Large Language Models show promise in emotion understanding, social reasoning, and empathy, yet they struggle with psychologically grounded tasks that require inferring implicit mental states in context-rich, ambiguous settings. These limitations arise from the absence of theory-aligned supervision and the difficulty of capturing nuanced mental processes in real-world narratives. To address this gap, we leverage expert-labeled, psychologically rich scenarios and propose a trajectory-aware reinforcement learning framework that explicitly imitates expert psychological thought patterns. By integrating real-world stimuli with structured reasoning guidance, our approach enables compact models to internalize social-cognitive principles, perform nuanced psychological inference, and support continual self-improvement. Comprehensive experiments across multiple benchmarks further demonstrate that our models achieve expert-level interpretive capabilities, exhibiting strong out-of-distribution generalization and robust continual learning across diverse, challenging, and psychologically grounded tasks.
Accelerating the deep transformation and upgrading of industrial structure and forming new quality productive forces are essential components for China to achieve the great rejuvenation of the Chinese Dream. After more than 40 years of rapid development, China has entered the "new normal" of development, making the advancement of new quality productive forces an urgent task. This paper reviews the evolution of China's industrial structure, argues the necessity for a new round of deep industrial transformation, and explores the impact of industrial structure transformation and upgrading on the level of new quality productive forces using various methods. The research findings are as follows:(1)The deep transformation and upgrading of the industrial structure can significantly promote the development of new quality productive forces, but there are obvious regional differences.(2)The core indicator of the improvement in the level of new quality productive forces is the enhancement of total factor productivity. Furthermore, this paper summarizes past industrial development processes and the challenges faced, and analyzes and discusses the potential challenges that may arise in promoting the development of new quality productive forces through deep industrial structure transformation, based on empirical research results.
In industrial environments, data acquisition accuracy is crucial for process control and optimization. Wireless telemetry has proven to be a valuable tool for improving efficiency in well-testing operations, enabling bidirectional communication and real-time control of downhole tools. However, high sampling frequencies present challenges in telemetry, including data storage, transmission, computational resource consumption, and battery life of wireless devices. This study explores how optimizing data acquisition strategies can reduce aliasing effects and systematic errors while improving sampling rates without compromising measurement accuracy. A reduction of 80% in sampling frequency was achieved without degrading measurement quality, demonstrating the potential for resource optimization in industrial environments.
Christian Internò, Andrea Castellani, Sebastian Schmitt
et al.
Industrial Non-Intrusive Load Monitoring (NILM) is limited by the scarcity of high-quality datasets and the complex variability of industrial energy consumption patterns. To address data scarcity and privacy issues, we introduce the Synthetic Industrial Dataset for Energy Disaggregation (SIDED), an open-source dataset generated using Digital Twin simulations. SIDED includes three types of industrial facilities across three different geographic locations, capturing diverse appliance behaviors, weather conditions, and load profiles. We also propose the Appliance-Modulated Data Augmentation (AMDA) method, a computationally efficient technique that enhances NILM model generalization by intelligently scaling appliance power contributions based on their relative impact. We show in experiments that NILM models trained with AMDA-augmented data significantly improve the disaggregation of energy consumption of complex industrial appliances like combined heat and power systems. Specifically, in our out-of-sample scenarios, models trained with AMDA achieved a Normalized Disaggregation Error of 0.093, outperforming models trained without data augmentation (0.451) and those trained with random data augmentation (0.290). Data distribution analyses confirm that AMDA effectively aligns training and test data distributions, enhancing model generalization.
Anna M. Krol, Marvin Erdmann, Rajesh Mishra
et al.
In this paper, we show the design and implementation of a quantum algorithm for industrial shift scheduling (QISS), which uses Grover's adaptive search to tackle a common and important class of valuable, real-world combinatorial optimization problems. We give an explicit circuit construction of the Grover's oracle, incorporating the multiple constraints present in the problem, and detail the corresponding logical-level resource requirements. Further, we simulate the application of QISS to specific small-scale problem instances to corroborate the performance of the algorithm, and we provide an open-source repository with our code, available on github.com/anneriet/QISS . Our work shows how complex real-world industrial optimization problems can be formulated in the context of Grover's algorithm, and paves the way towards important tasks such as physical-level resource estimation for this category of use cases.
