Control Process Research Articles

On minimum cost of early fault detection with a maximum discounted effort reward in a feedback machining system under quality control process

ABSTRACT Preventive maintenance for companies contributes to the continuity and sustainability of their business, as it increases the life of equipment and improves its reliability. This study investigates corrective and proactive maintenance to develop a quality control policy. A feedback machining system model with a finite supply and standbys is described, along with the corrective maintenance dependent on time. Additionally, the system incorporates an inspector to guarantee the quality of the repaired units and a known number of servers to repair the damaged units. We utilize the probability of effective units in the system to calculate the likelihood of early defect discovery in the steady state to support the idea of preventive maintenance. We analyze the search effort, which is constrained by a Gaussian distribution, as a function with a discounted parameter to reduce the detection cost and increase the detection probability. This model’s applicability for various system capacities is addressed.

Effectiveness of emotion-focused therapy: Main results of a practice–research network study

Background Emotion-Focused Therapy is one of the evidence-based psychotherapies for a range of psychological problems. While most evidence was gained from randomized controlled studies or process research at university settings, there is a need for more outcome research conducted within practitioner–researcher networks assessing the effectiveness of Emotion-Focused Therapy in private practice. Methods A total of N = 70 clients with a variety of psychiatric disorders have been included in the bi-national practitioner researcher network for Emotion-focused Therapy Switzerland–Germany. Symptom change was assessed using CORE-OM, BDI-II, IIP and WSAS. Therapist adherence was assessed by the self-reported version of the PCEPS. Pre–post-follow-up analyses were conducted using paired sample t-tests and hierarchical linear modeling. Results Pre–post-assessment shows small to medium effect sizes for all outcome measures (except for interpersonal problems which did not change). Symptom reduction is maintained for CORE, BDI and WSAS, and increased for IIP at 3 and 6 months follow-up. Therapist adherence is good to excellent in this sample. Conclusions Emotion-Focused Therapy is effective in the real-world practice, as suggested by the small to moderate effect sizes. The smaller effects found in the current study as compared to randomized controlled trials are consistent with the literature and suggest that EFT remains moderately effective when applied in a naturalistic context.

Platelet activation and signaling in thrombus formation.

In thrombosis and hemostasis, the formation of a platelet-fibrin thrombus or clot is a highly controlled process that varies, depending on the pathological context. Major signaling pathways in platelets are well established. However, studies with genetically modified mice have identified the contribution of hundreds of additional platelet-expressed proteins in arterial thrombus formation and bleeding. Using phenotype information of 540 mouse genes, involved in arterial thrombosis and hemostasis, we review current insights into established and novel platelet signaling mechanisms. We discuss pathways involved in platelet adhesion, shape change, integrin activation, intracellular vesicle trafficking and protein processing, granule secretion, aggregate formation and procoagulant activity. Specific attention is paid to the signaling routes used by ITAM-linked, ITIM-linked and G protein-coupled receptors, as well as downstream events feeding into GTPase regulation and protein kinase activation. We further summarize known alterations in platelet responses under conditions of venous, inflammatory and infection-dependent thrombosis, taking into account interactions of platelets with the endothelium, leukocytes and red blood cells. Understanding the genes and proteins involved in platelet signaling in the context of hemostasis, thrombosis and inflammation may lead to improved therapies to prevent and treat thrombotic disorders.

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs’ ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generated detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the performance of GPT-4o-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. The GPT-4o-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. This methodology confirmed LLMs’ benefits as a traffic controller in real-world applications. We demonstrated that LLMs can offer precise recommendations to drivers in real time including yielding, slowing, or stopping based on vehicle dynamics. This study demonstrates LLMs’ transformative potential for traffic control, enhancing efficiency and safety at intersections.

Prioritizing working memory resources depends on prefrontal cortex.

