Occupational Data Research Articles

Towards self-learning control of HVAC systems with the consideration of dynamic occupancy patterns: Application of model-free deep reinforcement learning

This study proposes a self-learning control system that aims to learn occupancy profiles, building energy consumption patterns, and lag-time of the heating, ventilation, and air-conditioning (HVAC) systems. The control system learns by interacting with the environment with no need to develop building models and occupancy prediction models. The controller is developed based on a double deep Q-networks (DDQN) algorithm, as a model-free reinforcement learning method. The system's performance is evaluated and compared with that of a model predictive control (MPC) system under two scenarios of perfect and actual occupancy predictions based on occupancy data collected from 20 residential units. The MPC is assisted by a genetic algorithm and supervised learning models for predicting future occupancy patterns, indoor operative temperature, and building energy consumption. The results show that in the case of using perfect occupancy prediction, the self-learning controller operates almost as well as the MPC while not requiring any models. When occupancy prediction uncertainty is added to the problem, the proposed method outperforms the MPC in terms of thermal comfort by increasing the average temperature deviation and deviation period by 0.24 °C and 7.87%, respectively. However, the DDQN agent causes significant thermal comfort violations during the initial training period. The system causes up to a 2.8% longer deviation period and a 0.32 °C higher average temperature deviation, compared with the performance of the fully-trained system.

Dengue Fever Based on Epidemiological Situation: Current Outbreak in Timor-Leste on January 2020 Until February 2022

Background: The dengue Fever is the fastest emerging arboviral infection spread by Aedes mosquitos with superior in public health impact in over 100 tropical and sub-tropical in South East Asia, western Pacific and South and Central America. More up to 2.5 billion people globally lives under the threat of dengue fever and its severe forms dengue hemorrhagic fever (DHF) or dengue shock syndromes (DSS). More than 75% of these people, or approximately 1.8 billion, live in the Asia Pacific Region. As the diseases spreads to new geographical areas, the frequency of the outbreaks is increasing along with a changing disease epidemiology. It is estimated that 50 million people suffering of DHF required hospitalization each year, a must large proportion of whom (approximately 90%) are children less than five years old. About 2.5% of those affected with dengue die of the illness. Objective: The study to spread knowledge of present situation of dengue fever and dengue hemorrhagic fever and dengue fever shock syndrome this occurs in Timor-Leste in January 2020 until February 2022 by epidemiological approach. Research Design: The study adopted an analysis descriptive cross-sectional research based on data epidemiological health system information the Ministry of Health, Hospitals and Health Centers. Subject and sampling: The total number of study subjects was 506 (48 health professionals 1451 caregivers and their children under 5 years and 29 managers on health care settings). Setting: This investigation was carried out in the twenty-nine primary health settings affiliated in Health Municipality of Timor-Leste. Study tools: Three tools where use for data collection 1-Health professionals occupational and educational qualifications data assessment sheet. 2-Observation checklist of case management action. 3-Manager’s feedback self- administrate questionnaire. Results: Current results from died showed that 0.7% in 2020 and 1.3% actual 2022 and perception of new borne care suffering 3.8% for the dengue fever not mistake, that has good idea 96.2% he said understanding of DF, DHF and DSS. Recommendations: The research recommended that DF-DHF evaluation and supervision need more implementation in service training have been adequate the health professionals care and the participation of population cleanup home and environmental just more importance the people to understanding real situation process in current and future of Timor-Leste. Conclusions: The level of adherence of DF-DHF regarding children’s case management of dengue fever of DSS status was poor for closely partial of situation associated less than one one-fifth presented good level observance.

In search of the ‘buffering’ effect in the job demands–control model: The role of teamwork HRM practices and occupations

The job demands–control/support (JDC/JDCS) models are highly influential in the HRM and employee well-being literature. Despite the high face validity, however, research has failed to find convincing empirical support for the ‘buffer’ hypothesis suggested by the JDC/JDCS models. In this article the authors explore this issue from three perspectives. First, they test the controversial ‘buffer’ hypothesis using a large nationally representative matched employer–employee sample from Britain. Second, they examine the role of teamwork HRM practices as a moderator of the buffering effect of job control against job demands on employee well-being. Finally, incorporating occupational level data into the analysis, the authors further explore the moderating effects of teamwork under different occupation-specific work intensity. The analysis suggests that there is strong evidence supporting the ‘buffering’ hypothesis. Also, it was found that teamwork moderates the buffering effect for employee intrinsic job satisfaction. Finally, the moderating effect of teamwork differs between occupations with different levels of work intensity.

