An indisputable fact about our planet is that its atmospheric temperature has risen dramatically during the past century. Combustion of fossil fuels and their subsequent greenhouse gas emissions are thought to be the main contributors to recent changes within the Earth’s ecosystem. The transportation sector and electricity generating power plants are each responsible for approximately one-third of these emissions. Shifting towards a cleaner and renewable resources to generate electricity is believed to omit a big portion of polluting substances. Improvements in vehicles’ fuel efficiency and the introduction of alternative fuels besides strategic plans to control travel demand are among the most promising approaches to alleviate emissions from the transportation sector. Recent technology advancements, however, drew much attention to the production and manufacturing of alternative fuel vehicles, electric vehicles in particular. Since these vehicles use electricity as part of or all their powertrain, assessing the amount of emissions they produce is closely tied to the cleanliness of the electricity source. In order for a valid comparison to be made between internal combustion and electric vehicles, hence, a life cycle assessment procedure needs to be followed from production stages to terminal life of vehicles. Involvement of numerous affecting factors during the lifetime of a vehicle on one hand, and the ambiguity in the exact source of electricity used to charge electric vehicles on the other hand bring about more complexities. The latter case is more commonly known as the marginal grid problem, which deals with how a combination of sources used to generate electricity can influence the life cycle emissions. There are also other concerns regarding the growth in fuel-efficient and electric vehicles. Transportation planners argue that new developments in the vehicle industry may attract more people to owning and driving cars. This phenomenon which is better known as a rebound effect not only will result in increased traffic congestion, but it can also outpace the environmental benefits from utilizing electric vehicles. Moreover, since fuel taxes comprise the majority of Highway Trust Funds, alternative ways to compensate for state and federal revenues should be devised. This paper is an attempt to review the existing literature to better elaborate on the role of the transportation sector in controlling climate change threats. More specifically, issues around the use of electric vehicles and how they can contribute to more environmentally friendly communities are discussed.

As part of the efforts to reduce the environmental impacts caused by the aviation sector, the use of bio-based instead of fossil-based materials has been assessed as a possible mitigation option. Natural Fibre Polymer Composites have proven to have a higher environmental performance in the automotive sector and are emerging as an option for weight reduction in aircraft. This study quantifies, though Life Cycle Assessment, the environmental performance of specific flax-based composite panels intended for aircrafts as interior fitting elements (i.e. partition panels, tray tables, baggage compartments) compared to a glass fibre/epoxy composite with a honeycomb core. Through system expansion, the fate of co-products issued from the production of the flax fibre technical textile used as reinforcement in the biocomposite material were considered in the assessment. Results showed that for an application in the aeronautics sector, the weight of the panels is the upmost critical parameter shaping the overall environmental performance of panels. Focusing on the panel production only, the biocomposite panel showed a higher environmental performance in the categories of climate change and marine eutrophication compared to the conventional panel, and the fire suppressant agent was identified as the main contributor to the environmental impacts of the bio-based panel. Yet these gains were negligible when considering the full life cycle of the panels, due to the higher weight (14%) of the bio-based panels; which is linked to the bio-based panel being at a prototype stage.In order to improve the environmental performance of the biocomposite panel and thus reduce its weight, it was shown relevant to optimize geometry of the panel itself, especially its core, so less resin could be used.

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for high-quality digitization of content from pages and whiteboards to sharable online material. But capturing this information is not always easy, nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts, by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.

Safety of source water streams is an urgent environmental issue, while protections in monsoon controlled subtropical regions face difficulties because of the lack of small scaled observation and analysis in small source water catchments. Basing on continuous weekly water quality data (2014-2017) in Pingqiao River Catchment, the annual average values of TN, NO₃, NH₄ and TP are 3.36, 1.64, 0.28 and 0.02 (mg/L) respectively. During dry, normal and wet seasons, the variability of parameters is over 35%, which indicates an obvious seasonality. Multiple methods are combined in order to assess the water quality and find the driving factors during dry, normal and wet seasons. This study suggests precipitation and fertilization are the mainly seasonal factors, which can make water quality better in wet season than dry season due to the dilution effect. The mechanism between seasonality and compound of nutrients can also be traced by log(TN:TP), and log(NO₃:NH₄). Among six main land use types (forest, tea plantation, cropland (paddy), urban, bare soil and water), the former three ones are influential mostly during dry and wet season. Tea plantation has the largest nutrients discharge amount per area, which is similar to cropland in dry season. By contrary, forest has the double power in reducing nitrogen release in wet and normal seasons. When transformed into paddies, croplands can lower the phosphorus concentration. Conclusions of this study can be used in southeastern China and similar regions on source water protection and agricultural plans.

