Work Scenarios Research Articles

OVERVIEW OF CAREER MANAGEMENT IN DENTISTRY: CHALLENGES AND TRENDS FOR THE FUTURE FROM THE STUDENTS' PERSPECTIVE

Career management has faced several innovations, due to the economy, politics and social contexts. Such changes are justified by technological advances, globalization, communication processes, outsourcing, and unemployment (PEREZ, 2021). It is relevant to reflect and outline a career plan, in order to find a way to put it into practice, to have the attitude of moving towards the achievement of both personal and professional goals. To describe the perception of career management, as seen by students in the health area at a private university in Belo Horizonte. This is a quantitative research, with the purpose of obtaining results that answer the guiding problem of this study. All dentistry students from Brazilian public and private universities were included, who answered a semi-structured questionnaire, reaching the following results: Gender 76.9% of the interviewees are female and 23.1% male; Age 58% of the students are in the 25-year age group; working in the area represent 38%; 79.3% of these students are studying their first undergraduate degree; 20.7% are in their second degree. Regarding the income profile, 37.9% receive between 1 and 2 thousand reais per month, and 50.7% have a salaried employment relationship, and 70.9% have never taken a vocational test to choose dentistry. Therefore, it is possible to conclude that in the current times, important changes in the work scenario are visible. Having achieved the objectives of this study, evidencing the commitment of dentistry students, however, contrary to expectations, it was noted that the individual initiative to develop the career is unsatisfactory, as well as there is no promotion by the HEIs due to the lack of disciplines on clinical career management and offices during the course.

Evaluating Typing Performance in Different Mixed Reality Manifestations using Physiological Features

Mixed reality enables users to immerse themselves in high-workload interaction spaces like office work scenarios. We envision physiologically adaptive systems that can move users into different mixed reality manifestations, to improve their focus on the primary task. However, it is unclear which manifestation is most conducive for high productivity and engagement. In this work, we evaluate whether physiological indicators for engagement can be discriminated for different manifestations. For this, we engaged participants in a typing task in three different mixed reality manifestations (augmented reality, augmented virtuality, virtual reality) and monitored physiological correlates (EEG, ECG, and eye tracking) of users' engagement and workload. We found that users achieved best typing performances in augmented reality and augmented virtuality. At the same time, physiological engagement peaked in augmented virtuality, while workload decreased. We conclude that augmented virtuality strikes a good balance between the different manifestations, as it facilitates displaying the physical keyboard for improved typing performance and, at the same time, allows one to block out the real world, removing many real-world distractors.

End-to-end underwater acoustic source separation model based on EDBG-GALR

In practical work scenarios, it is common for multiple vessels to be present in close proximity within the same water area. In such cases, the acoustic signals emitted by these vessels often overlap, causing interference and reducing the accuracy of vessel identification. This paper proposes an improved GALR end-to-end underwater acoustic source separation algorithm, with ECA-DE (Efficient Channel Attention-Deep Encoder) and Bi-GRU (Bidirectional Gated Recurrent Unit), which consists of an encoder, separation blocks, and a decoder (EDBG-GALR). Addressing the issue of GALR encoder’s limited expressiveness due to its single-layer one-dimensional convolutional layer, we introduce a new deep encoder, ECA-DE, to enhance the encoder’s capability to process temporal signals and improve the efficiency of the separator’s input. Additionally, we integrate Bi-GRU into the GALR separation block’s local modeling module to enhance the local modeling ability of sequence features, thereby reducing the model’s computational and parameter requirements. Experimental results demonstrate that the proposed EDBG-GALR method can effectively separate mixed multi-target signals into multiple single-target signals, achieving a maximum scale-invariant signal-to-noise ratio (SI-SNR) improvement of 3.32 dB and a signal distortion ratio (SDR) improvement of 3.38 dB over baseline methods. These results highlight the practical applicability of EDBG-GALR in complex underwater environments.

The Impact of Generative Artificial Intelligence Technologies on Chinese Librarians' information Behavior and Ethical Discussion: An Empirical Study Based on a Small Sample

This study used a combination of questionnaires and interviews to survey 68 librarians in mainland China. The questionnaire was divided into three parts: (1) providing descriptive statistics of the interviewed librarians; (2) exploring the impact of generative technologies on librarians' information behaviour from work scenarios; (3) investigating librarians' concerns about ethics and strategies for coping with ethical challenges. The results show that generative AI technologies had a greater impact on information seeking, information encountering, and information using behaviours, and an insignificant impact on information sharing behaviours. In addition, the results of the study reflect that 67.65% of librarians showed a very high level of concern about privacy and security; 66.18% of them believed that the content generated by the tools needed further validation. The study also provided six recommendations from the perspective of libraries and librarians to address ethical challenges such as the spread of disinformation and bias.

