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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]

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]

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]

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]

Social prescribing (SP) embodies a comprehensive approach to addressing the social determinants of health. Access to Resources in the Community (ARC) is an innovative SP program offering bilingual services that involves a single point of entry for health and social needs and introduces practice changes to assist primary care providers in engaging patients, along with a nonclinical lay navigator who supports patients in accessing relevant community resources. The ARC team has created a SP toolkit offering practical guidance for setting up, implementing, monitoring the progress of and evaluating SP programs. The four ARC guides can be easily customized for application in diverse practice and research settings. [ABSTRACT FROM AUTHOR]

Although service customization is widely adopted in the tourism industry to cater to the heterogeneous needs of customers, the high interaction in customization often results in a value decrease for the service provider and tourists (i.e., value codestruction). However, the literature has not fully explored the restoration process when value codestruction occurs during the consumption stage of customized tourism. Based on conservation of resources theory, this study identified a systematic value restoration framework by investigating the factors of codestruction situation, restoration approach, and customer characteristics. Our experiments indicate that value can only be restored effectively when the restoration approach is congruent with the value reducer type. Moreover, customers with low self-efficacy rely more on such a congruency effect. The findings provide managers and tourists with insights into how to optimize tourism experiences by displaying flexibility in dealing with value codestruction in customized tourism. [ABSTRACT FROM AUTHOR]

The use of mobile devices for web browsing has increased extraordinarily in recent years, becoming the main source of information. Unfortunately, developers cannot meet the needs of all users. As a result, users have been forced to adapt web content to their needs. Additionally, users face more costly interactions than on desktop computers due to the smaller screen size of cell phones. These interactions include URL typing, scrolling, taping, branching and tab-switching. If the number of web resources is large, the activity becomes unpleasant. Furthermore, should the quantity of interactions be extensive, the web experience also becomes unpleasant. Moreover, all these interactions lead to higher costs in terms of phone time and battery life, as well as more costly navigation. Web augmentation enhances the user web experience by adding content from different resources, modifying the original layout, or reducing interactions during web searches, among other benefits. To reduce user interactions and satisfy their information necessities, we propose MAWA, an extension for Firefox mobile that allows users to adapt any web page to their needs through web augmentation techniques. To validate our proposal, an evaluation of the tool has been carried out. The article presents the benefits introduced by MAWA as a response to the inconveniences experienced by users when performing their activities in the mobile browser. The results obtained show that MAWA is able to reduce the number of interactions significantly, as well as to reduce battery consumption and the time needed to obtain the desired information. In addition, users perceive that navigation is less cluttered. [ABSTRACT FROM AUTHOR]

Despite the growing attention on the use of online scaffolding to enhance argumentation skills, there has not been a comprehensive review conducted in this specific area. There is a lack of understanding of both the current state of online scaffolding for acquiring argumentation skills and the specific research gaps that exist. This article presents a scoping review of 32 empirical studies, published between 2000 and 2023, that addressed online scaffolding in the context of argumentation skills. Overall, the review indicates that online scaffolding is generally associated with positive results in developing argumentation skills. Surprisingly, customisation of scaffolding was often neglected in online scaffolding designs. When it is applied, limited consideration was paid to contingency, fading and transfer of responsibility aspects in online scaffolding customisation designs. The authors discuss the implications of their findings in terms of the design for online scaffolding and share recommendations for further research. [ABSTRACT FROM AUTHOR]

