Common Objects Research Articles

Decentralized micro water-energy co-optimization for small communities

The water-energy nexus encompasses the interdependencies between water and energy resources identifying the existing links between the production and distribution of these resources. Therefore, understanding the water-energy nexus is crucial for developing sustainable and integrated resource management approaches. This paper proposes a decentralized co-optimization model for a micro water-energy nexus system (MWEN), aiming to optimize the combined supply of both resources to end consumers. The approach respects the separate ownership and management of the water and energy sectors while bridging the gap between their optimized operations. An enhanced version of the alternating direction method of multipliers (ADMM) is proposed, the objective-based ADMM (OB-ADMM), which is able to robustly optimize each system independently towards a common objective, only sharing information about the power consumption of water management, providing privacy for each resource provider.

What is narrativity?

AbstractIn recent years, narrative accounts of the self have gained increasing attention. It is widely accepted that humans are storytelling creatures, that stories shape our self‐conception, and that we fail to be agents without a narrative framework. While there is less agreement on what constitutes a narrative, it is generally understood to be more than a chronological listing of life events; it is also an account of the explanatory relationships among these events—a story of how events lead to other events. However, specifying the nature of this explanatory relationship has proven difficult. As critics have pointed out, narrativists often resort to simplistic notions of narrative when faced with criticism. The concept of narrative explanation needs to be elaborated in a way that is both substantive enough to exclude trivial behaviours from being considered narratives and nonrestrictive enough to accommodate ordinary lives that may not be particularly story‐like. In this paper, I review existing accounts of narrative explanation and propose a teleological account, according to which narratives consist of goal‐directed explanations of a sequence of events. I argue that a teleological account of narrative explanation can be a fruitful way to clarify the concept of “narrative” in the context of the narrative self by addressing the most common objections.

Modeling action: Recasting the causal theory

AbstractContemporary action theory is generally concerned with giving theories of action ontology. In this paper, we make the novel proposal that the standard view in action theory—the Causal Theory of Action—should be recast as a “model,” akin to the models constructed and investigated by scientists. Such models often consist in fictional, hypothetical, or idealized structures, which are used to represent a target system indirectly via some resemblance relation. We argue that recasting the Causal Theory as a model can not only accomplish the goals of causal theorists, but also give the theory greater flexibility in responding to common objections.

New Strategies in Archaeometric Provenance Analyses of Volcanic Rock Grinding Stones: Examples from Iulia Libica (Spain) and Sidi Zahruni (Tunisia)

Archaeometry can help archaeologists in many ways, and one of the most common archaeometric objectives is provenance analysis. Volcanic rocks are often found in archaeological sites as materials used to make grinding tools such as millstones and mortars or as building materials. Petrographic characterization is commonly applied to identify their main mineralogical components. However, the provenance study of volcanic stones is usually undertaken by comparing geochemical data from reference outcrops using common descriptive statistical tools such as biplots of chemical elements, and occasionally, unsupervised multivariate data analysis like principal component analysis (PCA) is also used. Recently, the use of supervised classification methods has shown a superior performance in assigning provenance to archaeological samples. However, these methods require the use of reference databases for all the possible provenance classes in order to train the classification models. The existence of comprehensive collections of published geochemical analyses of igneous rocks enables the use of the supervised approach for the provenance determination of volcanic stones. In this paper, the provenance of volcanic grinding tools from two archaeological sites (Iulia Libica, Spain, and Sidi Zahruni, Tunisia) is attempted using data from the GEOROC database through unsupervised and supervised approaches. The materials from Sidi Zahruni have been identified as basalts from Pantelleria (Italy), and the agreement between the different supervised classification models tested is particularly conclusive. In contrast, the provenance of the materials from Iulia Libica remained undetermined. The results illustrate the advantages and limitations of all the examined methods.

