numbers. Global collaboration of investigators, clinicians, industry and patients over many years will therefore be required to transition to a precision medicine approach and ultimately realize treatment advances seen in other medical fields. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Rethinking Resistance as Relational – Resisting Psychologization in Psychology: Lessons from Carrère’s Between Two Worlds

AbstractPsychology, and in particular mainstream positive psychology, is fuelled by discourses on resistance strategies, understood as the individual capacity to resist and adapt to negative and oppressive thoughts, circumstances, experiences, and social structures. This self-strategy of resistance is evident in positive psychology’s notions of resilience: grit, life-crafting, and job-crafting behaviors, for example. While positive psychology would have us believe these strategies are associated with overcoming hardship and living a good life, they risk imbricating people in their own oppressions. In this paper, we engage in a reading of Carrère’s Between Two Worlds (original title Ouistreham) (2021), a movie featuring multiple examples of resistance. The movie shows how precarious workers enact self-strategies of resistance to fight for a decent and bearable life. They persevere despite ongoing hardships, seek joy amidst tragic life events, and find meaning in menial labor. However, resistance also appears as relational and political, and thus escapes and exceeds self-focused psychological categories of resistance. Resistance appears as the refusal to be understood solely in individual and individualizing ways – as a psychologized and knowable subject – and is characterized by relational, contextual, and political tactics. The movie profanes established positive psychology’s individualist focus on resistance. This profanation of self-strategies of resistance affords an opportunity to rethink resistance beyond the individual and compels us to problematize the tendency to psychologize and individualize social phenomena. In doing so, our paper too resists the determination of psychological language (i.e., psychologization) and advances ideas for alternative resistances in, to, and of psychology.

Indigenous voices: reimagining Indigenous education through a discourse of excellence

AbstractThe persistent deficit positioning of Indigenous students has meant that the combined terms ‘Indigenous education’ and ‘excellence in education’ have been kept separate in mainstream discourse. Excellence in education is an under-theorised concept that must consider intercultural and diverse perspectives. Consequently, this paper aims to understand excellence in education through Indigenous peoples’ perspectives regarding how excellence in Indigenous education is (or could be) enacted in schools. This paper reports on findings from a pilot study with Indigenous community members, principals, teachers, and support staff. The research aimed to address the question: How do Indigenous education practitioners define excellence in Indigenous education? This qualitative study used appreciative inquiry, which allowed for an in-depth understanding of the phenomenon and multiple contextual and localised examples from everyday community members, educational practitioners, and leaders. The data show that employing a language underpinned by strengths can change the conversation, expectations, and aspirations in Indigenous education, as framing through excellence may shift the ideology of policy and, thus, the interpretation of enactment in Indigenous education.

Multigenic Hairpin Transgenes in Tomato Confer Resistance to Multiple Orthotospoviruses Including Sw-5 Resistance-Breaking Tomato Spotted Wilt Virus.

Tomato spotted wilt virus (TSWV) and related thrips-borne orthotospoviruses are a threat to food and ornamental crops. Orthotospoviruses have the capacity for rapid genetic change by genome segment reassortment and mutation. Genetic resistance is one of the most effective strategies for managing orthotospoviruses, but there are multiple examples of resistance gene breakdown. Our goal was to develop effective multigenic, broad-spectrum resistance to TSWV and other orthotospoviruses. The most conserved sequences for each open reading frame (ORF) of the TSWV genome were identified, and comparison with other orthotospoviruses revealed sequence conservation within virus clades; some overlapped with domains with well-documented biological functions. We made six hairpin constructs, each of which incorporated sequences matching portions of all five ORFs. Tomato plants expressing the hairpin transgene were challenged with TSWV by thrips and leaf-rub inoculation, and four constructs provided strong protection against TSWV in foliage and fruit. To determine if the hairpin constructs provided protection against other emerging orthotospoviruses, we challenged the plants with tomato chlorotic spot virus and resistance-breaking TSWV and found that the same constructs also provided resistance to these related viruses. Antiviral hairpin constructs are an effective way to protect plants from multiple orthotospoviruses and are an important strategy in the fight against resistance-breaking TSWV and emerging viruses. Targeting of all five viral ORFs is expected to increase the durability of resistance, and combining them with other resistance genes could further extend the utility of this disease control strategy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

