Multiple Items Research Articles

Examining the neural basis of unitization: A review.

Associative memory refers to the ability to form and remember associations between individual pieces of information rather than memory for a single object or word. Encoding associations in memory tends to be a more difficult task than item (only) encoding, because associative memory requires encoding multiple items as well as the specific links amongst the items. Accordingly, researchers have worked to identify interventions and strategies to reduce the effort and neural resources required for successful associative memory processing. Unitization is one such strategy that has traditionally been defined as the process by which two or more discrete items are processed, or encoded, such that they are perceived as a single ensemble. The current review explores the neural research on unitization while considering the behavioral benefits that accompany the process.

Storage Location Assignment for Improving Human–Robot Collaborative Order-Picking Efficiency in Robotic Mobile Fulfillment Systems

The robotic mobile fulfillment (RMF) system is a parts-to-picker warehousing system and a sustainable technology used in human–robot collaborative order picking. Storage location assignment (SLA) tactically benefits order-picking efficiency. Most studies focus on the retrieval efficiency of robots to solve SLA problems. To further consider the crucial role played by human pickers in RMF systems, especially in the context that the sustainable performance of human workers should be paid attention to in human–robot collaboration, we solve the SLA problem by aiming to improve human–robot collaborative order-picking efficiency. This study specifically makes decisions on assigning multiple items of various products to the slots of pods in the RMF system, in which human behavioral factors are taken into account. To obtain the solution in one mathematical model, we propose the heuristic algorithm under a two-stage optimization method. The results show that assigning correlated products to pods improves the retrieval efficiency of robots compared to class-based assignment. We also find that assigning items of each product to slots of pods, considering behavioral factors, benefits the operation efficiency of human pickers compared to random assignment. Improving human–robot collaborative order-picking efficiency and increasing the capacity usage of pods benefits sustainable warehousing management.

Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent

Session-based recommendation aims to predict the next item an anonymous user is most likely to click based on its limited historical behaviors. However, in session-based recommendation, the anonymity and short sequences often lead to the issue of data sparsity, which can affect the performance of recommendation models. Existing recommendation methods mostly focus on individual items in the session sequence, without considering the implicit user continuous intent underlying multiple continuous items. Moreover, current graph-based recommendation models do not fully take into account the direction of information transfer among shared items when calculating their similarities. This paper proposes, TCAUIS, a topic classification augmented user intend self-supervised hypergraph neural network for session-based recommendation, which contains continuous topic intent of users within a session and the information of co-occurrence items. The TCAUIS models a individual session sequence as a topic intent hypergraph and co-occurrence graph respectively, and employs latent Dirichlet allocation to classify the topic of the dataset and fuses the topic hyperedges into the hypergraph to alleviate the sparsity of the data and enhance the session information. Additionally, the hypergraph attention networks and attention mechanisms are leveraged to capture the complex high-order information between nodes in both the hypergraphs and co-occurrence graphs. The continuous topic intent of users and information of co-occurrence relationship are effectively integrated using a self-supervised mechanism that maximizes the mutual information between the representations of two sessions. Extensive experimental results show that the proposed TCAUIS model outperforms the state-of-the-art models on three real datasets Diginetica, Yoochoose1/64 and Nowplaying.

Children's rehearsal development parallels that of self-talk in other executive functions.

Rehearsal is a form of self-talk used to support short-term memory. Historically, the study of rehearsal development has diverged from the study of self-talk more generally. The current experiment examines whether two characteristics of self-talk (impact of task difficulty and self-talk's narrative vs. planning purpose) are also observed in rehearsal. Eighty children, ages 4-7, were tasked with remembering the three-item and six-item lists over a 15-s delay. Children's spontaneous use of fixed rehearsal (i.e., immediate repetition of a just-presented item) and cumulative rehearsal (i.e., cycling through multiple items at a time) was documented from video recordings. Four-year-olds narrated item presentations using fixed rehearsal. Six- and seven-year-olds proactively planned for recall by engaging cumulative rehearsal. Five-year-olds used both forms of rehearsal, but their frequency of cumulative rehearsal was dependent on list length. Therefore, rehearsal is susceptible to task manipulations that affect other forms of self-talk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