How the prefrontal cortex contributes to working memory remains controversial, as theories differ in their emphasis on its role in storing memories versus controlling their content. To adjudicate between these competing ideas, we tested how perturbations to the human (both sexes) lateral prefrontal cortex impact the storage and control aspects of working memory during a task that requires human subjects to allocate resources to memory items based on their behavioral priority. Our computational model made a strong prediction that disruption of this control process would counterintuitively improve memory for low-priority items. Remarkably, transcranial magnetic stimulation of retinotopically-defined superior precentral sulcus, but not intraparietal sulcus, unbalanced the prioritization of resources, improving memory for low-priority items as predicted by the model. Therefore, these results provide direct causal support for models in which the prefrontal cortex controls the allocation of resources that support working memory, rather than simply storing the features of memoranda.Significance statement Although higher-order cognition depends on working memory, the resources that support our memory are severely limited in capacity. To mitigate this limitation, we allocate memory resources according to the behavioral relevance of items. Nonetheless, the neural basis of these abilities remains unclear. Here, we tested the hypothesis that a region in lateral prefrontal cortex controls prioritization in working memory. Indeed, perturbing this region with transcranial magnetic stimulation disrupted the prioritization of working memory resources. Our results provide causal evidence for the hypothesis that prefrontal cortex primarily controls the allocation of memory resources, rather than storing the contents of working memory.

A big data dynamic approach for adaptive music instruction with deep neural fuzzy logic control

BackgroundMusic training for learners has improved greatly in recent years with the inclusion of information technology and optimization methods. The improvements focus on assisted learning, instruction suggestions, and performance assessments.PurposeAn adaptive instructive suggestion method (AISM) using deep neural fuzzy control (FC) is introduced in this paper to provide persistent assistance for technology-based music classrooms. This proposed method reduces learning errors by pursuing instructions based on the learner’s level. The instructions are adaptable depending on the error and level independent of different suggestions. The suggestions are replicated for similar issues across various music learning classrooms, retaining the constant fuzzification.Materials and methodsThe fuzzy control deviates at every new level, and errors are identified over the deviations from the instructions pursued. This control process verifies the input based on instruction deviations to prevent error repetitions. Therefore, the fuzzification relies on error normalization using common adaptive suggestions for different learning sessions. If the fuzzy control fails to match the existing instruction pursued, then new instructions are augmented to reduce errors that serve as the FC constraint. This constraint is pursued by unresolved previous errors to improve learning efficacy.ResultsThus, compared to other methods, the system improves adaptability by 13.9%, efficiency analysis by 9.02%, and constraint detection by 10.26%.

Development of Prototype Software for the Hardware-Software Complex "Marking Inspection"

The results of the analysis of existing approaches to labeling of goods are presented, the requirements of legislation and regulations in this area are considered. Based on the obtained data, a software prototype has been developed that can automate the process of marking verification, which will provide a high level of control and minimize errors. It is expected that the developed prototype will contribute to the efficiency of the labeling process, ensuring not only compliance with regulations, but also improving the overall safety and quality of products offered on the market. The software product is focused on universal application and can be integrated into a variety of business spheres, including production of goods, provision of services, warehouse management and even in areas where a complex process of control over the production and labeling of goods is required.

Mitochondrial fission and fusion in neurodegenerative diseases:Ca2+ signalling.

Neurodegenerative diseases (NDs) are a group of disorders characterized by the progressive loss of neuronal structure and function. The pathogenesis is intricate and involves a network of interactions among multiple causes and systems. Mitochondria and Ca2+ signaling have long been considered to play important roles in the development of various NDs. Mitochondrial fission and fusion dynamics are important processes of mitochondrial quality control, ensuring the stability of mitochondrial structure and function. Mitochondrial fission and fusion imbalance and Ca2+ signaling disorders can aggravate the disease progression of NDs. In this review, we explore the relationship between mitochondrial dynamics and Ca2+ signaling in AD, PD, ALS, and HD, focusing on the roles of key regulatory proteins (Drp1, Fis1, Mfn1/2, and Opa1) and the association structures between mitochondria and the endoplasmic reticulum (MERCs/MAMs). We provide a detailed analysis of their involvement in the pathogenesis of these four NDs. By integrating these mechanisms, we aim to clarify their contributions to disease progression and offer insights into the development of therapeutic strategies that target mitochondrial dynamics and Ca2+ signaling. We also examine the progress in drug research targeting these pathways, highlighting their potential as therapeutic targets in the treatment of NDs.

