Processing Model Research Articles

Promoting emotion understanding in middle childhood: A systematic review of school-based SEL programs

Emotion understanding (EU) describes the ability to identify, interpret, and communicate about emotions, and is often targeted by social-emotional learning (SEL) programs. Still, the theoretical framing of SEL programs and their impact on specific areas of social-emotional development, such as EU, for different age bands is not always transparent. This systematic review synthesized emotion-focused content in SEL programs used in quantitative outcome studies in middle childhood to identify which EU components are targeted and examine content congruence with an integrated EU development model drawing on the Pons EU developmental model and the Crick and Dodge social-information processing (SIP) model that posits emotion identification as fundamental to social decision-making in childhood. A total of 38 programs for Grades 3 to 5 across 54 studies in 20 countries were reviewed. Program aims, lesson topics, and activities were extracted and mapped to a 10-component EU framework integrating the nine Pons model components (‘recognition, external cause, reminder, desire, belief, hiding, regulation, mixed, morality’) with one based on the SIP model (‘decision/action’). At least 87% of emotion-focused SEL content targeted EU components of recognition, regulation, and social decision-making. Findings indicate a good level of congruence between emotion-focused SEL program content and prevailing EU development models. Many programs emphasized the external causes of emotions, underscoring the importance of scenarios to explain emotions—discussed further in light of cross-cultural variation in emotion socialization. We encourage SEL intervention research to be more transparent in reporting SEL program content and activities to move toward causal explanations of program impact.

Application of Extreme Machine Learning for Smart Agricultural Robots to Reduce Manoeuvering Adaptability Errors

The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.

Human perception of self-motion and orientation during galvanic vestibular stimulation and physical motion

Galvanic vestibular stimulation (GVS) is an emergent tool for stimulating the vestibular system, offering the potential to manipulate or enhance processes relying on vestibular-mediated central pathways. However, the extent of GVS’s influence on the perception of self-orientation pathways is not understood, particularly in the presence of physical motions. Here, we quantify roll tilt perception impacted by GVS during passive whole-body roll tilts in humans (N = 11). We find that GVS systematically amplifies and attenuates perceptions of roll tilt during physical tilt, dependent on the GVS waveform. Subsequently, we develop a novel computational model that predicts 6DoF self-motion and self-orientation perceptions for any GVS waveform and motion by modeling the vestibular afferent neuron dynamics modulated by GVS in conjunction with an observer central processing model. This effort provides a means to systematically alter spatial orientation perceptions using GVS during concurrent physical motion, and we find that irregular afferent dynamics alone best describe resultant perceptions.

The ethical dimensions of utilizing Artificial Intelligence in palliative care

Palliative care aims to improve the quality of life for seriously ill individuals and their caregivers by addressing their holistic care needs through a person- and family-centered approach. While there have been growing efforts to integrate Artificial Intelligence (AI) into palliative care practice and research, it remains unclear whether the use of AI can facilitate the goals of palliative care. In this paper, we present three hypothetical case examples of using AI in the palliative care context, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers’ unmet needs. Using these cases, we examine the ethical dimensions of utilizing AI in palliative care by applying five widely accepted moral principles that guide ethical deliberations in AI: beneficence, nonmaleficence, autonomy, justice, and explicability. We address key ethical questions arising from these five core moral principles and analyze the potential impact the use of AI can have on palliative care stakeholders. Applying a critical lens, we assess whether AI can facilitate the primary aim of palliative care to support seriously ill individuals and their families. We conclude by discussing the gaps that need to be further addressed in order to promote ethical and responsible AI usage in palliative care.

The Concept of Innate Sexual Priors in the Brain: A Theory on Why We Are Attracted to What We Are Attracted to