Due to the heterogeneity gap in multi-view data, researchers have been attempting to apply these data to learn a co-latent representation to bridge this gap. However, multi-view representation learning still confronts two challenges: (1) it is hard to simultaneously consider the performance of downstream tasks and the interpretability and transparency of the network; (2) it fails to learn representations that accurately describe the class boundaries of downstream tasks. To overcome these limitations, we propose an interpretable representation learning framework, named interpretable multi-view proximity representation learning network. On the one hand, the proposed network is customized by an explicitly designed optimization objective that enables it to learn semantic co-latent representations while maintaining the interpretability and transparency of the network from the design level. On the other hand, the designed multi-view proximity representation learning objective function encourages its learned co-latent representations to form intuitive class boundaries by increasing the inter-class distance and decreasing the intra-class distance. Driven by a flexible downstream task loss, the learned co-latent representation can adapt to various multi-view scenarios and has been shown to be effective in experiments. As a result, this work provides a feasible solution to a generalized multi-view representation learning framework and is expected to accelerate the research and exploration in this field. [ABSTRACT FROM AUTHOR]

As a core component of electric vehicles, the state of health (SOH) of lithium-ion battery has a direct impact on vehicle performance and safety. Existing data-driven models primarily focus on feature extraction, often overlooking the processing of multi-level redundant information and the utilization of multi-stage battery features. To address the issues, this paper proposes a novel data-driven method, named multi-stage mixed attention U-Net (MMAU-Net), for SOH estimation, which is based on both the phased learning and an encoder–decoder structure. First, the geometric knee-point division method is proposed to divide the battery life cycle into multiple stages, which allows the model to learn the distinctive features of battery degradation at each stage. Second, to adeptly capture degraded features and reduce redundant information, we propose a mixed attention U-Net model for the SOH prediction task, which is constructed upon the fundamental U-Net backbone and is enhanced with time step attention and feature attention modules. The experimental results validate the proposed method's feasibility and efficacy, demonstrating an acceptable performance across a spectrum of evaluative metrics. Consequently, this study offers a research within the domain of battery health management. [ABSTRACT FROM AUTHOR]

The combination of 3D‐printing technology and microfluidics has garnered widespread interest for creating new designs and has opened a large window for creativity. However, some laboratories do not have the necessary technology to explore this potential advantage. In this paper, we demonstrate how to explore target technologies to fabricate a single version of a polydimethylsiloxane (PDMS)‐based electroanalytical platform using a low‐cost protocol and disposable materials. This protocol is cost‐effective in terms of equipment and materials, allowing for the creation of a circular channel, a T‐junction system, and two designs of electrochemical detectors customized onto a single platform. The interesting new device has some important characteristics, such as fabrication following green and sustainable protocols. Additionally, it can address the limitations associated with flow‐based analysis, for which the classical three‐electrode configuration is inadequate. It has also demonstrated the feasibility of using the device in analytical applications for the determination of salicylic acid in commercial dermocosmetic samples, including aqueous samples. [ABSTRACT FROM AUTHOR]

This study investigated how "teacher" avatar customization and exposure to "learner" virtual humans who display mind-affected participants' behavior and emotions in a re-creation of Milgram's obedience experiment. Female participants customized avatars that merged their physical self with hero, antihero, or villain archetypes and then interacted with a mindful or non-mindful virtual human; 82.8% of participants went above the maximum electric shock intensity. Women who customized hero avatars delivered lower voltage shocks to a virtual human compared with those who customized antihero and villain avatars. Virtual humans' display of mind did not affect shock intensity, guilt, or negative emotion though participants reported increased shame after shocking a mindful versus a non-mindful virtual human. Customizing antihero avatars increased shame and negative affect, especially after interacting with mindful virtual humans. We discuss the implications of these findings for the Proteus effect and Computers Are Social Actors perspective in extreme virtual encounters. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations. [ABSTRACT FROM AUTHOR]

