Process Information Research Articles

NeuroART: Real-Time Analysis and Targeting of Neuronal Population Activity during Calcium Imaging for Informed Closed-Loop Experiments.

Two-photon calcium imaging allows for the activity readout of large populations of neurons at single cell resolution in living organisms, yielding new insights into how the brain processes information. Holographic optogenetics allows us to trigger activity of this population directly, raising the possibility of injecting information into a living brain. Optogenetic triggering of activity that mimics "natural" information, however, requires identification of stimulation targets based on real-time analysis of the functional network. We have developed NeuroART (Neuronal Analysis in Real Time), software that provides real-time readout of neuronal activity integrated with downstream analysis of correlations and synchrony and of sensory metadata. On the example of auditory stimuli, we demonstrate real-time inference of the contribution of each neuron in the field of view to sensory information processing. To avoid the limitations of microscope hardware and enable collaboration of multiple research groups, NeuroART taps into microscope data streams without the need for modification of microscope control software and is compatible with a wide range of microscope platforms. NeuroART also integrates the capability to drive a spatial light modulator (SLM) for holographic photostimulation of optimal stimulation targets, enabling real-time modification of functional networks. Neurons used for photostimulation experiments were extracted from Sprague Dawley rat embryos of both sexes.

The Effectiveness of Updating Data on Small and Medium Industrial Business Actors in the Industrial Sector at the Cirebon City Cooperatives, Small and Medium Enterprises, Trade and Industry Office

The development of Science and Technology (IPTEK) affects various aspects of human life, including in the context of organizations and government agencies. In Cirebon City, the management of Small and Medium Industries (SMEs) by the Trade and Industry Small and Medium Business Cooperative Office faces challenges in updating data that has not been fully computerized. This study uses a descriptive qualitative approach to evaluate the effectiveness of the IKM data update process. The study results show that the number of SMEs is increasing every year, although there are still obstacles, such as incomplete information and manual processes that slow down efficiency. The application of digital technologies, such as applications for data collection and update, is proposed as a solution to improve the efficiency and accuracy of processes. It is hoped that better data updates, facilitated by digital technologies, can significantly support the development of the creative economy of Cirebon City through the identification of more appropriate needs and opportunities, as well as the optimization of government programs in the SME sector.

Right to be forgotten in the Era of large language models: implications, challenges, and solutions

AbstractThe Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja González, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. It was a significant emergent right as the result of the evolution of technology. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and guardrails. With the rapid advancement of AI and the increasing need of regulating this powerful technology, learning from the case of RTBF can provide valuable lessons for technical practitioners, legal experts, organizations, and authorities.

Enhancing user experience and trust in advanced LLM-based conversational agents

This study explores the enhancement of user experience (UX) and trust in advanced Large Language Model (LLM)-based conversational agents such as ChatGPT. The research involves a controlled experiment comparing participants using an LLM interface with those using a traditional messaging app with a human consultant. The results indicate that LLM-based agents offer higher satisfaction and lower cognitive load, demonstrating the potential for LLMs to revolutionize various applications from customer service to healthcare consultancy and shopping assistance. Despite these positive findings, the study also highlights significant concerns regarding transparency and data security. Participants expressed a need for clearer understanding of how LLMs process information and make decisions. The perceived opacity of these processes can hinder user trust, especially in sensitive applications such as healthcare. Additionally, robust data protection measures are crucial to ensure user privacy and foster trust in these systems. To address these issues, future research and development should focus on enhancing the transparency of LLM operations and strengthening data security protocols. Providing users with clear explanations of how their data is used and how decisions are made can build greater trust. Moreover, specialized applications may require tailored solutions to meet specific user expectations and regulatory requirements. In conclusion, while LLM-based conversational agents have demonstrated substantial advantages in improving user experience, addressing transparency and security concerns is essential for their broader acceptance and effective deployment. By focusing on these areas, developers can create more trustworthy and user-friendly AI systems, paving the way for their integration into diverse fields and everyday use.

Optimal filtering of an information process depended on noninformative parameters described by Marcovian chain when radio signals are receiving

In radio navigation and radio location tasks tracking systems on radio signals parameters often operate when object acceleration has stepwise changing. If we synthesize various tracking systems using optimal filtering theory it is necessary to represent the estimated process as a multidimensional Marcovian process. But such representation for stepwise changed acceleration is a rough approximation that lead to estimates accuracy degradation. One of possible approach to such disambiguation is to describe acceleration as Marcovian chain with finite number of states. This approach is developed in the article. Objective. To synthesize an optimal filtering algorithm of information process depended on noninformative parameters described by Marcovian chain using the observation grouping method when receiving radio signals and to implement simulation for an illustrative task.

Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity

The complexity of brain activity reflects its ability to process information, adapt to environmental changes, and transition between states. However, it remains unclear how schizophrenia (SZ) affects brain activity complexity, particularly its dynamic changes. This study aimed to investigate the abnormal patterns of brain activity complexity in SZ, their relationship with cognitive deficits, and the impact of antipsychotic medication. Forty-four drug-naive first-episode (DNFE) SZ patients and thirty demographically matched healthy controls (HC) were included. Functional MRI-based sliding window analysis was utilized for the first time to calculate weighted permutation entropy to characterize complex patterns of brain activity in SZ patients before and after 12 weeks of risperidone treatment. Results revealed reduced complexity in the caudate, putamen, and pallidum at baseline in SZ patients compared to HC, with reduced complexity in the left caudate positively correlated with Continuous Performance Test (CPT) and Category Fluency Test scores. After treatment, the complexity of the left caudate increased. Regions with abnormal complexity showed decreased functional connectivity, with complexity positively correlated with connectivity strength. We observed that the dynamic complexity of the brain exhibited the characteristic of spontaneous, recurring “complexity drop”, potentially reflecting transient state transitions in the resting brain. Compared to HC, patients exhibited reduced scope, intensity, and duration of complexity drop, all of which improved after treatment. Reduced duration was negatively correlated with CPT scores and positively with clinical symptoms. The results suggest that abnormalities in brain activity complexity and its dynamic changes may underlie cognitive deficits and clinical symptoms in SZ patients. Antipsychotic treatment partially restores these abnormalities, highlighting their potential as indicators of treatment efficacy and biomarkers for personalized therapy.

Key Factors Influencing Gelation in Plant vs. Animal Proteins: A Comparative Mini-Review.

This review presents a comparative analysis of gelation properties in plant-based versus animal-based proteins, emphasizing key factors such as pH, ionic environment, temperature, and anti-nutritional factors. Gelation, a crucial process in food texture formation, is influenced by these factors in varying ways for plant and animal proteins. Animal proteins, like casein, whey, meat, and egg, generally show stable gelation properties, responding predictably to pH, temperature, and ionic changes. In contrast, plant proteins such as soy, pea, wheat, and oilseed show more variable gelation, often requiring specific conditions, like the presence of NaCl or optimal pH, to form effective gels. Animal proteins tend to gel more reliably, while plant proteins require precise environmental adjustments for similar results. Understanding these factors is crucial for selecting and processing proteins to achieve desired textures and functionalities in food products. This review highlights how changing these key factors can optimize gel properties in both plant- and animal-based proteins.

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network

BackgroundRecently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing.ResultsHere, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it’s also useful to analyze interactions in other types of protein complexes.

Information on Gender Identity as Personal Data under EU and US Data Protection Models

Abstract One of the most important legal issues concerning gender identity is ensuring that no one is discriminated against in any type of environment and that individuals’ needs are considered seriously during the legislation process. Even though this can be questioned, if one needs to process information on gender to achieve an inclusive and diverse society and law, it seems that at this point in the history of society, there are no better measures to ensure a non-discriminatory environment than processing information on gender identity. Under the current personal data protection landscape, both in the European Union and the United States, it is not clear what the conditions are for processing information on the gender of individuals. Therefore, the authors of this article analyse legal requirements from both jurisdictions, also in the light of the question of the adequacy of personal data protection in the US under article 45 of the General Data Protection Regulation.

DESIGN OF WEB-BASED ACADEMIC SERVICE SATISFACTION INFORMATION SYSTEM OF WEST SUMATERA SHIPPING POLYTECHNIC

The design of a web-based information system for academic service satisfaction at the West Sumatra Shipping Polytechnic aims to overcome the problems of the survey system which is still manual. This can slow down the process of information at the West Sumatra Shipping Polytechnic. The system design in this Final Project (TA) was built using a website platform and run online. The research method uses the System Development Life Cycle (SDLC) with the Waterfall model approach which consists of 5 stages, namely, a) Analysis b) Design c) Implementation d) Maintenance e) Planing. The programming language used is PHP, and the database application uses the MySql data processor as the database. The system design tool uses the unified Modeling Language (UML) method using 5 (five) diagrams according to design needs, namely Usecase Diagrams, Activity Diagrams, Sequence Diagrams, Class Diagram and database design. This final project produces an effective and efficient Web-based design of an information system for academic service satisfaction at the West Sumatra Shipping Polytechnic that produces accurate and timely survey resultss

TOOL CONDITION MONITORING IN MACHINING USING ROBUST RESIDUAL NEURAL NETWORKS

Tool condition monitoring (TCM) aims to improve process efficiency, quality and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal computer numerical control (CNC) signals. The Robust-ResNet architecture uses skip connections to move through multiple convolutional layers, avoiding the vanishing gradient problem of other neural network algorithms. The study includes an evaluation of the binding of process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture has been trained and optimised using an open access data set of face milling time series. In this particular case, AC and DC signals have been used together with the corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in the prediction of tool wear using internal signals provided by the CNC itself. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality and increase production efficiency in milling manufacturing processes.