AbstractSexuality is an integral part of human nature, yet we know little about its origins and underlying mechanisms. Understanding how the brain processes sexual stimuli is crucial for advancing our knowledge of the origins of sexuality and its variations. Only if a stimulus was internally evaluated as ‘attractive’ the information can progress to evoke sexual desire or arousal. Importantly, such evaluation processes require an internal reference against which external information is compared. These processes, however, remain largely unexplored, and hence we still do not understand why we are attracted to what we are attracted to. By synthesizing the existing literature and integrating existing models, this narrative review proposes a novel neuroscientific framework on sexuality, addressing key theoretical gaps. As a result, the concept of sexual priors in the brain is introduced. Within this framework, sexual priors refer to mental ‘images’ representing a collection of internally stored information of what we regard as sexually attractive. Such mental representations act as reference in early internal evaluation processes of sexual stimuli. It is suggested that sexual priors are (partly) innate and sex-specific, contributing to heterosexuality. Evolutionary aspects that could explain the development of these priors are discussed. Furthermore, the framework provides possible explanations for variations in sexual orientation through potential cross-sex shifts in sexual prior content. The refinement of sexual prior content over time may explain the diversity of sexual interests and attraction patterns among humans. Moreover, the concept can be applied to a variety of psychiatric conditions related to atypical and maladaptive sexual behaviors. A proposition is put forward regarding brain areas and networks that may be involved in the formation of sexual priors, serving as potential targets for future research. In summary, by integrating sexual priors into models of sexual stimuli processing, this article fills theoretical gaps while inspiring further research. This work aims to enhance understanding of sexuality, which ultimately could increase social awareness, foster tolerance, and promote psychological well-being.

Research on Multi-Scale Signal Processing and Efficient Model Construction Strategies in Lithium-Ion Battery State Prediction

Abstract To tackle the question of limited generalization and inefficiency in predicting state of health (SOH) and state of charge (SOC) in lithium-ion batteries across diverse sequence lengths, a novel hybrid model is developed. This model integrates multivariate variational mode decomposition (MVMD), informer, and long short-term Memory (LSTM) networks. Initially, battery health features are extracted from the charge and discharge curves, which are then validated for their relevance to SOH and SOC via correlation analysis and random forest algorithms. These features undergo multi-scale decomposition using MVMD, thereby encapsulating the intricate dynamics of battery state changes across various time scales. This decomposition enhances the model's adaptability to different sequence lengths, bolstering its generalization capability. Subsequently, the informer model is utilized to identify temporal patterns within the decomposed features. Finally, LSTM exploits its capacity to capture temporal dependencies for further refinement of the predictions. This hybrid strategy yields substantial enhancements in both efficiency and accuracy. Compared to the transformer model, the proposed hybrid model demonstrates a 30% reduction in SOH prediction error and a 22% decrease in SOC prediction error, concurrently slashing training time significantly. Spanning diverse sequence lengths and battery types, demonstrates the model's strong generalization capabilities.

Assessing Whether Negative Parenting Cognitions Bias Parent Report of Preschoolers' Externalizing Symptoms: A Regularized Moderated Non-Linear Factor Analysis Approach.

Parent report is frequently used to assess children's psychopathology, however, researchers have expressed concerns about the validity of parent reports. Some parental characteristics, attitudes, or beliefs may systematically bias a parent's report of their child's behaviors and functioning. Informed by social information processing models of parenting risk, parents with more difficulties reflecting on and interpreting their children's behavior may be at risk for less accurate reports. The present study conducted two regularized moderated non-linear factor analyses with LONGSCAN data to examine how parents' self-reported negative parenting cognitions were associated with structural parameters of parent-reported child aggression and attention problems. While differential item functioning (DIF) was present on the aggression and attention problems subscales as a function of negative parenting cognitions, the DIF was small in magnitude, inconsistent in directionality and did not significantly alter factor-level parameters. Negative parenting cognitions did demonstrate a small but significant negative impact on all latent externalizing factors (aggression and attention problems), such that caregivers with fewer negative parenting cognitions endorsed fewer items, and this was associated with a lower mean of each latent variable. Given that accounting for DIF did not contribute to meaningful differences in impact parameters or improve criterion validity, findings suggest that the aggression and attention subscales are functionally invariant to negative parenting cognitions, suggesting that externalizing symptoms can be reliability compared across parents of varying parenting cognitions.

How does depressive cognition develop? A state-dependent network model of predictive processing.

Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical "negative prediction mechanism" is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual's capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update individual's brain so that it increasingly predicts and reinforces negative experiences over time-pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hybrid 3D bioprinting for advanced tissue-engineered trachea: merging fused deposition modeling (FDM) and top-down digital light processing (DLP).