Industrial Internet of Things (IIoT) technologies have revolutionized industrial processes, enabling smart automation, real-time data analytics, and improved operational efficiency across diverse industry sectors. IIoT testbeds play a critical role in advancing IIoT research and development (R&D) to provide controlled environments for technology evaluation before their real-world deployment. In this article, we conduct a comprehensive literature review on existing IIoT testbeds, aiming to identify benchmark performance, research gaps and explore emerging trends in IIoT systems. We first review the state-of-the-art resource management solutions proposed for IIoT applications. We then categorize the reviewed testbeds according to their deployed communication protocols (including TSN, IEEE 802.15.4, IEEE 802.11 and 5G) and discuss the design and usage of each testbed. Driven by the knowledge gained during this study, we present suggestions and good practices for researchers and practitioners who are planning to design and develop IIoT testbeds for connectivity research.
The multifaceted nature of body experience, encompassing affective, cognitive, and perceptual components, plays a critical role in the development and persistence of conditions such as anorexia nervosa (AN). Recent research attempted to address such disturbance to improve therapeutical effectiveness, using mirror exposure (ME) and body-swapping (BS) techniques in immersive virtual reality (VR). Procedures from a third-person perspective—as mirror-like experiences—were primarily used to address bodily cognitive and affective components, whereas first-person ones—as BS—were used to correct distorted body perception. This study investigated the effect of spatial reference frame on body self-consciousness and body perception during BS. Data revealed two main findings: Even though embodiment was significantly enhanced when participants engaged with a virtual body from a first-person viewpoint, both egocentric and allocentric perspectives influenced body size estimation (BSE), suggesting that both spatial perspectives critically form and sustain body self-perception. We discussed predictive coding theory to explain spatial perspective influence on body experience, highlighting the potential therapeutic benefits of incorporating first and third-person perspectives on body illusions (BIs) for more effective interventions targeting body-related disturbances.
Emma R. Douma, Svenja Wirtz, Manuela Sestayo Fernandez
et al.
Background: Cardiac rehabilitation (CR) reduces recurrent cardiac events and mortality in patients with cardiovascular diseases (CVD). Innovative eHealth methods can facilitate CR uptake and effectiveness by addressing barriers associated with clinic-based rehabilitation. Tailoring eHealth-based CR to patient preferences is needed to further enhance CR. Purpose: To identify preferred behavior change techniques (BCTs) as well as barriers and facilitators for the different health behaviors targeted in eHealth-based CR among patients who have been referred to CR. Methods: Thirty-nine patients were interviewed in nine focus groups in The Netherlands, Germany, and Spain. A thematic analysis, using a combined deductive and inductive approach to coding, was conducted to identify BCTs and barriers and facilitators to behavior change. Behaviors under investigation included physical activity, medication adherence, eating a cardiac healthy-diet, stress reduction and smoking cessation. Results: The perceived helpfulness of BCTs depended on the specific behavior targeted. Common barriers were negative emotional state and physical limitations. A desire to feel physically or mentally well and having experienced a cardiac life event were the most common facilitators across health behaviors. Specific BCTs, barriers and facilitators were found for each of the health behavior. Conclusions: Behavior change techniques that patients preferred for each health behavior targeted in eHealth-based CR were identified. A negative emotional state, experiencing a life event, and improving physical functioning are important barriers and facilitators in multiple behaviors targeted in eHealth-based CR programs. Additional tailoring of interventions to patient preferences for BCTs and patient-specific barriers and facilitators per health behavior could lead to further improvement of eHealth-based CR.
Thembekile K. Khoza, Tshegofatso Mabitsela, Petrus Nel
Orientation: Organisations are increasingly integrating technology to enhance employee productivity and strategic goals. However, the acceptance of such technology is dependent on employees’ attitudes.
Research purpose: This study seeks to understand: (1) the influence of employees’ levels of technology readiness and technology acceptance on their work engagement and (2) whether technology acceptance mediates the relationship between technology readiness and work engagement.
Motivation for the study: Drawing from the Technology Readiness and Acceptance Model (TRAM), the Worker-Centric Design and Evaluation Framework for Operator 4.0 and the Job Demands-Resources (JD-R) theory, we posit that technology readiness and acceptance function as resources aiding employees in effectively navigating workplace technology, potentially fostering greater engagement.
Research approach/design and method: Using a quantitative, cross-sectional approach, we examined 143 working adults’ technology readiness, technology acceptance and work engagement. Structural equation modelling (Partial Least Squares) guided the evaluation of hypotheses.
Main findings: Results revealed a positive relationship between technology readiness and work engagement. Interestingly, technology acceptance showed a negative link with work engagement. Furthermore, technology acceptance partly mediated the direct link between technology readiness and work engagement. Notably, the significant and negative indirect effect suggests a potential suppressor variable role for technology acceptance.
