Hasil untuk "Automation"

Astrid Heusel, Hubert Gabrys, Lotte Wilke et al.

Introduction and background: Same-day radiotherapy, integrating consultation, simulation, planning, and treatment within a single day, is an emerging approach in radiation oncology. Advances in imaging integration, automation, and adaptive planning have enabled shorter timelines between consultation and treatment initiation, but prospective clinical evidence remains limited. This systematic review evaluates indications, feasibility, treatment accuracy, and patient satisfaction with same-day radiotherapy. Materials and methods: A systematic literature search was conducted in August 2025 using PubMed, Embase, and the Cochrane Library in accordance with PRISMA 2020 guidelines. Eligible studies included prospective or observational reports of radiotherapy for solid malignancies delivered on the same day as treatment planning. Results: Of 124 identified records, 13 studies met inclusion criteria. Most were prospective (n = 9), with four retrospective studies. Median study size was 39 patients, with a median ECOG performance status of 1. Treatment intent was predominantly palliative (54%), followed by curative (31%) and ablative (15%). Bone metastases were the most common indication; curative treatments included lung, prostate, brain, breast, and uveal tumors. Stereotactic radiotherapy (54%) and hypofractionated external-beam radiotherapy (38%) were most frequently used. Procedure times ranged from 25 to 470 min. Patient satisfaction, reported in three studies, was consistently good. Treatment accuracy was only assessed in three (23%) studies. Conclusion: Current evidence supports the feasibility of same-day radiotherapy, particularly in palliative settings, with favorable patient satisfaction and robust treatment accuracy, where reported. Larger prospective studies are required to standardize workflows and assess outcomes in curative settings, as well as health-system, financial, and legal implications.

Ahmad Rahdari, Elham Keshavarz, Ehsan Nowroozi et al.

The increasing need to process large, high-dimensional datasets and the substantial computational power required have made the use of distributed cloud servers essential. These servers provide cost-effective solutions that make storage and computing accessible to ordinary users. However, they might face significant vulnerabilities, including data leakage, metadata spoofing, insecure programming interfaces, malicious insiders, and denial of service. To gain public trust in distributed computing, addressing concerns related to privacy and security while ensuring high performance and efficiency is crucial. Multiparty computation, differential privacy, trusted execution environments, and federated learning are the four major approaches developed to address these issues. This survey paper reviews and compares these four approaches based on a structured framework, by highlighting recent top-tier research papers published in prestigious journals and conferences. Particular attention is given to progress in federated learning, which trains a model across multiple devices without sharing the actual data, keeping data private and secure. The survey also highlights federated learning techniques, including secure federated learning, by detecting malicious updates and privacy-preserving federated learning via data encryption, data perturbation, and anonymization, as new paradigms for building responsible computing systems. Finally, the survey discusses future research directions for connecting academic innovations with real-world industrial applications.

Anja Kunic, Davide Angeletti, Giuseppe Marrone et al.

Matthew Benson, Ryan W. Parker, Melisa Ekin Gulseren et al.

The next generation of infrared (IR) sensors may enable unprecedented applications in fields like spectroscopy, health monitoring, and communication systems. For instance, metasurface-enhanced micro-electromechanical system (MEMS)-based IR sensors have demonstrated excellent performance in terms of responsivity and spectral-selectivity. However, it is burdensome to experimentally determine the performance limits of this and other IR sensing technologies as it requires time-consuming and expensive systematic testing not always feasible in research settings. To address this challenge, an automated solution for characterizing miniaturized sensing devices is presented in this paper and applied to experimentally determine the performance limits of MEMS-based IR sensors. The system offers low-cost, rapid, and automated characterization of on-chip IR sensors, determining key performance metrics such as noise, responsivity, and noise-equivalent power. The platform is flexible, easily adapted to different types of devices, chip layouts, and light sources, and is designed to test a large number of sensors within the same wafer &#x2212;spending ~20 seconds per device&#x2212; using a combination of optical and radiofrequency interrogation techniques. The system has been applied to test over 1500 MEMS-based IR sensors. Collected data revealed hidden trade-offs between responsivity and noise with respect to the device thickness and allowed a statistical analysis of sensing response versus device geometrical dimensions. The best-performing devices exhibit a quality factor, responsivity, fluctuation induced noise, and noise-equivalent power of 2391, 164 Hz/nW, 0.257 Hz/<inline-formula> <tex-math notation="LaTeX">$\sqrt {\mathbf {Hz}}$ </tex-math></inline-formula>, and 5.01 pW/<inline-formula> <tex-math notation="LaTeX">$\sqrt {\mathbf {Hz}}$ </tex-math></inline-formula> respectively. The proposed automated platform provides an efficient and cost-effective solution for characterizing the next generation of IR sensing devices.

