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Cool roofs are roofing systems designed to reflect significant solar radiation, reducing heat absorption and subsequent cooling energy demands in buildings. This paper provides a comprehensive review of cool roof technologies, covering performance standards, material options, energy-saving potential, and hygrothermal considerations. The review examines provisions in current codes and standards, which specify minimum requirements for solar reflectance, thermal emittance, and solar reflectance index (SRI) values. These criteria often vary based on factors like roof slope, climate zone, and building type. Different cool roof materials are explored, including reflective paints and coatings that can be applied to existing roofs as cost-effective solutions. Several studies have demonstrated the energy performance benefits of cool roofs, showing significant reductions in cooling loads, indoor air temperatures, peak cooling demand, and overall cooling energy consumption compared to traditional roofs. However, hygrothermal performance must be evaluated, especially in cold climates, to optimize insulation levels and avoid moisture accumulation risks, as reduced heat absorption can alter moisture migration patterns within the building envelope. While cool roofs provide substantial energy savings in hot climates, further research is needed to validate modeling approaches against real-world studies, investigate the impact of seasonality and green spaces on cool roof efficacy and urban heat island mitigation, and explore energy-saving potential, moisture control, and condensation risks in cold and humid environments.

The building sector is crucial in keeping the environment healthy, mainly because of its energy and material usage. Roofs are one of the most important components to consider, as they not only shield the building from the elements but also have a big impact on the environment. The paper provides a state-of-the-art review of the life cycle assessment (LCA) application in the roofing industry. The review examines three main focus areas: (1) LCA of different roofing materials, (2) LCA of roofing systems, and (3) whole-building LCA. Key takeaways from the literature review demonstrate that there is significant variability in LCA methods and impact categories assessed across roofing studies. Only a few studies have explored the complete urban scale in LCA assessments of roofing components. Future research can include utilizing the potential of LCA at urban scales, which can offer a full understanding of the environmental impacts associated with roofing materials in urban settings.

One of the parameters that influences the moisture performance of the wood framed wall assembly is the material properties of exterior cladding. The uncertainties of its properties, would result in a range of wall performance. The objective of this study was to investigate the impact of uncertainties in cladding material properties on moisture performance of wood framed wall assembly under different climatic conditions. A wood framed (2×6 wood stud) wall with exterior brick cladding was simulated assuming 1% rain leakage deposited on the exterior side of sheathing membrane. A parametric study was carried out to analyze the impact of the cladding properties on the moisture response of OSB. The simulations were conducted in five different cities located in different climate zones across Canada. The aim was to identify the most influential cladding property on the moisture response of OSB, i.e., mould growth index and moisture content, to the varying cladding properties under different climatic conditions i.e., different cities under historical and future conditions. In general, it was found that liquid diffusivity is the parameter that has the most influence on moisture response of OSB in all the five cities. Also, the significance of this influence varies depending on the climatic conditions.

Hygrothermal simulations can be used as a reliable tool in analysing moisture performance. For an efficient analysis, it is important to appropriately select the wall orientation in the simulations. ASHRAE 160 recommends to using orientation with highest amount of annual wind-driven rain (WDR) and the orientation with the least annual solar radiation. The objective of this work was to identify the orientation which leads to the worst moisture performance of different wall assemblies under historical climate in different Canadian cities. Four cardinal orientations (North, East, South, and West) and orientation receiving the highest amount of annual WDR (Default) were tested in this study. The simulations were carried out assuming three scenarios of moisture loads for four different wood-frame (2×6 wood stud) wall systems that differ by their claddings: brick, fibreboard, stucco, and vinyl. With an assumption of no WDR, north facing wall always leads to the worst moisture performance. In the presence of WDR, with and without water source, default orientation leads to the worst moisture performance with few exceptions. As default orientation was based on total sum of WDR, it sometimes may not lead to worst performance and hence hourly distribution of WDR should be taken into consideration.