This paper introduces TC-Llama 2, a novel application of large language models (LLMs) in the technology-commercialization field. Traditional methods in this field, reliant on statistical learning and expert knowledge, often face challenges in processing the complex and diverse nature of technology-commercialization data. TC-Llama 2 addresses these limitations by utilizing the advanced generalization capabilities of LLMs, specifically adapting them to this intricate domain. Our model, based on the open-source LLM framework, Llama 2, is customized through instruction tuning using bilingual Korean-English datasets. Our approach involves transforming technology-commercialization data into formats compatible with LLMs, enabling the model to learn detailed technological knowledge and product hierarchies effectively. We introduce a unique model evaluation strategy, leveraging new matching and generation tasks to verify the alignment of the technology-commercialization relationship in TC-Llama 2. Our results, derived from refining task-specific instructions for inference, provide valuable insights into customizing language models for specific sectors, potentially leading to new applications in technology categorization, utilization, and predictive product development. [ABSTRACT FROM AUTHOR]

The von Neumann architecture is no longer sufficient for handling large-scale data. In-memory computing has emerged as the potent method for breaking through the memory bottleneck. A new 10T SRAM bitcell with row and column control lines called RC-SRAM is proposed in this article. The architecture based on RC-SRAM can achieve bi-directional and operand-controllable logic-in-memory and search operations through different signal configurations, which can comprehensively respond to various occasions and needs. Moreover, we propose threshold-controlled logic gates for sensing, which effectively reduces the circuit area and improves accuracy. We validate the RC-SRAM with a 28 nm CMOS technology, and the results show that the circuits are not only full featured and flexible for customization but also have a significant increase in the working frequency. At VDD = 0.9 V and T = 25 °C, the bi-directional search frequency is up to 775 MHz and 567 MHz, and the speeds for row and column Boolean logic reach 759 MHz and 683 MHz. [ABSTRACT FROM AUTHOR]

In today's research environment characterized by exponential data growth and increasing complexity, the selection of appropriate statistical tests, tailored to research objectives and data distributions, is paramount for rigorous analysis and accurate interpretation. This article explores the growing prominence of bootstrapping, an advanced statistical technique for multiple comparisons analysis, offering flexibility and customization by estimating sample distributions without assuming population distributions, thus serving as a valuable alternative to traditional methods in various data scenarios. Computer simulations were conducted using data from cardiovascular disease patients. Two approaches, spontaneous partly controlled simulation and fully constrained simulation using self-written R scripts, were utilized to generate datasets with specified distributions and analyze the data using tests for comparing more than two groups. The utilization of the bootstrap method greatly improves statistical analysis, especially in overcoming the constraints of conventional parametric tests. Our research showcased its effectiveness in comparing multiple scenarios, yielding strong findings across diverse distributions, even with minor inflation in p values. Serving as a valuable substitute for parametric approaches, bootstrap promotes careful consideration when rejecting hypotheses, thus fostering a deeper understanding of statistical nuances and bolstering analytical rigor. [ABSTRACT FROM AUTHOR]

The proliferation of fake news poses a significant challenge in today's information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing interest in strategies for detecting cross-domain misinformation. However, traditional machine learning (ML) approaches often struggle with the nuanced contextual understanding required for accurate news classification. To address these challenges, we propose a novel contextualized cross-domain prompt-based zero-shot approach utilizing a pre-trained Generative Pre-trained Transformer (GPT) model for fake news detection (FND). In contrast to conventional fine-tuning methods reliant on extensive labeled datasets, our approach places particular emphasis on refining prompt integration and classification logic within the model's framework. This refinement enhances the model's ability to accurately classify fake news across diverse domains. Additionally, the adaptability of our approach allows for customization across diverse tasks by modifying prompt placeholders. Our research significantly advances zero-shot learning by demonstrating the efficacy of prompt-based methodologies in text classification, particularly in scenarios with limited training data. Through extensive experimentation, we illustrate that our method effectively captures domain-specific features and generalizes well to other domains, surpassing existing models in terms of performance. These findings contribute significantly to the ongoing efforts to combat fake news dissemination, particularly in environments with severely limited training data, such as online platforms. [ABSTRACT FROM AUTHOR]

