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We consider a general assembly system that produces variations on a single product. Customers can choose to customize any aspect of the product which corresponds to one or more stages of the assembly system. We assume that demand for the original product and the demand for any customization at any stage are all random with known probability distributions. We model this problem as a multi-echelon inventory management problem. This problem has not been studied before. Allowing for customization at any stage in an assembly system makes this problem significantly more complex than similar problems considered in the past. To solve this problem, we 'split' the assembly line into two parallel assembly lines which we call uncommitted and committed lines. We show that the usual method of reducing the assembly system to a series is not possible in general. We develop a heuristic policy which can be used to then reduce the system into a series system. We also present numerical calculations which highlight the efficacy of the heuristic policy. [ABSTRACT FROM AUTHOR]

Innovative methods for substrate patterning provide intriguing possibilities for the development of devices based on ordered arrays of semiconductor nanowires. Control over the nanostructures' morphology in situ can be obtained via extensive theoretical studies of their formation. In this paper, we carry out an investigation of the ordered nanowires' formation kinetics depending on the growth mask geometry. Diffusion equations for the growth species on both substrate and nanowire sidewalls depending on the spacing arrangement of the nanostructures and deposition rate are considered. The value of the pitch corresponding to the maximum diffusion flux from the substrate is obtained. The latter is assumed to be the optimum in terms of the nanowire elongation rate. Further study of the adatom kinetics demonstrates that the temporal dependence of a nanowire's length is strongly affected by the ratio of the adatom's diffusion length on the substrate and sidewalls, providing insights into the proper choice of a growth wafer. The developed model allows for customization of the growth protocols and estimation of the important diffusion parameters of the growth species. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) represent a fusion of compelling porous structures, alluring physical and chemical attributes, and extensive possibilities for application. The inherent capability of employing these organic constituents has paved the path for the construction of MOFs, permitting imaginative pre-design and post-synthetic adjustments through apt reactions. This adaptability not only enriches the structural variety of MOFs but also uncovers pathways for customizing their characteristics to align with precise application needs. In this context, click chemistry has emerged as a potent and resilient tool in the creation and modification of diverse functional materials, with a promising application in MOF structures. These MOF architectures undergo postsynthetic modifications through the application of "click" chemistry. This approach, characterized by its efficiency and selectivity, proves instrumental in tailoring and enhancing the properties of MOFs, thereby expanding their utility across a spectrum of scientific and industrial applications. Herein, we illuminate recent MOF structures achieved through the postsynthetic application of "click" chemistry. Our exploration delves into the forefront of strategies propelling the postsynthetic modification process, with a dedicated focus on the structural complexities, synthesis methodologies, and the prospective applications of these modified MOF architectures. Our intention is to actively contribute to the continuous discourse on applications of click chemistry in the development of MOF architectures, encouraging scientists to dedicate their expertise and efforts towards the advancement of these multifunctional materials. [ABSTRACT FROM AUTHOR]

With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for the scientific study and applied use of customized behavior-change interventions. To facilitate this development, we propose a classification system that divides psychological targeting into two approaches that differ in the process by which interventions are designed: audience-to-content matching or content-to-audience matching. This system is both integrative and generative: It allows us to (a) integrate existing research on personalized interventions from different psychological subdisciplines (e.g., political, educational, organizational, consumer, and clinical and health psychology) and to (b) articulate open questions that generate promising new avenues for future research. Our objective is to infuse personality science into intervention research and encourage cross-disciplinary collaborations within and outside of psychology. To ensure the development of personality-customized interventions aligns with the broader interests of individuals (and society at large), we also address important ethical considerations for the use of psychological targeting (e.g., privacy, self-determination, and equity) and offer concrete guidelines for researchers and practitioners. [ABSTRACT FROM AUTHOR]

A large number of perceptual image hashing schemes have been developed in recent decades, and they usually calculated the hash distances using Euclidean distance, correlation coefficient, normalized Hamming distance, etc. These hash distances are not customized measurements for hash sequences, which are all single values. To this end, a new distance measurement method, i.e. a distance vector instead of a single value, is proposed in this paper. Since a hash sequence can be regarded as the compressed representation of image perceptual content, the correlation between images is maintained between hash sequences. In our method, the correlation between hash sequences is captured by residuals and co-occurrence matrix to form a distance vector. The correlation differences between perceptually similar and distinct images can be captured in the obtained distance vectors. Using a binary classifier, it is easy to classify the distance vectors of perceptually similar image pairs and perceptually distinct image pairs. As a result, content authentication accuracy of perceptual image hashing can be effectively increased, which is verified by experimental results. [ABSTRACT FROM AUTHOR]

The integration of 3D printing and design technologies into the fashion industry has created new opportunities for customization and flexibility in apparel production. These innovations enable faster creation of new clothing models and allow designers to experiment with unique designs, distinguishing themselves from conventional mass production. However, the widespread adoption of 3D-printed clothing faces limitations due to the rigidity of current materials, which impacts flexibility and comfort. Despite these challenges, designers like Iris van Herpen and Victoria Beckham have successfully incorporated 3D-printed elements into high-fashion collections, highlighting the technology's artistic potential. Current applications are primarily found in accessories and avant-garde designs, where rigidity poses fewer issues. The future of 3D printing in fashion lies in personalized, eco-friendly production with further advancements in material science and cost reductions. [ABSTRACT FROM AUTHOR]