AI-based real-time hand gesture-controlled virtual mouse

ABSTRACT Hand gesture control technology is an emerging alternative to conventional input devices like keyboards and mice, enabling users to interact with computers through natural hand movements. Although, many researchers worked on gesture recognition and its challenges, yet wide range of gestures has not been addressed. In this direction, the author proposed a novel gesture-controlled virtual mouse system that uses hand gestures and hand tip detection with a webcam or internal camera for computer navigation that accurately recognizes various gestures, including cursor movement, left and right clicks, double clicks, scrolling, drag and drop, multiple item selection, volume control. To deal with this real-world problem, the authors proposed a solution for limited space situations and assist individuals with hand impairments who cannot use a physical mouse. Furthermore, it surpasses the limitations of traditional mice, such as mechanical wear and tear, adaptability to different environments, and restricted functionalities. By leveraging hand gestures and tip detection through a camera proposed system offers an efficient and adaptable approach to operating a PC mouse. The proposed methodology uses computer vision and AI for the accurate detection of gestures, even in challenging environments. It also offers a comprehensive set of gestures for various actions and functions.

An Automated Item Pool Assembly Framework for Maximizing Item Utilization for CAT

AbstractComputerized adaptive testing (CAT) has gained deserved popularity in the administration of educational and professional assessments, but continues to face test security challenges. To ensure sustained quality assurance and testing integrity, it is imperative to establish and maintain multiple stable item pools that are consistent in terms of psychometric characteristics and content specifications. This study introduces the Honeycomb Pool Assembly (HPA) framework, an innovative solution for the construction of multiple parallel item pools for CAT that maximizes item utilization in the item bank. The HPA framework comprises two stages—cell assembly and pool assembly—and uses a mixed integer programming modeling approach. An empirical study demonstrated HPA's effectiveness in creating a large number of parallel pools using a real‐world high‐stakes CAT assessment item bank. The HPA framework offers several advantages, including (a) simultaneous creation of multiple parallel pools, (b) simplification of item pool maintenance, and (c) flexibility in establishing statistical and operational constraints. Moreover, it can help testing organizations efficiently manage and monitor the health of their item banks. Thus, the HPA framework is expected to be a valuable tool for testing professionals and organizations to address test security challenges and maintain the integrity of high‐stakes CAT assessments.

Single-lot, lot-streaming problem for a 1 + m hybrid flow shop

In this paper, we consider an application of lot-streaming for processing a lot of multiple items in a hybrid flow shop (HFS) for the objective of minimizing makespan. The HFS that we consider consists of two stages with a single machine available for processing in Stage 1 and m identical parallel machines in Stage 2. We call this problem a 1 + m TSHFS-LSP (two-stage hybrid flow shop, lot streaming problem), and show it to be NP-hard in general, except for the case when the sublot sizes are treated to be continuous. The novelty of our work is in obtaining closed-form expressions for optimal continuous sublot sizes that can be solved in polynomial time, for a given number of sublots. A fast linear search algorithm is also developed for determining the optimal number of sublots for the case of continuous sublot sizes. For the case when the sublot sizes are discrete, we propose a branch-and-bound-based heuristic to determine both the number of sublots and sublot sizes and demonstrate its efficacy by comparing its performance against that of a direct solution of a mixed-integer formulation of the problem by CPLEX®.

Emotional labour and turnover intention among nurses in China: Mediating effects of nurse-patient relationship and self-rated health.

