Automatic Processing Research Articles

Intangible Capital: Digital Colors in Romanesque Cloisters

This paper explores the possibility of counteracting the crisis of culture and institutions by investing in the identity values of the user-actor within digital spaces built for the purpose. The strategy is applied to the analysis of three Catalan cloisters (Spain), with a focus on the representation of the cloister of Sant Cugat (Barcelona). Heuristic picklocks are found in the semantic richness proposed by Marius Schneider exclusively on the verbal level. The authors interpret the contents and transcribe them into graphic signs and digital denotations of sounds and colors. They organize proprietary ontologies, or syntagmatic lines, to be entrusted to the management of computer algorithms. The syncretic culture that characterized the medieval era allowed the ability to mediate science and faith to be entrusted to the mind of the praying monk alone in every canonical hour. The hypothesis that a careful direction has programmed the ways in which to orient souls to “navigate by sight” urges the authors to find the criteria that advanced statistics imitates to make automatic data processing “Intelligent”. In step with the times and in line with the most recent directions for the Safeguarding of Heritage, the musical, astral, and narrative rhythms feared by Schneider are used to inform representative models, to increase not only the visual perception of the user (XR Extended Reality) but also to solicit new analogies and illuminating associations. The results return a vision of the culture of the time suitable for shortening the distances between present and past, attracting the visitor and, with him, the resources necessary to protect and enhance the spaces of the Romanesque era. The methodology goes beyond the contingent aspect by encouraging the ‘remediation’ of contents with the help of machine learning.

Анализ отзывов пациентов с использованием машинного обучения и лингвистических методов

With the advancement of digitalization, traditional methods of surveying consumers to assess their satisfaction with service quality are being replaced by approaches based on the automatic processing of text data from social media. This study aims to determine the degree of patient satisfaction with the quality of medical services by developing and testing an algorithm for classifying Russian-language text reviews collected from social media platforms. The focus is on analyzing the sentiment (positive/negative) of patient reviews about medical institutions and doctors, as well as identifying the review's subject—either the quality of medical services provided or the organization of patient care by the institution. A method was developed for classifying text reviews about the work of medical institutions posted by patients on two Russian doctor review platforms. Approximately 60,000 reviews were analyzed. Machine learning techniques, including various artificial neural network architectures, were tested. The classification algorithm demon-strated high efficiency, with the best performance achieved using a recurrent neural network architecture (accuracy = 0.9271). Incorporating named entity recognition into text analysis further enhanced the classification efficiency across all neural network-based classifiers. To improve classification quality, the study highlights the need for semantic seg-mentation of reviews by their subject and sentiment, followed by the separate analysis of these fragments.

Package Positioning Based on Point Registration Network DCDNet-Att

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.

Developing an automatic treatment record review system for quality assurance of patient treatment delivery in radiation therapy

Background and purposeTreatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists. However, it is time-consuming, tedious, and error-prone. To assist this process, a treatment record review system (TRRS) is developed to automatically review items related to treatment delivery record.MethodsThe treatment record is firstly extracted from oncology information system (OIS). Based on the daily patient treatment information, the original plan from the treatment planning system is identified. Then the original plan and the delivered plan are correlated. Eight review categories (parameter consistency, treatment completeness, treatment progression, image guidance, override, treatment couch, documentation, and treatment mode) are created. Tailored rules are designed for various review items to automate the review process. As a result, for each daily treatment record, a reviewed flag (pass, failure, warning, and N/A) is assigned by the TRRS. Finally, this system is evaluated by 6 months patient treatment records collected in our institute and compared to the manual process on the same data.ResultsTRRS processed a total of 76,651 treatment fractions from 4230 patients with an average of 574 treatments per day. The percentage of the detected anomalies among the total records was 0.76%. The average processing time was 3.9 s and 282 s per treatment record for the automatic and manual processes, respectively. Comparing with the manual process, the time efficiency of TRRS is improved by a factor of 72. The average numbers of anomalies detected by the automatic and manual processes are 21 and 13 per day, respectively. TRRS detects 61.5% more anomalies than those of the manual process.ConclusionTRRS is not only efficient in processing a large amount of treatment records on a daily basis but also effective in finding more anomalies than those of physics weekly check. The application of the TRRS could significantly reduce the workload of the review physicists and let them focus on more important works related to patient safety.