Recent years have seen a growth in customized products and services. As a prerequisite for customization, private information on individual customers' quality preferences needs to be uncovered. Sellers can listen to customers about their stated or self-reported preferences through direct communication (e.g., conversation, survey). Alternatively, customer preferences can be inferred from their behavior when they are given the rights to self-design the quality. In this research we endogenize the viability of customization by investigating whether and when customers may reveal their stated/inferred preferences truthfully. We find that, for either preference-learning approach, customers would voice their preferences faithfully if and only if they are sufficiently heterogenous. Equilibrium preference revelation, and hence endogenous customization, tend to be sustained by intermediate seller bargaining power or nonextreme production/selling costs. We examine how the preference-learning approaches may differ in the endogenous feasibility of customization, equilibrium qualities, and the parties' expected payoffs. We show that giving up the design right need not always be harmful for the seller, and gaining it can make the buyers worse off, especially when fixed costs of customization are considered. This paper was accepted by Eric Anderson, marketing. Funding: This work was supported by a DAG grant offered by the Hong Kong Research Grant Council. [ABSTRACT FROM AUTHOR]

The passive sensing and active control of electromagnetic (EM) waves have always been attractive in electronic and information areas, especially during the intelligent era. Here a new method is presented to achieve the angle sensing of incident wave and adaptive control of backward scattering using the intelligent metasurface. The proposed unit cells have the ability to dynamically manipulate the receiving and reflection of the EM energy respectively. The angle sensing of incident waves can be actualized using the method of compressive sensing based on multiple receiving patterns, which are generated by randomly switching the receiving and reflection states of the unit cells. Afterward, the customized performances of backward scattering waves according to the cognitive incident angle can be realized by controlling the programmable reflective phases of unit cells correspondingly. One prototype composed of the metasurface and the module for sensing and adaptive feedback control is fabricated. The whole intelligent metasurface with customizing the function of retro‐reflection or low scattering is measured without human intervention and the good results acquired can verify the validity of the proposed concept and design. [ABSTRACT FROM AUTHOR]

The visualization of complex spatial data is typically achieved using the Windows, Icons, Menus, and Pointer (WIMP) method. However, this approach has limitations, including a steep learning curve, non-intuitive interfaces, and limited visualization capabilities. To overcome these challenges, this study proposes an augmented reality-based (AR-based) visualization and interaction method for complex spatial data. By customizing the rendering pipeline, various visualization effects, such as clipping and lighting adjustments, can be achieved. Additionally, a prototype system called ARSpatialDX is developed. Furthermore, a user study is conducted to compare the AR and WIMP methods. The results indicate that AR outperforms WIMP in terms of virtual-reality matching, perception of physical properties, attractiveness, stimulation, perspicuity, and novelty aspects. Users generally find the direct interaction with data using their hands in AR to be highly interesting and express a strong willingness to continue using it. [ABSTRACT FROM AUTHOR]

This study proposed a hydroponic system with the capacity to acquire high-resolution in situ mass data for non-destructive evaluation of water circulation in lettuce. The system customizes the watering profile, enables high-frequency in situ weight measurement, and monitors multidimensional environment changes. Key air, water, and light parameters were collected to evaluate the plant response, susceptibility, and adaptability to environmental conditions. Multiple physiological indices were defined to characterize the properties of two lettuce varieties in response to different environmental factors. [ABSTRACT FROM AUTHOR]

For animals to locate resources and stay safe, navigation is an essential cognitive skill. Blind people use different navigational strategies to encode the environment. Path integration significantly influences spatial navigation, which is the ongoing update of position and orientation during self-motion. This study examines two separate things: (i) how guided and non-guided strategies affect blind individuals in encoding and mentally representing a trajectory and (ii) the sensory preferences for potential navigational aids through questionnaire-based research. This study first highlights the significant role that the absence of vision plays in understanding body centered and proprioceptive cues. Furthermore, it also underscores the urgent need to develop navigation-assistive technologies customized to meet the specific needs of users. [ABSTRACT FROM AUTHOR]

Universal adversarial perturbation (UAP) exhibits universality as it is independent of specific images. Although previous investigations have shown that the classification of natural images is susceptible to universal adversarial attacks, the impact of UAP on face recognition has not been fully investigated. Thus, in this paper we assess the vulnerability of face recognition for UAP. We propose FaUAP-FBF, which exploits the frequency domain by learning high, middle, and low band filters as an additional dimension of refining facial UAP. The facial UAP and filters are alternately and repeatedly learned from a training set. Furthermore, we convert non-target attacks to target attacks by customizing a target example, which is an out-of-distribution sample for a training set. Accordingly, non-target and target attacks form a uniform target attack. Finally, the variance of cosine similarity is incorporated into the adversarial loss, thereby enhancing the attacking capability. Extensive experiments on LFW and CASIA-WebFace datasets show that FaUAP-FBF has a higher fooling rate and better objective stealthiness metrics across the evaluated network structures compared to existing universal adversarial attacks, which confirms the effectiveness of the proposed FaUAP-FBF. Our results also imply that UAP poses a real threat for face recognition systems and should be taken seriously when face recognition systems are being designed. [ABSTRACT FROM AUTHOR]