In this present study, we introduce an innovative hybrid 3D bioprinting methodology that integrates fused deposition modeling (FDM) with top-down digital light processing (DLP) for the fabrication of an artificial trachea. Initially, polycaprolactone (PCL) was incorporated using an FDM 3D printer to provide essential mechanical support, replicating the structure of tracheal cartilage. Subsequently, a chondrocyte-laden glycidyl methacrylated silk fibroin (Sil-MA) hydrogel was introduced via top-down DLP into the PCL scaffold (PCL-Sil scaffold). &#xD;The mechanical evaluation of PCL-Sil scaffolds showed that they have greater flexibility than PCL scaffolds, with a higher deformation rate (PCL-Sil scaffolds: 140.9±5.37% vs. PCL scaffolds: 124.3±6.25%) and ability to withstand more force before fracturing (3.860±0.140 N for PCL-Sil scaffolds vs. 2.502±0.126 N for PCL scaffolds, ***P < 0.001). Both types of scaffolds showed similar axial compressive strengths (PCL-Sil scaffolds: 4.276±0.127 MPa vs. PCL scaffolds: 4.291±0.135 MPa). Additionally, PCL-Sil scaffolds supported fibroblast proliferation, indicating good biocompatibility. In vivo testing of PCL-Sil scaffolds in a partial tracheal defect rabbit model demonstrated effective tissue regeneration. The scaffolds were pre-cultured in the omentum for two weeks to promote vascularization before transplantation. Eight weeks after transplantation into the animal, bronchoscopy and histological analysis confirmed that the omentum-cultured PCL-Sil scaffolds facilitated rapid tissue regeneration and maintained the luminal diameter at the anastomosis site without signs of stenosis or inflammation. Validation study to assess the feasibility of our hybrid 3D bioprinting technique showed that structures, not only the trachea but also the vertebral bone-disc and trachea-lung complex, were successfully printed. &#xD.

Enhancing systematic review efficiency in hand surgery using artificial intelligence (natural language processing) for abstract screening.

The aim of the present study was to train a natural language processing model to recognize key text elements from research abstracts related to hand surgery, enhancing the efficiency of systematic review screening. A sample of 1600 abstracts from a systematic review of distal radial fracture treatment outcomes was annotated to train the natural language processing model. To assess time-saving potential, 200 abstracts were processed by the trained models in two experiments, where reviewers accessed natural language processing predictions to include or exclude articles. The natural language processing model achieved an overall accuracy of 0.91 in recognizing key text elements, excelling in identifying study interventions. Use of the natural language processing reduced mean screening time by 31% without compromising accuracy. Precision varied, improving in the second experiment, indicating context-dependent performance. These findings suggest that natural language processing models can streamline abstract screening in systematic reviews by effectively identifying original research and extracting relevant text elements.Level of evidence: IV.

Human-computer interaction interface design of flight simulator based on situation awareness.

To meet the comprehensive requirements of drivers for Human-Computer Interaction (HCI) during operation and to enhance situation awareness (SA), this paper proposes a design and evaluation method for flight simulator HCI interfaces grounded in SA. Firstly, the widely adopted Endsley's SA model, which comprehensively covers processes relevant to individual drivers was selected as the foundational framework. The study concentrates on flight, cruise, and landing scenarios within the simulator, analyzing and restructuring the information architecture of the HCI interface. Secondly, a self-assessment scale was developed based on the three-level model of information processing for SA to measure drivers' SA cognitive levels. The core scenarios of the simulation training were analyzed to establish a design-making process. Thirdly, invite participants were invited to engage in simulated driving sessions on the flight simulator, where their SA cognitive levels were assessed. An analysis of detail features was performed on samples with high SA cognitive levels. Subsequently, drivers' requirements for the control layout features of the HCI interface design were gathered through surveys. The design detail features of the HCI interface associated with high SA cognitive levels were identified and synthesized into four distinct dimensions to guide the detailed design process. Finally, participants engaged in simulated driving using the finalized interface design, and their SA cognitive levels were reassessed. The results demonstrated that the HCI interface design derived from this method significantly improved SA cognitive levels compared to the original design.