Purpose: To propose a straightforward and time‐efficient quality assurance (QA) approach of beam time delay for respiratory‐gated radiotherapy and validate the proposed method on typical respiratory gating systems, Catalyst™ and AlignRT™. Methods: The QA apparatus was composed of a motion platform and a Winston‐Lutz cube phantom (WL3) embedded with metal balls. The apparatus was first scanned in CT‐Sim and two types of QA plans specific for beam on and beam off time delay, respectively, were designed. Static reference images and motion testing images of the WL3 cube were acquired with EPID. By comparing the position differences of the embedded metal balls in the motion and reference images, beam time delays were determined. The proposed approach was validated on three linacs with either Catalyst™ or AlignRT™ respiratory gating systems. To investigate the impact of energy and dose rate on beam time delay, a range of QA plans with Eclipse (V15.7) were devised with varying energy and dose rates. Results: For all energies, the beam on time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 92.13 ±$ \pm $ 5.79 ms, 123.11 ±$ \pm $ 6.44 ms, and 303.44 ±$ \pm $ 4.28 ms, respectively. The beam off time delays in AlignRT™ V6.3.226, AlignRT™ V7.1.1, and Catalyst™ were 121.87 ±$ \pm $ 1.34 ms, 119.33 ±$ \pm $0.75 ms, and 97.69 ±$ \pm $ 2.02 ms, respectively. Furthermore, the beam on delays decreased slightly as dose rates increased for all gating systems, whereas the beam off delays remained unaffected. Conclusions: The validation results demonstrate the proposed QA approach of beam time delay for respiratory‐gated radiotherapy was both reproducible and time‐efficient to practice for institutions to customize accordingly. [ABSTRACT FROM AUTHOR]

Bioinformatics tools are essential for performing analyses in the omics sciences. Given the numerous experimental opportunities arising from advances in the field of omics and easier access to high-throughput sequencing platforms, these tools play a fundamental role in research projects. Despite the considerable progress made possible by the development of bioinformatics tools, some tools are tailored to specific analytical goals, leading to challenges for non-bioinformaticians who need to integrate the results of these specific tools into a customized pipeline. To solve this problem, we have developed the BioPipeline Creator, a user-friendly Java-based GUI that allows different software tools to be integrated into the repertoire while ensuring easy user interaction via an accessible graphical interface. Consisting of client and server software components, BioPipeline Creator provides an intuitive graphical interface that simplifies the use of various bioinformatics tools for users without advanced computer skills. It can run on less sophisticated devices or workstations, allowing users to keep their operating system without having to switch to another compatible system. The server is responsible for the processing tasks and can perform the analysis in the user's local or remote network structure. Compatible with the most important operating systems, available at https://github.com/allanverasce/bpc.git. [ABSTRACT FROM AUTHOR]

Understanding how Artificial Intelligence (AI) impacts organizational functions supports stakeholders to prepare accordingly and profit from these developments. Adopting a grounded theory approach, this study uses three interlinked stages (in-depth interviews, focus groups and a questionnaire-based survey) to explore the impact of AI on the marketing function of hotels. The results identify ten trends related to AI's contribution to hotel marketing, clustered in four themes. AI reengineers internal processes and procedures by enabling data and content as catalysts of competitiveness; empowering the augmented worker and performing mass personalization and customization. AI also impacts relationships with stakeholders by determining return on investment; improving sustainability; and governing legal aspects and ethics regarding data use. AI supports networks to which the organizations belong by concentrating and integrating organizations and transforming distribution models. AI transforms customer processes and services by engaging smart and predictive customer care and by employing predictive and augmented product and service design. The study illustrates the changes that AI will likely bring to hospitality and tourism marketing, developing a research agenda and raising discussion points for academic and industry practitioners respectively. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have gained immense attention and are being increasingly applied in various domains. However, this technological leap forward poses serious security and privacy concerns. This paper explores a novel approach to data stealing attacks by introducing an adaptive method to extract private training data from pre-trained LLMs via backdooring. Our method mainly focuses on the scenario of model customization and is conducted in two phases, including backdoor training and backdoor activation, which allow for the extraction of private information without prior knowledge of the model's architecture or training data. During the model customization stage, attackers inject the backdoor into the pre-trained LLM by poisoning a small ratio of the training dataset. During the inference stage, attackers can extract private information from the third-party knowledge database by incorporating the pre-defined backdoor trigger. Our method leverages the customization process of LLMs, injecting a stealthy backdoor that can be triggered after deployment to retrieve private data. We demonstrate the effectiveness of our proposed attack through extensive experiments, achieving a notable attack success rate. Extensive experiments demonstrate the effectiveness of our stealing attack in popular LLM architectures, as well as stealthiness during normal inference. [ABSTRACT FROM AUTHOR]