This study tested the mediating role of the nurse-patient relationship and self-rated health in the effect of emotional labour on turnover intention among nurses in China. The underlying mechanism behind the effect of emotional labour on turnover intention remains inadequately understood. Nurses with a high level of emotional labour are predisposed to experiencing poor health and tension in their relationships with patients, which may increase turnover intention. A cross-sectional survey of 527 nurses in a public tertiary hospital in Qiqihar, located in China's Heilongjiang province, was conducted. Emotional labour and turnover intention were assessed using existing validated scales containing multiple items, while the nurse-patient relationship and self-rated health were assessed using single items, respectively. Baron and Kenny's causal steps and the Karlson/Holm/Breen method were adopted to test the mediating effects of the nurse-patient relationship and self-rated health in the association between emotional labour and turnover intention after adjusting for variations in sociodemographic and job characteristics. Emotional labour was positively associated with turnover intention. Self-rated poor health and a disharmonious nurse-patient relationship partially mediated the positive effect of emotional labour on turnover intention. Emotional labour significantly affects the turnover intention of nurses working in public tertiary hospitals in China, and this effect is partially mediated by self-rated health and the nurse-patient relationship. Giving more attention to nurses' negative emotions and work attitudes is crucial. Developing comprehensive strategies for enhancing nurses' emotional management ability, promoting their physical and psychological well-being, and improving nurse-patient relationship to reduce nurses' turnover.

An Analysis of Test Items of English Subject at Eighth Grade of SMP Muhammadiyah Pasir Pengaraian

This research is aimed at describing the quality of the English tests and the index of difficulty. This research was conducted because of the researcher found several problems faced by eighth students grade of SMP Muhammadiyah Pasir Pengaraian when submitted the examination result. These problems were most of students had limited vocabularies so they could not answer the question of the test, they could not understand the question because it did not have clear. Based on the problems above this research is aimed at describe the quality of test that used in final examination. This research is descriptive quantitative research. The population of the research were all of the eighth grade students at SMP Muhammadiyah Pasir Pengaraian. They were 84 students as the sample of the research. The instrument of the research were documentation in the form of the sheet of the students’ test and it was analyzed by using Ms. Excel. Based on the result of the research, there were 30 questions answered by the students. According to the categories of the questions as follows; 34 students they were belong to easy test (40%), 25 students answered difficult test, they were belong to difficult test (30%) and 42 students answered moderate test. They were belong to moderate test(50%). The kinds of test were matching word, arranging the statement, essay and multiple choice items. English test were included in moderate tests. The tests were good but there were some unclear instructions so that most of students could not answer. It can be concluded that arrangement of the question were good, but the form of the questions have did not clear instructions, so most of the students did not understand the questions because of the meaning of the vocabulary most of them did not know. Therefore the teacher must have the knowledge to make good quality questions so that students can answer the questions.

Qualitative and Quantitative Study on Students’ Critical Thinking of Conceptual E-Scaffolding in Blended Learning Context

Critical thinking skills are one of the rational thinking skills that students really need in solving problems or conveying ideas. In fact, students' critical thinking abilities are still relatively low. A feasible alternative for developing students' critical thinking skills is by implementing electronic-based scaffolding. This research uses mixed methods with an explanatory research design. The experimental group received conceptual treatment of e-scaffolding blended learning with the Problem-Based Learning model and the control group received no additional treatment. experimental group and control group, the research subjects were first semester students of IKIP Budi Utomo Malang. Because there were two research groups, 46 students were included in the control group. The instruments used in this research were pre-test and post-test. The pre-test consists of 18 multiple choice items with a reliability coefficient of 0.735 and the critical thinking post-test consists of 14 essay questions with a reliability coefficient of 0.762. In addition, semi-structured and think aloud interview guidelines were also used. Quantitative data was obtained from the distribution of pre-test and post-test questions. Meanwhile, qualitative data was obtained from semi-structured interviews and think aloud. Quantitative data was then analyzed using parametric ANACOVA analysis while qualitative data was analyzed using a path analysis model. The results of the research show 1) the critical thinking abilities of students who learn with conceptual e-scaffolding in blended learning in Problem-Based Learning are higher than students who learn with conventional methods, 2) the application of conceptual e-scaffolding in Problem-Based Learning. The link map form in the Problem Based Learning model has a positive effect on students' critical thinking abilities. This is reflected in the behavior and way of thinking of experimental group students which shows the development of critical thinking skills during the learning process. Apart from that, conceptual e-scaffolding in the form of a link map helps students obtain information easily and use it in understanding concepts in geometric optics material