Impact of space utilization and work time flexibility on energy performance of office buildings

This study investigates the impact of different space utilization on energy use intensity, heating load, cooling load, and thermal comfort of occupants using a combination of empirical data gathering and simulation-based studies. The increasing worldwide preoccupation with energy consumption and environmental sustainability has led to increased attention on optimizing interior spaces to mitigate total energy demand. The considered case study for conducting this research was the Norwegian living lab namely Zero Emission Building (ZEB) Flexible Lab in Trondheim, Norway. In this study, 10 different scenarios of occupancy schedules based on flexible arrangements from standard workweeks to extensive remote work configurations were designed and analyzed using IDA ICE 5.0. The findings demonstrate significant reductions in energy use across scenarios with increased teleworking and compressed work weeks. The remote scenario achieved the most significant decrease in Energy Use Intensity (EUI), with a reduction of 46 % compared to the base case. Similarly, the implementation of flexible hours and remote working in scenarios resulted in a reduction of electric heating demand by up to 23 %, underscoring the potential of occupancy-based strategies in enhancing building energy efficiency. However, uncomfortable hours increased by 59 % in the 2-day remote working scenario compared to the base case, demonstrating the need to consider climate conditions when implementing remote work. The research offers valuable insights into the complex connections between flexible arrangements and energy efficiency, considering many elements such as occupancy schedule and use dynamics. This article offers a comprehensive analysis that may give architects, building managers, and policymakers valuable insights. This study contributes to the developing sustainable architecture by emphasizing the impact of dynamic occupancy on the energy performance of office buildings.

Synthesis and Performance Evaluation of Metallocene Polyalphaolefins (mPAO) Base Oil with Anti-Friction and Anti-Wear Properties

Anti-wear and anti-oxidation abilities are two key properties of lubricants that play a crucial role in ensuring long-term stable equipment operation. In this study, we aimed to develop a base oil with good anti-oxidation and anti-wear properties for use under extreme pressure. The as-prepared metallocene polyalphaolefin (mPAO) was chemically modified using the trifluoromethanesulfonic acid (TfOH) catalysis through an alkylating reaction with triphenyl phosphorothioate (TPPT). During the experiments, when the reaction temperature exceeded 70 °C or the concentration of TfOH exceeded 2.67%, the β-scission reaction in the alkylation process became significantly more pronounced. The physical and chemical properties of TPPT-modified mPAO (T-mPAO) were evaluated by nuclear magnetic resonance spectroscopy, Fourier trans-form infrared spectroscopy, gel–permeation chromatography, and ASTM standards. T-mPAO showed significantly improved antioxidant capacity, with the initial oxidation temperature increasing by 32 °C compared to the base oil, and it exhibited the slowest increase in acid number in the 96-h oven oxidation test. The tribological tests showed that T-mPAO had the lowest friction coefficient, wear track, and wear rate (72.7% lower than that of mPAO) as well as the highest PB (238 kg) and PD (250 kg) among all tested samples. Compared to mPAO, the average friction coefficient of TPPT-modified mPAO in the four-ball friction test was reduced by 30.5%, and by 16.4% in the TE77 reciprocating friction test. Based on the experimental results, T-mPAO had strong anti-oxidation ability and excellent lubricating performance.The successful synthesis of multifunctional mPAO has enabled lubricant base oil additization, making it possible to use it in more demanding work scenarios, greatly broadening its application scope and making lubricant formulation blending more flexible.

Impressive Electrochemical Characteristics of Freeze-Dried Nanosheet Structured Mn2P2O7 for Affordable Electrochemical capacitor

The electrochemical capacitive performance of supercapacitors (SCs) is mainly evaluated by active electrode material, which plays a critical participation. At present, transitional metallic pyrophosphates (TMPs) have grabbed more interest as active electrodes in SC configuration. In this work scenario, we informed a novel strategic synthesis route for constructing Mn2P2O7 (MP) through an ultrasonic probe-assisted chemical reaction approach. The electrode in the form of a monoclinic, freeze-dried nanosheet structure characterized by XRD, FESEM- TEM, and its signature chemical bonds and chemical composition were confirmed via FTIR and EDS analysis, respectively. As prepared active sample was successfully sent into electrochemical investigation tools such as Cyclic Voltagrams (CV), Galvanostatic charge/discharge (GCD), electrochemical impedance analysis (EIS), and Cycling life span (stability) using three and two-electrode set-up. These tests indicate that the active sample holds an impressive capacitive value of 558 F/g at scanning range of 2 mV/s, a specific capacity of 420 C/g at 1 A/g, magnificent rate capability, 90% retention over 10,000 successive voltammograms in three-electrode measurements. Additionally, the fabricated symmetric supercapacitor device shows superior energy density, power density, and good rate capability in two electrode investigations. The mentioned prominent results reflect that engineered MP and its characteristics are the efficient fruits in manufacturing affordable electrochemical capacitors.