Cranioplasty is one of the most common neurosurgical procedure performed to repair cranial defect. Many materials and fabrication technique are used to prepare cranial implant in cases where autologous bone is not available. Polymethyl Methacrylate (PMMA) is one of the most common polymer used as bone substitute. PMMA fabricated using 3D printed models have shown better fit, symmetrical shape, and restore esthetic looks of patients. The use of 3D printed implants in medical procedures has several advantages over traditional manufacturing methods. 3D printing allows for greater precision, customization, and quicker implant time. [ABSTRACT FROM AUTHOR]

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are widely utilized in image classification. Nevertheless, CNNs typically require substantial computational resources, posing challenges for deployment on resource-constrained edge devices and limiting the spread of AI-driven applications. While various pruning approaches have been proposed to mitigate this issue, they often overlook a critical fact that edge devices are typically tasked with handling only a subset of classes rather than the entire set. Moreover, the specific combinations of subcategories that each device must discern vary, highlighting the need for fine-grained task-specific adjustments. Unfortunately, these oversights result in pruned models that still contain unnecessary category redundancies, thereby impeding the potential for further model optimization and lightweight design. To bridge this gap, we propose a task-level customized pruning (TLCP) method via utilizing task-level information, i.e., class combination information relevant to edge devices. Specifically, TLCP first introduces channel control gates to assess the importance of each convolutional channel for individual classes. These class-level control gates are then aggregated through linear combinations, resulting in a pruned model customized to the specific tasks of edge devices. Experiments on various customized tasks demonstrate that TLCP can significantly reduce the number of parameters, by up to 33.9% on CIFAR-10 and 14.0% on CIFAR-100, compared to other baseline methods, while maintaining almost the same inference accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive investigation into pore pressure prediction methodologies, specifically addressing challenges in the upper zones of a field in southwestern Iran characterized by intricate geological complexities. Our analysis centers on the application of the drilling exponent (D-exponent) methodology, utilizing drilling parameters for accurate pore pressure estimation. Three methodologies are scrutinized: a section-specific approach, a site-specific analysis establishing unique normal compaction trend lines, and a novel method refining the normal compaction trend line mathematically. The Zamora method stands out as superior, especially in geologically complex regions like the Gachsaran formation, where overburden pressure may not be the primary stress. Implementing the Zamora method yields a substantial reduction in mean absolute percentage error (MAPE), enhancing pore pressure prediction accuracy by up to 9%. Additionally, a newly proposed method enhances accuracy by 7%. These findings underscore the importance of customizing pore pressure prediction techniques to the distinct geological conditions of specific regions. The outcomes not only advance our understanding of pore pressure prediction but also offer practical guidance for similar geological settings. [ABSTRACT FROM AUTHOR]

AbstractA common issue in the second/foreign language (L2) classroom is that teachers’ insights are not often included in the development of CALL materials. To understand how teachers can customize CALL materials that meet the needs of their students, our previous conceptual paper proposed a three-level approach to show how teachers can leverage basic computers skills to categorize pre-existing digital materials to stimulate L2 interaction, namely by using them as-is, modifying them, and/or using online tools to create them from scratch. This one-group mixed-methods study implements these ideas by: (1) examining the types of CALL materials teachers can customize to stimulate L2 interaction, and (2) assessing participants’ perceptions of how they used the approach. The study took place in a CALL teacher training course for eight weeks at an English-speaking university in Montréal and featured readings, lectures, lab sessions, and projects directed at customizing CALL materials. To understand the types of CALL materials participants could customize (goal #1), mixed-methods data were collected in the form of online ESL courses built by participants and a design-choice log where they recorded details about the tools they used, providing an overview of each activity. A reflective discussion was held using the Socratic-Wheel technique to understand how participants used the approach (goal #2). The results indicate that participants customized CALL materials at all three levels, targeting L2 interaction with (e.g. online flashcards) and through (e.g. synchronous messages) computers. Responses from the reflective discussion revealed that participants perceived becoming aware of their abilities in CALL, thus endorsing the pedagogical effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Introduction and Hypothesis: This study surveyed urogynecologists and Advanced Practice Providers (APPs) in the USA to gauge their interest and willingness to embrace customizable pessaries as a viable treatment option. We hypothesize that clinicians might be interested in using customizable pessaries in their practice. Methods: A cross-sectional survey was conducted among urogynecologists and their APPs who fit pessaries to gain insights into their experiences with standard pessaries and perspectives on the value and feasibility of customizable devices. The survey was distributed through email lists associated with women's health and pelvic floor disorders and gathered data on the difficulty in fitting pessaries and the perceived advantages of integrating customizable options into clinical practice. Results: There were 122 participants, including 76 physicians and 46 APPs. Thirty-five percent of clinicians advocated for pessaries as a first-line therapy for pelvic floor disorders. APPs were more inclined to recommend pessaries as a primary therapy than physicians (p < 0.01). Fifty-three percent of providers reported occasional difficulties, and 12% reported frequent difficulties fitting patients with standard-shaped pessaries. APPs were significantly more likely to encounter fitting issues due to pessary shape (p = 0.023). Clinicians suggested additional practices, such as modifying pessaries to enhance retention in patients with an enlarged genital hiatus, shortened vaginas, or apical narrowing. Conclusions: This study indicates that clinicians are inclined to incorporate customizable pessaries into their treatment protocols for pelvic floor disorders, emphasizing the need for continued innovation in pessary customization that prioritizes a patient-centric approach to pelvic floor disorder management. [ABSTRACT FROM AUTHOR]