LaANIL: ANIL with Look-Ahead Meta-Optimization and Data Parallelism

Meta-few-shot learning algorithms, such as Model-Agnostic Meta-Learning (MAML) and Almost No Inner Loop (ANIL), enable machines to learn complex tasks quickly with limited data and based on previous experience. By maintaining the inner loop head of the neural network, ANIL leads to simpler computations and reduces the complexity of MAML. Despite its benefits, ANIL suffers from issues like accuracy variance, slow initial learning, and overfitting, hardening its adaptation and generalization. This work proposes “Look-Ahead ANIL” (LaANIL), an enhancement to ANIL for better learning. LaANIL reorganizes ANIL’s internal architecture, integrating parallel computing techniques (to process multiple training examples simultaneously across computing units) and incorporating Nesterov momentum (which accelerates convergence by adjusting the learning rate based on past gradient information and extracting informative features for look-ahead gradient computation). These additional features make our model more state-of-the-art capable and better edge-compatible and thus improve few-short learning by enabling models to quickly adapt to new information and tasks. LaANIL’s effectiveness is validated on established meta-few-shot learning datasets, including FC100, CIFAR-FS, Mini-ImageNet, CUBirds-200-2011, and Tiered-ImageNet. The proposed model achieved an increased validation accuracy by 7 ± 0.7% and a variance reduction by 44 ± 4% in two-way two-shot classification as well as increased validation by 5 ± 0.4% and a variance reduction by 18 ± 2% in five-way five-shot classification on the FC100 dataset and similarly performed well on other datasets.

Broomcorn Millet

Broomcorn millet (Panicum miliaceum) was one of the most important and enigmatic crops of the ancient world. The integration of millet into existing crop systems drove significant transformations in past societies. Thanks to the environmental adaptability and short growing period of millet, many societies across Eurasia were dependent on millet cultivation for food security. For modern researchers, broomcorn millet also possesses unique botanical and biochemical characteristics that make it an ideal candidate for tracing its ancient dispersal and integration, which in turn provides a unique avenue for understanding the broader mechanisms of dietary transformations. This paper offers a review of the multiproxy evidence for the initial broomcorn millet dispersal across Eurasia. In light of millet’s unique biomolecular properties, multiple archaeological examples are drawn on to describe how millet consumers can be traced down to demographic categories of sex, age, social status, and individual mobility history. In combination with other research methods, this paper reviews evidence for past millet preparation for human consumption, using various archaeological sites as case studies, along with offering a theoretical reasoning for the discontinuities in millet exploitation over time, which is likely to be the result of past climate change.

ESG Practices In Publicly Capital Construction Companies

Objective: The study aimed to qualitatively evaluate the use of ESG concepts by publicly traded Brazilian companies in the construction and development sector. Theoretical Framework: The discussion about the socio-environmental responsibility of companies has gained prominence in recent years with ESG (Environmental, Social and Governance). Considering the relevance of the construction and civil development sector to the economy, it is not exempt from the aforementioned socio-environmental responsibilities. Method: It was necessary to identify, among all the aspects involved in the concept of corporate sustainability, which were most relevant for companies in the respective segment, and then a survey was carried out to identify which practices companies adopt on a daily basis to minimize negative impacts. From the case study of the three companies chosen for the analysis, multiple examples of ESG initiatives were collected and it was possible to make a comparison between them. Results and Discussion: Company A stands out in the environmental pillar as it already offsets part of its GHG emissions. While on the social side, Companies A and B have initiatives that support projects in the education area. And in relation to governance, the three companies have positive indicators regarding independent members on the board. Research Implications: The limitations of the study are based on the superficiality of data from other companies studied during the company selection process, which do not allow for a broader analysis of the sector. Originality/Value: This study contributes to the literature by providing support for construction companies and developers to improve their ESG practices.