HUANG Junbo, LUO Zhexuan, LI Junpeng et al.

Navigation and positioning technology is the core technology for UAV to perform tasks stably. In this paper, the tight integrated navigation algorithm of Inertial Navigation System (INS) and Ultra Wide Band (UWB) is studied to meet the needs of UAV indoor navigation and positioning. Firstly, the strapdown inertial navigation system and UWB positioning system are modeled. Then, the loose combination and tight combination of UWB and INS are designed to fuse the information of UWB and INS, restrain the drift error of INS, and reduce the impact of non line of sight environment and communication interruption on the accuracy of UWB positioning system. The state equation and observation equation of UWB/IMU loose and tight integrated navigation algorithm are designed. Finally, a simulation experiment is designed to compare the tight combination with loose combination of UWB and INS. The simulation experiment proves that the tight combination of UWB with INS has higher precision and stronger anti-interference ability than the loose combination.

Alberto Hernández-Marcos, Eduardo Ros

Abstract In nature, intelligent living beings have developed emotions to modulate their behavior as a fundamental evolutionary advantage. However, researchers seeking to endow machines with this advantage lack a clear theory from cognitive neuroscience describing emotional elicitation from first principles, namely, from raw observations to specific affects. As a result, they often rely on case-specific solutions and arbitrary or hard-coded models that fail to generalize well to other agents and tasks. Here we propose that emotions correspond to distinct temporal patterns perceived in crucial values for living beings in their environment (like recent rewards, expected future rewards or anticipated world states) and introduce a fully self-learning emotional framework for Artificial Intelligence agents convincingly associating them with documented natural emotions. Applied in a case study, an artificial neural network trained on unlabeled agent’s experiences successfully learned and identified eight basic emotional patterns that are situationally coherent and reproduce natural emotional dynamics. Validation through an emotional attribution survey, where human observers rated their pleasure-arousal-dominance dimensions, showed high statistical agreement, distinguishability, and strong alignment with experimental psychology accounts. We believe that the framework’s generality and cross-disciplinary language defined, grounded on first principles from Reinforcement Learning, may lay the foundations for further research and applications, leading us toward emotional machines that think and act more like us.

Khushvaktov Saydulla, Djumanov Jamoljon, Anorboev Erkin et al.

This article explores the integration of modern information technologies, such as Geographic Information Systems (GIS) and automated water meter devices, into daily water distribution practices. By leveraging these advanced tools, agricultural managers can significantly enhance their operational decision-making processes, especially in response to dynamic changes in irrigation circuit conditions. The implementation of GIS allows for detailed spatial analysis and visualization of water resources, while automated water meters provide real-time data on water consumption and availability. This combination not only improves resource management but also facilitates timely adjustments based on various factors, including water availability and weather parameters. As conditions change, such as rainfall or temperature fluctuations, operators are better equipped to make informed decisions that optimize water use and minimize waste. Ultimately, the incorporation of these technologies represents a transformative shift towards smarter irrigation practices, ensuring sustainable water distribution and improved agricultural productivity in an era of increasing environmental challenges.

Grzegorz Tytko, Wuliang Yin, Yao Luo et al.

Eddy current testing of tubes made of materials conducting electric current enables scrutinising their structure, geometric dimensions, and detecting flaws, thus limiting the risk of leakage and failure. This paper deals with investigating the feasibility of performing effective inspections in which the sensor is inserted into the interior of the tube. In the first step, a numerical model was created using the finite element method, and afterwards, two I-core sensors with a similar number of turns but of different geometrical dimensions were constructed. Both sensors have a removable core, thanks to which the influence of the core on the impedance of the sensors was examined. The utilised ferrite cores were of two types: with an air gap and without an air gap. The measurements were carried out for tubes made of magnetic and non-magnetic steel. Defects of different shape and different geometric dimensions were made in the tubes. In all cases, the changes in resistance and reactance for the I-core sensor were many times greater than for the air-core sensor. At the same time, the obtained results confirmed the effectiveness of internal inspections of conductive tubes with the use of the constructed sensor.