SELECTION OF TOOLS AND MACHINING PARAMETERS FOR PIPE WALLS

This review paper examines the techniques and factors involved in pipe wall processing, with a focus on the essential technologies utilized in this field. The study revealed crucial elements, such as ASTM A350-LF2 Class 1 carbon steel, renowned for its exceptional mechanical qualities and ability to withstand low temperatures. An exhaustive analysis has been conducted on the chemical makeup of this steel to comprehend its capacity to adjust and endure in various operational environments. The study focused on examining several particle separation processes, including as turning, milling, drilling, and grinding, in order to determine the exact equipment and criteria needed for each technique. The cooling and lubricating process received particular emphasis, with thorough investigation of several agents, including CASTROL HYSOL T15, and their impact on surface quality and tool longevity. The utilization of these compounds has been discovered to substantially decrease tool temperature and enhance machining quality. Upon closer examination of the processing techniques specified in section A-A of the tube wall, it was determined that advanced procedures such as floating plug drawing and micro-sized spiral tube hydroforming were discovered. These techniques allow the attainment of superior surface quality and precise dimensions, which are crucial for guaranteeing the structural integrity of the pipe. Cutting tools, processing equipment, and coolants and lubricants are essential components in the manufacturing of pipe walls. Nanoparticulate cooling and lubrication systems, which are advanced technologies, have demonstrated their effectiveness in minimizing friction and heat during machining. As a result, these systems enhance productivity and prolong the lifespan of tools. According to the article, the essential components for attaining high-quality manufacture of pipe walls are cutting tools, processing equipment, and coolants and lubricants. Additional study is advised to enhance processing parameters, create eco-friendly coolants and lubricants, and utilize artificial intelligence for predictive modeling and optimization of processing. This establishes the groundwork for future research and innovation in the pipe wall production business, with the objective of attaining enhanced efficiency and sustainability in production processes.

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

A mapping-free natural language processing-based technique for sequence search in nanopore long-reads.

In unforeseen situations, such as nuclear power plant's or civilian radiation accidents, there is a need for effective and computationally inexpensive methods to determine the expression level of a selected gene panel, allowing for rough dose estimates in thousands of donors. The new generation in-situ mapper, fast and of low energy consumption, working at the level of single nanopore output, is in demand. We aim to create a sequence identification tool that utilizes natural language processing techniques and ensures a high level of negative predictive value (NPV) compared to the classical approach. The training dataset consisted of RNA sequencing data from 6 samples. Multiple natural language processing models were examined, differing in the type of dictionary components (word length, step, context) as well as the encoding length and number of sequences required for algorithm training. The best configuration analyses the entire sequence and uses a word length of 3 base pairs with one-word neighbor on each side. For the considered FDXR gene, the achieved mean balanced accuracy (BACC) was 98.29% and NPV was 99.25%, compared to minimap2's performance in a cross-validation scenario. The next stage focused on exploring the dictionary components and attempting to optimize it, employing statistical techniques as well as those relying on the explainability of the decisions made. Reducing the dictionary from 1024 to 145 changed BACC to 96.49% and the NPV to 98.15%. Obtained model, validated on an external independent genome sequencing dataset, gave NPV of 99.64% for complete and 95.87% for reduced dictionary. The salmon-estimated read counts differed from the classical approach on average by 3.48% for the complete dictionary and by 5.82% for the reduced one. We conclude that for long Oxford nanopore reads, a natural language processing-based approach can reliably replace classical mapping when there is a need for fast, reliable and energy and computationally efficient targeted mapping of a pre-defined subset of transcripts. The developed model can be easily retrained to identify selected transcripts and/or work with various long-read sequencing techniques. Our results of the study clearly demonstrate the potential of applying techniques known from classical text processing to nucleotide sequences.

Reducing UVR Exposure in Pregnant Women and Infants: A Pilot Study.

Sun protection during pregnancy is critical for both maternal and infant skin cancer prevention, yet gaps remain in addressing this behavior in pre and postnatal settings. This pilot study aimed to evaluate the feasibility and acceptability of a peer-led intervention for expectant mothers' sun protection behaviors for themselves and their newborns and examine the preliminary effects on mothers' skin cancer knowledge, attitudes, and sun protection behaviors for themselves and their infants. Expectant mothers were recruited from medical clinics and community settings and were asked to complete surveys and interviews. The intervention, informed by the Cognitive-Social Health Information Processing model, consisted of two peer coach-led remote sessions. The majority of mothers (81%) attended both intervention sessions and reported increased understanding of (88%) and improved sun protection practices for themselves (79%; such as wearing sunscreen and reapplying sunscreen while outside). Additionally, 89% agreed that SUNRISE content is beneficial for new mothers. Moreover, maternal self-efficacy for implementing sun protection, intention to implement sun-safe behaviors for their infants, and knowledge of infant skin cancer prevention also increased with large effect sizes (d = 0.8-1.3). Mothers reported significantly decreased barriers to sun safety pre- to post-intervention. Feasibility of recruitment and delivery of a peer-led intervention focused on sun safety in women and their infants was high, and the intervention was acceptable to participating women. Mothers improved their sun protection behaviors over time, and implementation of sun protection behaviors for their infants was high. Intervention efficacy with a larger and more diverse sample is warranted, with longer follow-up.