High-Level Synthesis (HLS) tools have revolutionized FPGA application development by providing a more efficient and streamlined approach, significantly impacting digital design methodologies. Despite the capability of FPGAs to customize numerical representations in data paths, most HLS projects have focused on fixed-point precision, while floating-point representations remain limited to vendor-provided single, double, and half-precision formats. This paper proposes a customized floating-point library compatible with HLS to address these limitations. This library allows programmers to define the number of exponent and mantissa bits at compile time, providing greater flexibility and enabling the use of mixed precision. Moreover, this library includes optimized implementations of common components such as vector summation (VSUM), dot-product (DP), and matrix-vector multiplication (MVM). Results demonstrate that the proposed library reduces latency and resource utilization compared to vendor IP blocks, particularly in VSUM, DP, and MVM operations. For example, the mvm operation involving a 32 × 32 matrix, using vendor IP requires 22 clock cycles, whereas CuFP completes the same task in just 7 clock cycles, using approximately 60% fewer DSPs, 10% fewer LUTs, and 60% fewer FFs. [ABSTRACT FROM AUTHOR]

Customized topography-guided epi-on CXL in high oxygen is a viable alternative to customized topography-guided epi-off CXL in room air, with less subjective ocular discomfort and faster visual recovery. Purpose: To compare clinical outcomes of customized transepithelial (epi-on) corneal crosslinking (CXL) in high oxygen and customized CXL with epithelial removal (epi-off) in room air for keratoconus (KC). Setting: Umeå University Hospital, Umeå, Sweden. Design: Prospective, randomized, single-masked, intraindividually comparing study. Methods: 32 participants with bilateral progressive KC were treated with bilateral customized topography-guided CXL, 30 mW/cm2; 7.2 to 15 J/cm2 and were randomized to epi-on in one eye (32 eyes) and epi-off in the fellow eye (32 eyes). Uncorrected distance visual acuity (UDVA) and corrected distance visual acuity (CDVA), maximal keratometry (Kmax), subjective ocular discomfort, low-contrast visual acuities (LCVAs) at 10% and 2.5% contrast, ocular and anterior corneal wavefront aberrations, manifest refractive spherical equivalent, endothelial cell count (ECC), and adverse events were assessed through 24 months. Results: Both treatments showed improvements at 24 months in UDVA; −0.16 ± 0.24 (P <.001) and −0.13 ± 0.20 logMAR (P =.006), respectively, CDVA; −0.10 ± 0.11 (P <.001) and −0.10 ± 0.12 (P =.001), Kmax; −1.74 ± 1.31 (P <.001) and −1.72 ± 1.36 D (P <.001). LCVA 10% improved for both protocols (P <.001), but LCVA 2.5% improved for epi-on CXL only (P =.001). ECC was unaltered, and no adverse events occurred. The epi-on eyes had significantly less discomfort symptoms during the whole first week posttreatment (P <.05). Conclusions: High-oxygen customized epi-on CXL is a viable alternative to room air customized epi-off CXL, with faster improvements in CDVA and LCVA and less early ocular discomfort. [ABSTRACT FROM AUTHOR]

