Joint Object Research Articles

Stochastic surgery selection and sequencing under dynamic emergency break-ins

Anticipating the impact of urgent emergency arrivals on operating room schedules remains methodologically and computationally challenging. This paper investigates a model for surgery scheduling, in which both surgery durations and emergency patient arrivals are stochastic. When an emergency patient arrives he enters the first available room. Given the sets of surgeries available to each operating room for that day, as well as the distributions of the main stochastic variables, we aim to find the per-room surgery sequences that minimise a joint objective, which includes over- and under-utilisation, the amount of cancelled patients, as well as the risk that emergencies suffer an excessively long waiting time. We show that a detailed analysis of emergency break-ins and their disruption of the schedule leads to a lower total cost compared to less sophisticated models. We also map the trade-off between the threshold for excessive waiting time, and the set of other objectives. Finally, an efficient heuristic is proposed to accurately estimate the value of a solution with significantly less computational effort.

Curriculum learning for distant supervision relation extraction

Relation extraction under distant supervision leverages the existing knowledge base to label data automatically, thus greatly reduced the consumption of human labors. Although distant supervision is an efficient method to obtain a large amount of labeled data, the training dataset labeled by distant supervision suffers from noise problem resulting in poor generalization ability of the relation extractor. To alleviate the noise problem, we propose a novel relation extraction method based on curriculum learning. Curriculum learning is utilized to guide the training process of relation extractor, specifically through the predefined curriculum-driven mentor network. Mentor network can dynamically adjust the weights of sentences during training, giving lower weights to noisy sentences and higher weights to truly labeled sentences. Relation extractor and mentor network are trained collaboratively to optimize joint objective. The experimental results show that the proposed method can improve the generalization ability of relation extractor in a noisy environment and obtains better performance for relation extraction.

Peripheral and central nervous system alterations in a rat model of inflammatory arthritis.

It is consistently reported that in inflammatory arthritis (IA), pain may continue despite well-controlled inflammation, most likely due to interactions between joint pathology and pain pathway alterations. Nervous system alterations have been described, but much remains to be understood about neuronal and central non-neuronal changes in IA. Using a rat model of IA induced by intra-articular complete Freund's adjuvant injection, this study includes a thorough characterization of joint pathology and objectives to identify peripheral innervation changes and alterations in the spinal dorsal horn (DH) that could alter DH excitatory balancing. Male and female rats displayed long-lasting pain-related behavior, but, in agreement with our previous studies, other pathological alterations emerged only at later times. Cartilage vascularization, thinning, and decreased proteoglycan content were not detectable in the ipsilateral cartilage until 4 weeks after complete Freund's adjuvant. Sympathetic and peptidergic nociceptive fibers invaded the ipsilateral cartilage alongside blood vessels, complex innervation changes were observed in the surrounding skin, and ipsilateral nerve growth factor protein expression was increased. In the DH, we examined innervation by peptidergic and nonpeptidergic nociceptors, inhibitory terminal density, the KCl cotransporter KCC2, microgliosis, and astrocytosis. Here, we detected the presence of microgliosis and, interestingly, an apparent loss of inhibitory terminals and decreased expression of KCC2. In conclusion, we found evidence of anatomical, inflammatory, and neuronal alterations in the peripheral and central nervous systems in a model of IA. Together, these suggest that there may be a shift in the balance between incoming and outgoing excitation, and modulatory inhibitory tone in the DH.

Multi-context aware user–item embedding for recommendation

Real recommender systems usually contain various auxiliary information. Some of the most recent works make meaningful exploration of incorporating auxiliary information into the representation model for competitive recommendation. However, learning user and item representations still faces two challenges: (1) existing works do not well address the problem of integrating multi-type auxiliary information; (2) learning representations for inactive users is still challenging due to the high sparsity of explicit user–item associations. In order to tackle these problems, in this paper, the attributed heterogeneous network and bipartite interaction network are employed to incorporate various auxiliary information and user–item associations. A joint objective function and an efficient algorithm are devised for the representation learning. Experimental results show that the proposed algorithm has significant advantages over the state-of-the-art baselines. What is remarkable is that our proposed method is demonstrated to be especially useful for dealing with low-active users in the system.

Legal aspects of the Police cooperation with the National Forest Holding

This article is devoted to the analysis of legal regulations constituting the basis for the Police cooperation with the State Forests National Forest Holding. The paper explains the concept of “cooperation” based on the doctrine of administrative law. The cooperation is aimed at facilitating and improving the implementation of assigned tasks and achieving joint objectives in ensuring safety and public order in forest areas. Greater effects can be achieved by both entities only if they cooperate with each other, conducting joint activities, providing mutual assistance in conducting official activities and exchanging information, than when each of those units would operate independently.