Performance Evaluation of Time and Cost In The Bungur-Kedoyo Tulungagung Road Construction Project Using The Earned Value Method

Road infrastructure is essential for supporting transportation, fostering economic growth, and enhancing community mobility. However, the Bungur-Kedoyo road construction project in Tulungagung has faced challenges in time and cost management, impacting overall project performance. This study aims to evaluate the project's time and cost performance using the Earned Value Method (EVM), a robust tool that compares planned work, completed work, and actual costs to provide a comprehensive performance analysis. Data from the 25th-week project report revealed delays, with a Schedule Variance (SV) of -Rp. 315,481,514.90 and a Schedule Performance Index (SPI) of 0.970, indicating slower progress than initially planned (SPI < 1). These results highlight the need for improved project scheduling and enhanced control mechanisms to mitigate delays and manage costs effectively. By identifying inefficiencies and areas requiring intervention, the study provides actionable recommendations for improving project management practices. It concludes that integrating EVM into time and cost control processes can significantly enhance project efficiency and reliability. This research contributes to advancing infrastructure development by offering insights that can be applied to similar projects, ensuring timely and cost-effective delivery while promoting sustainable growth in the road construction sector.

Transforming frontiers: The next decade of differential equations and control processes

Mathematics serves as the fundamental basis for innovation, propelling technological advancement. In the forthcoming decade, the convergence of differential equations and control processes is poised to redefine the frontiers of scientific exploration. The integration of artificial intelligence and machine learning with differential equations is set to inaugurate a new era of problem-solving, enabling the extraction of latent physical insights and accelerating solution discovery. Multi-scale modeling, with its capacity to span disparate physical domains, has the potential to resolve long-standing puzzles in fields such as fluid mechanics and nanoscience. Furthermore, the integration of fractal geometry with differential equations holds the promise of novel perspectives for understanding and optimizing complex systems, ranging from urban landscapes to turbulent flows. The integration of artificial intelligence (AI) with control innovations is poised to play a pivotal role in the development of next-generation technologies, with the potential to transform diverse sectors such as medicine, communication, and autonomous systems. This paper explores these developments, highlighting their potential impacts and emphasizing the necessity for interdisciplinary collaboration to leverage their full potential.

Transient Allosteric Regulation of Catalysis by Effector Switching in a Pt2L4 Cage.

The complexity of allosteric enzymatic regulation continues to inspire synthetic chemists seeking to emulate interconnected biological systems. In this work, a Pt2L4cage capable of catalyzing the cyclization reaction of an alkynoic tosyl amide is orthogonally coupled to a diacid-catalyzed carbodiimide-hydration cycle. This new Pt-catalyzed cyclization reaction is demonstrated to exhibit electronic regulation by inclusion of different guest effectors. The orthogonal diacid-catalyzed carbodiimide hydration cycle produces transiently diverse guests that influence the rate of the Pt-catalyzed cyclization reaction to different extents. Further complexity can be introduced to the system through displacing the transiently-formed, weakly bound anhydride guest with the stronger binding fumaronitrile, affecting the catalytic rate to a larger extent for the duration of the orthogonal reaction cycle. The modulation of a Pt-catalyzed cyclization reaction can thus be regulated transiently over the course of the reaction-either up- or down-regulating the turnover frequency (TOF)-via coupling with a temporally controllable orthogonal process. This study demonstrates that principles of allosteric enzymatic regulation can also be applied to simple artificial systems.

Transient Allosteric Regulation of Catalysis by Effector Switching in a Pt2L4 Cage

The complexity of allosteric enzymatic regulation continues to inspire synthetic chemists seeking to emulate interconnected biological systems. In this work, a Pt2L4 cage capable of catalyzing the cyclization reaction of an alkynoic tosyl amide is orthogonally coupled to a diacid‐catalyzed carbodiimide‐hydration cycle. This new Pt‐catalyzed cyclization reaction is demonstrated to exhibit electronic regulation by inclusion of different guest effectors. The orthogonal diacid‐catalyzed carbodiimide hydration cycle produces transiently diverse guests that influence the rate of the Pt‐catalyzed cyclization reaction to different extents. Further complexity can be introduced to the system through displacing the transiently‐formed, weakly bound anhydride guest with the stronger binding fumaronitrile, affecting the catalytic rate to a larger extent for the duration of the orthogonal reaction cycle. The modulation of a Pt‐catalyzed cyclization reaction can thus be regulated transiently over the course of the reaction—either up‐ or down‐regulating the turnover frequency (TOF)—via coupling with a temporally controllable orthogonal process. This study demonstrates that principles of allosteric enzymatic regulation can also be applied to simple artificial systems.