Remote sensing technologies may play a fundamental role in the environmental assessment of open-cast mining and the accurate quantification of mine land rehabilitation efforts. Here, we developed a systematic geographic object-based image analysis (GEOBIA) approach to map the amount of revegetated area and to quantify the land-use changes in open-cast mines in the Carajás region situated in the eastern Amazon. Based on high-resolution satellite images from 2011 to 2015 from different sensors (GeoEye, WorldView-3 and Ikonos), we quantified forests, cangas (natural metalliferous savanna ecosystems), mine land, revegetated areas and water bodies. Based on the GEOBIA approach, threshold values were established to discriminate land cover classes using spectral bands, and the NDVI and NDWI indices and LiDAR digital ground and slope models. The overall accuracy was higher than 90%, and the Kappa indices varied between 0.82 and 0.88. During the observation period, the mining complex expanded; for that, canga and forest vegetation was converted to mine land. At the same time, the amount of revegetated area increased. Thus, we conclude that our approach is capable of providing consistent information regarding land cover changes in mines, with a special focus on the amount of revegetation necessary to fulfill environmental liabilities.

Riparian restoration is an important objective for landscape managers seeking to redress the widespread degradation of riparian areas and the ecosystem services they provide. This study investigated the long-term outcomes of ‘one-off’ restoration activities undertaken in the Upper Murrumbidgee Catchment, NSW, Australia. The objective of the restoration was to protect and enhance riparian vegetation and control erosion, and consequently reduce sediment and nutrient delivery into the Murrumbidgee River. To evaluate the outcomes 10 years after restoration, rapid riparian vegetation and geomorphological assessments were undertaken at 29 sites spanning the four different restoration methods used (at least five replicates per treatment), as well as at nine comparable untreated sites. We also trialed the use of aerial imagery to compare width of riparian canopy vegetation and projective foliage cover prior to restoration with that observed after 10 years. Aerial imagery demonstrated the width of riparian canopy vegetation and projective foliage cover increased in all restored sites, especially those with native plantings. The rapid assessment process indicated that 10 years after riparian restoration, the riparian vegetation was in a better condition at treated sites compared to untreated sites. Width of riparian canopy vegetation, native mid-storey cover, native canopy cover and seedling recruitment were significantly greater in treated sites compared to untreated sites. Geomorphological condition of treated sites was significantly better than untreated sites, demonstrating the importance of livestock exclusion to improve bank and channel condition. Our findings illustrate the value of ‘one-off’ restoration activities in achieving long-term benefits for riparian health. We have demonstrated that rapid assessments of the vegetation and geomorphological condition can be undertaken post-hoc to determine the long-term outcomes, especially when supported with analysis of historical aerial imagery.

Most of the municipalities in the Gulf region are facing performance related issues in their municipal solid waste management (MSWM) systems. They lack to possess a deliberate inter-municipality benchmarking processes. Instead of identifying the performance gaps for their key components (e.g., personnel productivity, operational reliability, etc.) and adopt proactive measures, the municipalities primarily rely on an efficient emergency response. A novel hierarchical modeling framework, based on deductive reasoning, is developed for performance assessment of MSWM systems. Fuzzy rule based modeling using Simulink-MATLAB was used for performance inferencing at different levels, i.e., component, sub-components, etc. The model is capable of handling the inherent uncertainties due to limited data and imprecise knowledge base. The model’s outcomes can exclusively assist the managers working at different levels of organizational hierarchy for effective decision-making. Performance of the key component, assists the senior management to assess the overall compliance level of performance objectives. Subsequently, operation management can hone in the sub-components to acquire useful information for intra-municipality performance management. While, individual indicators are useful for inter-municipality benchmarking. The model has been implemented on two municipalities operating in Qassim Region, Saudi Arabia. The results demonstrate the model’s pragmatism for continuous performance improvement of MSWM systems in the country and elsewhere.

Sensory evaluation has been widely applied in assessing the quality of many consumer products that directly serve human needs such as food, beverages, clothing, etc. This paper first examined the inherent deficiencies in this approach, due mainly to the essentially subjective nature of human sensory preference. It then argued that instinctively designating certain materials attributes as sensory perceptions is unnecessary; considering every scientific concept is in essence the processed results of our sensory organs/brains, i.e., all quantities were initially human perceptions. Scientific advances have inevitably generated gradual transformation of such human perceptions as warmth and heaviness into objective parameters measurable in physical quantities like temperature and kilograms. Then using existing successful examples, it demonstrated firmly how the sensory attributes can be assessed by more reliable instrumental methods, and envisioned the key steps to turning a perception into scientifically measurable parameter(s).