Stress Assessment for Augmented Reality Applications Based on Head Movement Features.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

A Study on the Implementation Path of the Blended Teaching Model in Geological Hazards Investigation and Evaluation

Geological Hazards Investigation and Evaluation is the core course of Environmental Geological Engineering, aiming to cultivate skilled talents with solid theoretical knowledge and excellent practical skills. At present, the course faces several issues, including a teaching environment disconnected from real-world work scenarios, course content that deviates from job-related tasks, a lack of digital teaching resources, and reliance on a single teaching method, leading to students’ poor feedback from employers. Based on the concept of outcome-based education, the course team of Geological Hazards Investigation and Evaluation establishes a “five-step double-rotation” blended teaching model with the help of a Small Private Online Course platform. The program is designed to improve the teaching environment and expand the digitalized teaching resources in order to improve students’ learning motivation, enhance learning effectiveness, and cultivate skillful talents who meet employers’ satisfaction.

Adaptive intelligent agent for cloud edge collaborative industrial inspection driven by multimodal data fusion and deep transformation networks

Currently, the rapid development of the industrial Internet has led to the creation of a massive number of intelligent agents that are widely and distributively applied in various edge scenarios. The work conditions in these edge scenarios are complex, uncertain, and random. Traditional manual updates or human judgments are used for task decision-making in large-scale distributive intelligent agent edge work scenarios, which lack dynamic perception and autonomous recognition capabilities for edge work conditions. This inevitably leads to low decision-making accuracy, poor reliability, and ultimately, task failure. To address this issue, this study proposes an adaptive task identification strategy based on cloud-edge collaboration. This method utilizes a cloud-edge collaborative industrial intelligent application architecture to achieve cloud-based training and encapsulation of the task model, with online calling at the edge-end. Then, edge-end intelligent agents identify edge work conditions through multi-source data fusion, enabling accurate task decision-making. Finally, the edge-end requests the cloud for task model matching. The effectiveness of the proposed method is validated in an industrial safety situation virtual detection system.

Introduction to the Special Issue: Family Science Careers Through the Eyes of Theory

This manuscript is part of a special issue of Family Science Review entitled Family Science Careers Through the Eyes of Theory, edited by Raeann Hamon, Ph.D., CFLE. The authors of these deliberately unconventional manuscripts were asked to select and describe a career that a professional with a family science background might pursue. After outlining the professional role, authors reflected upon the family theories that most influence the way they approach their work and perform their professional duties. Authors briefly review the scholarly literature on selected family theories, provide case studies or work scenarios as illustrations of theory in action, and discuss the strengths and weaknesses of the theories in their unique professional contexts. The Special Issue articles are designed to be used individually or in combination, and feature articles about careers in early intervention, special education, family court, child life, and higher education.

A 2 : Towards Accelerator Level Parallelism for Autonomous Micromobility Systems

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI accelerators. An emerging paradigm called Accelerator Level Parallelism (ALP) suggests managing accelerators holistically. However, there lacks a specialized and practical solution populating ALP for an AMS, where the varying real-time requirements under different working scenarios bring an opportunity to dynamically tradeoff between latency and efficiency. Furthermore, accelerator heterogeneity introduces enormous configuration space, and the shared-memory architecture results in dynamic bandwidth interference. In this paper, we propose A 2 , a novel AMS resource manager optimizing energy and memory space efficiency under variable latency constraints. We gain insight from prior Learn&Control scheme to design an Analyze&Adapt scheme specialized for heterogeneous AI accelerators under shared-memory architecture. It features analyzing the system thoroughly offline to support two-step adaptation online. We build a prototype of A 2 and evaluate it on a commercial edge platform. We show that A 2 achieves 32.8% improvements in power and 13.8% in memory compared with control-based methods. As for timeliness enhancement, A 2 reduces the deadline violation rate by 9.2 percentage points (12.8% → 3.6%) on average compared to directly porting Learn&Control methods.