Organizing sustainable and fair agri-food systems: exploring the role of north-north alternative food networks in the European Union

ABSTRACT Alternative Food Networks (AFNs) have grown globally since the late 1990s to promote responsible consumption and production in the agri-food sector, although their expansion varies across countries and socio-economic contexts. Initially, the Fair Trade movement and development organizations established AFNs to address socio-environmental challenges faced by producers in the Global South. However, as cooperation and development agendas evolve, more Fair Trade and development actors are forming North-North AFNs (N-N AFNs) to tackle socio-economic imbalances in agri-food systems of the Global North, especially in the EU. Despite their prevalence, these initiatives and their structures lack extensive academic study. Drawing on Le Velly’s framework for the analysis of AFNs, this paper conducts an in-depth qualitative analysis of three N-N AFN initiatives in Belgium, Italy, and Greece. The analysis demonstrates that while these initiatives share common objectives in terms of fairness and shortness of the agri-food system, variations arise in terms of its accessibility and environmental sustainability. Furthermore, the findings emphasize that there is no one-size-fits-all model for N-N AFNs across Europe, as different organizational arrangements – such as farmers’ cooperatives, consumers’ cooperatives, and business partnerships – emerge in response to specific project goals within each national context.

Incitement to misuse of corticosteroids by Arab YouTubers in a local context.

The ubiquity of social media has ushered in an era where uncontrolled content sharing extends to all subjects, including sensitive topics such as medication consumption. To quantify the prevalence of YouTube videos providing information on glucocorticoids and to underscore the risks associated with inaccurate information, which might inadvertently promote inappropriate use of these medications. The YouTube videos were selected using predefined keywords from February 20 to March 4, 2023. The videos were categorized into two groups. Category 1 promotes the misuse of corticosteroids, while Category 2 raises awareness about the risks associated with these drugs. In total, 843 YouTube videos were included. Approximately 76% of the creators were women. Of these, category 1 videos (69.63%) predominated over Category 2 videos (30.37%). Regarding Category 1, dexamethasone was mentioned in 41.53% of cases, followed by hydrocortisone (17.30%). According to these YouTubers, these products/medications are mainly obtained from community pharmacies (58.09%), online shops (20.01%), and through illicit markets and the black market (13.46%). Weight gain was the most common objective, according to 32.62% of the YouTubers. This study highlights the prevalence of YouTube videos regarding the misuse of corticosteroids. The common focus on weight gain as an objective underscores the importance of educating content creators and viewers about responsible corticosteroid use. Targeted interventions are needed to promote safe and informed medication practices within this online environment.

Pre-organization theory: an evolutionary approach integrating memetics, inducement-contribution theory and generalized darwinism

PurposeOrganization theory seeks to explain how people coordinate their behaviors to achieve common objectives, but it has offered little insight into how organizations emerge from such coordination. Fully understanding entities requires knowing their origins. The purpose of this paper is to draw attention to and to suggest an approach for fortifying a foundational weakness in organization theory: pre-organization theory.Design/methodology/approachTo develop pre-organization theory, this paper employs an evolutionary approach that integrates three theories. This paper first employs memetics to articulate a unit of selection, the i-memeplex, and next introduces inducement-contribution theory to tailor the i-memeplex to pre-organization, yielding a founder’s mental map for exchanges of inducements and contributions. It then applies generalized Darwinism to complete its evolutionary theory of pre-organization.FindingsMemetics, inducement-contribution theory, and generalized Darwinism can be integrated to create a promising theoretical solution, but further investigation is needed to assess the empirical and practical value of pre-organization theory.Originality/valueThis paper contributes to organization theory by (1) explicating a foundational weakness in organization theory – its lack of pre-organization theory – and (2) integrating a novel set of theories to develop an evolutionary theory of pre-organization.

Grounding rod hanging and removing robot with hand-eye self-calibration capability in substation

In this paper, a robot system for hanging and removing grounding rods is designed by integrating 3D recognition technology, hand-eye self-calibration technology, and automatic operation technology. Specifically, a novel hand-eye self-calibration algorithm is proposed that only uses common objects in the actual scene, which differs from traditional robot hand-eye calibration in that it requires a dedicated calibration board to assist with offline completion. Addressing the problem that existing self-calibration methods cannot be optimized as a whole, resulting in low accuracy and instability of the solution, a multi-stage objective function optimization self-calibration algorithm is proposed. An optimization method based on the minimization of re-projection error is designed to compensate for the results, which uses an efficient Oriented fast and rotated brief (ORB) feature extraction algorithm and introduces a scoring mechanism to retain more correct matching points in the feature matching stage. Experimental verifications are conducted using both public dataset and practical robot system. In the dataset experiment, our method demonstrates superior accuracy and robustness compared to existing self-calibration methods. Furthermore, the practical robot platform experiment confirms the feasibility and efficacy of our approach across various wind speeds and lighting conditions.

