Search Results (294)

Food waste is a global sustainability issue, and in Japan, approximately half of all food waste is generated in households. This study focused on refrigerator management behaviors aimed at using up the food inventory in the home. An intervention study involving 119 households with two or more members across Japan, with a two-week baseline period and a two-week intervention, was conducted. Target behaviors were set as “search food that should be eaten quickly,” “move it to a visible place,” and “use the foods that should be eaten quickly,” and tools to support these behaviors were selected, including an organizer for the refrigerator, photos, and food management apps. Each tool was assigned to approximately 30 households, and a control group was established. Food waste was measured using a cloud-based automatic weighing system, and all participants were asked to separate avoidable food waste at home and dispose of it in the designated waste bin. During the intervention period, the average weekly food waste per household decreased by 29% to 51% in the intervention group, while there was little change in the control group. An analysis using a two-way mixed ANOVA revealed a marginally significant interaction (p < 0.10), indicating moderate effectiveness. Among the behaviors contributing to reduced food waste, three actions—“having trouble not being able to recall food inventory at home during shopping,” “moving foods that should be used sooner,” and “organizing refrigerator”—showed significant interaction effects (p < 0.05) in a two-way mixed ANOVA, indicating the effectiveness of the intervention. Full article

High unemployment rates, inaccessible housing markets, and funding challenges create barriers to finding suitable housing for adults with Autism Spectrum Disorder (ASD) who have less obvious support needs, also known as autistic adults. While public and community housing services in Aotearoa New Zealand (AoNZ) may be an option, a lack of accessible designs leaves families uncertain about future care options. This paper, part of the MBIE-funded Public Housing and Urban Regeneration: Maximising Wellbeing research programme in partnership with registered Community Housing Provider, Te Toi Mahana (TTM), takes an exploratory approach to ask how public and community housing can support and help enable semi-independent living for autistic adults. It investigates how design elements—such as dwelling layouts, material choices, colour schemes, lighting, acoustics, shared and community spaces, and external environments—impact the wellbeing of autistic adults. By extension, insights may also inform private housing design. The study focuses on autistic adults who may be considered ‘mid-to-high’ functioning or those who have been previously diagnosed with Asperger’s Syndrome, whose housing needs are often overlooked. It develops guiding principles and detailed guidance points for public and community housing, informed by the literature, case studies, and data from a photo elicitation study and interviews undertaken with autistic adults in AoNZ. These guiding principles are tested through the speculative redesign of a large TTM site in Newtown, Wellington, AoNZ. Findings should be of interest to government agencies, housing providers, architects, stakeholders, and others involved in shaping the built environment, as well as autistic adults and their supporters, both in AoNZ and internationally. Full article

Despite the importance of Urban Green Infrastructure (UGI) as a provider of multiple Ecosystem Services (ESs), some concerns have been raised regarding Ecosystem Disservices (EDs) associated with UGI design and management, namely, the link between Urban Green Spaces’ (UGSs) attributes and invasion spatial patterns. This research takes the UGI of Coimbra, a medium-sized Portuguese city, as a case study to explore the relationships between UGS attributes and the occurrence of invasive plant species. The methodology involved aerial photo-interpretation and full patch survey to collect data about UGSs types, maintenance level and occurrence of invasive plant species, and landscape metrics analysis. Our results showed that the UGI of Coimbra exhibits a large prevalence of small UGSs with regular maintenance and the occurrence of invasive plant species in a low number of patches (17%). Although these patches correspond to 64% of the UGI. The area of recent sprawl (zone 2) registers higher occurrence of invasive plant species across different UGSs types, with higher prevalence in patches with no or low maintenance. Mapping the occurrence of invasive plant species in UGS is of utmost importance to implement appropriate maintenance practices, allowing medium-sized cities like Coimbra to optimize ESs associated with UGI and minimize potential EDs. Full article

