The Risk of Greenwashing in Corporate Social Responsibility Communications

There is a growing expectation from consumers that companies recognize the environmental impact of their businesses and engage in corporate social responsibility (CSR) initiatives. While the demand for CSR has increased, so has the prevalence of greenwashing, which has caused consumers to be more skeptical about a company’s motives when CSR is promoted. Marketing practitioners are faced with the challenge of balancing the demand for corporate responsibility with the skepticism of greenwashing. This study consists of a survey of 22 marketing practitioners in Canada to explore their experiences when developing CSR-related communications and how they establish trust with audiences to reduce the perception of greenwashing. The results illustrate that practitioners manage the risk of greenwashing by developing messages that do not self-promote, showcasing concrete action and evidence to support claims, relying on positive marketing appeals such as pride and compassion, and tailoring messages based on the audience and industry a company belongs to

A Practical Data-Driven Multi-Model Approach to Model Predictive Control: Results from Implementation in an Institutional Building

Model-based Predictive Control (MPC) is an effective solution to improve building controls. It consists of the use of weather and occupancy forecasts along with a control-oriented model to predict the behaviour of the building a few hours or days ahead, and thus optimize the operation of its systems. Although the potential of MPC is widely recognized, and plentiful operational data is often available, the development of a model requires a great deal of effort, significant technical expertise and knowledge of building systems. The challenge of creating a model is a hurdle that makes the on-site implementation of MPC in buildings relatively rare. This study tackles the development of a multi-model approach to optimize the operation of electric and natural gas boilers in an institutional building to reduce greenhouse gas (GHG) emissions while maintaining the required level of comfort. This methodology leverages Machine Learning techniques to rapidly develop and calibrate control-oriented models using a limited number of input variables (indoor air temperature and temperature set-points, weather conditions, power meter data). The proposed multi-model approach consists of five models used to estimate the building total heating demand, the electric baseload, the natural gas boiler power, and the indoor air temperature under free floating conditions and during warming-up periods in the morning. The models are calibrated and validated with operational data and they are then used to optimize the transition between nighttime and daytime indoor air temperature. Since these are black-box models that require only a basic understanding of the building system and a few inputs, the model development was considerably reduced while the modularity of the proposed method makes it flexible. Such an approach could therefore be easily replicated in other buildings equipped with similar pieces of equipment. This methodology has been implemented in a Canadian institutional building, located in Varennes (QC). Results in 2020-21 showed that the COVID-19 pandemic has significantly impacted building performance and reduced energy use, thus creating a new baseline. The MPC strategy allowed to achieve an additional 20.2% GHG emission reduction compared to this new baseline while thermal comfort was improved. Nevertheless, energy costs increased, which was mainly due to the impact of the pandemic, which eventually made the pre-COVID-19 model and optimization parameters outdated; lower costs are expected with model recalibration, currently ongoing

Improvement in accuracy of diagnosis of carotid artery stenosis with duplex ultrasound scanning with combined use of linear array 7.5 MHz and convex array 3.5 MHz probes: validation versus 489 arteriographic procedures1 1Competition of interest: none.Published online Mar 6, 2003.

AbstractObjective: Validity of a method to improve the accuracy of carotid artery duplex scanning was tested in comparison with arteriography.Study Design: In 489 patients who had not previously undergone arteriography, 978 carotid arteries were examined with duplex ultrasound scanning. In method A, a linear array 7.5 MHz transducer with pulsed-wave 4.7 MHz Doppler scanning was used. For the diagnosis and grading of carotid stenosis, peak systolic and end-diastolic velocity of the Doppler waves were recorded. Method B consisted of complete ultrasound imaging and color-flow mapping with a convex array 3.5 MHz transducer with pulsed-wave 2.8 MHz Doppler scanning in all patients who had previously undergone method A. Further velocity measurements were performed at the sites of stenosis. The results of methods A and B were compared with data from neurologic assessment and arteriographic studies.Results: Method B showed significantly higher diagnostic agreement with arteriography than did method A (K 95% confidence interval [CI], 0.87–0.93 vs 0.79–0.85; P < .05), and the number of mistakes in grading stenosis was significantly lower (primarily because of decreased overestimation) in patients with internal carotid kinking (>60 degrees of angulation) (P < .05), distal stenosis (>20 mm from bifurcation) (P < .01), or wide acoustic shadowing (>1 cm) (P < .01) and in those without these conditions (P < .05). Compared with arteriography, diagnostic accuracy with the new method proved higher for carotid stenoses 50% or greater, 60% or greater, 70% or greater, and 80% or greater; no statistically significant difference was found for carotid stenosis 96% or greater or for carotid occlusion. Compared with data from neurologic assessment and arteriography, method B proved more accurate than method A in designating patients for carotid endarterectomy (P = .014).Conclusions: The new method significantly improved diagnostic reliability of duplex ultrasound scanning, especially in carotid arteries with kinking, distal stenosis, or wide acoustic shadowing (32.2% of all arteries studied). In clinical practice, we suggest additional use of a lower frequency transducer in cases in which these three conditions are found or suspected at first scanning