A Time Course Analysis of the Conceptual and Affective Meanings of Words.

Words are the basic units of language and vital for comprehending the language system. Lexical processing research has always focused on either conceptual or affective word meaning. Previous studies have indirectly compared the conceptual and affective meanings of words. This study used emotion-laden words, a special type of dual-meaning word, to directly compare the time course of processing conceptual and affective word meanings. Free association was applied in Experiment 1 to investigate the time course of conceptual and affective meanings in dual-meaning words. The results showed that conceptual-meaning processing was superior to affective-meaning processing. In Experiment 2, the semantic/affective priming paradigm was used to directly compare the time courses of processing conceptual and affective word meanings by manipulating stimulus onset asynchrony (SOA) in different ways. The results showed that semantic and affective priming effects could be obtained under short SOA conditions, with no differences between them. Consistent with Experiment 1, only the semantic priming effect was observed in the long SOA condition. These findings suggest that the conceptual and affective meanings of words have different time courses. The conceptual meaning of words includes automatic and controlled processing, whereas the affective meaning mainly involves automatic processing.

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson’s disease

Background and objectivesPatients with synucleinopathies such as multiple system atrophy (MSA) and Parkinson’s disease (PD) frequently display speech and language abnormalities. We explore the diagnostic potential of automated linguistic analysis of natural spontaneous speech to differentiate MSA and PD.MethodsSpontaneous speech of 39 participants with MSA compared to 39 drug-naive PD and 39 healthy controls matched for age and sex was transcribed and linguistically annotated using automatic speech recognition and natural language processing. A quantitative analysis was performed using 6 lexical and syntactic and 2 acoustic features. Results were compared with human-controlled analysis to assess the robustness of the approach. Diagnostic accuracy was evaluated using sensitivity analysis.ResultsDespite similar disease duration, linguistic abnormalities were generally more severe in MSA than in PD, leading to high diagnostic accuracy with an area under the curve of 0.81. Compared to controls, MSA showed decreased grammatical component usage, more repetitive phrases, shorter sentences, reduced sentence development, slower articulation rate, and increased duration of pauses, whereas PD had only shorter sentences, reduced sentence development, and longer pauses. Only slower articulation rate was distinctive for MSA while unchanged for PD relative to controls. The highest correlation was found between bulbar/pseudobulbar clinical score and sentence length (r = −0.49, p = 0.002). Despite the relatively high severity of dysarthria in MSA, a strong agreement between manually and automatically computed results was achieved.DiscussionAutomated linguistic analysis may offer an objective, cost-effective, and widely applicable biomarker to differentiate synucleinopathies with similar clinical manifestations.

Larry l. Jacoby (1944-2024).

Memorializes Larry L. Jacoby (1944-2024), a pioneering cognitive psychologist, who passed away in Pittsburgh, Pennsylvania, on March 15, 2024. Jacoby earned his undergraduate degree at Washburn University, where he took pride in being a football lineman and met his lifelong partner, Carole. He pursued graduate studies at Southern Illinois University at Carbondale, earning both his MA and PhD under the supervision of Robert Radtke (in 1970). Larry began his academic career at Iowa State before spending nearly 25 years at McMaster University. He later held positions at the University of Utah and New York University, and he served as the David Wechsler chair at the University of Texas at Austin. He concluded his career at Washington University in St. Louis, where he was a faculty member for nearly 2 decades before retiring. Larry was best known for his innovative research on unconscious influences on memory, memory attributions, and the effects of cognitive aging on memory. In the late 1980s, he introduced the "logic of opposition," an innovative method for examining unconscious, automatic processes. This research culminated in the development of the "process dissociation procedure" in 1991, a highly influential method that quantifies the separate contributions of conscious recollection and familiarity-based responses during task performance. His impact extended broadly to fields such as cognitive neuroscience and social, personality, and developmental psychology. Larry's contributions were recognized with numerous accolades, including the William James Fellow Award from the Association for Psychological Science and the Norman Anderson Lifetime Achievement Award from the Society of Experimental Psychologists. His impact on the field continues through the work of his many close colleagues and a generation of researchers he thoughtfully mentored. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A Classification Model Based on Interval Rule Inference Network with Interpretability

Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement.

Feasibility Testing of the Automatic Design of Three-Unit Implant Fixed Dental Prostheses with Different Dental CAD Software: A Pre-Clinical Pilot Trial.

Background/Objectives: The technical development of implant-supported fixed dental prostheses (iFDP) initially concentrated on the computer-aided manufacturing of prosthetic restorations (CAM). Advances in information technologies have shifted the focus for optimizing digital workflows to AI-based processes for design (CAD). This pre-clinical pilot trial investigated the feasibility of the automatic design of three-unit iFDPs using CAD software (Dental Manger 2021, 3Shape; DentalCAD 3.1 Rijeka, exocad GmbH). Methods: Two clinical scenarios based on a full dentition were created virtually. Physical models were produced and digitized using two intraoral scanners applying quadrant or full-arch scans (Trios3, 3Shape, Copenhagen, Denmark; and Primescan AC, Dentsply Sirona, Bensheim, Germany). For each scenario, iFDP designs were generated automatically using two laboratory software systems (Dental Manger 2021, 3Shape; DentalCAD 3.1 Rijeka, exocad GmbH), resulting in 80 STL datasets (2 scenarios × 2 scan strategies × 2 IOS systems × 5 scan repetitions × 2 software). The files were analyzed clinically for the contact schemes and pontic area. One of the automated designs for each scenario was manually post-processed and one iFDP design for each scenario was manually created by experienced dental technicians (control). The time required for all the design processes was recorded. Results: The automatic design of iFDPs without manual adjustment did not lead to clinically acceptable restorations. The time required for the automatically generated/manually adjusted iFDPs designs was not significantly different to that for the manually designed restorations. Conclusions: Current laboratory software can not automatically generate three-unit iFDPs with clinically acceptable results in terms of the interproximal and occlusal contacts and the pontic design. The automatic iFDP design process currently requires manual adjustment, which means there is no benefit in terms of the working time compared with manually created restorations.

Therapist's perfectionism as a target of clinical supervision. A case-study

Perfectionism is characterized by beliefs and feelings that drive individuals to pursue unattainable standards of excellence and impeccability, often at the expense of their psychological and physical well-being. It correlates with many psychopathologies and a heightened suicide risk; and it can be also an aspect of a multiplicity of personality disorders. Perfectionism seems a common phenomenon among psychological practitioners too, and the very few studies that focus on it overall suggest that perfectionism in therapists is negatively correlated with therapy effectiveness, and positively correlated with therapist emotional distress (e.g., negative emotions toward patient characteristics or demands); but it is not clear the mechanism underlying these correlations. In this paper we present a hypothesis on the process underlying the relationship between therapist perfectionism, therapist emotional distress, and ineffectiveness of therapeutic interventions. Starting with a parallel analysis of a clinical case and supervision over it, we show that perfectionism may be part of an overall organization of the meaning of the experience that the therapist brings into the relationship, which we call Prototypical Adaptive Modality of Existence (PAME). We show how the therapist's perfectionist PAME functions as an automatic process that hinders intersubjective attunement with the patient; finally, we show how a supervisory process focused on the shared exploration of the therapist's perfectionist PAME, of its developmental genesis, and of its impact on the supervisory relationship can modulate the therapist's perfectionism, foster attunement with the patient, functionally modify the line of intervention, and foster a positive outcome.