A novel approach for designing acceptance sampling plans is presented. It is based on the Bayesian conformity assessment framework from JCGM 106. In particular, the concept of conformance probability plays a central role in the new approach. The new approach takes into account both the producer's and the consumer's perspective by allowing each to have their own prior. In parallel, the JCGM 106 framework allows the definition of Bayesian consumer's and producer's risks which complement the "classical" risks defined in the ISO 2859 and ISO 3951 series. The new approach also casts a new light on the plans from the ISO standards.

Older adults in low-and middle-income countries experience a disproportionate burden of non-communicable diseases (NCDs). Unintentional injuries are among the major NCDs, and falls are the second leading cause of these injuries and deaths worldwide, including in Thailand. We aimed to culturally adapt the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) for Thai older adults and explore the feasibility, appropriateness, and acceptability of using STEADI in primary care via trained community health workers (CHWs) and care managers (CMs). STEADI takes a coordinated care approach that consists of three steps: screening, assessing, and intervening. In Step a, CHWs screened fall risk in 20 community-dwelling older adults using three key questions and found that all of them had fall risk, then CHWs screened with a Stay Independent questionnaire (range 0-14) and found that 100% have high fall risk (total scores 9.7± 2.4). In Step b, CMs assessed balance, vision, footwear, postural hypotension, medications, and CHWs assessed home hazards. They found that 50% had poor balance, 70% took 4+ medications,75% fell on the walkway, and 70% had no bathroom modifications. In Step c, individual participants received fall prevention interventions to mitigate their specific fall risk factors. CHWs and CMs indicated high acceptability (19.20±.1.31 of 20 total), appropriateness (18.80± 1.79 of 20 total), and feasibility (18.60±1.67 of 20 total) of the Thai-STEADI intervention. Our study showed that the community-based multifactorial Thai-STEADI delivered by CHWs and CMs is feasible and acceptable to prevent falls in older adults with limited access to health care.

Geriatric decisional capacity assessment is a complex medico-legal activity that evaluates normal subjects involved in specific civil matter like wealth transfer, access to treatment or voluntary hospitalization. Our objective was to evaluate how geriatric extrajudicial capacity exams impacted a general psychiatric unit over a decade. We also assessed the value of a surrogate biomarker (neutrophils to lymphocyte ratio) as a screening factor in the capacity assessment process. 72 cases were assessed in one psychiatric unit over a decade (2009-2018). As population was fast ageing, the total number of capacity exams per year doubled in time. None of the subjects had an active diagnostic of severe cognitive impairment or active psychiatric disease. The percentage of subjects receiving an “absence of capacity” conclusion remained constant over time (approx. 20% of all cases). 81% of the subjects within this subgroup had a clinical diagnostic of mild cognitive impairment compared to 46% in the “presence of capacity” subgroup (p<0.001). The surrogate biomarker neutrophils to lymphocyte ratio was significantly higher in the “absence of capacity” subgroup, mainly related to a cognitive impairment diagnostic. The ratio has to be validated by larger studies before to be included in the capacity assessment process as an early screening tool.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

In various domains such as energy, manufacturing, and maritime, cyber-physical systems (CPS) have seen increased interest. Both academia and industry have focused on the cybersecurity aspects of such systems. The assessment of cyber risks in CPS is a popular research area with many existing approaches that aim to suggest relevant methods and practices. However, few works have addressed the extensive and objective evaluation of the proposed approaches. In this paper, a standard-aligned evaluation methodology is presented and empirically conducted to evaluate a newly proposed cyber risk assessment approach for CPS. The approach, which is called FMECA-ATT&CK is based on Failure Mode, Effects & Criticality Analysis (FMECA) risk assessment process and enriched with the semantics and encoded knowledge in the Adversarial Tactics, Techniques, and Common Knowledge framework (ATT&CK). Several experts were involved in conducting two risk assessment processes, FMECA-ATT\&CK and Bow-Tie, against two use cases in different application domains, particularly an autonomous passenger ship (APS) as a maritime use case and a digital substation as an energy use case. This allows for the evaluation of the approach based on a group of characteristics, namely, applicability, feasibility, accuracy, comprehensiveness, adaptability, scalability, and usability. The results highlight the positive utility of FMECA-ATT&CK in model-based, design-level, and component-level cyber risk assessment of CPS with several identified directions for improvements. Moreover, the standard-aligned evaluation method and the evaluation characteristics have been demonstrated as enablers for the thorough evaluation of cyber risk assessment methods.