Performance of ChatGPT-3.5 and ChatGPT-4o in the Japanese National Dental Examination.

In this study, we compared the performance of ChatGPT-3.5 to that of ChatGPT-4o in the context of the Japanese National Dental Examination, which assesses clinical reasoning skills and dental knowledge, to determine their potential usefulness in dental education. ChatGPT's performance was assessed using 1399 (55% of the exam) of 2520 questions from the Japanese National Dental Examinations (111-117). The 1121 excluded questions (45% of the exam) contained figures or tables that ChatGPT could not recognize. The questions were categorized into 18 different subjects based on dental specialty. Statistical analysis was performed using SPSS software, with McNemar's test applied to assess differences in performance. A significant improvement was noted in the percentage of correct answers from ChatGPT-4o (84.63%) compared with those from ChatGPT-3.5 (45.46%), demonstrating enhanced reliability and subject knowledge. ChatGPT-4o consistently outperformed ChatGPT-3.5 across all dental subjects, with significant improvements in subjects such as oral surgery, pathology, pharmacology, and microbiology. Heatmap analysis revealed that ChatGPT-4o provided more stable and higher correct answer rates, especially for complex subjects. This study found that advanced natural language processing models, such as ChatGPT-4o, potentially have sufficiently advanced clinical reasoning skills and dental knowledge to function as a supplementary tool in dental education and exam preparation.

Exploring knowledge management technologies to enhance sustainability and mitigate technostress from a collaborative perspective

Purpose The relevance of digital technology and knowledge management (KM) has been increasing continuously owing to their importance in sustainability. However, research on which technology leads to collaboration in KM and how it affects sustainability is lacking. Therefore, this study aims to analyze knowledge collaboration management technologies for sustainable growth based on academic literature and technology patents. Design/methodology/approach This paper analyzed current technology and academic trends in KM using BERTopic, an artificial intelligence natural language processing model, and proposed future promising candidate technologies and potential application areas in terms of collaboration and sustainability. Findings From the textual data of research papers and patents, this paper extracted 10 and 47 major topics, respectively. Moreover, five potential and feasible candidate patents along with their applicable fields were recommended. Originality/value The strength of this study is its analysis of the technologies affecting collaboration and sustainability in KM. This paper found that new digital technologies (i.e. artificial intelligence, cloud computing and big data) impact organizations’ KM and have the potential for collaboration. Digital technologies have a positive impact on corporate smartness directly and sustainability and inclusion indirectly. This paper argued that organizations should understand and identify various promising technological factors and apply them to their KM from a collaboration perspective.

Television and the Origins of Visual Pattern Recognition AI in Japan: a ‘Quest for a Seeing Machine’

Abstract This article traces the genealogy of contemporary data-driven computer vision to developments at the Japan Broadcasting Corporation (NHK) in the 1960s. Specifically, it examines NHK’s Visual and Auditory Information Science Unit and its role in the invention of the world’s first deep convolutional neural network. The use of television to collect viewer behaviour data enabled modelling of eye-brain information processing, in particular mechanisms of feature extraction. This, in turn, linked Fukushima Kunihiko’s formative work on signal compression to the development of a pattern recognition machine, resulting in the creation of the world’s first convolutional neural network. Recovering this history is important for two reasons. First, it helps counter a trend of ‘digital universalism’ that covertly homogenizes local differences into a single culture of artificial intelligence in the Cold-War-era US. Second, it reveals the largely ignored role of television in the genesis of digital image technologies and AI more broadly.

Wrongness and Blame Judgments and Their Dynamics: Toward a Three-Input Processing Model of Moral Judgment

Wrongness and Blame Judgments and Their Dynamics: Toward a Three-Input Processing Model of Moral Judgment

Heart Failure Prediction Using Machine Learning

This study explores the use of XGBoost and Shapley values for heart failure prediction, in addition to processing models such as neural network, CNN, and SVM. By comparing the performance of these different machine learning algorithms, we aim to develop a robust and accurate predictive model for identifying individuals at risk of heart failure. Our findings highlight the strengths and limitations of each approach, providing valuable insights for future research in this area. Ultimately, this research aims to improve the early detection and management of heart failure, leading to better patient outcomes and more efficient healthcare practices. Key Words: heart failure prediction, XGBoost, SHAPley values, neural network, CNN, SVM, machine learning algorithms, early detection, death events.