Real-world evidence for factors associated with maintenance treatment practices among US adults with autoimmune hepatitis.

While avoidance of long-term corticosteroids is a common objective in the management of autoimmune hepatitis (AIH), prolonged immunosuppression is usually required to prevent disease progression. This study investigates the patient and provider factors associated with treatment patterns in US patients with AIH. A retrospective cohort of adults with the incident and prevalent AIH was identified from Optum's deidentified Clinformatics Data Mart Database. All patients were followed for at least 2 years, with exposures assessed during the first year and treatment patterns during the second. Patient and provider factors associated with corticosteroid-sparing monotherapy and cumulative prednisone use were identified using multivariable logistic and linear regression, respectively.The cohort was 81.2% female, 66.3% White, 11.3% Black, 11.2% Hispanic, and with a median age of 61 years. Among 2203 patients with ≥1 AIH prescription fill, 83.1% received a single regimen for >6 months of the observation year, which included 52.2% azathioprine monotherapy, 16.9% azathioprine/prednisone, and 13.3% prednisone monotherapy. Budesonide use was uncommon (2.1% combination and 1.9% monotherapy). Hispanic ethnicity (aOR: 0.56; p = 0.006), cirrhosis (aOR: 0.73; p = 0.019), osteoporosis (aOR: 0.54; p =0.001), and top quintile of provider AIH experience (aOR: 0.66; p = 0.005) were independently associated with lower use of corticosteroid-sparing monotherapy. Cumulative prednisone use was greater with diabetes (+441mg/y; p = 0.004), osteoporosis (+749mg/y; p < 0.001), and highly experienced providers (+556mg/y; p < 0.001). Long-term prednisone therapy remains common and unexpectedly higher among patients with comorbidities potentially aggravated by corticosteroids. The greater use of corticosteroid-based therapy with highly experienced providers may reflect more treatment-refractory disease.

Creatio ex Omnibus: Pantheism and Its Values and Ethics

ABSTRACT This paper refutes the common objection against pantheistic models of divinity that they are valueless or bereft of ethics due to the ambiguous model of God they espouse, arguing instead that pantheism can be construed as an inherently ethical model of divinity. It addresses common assumptions about omnibenevolent deities and the values they do or do not inspire. It then discusses the values that are arguably both immanent and inherent in nature itself. Finally, this paper makes the case that the ambiguity that pantheistic models of God acknowledge in nature is actually a necessary precondition for goodness itself.

THE IMPACT OF CRISIS ON INTERECOSYSTEMIC INNOVATION: DYNAMICS OF ENEL’S HYDROGEN ECOSYSTEM

Crisis highlights conflicts but also helps disparate organisations rally around common objectives through forming ecosystemic relationships. To improve future crisis response, we study the influence of crisis-driven transitions on innovation in ecosystems. We illustrate the study with the dynamics of hydrogen innovation at Enel. Here, innovation caters to multiple value propositions; therefore, we define interecosystemic innovation as an innovation that addresses the value propositions of more than one ecosystem. The unexpected disruption caused by the energy crisis focuses attention on a single value proposition, thus fomenting the dynamic, temporal sequencing of interecosystemic innovation. This increases activity in the ecosystem core as well as interaction with the ecosystem periphery, allowing more rapid innovation iterations throughout the ecosystem. When operating in the intercept of ecosystems, firms need to consider the multiple value propositions of ecosystem participants to identify and mitigate tensions that may impede innovation.

Construction Activity Recognition Method Based on Object Detection, Attention Orientation Estimation, and Person Re-Identification

Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker’s activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency.

Diversity-Learning Block: Conquer Feature Homogenization of Multibranch.

Visual Geometry Group (VGG)-style ConvNet is an neural-network process units (NPU)-friendly network; however, the accuracy of this architecture cannot keep up with other well-designed network structures. Although some reparameterization methods are proposed to remedy this weakness, their performance suffers from the homogenization issue of parallel branches, and the preset shape of convolution kernels also influences spatial perception. To address this problem, we propose a diversity-learning (DL) block to build the DLNet, which could adaptively learn various features to enrich the feature space. To balance floating point of operations (FLOPs) and accuracy, groupwise operation is introduced and finally, a lightweight DL ConvNet DLGNet is obtained. Extensive evaluations have been conducted on different computer vision tasks, e.g., image classification [Canadian Institute For Advanced Research (CIFAR) and ImageNet], object detection [PASCAL visual object classes (VOC) and Microsoft Common Objects in Context (MS COCO)], and semantic segmentation (Cityscapes). The experimental results show that our proposed DLGNet can achieve comparable performance with the state-of-the-art networks while the speed is 183% faster than GhostNet and even over 600% faster than MobileNetV3 with similar accuracy when running on NPU.