Polyetheretherketone (PEEK), with good biocompatibility and similar mechanical properties to natural bone, is extensively employed in the manufacture of prostheses. However, the precision and mechanical properties of current implants are major challenges for clinical applications. In this study, the effect of pore size, raster angle and printing temperature were investigated on length, width, thickness, material consumption, compressive strength and Young's modulus. Taguchi design of experiment method was used to reduce the number of experiments and optimize the printing process parameters. Finally, predictive analysis was exploited to give the optimal set of process parameters. Experimental results indicated that the approach applied in this work provided more accurate predictions and control of the response variables. The maximum compressive strength and compressive modulus of PEEK scaffolds reached 43.4 MPa and 253.3 MPa, respectively. Therefore, the methodology of present work has the potential to meet the demand of design precision and manufacture of customized bone substitutes. [ABSTRACT FROM AUTHOR]

Electrospun nanofiber membranes (ENMs) have emerged as a cutting‐edge solution for membrane distillation (MD), recognized for their highly porous and interconnected architecture. This distinctive structure enables them to offer minimal mass transfer resistance, making them exceptionally suited for high‐efficiency membrane‐based separation processes. However, the very porosity that defines their strength also renders them vulnerable to fouling, scaling, and wetting during operation, which in turn compromises their performance. Current research efforts are geared toward overcoming these obstacles by refining the surface design and characteristics of ENMs. This review delves into the latest advancements in surface‐enhanced electrospun nanofiber membranes tailored for MD applications. It discusses the existing gaps in research and provides forward‐looking insights into the future of ENMs, spotlighting the development of membranes with precisely tunable surface attributes for optimized performance. [ABSTRACT FROM AUTHOR]

In this work, a coupled acoustic system with parallel cavities is designed by adopting the technique of adiabatic passage. The required space-varying coupling strengths between adjacent cavities can be modulated by meticulously customizing the slit heights along the propagation direction, and efficient energy transfer is realized from one cavity to another. Meanwhile, one-way energy transfer behavior is achieved as well by utilizing a lossy cavity to serve as an intermediate connector of the coupler. Moreover, an available method to optimize the coupling actions is adopted to further reduce the device width so as to achieve desirable energy transfer in a compact configuration. Our work provides an efficient approach for acoustic energy transfer between desired cavities, which may have potential applications in acoustic communication, detection, and filtering. [ABSTRACT FROM AUTHOR]

Field-Programmable Gate Arrays (FPGAs) play a significant and evolving role in various industries and applications in the current technological landscape. They are widely known for their flexibility, rapid prototyping, reconfigurability, and design development features. FPGA designs are often constructed as compositions of interconnected modules that implement the various features/functionalities required in an application. This work develops a novel tool FEINT, which facilitates this module composition process and automates the design-level modifications required when introducing new modules into an existing design. The proposed methodology is architected as a "template" insertion tool that operates based on a user-provided configuration script to introduce dynamic design features as plugins at different stages of the FPGA design process to facilitate rapid prototyping, composition-based design evolution, and system customization. FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort. For example, FEINT can help insert defensive monitoring, adversarial Trojan, and plugin-based functionality enhancement features. FEINT is scalable, future-proof, and cross-platform without a dependence on vendor-specific file formats, thus ensuring compatibility with FPGA families and tool versions and being integrable with commercial tools. To assess FEINT's effectiveness, our tests covered the injection of various types of templates/modules into FPGA designs. For example, in the Trojan insertion context, our tests consider diverse Trojan behaviors and triggers, including key leakage and denial of service Trojans. We evaluated FEINT's applicability to complex designs by creating an FPGA design that features a MicroBlaze soft-core processor connected to an AES-accelerator via an AXI-bus interface. FEINT can successfully and efficiently insert various templates into this design at different FPGA design stages. [ABSTRACT FROM AUTHOR]