Neurodevelopmental and Neuropsychiatric Perspectives on Respiratory Control: Understanding Congenital and Developmental Disorders

: Breathing is an automatic process generated by the central nervous system, crucial for the homeostasis of several body processes. This vital process is underpinned by an intricate network in which distinct functional and anatomical factors and structures play a role. Transcription factors (i.e., PHOX2B and Pbx proteins), as well as neuromodulators (i.e., serotonin, noradrenaline, GABA, and glycine), have been demonstrated as implicated in the regulation of breathing. Besides, the several intertwined excitatory and inhibitory brainstem neural circuits comprising the so-called central pattern generator (CPG) have recently demonstrated a potential role of cerebellar structures and circuits in coordinating the complex and coordinated respiratory act in eupnea. A disruption affecting one of these components, which may also occur on a genetic basis, may indeed result in complex and heterogeneous disorders, including neurodevelopmental ones (such as Rett and Prader-Willi syndrome), which may also present with neuropsychiatric and breathing manifestations and potentially lead to sudden infant death syndrome (SIDS). Herein, we discuss the main factors and systems involved in respiratory control and modulation, outlining some of the associated neurodevelopmental disorders (NDDs) deriving from an impairment in their expression/ function. Further studies are needed to deepen our knowledge of the complexity underpinning “breathing” and the relation between respiratory implications and congenital and developmental disorders.

SAR Image Colorization with a Deep Learning Model for All-Inclusion Understanding (H)

The procedure for enhancing halftone photographs with color to create eye-catching pigmented pictures is known as image colorization. This involves the application of colors in line with the intended purpose of the image. However, traditional methods of colorization available today often include the need for end user’s intervention in the form providing color points and doodle drawings, as well as use of color images for reference in which case colors are transferred to the target image or using multiple color images to predict what the colored result would look like; however, the majority of colorizes developed tend to produce colorized images that are not realistic in most instances due to unskilled users who attempt to apply the devices, wrong color transfer or limited color image library. In this paper, to overcome these weaknesses, a new method that consists of two modules is proposed to colorize certain regions of an image by fusing semantic segmentation and seamless region filling. In the first module, input image foreground and background regions masks and categories are extracted each extracted region's reference image is taken from a per-sorted color image database using a Mask R-CNN model. Using colorization approaches based on the VGG and U-Net models, respectively, the second module entails coloring the image's backdrop and other areas. Afterward, the final, fully colored image is created by combining the photographs using the poission editing process. The tests show that our method enables not only the proper reference image selection based on the semantic information of various image components, but also incorporation of the colored results to create a believable colored image. Proposing to use different CNNbased models in a combination where each is used according to its own strength, In contrast to other methods now in use, our scheme gives a deeper creative visual impression while avoiding the disadvantage of single-step techniques' failure frequency. Keywords: Mask R-CNN, Input Photograph, Image Sections, Coloured Photographs, Conceptual Data, Visuals, Black and White Images, Semantic Pix elation, Reference Picture, Base Image, Images Stockpile, Outlying Areas, Image Library, Segregation Methods, Doodle, Conventional Neural Network Based Designs, U-Net Architecture, One-shot Approach, Strategy Present in This Manuscript, Zonal Regions, Principal Zones, Large Image, Color space, Competitive Half toning Algorithm, Zonal Context, Focus Area, Artistic Incorporation, Partially Automatic Process, Cutting Out Section of the Picture, Instance Level Segmentation