Background: Low-level physical activity (PA) among Chronic Low Back Pain (cLBP) is associated with various biopsychosocial factors. This research aimed to study the predictors of PA behavior among cLBP patients. Methods: In the present study 300 eligible patients with cLBP who referred to comprehensive health service centers in the Shahid Beheshti University of Medical Sciences (SBUMS) in Tehran, Iran were random selected.To diagnose the predictors of PA behavior, all the Theory of Reasoned Action (TRA) constructs were examined as risk factors to see if they influence on the probability of PA behavior occurrence and were interpreted through odds ratio (OR). SPSS version 19 was used to analyze the data. Results: Totally 280 cLBP patients with mean age of 57.07 ±13.09 years old participated in the study. This study showed that motivation to comply significant predictor the cLBP patients for subjective norm OR (%95CI): 2.095(0.116-2.792), p-value<0.001), intention was significant predictor for perform the PA behavior OR (%95CI): 1.431(0.138-1.538), p-value <0.001), behavior beliefs could predictor for attitude OR (%95CI): 1.276(0.106-1.355), p-value= 0.002). attitude, normative beliefs, subjective norm ,and evaluation outcome behavior could predictors the cLBP patients for intention to perform the PA behavior OR (%95CI): 1.188(0.032-1.312), p-value<0.001)., OR (%95CI): 1.158(0.076-2.208), p-value=0.003) ., OR (%95CI): 1.104(0.076-1.128), p-value<0.001) ., OR (%95CI): 0.814(0.301-1.440), p-value=0.007). Conclusions: This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm).

The functional capacity of the cervical spine plays a central role in healthy living. This study evaluated and created a comprehensive strength, range of motion (ROM), and musculoskeletal pain profile of the cervical spine in young adults. 60 physically active individuals (30 males/30 females; 19-24 year-old) participated in the study. Cervical ROM (forward flexion, extension, lateral flexion, and rotation), maximal isometric strength (forward flexion, extension, lateral flexion), and musculoskeletal pains were assessed using an electronic goniometer, a handheld dynamometer, and the Nordic questionnaire, respectively. Data analysis revealed that: a) fe-males had higher ROM values, during extension and lateral flexion, than males (p<0.05), b) males had higher absolute strength values than females in all movement directions, higher rela-tive strength values/body mass (at forward flexion) and flexion/extension ratio values (p<0.05), c) ROM and strength values were not significantly different between right-left sides (p>0.05), d) a great proportion (43.3%) of the participants had cervical pains (no sex-related differences) and e) cervical strength showed significant small negative correlation with pains (p<0.05; r=-0.225 to -0.345). The reference values created may be used by health and fitness professionals to exten-sively evaluate the cervical spine and to design specific exercise programs for injury prevention and rehabilitation.

Introduction: Despite the introduction of increasingly multifaceted diagnostic techniques and the general progress of emergency abdominal and vascular surgery, the outcome of treatment of patients with acute impaired intestinal circulation remains unsatisfactory. The non-invasive and high-resolution technique of optical coherence tomography (OCT) can be used intraoperatively to assess intestine viability and associated conditions that frequently emerge under conditions of impaired blood circulation. This study aims to demonstrate the effectiveness of multimodal (MM) OCT for intraoperative diagnostics of both the microstructure (cross-polarization OCT mode) and microcirculation (OCT angiography mode) of the small intestine wall in patients with acute mesenteric ischemia (AMI). Methods and Participants: A total of 18 patients were enrolled in the study. Nine of them suffered from AMI in segments II-III of the superior mesenteric artery (AMI group), where the ischemic segments of the intestine were examined. Nine others were operated for adenocarcinoma of the colon (control group), thus allowing areas of their normal small intestine to be examined for comparison. Data on the microstructure and microcirculation in the walls of the small intestine were obtained intraoperatively from the side of the serous membrane using the MM OCT system (IAP RAS, Russia) before bowel resection. The MM OCT data were compared with the results of histological examination. Results: The study finds that MM OCT visualized the damage to serosa, muscularis externa, and blood vessels localized in these layers in 100% of AMI cases. It also visualized the submucosa in 33.3% of AMI cases. The MM OCT image of non-ischemic (control group), viable ischemic and necrotic small intestines (AMI group) differed significantly across stratification of the distinguishable layers, the severity of intermuscular fluid accumulations and the type and density of the vasculature. Conclusion: The MM OCT diagnostic procedure optimally meets the requirements of emergency surgery. Data on the microstructure and microcirculation of the intestinal wall can be obtained simultaneously in real time without requiring contrast agent injections. The depth of visualization of the intestinal wall from the side of the serous membrane is sufficient to assess the volume of the affected tissues. However, the methodology for obtaining MM OCT data needs to be improved to minimize the motion artefacts generated in actual clinical conditions.