Design of a Novel Bio-Inspired Three Degrees of Freedom (3DOF) Spherical Robotic Manipulator and Its Application in Human–Robot Interactions

Studying the interactions between biological organisms and their environment provides engineers with valuable insights for developing complex mechanical systems and fostering the creation of novel technological innovations. In this study, we introduce a novel bio-inspired three degrees of freedom (DOF) spherical robotic manipulator (SRM), designed to emulate the biomechanical properties observed in nature. The design utilizes the transformation of spherical Complex Spatial Kinematic Pairs (CSKPs) to synthesize bio-inspired robotic manipulators. Additionally, the use of screw theory and the Levenberg–Marquardt algorithm for kinematic parameter computation supports further advancements in human–robot interactions and simplifies control processes. The platform directly transmits motion from the motors to replicate the ball-and-socket mobility of biological joints, minimizing mechanical losses, and optimizing energy efficiency for superior spatial mobility. The proposed 3DOF SRM provides advantages including an expanded workspace, enhanced dexterity, and a lightweight, compact design. Experimental validation, conducted through SolidWorks, MATLAB, Python, and Arduino, demonstrates the versatility and broad application potential of the novel bio-inspired 3DOF SRM, positioning it as a robust solution for a wide range of robotic applications.

Structural equation modeling of the impact of disease activity on inflammatory bowel disease control: the mediating roles of self-efficacy and self-management behaviors

BackgroundMaintaining effective disease control in patients with inflammatory bowel disease (IBD) is both a significant goal and challenge. Drawing on the Common-Sense Model of Self-Regulation (CSM) and related research, this study investigates how IBD activity status influences disease control through both direct and indirect pathways.MethodsA cross-sectional survey was conducted among 310 IBD patients who attended a tertiary general hospital, the leader of the IBD Alliance Group in Chongqing City, between March and August 2024. Structural equation modeling (SEM) was utilized to assess the role and magnitude of various influencing factor pathways. Relying on AMOS26 software, the path effects and magnitude of various factors in the disease control process were analyzed using structural equation modeling (SEM) to test hypothetical models.ResultsA total of 306 valid questionnaires were collected, with a mean IBD-control score of 12.14 ± 3.665. There was a negative link between disease activity and IBD-control (P < 0.01) and a positive correlation between chronic illness management self-efficacy, IBD self-management behavior, and IBD-control (P < 0.01). Path analysis showed that IBD activity negatively predicted IBD control (β = -0.715, P = 0.01). Chronic disease management self-efficacy partially mediated this relationship (β = -0.071, P = 0.012). A significant chain-mediated pathway was identified, where IBD activity affected IBD control via self-efficacy guided by self-management behavior (β = -0.025, P = 0.007). However, the pathway where IBD activity influenced control through self-efficacy and subsequently self-management behavior showed only marginal significance (P = 0.074).ConclusionEffective self-management behaviors improve IBD control. High disease activity may reduce chronic disease management self-efficacy, impairing IBD control. Positive feedback loops involving self-management behaviors and enhanced self-efficacy are crucial for better disease control, as patients who perceive positive outcomes are more motivated to maintain these behaviors.

Quality Control of Furniture Products With SQC and FMEA Methods at UD. XYZ

&lt;p&gt;&lt;strong&gt;In the era of globalization, increasingly fierce industrial competition forces companies to improve product quality and production efficiency. This study is focused on UD. XYZ iron fabrication company, which experienced a bench product defect rate of 13.68%, exceeding the intended tolerance standard of 9%. The purpose of this study is to analyze the quality control processes of bench furniture products at UD. XYZ to reduce the defect rate. For this purpose the writer used the Statistical Quality Control (SQC) and Failure Mode and Effect Analysis (FMEA) methods. Based on the data obtained, there are six types of defects that occur during the production process of furniture bench products with the largest percentage of defects being missing color (19.9%), followed by broken bench pads (19.1%), and broken rubber feet (17.7%). Based on the RPN calculation, the first highest RPN is obtained for the type of missing color defect of 210, which is caused by the lack of worker accuracy. Second, the bench pad broke at 210, which is caused by the worker being too hard when hitting the bench pad. Third, the rubber foot broke by 180 which is caused by the worker hitting the rubber foot too hard. To reduce the defect rate it is recommended for the company management to provide more supervision and training to the workers.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt; – &lt;em&gt;Failure Mode and Effect Analysis, Furniture, Statistic Quality Control, Quality Control.&lt;/em&gt;&lt;/p&gt;

Targeted migration mechanisms of nitrogen-containing pollutants during chemical looping co-gasification of coal and microalgae.