Practical/managerial implications: Practically, organisations should recognise the dual impact of technology on work engagement. When technology is perceived as a demand, interventions are crucial to counteract its adverse effects on engagement.
Contribution/value-add: The study brings a multidisciplinary perspective to understanding how personal resources in the face of workplace technology can foster work engagement.
This study reports the results of applying the cross-lingual bug localization approach proposed by Xia et al. to industrial software projects. To realize cross-lingual bug localization, we applied machine translation to non-English descriptions in the source code and bug reports, unifying them into English-based texts, to which an existing English-based bug localization technique was applied. In addition, a prototype tool based on BugLocator was implemented and applied to two Japanese industrial projects, which resulted in a slightly different performance from that of Xia et al.
Cyberattacks on industrial control systems (ICS) have been drawing attention in academia. However, this has not raised adequate concerns among some industrial practitioners. Therefore, it is necessary to identify the vulnerable locations and components in the ICS and investigate the attack scenarios and techniques. This study proposes a method to assess the risk of cyberattacks on ICS with an improved Common Vulnerability Scoring System (CVSS) and applies it to a continuous stirred tank reactor (CSTR) model. The results show the physical system levels of ICS have the highest severity once cyberattacked, and controllers, workstations, and human-machine interface are the crucial components in the cyberattack and defense.
Sophie A Derksen, Xenia Brancart, Gina Rossi Department of Psychology, Personality and Psychopathology Research Group (PEPS), Vrije Universiteit Brussel (VUB), Brussels, BelgiumCorrespondence: Sophie A Derksen, PE-PEPS, Vrije Universiteit Brussel (VUB), Pleinlaan 2, Brussels, 1050, Belgium, Email sophiederksen@outlook.comIntroduction: With a rapidly aging population, age-neutrality of psychological instruments is becoming ever more important for accurate assessment of older adults. This study aims to test the age-neutrality of the Dutch Behavioral Inhibition System/Behavioral Approach System (BIS/BAS) scales by means of Differential Item Functioning (DIF) and Differential Test Functioning (DTF) analyses.Methods: DIF and DTF analyses were conducted using an odds ratio approach. Potential DIF was examined for the two primary scales and three BAS subscales in 390 Dutch-speaking participants divided over three age groups.Results: Overall, the BIS-BAS scales lacked age-neutrality when comparing older adults to young adults: eight out of 20 items (ie, 40%) displayed DIF according to the adjusted Bonferroni corrected cut-offs, which exceeds the threshold of 25% for large DIF. Thus, for 40% of the test items, items were differently endorsed by young and older adults with the same position on the construct measured by the item. Therefore, the implications of the item-level DIF on the scale level were investigated across age groups. DTF analyses revealed large DTF for all BIS and BAS scales according to the adjusted Bonferroni corrected cut-offs.Discussion: DIF found in items on the BIS scale and BAS-Drive and BAS-Fun Seeking scale can probably be explained by a difference in degree of expression across age groups. A solution could be the development of age-specific norms. DIF on BAS-Reward Responsiveness subscale could be partly due to measuring a different construct across age groups. Rephrasing or replacing such items with DIF may increase age-neutrality of the BIS/BAS Scales.Keywords: older adults, BIS/BAS questionnaire, personality, assessment, differential item functioning
This study aims to analyse the influence of executive coaching on business results, in line with the Balanced Scorecard framework. First, the findings from general management literature are integrated to design a framework for assessing the impact of executive coaching on business results. This methodology was tested with a within subject, ABA single case design of an Industrial Director, incorporating the vision of organizational stakeholders and including objective management data covering two years. The process unravels the underlying mechanism by which organizational results of executive coaching are obtained, thereby providing method for future application to the assessment of executive coaching outcomes.