Outlier item detection in bundle recommendation via the attention mechanism

Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once. However, the intricate correlations between items, varied user preferences, and the pronounced data sparsity in combinations present significant challenges for bundle recommendation algorithms. Furthermore, current bundle recommendation methods fail to identify mismatched items within a given set, a process termed as “outlier item detection”. These outlier items are those with the weakest correlations within a bundle. Identifying them can aid users in refining their item combinations. While the correlation among items can predict the detection of such outliers, the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning phase. This limitation can hinder the algorithm’s performance. To tackle these challenges, we introduce an encoder–decoder architecture tailored for outlier item detection. The encoder learns potential item correlations through a self-attention mechanism. Concurrently, the decoder garners efficient inference frameworks by directly assessing item anomalies. We have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

Session-based recommendation plays an important role in daily life and exists in many scenarios, such as online shopping websites and streaming media platforms. Recently, some works have focused on using graph neural networks (GNNs) to recommend new items in session-based scenarios. However, these methods have encountered several limitations. First, existing methods typically ignore the impact of items’ visited time in constructing session graphs, resulting in a departure from real-world recommendation dynamics. Second, sessions are often sparse, making it challenging for GNNs to learn valuable item embedding and user preferences. Third, the existing methods usually overemphasize the impact of the last item on user preferences, neglecting their interest in multiple items in a session. To address these issues, we introduce a time-sensitive graph neural network for new item recommendation in session-based scenarios, namely, TSGNN. Specifically, TSGNN provides a novel time-sensitive session graph constructing technique to solve the first problem. For the second problem, TSGNN introduces graph augmentation and contrastive learning into it. To solve the third problem, TSGNN designs a time-aware attention mechanism to accurately discern user preferences. By evaluating the compatibility between user preferences and candidate new item embeddings, our method recommends items with high relevance scores for users. Comparative experiments demonstrate the superiority of TSGNN over state-of-the-art (SOTA) methods.

Will Fit Work Here? Using Multiple Data Sources to Adapt a Student–Institution Fit Instrument to a New Institutional Context

Several authors (Bowman & Denson, 2014; Gilbreath et al., 2011) have claimed that measures of fit between a student and the institution they are attending should be useful for institutional decision-making. These student–institution fit instruments were originally developed for research with specific student populations in one institution, but have not been assessed at other institutions. Institutional research offices rarely assess surveys or other instruments that are developed at one institution to determine whether those same instruments would provide results that are reliable and valid in different institutional contexts. The purpose of this study was to determine if the factor structure of a measure of student–institution fit would be appropriate to use at another institution. A survey that included a student–institution fit instrument developed by Gilbreath et al. (2011) was administered to a random sample of first-year students and new external transfers at a large, public university in the Midwest. Confirmatory factor analysis was used to determine if the underlying factor structure in the original instrument provides an appropriate fit for data obtained from a new sample. In addition, open-ended comments were collected to gain insight into students’ interpretation of each item. The results suggested changes from the original scale to create a more robust measure. Specifically, Great Support Services was moved from the Social Environment fit to the Academic Environment fit, and covariances between multiple items were integrated. Similar methods could be used by institutional researchers at other institutions to gather data on students’ interpretation of survey items, to evaluate the underlying factor structure of external instruments, and to create appropriate modifications to account for their own institutional contexts.

Test Item Analysis: A Catalyst for Quality Assurance of Assessment in Outcome-Based Education

Due to the evolving demands of the educational system, where a greater focus is placed on learning outcomes for students rather than merely degrees, outcome-based education (OBE) has grown in favor. An OBE system involves a clear picture of what is important for students to be able to do, organizing the curriculum, instruction and assessment to make sure this learning ultimately happens. Assessment and evolution is one of the important steps in Outcome based education. Purpose of the assessment and evolution is to final the lacuna and loopholes of teaching learning process and intended outcome that are finalized for particular course. Test item analysis is one of the prime components of assessment, which is the process of statistically analyzing student responses to individual exam questions. Facilitation value, discriminative index, and effectiveness of the distraction are the components of test item analysis. Item analysis helps in analyzing the difficulty level of items; whether the items are challenging, easy, comparatively difficult, or relatively easy, to discriminate amongst pupils who have different performance levels and identifies the effectiveness of the distractions in the multiple choice items. Keywords—Item Analysis, Facilitation value, Discrimination Index, Effectiveness of the Distractors’