The Relationship between Physiotherapist and Patient: A Qualitative Study on Physiotherapists' Representations on This Theme.

The physiotherapist represents a resource for the psychophysical well-being of an individual. Specific characteristics of the physiotherapist-patient relationship can influence the outcome of rehabilitation. This study aimed to explore physiotherapists' representations on how they perceive their relationship with their patients, in order to highlight helpful elements in promoting the outcome of the intervention. In this study, 50 physiotherapists (27 females and 23 males; mean age = 42 years; sd = 12.2) participated in an individual interview, conducted remotely via videocall. Socio-demographic and occupational data were collected. The interviews were recorded and transcribed. Texts were analyzed using emotional text mining (ETM). Participants organized their work by means of three categories: (1) work with the patient, in which the physiotherapists described two complementary elements of the therapy, which are the observable-technical aspects of their work and the internal predispositions; (2) the healing process, highlighting the aims of their intervention, including the physical pathology and the relationship with the patients; (3) physiotherapist as a psychologist, describing the attempt to understand patients' emotional experience to gradually transition to the practical intervention. Understanding the emotional and relational processes that form the basis of physiotherapist practice can contribute to the development of interventions in which the body and the mind can be integrated, resulting in a real person-centered point of view.

Adaptive damper control for HVAC systems based on human occupancy and indoor parameters: A development study

Occupancy-based strategies for the control of ventilation systems in buildings are effective for achieving energy savings and user comfort. Savings in energy consumption of more than 50% can be achieved by controlling heat, ventilation, and air conditioning (HVAC) systems with accurate sensory and occupancy information. In this study, the flow through the damper of the variable area valve (VAV) system and the speed of the blower’s variable frequency drive (VFD) are controlled in the HVAC system, on the basis of human occupancy and indoor parameters, namely, temperature and humidity, segment-wise in the building. In the proposed model, the flapper angle of the VAV is estimated using the indoor temperature, external temperature, and number of occupants. The occupancy data are fed to the controller proposed to regulate the flow through the ducts of the system, which is based on the flapper angle of the VAV, in order to maintain human comfort. The proposed scheme makes it possible to detect abnormalities in energy utilization and to trace maximum utilization in the building based on occupancy, with the control parameters of the HVAC adjusted for a comfortable indoor environment. Performance evaluation of the VAV system with its proposed control strategy, temperature, and flow distribution is simulated using Fluent software. A laboratory grade prototype incorporating the proposed control strategy is then developed, tested under three different conditions, and the results are reported. The experimental results show that an energy saving of 18% can be achieved.

Protection status, human disturbance, snow cover and trapping drive density of a declining wolverine population in the Canadian Rocky Mountains

Protected areas are important in species conservation, but high rates of human-caused mortality outside their borders and increasing popularity for recreation can negatively affect wildlife populations. We quantified wolverine (Gulo gulo) population trends from 2011 to 2020 in > 14,000 km2 protected and non-protected habitat in southwestern Canada. We conducted wolverine and multi-species surveys using non-invasive DNA and remote camera-based methods. We developed Bayesian integrated models combining spatial capture-recapture data of marked and unmarked individuals with occupancy data. Wolverine density and occupancy declined by 39%, with an annual population growth rate of 0.925. Density within protected areas was 3 times higher than outside and declined between 2011 (3.6 wolverines/1000 km2) and 2020 (2.1 wolverines/1000 km2). Wolverine density and detection probability increased with snow cover and decreased near development. Detection probability also decreased with human recreational activity. The annual harvest rate of ≥ 13% was above the maximum sustainable rate. We conclude that humans negatively affected the population through direct mortality, sub-lethal effects and habitat impacts. Our study exemplifies the need to monitor population trends for species at risk—within and between protected areas—as steep declines can occur unnoticed if key conservation concerns are not identified and addressed.