Oil has become the object of global exploits and fierce competition among the major world powers as it is a key strategic non-renewable resource. Transnational petroleum investment is therefore an important mechanism available to countries and international corporations to control oil resources even though there are numerous inherent uncertainties and risks. A new risk assessment index system is proposed in this paper based on use of the Delphi expert scoring system and fuzzy comprehensive evaluation that aims to minimize the potential risks inherent to multinational petroleum investment. This approach encapsulates political, legal, socioeconomic, and infrastructural factors to develop a technical method that can be used for transnational petroleum investment risk assessment. An evaluation of oil investment risk within a case study area is also presented; results provide reference data that can be applied by national and international oil companies to mitigate risks of transnational oil investment.

The sustainability of cities often changes as rapid urban developments and population growth affect the carrying capacity. Understanding the dynamics of carrying capacity then becomes crucial for tourist-oriented cities. This study focused on Baguio City, the "Summer Capital of the Philippines," attracting large crowds on holidays and actively promoting various events on non-holidays, thereby having temporal context variations classified as regular days, days with events, and holidays. Using the enhanced urban carrying capacity load number (EUCCLN) model, load number (LN) changes for different temporal contexts are calculated based on spatial indicators grouped into air, traffic, economy, and waste. The city experiences the worst pressures on air quality during holidays with $99.23\%$ districts under very high to critical levels followed by regular and days with events. However, the total LN is balanced by favorable economic conditions with only $9.23\%$ at critical. EUCCLN effectively pinpoints areas experiencing critical states at any time, advocating targeted government monitoring. We demonstrate that Night Time Light, for places with no economic data available, can be used as a proxy to highlight areas requiring improved urban vibrancy. This work suggests that EUCCLN monitoring urban pressures can easily be expanded to nowcasting with real-time data.

: Chemical accidents can occur anywhere. The need for chemical management in Korea was realized following the 2012 Gumi hydrofluoric acid accident in 2012. The Chemicals Control Act was enacted in 2015. This system evaluates the risks (high, medium, low) and consequent safety management at all plants that handle hazardous chemical substances. However, the system was criticized as excessive when most plants were designated high-risk without considering their size. Thus, laboratories and hospitals handling very small quantities were subject to regulation. Accordingly, in 2021 Korea revised the system to include off-site consequence analyses and a Korean-style risk analysis. Plants handling very small quantities, such as laboratories and hospitals, were exempt from regulation. In this study, plating and paint manufacturing companies, which were classified as high-risk in the previous system, even though they were medium-size business plants, were re-evaluated as low-risk plants. In the Korean-style risk analysis, it is possible to see at a glance what is lacking in the plants, such as cooperation between local residents and local governments and the construction of safety facilities according to the type of accident scenario. The revised system is a reasonable regulation for medium business plants.

Our objective was to evaluate whether pregnancy is prolonged by use of a proteomics-based maternal serum screening test followed by treatment interventions. This is a secondary analysis of the PREVENT-PTB randomized trial comparing screening with the PreTRM test versus no screening. The primary trial analysis found no significant between-group difference in preterm birth rate. Rather than considering a dichotomous outcome (preterm vs term), we treated gestational age at birth as a continuous variable using survival analysis. We also evaluated between-group difference in NICU length of stay and duration of respiratory support. Results indicated that pregnancy was significantly prolonged in subjects screened with PreTRM test compared to controls (adjusted hazard ratio 0.53, 95% confidence interval 0.36-0.78, P &lt;0.01). Newborns of screened subjects had significantly shorter NICU stay but no significant decrease in duration of respiratory support. In the PreTRM screen-positive group, interventions that were associated with pregnancy prolongation included care management and low-dose aspirin but not 17-hydroxyprogesterone caproate. We concluded that screening with the PreTRM test followed by interventions for screen-positive pregnancies can prolong pregnancy and reduce NICU-LOS.