Beyond Good or Bad: The Four Evaluative Quadrants of Relationships

Academic Abstract Traditionally, theoretical and empirical accounts have considered relationship evaluations along one single dimension ranging from positive to negative. However, in this theoretical work, we stress the importance of using a bi-dimensional conceptualization of relationship evaluations in which positive and negative dimensions can vary independently. In doing so, we describe the four evaluative quadrants experienced in relationships and outline their unique interpersonal processes and outcomes, both from the perspective of the person experiencing them (i.e., actor effects) and from the perspective of the recipient of such evaluations (i.e., partner effects) and considering both explicit (i.e., deliberative) and implicit (i.e., automatic) processes. We also provide a framework that predicts how relationship properties are likely to influence relationships evaluations, and we introduce the Trajectories of Relationship Evaluation Model (TREM) that describes changes in evaluations over time and the factors that influence such changes. Public Abstract The way we evaluate other people has important implications for how we relate to others and for our psychological and physical health. However, previous research has mostly focused on positive or negative evaluations in relationships. But there are two other types of evaluations that people commonly experience in relationships: ambivalence and indifference. In this work, we argue that it is important to study all four different evaluative types in relationships (i.e., mostly positive, mostly negative, ambivalence, and indifference) because they each uniquely predict certain relationship dynamics and processes. We discuss the consequences of these different types of evaluations for the person who holds the evaluation and for the person who is the target of such evaluation, and we discuss how these evaluations affect both deliberative and automatic processes. Finally, we propose a model (TREM) of how relationship evaluations evolve over time and of the factors that influence the changes in evaluations.

CAECENET: An automatic system processing photometer and ceilometer data from different networks to provide columnar and vertically-resolved aerosol properties.

This work introduces CAECENET, a new system capable of automatically retrieving columnar and vertically-resolved aerosol properties running the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm using sun-sky photometer (aerosol optical depth, AOD; and sky radiance measurements) and ceilometer (range corrected signal; RCS) data as input. This method, so called GRASPpac, is implemented in CAECENET, which assimilates sun-sky photometers data from CÆLIS database and ceilometer data from ICENET database (Iberian Ceilometer Network). CAECENET allows for continuous and near-real-time monitoring of both vertical and columnar aerosol properties. The main characteristics and workflow of CAECENET are explained in detail. This work also explores the potential of CAECENET to monitor and analyze the evolution of transported aerosol events on a regional scale by means of the distribution of CAECENET stations across the Iberian Peninsula. As an example, this paper analyzes, using the CAECENET products, the case of a Saharan dust outbreak that occurred between the 3rd and 5th of October 2022. This was an intense event, with AOD at 440 nm values around 0.5 in Madrid and Valladolid, and reaching 1.55 in Granada. Transport from the Canadian wildfires at the end of June 2023 is also studied. Despite the long-range transport of the smoke particles in this event, measured volume concentrations reached and surpassed 80 μm3/cm3 in some stations. The results obtained point to the utility of this CAECENET tool for analyzing changes in the height and speed of the event propagation, in the aerosol concentration, and how this affects the optical properties.

Algorithm for Detecting Reference Points on a Digital Electrocardiogram in Real Time

Relevance. The use of digital electrocardiographs and cardiac monitors with built-in algorithms for automatic processing, analysis and interpretation of electrocardiograms allows the doctor to effectively diagnose cardiac arrhythmias. It is known that in order to provide emergency care to a patient, the duration of arrhythmia diagnostics should not exceed several tens of seconds, which requires the emergence of new algorithms for detecting informative features indicating arrhythmia, operating in real time. The need to introduce new and effective technologies for diagnosing cardiovascular diseases is also reflected in public health development programmes.Research goal. Development and quality indicators analysis of the algorithm for reference points detection on a digital electrocardiogram, bearing informative signs for the procedure of arteries diagnosis.The methods used. The study is based on an analysis of existing approaches to the problem of reference points detection on digital electrocardiogram, as well as conducting a test of the proposed algorithms by mathematical modelling methods. The quality indicators of the algorithms defined in accordance with the principles of signal detection theory and diagnostic testing, at the junction of which the task of electrocardiogram reference point detection is located. The proposed algorithm was tested on materials of MIT-BIH Arrhythmia Database, which is widely used for verification and validation of real-time digital electrocardiogram signal processing algorithms.The results. The study proposes an algorithm for detecting reference points on a digital electrocardiogram that carry informative features for the arrhythmia diagnostic procedure. The proposed algorithm is based on digital signal filtering using a decision rule based on a three-step two-threshold principle of pre-processed electrocardiogram signal values comparison on a sliding window. An experiment on the materials of the open verified MIT-BIH Arrhythmia DB showed that the quality of the proposed algorithm for detecting reference points is higher than that of the algorithms used in modern digital electrocardiographs and cardiac monitors. The proposed algorithm based on digital signal filtering and the three-step two-threshold decision rule have elements of scientific novelty.The significance. The results of this work can be used in the development of digital heart rate monitors, cardiac devices and for automatic processing, analysis and real-time computer-assisted digital electrocardiogram signal interpretation.