Motivation Molecular dynamics simulation is very useful but computationally demanding method of studying dynamics of biomolecular systems. Many enhanced sampling methods were developed in order to obtain the desired results in available computational time. Metadynamics and its variants are common enhanced sampling methods used for this purpose. Metadynamics simulations allow the user to gather large amounts of data, which have to be analyzed to elucidate the properties of the studied system. Results Here, we present metadynminer.py, a Python package that allows easy and user-friendly analysis and visualization of the results obtained from metadynamics simulations. The built-in functions automate frequent tasks and make the package easy to use for new users, while its many customization options and object-oriented nature allow for integration into specialized data analysis workflows by more advanced users. Availability and implementation The "metadynminer.py" Python package is available under the GPL-3.0 license via PyPi and Conda. The development version is available on GitHub along with issue support (https://github.com/Jan8be/metadynminer.py). Documentation, tutorial and Jupyter Notebook (provided through the public mybinder.org service) are available at https://metadynreporter.cz. [ABSTRACT FROM AUTHOR]

In recent years, additive manufacturing (AM) has been recognized as a transformative force in the dental industry, with the ability to address escalating demand, expedite production timelines, and reduce labor-intensive processes. Despite the proliferation of three-dimensional printing technologies in dentistry, the absence of well-established post-processing protocols has posed formidable challenges. This comprehensive review paper underscores the critical importance of precision in post-processing techniques for ensuring the acquisition of vital properties, encompassing mechanical strength, biocompatibility, dimensional accuracy, durability, stability, and aesthetic refinement in 3D-printed dental devices. Given that digital light processing (DLP) is the predominant 3D printing technology in dentistry, the main post-processing techniques and effects discussed in this review primarily apply to DLP printing. The four sequential stages of post-processing support removal, washing, secondary polymerization, and surface treatments are systematically navigated, with each phase requiring meticulous evaluation and parameter determination to attain optimal outcomes. From the careful selection of support removal tools to the consideration of solvent choice, washing methodology, and post-curing parameters, this review provides a comprehensive guide for practitioners and researchers. Additionally, the customization of post-processing approaches to suit the distinct characteristics of different resin materials is highlighted. A comprehensive understanding of post-processing techniques is offered, setting the stage for informed decision-making and guiding future research endeavors in the realm of dental additive manufacturing. [ABSTRACT FROM AUTHOR]

AbstractPrevious research on Live Stream Commerce (LSC) has seldom addressed the topic from the combined perspectives of personal selling and Human–Computer Interaction (HCI), which collectively involve humans, computers, technology, and data. This study investigates the intricate relationship between HCI and LSC through the lens of Assemblage Theory. By exploring how streamer-sellers leverage technology to innovate and add value in digital commerce, it offers a fresh research perspective and framework. This research underscores the pivotal role of streamers, redefining LSC as a model of personal selling that is significantly enhanced by HCI. Employing a qualitative approach, the study identifies seven value themes across four LSC assemblages: Product Discovery and Satisfying Cognitive Needs within the Live Streaming Assemblage; Attractive Deals within the Transaction Assemblage; Confidence Creation and Understandable Communication within the Social Assemblage; and the less-discussed Tailored Satisfaction and Mass Customization within the Foundational Layer Assemblage. Guided by Assemblage Theory, this research proposes a novel methodology and identifies new key variables for analyzing the intricate dynamics between streamers, viewers, technology, and data in LSC. For instance, HCI-driven AI and algorithms can empower streamers to target viewers and discern user needs, thereby enabling mass customization and surpassing traditional retail customization standards. This study contributes theoretically to personal selling and LSC research. [ABSTRACT FROM AUTHOR]

In the past decades, European Union (EU) agencies have proliferated to address a plethora of governance problems. When designing EU agencies, EU legislators confront a tension: Legislators want agencies to be competent problem‐solvers, but they also want to keep agencies under control. How do EU legislators balance these two imperatives? We argue that agencies' political principals do not necessarily have to trade competence for control, and vice versa, but can draw on different institutional strategies – manipulating agencies' decision‐making and decision‐maker independence – to mitigate the competence‐control dilemma. Drawing on an original dataset on EU agencies' formal independence, we demonstrate empirically that principals customize EU agencies' independence to match the respective competence and control demands that come with specific agency tasks: regulation, authorization, implementation and information. The paper makes an important contribution to the literature on the drivers of the EU's agencification and to debates on the institutional design of regulatory institutions in the EU and beyond. [ABSTRACT FROM AUTHOR]

Users often encounter tedious recommendations as they are continuously exposed to the recommendation system. In response to this issue, serendipity in a recommendation system has been introduced to generate novel and unexpected recommendations while keeping them relevant to users' previous preferences. This study proposes an interactive feedback loop for a serendipity in a recommendation system that allows users to directly explore content via counterfactual manipulation of features. Specifically, users indicate their preferences through the "what-if" based customization of content meta-information, and these modifications influence their usage history, thereby enabling the elicitation of serendipitous items. To validate the proposed feedback loop, we conducted a scenario-based experiment and compared system-initiated and user-intervened recommendations. The results reveal that counterfactual exploration can help to generate serendipitous recommendations. This study contributes to providing a user-friendly recommendation system that can retrieve preference-reflected recommendations through user interaction. [ABSTRACT FROM AUTHOR]