Sarah Khavandi, Fatema Zaghloul, Aisling Higham et al.

BackgroundWhile digital health innovations are increasingly being adopted by health care organizations, implementation is often carried out without considering the impacts on frontline staff who will be using the technology and who will be affected by its introduction. The enthusiasm surrounding the use of artificial intelligence (AI)–enabled digital solutions in health care is tempered by uncertainty around how it will change the working lives and practices of health care professionals. Digital enablement can be viewed as facilitating enhanced effectiveness and efficiency by improving services and automating cognitive labor, yet the implementation of such AI technology comes with challenges related to changes in work practices brought by automation. This research explores staff experiences before and after care pathway automation with an autonomous clinical conversational assistant, Dora (Ufonia Ltd), that is able to automate routine clinical conversations. ObjectiveThe primary objective is to examine the impact of AI-enabled automation on clinicians, allied health professionals, and administrators who provide or facilitate health care to patients in high-volume, low-complexity care pathways. In the process of transforming care pathways through automation of routine tasks, staff will increasingly “work at the top of their license.” The impact of this fundamental change on the professional identity, well-being, and work practices of the individual is poorly understood at present. MethodsWe will adopt a multiple case study approach, combining qualitative and quantitative data collection methods, over 2 distinct phases, namely phase A (preimplementation) and phase B (postimplementation). ResultsThe analysis is expected to reveal the interrelationship between Dora and those affected by its introduction. This will reveal how tasks and responsibilities have changed or shifted, current tensions and contradictions, ways of working, and challenges, benefits, and opportunities as perceived by those on the frontlines of the health care system. The findings will enable a better understanding of the resistance or susceptibility of different stakeholders within the health care workforce and encourage managerial awareness of differing needs, demands, and uncertainties. ConclusionsThe implementation of AI in the health care sector, as well as the body of research on this topic, remain in their infancy. The project’s key contribution will be to understand the impact of AI-enabled automation on the health care workforce and their work practices. International Registered Report Identifier (IRRID)PRR1-10.2196/49374

LI Junshi

The electromagnetic pilot valve is one of the core components for realizing unmanned mining in the fully mechanized working face. The current mine electromagnetic pilot valve test methods use emulsion pump as a liquid source. The power consumption is large. In addition, the system pressure regulation uses manual adjustment, which is inefficient. In order to solve the above problems, a low-power test platform for reversing characteristics of mine electromagnetic pilot valve is designed. The platform consists of two parts: hydraulic system and measurement and control system. The hydraulic system mainly includes electric proportional valve, gas-liquid booster pump, valve to be tested, and accumulator. The double-headed gas-liquid booster pump is used as the liquid source so as to reduce energy consumption. The electric proportional valve is used to adjust the inlet air pressure of the gas-liquid booster pump. The inlet pressure of the tested valve is adjusted to the required value to realize the automatic regulation of the system pressure. The measurement and control system consists of upper computer, acquisition card, program control power supply, electric proportional valve controller (PID controller), and various sensors. According to the requirements of sensor installation and precision, the laser displacement sensor is selected to test the displacement of the electromagnet ejector rod. The PID controller is used to adjust the pressure of the liquid inlet of the electromagnetic pilot valve. The platform can realize the real-time dynamic monitoring and data storage of various performance indexes of the electromagnetic pilot valve in the reversing process. The indexes include voltage, current, inlet and outlet pressure, ejector rod displacement, dynamic response time and real-time power consumption, etc. The maximum power is only 800 W, which improves the test safety and efficiency, greatly reduces the system energy consumption, and provides an efficient and reliable test and verification means for researchers.

Stephan Allgeier, Andreas Bartschat, Sebastian Bohn et al.