Fractional Block Method for the Solution of Fractional Order Differential Equations

The construction of the fourth-order 2-point Fractional Block Backward Differentiation Formula (2FBBDF(4)) to solve the fractional order differential equations (FDEs) is presented in this paper. The method is developed using the fractional linear multistep method (FLMM) linked with the linear difference operator. This paper aims to approximate the fractional order problems via 2FBBDF(4), which is normally used to solve ordinary differential equations. The criteria for the stability of the method are analyzed in order to solve FDE problems. Consequently, the method is determined to be \textit{A}-stable for different values of α within the interval (0,1) . Then, Newton's iteration is implemented in this method to solve the problems. Multiple numerical examples of linear, nonlinear, and system FDEs are provided for the scenario where the order α lies within the range of 0 and 1 . Ultimately, the numerical results confirm that the proposed method performs at a similar level to the existing methods.

EmFORE: Learning Email Folder Classification Rules by Demonstration

Tools that help with email folder management are limited, as users have to manually write rules to assign emails to folders. We present EMFORE, an iterative learning system that automatically learns and updates such rules from observations. EMFORE is fast enough to suggest and update rules in real time and suppresses mails with low confidence to reduce the number of false positives. EMFORE can use different rule grammars, and thus be adapted to different clients, without changing the user experience. Previous methods do not learn rules, require complete retraining or multiple new examples after making a mistake, and do not distinguish between inbox and other folders. EMFORE learns rules incrementally and can make the neutral decision of leaving emails in the inbox, making it an ideal candidate for integration in email clients.

Oxidation, reduction and semi-classical limit for quantum matrix geometries

Matrix configurations define noncommutative spaces endowed with extra structure including a generalized Laplace operator, and hence a metric structure. Made dynamical via matrix models, they describe rich physical systems including noncommutative gauge theory and emergent gravity. Refining the construction in [25], we construct a semi-classical limit through an immersed submanifold of complex projective space based on quasi-coherent states. We observe the phenomenon of oxidation, where the resulting semi-classical space acquires spurious extra dimensions. We propose to remove this artifact by passing to a leaf of a carefully chosen foliation, which allows to extract the geometrical content of the noncommutative spaces. This is demonstrated numerically via multiple examples.

Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

Deep smoothness weighted essentially non-oscillatory method for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators

Iterative physical optics method based on efficient occlusion judgment with bounding volume hierarchy technology

This paper builds a binary tree for the target based on the bounding volume hierarchy technology, thereby achieving strict acceleration of the shadow judgment process and reducing the computational complexity from the original O(N3) to O(N2logN). Numerical results show that the proposed method is more efficient than the traditional method. It is verified in multiple examples that the proposed method can complete the convergence of the current. Moreover, the proposed method avoids the error of judging the lit-shadow relationship based on the normal vector, which is beneficial to current iteration and convergence. Compared with the brute force method, the current method can improve the simulation efficiency by 2 orders of magnitude. The proposed method is more suitable for scattering problems in electrically large cavities and complex scenarios.

Sendemails: An automated email package with multiple applications

In this article, we illustrate the sendemails command, which allows users to automatically send emails with Stata through PowerShell. Researchers can use this package to perform several email tasks, such as contacting students or colleagues with standardized messages. Additionally, researchers can perform more complex tasks that entail sending randomized messages with multiple attachments from multiple accounts; these tasks are often necessary to conduct correspondence audit tests. This article introduces the sendemails command and illustrates multiple examples of its application. The online appendix discusses an application of this package to correspondence audits.

A Region-Based Approach for the Operational Reliability Evaluation of Power Systems With Renewable Energy Integration

Power system operational reliability evaluation is computationally time-consuming due to the need to analyze numerous system states resulting from uncertain renewable energies and loads as well as random outages of components. This paper presents a load feasible region (LFR) approach to accelerate the process of operational reliability evaluation. The LFR of each state, which is defined as a polyhedron in the load space, can be built in advance offline. The inner points of the LFR correspond to the loads that can be satisfied under the system operational constraints. Then, the preestablished LFR can be corrected with little computing effort considering the renewable energy fluctuation during the operation phase. The optimal load shedding under each state can be obtained directly by calculating the minimum Manhattan distance to the LFR's boundary. Multiple application examples are presented to demonstrate the efficiency and scalability of the proposed region-based approach.