Association between job exposures and employment status 2 years after medical rehabilitation

Abstract Background Research shows an important association between job exposures and various health- and employment-related outcomes. On contrast, little is known about the impact of job exposures on the employment status after medical rehabilitation. Thus, the aim of this study is to investigate the association between job exposures and employment, unemployment, and disability pension entry 2 years after rehabilitation. Methods A retrospective cohort study was performed based on the scientific use file “SUFRSDLV15B” of the German Pension Insurance containing rehabilitation and occupational data at a monthly level. We included n = 597,021 insured persons aged 18 to 63 years completing a medical rehabilitation between 2008 and 2013 in Germany and traced their employment status over a 24-month follow-up period. Job exposures were operationalised with the Overall Job Exposure Index (Kroll, 2015) by applying job-exposure-matrices. Results Persons with high job exposures in comparison to those with low job exposures were less likely to be employed (87.6% vs. 92.6%) and more likely to be unemployed (13.9% vs. 7.7%) and to draw disability pension (4.8% vs. 4.4%) for at least one month in the 2-year-period after rehabilitation. One minus survival curves showed that the differences were already evident in the first month after rehabilitation and further increased during the following 24 months. Cox regressions revealed that these associations remained stable when adjusting for gender, age, and employment status before rehabilitation. Conclusions The results underline the importance of addressing job exposures during rehabilitation to enhance return-to-work and stay-at-work after rehabilitation. These findings could help to identify particularly vulnerable groups of insured persons based on routine data at an earlier stage than has been the case so far and to give them access to structured workplace-oriented medical rehabilitation programmes that have been established in recent years. Key messages • High job exposures are associated with less work participation after medical rehabilitation. • To increase return-to-work, it may be useful to address job exposures in rehabilitation more than before.

Occupational role and Covid-19 among foreign-born healthcare workers: a registry-based study

Abstract Background Studies have shown an elevated risk of Covid-19 among foreign-born healthcare workers (HCWs), but data on the distribution of the risk in different occupational roles are lacking. Such data are needed for the effective control of Covid-19 risk among HCWs. Here, we examined the risk of Covid-19 infection and hospitalization in foreign-born HCWs in different occupational roles in Sweden. Methods We prospectively linked occupational data (2018-2019) of 783950 employed foreign-born (20-65 years) workers to Covid-19 data registered between 1 January 2020 and 30 September 2021. We used Cox proportional hazards regression to estimate the risk of Covid-19 infection and hospitalization in foreign-born HCWs in eight different occupational groups compared to non-HCWs, and to assess whether the associations varied by region of birth. The analyses were adjusted for sociodemographic and socioeconomic factors, comorbidities, and Covid-19 vaccination. Results All HCWs had a higher risk of Covid-19 outcomes than non-HCWs, but the risk differed by occupational role. Assistant nurses had the highest risk both for Covid-19 infection (HR 1.80; 95%CI 1.74-1.87) and hospitalization (HR 1.85; 95%CI 1.57-2.18); other allied HCWs had the lowest risk (infection: HR 1.23; 95%CI 1.11-1.36; hospitalization: HR 1.02; 95%CI 0.63-1.67)). In some healthcare occupations, the relative risk of Covid-19 varied by region of birth. For example, physicians and dental nurses/hygienists of African and Asian origin had a higher risk of Covid-19 infection than European-born in the same occupation. In contrast, European-born assistant nurses had a greater risk of both outcomes than non-European-born in the same occupation. Conclusions The risk of Covid-19 among foreign-born HCWs varied by occupational role and region of birth. Public health efforts that target occupational exposures as well as incorporate culturally responsive measures may help to reduce Covid-19 risk among foreign-born HCWs. Key messages

The effect of population model choice and network topologies on extinction time patterns in dendritic ecological networks