Abstract The morphometric assessment of the corneal subbasal nerve plexus (SNP) by confocal microscopy holds great potential as a sensitive biomarker for various ocular and systemic conditions and diseases. Automated wide-field montages (or large-area mosaic images) of the SNP provide an opportunity to overcome the limited field of view of the available imaging systems without the need for manual, subjective image selection for morphometric characterization. However, current wide-field montaging solutions usually calculate the mosaic image after the examination session, without a reliable means for the clinician to predict or estimate the resulting mosaic image quality during the examination. This contribution describes a novel approach for a real-time creation and visualization of a mosaic image of the SNP that facilitates an informed evaluation of the quality of the acquired image data immediately at the time of recording. In cases of insufficient data quality, the examination can be aborted and repeated immediately, while the patient is still at the microscope. Online mosaicking also offers the chance to identify an overlap of the imaged tissue region with previous SNP mosaic images, which can be particularly advantageous for follow-up examinations.

Jianhao Yin, Junfeng Wu, Chunqi Gao et al.

With the continuous development of industrial aquaculture and artificial intelligence technology, the trend of the use of automation and intelligence in aquaculture is becoming more and more obvious, and the speed of the related technical development is becoming faster and faster. Individual fish recognition could provide key technical support for fish growth monitoring, bait feeding and density estimation, and also provide strong data support for fish precision farming. However, individual fish recognition faces significant hurdles due to the underwater environment complexity, high visual similarity of individual fish and the real-time aspect of the process. In particular, the complex and changeable underwater environment makes it extremely difficult to detect individual fish and extract biological features extraction. In view of the above problems, this paper proposes an individual fish recognition method based on lightweight convolutional neural network (LIFRNet). This proposed method could extract the visual features of underwater moving fish accurately and efficiently and give each fish unique identity recognition information. The method proposed in this paper consists of three parts: the underwater fish detection module, underwater individual fish recognition module and result visualization module. In order to improve the accuracy and real-time availability of recognition, this paper proposes a lightweight backbone network for fish visual feature extraction. This research constructed a dataset for individual fish recognition (DlouFish), and the fish in dataset were manually sorted and labeled. The dataset contains 6950 picture information instances of 384 individual fish. In this research, simulation experiments were carried out on the DlouFish dataset. Compared with YOLOV4-Tiny and YOLOV4, the accuracy of the proposed method in fish detection was increased by 5.12% and 3.65%, respectively. Additionally, the accuracy of individual fish recognition reached 97.8%.

DING Zhen1, ZHANG Yuchen2

The environment of the fully mechanized working face in coal mines is harsh and the dust concentration is high. When ultra wide band (UWB) is used for positioning, it is easy to be interfered by dust. UWB ranging will have certain errors, so the positioning precision cannot meet the requirements of intelligent coal mines. To order to solve this problem, the principle of UWB ranging is analyzed, and it is pointed out that the main factors affecting the precision of UWB ranging are multipath effect and non-line-of-sight propagation. The generation of multipath effect and non-line-of-sight propagation is closely related to dust concentration, and the influence of dust concentration on the precision of UWB ranging in coal mines is explored through experiments. The experimental results show that the UWB ranging error increases as the dust concentration increases, and the growth rate of UWB ranging error accelerates.