C-reactive protein (CRP) and leukocytes are blood biomarkers involved in "Inflamm-Aging", which is a risk factor for the onset and progression of age-related diseases. Studies show that higher serum concentrations of these biomarkers are associated with functional disability, increased risk of low muscle strength, decreased muscle mass and mortality in the elderly. The objective was to estimate the predictive power and discriminating criteria of C-reactive protein and leukocyte concentrations for the risk of adverse health factors in the elderly within 30 days after hospital discharge (HD). Prospective cohort study using exploratory methods and blood biomarkers with 135 older adults admitted to medical and surgical clinics at a government hospital. The elderly were monitored at home after 30 days of HD for adverse health factors (rehospitalization, falls, amount of medication consumed, disability in basic and instrumental activities of daily living and mortality). CRP> 2.4; ≥ 0.7 and> 24.7 mg / dL and leukocytes ≥ 6.410; ≥ 8.690 and> 8.310 mm³ were discriminant for rehospitalization, falls and mortality within 30 days after HD, respectively. The cut-off points described may be used as a reference in the screening of hospitalized elderly vulnerable to adverse health events after hospital discharge.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modelling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modelling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modelling and assessment in Japan.

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.

The WHO European Region defined the role of a new central professional for primary care, the Family and Community Nurse (FCN). The introduction of FCN in the framework of health policies highlight a key role of nurses in addressing the families’ and communities’ needs. A scoping review was conducted in order to identify and describe the available tools which has been adopted for the assessment of the community health needs by FNC. A comprehensive literature review on Embase, Cochrane Library, PubMed, CINAHL, Scopus and PsycInfo databases was conducted including all studies up to May 2021. A total of 1563 studies were identified and 36 of them were included. The literature review had made possible to identify studies employing twelve different community assessment's tools or modalities. Referring the WHO framework proposed in 2001 some common themes have been identified with an uneven distribution, as profiling the population, deciding on priorities for action and public healthcare programmes, implementing the planned activities, evaluation of health outcomes, multidisciplinary activity, flexibility and involving the community. To the best of our knowledge, this work is the first attempt to provide an overview of community assessment tools, keeping the guidance provided by the WHO as a reference.

Over 130 countries have committed to reaching net-zero CO2 or GHG emissions by 2050, yet this ambition is rarely underpinned by robust policies. By applying a detailed integrated assessment modelling approach for Brazil, we assess, for the first time, the extent to which the existing and planned local policies could put Brazil on the path to its net zero pledge. This includes quantifying the role of nature-based solutions, such as protection and restoration, and engineered solutions, such as bioenergy with carbon capture and storage (BECCS). We show protection is the single most important climate mitigation measure at relatively low costs, whereas relying heavily on engineered solutions would jeopardise Brazil’s chances of achieving its net zero pledge. We also show that the mismatch between Brazil's short- and long-term climate targets reflects current weak environmental governance. Our analysis reinforces the urgent need for Brazil to eliminate illegal deforestation and go beyond to help fight climate change whilst curbing biodiversity loss.

The use of nanomaterials has been increasing in recent times, and they are widely used in industries such as cosmetics, drug, food, water treatment and agriculture. The rapid development of new nanomaterials demands a set of approaches to evaluate the potential toxicity and risks related to them. In this regard, nanosafety has been using and adapting already existing methods (toxicological approach), but the unique characteristics of nanomaterials demand new approaches (nanotoxicology) to fully understand the potential toxicity, immunotoxicity and (epi)genotoxicity. Also, new technologies, such as organ-on-chip and sophisticated sensors, are under development and/or adaptation. All the information generated is used to develop new in silico approaches trying to predict the potential effects of newly developed materials. The overall evaluation of how from the production to final disposition chain of nanomaterials is evaluated under Life Cycle Assessment (LCA), which is becoming an important element of nanosafety considering sustainability and environmental impact. In this review we give an overview of all these elements of nanosafety.

Cold brew coffee is a new trend in coffee industry. This paper presents pilot studies into several aspect of this beverage. Using an online survey, the current practices of cold brew coffee preparation were investigated identifying a rather large variability with a preference for extraction of medium roasted Arabica coffee using 50-100 g/l at 8°C for about 1 day. Sensory testing using ranking and triangle tests showed that cold brew may be preferred over iced coffee (cooled down hot extracted coffee). Extraction experiments at different conditions combined with nuclear magnetic resonance (NMR) analysis showed that the usual extraction time may be longer than necessary as most compounds are extracted within only a few hours, while increasing turbulence (e.g. using ultrasonication) and temperature may additional increase the speed of extraction. NMR analysis also revealed a possible chemical differentiation between cold brew and hot brew using multivariate data analysis. Decreased extraction time and reduced storage times could be beneficial for cold brew product quality as microbiological analysis of commercial samples detected samples with spoilage organisms and contamination with Bacillus cereus.