Running static analysis rules in the wild as part of a commercial service demands special consideration of time limits and scalability, given the large and diverse real-world workloads that the rules are evaluated on. Furthermore, these rules do not run in isolation, which exposes opportunities for reuse of partial evaluation results across rules. In our work on Amazon CodeGuru Reviewer, and its underlying rule-authoring toolkit known as the Guru Query Language (GQL), we have encountered performance and scalability challenges, and identified corresponding optimization opportunities, such as caching, indexing, and customization of data-flow specification, which rule authors can take advantage of as built-in GQL constructs. Our experimental evaluation of a dataset of open-source GitHub repositories shows 3× speedup and perfect recall using indexing-based configurations, 2× speedup and 51% increase on the number of findings for caching-based optimization. Customizing the data-flow specification, such as expanding the tracking scope, can yield a remarkable increase in the number of findings, as much as 136%. However, this enhancement comes at the expense of a longer analysis time. Our evaluations emphasize the importance of customizing the data-flow specification, particularly when users operate under time constraints. This customization helps the analysis complete within the given time frame, ultimately leading to improved recall. [ABSTRACT FROM AUTHOR]

Originally developed for application in dissimilar joints, the dynamic feeding technology applied to the GMAW process is gaining popularity with applications in wire arc additive manufacturing (WAAM). Given the growing importance of additive manufacturing (AM) in industrial production, it is essential to better understand the main tool of WAAM technology. Acknowledging the limitations imposed on current waveform parameterization in commercial welding sources, this work used a proprietary welding source, named MIG-AD, which allows full control of parameters through dedicated hardware and open-source software. This study focuses on the impact of arc phase parameters on metal transfer, namely arc pulse time, arc pulse current level, and arc base current level. The results show the impact of altering each parameter on droplet formation, wire retraction, detachment frequency, and metal bridge elongation, as well as the stability and quality of the weld bead. This research offers valuable insights for optimizing welding parameters, aiming for greater control and process customization, making it a viable alternative for advanced research in WAAM, cladding, computational simulation, and artificial intelligence applied to welding. [ABSTRACT FROM AUTHOR]

In four studies, we found that receiving a customized gift leads recipients to appreciate the gift more highly because of vicarious feelings of pride. This vicarious pride is the same feeling that the customizer experiences after having self‐customized a product. In the first two experiments with real‐life pairs of friends, vicarious pride was documented among recipients of customized gifts. The findings show that the relationship between customization and gift appreciation is mediated by vicarious pride. Study 3 and Study 4 replicate the results of the first series of experiments, and reveal the role of vicarious pride in enhancing state self‐esteem and, in turn, gift appreciation. Study 4 also tests the effect of a relational variable (i.e., relationship anxiety) on this relationship. Specifically, relationship anxiety affects vicarious pride, and then consequently state self‐esteem and gift appreciation, which are only enhanced by customization when the relationship is not anxious. As a practical implication, this research emphasizes the importance of communicating gift customization to gift recipients to enhance their gift experience and appreciation. [ABSTRACT FROM AUTHOR]

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion. [ABSTRACT FROM AUTHOR]

This study investigates the combined impact of Trinidad Lake Asphalt (TLA) and styrene–isoprene–styrene (SIS) on the performance of PG 64-22 asphalt binder. The study specifically examines how the addition of TLA and SIS affects the stiffness, viscoelastic properties, and ability to resist deformation of the asphalt binder, using the Superpave test method. The findings demonstrate that the addition of SIS improves the ability to withstand deformation, with the best results seen in the mixture containing 20% TLA and 5% SIS. This blend achieves a harmonious combination of stiffness, pliability, and ease of use. Increasing the SIS content to 10% and combining it with a 20% TLA greatly enhances resistance to rutting, but it also decreases workability. In addition, a mixture consisting of 10% TLA and 10% SIS provides excellent resistance to fatigue and possesses viscoelastic properties that are well suited for reducing oxidative aging and cracking. Although SIS typically enhances the performance of binders, an excessive quantity can result in unwanted rigidity, requiring meticulous optimization. The results highlight the importance of customizing TLA–SIS combinations according to specific environmental conditions in order to optimize advantages. Subsequent studies should investigate the prolonged impacts and alternative combinations of additives to further improve the performance of binders. [ABSTRACT FROM AUTHOR]

The escalating demands for smart biomedical systems ignite a significantly growing influence of three‐dimensional (3D) printing technology. Recognized as a revolutionary and potent fabrication tool, 3D printing possesses unparalleled capabilities for generating customized functional devices boasting intricate and meticulously controlled architectures while enabling the integration of multiple functional materials. These distinctive advantages arouse a growing inclination toward customization and miniaturization, thereby facilitating the development of cutting‐edge biomedical systems. In this comprehensive review, the prevalent 3D printing technologies employed in biomedical applications are presented. Moreover, focused attention is paid to the latest advancements in harnessing 3D printing to fabricate smart biomedical systems, with specific emphasis on exemplary ongoing research encompassing biomedical examination systems, biomedical treatment systems, as well as veterinary medicine. In addition to illuminating the promising potential inherent in 3D printing for this rapidly evolving field, the prevailing challenges impeding its further progression are also discussed. By shedding light on recent achievements and persisting obstacles, this review aims to inspire future breakthroughs in the realm of smart biomedical systems. [ABSTRACT FROM AUTHOR]