Optimal Dynamic Resource Allocation for Multi-Point Communication in WBAN

Considering the characteristics of lower power resources in multi-point wireless body area networks (WBANs), the wireless energy and information transmission in full duplex (FD) mode is considered. We propose a transmission protocol based on time division multiple access, which includes two phases. In the inactive phase, the signal access point (AP) broadcasts Radio Frequency (RF) energy to the sensors. When the energy collected by all sensors reaches their respective energy thresholds, the active phase will start. In the active phase, the sensors send back the collected physiological information parameters to the AP, while sensors still collecting the energy transmitted from the AP to achieve the purpose of energy accumulating. Our goal is to obtain the optimal time allocation ratio through the joint objective linear programming method to obtain the maximum average sum-throughput. The simulation results compared with the AP with FD and the sensors with half duplex (HD) show that our proposed optimization method can improve system throughput obviously.

The restoration of severely damaged churches – Implications and opportunities on cultural heritage conservation, thermal comfort and energy efficiency

• An interdisciplinary methodology is applied on a recently restored church. • Multi-objective analysis performed for artwork conservation, thermal comfort, energy efficiency. • The experimental campaign highlighted the improvable artwork conservation conditions. • A calibrated dynamic simulation model was used to hypothesize HVAC systems. • The joint objective of artworks conservations and thermal comfort is obtainable only with high energy consumption. Indoor microclimate and energy performance analyses of historic buildings require tailored methodologies, because of their complexities, e.g. presence of artworks, lack of documents or project data and employed structure and materials. Given such difficulties, there are a few interdisciplinary methodologies, capable of carrying out multi-objective analyses for this kind of buildings, and they are often based on in situ monitoring that, however, may not be able to predict the effects deriving from different conceivable technologies and control strategies. In this work, an interdisciplinary methodology is employed for evaluating cultural heritage conservation conditions, occupants ‘thermal comfort and energy performance of a specific historic building category, such as churches, on the basis of experimental and numerical approach. The methodology was applied to the case study of an ancient Italian church, recently restored following the earthquake that hit L’Aquila in 2009. After the refurbishment of the church, the statistical analysis of temperature and relative humidity experimental data allowed to observe that the conservation conditions of artistic heritage just restored may be non-correct, due to remarkable thermo-hygrometric fluctuations of the indoor microclimate. Therefore, starting from the current condition of absence of HVAC system, calibrated dynamic simulation models of the church allowed to hypothesize different technological solutions able to control the indoor microclimate and to evaluate the effects on artworks preservation, thermal comfort, and energy performance. The results of the multi-scenario analysis showed that suitable conservation conditions (PIs > 90%) and thermal comfort can be obtained by employing a complex heating/cooling and humidification/dehumidification system which determines a significant increase in energy consumption.

A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Recommender Systems

In real-world recommender systems, users' interest and products' characteristics tend to go through a distinct series of changes over time. Thus, designing a recommender system that can simultaneously track the temporal dynamics of both drifts becomes a significant task. However, most of the existing temporal recommender systems only focus on users' dynamics, ignoring changes in products' characteristics. In this study, we propose a Multi-Trans matrix factorization (MTMF) model with improved time weight to capture temporal dynamics. Firstly, we introduce a personalized time weight that combines the forgetting curve and item similarity to reduce the impact of outdated information and retain the influence of users' stable preferences. Then, we model user and item dynamics by learning the multiple transitions at the user-factor and factor-item latent space between the ongoing time period and all past time periods. Accordingly, we formulate a joint objective function and take a gradient-based alternating optimization algorithm to solve this joint problem. Experimental results on historical datasets MovieLens show that the recommendation accuracy of MTMF with improved time weight is superior to the existing temporal recommendation methods.

Emotions, time, and strategy: The effects of happiness and sadness on strategic decision-making under time constraints

Managers often have limited available time to make high-quality strategic decisions that are both original and feasible. This joint objective is important but difficult to achieve because under time constraints, managers must combine different cognitive processes that are often influenced by emotional states. However, research investigating how time constraints and emotions interact and impact the quality of strategic decisions is scarce. Building upon the affect infusion model and information processing perspective, our experimental study involving 174 managers shows that the impact of time constraints and emotions varies across the two decision-making tasks of strategic idea generation and strategic choice. In particular, compared to the managers in a neutral emotional state under low time constraints, the happy managers under high time constraints seemed to generate fewer original and fewer feasible strategic ideas and appeared to make worse original strategic choices, while compared to the managers in a neutral emotional state under low time constraints, the sad managers under high time constraints made better original strategic choices. Our study contributes novel insight into behavioral strategy research by showing how time constraints and emotions causally and jointly influence different quality dimensions in various tasks involving strategic decision-making.