Primena modifikovanog TA modela za analizu prihvata "kutija sa plenom" od strane igrača video igara u Srbiji

Business models in the video game industry have shifted from physical to digital. With microtransactions, game producers have been provided with the ability to charge for extra in-game content. Loot boxes are one of the most controversial forms of microtransactions in video games. These are the prize packages that consist of one or multiple virtual items, whose specific content remains unknown to the player prior to opening. This study is aimed at identifying the combination of the factors that act as motivators for players in Serbia to engage themselves in monetary transactions for the purchase of loot boxes. To address the requirements of the study, modifications were made to the fundamental Technology Acceptance Model (TAM) so as to encompass the additional variables that had been perceived as significant for players' decision-making processes. The findings of the study indicate the fact that the "perceived enjoyment, customization", and "perceived ease of use" variables are the important factors that can predict the "perceived usefulness of loot boxes" variable. Additionally, the "perceived usefulness of loot boxes" and "propensity for gambling" variables are significant predictors of players' intention to purchase. Furthermore, the "intention to purchase loot boxes" variable has a statistically significant impact on the "actual use of loot boxes" variable.

Steps toward identification of a novel cue-positive overactive bladder phenotype in women with high-bother urinary urgency.

The objective of this study was to investigate the relationship between situational cues (running water, stress, cold, etc.) and overactive bladder (OAB) symptoms. Women scheduled for urodynamic studies for clinical indications completed surveys to characterize OAB (ICIQ-OAB and OAB-V3) and responses to situational cues (validated long-form cues survey and a novel short-form cues survey). Participants were divided into two groups (Low-Bother urgency vs. High-Bother urgency), and OAB and cue survey responses were compared. A total of 47 participants were enrolled in the study with 36 meeting inclusion criteria (15 Low-Bother and 21 High-Bother) with an overall mean age of 60.0 ± 10.0 years. The High-Bother urgency group scored significantly higher on multiple cue items in the long-form (P<0.05) and only "running water" in the short-form cues survey (P<0.05). In addition, "running water" was the only cue that was scored higher in both surveys (P<0.05). This study showed that patients with High-Bother urgency may have increased symptom responses to environmental, mood, and cognitive cues. These findings suggest increased sensitivity to cues, especially "running water" in participants with bothersome OAB and the potential presence of a cue-specific OAB phenotype.

Multi-task learning for predicting quality-of-life and independence in activities of daily living after stroke: a proof-of-concept study.

A health-related (HR) profile is a set of multiple health-related items recording the status of the patient at different follow-up times post-stroke. In order to support clinicians in designing rehabilitation treatment programs, we propose a novel multi-task learning (MTL) strategy for predicting post-stroke patient HR profiles. The HR profile in this study is measured by the Barthel index (BI) assessment or by the EQ-5D-3L questionnaire. Three datasets are used in this work and for each dataset six neural network architectures are developed and tested. Results indicate that an MTL architecture combining a pre-trained network for all tasks with a concatenation strategy conditioned by a task grouping method is a promising approach for predicting the HR profile of a patient with stroke at different phases of the patient journey. These models obtained a mean F1-score of 0.434 (standard deviation 0.022, confidence interval at 95% [0.428, 0.44]) calculated across all the items when predicting BI at 3 months after stroke (MaS), 0.388 (standard deviation 0.029, confidence interval at 95% [0.38, 0.397]) when predicting EQ-5D-3L at 6MaS, and 0.462 (standard deviation 0.029, confidence interval at 95% [0.454, 0.47]) when predicting the EQ-5D-3L at 18MaS. Furthermore, our MTL architecture outperforms the reference single-task learning models and the classic MTL of all tasks in 8 out of 10 tasks when predicting BI at 3MaS and has better prediction performance than the reference models on all tasks when predicting EQ-5D-3L at 6 and 18MaS. The models we present in this paper are the first models to predict the components of the BI or the EQ-5D-3L, and our results demonstrate the potential benefits of using MTL in a health context to predict patient profiles.