Background and Objectives: Traditionally, we evaluate the size of a wound by using Opsite Flexigrid transparent film dressing, placing it over the wound, tracing the edges of the wound, and then calculating the area. However, this method is both time-consuming and subjective, often leading to varying results depending on the individual performing the assessment. In this study, our goal is to provide an objective method to calculate the wound size and solve variations in photo-taking distance caused by different medical practitioners or at different times, as these can lead to inaccurate wound size assessments. To evaluate this, we employed K-means clustering and used a QR code as a reference to analyze images of the same wound captured at varying distances, objectively quantifying the areas of 40 wounds. This study aims to develop an objective method for calculating the wound size, addressing variations in photo-taking distance that occur across different medical personnel or time points—factors that can compromise measurement accuracy. By improving consistency and reducing the manual workload, this approach also seeks to enhance the efficiency of healthcare providers. We applied K-means clustering for wound segmentation and used a QR code as a spatial reference. Images of the same wounds taken at varying distances were analyzed, and the wound areas of 40 cases were objectively quantified. Materials and Methods: We employed K-means clustering and used a QR code as a reference to analyze wound photos taken by different medical practitioners in the outpatient consulting room. K-means clustering is a machine learning algorithm that segments the wound region by grouping pixels in an image according to their color similarity. It organizes data points into clusters based on shared features. Based on this algorithm, we can use it to identify the wound region and determine its pixel area. We also used a QR code as a reference because of its unique graphical pattern. We used the printed QR code on the patient’s identification sticker as a reference for length. By calculating the ratio of the number of pixels within the square area of the QR code to its actual area, we applied this ratio to the detected wound pixel area, enabling us to calculate the wound’s actual size. The printed patient identification stickers were all uniform in size and format, allowing us to apply this method consistently to every patient. Results: The results support the accuracy of our algorithm when tested on a standard one-cent coin. The paired t-test comparing the first and second photos shot yielded a p-value of 0.370, indicating no significant difference between the two. Similarly, the t-test comparing the first and third photos shot produced a p-value of 0.179, also showing no significant difference. The comparison between the second and third photos shot resulted in a p-value of 0.547, again indicating no significant difference. Since all p-values are greater than 0.05, none of the test pairs show statistically significant differences. These findings suggest that the three randomly taken photo shots produce consistent results and can be considered equivalent. Conclusions: Our algorithm for wound area assessment is highly reliable, interchangeable, and consistently produces accurate results. This objective and practical method can aid clinical decision-making by tracking wound progression over time. Full article

Background: Medical photography is widely used in dermatology referrals to secondary healthcare, yet concerns exist regarding image quality and data security. This study aimed to evaluate the quality of clinical photographs used in dermatology referrals, to identify discrepancies between specialties’ perceptions, and to determine the general awareness of proper storage and security of clinical photographs. Methods: A 43-question survey, based on previously validated questionnaires, was administered to general and family medicine (GFM) doctors and to dermatologists at an academic referral hospital in Porto, Portugal. The survey assessed demographics, photo-taking habits, perceived photo quality, adequacy of clinical information, and opinions on the role of photography in the referral process. Quantitative statistical methods were used to analyze questionnaire responses. Results: A total of 65 physicians participated (18 dermatologists and 47 GFM doctors). Significant differences were observed between the two groups. While 36.2% of GFMs rated their submitted photos as high- or very-high-quality, none of the dermatologists rated the received photos as high-quality, with 83.3% rating them as average (p = 0.012). Regarding clinical information, 46.8% of GFMs reported consistently sending enough information, while no dermatologists reported always receiving sufficient information (p < 0.001). Most respondents (76.9%) agreed that the quality of photographs is important in diagnosis and treatment. Conclusions: The findings reveal a discrepancy between GFM doctors’ and dermatologists’ perceptions of photograph quality and information sufficiency in dermatology referrals. Standardized guidelines and educational interventions are necessary to improve the quality and consistency of clinical photographs, thereby enhancing communication between healthcare providers and ensuring patient data privacy and security. Full article