An adjoint-based optimization method for reducing the axial force of a reactor coolant pump

To alleviate the wear of a thrust bearing in a reactor coolant pump (RCP) while ensuring the hydraulic performance of the pump, an adjoint-based optimization method is proposed in this study. This method reduces the axial force of the RCP impeller and synchronously improves the impeller’s hydraulic efficiency. By combining the adjoint solution with the radial basis function (RBF)-based mesh deformation, the optimization proceeds along the gradient direction, which greatly reduces the time and cost of the calculation. In the adjoint method, the adjoint equations in the rotating coordinate system are established, a joint objective function of the head constraint, hydraulic efficiency, and axial force is expressed, and then the blade surface sensitivity to the joint objective function is determined. In the RBF mesh deformation, the control points on the blade strand are evenly spaced, which ensures the smoothness of the deformed 3D twisted blade. Using the proposed optimization method, the hydraulic axial force of the impeller is reduced by approximately 3.8%, while the hydraulic efficiency of a scaled RCP impeller is increased by approximately 3.2%, and the head remains at an almost constant value. The obtained results validate the feasibility of the adjoint method for optimizing the design of centrifugal pumps.

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another's actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object co-manipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over ${\bf 80}\%$ accuracy with both classifiers when identifying general interaction types.

Region-Based Segmentation and Object Detection

Object identification and multi-object picture separation are two firmly related processes and it can be enhanced when understood jointly by supporting data from one assignment to the next. Be that as it may, current best in object models are different portrayal for each space creation joint objects and leaving the categorization of numerous part of the scene uncertain. Picture element appearance highlights enable us to do well on classifying formless foundation classes, while the express portrayal of districts encourage the calculation of increasingly complex highlights essential for object detection. Vitally, our model gives a solitary bound together portrayal of the scene we clarify each picture elements of image and authorize it contains in the web between every random variable in our model.

Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor.

In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.

Quantitative Modeling and Analysis of Reliance in Physical Human–Machine Coordination

Abstract Smooth and efficient human–machine coordination in joint physical tasks may be realized through greater sensing and prediction of a human partner's intention to apply force to an object. In this paper, we define compliance and reliance in the context of physical human–machine coordination (pHMC) to characterize human responses in a joint object transport task. We apply an optimization framework to explain human intention and behavior. The weighting factor in the optimization problem, lambda (λ), is presented as a person's reliance on the machine in a joint physical task with varying constraints. We demonstrate that with an estimated λ, the intended two-dimensional motion of a person's trajectory can be captured. We also found a relationship between λ and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination. The extent to which λ may serve as an online measure of trust and reliance in a physical load sharing task requires further investigation under more complex task scenarios that involve greater degrees of vulnerability and uncertainty.

GIS Based Mapping of Optical Fiber Cable in a Part of ALWAR District, Rajasthan, India

In digital era, internet is an essential component of human being. The population of Alwar district is using internet resources and technology in abundance. But in recent years, it experienced a huge development in technology that reached to dark zone. In this work, we have studied internet facility based on Gram Panchayat levels to spread fiber optical cable for its accessibility to daily users. We have identified fiber optical cable with fault detection based on joint activity of cable and splitter in order to map issues using GIS platform. The Fiber Fault Localization System (FFLS) operates on ensemble of algorithms that maps, extricates, correlate and localize the fault by utilizing geographic data with remote sensing images. Centralized fiber fault localization requires (Optical Time Domain Reflectometer) OTDR to be integrated at OLT. A particular OTDR is obligatory per OLT to provide all the PONs of an OLT.We can used mobile application to capture OLT, ONT, splitter,Joint,BJC,SJC line and other object for identifying actual position of asserts with physical check. In which test fiber line using PON OTDR from one OLT to other connected splitter and ONT in Gram Panchayat.

The reciprocal development of the object of common space and the emergence of the collective agency in residents' workshops

This paper contributes to the study of agency by addressing the reciprocal development of collective agency and the object of common space. The aim is to investigate residents' collective agency in urban development from the perspective of cultural-historical activity theory (CHAT) with the help of applied ethnography. Residents' workshops focusing on the capacity to act together collectively provided a space for the residents to share their needs for a common space. The development of the collective agency of the residents began by expressing their individual needs in the development of their neighborhood. The collective elaboration of the fragmented needs in the residents' workshops enabled the formation of a joint object with the aid of cultural means and tools and with expansions of the object. In this study we provide an overview of the reciprocal development of residents' collective agency and the object of ‘space’. Our findings call attention to 1) the significance of the joint object in coordinating the fragmented needs of the residents, 2) new participatory practices which enable the long process of transitions in the formation of the joint object, and 3) guidance and support that allowed the residents' local place-based knowledge to emerge and promote their participation.