Primary Plasma cell leukemia (pPCL) is an aggressive variant of plasma cell dyscrasias. Diagnostic criteria of plasma cell leukemia were recently updated by international myeloma working group to with more than 5% circulating plasma cells or absolute plasma cell count of more than 500/µL. We performed a retrospective analysis of patients diagnosed with pPCL in our department from 2017 to 2022. Clinical characteristics including the symptoms at presentation, organomegaly, bony involvement and extramedullary involvement were collected. Laboratory parameters including the biochemistry serum protein electrophoresis, serum immunofixation, serum free light chain assay, immunoglobulin profile were sent. Treatment and follow up data was collected. Fifteen patients were diagnosed (8 females and 7 males), median age 59 years (34–70). Six were lost to follow up and nine patients who received treatment at our hospital were analyzed for survival outcome. First line treatment was bortezomib- dexamethasone and immunomodulatory drugs (IMiD). Six (66%) achieved partial response or more and 3 had progressive disease. Five of the nine patients (55%) underwent autologous transplantation. Two out of 5 patients (40%) in the transplant group and 3 of the 4 patients (75%) in the non transplant group have died of the progressive disease. Overall survival was 45% at a median follow up of 14 months. Median OS for patients who underwent auto SCT was 16 months (12–22) versus 10 months (8–12) for patients who did not undergo transplant (Student t test; p value 0.018). Three of the patients achieved MRD negativity after transplant and post transplant consolidation therapy. Survival appears to be improved in patients who respond to initial therapy and are able to achieve MRD negativity which should be the goal of treatment in these patients.

Businesses can benefit from customer feedback in different modalities, such as text and images, to enhance their products and services. However, it is difficult to extract actionable and relevant pairs of text segments and images from customer feedback in a single pass. In this paper, we propose a novel multi-modal method that fuses image and text information in a latent space and decodes it to extract the relevant feedback segments using an image-text grounded text decoder. We also introduce a weakly-supervised data generation technique that produces training data for this task. We evaluate our model on unseen data and demonstrate that it can effectively mine actionable insights from multi-modal customer feedback, outperforming the existing baselines by \\(14\\) points in F1 score.

We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.

Summarizing customer feedback to provide actionable insights for products/services at scale is an important problem for businesses across industries. Lately, the review volumes are increasing across regions and languages, therefore the challenge of aggregating and understanding customer sentiment across multiple languages becomes increasingly vital. In this paper, we propose a novel framework involving a two-step paradigm \\textit{Extract-then-Summarise}, namely MARS to revolutionise traditions and address the domain agnostic aspect-level multilingual review summarisation. Extensive automatic and human evaluation shows that our approach brings substantial improvements over abstractive baselines and efficiency to real-time systems.

The recognition of mathematical expressions remains a challenging task, particularly due to the segmentation sub-stage, which plays a critical role in the overall recognition process. Despite significant advancements in mathematical expression recognition, existing research has primarily focused on the recognition phase, often overlooking the segmentation issues that arise in diverse domains such as computer vision and image processing. This study aims to address the segmentation problem in handwritten mathematical text and expression recognition through a comprehensive analysis and the development of an optimal solution. Classical segmentation methods were explored, classified, and tested on various datasets of mathematical expressions, with multiple comparative case analyses conducted. Based on these findings, an optimal neural network-based segmentation approach was proposed. The model demonstrated effective segmentation performance, achieving competitive mean Intersection over Union (IOU) scores of 79.4%, 83.5%, 81.3%, 74.6%, and 79.6% on the CROHME 2014, CROHME 2016, CROHME 2019, Aidapearson, and HasyV datasets, respectively. The results highlight the success of the proposed neural network-based solution in overcoming the limitations of traditional segmentation methods and affirm its potential in enhancing mathematical expression recognition systems.

Therapeutic methods to modulate skin pigmentation has important implications for skin cancer prevention and for treating cutaneous hyperpigmentary conditions. Towards defining new potential targets, we followed temporal dynamics of melanogenesis using a cell-autonomous pigmentation model. Our study elucidates 3 dominant phases of synchronized metabolic and transcriptional reprogramming. The melanogenic trigger is associated with high MITF levels along with rapid uptake of glucose. The transition to pigmented state is accompanied by increased glucose channelisation to anabolic pathways that support melanosome biogenesis. SREBF1-mediated up-regulation of fatty acid synthesis results in a transient accumulation of lipid droplets and enhancement of fatty acids oxidation through mitochondrial respiration. While this heightened bioenergetic activity is important to sustain melanogenesis, it impairs mitochondria lately, shifting the metabolism towards glycolysis. This recovery phase is accompanied by activation of the NRF2 detoxication pathway. Finally, we show that inhibitors of lipid metabolism can resolve hyperpigmentary conditions in a guinea pig UV-tanning model. Our study reveals rewiring of the metabolic circuit during melanogenesis, and fatty acid metabolism as a potential therapeutic target in a variety of cutaneous diseases manifesting hyperpigmentary phenotype.