The chemical looping co-gasification of nitrogen-containing algal biomass and coal could effectively realize the high-value utilization of gasification products, but the mechanism of conversion of nitrogen-containing pollutants is not clear. In this work, the effects of the different ratios of microalgae on the co-gasification process were first explored, and the results showed that the 40 % coal + 60 % microalgae blending had the best synergistic effect, with a comprehensive synergistic index (CSI) of 1.35 as the maximum value. The effects of temperature, the ratio of OCs to feed (O/C) and steam flow rate on the evolution of nitrogen-containing species in the co-gasification products were explored based on the optimal mixing ratio, and the results showed that elevating the reaction temperature promotes the generation of nitrogen oxide precursors, and an appropriate amount of steam could inhibit the generation of NOx. The nitrogen in coke mainly came from the Maillard reaction between carbonyl (-CO) compounds and amino (-NH2) compounds and its main forms are pyridine nitrogen (N-5) and pyrrole nitrogen (N-6). Based on the nitrogen accumulation forms in the products of each phase, the mechanism of nitrogen transport and conversion in the chemical looping co-gasification of coal and microalgae (CM-CLCG) process was proposed. This study provides a reference for the synergistic utilization of coal and biomass and the control process of nitrogen-containing pollutants.

Voice Based Hot Cold- Water Dispenser Using Arduino

Technology is a never-ending process. To be able to design a product using the current technology that will be valuable to the lives of others is a huge contribution to the neighbourhood. Voice Based Water Dispenser Automation method using controller is the plan will be very useful for old age people and disabled people, basically one’s who cannot achieve basic actions efficiently. It is the idea Corresponds to the new area of automation and technology. This presents the design and implementation of a low cost but flexible Secure voice based hot and cold-water dispenser system. The Between the cell phone and the controller board is wireless. Voice Command sends from mobile to the micro controller, to understand whether the water required by the person should be hot or cold. The Micro controller processes the information to the IR sensor to Determine where the glass is placed below the pipe or not. The method uses IR sensors to detect the presence of stream beaker and then the IR sensor sends the signal to the micro controller about the presence of the glass, accordingly the motor starts and the Water flows though the pipes from the particular jar (hot/cold). Key Words: IR sensor, Water glass, Micro controller, Blue-tooth.

DOE Robot Perspective: Past, Present, and Future

The master-slave robotics system was invented at Argonne National Laboratory in 1948 to protect an operator from the harmful effects of radiation. Worker safety has been the highest priority at the U.S. Department of Energy (DOE) as it continues its cleanup mission. Safety is achieved by separating the human from the nuclear source and shielding that source to a manageable level. Robotics with remote control is a natural solution for many of DOE’s cleanup tasks. Unlike conventional robotics, a remote-handling system always involves a human being within the control process. The main handling device is a manipulator because the majority of remote-handling tasks need the intuition and intelligence of a human operator. In this paper, we take a historical perspective to the technology when exploring robotic systems from the past, on what we are currently doing, and what technologies would enable new capabilities for the future. We look at past successes and failures, and we glean lessons learned that can be applied to current and upcoming robotic activities within DOE. We also take a look at some new and emerging technologies that when matured could have a positive impact on where robotics within DOE will be needed.

Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm

Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.

A steel scrap recognition model based on machine vision

Image recognition, a subset of artificial intelligence, has been applied to various industries, including steel recycling, to enhance efficiency and accuracy in quality control processes. This review paper provides an overview of the current state of image recognition technology in steel recycling, including the types of images used, the algorithms employed, and the benefits and limitations of the technology. The paper also discusses potential future directions for image recognition in steel recycling, such as integrating machine learning and deep learning frameworks to improve accuracy and developing mobile applications for on-site quality control. Overall, image recognition technology has shown great potential in the steel recycling industry, and further research and development in this field could lead to significant improvements in efficiency and quality control. Experimental results show that the precision of steel scrap classification is 0.92, and the precision of steel scrap quality judgment is 0.87. The empirical findings indicate that this technique can swiftly and precisely detect the type of steel scrap and evaluate its quality, it can also identify the existence of dangerous goods. The model can directly help enterprises reduce costs and increase efficiency, is conducive to the full recovery of scrap steel, and positively affects environmental protection and enterprise profits.