Special aspects of education, Industrial psychology
Xuan Zheng, Ling Zhang Department of Human Resources Management, Shanghai University of Finance and Economics, Shanghai, People’s Republic of ChinaCorrespondence: Ling Zhang, Department of Human Resource Management, College of Business, Shanghai University of Finance and Economics, 100 Wudong Road, Shanghai, 200433, People’s Republic of China, Email ling@163.sufe.edu.cnPurpose: By integrating the theory of workplace anxiety with a self-regulatory theory, this study investigates the mediating role of workplace anxiety in the relationship between leader bottom-line mentality and abusive supervision targeting the follower, as well as the moderating effect of rules climate on leaders’ emotion-behavior process.Methods: A total of 393 employees and 91 supervising managers participated in the multi-source, two-wave survey. To test our hypotheses, we performed the regression analysis and conducted bootstrapping analyses using the Hayes PROCESS Model.Results: Findings indicated that leader bottom-line mentality has a positive indirect relationship with abusive supervision via workplace anxiety. In addition, rules climate weakens abusive supervision for workplace anxiety, revealing a significant moderate effect.Conclusion: Leaders with high BLM encapsulate apprehension and tension, which are deficient in fostering workplace anxiety as an important affect process that facilitates abusive supervision. During this process, we identify rules climate as an important boundary condition for our model and theorization.Keywords: bottom-line mentality, workplace anxiety, abusive supervision, rules climate, self-regulation
The working conditions of large-scale industrial systems are very complex. Once a failure occurs, it will affect industrial production, cause property damage, and even endanger the workers' lives. Therefore, it is important to control the operation of the system to accurately grasp the operation status of the system and find out the failure in time. The occurrence of system failure is a gradual process, and the occurrence of the current system failure may depend on the previous state of the system, which is sequential. The fault diagnosis technology based on time series can monitor the operating status of the system in real-time, detect the abnormal operation of the system within the allowable time interval, diagnose the root cause of the fault and predict the status trend. In order to guide the technical personnel to troubleshoot and solve related faults, in this paper, an industrial fault diagnosis system is implemented based on the cubic DUCG theory. The diagnostic model of the system is constructed based on expert knowledge and experience. At the same time, it can perform real-time fault diagnosis based on time sequence, which solves the problem of fault diagnosis of industrial systems without sample data.
Modular Reconfigurable Robots (MRRs) represent an exciting path forward for industrial robotics, opening up new possibilities for robot design. Compared to monolithic manipulators, they promise greater flexibility, improved maintainability, and cost-efficiency. However, there is no tool or standardized way to model and simulate assemblies of modules in the same way it has been done for robotic manipulators for decades. We introduce the Toolbox for Industrial Modular Robotics (Timor), a Python toolbox to bridge this gap and integrate modular robotics into existing simulation and optimization pipelines. Our open-source library offers model generation and task-based configuration optimization for MRRs. It can easily be integrated with existing simulation tools - not least by offering URDF export of arbitrary modular robot assemblies. Moreover, our experimental study demonstrates the effectiveness of Timor as a tool for designing modular robots optimized for specific use cases.
Ciaran Hughes, Doug Finke, Dan-Adrian German
et al.
Quantum information science and technology (QIST) has progressed significantly in the last decade, such that it is no longer solely in the domain of research labs, but is now beginning to be developed for, and applied in, industrial applications and products. With the emergence of this new quantum industry, a new workforce trained in QIST skills and knowledge is needed. To help support education and training of this workforce, universities and colleges require knowledge of the type of jobs available for their students and what skills and degrees are most relevant for those new jobs. Additionally, students need to know how to tailor their degrees to best align with the current needs of the quantum industry. We report on the results from a survey of 57 companies in the quantum industry, with the goal of elucidating the jobs, skills, and degrees that are relevant for this new workforce. We find a range of job opportunities from highly specific jobs, such as quantum algorithm developer and error correction scientist, to broader jobs categories within the business, software, and hardware sectors. These broader jobs require a range of skills, most of which are not quantum related. Further, except for the highly specific jobs, companies that responded to the survey are looking for a range of degree levels to fill these new positions, from bachelors to masters to PhDs. With this knowledge, students, instructors, and university administrators can make informed decisions about how to address the challenge of increasing the future quantum workforce.
Simon Daniel Duque Anton, Daniel Fraunholz, Daniel Krohmer
et al.
Operational Technology (OT)-networks and -devices, i.e. all components used in industrial environments, were not designed with security in mind. Efficiency and ease of use were the most important design characteristics. However, due to the digitisation of industry, an increasing number of devices and industrial networks is opened up to public networks. This is beneficial for administration and organisation of the industrial environments. However, it also increases the attack surface, providing possible points of entry for an attacker. Originally, breaking into production networks meant to break an Information Technology (IT)-perimeter first, such as a public website, and then to move laterally to Industrial Control Systems (ICSs) to influence the production environment. However, many OT-devices are connected directly to the Internet, which drastically increases the threat of compromise, especially since OT-devices contain several vulnerabilities. In this work, the presence of OT-devices in the Internet is analysed from an attacker's perspective. Publicly available tools, such as the search engine Shodan and vulnerability databases, are employed to find commonly used OT-devices and map vulnerabilities to them. These findings are grouped according to country of origin, manufacturer, and number as well as severity of vulnerability. More than 13000 devices were found, almost all contained at least one vulnerability. European and Northern American countries are by far the most affected ones.