The article delves into the transformative impact of Industry 5.0 principles on production logistics, aiming to enhance manufacturing efficiency and responsiveness by integrating advanced concepts like the milkrun, real-time scheduling, and optimization within smart factories. Addressing evolving customer needs for rapid customization, it seeks to revolutionize traditional approaches to production logistics, establishing a more adaptive and dynamic manufacturing ecosystem. This aligns with the advent of Industry 5.0, which heralds a new era in technological advancements and digitalization, fostering unprecedented transparency in corporate processes. Going beyond the Fourth Industrial Revolution, it seamlessly integrates the corporate value chain and supply network, emphasizing the importance of leveraging integration opportunities between production management and logistics amidst intensifying market competition. The study meticulously examines the role of production logistics within the corporate logistics system, shedding light on the tangible benefits of real-time scheduling and aiming to provide crucial insights for businesses navigating this new industrial revolution. [ABSTRACT FROM AUTHOR]

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Purpose: The objective of this study is to investigate consumer motivations and purchase behaviors with respect to cookies with customized packaging and consumer interest in different types of customization in the Italian food industry. Design/methodology/approach: The study was conducted by performing a quantitative analysis of 642 surveys collected via Google Forms. A repeated-measures ANOVA, univariate ANOVA, chi-squared test, correlation analysis and linear regression analysis were conducted using SPSS. Findings: Customers buy cookies with customized packaging as personal gifts to someone they care about. The ability to insert their initials or a personalized phrase appears to increase customers' positive attitudes toward buying cookies with customized packaging. Those interested in this type of customization are willing to pay more, regardless of the type of customization. Research limitations/implications: In the survey, only some types of customization not previously extracted from a focus group are explored, and the analysis covers only the food industry and does not take a cross-sectional approach. In addition, we specifically refer to the Italian market, which means that results cannot be generalized. Practical implications: From a managerial perspective, our results highlight that food industry companies should take advantage of the opportunity to segment the demand for customized packaging with respect to consumers' attitudes and their motivation toward food products. Originality/value: From a theoretical perspective, this study analyzes consumer attitudes and behaviors toward purchasing cookies with customized packaging. From a managerial perspective, the results of the study highlight interesting courses of action for companies in the food industry that would like to use the tool of customization by intervening in terms of the packaging rather than the product itself. [ABSTRACT FROM AUTHOR]

Significant advancements have been witnessed in visual tracking applications leveraging ViT in recent years, mainly due to the formidable modeling capabilities of Vision Transformer (ViT). However, the strong performance of such trackers heavily relies on ViT models pretrained for long periods, limiting more flexible model designs for tracking tasks. To address this issue, we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders, called TrackMAE. During pretraining, we employ two shared-parameter ViTs, serving as the appearance encoder and motion encoder, respectively. The appearance encoder encodes randomly masked image data, while the motion encoder encodes randomly masked pairs of video frames. Subsequently, an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level. In this way, ViT learns to understand both the appearance of images and the motion between video frames simultaneously. Experimental results demonstrate that ViT-Base and ViT-Large models, pretrained with TrackMAE and combined with a simple tracking head, achieve state-of-the-art (SOTA) performance without additional design. Moreover, compared to the currently popular MAE pretraining methods, TrackMAE consumes only 1/5 of the training time, which will facilitate the customization of diverse models for tracking. For instance, we additionally customize a lightweight ViT-XS, which achieves SOTA efficient tracking performance. [ABSTRACT FROM AUTHOR]