Models of populations in habitat networks are vital for understanding and linking processes and patterns across individuals, environments, ecological interactions, and population structures. River ecosystem models combine the physical structure of the networks with the biological processes of the organisms using structural and functional models, respectively. Previous studies on dendritic river networks have employed different functional (population) models and either directly claimed or implied that the results illustrate general properties of actual river systems. However, these studies have used different approaches and assumptions when modeling population characteristics and behavior, and it is possible that inferences regarding a system may vary based on the combination of functional model and the spatial structure of a network. This study aims to understand if different functional models in river systems produce substantially different model results and, therefore, whether conclusions are model-dependent. We compare variation in extinction time and occupancy proportion of river networks with linear, trellis, dendritic and ring-lattice topologies, using three population models (uniform, age-class and individual based) and one metapopulation-based (patch-occupancy) model. Dendritic, linear, and trellis structures did not show notable differences among extinction times for any of the four models. The difference between topologies was higher for the patch-occupancy model compared to the three population models. There were significant differences in the variations of patch-occupancy between the metapopulation and the population models, but the three population models of differing complexity produced broadly similar results. Therefore, if the occupancy data is obtained based on local subpopulations, spatial arrangement and connectivity does not appear to be the sole predictor of single-species metapopulation responses. We conclude that the outputs from functional models are robust to assumptions and varying levels of detail as long as they contain at least some detail at the level of individuals within habitat nodes. Also, if we are modeling network-scale populations, models that include at least some detailed information on individuals are a far better choice than considering populations implicitly.

SeedTransNet: a directional translational network revealing regulatory patterns during seed maturation and germination.

Seed maturation is the developmental process that prepares the embryo for the desiccated waiting period before germination. It is associated with a series of physiological changes leading to the establishment of seed dormancy, seed longevity, and desiccation tolerance. We studied translational changes during seed maturation and observed a gradual reduction in global translation during seed maturation. Transcriptome and translatome profiling revealed specific reduction in the translation of thousands of genes. By including previously published data on germination and seedling establishment, a regulatory network based on polysome occupancy data was constructed: SeedTransNet. Network analysis predicted translational regulatory pathways involving hundreds of genes with distinct functions. The network identified specific transcript sequence features suggesting separate translational regulatory circuits. The network revealed several seed maturation-associated genes as central nodes, and this was confirmed by specific seed phenotypes of the respective mutants. One of the regulators identified, an AWPM19 family protein, PM19-Like1 (PM19L1), was shown to regulate seed dormancy and longevity. This putative RNA-binding protein also affects the translational regulation of its target mRNA, as identified by SeedTransNet. Our data show the usefulness of SeedTransNet in identifying regulatory pathways during seed phase transitions.

A Multi-dimensional Real World Spectrum Occupancy Data Measurement and Analysis for Spectrum Inference in Cognitive Radio Network

Spectrum Inference in contrast to Spectrum Sensing is an active technique for dynamically inferring radio spectrum state in Cognitive Radio Networks. Efficient spectrum inference demands real world multi-dimensional spectral data with distinct features. Spectrum bands exhibit varying noise floors; an effective band wise noise thresholding guarantees an accurate occupancy data. In this work, we have done an extensive real world spectrum occupancy data measurement in frequency range 0.7 GHz to 3 GHz for tele density wise varying locations at Pune, Solapur and Kalaburagi with time diversity ranging from 2 to 7 days. We have applied maximum noise (Max Noise), m-dB and probability of false alarm (PFA) noise thresholding for spectrum occupancy calculations in all bands and across all locations. Overall occupancy across these locations is 37.89 %, 18.90 % and 13.69 % respectively. We have studied signal to noise ratio (SNR), channel vacancy length durations (CVLD) and service congestion rates (SCR) as characteristic features of measured multi-dimensional spectrum data. The results reveal strong time, spectral and spatial correlations of these features across all locations. These features can be used for a multi-dimensional spectrum inference in cognitive radio based on machine learning.

A Kamm's Circle-Based Potential Risk Estimation Scheme in the Local Dynamic Map Computation Enhanced by Binary Decision Diagrams.

Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm’s circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs.

COVID-19 in the workplace: Self-reported source of exposure and availability of personal protective equipment by industry and occupation in Michigan.