Low-dose CT Denoising Using Edge Detection Layer and Perceptual Loss.

Low-dose CT imaging is a valid approach to reduce patients' exposure to X-ray radiation. However, reducing X-ray current increases noise and artifacts in the reconstructed CT images. Deep neural networks have been successfully employed to remove noise from low-dose CT images. This study proposes two novel techniques to boost the performance of a neural network with minimal change in the complexity. First, a non-trainable edge detection layer is proposed that extracts four edge maps from the input image. The layer improves quantitative metrics (PSNR and SSIM) and helps to predict a CT image with more precise boundaries. Next, a joint function of mean-square error and perceptual loss is employed to optimize the network. Using the perceptual loss helps to preserve structural detail; however, it adds check-board artifacts to the output. The proposed joint objective function takes advantage of the benefits offered by each loss. It improves the over-smoothing problem caused by mean-square error and the check-board artifacts caused by perceptual loss.

An ergonomic approach to sustainable development: The role of information environment and social‐psychological variables in the adoption of agri‐environmental innovations

AbstractSustainability is defined as meeting the human needs of current as well as future generations. This anthropocentric perspective leads to joint objectives between sustainability and ergonomics. In the present study, we adopted a systems ergonomic approach and, based on the Theory of Planned Behavior, we analyzed the paths by which the information environment can affect farmers' adoption of sustainable measures through the mediation of attitudes, social pressure, and behavioral control. One hundred ninety‐nine Italian farmers completed a questionnaire assessing exposure to impersonal, personal‐formal, and personal‐informal sources of information on the one hand and attitudes, perceived behavioral control, and subjective norms toward the adoption of two types of sustainable innovations (technological solutions and organizational/managerial practices) on the other. The results showed that attitudes and perceived behavioral control were the dominant determinants of farmers' adoption behavior, and personal‐formal sources of information were positively associated with perceived behavioral control. Possible interventions are discussed for farmers' information environment to promote the adoption of sustainable innovations.

Parental Linguistic Input and Its Relation to Toddlers' Visual Attention in Joint Object Play: A Comparison Between Children with Normal Hearing and Children With Hearing Loss.

Parent-child interactions are multimodal, often involving coordinated exchanges of visual and auditory information between the two partners. The current work focuses on the effect of children's hearing loss on parent-child interactions when parents and their toddlers jointly played with a set of toy objects. We compared the linguistic input received by toddlers with hearing loss (HL) and their chronological age-matched (CA) and hearing age-matched (HA) normal-hearing peers. Moreover, we used head-mounted eye trackers to examine how different parental linguistic input affected children's visual attention on objects when parents either led or followed children's attention during joint object play. Overall, parents of children with HL provided comparable amount of linguistic input as parents of the two normal-hearing groups. However, the types of linguistic input produced by parents of children with HL were similar to the CA group in some ways and similar to the HA group in other ways. Interestingly, the effects of different types of linguistic input on extending the attention of children with HL qualitatively resembled the patterns seen in the CA group, even though the effects were less pronounced in the HL group. We discuss the implications of these results for our understanding of the reciprocal, dynamic, and multi-factored nature of parent-child interactions.

Robust topology optimization of compliant mechanisms with uncertainties in output stiffness

SummaryThis work addresses the topology optimization approach to design robust compliant mechanisms with respect to uncertainties in the output stiffness, when compared to the traditional deterministic approach. To this end, two formulations are proposed: probabilistic and nonprobabilistic. The probabilistic formulation minimizes a joint objective function of expected output displacement plus a measure of its standard deviations, for given statistical distribution of the output stiffness. The nonprobabilistic formulation is written as minimization of a joint function of the median of output displacements, plus the width of the intervals that contains the extreme values of the output displacements, for a given interval of output stiffness. The Monte Carlo simulation method is used to evaluate expected values and standard deviations of output displacements in the probabilistic formulation and to assess results obtained with the deterministic approach. It is shown that both formulations lead to designs where output displacements are less sensitive to variations of output stiffness when compared to the traditional deterministic approach. Furthermore, as an additional benefit, it is observed that large variations of output stiffness can hinder the appearance of one‐node connected hinges, usually found in the deterministic design of compliant mechanisms.