Dispersion Engineered Metasurfaces for Broadband, High-NA, High-Efficiency, Dual-Polarization Analog Image Processing

Analog computing and image processing with optical metasurfaces holds a great potential for increasing processing speeds and reducing power consumption. Among different functionalities, spatial differentiation and edge detection have recently attracted much interest in this context. While a few demonstrations have achieved analog edge detection with compact metasurfaces, current approaches often suffer from trade-offs in terms of spatial resolution, overall throughput, polarization asymmetry, operational bandwidth and isotropy. Here, we exploit dispersion engineering to design and realize metasurfaces capable of performing isotropic 2D edge detection over a broad operational bandwidth and for any input polarization, while simultaneously maintaining high numerical aperture and record efficiency. Remarkably, we show that this performance can be achieved within a single-layer metasurface consisting of a silicon photonic crystal on glass. We demonstrate metasurfaces performing isotropic dual-polarization edge-detection with numerical apertures larger than 0.35, and operating within a spectral bandwidth of 35 nm (5 THz) around 1500 nm. Moreover, we introduce quantitative metrics to properly assess the efficiency of the analog image processing. Thanks to the low insertion loss and the dual-polarization response, our metasurface provides edge-enhanced images with high efficiency and contrast across a broad operational bandwidth and for arbitrary input polarization. Remarkably, the experimentally measured efficiencies are very close to the ones of any ideal passive edge-detector device with a given NA, and they are in fact comparable to the efficiency obtained by performing analytically the Laplacian mathematical operation on the input images. Our results pave the way for the application of metasurfaces for low-loss, high-efficiency and broadband optical computing and image processing.Comment: 15 pages, 5 figures in main text. 9 pages, 7 figures in supplemental materia

Risk and Maintenance Factors for Eating Disorders: An Exploration of Multivariate Models on Clinical and Non-Clinical Populations

The recognition of factors involved in the development and maintenance of eating disorders (EDs) may support the choice of therapeutic strategies and improve the prevention/treatment of eating pathologies and their outcomes. Based on this consideration, the overall purpose of the chapter is to investigate how some psychological characteristics link to EDs. It is organized as follows. First, the epidemiological aspects, risk, and maintaining factors for ED are outlined. Next, we present the findings from our two studies. The purpose of the first study was to identify predictors associated with the severity of eating symptomatology. Then, the objective of the second study was to provide an understanding of the relationship among perceived parental bonding, self-esteem, perfectionism, body shame, body mass index, and ED risk and mainly to test a predictive ED risk model in a non-clinical sample. In conclusion, the major findings and practical implications are discussed

Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century.

Managing soil organic matter (SOM) stocks to address global change challenges requires well-substantiated knowledge of SOM behavior that can be clearly communicated between scientists, management practitioners, and policy makers. However, SOM is incredibly complex and requires separation into multiple components with contrasting behavior in order to study and predict its dynamics. Numerous diverse SOM separation schemes are currently used, making cross-study comparisons difficult and hindering broad-scale generalizations. Here, we recommend separating SOM into particulate (POM) and mineral-associated (MAOM) forms, two SOM components that are fundamentally different in terms of their formation, persistence, and functioning. We provide evidence of their highly contrasting physical and chemical properties, mean residence times in soil, and responses to land use change, plant litter inputs, warming, CO2 enrichment, and N fertilization. Conceptualizing SOM into POM versus MAOM is a feasible, well-supported, and useful framework that will allow scientists to move beyond studies of bulk SOM, but also use a consistent separation scheme across studies. Ultimately, we propose the POM versus MAOM framework as the best way forward to understand and predict broad-scale SOM dynamics in the context of global change challenges and provide necessary recommendations to managers and policy makers