ObjectivesFragmented industry and occupation surveillance data throughout the COVID‐19 pandemic has left public health practitioners and organizations with an insufficient understanding of high‐risk worker groups and the role of work in SARS‐CoV‐2 transmission.MethodsWe drew sequential probability samples of noninstitutionalized adults (18+) in the Michigan Disease Surveillance System with COVID‐19 onset before November 16, 2020 (N = 237,468). Among the 6000 selected, 1839 completed a survey between June 23, 2020, and April 23, 2021. We compared in‐person work status, source of self‐reported SARS‐CoV‐2 exposure, and availability of adequate personal protective equipment (PPE) by industry and occupation using weighted descriptive statistics and Rao‐Scott χ 2 tests. We identified industries with a disproportionate share of COVID‐19 infections by comparing our sample with the total share of employment by industry in Michigan using 2020 data from the US Bureau of Labor Statistics.ResultsEmployed respondents (n = 1244) were predominantly female (53.1%), aged 44 and under (54.4%), and non‐Hispanic White (64.0%). 30.4% of all employed respondents reported work as the source of their SARS‐CoV‐2 exposure and 78.8% were in‐person workers. Work‐related exposure was prevalent in Nursing and Residential Care Facilities (65.2%); Justice, Public Order, and Safety Activities (63.3%); and Food Manufacturing (57.5%). By occupation, work‐related exposure was highest among Protective Services (57.9%), Healthcare Support (56.5%), and Healthcare Practitioners (51.9%). Food Manufacturing; Nursing and Residential Care; and Justice, Public Order, and Safety Activities were most likely to report having adequate PPE “never” or “rarely” (36.4%, 27.9%, and 26.7%, respectively).ConclusionsWorkplaces were a key source of self‐reported SARS‐CoV‐2 exposure among employed Michigan residents during the first year of the pandemic. To prevent transmission, there is an urgent need in public health surveillance for the collection of industry and occupation data of people infected with COVID‐19, as well as for future airborne infectious diseases for which we have little understanding of risk factors.

Mechanized Wood Extraction: Impacts on Operators’ Hearing Health

The activity of the mechanized extraction of wood, carried out by means of self-propelled forest machines, consists of moving the wood cut inside the forest stand from the planted forests to the roadside. During displacements, these machines normally travel over forest residues and stumps, which can demand greater motor power from the engine, in addition to the possible increase in occupational noise levels. In this context, we evaluated whether, when operating different self-propelled forest machines in the wood extraction of planted Eucalyptus forests, operators are exposed to different levels of occupational noise, especially to thresholds above those established for prevention purposes. Occupational noise data were collected during the workday, weighing six self-propelled forest machine operators who performed the wood-extraction operation, performed by two forwarders, two self-loading tractors, and two grapple skidders, in three areas with Eucalyptus plantations located in the southeastern region of Brazil. The sound-pressure level was collected using two integrative meters following the strategic measurement criterion recommendation for the whole day in the guidelines of the acoustic determination of occupational noise exposure and analyzed according to the American Conference of Government Industrial Hygienists. Operators of forest machines for the mechanized extraction of wood were exposed to occupational noise levels above the recommended level, which can affect health and well-being; this occupation also required above-average levels of attention during the execution of work activities.

Predictability quantification of occupant presence in high-rise residential apartment buildings

In recent decades, many studies have attempted to develop a reliable occupancy model using either rule-based, stochastic, data-driven, or agent-based approaches. These are based on the hypothesis that occupant presence can become predictable provided sufficient knowledge and data are provided. However, a different view propounds that occupant presence could follow a random-walk pattern or become unpredictable in certain types of rooms/buildings, for example, university labs and library buildings. In this study, the authors report the predictability of occupant presence in high-rise residential apartment buildings in South Korea. The authors collected occupant presence data from 31 households over 147 days using occupancy sensors installed in each household. The predictability of occupant presence was then analyzed using the normalized cumulative periodogram (NCP) and Bartlett’s test. It was found that (1) the predictability of occupant presence is significantly influenced by temporal and spatial resolutions, (2) extending measurement periods (e.g., 7 days vs 147 days) can increase the predictability of occupant presence, (3) for a measurement period of 7 days, the occupant presence for 14 households became unpredictable, and (4) the predictability of occupant presence significantly differs among 31 households.