DAVID PROCHÁZKA

Accounting and financial and non-financial reporting in the digital context Motivation Accounting is a product of an ever-changing environment, and at the same time, it interacts with it. Social, economic, cultural, and technological changes have a significant impact on how accounting is theorized, how research is conducted, how accounting policy is shaped on a macro scale, and how accounting functions in business practice. Half a century ago, accountants never dreamed that part of their work would be automated, nor that tabular numerical combinations, which constituted the main effect of their work, would one day be just one of many elements of extensive financial and non-financial reporting. Times have changed. The economy has gained a global and digital dimension, economic phenomena have become more complex, social and environmental awareness (including sustainability and circular economy issues) has increased, and new technologies have opened up extraordinary opportunities for people in terms of information processing and communication. These changes have had a significant impact on accounting and corporate reporting. In some sense, it matured, moving from recording "real" and "objective" economic transactions and presenting the effects of these events in a synthetic tabular form for recording, measuring, and describing economic as well as non-financial phenomena, the essence of which is often difficult even for professional accountants. The common feature of all these changes is the increasing emphasis on communication in accounting and corporate reporting. Accounting communication may be analyzed in numerous aspects: linguistic, visual, technological, cultural, or sociological. The articles submitted for the special edition will be aimed at discussing these aspects. Suggested topics We invite papers addressing the following research questions of immense interest: 1. How is financial and non-financial information reported by firms, and how is it processed by users? 2. What is the impact of new technologies and digitalization on accounting communication? 3. Does the increasing use of language and narratives in accounting reports improve communication? 4. How do economic, social, and cultural changes shape financial and non-financial reporting? 5. What are the opportunities and threats resulting from the digitalization and automation of accounting? 6. What is the current role of management accounting in creating financial and non-financial reports? 7. How does financial reporting reflect the non-financial aspects of business activity? 8. What should be the role of the professional accountant in contemporary business? 9. Do accounting standards (e.g., IFRS) respond to the challenges of the contemporary economy? 10. What is the future of financial and non-financial reporting? The articles (ca. 32,000-44,000 characters) may present the results of research conducted using different methods, e.g., theoretical approaches or empirical approaches (surveys, case studies, experiments, archival research). We want to underline that the articles should pertain to the theme of the special issue of ZTR and should not have been published earlier in hard copy nor electronic version, whether in magazines, books, or conference materials. The deadline for submitting papers is 30th June 2022. The accepted papers will be published in 2022 (ZTR, vol. 46, no. 4). Each manuscript should be prepared in accordance with the guidance given in the section "Instructions for authors" of our website: http://ztr.skwp.pl/resources/html/cms/FORAUTHORS. Articles should be submitted through the online ICI Publishers Panel: http://ztr.skwp.pl/resources/html/cms/DEPOSITSMANUSCRIPT When submitting an article, please enter that your article is for Special Issue 2022 with Guest Editor in the additional comment in the Publishers Panel Index Copernicus system. The coordinator of the Special Issue of ZTR with Guest Editor is Dr hab. Marek Masztalerz, prof. UEP (ztr@skwp.pl)

Heather Johnson

The purpose of this paper is to conduct a quantitative, integrative and systematic literature review of the moderating effects of dynamic capability associated with radical innovation and incremental innovation teams in the global pharmaceutical biotechnology industry. This paper utilizes a conceptual framework of dynamic capability and socio-technical theory to underpin the study. The study includes 250 articles which were originally surveyed, and then a final selection of 66 articles was based on a structured coding system. The study outcome reveals that knowledge sharing strengthens existing professional knowledge and enhances internal work coordination and consistency in employees’ behavior, and effectively integrates diverse team knowledge and experience. Open innovation has a positive effect on radical innovation and enables knowledge acquisition to form a symbiotic relationship with knowledge sharing. Learning orientation has a stronger effect on incremental innovation than on radical innovation. The limitations of the study are related to a systematic literature review for this research does not establish causality. The mediating effects of dynamic capability on teams are not explored for this research. The implications for management are as follows, teams must be given the autonomy to make decisions from a technical perspective. Tacit knowledge, open innovation, knowledge acquisition and learning orientation are areas in which priority must be given during and after acquisitions in the pharmaceutical biotechnology industry.

Ye Wu, Pengfei Ou, Jun Song et al.

Development of nanoparticles with multi-functionalities is of great importance. In this study, praseodymium sulfide (Pr _2 S _3 ) and molybdenum sulfide (MoS _2 ) nanoparticles were synthesized. The structural, morphological and optical properties of the as-obtained products were investigated by XRD, XPS, TEM, UV–vis-NIR spectroscopy, and photoluminescence spectroscopy. Pr _2 S _3 is found to be used in selective photodegradation of fluorescein sodium salt. MoS _2 can be utilized for selective photodegradation of rhodamine B. In the mixture of rhodamine B, fluorescein sodium salt and rhodamine 6 G, most of rhodamine B and part of fluorescein sodium salt are optically degraded by Pr _2 S _3 . In the mixture of rhodamine B, fluorescein sodium salt and rhodamine 6 G, part of fluorescein sodium salt and most of rhodamine B is degraded by MoS _2 . Moreover, they emit near-infrared fluorescence (800–1100 nm) when excited by the 785 nm light. Deep tissues imaging with high-contrast is shown, utilizing a nanoparticle-filled centrifuge tube covered with animal tissues (pig Bacon meat). Maximum imaging depth below the tissue surface of 1 cm is achieved. Our work provides a rapid yet efficient procedure to make nanoparticles for dual-application-potential in dye-photodegradation and near-infrared deep tissue imaging.