Behavioral Intervention Functions in Type 2 Diabetes Apps: Literature Review.

As type 2 diabetes (T2D) is expected to increase, self-management becomes more crucial. Mobile apps are increasingly supporting self-management with tasks like blood glucose monitoring and medication management. Understanding the behavioral intervention functions used by diabetes apps today, is essential for improving future apps and systems for diabetes management. To analyze the behavioral intervention functions used in apps for managing T2D that integrate the three main elements: medication management, nutrition tracking, and blood glucose management. We conducted a literature review on T2D diabetes apps using SCOPUS, PubMed, and PsycINFO. After screening and removing duplicates, we analyzed app details and behavioral intervention functions based on the Behavior Change Wheel (BCW) framework. We reviewed 644 scientific publications describing diabetes apps in clinical studies, narrowing it down to 20 studies, including 16 unique apps, after screening and exclusions. These studies were published between 2016 and 2024. Among the identified apps, automatic processing of medication data was reported in one study, while blood glucose data were automatically processed in 13 studies. Nutrition data processing varied. Most apps used Enablement and Persuasion as behavioral intervention functions, with Education and Training reported less frequently. Environmental Restructuring, Incentivization, Coercion, Restriction, and Modeling were not reported as being used in any studies. This review shows that while Enablement and Persuasion are common, other behavioral intervention functions seem to be underused or underreported. Future research could explore the potential of integrating additional behavioral intervention functions to enhance diabetes app efficacy and users' self-management.

Study of the internal environment quality monitoring system for a laboratory model of a mining separator at key sensitive points of operation and process control using artificial intelligence

Modeling the quality of the indoor environment in buildings using neural networks, as an element supporting automatic process control, has become extremely popular nowadays. By analogy, attempts are being made to use the experience gained in construction and implement it in industry. The publication proposes a method of modeling feedforward neural networks, thanks to which it is possible to obtain the most efficient network with one hidden layer in terms of the given quality criterion. This network was implemented in the control system of the mining separator operation as part of pilot studies. The research included testing a laboratory model of the separator placed in a sea container modified for the separator function, in which modern automation technologies and monitoring of environmental parameters were integrated. Among others, time, outside temperature, set temperature, temperature error and controller output were measured. The measurements were taken at the points of installation of devices sensitive to the working environment - controllers, I/O modules, X-ray (XRT-DE) and optical analysis (VIS-NIR), enabling precise examination of the composition and quality of mineral resources. The internal environmental conditions in the housings of the above-mentioned sensitive elements and in the server room were the basis for the analysis. The aim was to develop a performance model enabling effective improvement of the working environment of all electrical and mechanical devices affecting energy efficiency and the internal environment. Separators operate in a very diverse environment, such as: tropical forests, Canadian Tundra, or desert areas in Africa, as well as EU countries, the USA and Australia. These devices are used in both open pit and underground mines. The use of modern technologies and mobile solutions in the mining industry contributes to increased efficiency, operational safety and, consequently, minimizing the negative impact on the environment. The research results confirmed that precise monitoring and control to ensure environmental conditions at selected separator points is crucial to ensuring the continuity and quality of the separation process.