Importance of ventilation and occupancy to Mycobacterium tuberculosis transmission rates in congregate settings

BackgroundVentilation rates are a key determinant of the transmission rate of Mycobacterium tuberculosis and other airborne infections. Targeting infection prevention and control (IPC) interventions at locations where ventilation rates are low and occupancy high could be a highly effective intervention strategy. Despite this, few data are available on ventilation rates and occupancy in congregate locations in high tuberculosis burden settings.MethodsWe collected carbon dioxide concentration and occupancy data in congregate locations and public transport on 88 occasions, in Cape Town, South Africa. For each location, we estimated ventilation rates and the relative rate of infection, accounting for ventilation rates and occupancy.ResultsWe show that the estimated potential transmission rate in congregate settings and public transport varies greatly between different settings. Overall, in the community we studied, estimated infection risk was higher in minibus taxis and trains than in salons, bars, and shops. Despite good levels of ventilation, infection risk could be high in the clinic due to high occupancy levels.ConclusionPublic transport in particular may be promising targets for infection prevention and control interventions in this setting, both to reduce Mtb transmission, but also to reduce the transmission of other airborne pathogens such as measles and SARS-CoV-2.

Updated findings from the International Nuclear Workers Study (INWORKS)

BACKGROUND Despite being a recognized carcinogen, public exposure to ionizing radiation has increased in recent decades. An update to the International Nuclear Workers Study (INWORKS) is being undertaken to strengthen direct assessments of the risks from low dose, low dose rate exposure to penetrating forms of ionizing radiation. Follow-up has been extended by 10 or more years in each partner country. METHODS We are conducting a pooled analysis of cohort mortality studies of nuclear industry workers in the United Kingdom, France, and the United States of America. Individual annual estimates of whole-body dose due to external exposure to penetrating radiation are derived from personal occupational exposure monitoring data. Vital status has been ascertained through 2012, 2014, and 2016 for workers in the United Kingdom, France, and the United States of America, respectively. RESULTS The updated pooled study includes 309,932 workers and 10.7 million person-years of observation. Over the period of follow-up, there have been 103,553 deaths observed, of which 31,009 are deaths due to cancer. CONCLUSIONS This presentation will describe the motivation for an update of the INWORKS study, the major aims of the project, and the progress that we have made to-date. We will report on a major update of this highly-influential study; the person-time in INWORKS increases by a factor of 1.3 and the number of cancer deaths increases by a factor of 1.6. The updated INWORKS analyses will provide some of the most informative direct estimates of low dose radiation risks reported to-date.

Vehicle detection and classification with spatio-temporal information obtained from CNN

Vehicle detection and classification with convolutional neural networks (CNN) is encouraging and fascinating for intelligent transportation applications. In general, CNN is trained with huge labeled vehicle images, which are rare to find in developing country scenarios. This work proposes a vehicle detection technique with CNN that does not require any labeled vehicle dataset. A CNN is trained with road marks and these act as the background. When a vehicle occupies a road mark a logic 1 is recorded else 0 is saved in the database. This accumulated occupancy data is spatio-temporal information from which vehicle width and length are approximated and classified. This method does not require any GPU for training as well as for implementation in real-time. The detection accuracy of the proposed method on real-time videos is about 98.5%. The runtime performance of CNN model on NVIDIA Jetson Tx2 is 0.01 s, whereas on RaspberryPi4 is 0.03 s. The given method even works in shadow and low-light conditions.

Occupational Skill Premia around the World: New Data, Patterns and Drivers

Firms hire workers to undertake tasks and activities associated with particular occupations, which makes occupations a fundamental unit in economic analyses of the labor market. Using a unique dataset on pay in identically defined occupations in developing and advanced countries, we find that in most countries occupational skill premia narrowed substantially from the 1950s to the 1980s, then widened through the 2000s, creating a U-shaped pattern of change. The narrowing was due in part to the huge worldwide increase in the supply of educated workers. The subsequent widening was due in part to the weakening of trade unions and a shift in demand to more skilled workers associated with rising trade. The data indicate that supply, demand, and institutional forces are all drivers of occupational skill premia, ruling out simple single factor explanations of change. The paper concludes with a call for improving the collection of occupational wage data to understand future changes in the world of work.

A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering

As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods.