Identification Of Drivers Research Articles

Improved mapping of coastal salt marsh habitat change at Barnegat Bay (NJ, USA) using object-based image analysis of high-resolution aerial imagery

Tidal wetlands are valued for the ecosystem services they provide yet are vulnerable to loss due to anthropogenic disturbances such as land conversion, hydrologic modifications, and the impacts of climate change, especially accelerating rates of sea level rise. To effectively manage tidal wetlands in face of multiple stressors, accurate studies of wetland extent and trends based on high-resolution imagery are needed. We provide salt marsh delineations for Barnegat Bay, New Jersey, by means of object-based image analysis of high-resolution aerial imagery and digital elevation models. We performed trends analyses of salt marsh extent from 1995 to 2015 and estimated drivers of marsh area change. We found that in 1995, 8830 ± 390 ha were covered with marsh vegetation, while in 2015 only 8180 ± 380 ha of salt marsh habitat remained. The resulting net loss rate of 0.37% yr−1 is equivalent to historic loss rates since the 1970s, indicating that despite regionally accelerating relative sea level rise and purported eutrophication, salt marsh loss rates at Barnegat Bay remain steady. The main drivers of salt marsh loss are excavations for mosquito control (409 ha), edge erosion (303 ha) and ponding (240 ha). Upland migration of salt marsh did not completely mitigate these losses but accounted for a gain of 147 ha of tidal marsh habitat. The methodology presented herein yielded accurate salt marsh delineations (>90%) and trend detection (85%), outperforming low-resolution wetland delineations used in coastal management. This study demonstrates the suitability of high-resolution imagery for the detection of open water features. For the purposes of salt marsh change detection and the identification of change drivers, management and conservation agencies should make use of high-resolution imagery whenever feasible.

Radio frequency identification and tracking of vehicles and drivers by exploiting keyless entry systems

Modern commercially-available vehicles have active or passive keyless entry systems. In the case of an active keyless entry system, an individual presses a button on a key fob that transmits a radio frequency signal to its paired vehicle that unlocks the doors. In the case of a passive keyless entry system, a vehicle senses an approaching individual and signals the key fob to respond with a radio frequency signal. If the key fob is paired with the vehicle, then the vehicle unlocks the doors and may even start the engine. For user convenience, most modern vehicles have integrated active–passive keyless entry systems.A passive keyless entry system in a vehicle uses the four-way handshake protocol to detect a proximal key fob and authenticate the key fob as being paired with the vehicle. Any vehicle or individual that transmits the initial protocol signal forces a key fob to become a passive radio signal transmitter. Exploiting the four-way handshake protocol has privacy and security consequences. This research demonstrates that passive keyless entry key fobs – and by extension, their paired vehicles and drivers – can be identified and tracked in real time using radio frequency signals. Additionally, this research demonstrates that the identification and tracking of key fobs, paired vehicles and drivers can be performed using commercial off-the-shelf hardware costing less than $900.

Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions

The COVID-19 pandemic has required international scientific efforts to address important aspects of the pandemic. Data science and scientific modeling are extensively used to provide assessments and predictions for policy-making purposes. However, resulting communications need to be supported by a proper uncertainty quantification to assess variability in model predictions, by the identification of the key-uncertainty drivers. This information can be provided by statisticians with sensitivity analysis methods. Knowing the drivers of uncertainty supports effective policy-making. Concerning the COVID-19 pandemic diffusion, two recent investigations reveal intervention-related parameters as more important than epidemiological parameters in two different modeling exercises. This result can help prioritize policy decisions.

Temporal drivers of textural complexity: A pilot study and analysis of simultaneous temporal dominance of sensations and temporal textural complexity data

AbstractThe aim of this study was to assess the applicability of simultaneous methodologies to describe dynamic textural complexity. In a simultaneous task, temporal dominance of sensations (TDS) and temporal textural complexity (TTC) were performed in one session (n = 17). A further 18 participants performed TDS and TTC on separate occasions; data from the simultaneous and separate session were then compared. A further aim was to investigate the temporal nature of textural complexity—temporal drivers of textural complexity were examined, as well as ratings of textural complexity across time. Regression modeling and multivariate analyses indicated little difference in how the participants in the simultaneous and separate tasks performed; only minor differences in the use of TDS attributes were noted. Positive drivers included texture contrast and crumbly/particles, while smooth was a negative driver. Significant differences in TTC data separated into three time intervals were found; a trend of increasing textural complexity during mastication until a midway point followed by a decline emerged. Further research with larger samples is needed to support these findings. However, for the first time this work supports the use of simultaneous tasks in sensory evaluation and indicates the importance of temporality in direct ratings of complexity.Practical applicationsSimultaneous tasks are not commonly used in sensory evaluation, this research suggests their applicability and value; ultimately they may save time and money in research and product development. Direct ratings of sensory complexity are often reported prior to or after descriptive temporal evaluation; this may underestimate true perceived complexity, indicating the importance of considering temporal measures. Sensory complexity is considered an important sensory construct given its arousal potential; thus, identification of drivers of complexity may inform product development.

Cost measurement in value-based healthcare: a systematic review

ObjectiveAlthough value-based healthcare (VBHC) views accurate cost information to be crucial in the pursuit of value, little is known about how the costs of care should be measured. The aim...

Habitat suitability model and range shift analysis for American Chestnut (Castanea dentata) in the United States

American chestnut (Castanea dentata) was a multifunctional hardwood tree species that provide both economic and ecological benefits in the United States. It was a major source of timber before the chestnut blight (Cryphonectria parasitica) invasion wiped out the stands of this species from the forest between the early 1900s and 1950s. Our study focuses on the estimation of suitable habitat in the eastern US, range shift predictions, and identification of habitat drivers for American chestnut (AMC) in the eastern US to help corroborate the restoration efforts of C. dentata using an ensemble model from five species distribution modeling (SDMs) methods while also projecting the prediction until 2100 in response to the changing climate. 1806 occurrence records were obtained from four sources including the United States Department of Agriculture (USDA) Forest Service. The result from the SDM techniques shows a model accuracy of the true skill statistics (TSS) and Area-Under-the-Curve (AUC) of greater than 0.8 and 0.90 respectively, which were ensembled. The historic suitable habitat of AMC compared to the present habitat suitability prediction in the US shows a significant reduction. The mean temperature of the warmest quarter, precipitation seasonality, and temperature seasonality are the top three variables dictating the distribution of the American chestnut in the US. The future projection also shows up to 100% less suitable habitats by the end of the century under high emissions and high precipitation with a northeast range shift up to 476.9 km. Modeling the American chestnut's habitat suitability is imperative for a successful restoration program and these results highlight the potential need for greater conservation and use of southern genotypes.

Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies.

Melanoma is an aggressive skin cancer reliant on early detection for high likelihood of successful treatment. Solar UV exposure transforms melanocytes into highly mutated tumor cells that metastasize to the liver, lungs, and brain. Even upon resection of the primary tumor, almost thirty percent of patients succumb to melanoma within twenty years. Identification of key melanoma genetic drivers led to the development of pharmacological BRAFV600E and MEK inhibitors, significantly improving metastatic patient outcomes over traditional cytotoxic chemotherapy or pioneering IFN-α and IL-2 immune therapies. Checkpoint blockade inhibitors releasing the immunosuppressive effects of CTLA-4 or PD-1 proved to be even more effective and are the standard first-line treatment. Despite these major improvements, durable responses to immunotherapy and targeted therapy have been hindered by intrinsic or acquired resistance. In addition to gained or selected genetic alterations, cellular plasticity conferred by epigenetic reprogramming is emerging as a driver of therapy resistance. Epigenetic regulation of chromatin accessibility drives gene expression and establishes distinct transcriptional cell states. Here we review how aberrant chromatin, transcriptional, and epigenetic regulation contribute to therapy resistance and discuss how targeting these programs sensitizes melanoma cells to immune and targeted therapies.

Plant functional groups and phylogenetic regularity control plant community bioelement composition through calcium and magnesium

The identification of drivers of bioelement concentrations in plant communities is crucial for our understanding of ecosystem functioning. In this respect, soil nutrients, plant biodiversity and functional groups are reported to affect the plant community bioelement composition. However, given the predominant focus on species richness and single elements (or stoichiometric ratios) so far, only little is known about the patterns of the whole suite of bioelements at the community level and whether these patterns can be predicted by evolutionary relationships between co‐occurring species (phylogenetic diversity). To explore this knowledge gap, we used a comprehensive dataset of soil nutrients, plant community composition, phylogeny and aboveground tissue bioelement data from experimental sites established in central European semi‐natural grasslands. Plant community bioelement composition was strongly and exponentially related to community calcium and magnesium (bioelements reflecting deep phylogenetic differences). Plant community bioelement composition and stoichiometry were best indicated by functional group composition (graminoids and forbs) and phylogenetic regularity, whereas phylogenetic richness, phylogenetic divergence and species richness were poor predictors. Bioelement‐rich communities were poor in graminoids and rich in forbs, and tended to have evenly distributed phylogenetic distances (high regularity). No confounding effect of soil nutrients was observed. The links between functional group composition, phylogenetic regularity and plant community bioelement composition suggest that functional groups and phylogenetic diversity are mechanistically connected to ecosystem functions, such as primary production, decomposition, biogeochemical cycling or plant–herbivore interactions, through bioelements.

A Method of Car Driver's Phone Call Recognition Based on Human Joint Points

The distracted driving state of the car driver has caused serious harm to traffic safety and the personal safety of the driver. The most frequent and common distraction is the distracted driving behavior of the driver on the phone. In order to accurately identify the car driver's phone call is distracting driving behavior. A driver's phone call behavior recognition method based on the joint point information of the driver's body was proposed in this manuscript. After using the OpenPose network structure to extract the joint points of the human body, the driver’s angle of the driver's upper and lower arms and the normalized distance between the joint points are calculated and compared to relatively accurate identification of car drivers' phone calls and distracted driving behaviors The experimental results show that the method proposed in this paper has an accuracy rate of 97.23% for the distracted driving state of the car driver's phone call.

Driver Identification Using Optimized Deep Learning Model in Smart Transportation

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure, and comfortable for the people. Although vehicles have been automated up to a certain extent, it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified: 1) face recognition of the driver, and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short-Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score, and ROC AUC curve are considered to evaluate the performance of the model.

Linking the potentials of extended digital marketing impact and start-up growth: Developing a macro-dynamic framework of start-up growth drivers supported by digital marketing

The contemporary market positions digital marketing as a powerful mediator between efficient digital interaction, data interpretation opportunities, and business growth, while extending its impact potential to tackle different growth challenges.As start-ups usually have limited resources and struggle with customer engagement, retention, and other growth challenges, the low investment and dynamic elements of digital marketing tools can be used to support constructive digital interactions impacting start-up growth. Consequently, these links have outlined an extended digital marketing impact in the areas which build on marketing and sales and influence growth components such as product & market testing, customer engagement, and partnership development.Through a systematic literature review, we present a holistic overview encompassing start-up growth areas that use digital marketing. We also discuss how digital marketing efforts complement the start-up maturity. Furthermore, we analyze the differences in B2B and B2C digital marketing usage and discuss how emerging technologies impact digital marketing. The link between the extended digital marketing impact, start-up challenges, and growth areas result in the identification of start-up growth drivers supported by digital marketing. We propose a Macro-Dynamic framework identifying the start-up growth drivers from product, market, team, and finance areas and digital marketing tactics connected to the identified growth drivers.

An Intelligent Online Drunk Driving Detection System Based on Multi-Sensor Fusion Technology.

Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver's seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles.

Selecting the right therapeutic target for kidney disease

Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution 'patient-centric' omics data sets, advanced in silico tools (systems biology, connectivity mapping, and machine learning) and 'state-of-the-art' experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.

Significant association between FGFR1 mutation frequency and age in central giant cell granuloma

Central giant cell granulomas (CGCG) are rare intraosseous osteolytic lesions of uncertain aetiology. Despite the benign nature of this neoplasia, the lesions can rapidly grow and become large, painful, invasive, and destructive. The identification of molecular drivers could help in the selection of targeted therapies for specific cases. TRPV4, KRAS and FGFR1 mutations have been associated with these lesions but no correlation between the mutations and patient features was observed so far. In this study, we analysed 17 CGCG cases of an Italian cohort and identified an interesting and significant (p=0.0021) correlation between FGFR1 mutations and age. In detail, FGFR1 mutations were observed frequently and exclusively in CGCG from young (<18 years old) patients (4/5 lesions, 80%). Furthermore, the combination between ours and previously published data confirmed a significant difference in the frequency of FGFR1 mutations in CGCG from patients younger than 18 years at the time of diagnosis (9/23 lesions, 39%) when compared to older patients (1/31 lesions, 0.03%; p=0.0011), thus corroborating our observation in a cohort of 54 patients. FGFR1 variants in young CGCG patients could favour fast lesion growth, implying that they seek medical attention earlier. Our observation might help prioritise candidates for FGFR1 testing, thus opening treatment options with FGFR inhibitors.

The PREDICT Registry Australia: A Prospective Registry to Evaluate the Clinical Utility of a Biomarker Assay on Treatment Decisions in Patients with DCIS Following Breast Conserving Surgery

<h3>Purpose/Objective(s)</h3> The benefit of adjuvant radiation therapy (RT) for women with ductal carcinoma in situ (DCIS) treated with breast conserving surgery (BCS) remains controversial. Although level-I evidence supports the role of RT in reducing the risk of local recurrence, the absolute benefit of RT is variable and so it is important to develop better prognostic and predictive tools. The assay under evaluation is a biologic signature that provides a validated decision score (DS) for assessing 10-year risk of recurrence and RT benefit using individual tumor biology as assessed by clinical and pathologic biomarkers. The primary objective of the PREDICT registries is to understand the decision impact such a tool has on treatment decisions. The hypothesis is that the biomarker assay is associated with changes in treatment recommendations for surgical, hormonal and/or radiation therapy. <h3>Materials/Methods</h3> This is a multi-center, prospective, observational study for women diagnosed with DCIS of the breast. After diagnosis, sites will send the most representative tissue block or sections for testing. Treating physicians complete a treatment recommendation survey before and after receiving test results. Test results, treatment recommendations, patient preferences and clinicopathologic features will be stored in a de-identified registry for participating institutions from a variety of geographic regions across Australia. Women will be followed for up to 10 years with completion of a follow-up form. The study includes females age 26 or older who are candidates for BCS and eligible for RT and/or systemic treatment. Subjects must not have been previously treated for DCIS or have previous or current invasive or micro-invasive breast cancer. The primary endpoints are changes in treatment recommendations for surgical, radiation and hormonal therapy. Secondary endpoints are identification of key drivers for treatment recommendations, including clinicopathologic factors and patient preference. Changes in pre- and post-test treatment recommendations will be analyzed using McNemar's test (alpha level = 0.05). Multivariate logistic regression will be used to determine odds ratios of clinicopathologic factors leading to pre- and post-test treatment recommendations. Pre-test covariates include patient age, tumor size, palpability, margin status, hormone receptor status, nuclear grade, tumor necrosis, family history, race, ethnicity and patient preference, as well as physician specialty and post-test covariates will also include the DS. Differences in recurrence-free and overall survival will be assessed by Kaplan-Meier survival analysis using the log-rank test and/or the Cox Proportional Hazards model. We are planning to enroll up to 1,500 women from up to 100 sites across Australia. A similar registry in the US has enrolled 2,500 women from 67 sites. <h3>Results</h3> TBD <h3>Conclusion</h3> TBD

Identification of barriers and drivers to implementation of solar drying technologies

Identification of barriers and drivers to implementation of solar drying technologies

Planar chromatography-bioassays for the parallel and sensitive detection of androgenicity, anti-androgenicity and cytotoxicity

Anti-androgens entering the aquatic environment, e.g., by effluents from wastewater treatment plants or agricultural settings are contributing to endocrine disruption in wildlife and humans. Due to the simultaneous presence of agonistic compounds, common in vitro bioassays can underestimate the risk posed by androgen antagonists. On the other hand, cytotoxic effects might lead to false positive assessments of anti-androgenic effects in conventional bioassays. In the present study, a combination of normal phase high-performance thin-layer chromatography (NP-HPTLC) with a yeast-based reporter gene assay is established for the detection of anti-androgenicity as a promising tool to reduce interferences of androgenic and anti-androgenic compounds present in the same sample. To avoid a misinterpretation of anti-androgenicity with cytotoxic effects, cell viability was assessed in parallel on the same plate using a resazurin viability assay adapted to HPTLC plates. The method was characterized by establishing dose-response curves for the model compounds flutamide and bisphenol A. Calculated effective doses at 10% (ED10) were 27.9 ± 1.3 ng zone−1 for flutamide and 20.1 ± 5.1 ng zone−1 for bisphenol A. Successful distinction between anti-androgenicity and cytotoxicity was exemplarily demonstrated with 4-nitroquinoline 1-oxide. As a proof of concept, the detection and quantification of anti-androgenicity in an extract of a landfill leachate is demonstrated. This study shows that the hyphenation of HPTLC with the yeast anti-androgen screen is a matrix-robust, cost-efficient and fast screening tool for the sensitive and simultaneous detection of anti-androgenic and cytotoxic effects in environmental samples. The method offers a wide range of possible applications in environmental monitoring and contributes to the identification of anti-androgenicity drivers in the course of an effect-directed analysis.