Operando monitoring of gelation kinetics of polyacrylamide hydrogel using in-fiber dual-MZI

Operando monitoring of gelation kinetics and exploring the gelation law are crucial for optimizing the performance of hydrogels. In this paper, we propose a novel superposition sensing structure based on an integrated dual-Mach–Zehnder interferometer (MZI), which can simultaneously sense temperature and refractive index (RI), with an RI sensitivity of −10,173 nm/RIU and a temperature sensitivity of 8.14 nm/ °C. For the first time, its application for the operando monitoring of gelation kinetics of polyacrylamide hydrogel (PAH) is demonstrated. By establishing the kinetic curves of temperature and RI information in the gelation process, it is confirmed that the gelation process can be divided into three stages: initial lag, rapid gelation, and final saturation. In addition, two gelation laws are verified through experiments: (1) As the concentration of monomers and crosslinkers increases, RI changes, and the amount of heat released gradually increases. (2) As the concentration of catalysts increases, the gelation time decreases significantly. This superposition sensing technique based on dual-MZI can be further applied to monitor other complex dynamic biochemical processes, thereby providing insights into the laws and mechanisms of reaction processes.

ПРОБЛЕМЫ ГЕЙМИФИЦИРОВАННОГО ОБУЧЕНИЯ РКИ И МЕТОДЫ ИХ РЕШЕНИЯ

The article considers gamification in education as a principle for organizing the educational process, its pros and cons in teaching Russian as a foreign language, as well as ways to prevent the formation of negative habits among students. The authors draw attention to the fact that the introduction of new teaching methods requires the adaptation of teachers to modern strategies and to the continuous development of advanced technologies. The text demonstrates the need for conscious and accuracy when using gamification in order to avoid oversimplification and evaluation of results by external criteria. The danger of students becoming accustomed to easily obtaining results and superficial assimilation of material in gamified courses is discussed. The text also returns to the idea of the importance of recording and visualizing information in the educational process. The authors note the significance of students’ own analytical work, awareness when performing tasks, deep immersion in the process of cognition and the development of creative thinking. Finally, the article emphasizes that gamification is a method that requires caution, careful attitude, and an emphasis on formation of students' cognitive skills.

Transdisciplinary approach as a resource for the development of critical thinking in higher mathematical education

Introduction. In a modern digital and globalized society, the higher education system is faced with the task of developing critical thinking skills in graduates that allow them to solve complex problems through the integration of knowledge from various disciplines at a new transdisciplinary level of knowledge. Such integration seems possible based on the use of modeling methodology, methods of computational and discrete mathematics, and mathematical cybernetics, which form the foundation of the language of information technology and processes. The purpose of the study is to identify the role of the transdisciplinary approach in the development of students’ critical thinking through mathematics. Materials and methods. 76 students of the Vyatka State University majoring in 06.03.01 Biology took part in the study, covering 2022–2023. The main methods for measuring critical thinking, presented by the UF/EMI Critical Thinking Disposition Instrument and The Critical Thinking Barriers Scale (CTBS), were supplemented with diagnostic tools from specially designed tasks, taking into account specifics of teaching mathematics to future biotechnologists. Statistical analysis of the obtained results was performed using the Mann-Whitney U test. Results. A number of methodological conditions have been formulated to ensure the development of students' critical thinking in the process of teaching mathematics, which corresponds to the priority goals of modern higher education. The expediency of using contextual mathematical problems containing a real data set, the method of mathematical modeling, the inclusion of modern mathematical theories and their applications in the teaching content, a joint form of work in the classroom and work with scientific articles is substantiated. A quantitative analysis of the results confirmed the significance of the differences (Uemp = 65 < Ucr = 66; р < 0,01), which indicates the effectiveness of the intervention based on the proposed methodological conditions. Conclusion. The practical significance of the study is determined by the possibilities of using its results in improving the critical thinking skills of students in the process of teaching mathematics.

Social and general cognition are uniquely associated with social connectedness in later life.