Development of a semi-active MR inerter for seismic protection of civil structures

Civil engineering structures are susceptible to collapsing when exposed to severe vibrations. Therefore, it is essential to protect them from undesirable vibrations triggered by natural calamities like earthquakes or strong winds. This paper proposes an innovative semi-active Magnetorheological (MR) inerter system with a compact structure for seismic protection. The inerter system consists of four rubber bearings and the semi-active MR inerter. The inertance of the semi-active MR inerter can be switched according to different working scenarios. This unique operating principle enhances the adaptability of the system. To assess the performance of the proposed inerter system, a scaled three-storey building was constructed following scaling laws. Four scaled earthquake signals with different dominant frequencies were used as ground motion excitations. An inertance switch controller based on short-time Fourier transformation (STFT) methodology was built to determine the desired inertance of the inerter. Both the simulation and experimental results indicated that the proposed semi-active MR inerter system provides superior vibration mitigation capacity over the passive inerter systems. Specifically, the employment of the semi-active MR inerter effectively reduces the acceleration responses of the structures under different seismic excitations.

Unlocking the Potential of the Regression Discontinuity Design for Social Work Research

Purpose: This article introduces the regression discontinuity design (RDD) as a valuable quasi-experimental approach for evaluating social work interventions. Methods: The RDD is outlined, providing guidance on identifying and framing research questions within its structure. Detailed explanations of analytical procedures for estimating intervention effects under both sharp (treatment assignment strictly determined by a cutoff) and fuzzy (treatment assignment is influenced but not strictly determined by a cutoff) RDD conditions are included, along with common supplemental analyses to address potential validity threats. Results: Concrete examples of social work scenarios where RDD can be effectively employed are presented, showcasing innovative approaches that can enhance the design's utility. This paper also includes code for implementing these techniques. Conclusions: By examining the applications and methodologies of RDD, this article equips social work practitioners and researchers with a versatile tool to rigorously assess the impact of interventions and advance evidence-based practice within the field.

Digital Documentation and Architectural Heritage Restoration with 3-D Geometry: The Ambulatory of the Cathedral of Santiago de Compostela

ABSTRACT The SFM photogrammetry (Structure from Motion) is a technique developed in the field of computer vision that allows obtaining 3D models from images taken from different angles. Despite its extensive development, this technique faces methodological challenges when applied to complex heritage environments. Typically, heritage restoration documentation focuses on the initial and final stages when works have not yet begun or have already concluded, and the object of study is free of obstacles and workers, with its use in intermediate stages being uncommon. The aim of this article is to present geometric documentation using SFM-MVS photogrammetry across multiple stages of the roof restoration of the Cathedral of Santiago de Compostela (Spain), showcasing an expert application of this technique in a real working scenario with multiple challenges, adapting to complex scenes without interfering with project timelines and providing high-quality outcomes at a low cost, even in areas temporarily obscured by scaffolding or plastic coverings. It provides essential data for geometric assistance in restoration design, adaptation, and stakeout. Moreover, continuous 3D documentation enables the creation of 3D stop-motion models, offering an innovative perspective for understanding and communicating a wide range of restoration and construction projects.

A multi-domain adversarial transfer network for cross domain fault diagnosis under imbalanced data

In the intelligent fault diagnosis of rolling bearings, transfer learning methods extend the applicability of models to diverse working scenarios. However, real-world scenarios often suffer from data imbalance, which reduces diagnostic accuracy. To address this issue, this paper proposes a multi-domain adversarial transfer (MDAT) framework to enhance cross-domain fault diagnosis accuracy for rolling bearings under imbalanced data conditions. First, an enhanced information generation method is introduced to produce realistic and useable synthetic data to mitigate data imbalance. Subsequently, an adversarial multi-domain adaptation module is designed to learn invariant features across multiple domains. Finally, a domain reweighting method is proposed to improve domain alignment and enhance domain confusion. The effectiveness of the proposed method was validated through two case studies on rolling bearing fault diagnosis. The results demonstrated that MDAT achieved cross-domain diagnosis accuracies of 89.2% and 99.0% under imbalanced data conditions, confirming the effectiveness and superiority of the MDAT framework.

The future of interactive information radiators for knowledge workers

Abstract Information Radiators (IRs) provide context-specific pieces of information in a semi-public place where a group of people can see it while working or passing-by. They can simplify information sharing “out-of-the-box”, foster awareness and socialization, create serendipity and enhance collaboration. Recent sociotechnical developments such as the establishment of permanent hybrid work settings as well as advances in the area of Human Computer Interaction (HCI) such as the emergence of Augmented Reality (AR) and Virtual Reality (VR) are likely to impact how IRs are being used – or even challenge their usefulness. In this article we discuss those developments and their possible implications for the design and use of IRs in the context of knowledge work in the next decades. We argue that IRs will probably remain an important part of future office environments providing awareness, supporting serendipity and building a situated social place for matchmaking as well as informal communication. Using new display and interaction technologies (such as AR) they might even grow in importance by enabling fluid work scenarios.

GAN-Based Multi-Task Learning Approach for Prognostics and Health Management of IIoT

Health assessment (HA) & remaining useful life (RUL) estimation, the two essential pillars of the prognostics and health management (PHM) paradigm, help improve industrial equipment reliability while reducing maintenance costs. However, reported works treat HA and RUL estimation as disjoint problems though there exist unexploited similarities among these related issues. Additionally, in practical industrial working scenarios, equipment(s) stay in a normal condition for most of its lifespan, leading to a disproportionate training dataset, hampering the prediction accuracy. To overcome the above problems, we propose a data-driven multi-task learning framework aided by a novel least squares recurrent auxiliary classifier generative adversarial network (LS-R-ACGAN). LS-R-ACGAN employs recurrent neural networks (RNNs) in its generator & discriminator networks for multi-variate fault data generation while overcoming the vanishing gradient problem of ACGANs. Post-data-augmentation, a balanced training dataset, trains a multi-task learning model based on a deep gated RNN (DGRU) for joint HA and RUL estimation. Our simulations use the C-MAPSS dataset for testing the proposed approach’s accuracy. The final results showcase improvements by at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 3.54% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 2.38% on the RMSE and Score metric over existing state-of-the-art works suggesting its competitiveness and competence for real-world implementations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study attacks multiple presumptions concerning the maintenance of complex systems including, ample availability of fault samples alongside normal samples and solving only one of the problems, i.e., health assessment (HA) or remaining useful life (RUL) estimations rather than both. Popular artificial intelligence (AI) based solutions rely heavily upon the quantity of training data for desired performance outcomes. However, in real-life industrial scenarios, fault instances are rare, inherently resulting in an imbalanced dataset. Additionally, it is both labor-intensive and costly to repeat multiple experiments for gathering required data as the equipment(s) pass through short-lived fault states. Thus conventional AI-based solutions may end up producing biased predictions. This work proposes to solve the imbalanced training dataset problem along with joint HA and RUL estimations, in one solution pipeline, currently unexplored in majority of the existing works in the literature. LS-R-ACGAN generates ample diverse fault samples for reducing the degree of imbalance between normal and fault classes. Subsequently, after data augmentation, the balanced training dataset helps train a multi-task learning model for joint HA and RUL estimations. The proposed framework has been implemented and tested on a benchmark dataset proving its superiority over multiple existing methods in the literature.

La formación docente: una oportunidad para la transformación desde la investigación científica

The essay aims to analyze the contributions of scientific research in teacher training and its impact on the transformation of education. At the University and in training centers, the curriculum that guides teachers has the task of not only updating the contents under cutting-edge demands, but it also has the fundamental task of adapting to the contextual realities of their work scenarios. In higher education, scientific research is the resource that guarantees the transformation of education professionals, their own students, and their communities. Examples of this are the Political Constitution of 1991, Law 115 of 1994 and Decree 1330 of 2019 (Colombia), which explain the importance and management of providing teachers with professional or continuous training in accordance with scientific processes. From this perspective, new contributions are made to the topic, taking teacher training to the construction of democratic scenarios, to the transformation of territories, systems, educational practices, curricular action, experience, and to society from a critical stance that transcends the discussion of knowledge.

Fostering belonging and social connectedness in nursing: Evidence-based strategies: A discussion paper for nurse students, faculty, leaders, and clinical nurses

BackgroundThe phenomenon of loneliness among healthcare providers, particularly nurses, has garnered increasing attention due to its detrimental effects on individual well-being and professional retention. The isolation experienced by nurses has been linked to heightened turnover rates and intentions to leave the profession, posing significant challenges to healthcare systems globally. Recognizing loneliness as an epidemic in 2023, the U.S. Surgeon General highlighted the urgency of addressing this issue within the healthcare workforce. PurposeThis paper explores evidence-based strategies to mitigate loneliness and promote social connectedness among nurses, drawing insights from various stakeholders. It aims to offer actionable recommendations to enhance the nursing experience and retain professionals in the field. DiscussionStrategies include peer support programs, mentorship initiatives, wellness activities, and fostering open communication. Leveraging technology for virtual connections is also highlighted, especially in remote work scenarios. ConclusionA holistic approach is vital, combining individual, interpersonal, and systemic interventions to combat nurse loneliness. Prioritizing social connectedness fosters a supportive work environment, benefiting both nurses and patient care quality.