Crowdsourcing initiatives during the COVID-19 pandemic: empirical evidence from the Italian context

PurposeAcademics and practitioners are increasingly discussing the role of crowdsourcing initiatives in dealing with the challenges imposed by the COVID-19 pandemic, along with their role in an emergency context in general. However, empirical evidence about the role of crowdsourcing initiatives in an emergency context is still scarce. This paper aims to address this gap in knowledge.Design/methodology/approachThe authors adopted an inductive approach to investigate how companies leveraged crowdsourcing initiatives to address the issues posed by COVID-19. Data were collected through semi-structured interviews. The selection of the interviewees was based on a purposive sampling strategy. Data were analysed through thematic analysis.FindingsThe analysis led to the identification and examination of drivers (e.g. preserving relationships with customers), advantages (e.g. increased demand for services) and disadvantages (e.g. increased platform cost) of implementing crowdsourcing initiatives during COVID-19. By leveraging the findings, this paper suggests future research opportunities. Relevant future research opportunities include: (1) quantitatively evaluating the economic impact of crowdsourcing initiatives during a disruptive event and (2) examining how the sector in which the company operates influences the beneficial and detrimental effects of crowdsourcing initiatives on company performance.Originality/valueThe involvement of crowds as innovation partners has provided unique opportunities for companies to innovate and address the challenging scenario. The scientific literature about the role of crowdsourcing during COVID-19 is growing. However, there is still a scarcity of empirical evidence about the mechanisms underpinning the use of crowdsourcing during the pandemic. By leveraging semi-structured interviews, this paper addresses this relevant gap in knowledge.

Application of Data Mining to Small Data Sets: Identification of Key Production Drivers in Heterogeneous Unconventional Resources

Summary In this study, we developed a data mining-based multivariate analysis (MVA) workflow to identify correlations in complex high-dimensional data sets of small size. The research was motivated by the integration analysis of geologic, geophysical, completion, and production data from a 4-square-mile study field located in the Northern Denver-Julesburg (DJ) Basin, Colorado, USA. The goal is to establish a workflow that can extract learnings from a small data set to guide the future development of surrounding acreages. In this research, we propose an MVA workflow, which is modified significantly based on the random forest algorithm and assessed using the R2 score from K-fold cross-validation (CV). The MVA workflow performs significantly better in small data sets compared to traditional feature selection methods. This is because the MVA workflow includes (1) the selection of top-performing feature combinations at each step, (2) iterations embedded, (3) avoidance of random correlation, and (4) the summarization of each feature’s occurrence at the end. When the MVA workflow was initially applied on a complex synthetic small data set that included numerical and categorical variables, linear and nonlinear relationships, relationships within independent variables, and high dimensionality, it correctly identified all correlating variables and outperformed traditional feature selection methods. Following that, a field data set consisting of the information from 23 wells was investigated using the MVA workflow aiming at identifying the key factors that affect the production performance in the study area. The MVA workflow reveals the weak correlation between production and legacy well effect. The results show that the key factors affecting production in this study area are total organic carbon (TOC) percentage, open fracture densities, clay content, and legacy well effect, which should receive significant attention when developing neighboring acreage of the DJ Basin. More importantly, this MVA method can be implemented in other basins. Considering the heterogeneity of unconventional resources, it is worthwhile to identify the key production drivers on a small scale. The outperformance of this MVA method on small data sets makes it possible to provide valuable insights for each specific acreage.

Environmental and socio-psychological drivers of building users’ behaviours: a case study of tertiary institutional offices in Auckland

PurposeBetter identification of comfort preferences and occupant behaviour drivers is expected to improve buildings’ user-centred designs and energy operations. To understand the underline drivers of occupant behaviours in office buildings, this study aims to evaluate the inter-relationships among occupant energy behaviours, indoor environmental quality satisfaction, user control and social-psychological factors influencing occupant behaviours in New Zealand offices.Design/methodology/approachUsing an occupant perception survey, this study identifies the occupant behaviour patterns based on multi-domain comfort preferences. A case study was conducted in five office spaces of a university in Auckland, New Zealand. Data were collected from 52 occupants and analysed using descriptive and binary logistic regression analysis. Indoor environmental quality, user control, motivational, opportunity and ability factors were the independent variables considered. A model to predict the behaviours using environmental, building and social-psychological aspects was developed.FindingsThe results showed that the primary sources of indoor environmental quality discomfort were related to thermal and air quality, while occupants’ indoor environmental quality satisfaction correlated with their comfort preferences. The outcomes emphasise how the connection between building systems and occupants’ comfort preferences affect the choice of occupant behaviours in offices. Also, the primary occupant behaviours were drinking hot and cold beverages, opening/closing windows and internal doors and adjusting clothing. The binary logistic regression analysis showed that occupants’ perceived user control satisfaction is the main driver for increasing window actions. No other independent variable showed a statistically significant association with other behaviours.Originality/valueThis study adopted a novel approach to assess the combined effects of comfort preferences, occupant energy behaviours and various environmental, building and socio-psychological factors for modelling energy-saving behaviours in office buildings.