This study aimed to check whether physical signs of children’s presence in the urban space have an impact on sense of safety and preferences. We based the study on an intra- and inter-group design, whereby respondents assessed two types of space: housing estates and green areas. The photos were manipulated to create three scenarios: control (without elements related to children), denotation (spaces supplemented with children’s playgrounds), and connotation (spaces containing elements associated with children). Each scenario was assessed by a separate group of respondents, who were further split between men and women. Analyses have shown that in residential districts, spaces with child-related connotations foster a feeling of safety and enhance the general appeal of the place, especially among women. In turn, direct signs of the presence of children (denotation) have a lesser impact on the positive assessment of the space. In green areas, adding child-related details did not affect women’s feelings but lowered men’s evaluations of safety and preference. The findings suggest that elements associated with children should be used selectively, taking into account the type of space and differences in perception between the sexes. These observations can support the creation of sustainable urban environments: safe and inclusive housing estate and park designs, especially in the context of building cities that foster social integration without exclusions. This is a preliminary study that paves the way for further exploration of the topic. In order to be able to analyse adults’ preferences and safety in more depth, it seems interesting to investigate the shared perception of landscape by children and adults and the possibilities of participatory design of public spaces. Full article

With the popularity of low-altitude small unmanned aerial vehicles (UAVs), UAVs are often used to take candid photos or even carry out malicious attacks. Acoustic detection can be used to locate UAVs in order to prevent malicious attacks by UAVs. Aiming at the problem of a large error in the time delay estimation algorithm under a low SNR, a time delay estimation algorithm based on an improved weighted function combined with a generalized cubic cross-correlation is introduced. By analyzing and comparing the performance of generalized cross-correlation time delay estimation of different traditional weighting functions, an improved weighting function that combines improved smooth coherent transform (SCOT) and phase transform (PHAT) is proposed. Compared with the traditional generalized cross-correlation weighted function, the improved weighted function has a sharper and higher peak value, and the time delay estimation error is smaller at a low SNR. Secondly, by combining the improved weight function with the generalized cubic cross-correlation, the main peak value is further increased and sharpened, and the time delay estimation performance is better than that when combined with the generalized cubic cross-correlation and the generalized quadratic correlation. Experimental results show that in complex outdoor scenes, the positioning error of the unimproved GCC PHAT method is 45.22 cm, and the positioning error of the improved weighted function generalized cubic cross-correlation algorithm is no more than 22.1 cm. Compared with the unimproved GCC PHAT method, the performance is improved by 35.55%. It is proven that this method is helpful for improving the positioning ability of low-flying UAVs and can provide help for anti-terrorism security against malicious attacks by UAVs. Full article

Three studies investigated the effects of photo and note taking on memory for lecture content in in-person and online environments. Participants watched slideshow lecture videos and were instructed to only watch, take photos, take notes, or simultaneously take both photos and notes of the information on the slides. Memory for on-slide and only said information was tested using fill-in-the-blank questions. Experiment 1, conducted in-person, found a significant photo-taking impairment and an interaction for on-slide information such that there was a larger impairment when participants took both photos and notes compared to when they only took notes. Experiment 2 failed to replicate this interaction; there was an overall photo-taking impairment for on-slide information in an in-person learning environment. We additionally examined mind wandering as a potential mechanism driving these effects but found that it does not provide a sufficient explanation for our results. Experiment 3 used a design similar to Experiments 1 and 2 in an online environment and found a photo-taking benefit for on-slide information. Our results suggest that, in in-person classes, photo taking likely impairs learning, but, in online classes, photo taking may not be as harmful. Participants showed a note taking benefit in Experiments 2 and 3 across both class modalities. Full article