In this study, we revisit the fundamental setting of face-swapping models and reveal that only using implicit supervision for training leads to the difficulty of advanced methods to preserve the source identity. We propose a novel reverse pseudo-input generation approach to offer supplemental data for training face-swapping models, which addresses the aforementioned issue. Unlike the traditional pseudo-label-based training strategy, we assume that arbitrary real facial images could serve as the ground-truth outputs for the face-swapping network and try to generate corresponding input pair data. Specifically, we involve a source-creating surrogate that alters the attributes of the real image while keeping the identity, and a target-creating surrogate intends to synthesize attribute-preserved target images with different identities. Our framework, which utilizes proxy-paired data as explicit supervision to direct the face-swapping training process, partially fulfills a credible and effective optimization direction to boost the identity-preserving capability. We design explicit and implicit adaption strategies to better approximate the explicit supervision for face swapping. Quantitative and qualitative experiments on FF++, FFHQ, and wild images show that our framework could improve the performance of various face-swapping pipelines in terms of visual fidelity and ID preserving. Furthermore, we display applications with our method on re-aging, swappable attribute customization, cross-domain, and video face swapping. Code is available under https://github.com/ ICTMCG/CSCS. [ABSTRACT FROM AUTHOR]

Inspired by the starfish's unique ability to achieve flexibility and posture-holding with minimal energy expenditure, we present a novel bioinspired morphing structure. Our two-component design, consisting of a thermoplastic mesh and elastomeric jacket, effectively mimics the functions of the starfish's ossicles, mutable collagenous tissues, and derma. This structure exhibits a remarkable combination of self-healing, time-dependent shape memory, and self-posture-holding properties. Systematic variations in mesh geometry demonstrate precise control over structural stiffness and thermal response, enabling customization for specific applications. The structure's scalability and ease of fabrication further enhance its adaptability. We experimentally demonstrate the potential of our biomimetic morphing structure using several prototypes. This work lays the foundation for developing a new type of versatile morphing structures with applications in diverse fields, including robotics, biomedical devices, and adaptive structures. [ABSTRACT FROM AUTHOR]

With increasing competition in the food franchise industry, the success of a franchise operation depends on its ability to instigate franchisee patronage. This study focuses on franchisor characteristics that help in creating positive behavioural outcomes. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS software is used to analyze 266 U.S. food franchisees. Findings show the relative impact of franchisee evaluation of information quality, service quality, rewards and recognition, and customization on perceived value. Also, positive perceived value in franchisees influences their willingness to pay a premium and their referrer intentions towards franchise brands. Multigroup analysis using demographic characteristics shows that the impacts of information quality and service quality on perceived value are higher in male and older franchisees than in female and younger franchisees. In contrast, the effect of customization on perceived value is greater in female and younger franchisees. This study provides insights to develop business strategies. [ABSTRACT FROM AUTHOR]

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This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals. [ABSTRACT FROM AUTHOR]

In virtual reality (VR), users can embody a wide variety of avatars, fromdigital replicas of themselves through diverse human body styles and appearances to non-humanoid representations. Although choosing a body to inhabit is part of what makes VR such an engaging experience, various studies have shown how embodiment may change the way we perceive ourselves and others both inside and outside VR. In our study, we explored whether first-person versus third-person avatar customization would lead to changes in embodiment. Furthermore, participants were embodied in larger-sized avatars based on the hypothesis that embodiment would lead to a change in implicit bias toward larger-sized people. Our results show that third-person avatar customization led to a decrease in the perceived embodiment of the larger-sized avatar and that, on the contrary, higher embodiment was associated with a reduction in implicit biases toward larger-sized people in the first-person avatar customization mode. These findings suggest that third-person avatar customization leads to reduced feelings of embodiment, while first-person avatar customization may support more radical body changes. [ABSTRACT FROM AUTHOR]

Risks of esthetic complications have been reported following dental implant insertion; hence it was suggested that a customized healing abutment could improve the peri-implant tissue healing. Anatomically contoured healing abutments (also called customized healing abutments) help to contain and protect the slow – Resorbing bone grafts, resulting in bone and soft tissue volume augmentation. Even in healed sites, customized healing abutments have shown a favourable outcome as compared to standard healing abutments. This article describes a simple chairside fabrication of precisely contoured healing abutment which will eventually result in a natural contoured soft tissue enhancing the esthetics of the restoration. [ABSTRACT FROM AUTHOR]

Prolonged workplace standing is commonplace and associated with a range of lower limb issues. Evaluating footwear interventions aiming to modify plantar pressure during standing is essential as the body is static, creating a different requirement for footwear. Previous research associates medial midfoot pressure with greater perceived comfort and identifies arch height as the most variable element of foot shape. Targeting footwear mass customization within this area may better address differences within the target wearers. This study aims to evaluate a modular insole system for its ability to modify plantar pressure during standing. Twenty-five participants completed a static and dynamic standing protocol for 60 seconds whilst measuring in-shoe peak and mean plantar pressure (KPa) and contact area (%). Individuals wore three insole options targeted towards different medial arch shapes (A– low arch, B– medium arch, C– high arch) and rated them for comfort. Participants received guidance to self-identify foot shape (low, medium, or high arch). Comparisons were drawn across the three insole profiles and between the insole rated as most comfortable (preferred), and the insole that matched the self-identified foot shape (matched). As insole arch height increased, medial midfoot pressure and contact area significantly increased, alongside significant reductions in first metatarsal pressure and contact. Preference was spread across insoles A, B, and C (56%, 32%, 12% of participants, respectively). Sixteen participants had different matched and preferred insoles, with significantly greater medial midfoot pressure and contact in the matched insole. The modular insole enabled different wear experiences, however, results suggest that individuals selected insoles lower than their foot shape. Providing adequate medial arch support enables redistribution of pressure which may enable greater comfort during the workday. [ABSTRACT FROM AUTHOR]