This study aims at providing a systematic and critical review on the state-of-the-art of life cycle applications from the circular economy point of view. In particular, the main objective is to un-derstand how researchers adapt life cycle approaches for the measurement of the empirical cir-cular pathways of agri-food systems along the overall lifespan. To perform the literature review, PRISMA protocol was considered to conduct a review by qualitative synthesis. Specifically, an evaluation matrix has been set up to gather and synthesize research evidence, by classifying pa-pers according to several integrated criteria. The literature search was carried out employing scientific databases. Findings evidence that the most common circularity topics are about closed-loop production systems, i.e. nutrient recovery for agricultural purposes, production of renewable energy, valorization of residues and wastes as fertilizers, food waste, and agro-wastes recycling for agriculture. To evaluate the benefits/impacts of CE strategies, Life Cycle Assess-ment (LCA) proved to be the most common methodology applied by authors, as it can help to meet the main CE requirements slowing and closing resource loops.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

Aim: this scoping review was designed to identify studies that assess the adverse drug reactions (ADRs) for older people in Australian aged-care facilities. This review critically evaluated each published study to identify the risk of, or actual adverse drug events in older people. Inclusion criteria: This review considered any clinical studies that examined the adverse effects of medications in older people who were living in aged-care facilities. This review considered qualitative studies, analytical studies, RCTs, descriptive cross-sectional studies, and analytic observational studies that explored the use of medications and their adverse effects on older people in clinical settings (including aged care facilities). Methods: An initial search of the PubMed, OvidSP, EBSCOHost, MEDLINE, ScienceDirect, Wiley Online, SAGE, and SCOPUS databases, with full text was performed, followed by an analysis of the article’s title and abstract. Additionally, MeSH was used to describe the article. The initial round of the database search was based on inclusion criteria from studies that assessed tools or protocols aiming to identify the adverse effects of medications on the elderly population suffering chronic conditions or multiple co-morbidities. Two reviewers screened the retrieved papers for inclusion. The data presented in this review are in tabular forms and a narrative summary which aligns with the review’s objectives. Results: Seven studies were identified, and the extracted data from these studies were grouped according their characteristics and the auditing results of each study. Conclusion: There was no comprehensive or broadly adverse drug reaction assessment tool derived from Australian data that has been used on the elderly in an Australian healthcare setting.

Polycyclic aromatic hydrocarbons (PAHs) in street dust pose a serious problem threatening both environment and human health. Street dust were collected from five different land use patterns (traffic areas TRA, urban area URA, residential areas REA, mixed residential commercial areas MCRA and suburban areas SUA) in a Saudi coastal city, Jeddah, and one in rural area (RUA) in Hada Al Sham. This study aimed to investigate the status, profile, sources of PAHs and estimate their human health risk. The results revealed an average concentration of total PAHs of 3320 ng/g in street dust of Jeddah and 223 ng/g in RUA dust. PAHs with high molecular weight represented 83.38% of total PAHs in street dust of Jeddah, while the carcinogenic PAH compounds accounted 57.84%. The highest average concentration of total PAHs in street dust of Jeddah was found in TRA (4980 ng/g) and the lowest in REA (1660 ng/g). PAHs ratios indicated that the principal source of PAHs in street dust of Jeddah is pyrogenic, mainly traffic emission. Benzo(a)anthracene/ chrysene (BaA/CHR) ratio suggests that PAHs in street dusts of Jeddah come mainly from emission of local sources, while PAHs in RUA might be transported from the surrounding urban areas. The estimated Incremental Lifetime Cancer Risk (ILCR) associated with exposure to PAHs in street dusts indicated that both dermal contact and ingestion pathways are major contributed to cancer risk for both children and adults. Based on BaP_equivalence concentrations of total PAHs, ILCR_Ingestion, ILCR_dermal and cancer risk values for children and adults exposed to PAHs in street dust of different areas in Jeddah were found between 10⁻⁶ and 10⁻⁴, indicating potential risk. The sequence of cancer risk was TRA > URA > MCRA > SUA > REA. Only exposure to BaP and DBA compounds had potential risk for both children and adults.