How well do models of visual cortex generalize to out of distribution samples?

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predictive models of neuronal activity in the visual cortex. Nevertheless, prediction performance in these models is often investigated on stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a generalization gap in how predicting neuronal responses to synthetically generated out-of-distribution (OOD) stimuli. Here, we investigated how the recent progress in improving DNNs' object recognition generalization, as well as various DNN design choices such as architecture, learning algorithm, and datasets have impacted the generalization gap in neural predictivity. We came to a surprising conclusion that the performance on none of the common computer vision OOD object recognition benchmarks is predictive of OOD neural predictivity performance. Furthermore, we found that adversarially robust models often yield substantially higher generalization in neural predictivity, although the degree of robustness itself was not predictive of neural predictivity score. These results suggest that improving object recognition behavior on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex.

Polarized consensus-based dynamics for optimization and sampling

AbstractIn this paper we propose polarized consensus-based dynamics in order to make consensus-based optimization (CBO) and sampling (CBS) applicable for objective functions with several global minima or distributions with many modes, respectively. For this, we “polarize” the dynamics with a localizing kernel and the resulting model can be viewed as a bounded confidence model for opinion formation in the presence of common objective. Instead of being attracted to a common weighted mean as in the original consensus-based methods, which prevents the detection of more than one minimum or mode, in our method every particle is attracted to a weighted mean which gives more weight to nearby particles. We prove that in the mean-field regime the polarized CBS dynamics are unbiased for Gaussian targets. We also prove that in the zero temperature limit and for sufficiently well-behaved strongly convex objectives the solution of the Fokker–Planck equation converges in the Wasserstein-2 distance to a Dirac measure at the minimizer. Finally, we propose a computationally more efficient generalization which works with a predefined number of clusters and improves upon our polarized baseline method for high-dimensional optimization.

Targeted dream incubation at a distance: the development of a remote and sensor-free tool for incubating hypnagogic dreams and mind-wandering

Hypnagogia—the transitional state between wakefulness and sleep—is marked by “hypnagogic dreams,” during which our brains tend to forge connections among concepts that are otherwise unrelated. This process of creating novel associations during hypnagogic dreams is said to contribute to enhancing creativity, learning, and memory. Recently, researchers have proposed that mind-wandering—a form of spontaneous thought that is freely moving and characterized by transitioning thought content—might be subserved by processes similar to those engaged during hypnagogia, and may serve similar creative functions. However, to date, the relationship between hypnagogia and mind-wandering remains poorly understood, which is likely due in part to the fact that research into hypnagogia requires time-consuming, cumbersome, and costly polysomnography. In light of this, the present study had two primary aims: first, to test a novel tool—called Dormio Light—for cueing and indexing hypnagogic dream content in a cost- and time-effective manner, with the ability for remote administration; second, to use this tool to examine any relations between hypnagogic dreams and mind-wandering (defined as “freely moving thought”). Participants (N = 80, with 34 females) completed a task in which our tool prompted them to engage in hypnagogia and, separately, mind-wandering, with instructions to think about a common everyday object (Tree or Fork) while experiencing these cognitive states. Following each state, participants reported thought content and completed phenomenological questionnaires. Providing an initial validation of our tool, we successfully cued hypnagogic and mind-wandering thought content that was specific to our cues (e.g., Tree), with our incubation-rate results comparable to those found in laboratory-based studies. Further, we found evidence for some phenomenological differences between hypnagogia and mind-wandering reports. Our study offers a novel, cost- and time-effective tool with which to remotely cue and index hypnagogia and mind-wandering, and sheds light on the relationship between hypnagogia and mind-wandering, thereby providing future directions for research into these two cognitive states.

Neural adaptations to congenital deafness: enhanced tactile discrimination through cross-modal neural plasticity - an fMRI study.