This study compares a gratitude intervention with an Indian yoga intervention to determine its efficacy to raise happiness levels. A randomized control trial was conducted with 48 female participants in each intervention group for eight weeks. The results suggest that happiness levels improved from pre-intervention to mid- and post-intervention and from mid- to post-intervention in the gratitude and yoga intervention groups. Despite similar levels of happiness pre-intervention in both groups, the yoga intervention group outperformed the gratitude intervention group in improving participants' happiness levels at mid-intervention and post-intervention. The findings reveal the significance of customizing interventions to fit specific cultural contexts. [ABSTRACT FROM AUTHOR]

The segmentation of fundus tumors is critical for ophthalmic diagnosis and treatment, yet it presents unique challenges due to the variability in lesion size and shape. Our study introduces Fundus Tumor Segmentation Network (FTSNet), a novel segmentation network designed to address these challenges by leveraging classification results and prompt learning. Our key innovation is the multiscale feature extractor and the dynamic prompt head. Multiscale feature extractors are proficient in eliciting a spectrum of feature information from the original image across disparate scales. This proficiency is fundamental for deciphering the subtle details and patterns embedded in the image at multiple levels of granularity. Meanwhile, a dynamic prompt head is engineered to engender bespoke segmentation heads for each image, customizing the segmentation process to align with the distinctive attributes of the image under consideration. We also present the Fundus Tumor Segmentation (FTS) dataset, comprising 254 pairs of fundus images with tumor lesions and reference segmentations. Experiments demonstrate FTSNet's superior performance over existing methods, achieving a mean Intersection over Union (mIoU) of 0.8254 and mean Dice (mDice) of 0.9042. The results highlight the potential of our approach in advancing the accuracy and efficiency of fundus tumor segmentation. [ABSTRACT FROM AUTHOR]

In view of the problem that the static analysis tool has a high cost of expanding the vulnerability due to the strong coupling of the vulnerability scanning process code in the tool code, a smart contract vulnerability detection method based on meta-operations is proposed. This method can convert the most basic detection process in the tool into an abstract form of meta-operation and customize the vulnerability scanning process through the combination of different meta-operations and logical operators, so as to realize the detection of different vulnerabilities by writing a small amount of vulnerability scanning process syntax. The detection can improve the ability of the tool to expand the new vulnerabilities of smart contracts. The experiment results prove that the number of characters of the vulnerability description required to be written according to this method is only 8.9%~12.7% of the logic characters of the Slither tool vulnerability detection, and the false positive rate is 2% lower than that of the Slither tool. It is proved that this method can provide stronger scalability and flexibility based on ensuring the reliability of tool detection. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of cyber-attack detection in a discrete event system by proposing a novel model. The model utilizes graph convolutional networks to extract spatial features from event sequences. Subsequently, it employs gated recurrent units to re-extract spatio-temporal features from these spatial features. The obtained spatio-temporal features are then fed into an attention model. This approach enables the model to learn the importance of different event sequences, ensuring that it is sufficiently general for identifying cyber-attacks, obviating the need to specify attack types. Compared with traditional methods that rely on synchronous product computations to synthesize diagnosers, our deep learning-based model circumvents state explosion problems. Our method facilitates real-time and efficient cyber-attack detection, eliminating the necessity to specifically identify system states or distinguish attack types, thereby significantly simplifying the diagnostic process. Additionally, we set an adjustable probability threshold to determine whether an event sequence has been compromised, allowing for customization to meet diverse requirements. Experimental results demonstrate that the proposed method performs well in cyber-attack detection, achieving over 99.9 % accuracy at a 1 % threshold and a weighted F1-score of 0.8126, validating its superior performance. [ABSTRACT FROM AUTHOR]