Cadavres exquis. Automatic processes in the building extensions of OMA

This article examines the affinities between Rem Koolhaas’s architectural extension strategies and the surrealist concept of the ‘exquisite corpse’. The problem addressed is how these extensions, conceived as ideologically autonomous components, reconfigure the building as a collective palimpsest. The objective is to analyze how these practices align with the principles of ‘Manhattanism’ articulated in Delirious New York and how they resonate with the organic excrescences characteristic of Salvador Dalí’s artistic universe. The methodology employs a critical analysis of Koolhaas’s key projects alongside an interpretation of surrealist methodologies, assessing their conceptual convergence. The findings indicate that these extensions, formally autonomous and seemingly random, not only fulfill functional requirements but also challenge the rationalist foundations of architectural design, establishing an interdisciplinary dialogue between architecture and surrealist art.

Cognitive and linguistic predictors of word reading fluency in Turkish–Arabic bilingual and Turkish monolingual children

Objectives: The study investigated the role of linguistic and cognitive factors in Turkish word reading fluency (WREAD) among second-grade Turkish–Arabic simultaneous bilingual and Turkish monolingual children. It specifically focused on the impact of phonological awareness (PA), phonological memory (PM), rapid automatized naming (RAN), morphological awareness (MA), morphological fluency (MF), processing speed (PS), and vocabulary knowledge (VK) on reading fluency. Methodology: The study used a cross-sectional design and collected data from 127 children in Hatay, Turkey. The participants completed a battery of tests measuring PA, PM, RAN, MA, PS, VK, and WREAD. The tests were administered individually, and the scores, along with the time spent on each test, were recorded. Data and analysis: Data from the tests were analyzed using SPSS 22.0. Independent samples t-tests were conducted to determine the differences between bilingual and monolingual children in the linguistic and cognitive measures. Pearson r correlation analyses were conducted to illustrate the relationship among the variables and stepwise regression analyses to explore the extent to which these variables explained the variance in WREAD. Findings: The findings highlighted significant differences between Turkish–Arabic bilingual and Turkish monolingual children in PA and PS. While MF and RAN explained WREAD in the bilingual group, MF and PA were the strongest predictors of WREAD in the monolingual children. Originality: This study is the first to investigate word reading development in Turkish–Arabic simultaneous bilingual children, contributing novel insights into literacy acquisition in simultaneous bilingualism. In addition, the concept of MF has been proposed in the literature as a distinct measurement from MA. Significance: The study expands the existing knowledge on bilingual reading development, emphasizing the importance of timed cognitive and linguistic variables in predicting WREAD. It also sheds light on the educational needs of bilingual children in the Turkish context.

Food cravings are associated with increased self-regulation, even in the face of strong instigation habits: A longitudinal study of the transition to plant-based eating.

Frequently engaging in a positive health behaviour, like following a vegetarian or vegan (veg*n) diet, can bring benefits to both the individual and society. We investigated the roles of two psychological determinants of behaviour-instigation habits and self-regulation strategy use-in a cohort of individuals who were newly transitioning to a veg*n diet. In a longitudinal study over 6months (7 waves including baseline), 222 individuals transitioning to a veg*n diet reported their monthly habit strength, craving frequency, self-regulation strategies and animal product consumption. Our results supported the benefits of having a healthy habit, as stronger habits predicted fewer cravings and lower consumption of animal products, in line with the person's target diet. However, in contrast to some theoretical accounts, having a strong instigation habit did not reduce the use of self-regulation strategies; people with strong habits used multiple strategies to maintain their diet, especially when they experienced frequent diet-inconsistent cravings. These findings challenge the idea that habits eliminate the need for self-regulation, and suggest that habits do not fully circumvent motivational challenges in the pursuit of complex health behaviours. Our results are consistent with recent suggestions that automatic and intentional processes act simultaneously during the enactment of complex health behaviours.