Previous studies have not paid much attention to the effect of social media marketing activities on value co-creation behavior. Especially, up to now, no one has studied the effects of social media marketing activities dimensions on value co-creation behavior. This study applies the commitment-trust theory to develop and estimate this relationship through brand trust and brand commitment on social media in Vietnam. The snowball sampling technique was applied to gather 504 social media users through social media platforms in Vietnam. The proposed research model was tested through PLS-SEM using SmartPLS 4. The results highlighted that most social media marketing activities dimensions (including trendiness, electronic word of mouth, interaction, and customization) affect brand trust (pc are 0.42, 0.152, 0.112, 0.097, respectively, and p-values are all less than 0.05). Simultaneously, brand trust was found to have a positive effect on brand commitment (pc =0.405, p = 0.000). Furthermore, calculation results revealed that brand commitment contributed significantly and strongly to their co-value behavior towards brands (pc = 0.531, p = 0.000). On the contrary, the data do not support a direct impact of entertainment on brand trust (pc = 0.001, p = 0.990) and brand trust on co-value behavior (pc = 0.025, p = 0.466) at a significance level of less than 5%. Ultimately, the findings also suggest a guide to social media marketers to drive customer value co-creation behavior for brands. AcknowledgmentThis work was supported by the National Foundation for Science & Technology Development of Vietnam under Decision No. 131/QD-HDQL-NAFOSTED dated September 21, 2020.

Customers frequently gravitate toward unique products, and firms increasingly utilize mass customization strategies allowing customers to self-customize products according to their unique preferences. While existing research shows that customers are willing to pay extra for this uniqueness, the present investigation points to a potential cost of self-customization that has been largely overlooked thus far. Specifically, the authors argue that what creates value for the individual consumer-designer (i.e., the original customer of the self-customized product) might conversely be detrimental to potential customers on the secondhand market, particularly in the context of aesthetic (vs. functional) customization. Results of three distinct data sets (including an analysis of more than 500,000 preowned car sales listings) support this uniqueness-hurts-resale hypothesis and provide a series of more nuanced findings. Consistent with the theorizing and empirical studies, three follow-up experiments show that although consumer-designers' valuations are positively affected by uniqueness, uniqueness indeed negatively affects secondhand-market customers' willingness to pay. This is because the more unique a given configuration is to a given consumer-designer, the lower the likelihood that said design will meet secondhand-market customers' taste preferences. The findings point to a tension between maximizing utility at first purchase and minimizing the related cost of aesthetic customization at resale. [ABSTRACT FROM AUTHOR]

One of the most dramatic shifts in recent years has been consumers' increased use of smartphones for making purchases and choices—but does using a smartphone influence what consumers choose? This article shows that, compared with using a personal computer (PC), making choices using a personal smartphone leads consumers to prefer more unique options. The authors theorize that because smartphones are considerably more personal and private than PCs, using them activates intimate self-knowledge and increases private self-focus, shifting attention toward individuating personal preferences, feelings, and inner states. Consequently, making choices using a personal smartphone, compared with a PC, tends to increase the preference for unique and self-expressive options. Six experiments and several replications examine the effects of personal smartphone use on the preference for unique options and test the underlying role of private self-focus. The findings have important implications for theories of self-focus, uniqueness seeking, and technology's impact on consumers, as well as tangible implications for many online vendors, brands, and researchers who use mobile devices to interact with their respective audiences. [ABSTRACT FROM AUTHOR]

Influencers' follower count, or indegree, is a key criterion that advertisers use when devising influencer marketing campaigns. However, whether influencers with lower or higher follower count are more effective in generating engagement remains an open question. This multimethod research effort—involving an observational field data analysis, based on 802 Instagram marketing campaigns featuring more than 1,700 influencers, together with an eye-tracking study and laboratory experiments—establishes conclusive evidence of an inverted U-shaped relationship between influencers' follower count and engagement with sponsored content. A higher follower count implies broader reach but also cues a weaker relationship that reduces followers' engagement likelihood. That is, engagement first increases, then decreases, as influencer follower count rises. The authors further test the potential moderating effects of two campaign properties: content customization and brand familiarity. Higher content customization and lower familiarity of the sponsored brand signal that influencers value their relationships with followers and thereby flatten the inverted U-shaped relationship. Managers can leverage these novel results and the related actionable guidelines to improve their influencer marketing strategies. [ABSTRACT FROM AUTHOR]