Background: Contemporary fixed orthodontic appliances are shifting from non‐customized pre‐adjusted appliances to custom‐designed and printed appliances with novel digital setup systems. We are one step closer to precision dentistry and orthodontics using personalized mechanics and custom appliances. However, despite the evidential enhancement and other improvements to fixed appliances, tooth movement is still limited to five degrees of freedom. Opening or closing spaces still requires manually placing elastomeric chains or coil springs. Aim: In this article, we aimed to demonstrate how advancements in CAD/CAM technology, reverse engineering, and digital customization are helping orthodontics constantly evolve, enabling treatment with enhanced esthetics and minimal compliance. The clinical system (InBrace®, Irvine, CA) described in this article uses a patient‐specific, digitally designed multiloop NITI wire that delivers friction‐free, light, and continuous forces and activates automatically whenever the malocclusion deviates from the digital setup. Conclusion: Through digital customization, InBrace allows for automated tooth movement in all six degrees of freedom, including space opening or closure, via programmed non‐sliding mechanics. Clinical Significance: Precision orthodontics and personalized treatment have been significant developments in orthodontics recently. This article focuses on how a technologically advanced lingual appliance system could achieve targeted cosmetic results methodically via automation and personalization. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the present study was to compare birth weight (BW) distribution and proportion of BWs below or above specified percentiles in low‐risk singleton pregnancies in healthy South African (SA) women of mixed ancestry with expected values according to four BW references and to determine the physiological factors affecting BW. Methods: This was an ancillary study of a prospective multinational cohort study, involving 7060 women recruited between August 2007 and January 2015 in two townships of Cape Town, characterized by low socioeconomic status, and high levels of drinking and smoking. Detailed information about maternal and pregnancy characteristics, including harmful exposures, was gathered prospectively, allowing us to select healthy women with uncomplicated pregnancies without any known harmful exposures. In this cohort we compared the median BW and the proportion of BWs P90, 95 and 97 according to four reference standards (INTERGROWTH‐21st, customized according to the method described by Mickolajczyk, Fetal Medicine Foundation and revised Fenton reference) with expected values. Appropriate parametric and nonparametric tests were used, and sensitivity analysis was performed for infant sex, first trimester bookings and women of normal body mass index (BMI). Multiple regression was used to explore effects of confounders. Written consent and ethics approval was obtained. Results: The cohort included 739 infants. The INTERGROWTH‐21st standard was closest for the actual BW‐distribution and categories. Below‐expected BW was associated with boys, younger, shorter, leaner women, lower parity and gravidity. Actual BW was significantly influenced by maternal weight, BMI, parity and gestational age. Conclusion: Of the four references assessed in this study, the INTERGROWTH‐21st standard was closest for the actual BW distribution. Maternal variables significantly influence BW. Synopsis: The INTERGROWTH‐21st birth weight standard was the best of four references for this population, but maternal variables affect the actual birth weight (BW) and its deviation from the expected median. [ABSTRACT FROM AUTHOR]

In visual tracking, the target always undergoes appearance variations due to a variety of challenging situations, such as deformation and rotation. In this paper, we propose the target-guided feature modulation network based on siamese network to extract the more target-relevant features, which is guided by a template to modulate the search features that can be directly used for classification and localization. Specifically, we customize two target-guided feature modulation subnetworks for visual tracking, which are called template-guided spatial-attention modulation subnetwork and template-guided channel-attention modulation subnetwork, to achieve this proposal. The former controls the discriminative region based on the correlation of each search feature position with all template feature positions, whereas the latter readjusts the importance of each channel based on the response value of each channel feature of the template feature and the response value of each channel of the search feature. Extensive experiments on multiple datasets have demonstrated the effectiveness of the proposed approach in this paper. [ABSTRACT FROM AUTHOR]

This research explores the transformative role of integrating Explainable AI (XAI) with Learning Analytics to enhance educational insights. At the heart of this study is an exploration into how this integration influences decisionmaking, student engagement, and learning outcomes in diverse educational contexts. Our objectives are to assess the clarity and impact of XAI techniques in education, scrutinize their role in decision-making processes, analyze their effects on student engagement and academic performance, evaluate customization across varied environments, delve into ethical considerations and privacy issues, investigate stakeholder perceptions, and identify both challenges and opportunities for future implementation. Ultimately, this study aims to offer educators, administrators, and policymaker's valuable insights into leveraging XAI and Learning Analytics to enrich the educational experience, positioning data-driven technologies as pivotal in enhancing learning environments. [ABSTRACT FROM AUTHOR]

Noninvasive respiratory support delivered through a face mask has become a cornerstone treatment for adults and children with acute or chronic respiratory failure. However, an imperfect mask fit by using commercially available interfaces is frequently encountered, which may result in patient discomfort and treatment inefficiency or failure. To overcome this challenge, over the past decade, increasing attention has been given to the development of personalized face masks, which are custom-made to address the specific facial dimensions of an individual patient. With this scoping review, we aim to provide a comprehensive overview of the current advances and gaps in knowledge with regard to the personalization masks for CPAP and NIV. We performed a systematic search of the literature and identified and summarized a total of 23 studies. Most studies included were involved in the development of nasal masks. Studies that targeted adult respiratory care mainly focused on chronic (home) ventilation and included some clinical testing in a relevant subject population. In contrast, pediatric studies focused mostly on respiratory support in the acute setting, whereas testing was limited to bench or case studies only. Most studies were positive with regard to the performance (ie, comfort, level of air leak, and mask pressure applied to the skin) of personalized masks in bench testing or in human, healthy or patient, subjects. Advances in the field of 3-dimensional scanning and soft material printing were identified, but important gaps in knowledge remain. In particular, more insight into cushion materials, headgear design, clinical feasibility, and cost-effectiveness is needed before definite recommendations can be made with regard to implementation of large-scale clinical programs that personalize noninvasive respiratory support masks for adults and children. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD), a prevalent neurodevelopmental condition impacting cognitive, communicative, and behavioral aspects, typically manifests in early childhood due to genetic, environmental, and immunological factors. Employing a novel dataset termed children's ASD speech corpus (CASD-SC), the research makes use of short-time Fourier transform (STFT) layered convolutional neural networks (CNN), incorporating an image input layer and a sequence input layer. The analysis encompasses data both with and without augmentation, exploring various CNN configurations. Results showcase that the log spectrogram-based STFT layered CNN model achieves 86.6% accuracy for the raw data, while the pre-emphasis filter (PEF) with learnables-based STFT layered CNN model attains 99.1% accuracy for the data with augmentation for detecting ASD in children. This investigation bridges the literature gap by evaluating child-specific raw speech data. The study underscores the significance of processing and training efficiency in ASD diagnosis and promotes early intervention techniques by improving ASD detection in children. [ABSTRACT FROM AUTHOR]