This study explores the compensatory neural mechanisms associated with congenital deafness through anexamination of tactile discrimination abilities using high-resolution functional magnetic resonance imaging (fMRI). To analyze the neural substrates underlying tactile processing in congenitally deaf individuals and compare themwith hearing controls. Our participant pool included thirty-five congenitally deaf individuals and thirty-five hearing controls. All participantsengaged in tactile discrimination tasks involving the identification of common objects by touch. We utilized ananalytical suite comprising voxel-based statistics, functional connectivity multivariate/voxel pattern analysis (fc-MVPA), and seed-based connectivity analysis to examine neural activity. Our findings revealed pronounced neural activity in congenitally deaf participants within regions typically associatedwith auditory processing, including the bilateral superior temporal gyrus, right middle temporal gyrus, and rightrolandic operculum. Additionally, unique activation and connectivity patterns were observed in the right insula andbilateral supramarginal gyrus, indicating a strategic reorganization of neural pathways for tactile informationprocessing. Behaviorally, both groups demonstrated high accuracy in the tactile tasks, exceeding 90%. However, thedeaf participants outperformed their hearing counterparts in reaction times, showcasing significantly enhancedefficiency in tactile information processing. These insights into the brain's adaptability to sensory loss through compensatory neural reorganization highlight theintricate mechanisms by which tactile discrimination is enhanced in the absence of auditory input. Understandingthese adaptations can help develop strategies to harness the brain's plasticity to improve sensory processing inindividuals with sensory impairments, ultimately enhancing their quality of life through improved tactile perceptionand sensory integration.

Moral Uncertainty and Public Justification

Moral uncertainty and disagreement pervade our lives. Yet we still need to make decisions and act, both individually and politically. So, what should we do? Moral uncertainty theorists provide a theory of what individuals should do when they are uncertain about morality. Public reason liberals provide a theory of how societies should deal with reasonable disagreements about morality. They defend the public justification principle: state action is permissible only if it can be justified to all reasonable people. In this article, we bring these two approaches together. Specifically, we investigate whether considerations of moral uncertainty support public reason liberalism: given moral uncertainty, should we favor public justification? We argue that while moral uncertainty theory cannot vindicate an exceptionless public justification principle, it supports adopting public justification as a pro tanto principle &amp;ndash; albeit one that can be overridden when the moral stakes are high. It also provides new answers to some intramural debates among public reason liberals and new responses to some common objections.

Safety and dream scepticism in Sosa’s epistemology

A common objection to Sosa’s epistemology is that it countenances, in an objectionable way, unsafe knowledge. This objection, under closer inspection, turns out to be in far worse shape than Sosa’s critics have realised. Sosa and his defenders have offered two central response types to the idea that allowing unsafe knowledge is problematic: one response type adverts to the animal/reflective knowledge distinction that is characteristic of bi-level virtue epistemology. The other less-discussed response type appeals to the threat of dream scepticism, and in particular, to the idea that many of our everyday perceptual beliefs are unsafe through the nearness of the dream possibility. The latter dreaming response to the safety objection to Sosa’s virtue epistemology has largely flown under the radar in contemporary discussions of safety and knowledge. We think that, suitably articulated in view of research in the philosophy and science of dreaming, it has much more going for it than has been appreciated. This paper further develops, beyond what Sosa does himself, the dreaming argument in response to those who think safety (as traditionally understood) is a condition on knowledge and who object to Sosa’s account on the grounds that it fails this condition. The payoffs of further developing this argument will be not only a better understanding of the importance of insights about dreaming against safety as a condition on knowledge, but also some reason to think a weaker safety condition, one that is relativised to SSS (i.e., skill/shape/situation) conditions for competence exercise, gets better results all things considered as an anti-luck codicil on knowledge.

Innovation in Transparent Technology with a Deep Dive into Iot-Connected Smart Glass

The goal of the project "Innovation In Transparent Technology With A Deep Dive Into IoT-Connected Smart Glass" using YOLO Image Processing is to create a cutting-edge assistive technology solution that will increase the mobility and freedom of people who are blind or visually impaired. The goal of this project is to incorporate wearable smart glasses with real-time object recognition utilizing the YOLO (You Only Look Once) algorithm. The Arduino Nano microprocessor, buzzer, and sensors that are built into the smart glasses will allow for the real-time recognition of common objects and obstacles within the user's surroundings. The system will use an easy-to-use, accessible user interface to communicate detected objects and navigation hints to the user through buzzer and vibration.