The research presented in this article seeks to identify the possible causes of reinforcement shadows (RS) on the surface of concrete test specimen produced under laboratory conditions. Different hypotheses about RS were selected based on factory practices and simulated in the study. The test specimens were cast horizontally in contact with steel form-facing surfaces coated with a water-soluble release agent. In addition, two scenarios were analysed during specimen production: reinforcing mesh was fixed using plastic spacers or tie wire. The analysis of the reinforcement shadows was based on visual inspection, taking photos, surface moisture content measurements, and colour variation analysis using the Natural Colour System. It was concluded that RS, which are typically characterized by darker lines, can be defined by the percentage of black colour present in the shadowed area compared to the percentage of black colour in the surrounding area. This percentage can be quickly assessed on a factory scale using digital colour readers that provide timely information. The reduced concrete cover thickness from 35 mm to 10 mm revealed light horizontal dark lines on the exposed surface. It was hypothesised that the gap of less than 10 mm between the reinforcing bars and the steel form-facing plate, along with the sieving effect of the fresh concrete, can retard the cement paste hydration process, resulting in unhydrated ferrite phases that contribute to the dark colour of the unhydrated cement. The release agent sprayed on the steel form-facing surface straight through the reinforcing mesh created a RS effect of the reinforcement on the exposed concrete surface. The absence of a release agent under steel rebars decreased the wettability at the interface between the formwork and fresh concrete, resulting in dark lines during the curing process. It is important to avoid such cases when manufacturing precast reinforced concrete elements. Quantitatively assessing RS and proposing a standardized method for calculation and categorization could be a new research direction in the future. Full article

The worldwide growing adoption of Light Electric Vehicles (LEVs) indicates that such technology might in the near future be decisive for improving the sustainability of transportation. The segment of LEVs has some peculiar features compared to electric mobility in general, which then deserve a devoted investigation. Stakeholders are called to implement the most appropriate technology depending on the context, by taking into account multi-faceted factors, which are the investigation object of this work. At first, a methodology is formulated for estimating the power and energy impact of LEVs recharging. Based on this, and assessed that the load constituted by LEVs is in general modest but might create some problems in lowly structured networks, it becomes conceivable to develop Charging Station (CS) technologies which are alternative to the grid connection at a point of delivery. Yet, it is fundamental to develop accurate methodologies for the techno-economic and environmental analysis. This work considers a use case developed at the University of Brescia (Italy): a CS operating off-grid, powered by PhotoVoltaics (PV). Its peculiarity is that it is transportable, which makes it more appealing for rural/remote areas or when the charging demand is highly not homogeneous in time. On these grounds, this work specializes to a context where the proposed solution might be more appealing: small isolated islands, in particular Favignana in Sicily (Italy). It is estimated that the adoption of the proposed off-grid CS is by far advantageous as regards the greenhouse gases emissions but it is more economically profitable than the grid connection only if the number of users per day is less than order of 200. Hence this work provides meaningful indications on the usefulness of off-grid CS powered by PV in peculiar contexts and furnishes a general method for their techno-economic and environmental assessment. Full article

The aim of this study was to develop a mobile application for Android devices dedicated to the classification of pathological changes in human eye optical coherence tomography (OCT) B-scans. The classification process is conducted using convolutional neural networks (CNNs). Six models were trained during the study: a simple convolutional neural network with three convolutional layers, VGG16, InceptionV3, Xception, Joint Attention Network + MobileNetV2 and OpticNet-71. All of these models were converted to TensorFlow Lite format to implement them into a mobile application. For this purpose, three models with the best parameters were chosen, taking accuracy, precision, recall, F1-score and confusion matrix into consideration. The Android application designed for the classification of OCT images was developed using the Kotlin programming language within the Android Studio integrated development environment. With the application, classification can be performed on an image chosen from the user’s files or an image acquired using the photo-taking function. The results of the classification are displayed for three neural networks, along with the respective classification times for each neural network and the associated image undergoing the classification task. The mobile application has been tested using various smartphones. The testing phase included an evaluation of image classification times and score accuracy, considering factors such as image acquisition method, i.e., camera or gallery. Full article

The ICARUS-T600 liquid argon time projection chamber detector takes data at a shallow depth as the far detector of the Short Baseline Neutrino program at Fermilab, searching for sterile neutrinos with the Booster and Main Injector neutrino beams. The ICARUS trigger system exploits the temporal coincidence of the beams with scintillation light signals detected by 360 photo-multiplier tubes in limited TPC regions. The trigger efficiency measurement leverages cosmic rays collected without any scintillation light requirement, with timing from an external cosmic ray tagger system. The efficiency measured with stopping muons roughly saturates at $E_{\mathsf{\mu }}$∼300 MeV, covering most of the expected energy range of charged-current neutrino interactions. For the latest ICARUS physics runs, special “adder” boards performing the analog sum of light signals were introduced as a complementary trigger to possibly recover low-energy neutrino interactions. Full article

The transition between the inflammatory phase and the proliferative phase is critical for wound healing. However, the development of proper switchers that can regulate this transition is facing great challenges. Macrophages play versatile roles in all wound healing phases because they can readily switch from pro-inflammatory M1 phenotypes to anti-inflammatory M2 phenotypes in response to different microenvironment stimuli. Herein, taking advantage of enhanced electron transfer by coupling MoS₂ with a highly conductive activated carbon fiber (ACF) network, a MoS₂-ACF heterojunction structure was constructed as a macrophage M1-M2 phenotype switcher (MAPS) for regulating inflammation–proliferation transition to accelerate wound healing. In the early stages of wound repair, MAPS-mediated photothermal effects with near-infrared laser irradiation could promote macrophage reprogramming to the M1 phenotype, which can expedite inflammation. NIR photo-induced hyperthermia, together with M1 macrophages, directly and indirectly kills bacteria. Later, during the healing process, the MAPS could further reprogram macrophages towards the M2 phenotype via its inherent reactive oxygen species (ROS) scavenging ability to resolve inflammation, promoting cell proliferation. Therefore, MoS₂-ACF heterojunction structures provide a new strategy to modulate inflammation–proliferation transition by rebalancing the immuno-environmental equilibrium of macrophage M1/M2 phenotypes. Full article

A magnetic shell-structured nano-catalyst was prepared by self-polymerization of dopamine wrapped by ferric oxide as the carrier, which was loaded with palladium nanoparticles (Pd/PDA@Fe₃O₄). The presence of magnetic Fe₃O₄ made it easy for nanoscale palladium particles to recover and prevent the loss of palladium nanoparticles that is unavoidable in traditional usage and preparation procedures. The catalyst was characterized by X-ray diffraction, fourier transform infrared spectroscopy, scanning electron microscopy, thermal weight loss analysis, Raman spectroscopy, X-ray photo-electron spectroscopy, and magnetic properties analysis. The catalytic performance of the prepared catalyst was investigated taking 4-nitrophenol (10 mg/L) and rhodamine B (15 mg/L) as the target pollutants. The results showed that under the conditions of 35 °C, pH = 7 and a catalyst dosage of 3 mg, the catalytic reduction efficiency of 4-nitrophenol, rhodamine B, and the mixture of them all can reach 99%. The catalytic efficiency of Pd/PDA@Fe₃O₄ remained above 90% after being used 10 times. The shell structure of Fe₃O₄ made it possible and easy to recover and recycle the nanoscale palladium, which was a real problem in the usage of nano-catalysts. At the same time, the problem of separation and recovery of palladium nano-catalyst is solved by magnetism, which provides research ideas for the recycling and utilization of nano-materials. Full article

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, the complexity and variety of scenarios render this a challenging task. In this paper, we propose a fine-grained crop disease classification network integrating the efficient triple attention (ETA) module and the AttentionMix data enhancement strategy. The ETA module is capable of capturing channel attention and spatial attention information more effectively, which contributes to enhancing the representational capacity of deep CNNs. Additionally, AttentionMix can effectively address the label misassignment issue in CutMix, a commonly used method for obtaining high-quality data samples. The ETA module and AttentionMix can work together on deep CNNs for greater performance gains. We conducted experiments on our self-constructed crop disease dataset and on the widely used IP102 plant pest and disease classification dataset. The results showed that the network, which combined the ETA module and AttentionMix, could reach an accuracy as high as 98.2% on our crop disease dataset. When it came to the IP102 dataset, this network achieved an accuracy of 78.7% and a recall of 70.2%. In comparison with advanced attention models such as ECANet and Triplet Attention, our proposed model exhibited an average performance improvement of 5.3% and 4.4%, respectively. All of this implies that the proposed method is both practical and applicable for classifying diseases in the majority of crop types. Based on classification results from the proposed network, an install-free WeChat mini program that enables real-time automated crop disease recognition by taking photos with a smartphone camera was developed. This study can provide an accurate and timely diagnosis of crop pests and diseases, thereby providing a solution reference for smart agriculture. Full article