SIIR: Symmetrical Information Interaction Modeling for News Recommendation.

Accurate matching between user and candidate news plays a fundamental role in news recommendation. Most existing studies capture fine-grained user interests through effective user modeling. Nevertheless, user interest representations are often extracted from multiple history news items, while candidate news representations are learned from specific news items. The asymmetry of information density causes invalid matching of user interests and candidate news, which severely affects the click-through rate prediction for specific candidate news. To resolve the problems mentioned above, we propose a symmetrical information interaction modeling for news recommendation (SIIR) in this article. We first design a light interactive attention network for user (LIAU) modeling to extract user interests related to the candidate news and reduce interference of noise effectively. LIAU overcomes the shortcomings of complex structure and high training costs of conventional interaction-based models and makes full use of domain-specific interest tendencies of users. We then propose a novel heterogeneous graph neural network (HGNN) to enhance candidate news representation through the potential relations among news. HGNN builds a candidate news enhancement scheme without user interaction to further facilitate accurate matching with user interests, which mitigates the cold-start problem effectively. Experiments on two realistic news datasets, i.e., MIND and Adressa, demonstrate that SIIR outperforms the state-of-the-art (SOTA) single-model methods by a large margin.

Incorporating transportation costs into the single and multiple items newsvendor problems.

The efficiency and responsiveness of supply chains are vitally dependent on inventory replenishment and transportation decisions. In this paper, we study a supply chain consisting of a single retailer ordering seasonal products within the newsvendor framework. The primary objective of the paper is to investigate the retailer's decision-making process, aimed at determining the optimal replenishment quantities and selecting the appropriate mix and size of the truck fleet. Initially, we formulate a mathematical model where the retailer exclusively manages a limited fleet of its own trucks for inbound transportation of a single seasonal product. In this context, we determine a lower breakeven point for the fixed transportation cost than what has been previously proposed in the literature. Subsequently, we examine a commonly encountered transportation scenario where the retailer has the opportunity to expand its fleet size by leasing trucks from the external market. The outcomes of the numerical example indicates that the flexibility resulting from the utilization of different types of trucks can lead to reduced overall costs. We also address the practical transportation problem of efficiently shipping various seasonal products solely with the retailer's own trucks. For this complex problem, we propose an optimal solution procedure based on Lagrangian method. We show that the joint replenishment of multiple products results in cost savings and enhances utilization of the trucks' capacities.

Cost Efficiency and Profitability through Intelligent Warehouse: Case Study of Unmanned Warehouse “Asia One” in JD Logistics

With the general environment of consumer purchasing power and the popularity of flexible supply chains, many warehousing companies have been based on their own needs for intelligent warehouse transformation, some warehouses for a comprehensive unmanned transformation, while some through the human-machine collaboration to control storage costs. But most of these warehouses to achieve transformation are only used to store one or a class of goods, such as the medicine and equipment industry. Able to carry out a large number and multiple items stored at the same time, intelligent warehouse construction is not popular enough, this paper will be the existing large-scale intelligent warehouse working principle, efficiency results and profit costs, and other aspects of the analysis to explore the large-scale warehouse intelligent transformation can bring specific benefits to the enterprise. The representatives of large, intelligent warehouses Amazon and Jingdong’s large and unmanned warehouses have a strong benchmark. In this paper, we select Jingdong’s large, unmanned warehouse, “Asia No. 1” and explain the working process and the role of hardware and software systems to find out what advantages the operation of these machines can bring to enterprises, what the competitiveness of intelligent equipment compared with manual labor, and how to help enterprises increase benefits and reduce costs.