The mechanisms by which older adults maintain large, complex social networks are not well understood. Prior work has primarily focused on general cognitive ability (e.g., executive function, episodic memory), largely overlooking social cognition-the ability to process, store, and remember social information. Because social cognition plays a key role in navigating social interactions and is distinct from general cognition, we examined whether general and social cognition uniquely predicted the nature of older adults' personal social networks. Our study leveraged comprehensive measures of general cognition (executive function, episodic memory), social cognition (face memory and dynamic measures of cognitive and affective theory of mind), and a rigorous measure of personal social networks from 143 community-dwelling older adults. We found that, when modeled together and controlling for sociodemographic variables, only executive function and dynamic cognitive theory of mind positively predicted having social networks with relatively unfamiliar, loosely connected others, accounting for 17% of the unique variance in older adults' social connectedness. Interestingly, having a social network comprised primarily of close, tightly knit relationships was negatively associated with affective theory of mind performance. Findings are discussed in the context of the social-cognitive resource framework-which suggests that social cognition may be more engaged in relatively unfamiliar, versus close, interactions. Specifically, our results show that social-cognitive processes may be relatively automatic for individuals whose primary social relationships are very close but may be more strongly engaged for individuals whose interactions include at least some relatively less close relationships. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Nursery Habitat Requirements for the Blue Swimming Crab: Implications for Larval Development

Overfishing causes a significant decline in blue swimming crab (Portunus pelagicus) populations, an essential marine resource for Thailand. This research aimed to identify suitable nursery areas for this species throughout the four distinct monsoon seasons. This is the first comprehensive study of habitat characteristics: water temperature, pH, salinity, dissolved oxygen, marine sediment, seagrass beds, and suitability assessment of nursery areas, using a combined approach of Analytic Hierarchy Process and Geographic Information Systems. Our findings reveal that highly suitable nursery habitats comprise a small percentage of the total study area, varying with the monsoon season: 1.66% in the Northeast Monsoon (NEMS), 1.00% in the NEMS transition (NEMST), 1.11% in the Southwest Monsoon (SWMS), and 1.97% in the SWMS transition (SWMST). These areas are concentrated along the coastlines of Sed Island, Phum Riang, Takrob, Tha Thong, and Donsak. Sed Island was designated as a fishery refuge zone in 2022. The identified nursery areas coincide with existing crab banks, highlighting the model's accuracy. Conversely, unsuitable areas were predominantly in shellfish farming zones and low-salinity estuaries. By identifying suitable nursery grounds for the blue swimming crab, this study offers valuable information that can support Thailand's sustainable management and conservation efforts for this species. Doi: 10.28991/HEF-2024-05-03-01 Full Text: PDF

From Pursuit of the Universal AGI Architecture to Systematic Approach to Heterogeneous AGI (SAGI): Addressing Alignment, Energy & AGI Grand Challenges

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to unprecedented increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to AI lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. In this paper, we posit that system design is the missing piece in overcoming current AI the grand challenges. We present a Systematic Approach to AGI (SAGI) that utilizes system design principles to overcome the energy wall and the alignment challenges. This paper asserts that artificial intelligence can be realized through a multiplicity of design-specific pathways, rather than a singular, overarching AGI architecture. AGI systems may exhibit diverse architectural configurations and capabilities, contingent upon their intended use cases. We argue that AI alignment, the most difficult among the grand challenges, is not attainable without a way to reflect the complexity of the human moral system and its subsystems in the AGI architectures. We claim that AGI approaches such as symbolicism, connectionism and others are not fundamental to AGI but emergent from the system design processes. Hence, we focus on employing system design principles as a guiding framework, rather than solely concentrating on a universal AGI architecture.

Verifying Welding Processes Selection for a Case Study Using an Analytic Hierarchical Process

This paper aims to develop a framework for differentiating the selection of welding processes using specialized knowledge-based expert systems, which are particularly useful in dealing with issues that require complex decision-making. A program was created and programmed using the process information maps (PRIMAs) matrix in Visual Basic Access for selecting welding processes in a study. The program is in two stages. In the first stage, it excludes non-candidate operations according to several criteria, the most important of which is the type of metal and its thickness. The second stage is arranging operations using multi-criteria methods for decision-making through research to resolve complicated problems involving multiple factors. The hierarchical analysis process (AHP) was used. It is considered one of the most frequently used multi-criteria decision-making methods. The selection of analytic hierarchical process. AHP criteria depend on experience and knowledge rather than specific data for selecting alternatives and determining weights. The methodology is straightforward, easy to comprehend, and applicable to various domains needing intricate decision-making. The program results are compared with those of a previously published case to choose the best welding processes for a study case, which is a home radiator, and results matching the candidate processes were discovered.

USE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN TEACHING LEARNING PROCESS IN HIGHER SECONDARY SCHOOL EDUCATION WITH RESPECT TO TEACHERS PERCEPTION

USE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN TEACHING LEARNING PROCESS IN HIGHER SECONDARY SCHOOL EDUCATION WITH RESPECT TO TEACHERS PERCEPTION