Background: Absolute uterine factor infertility (AUFI) is a kind of infertility that is completely attributable to uterine absence (surgical or congenital for women with Mayer-Rokitansky-Küster-Hauser syndrome: MRKH) or anatomic or functional abnormality that prevents embryo implantation or completion of pregnancy to term. Until recently, the only viable option to parenthood for couples with AUFI were adoption or surrogacy. Since a first attempt of uterus transplant (UTx) in 2000, nine babies were born from women with a transplanted uterus from 2014, eight of which in Sweden, and one in the United States. These promising results are raising immense hopes for the women with AUFI and there is optimism about the possibility for UTx to become part of clinical care even though, besides encouraging results, the procedure has also resulted in increased risks and harms for both the donors and recipients and increased risks of premature birth for the fetus. At present UTx is still considered as experimental and requiring more research and safety assessment before becoming a therapeutic option for AUFI. The transition from experimental procedure to therapeutic care would result in less strict ethical scrutiny for UTx and in the possibility for patients to get reimbursement for the procedure by the relevant healthcare insurance or public healthcare providers. In turn, an increase in the number of UTx performed yearly by specialized surgical teams would result in a general improvement of the “field strength”. However, at present it is difficult to establish the amount of evidence that we need in order to consider UTx as no longer experimental but routine clinical practice. The literature on UTx provides recommendations on the different outcomes that should be monitored in this experimental phase but no study is anticipating the number of subjects that should be followed and for how long. Conclusion: As for other transplants that have become routine practice, like renal transplant and heart transplant, it is likely that the decision on “routine practice readiness” will result from available cumulated evidences, from expert capacity to find a consensus on best practices and on political considerations as well, including pressures form patients and patient groups.

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.

Cardiovascular diseases (CVD) are rising rapidly among the postmenopausal woman but they are less likely to identify their risk by an appropriate risk assessment tool. This review evaluates available literature on cardiovascular risk assessment among postmenopausal women to provide a concise view of risk factors and disease burden among them, present risk assessment systems including their drawbacks, emergence of new risk factors and their role in risk prediction, and finally use of hormone replacement therapy during menopause. Results demonstrate that menopause is a transition point for developing CVD not due to physiological changes only but psychosocial factors like depression and marital stress are also responsible. Both conventional and emerging risk factors burden are high among postmenopausal women. Though data regarding CVD risk assessment among postmenopausal population is lacking but existing evidences claimed underestimation or overestimation of risk among women. Moreover application of different tools on same population has revealed significant variation in result. In this regard, recalibration of conventional tools with local data and new risk factors has showed improvement of risk prediction. Hormone replacement therapy during early menopause has reported beneficial to prevent CVD but in secondary prevention it has no role. All of these findings demand further studies on cardiovascular risk assessment, especially in developing countries where women after menopause are not in consideration of health strategy makers.

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.

The purpose of this paper was to analyze the development trend of hazardous chemical packaging towards low carbon economy from both qualitative and quantitative perspectives. Four types of relatively small volume packaging with volume/weight less than 450L/400kg, respectively, and three intermediate bulk containers (IBCs), which are widely used for hazardous chemicals were studied to calculate the carbon footprint (CF) from cradle to grave using life cycle assessment (LCA) method and to predict the future carbon emission of hazardous chemical packaging in the next five years (2016-2020), based on the export data of Tianjin Port in China. Grey model (GM) was adopted in the prediction. The results showed that majority of IBCs have lower carbon footprint than other types when the packaging contained same amount of same hazardous chemical. With the development of international trading, the demand of hazardous chemicals will increase as well. As the result, carbon emission generated by hazardous chemical packaging will increase accordingly. However, based on GM simulation result, increasing the amount of IBC use will effectively reduce the relative amount of carbon emission.

Electricity distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution network reliability by incorporating component lifetime models, time-varying component failure rates as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system is modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions, and the energy not supplied are complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.

From 2020 to 2023 many people around the world were forced to wear masks for large proportions of the day based on mandates and laws. We aimed to study the potential of face masks for the content and release of inanimate toxins. A scoping review of 1003 studies was performed (database search in PubMed/MEDLINE, qualitative and quantitative evaluation). Twenty-four studies were included (experimental time 17 min to 15 days) evaluating content and/or release in 631 masks (273 surgical, 228 textile and 130 N95 masks). Most studies (63%) showed alarming results with high micro- and nanoplastics (MPs and NPs) release and exceedances could also be evidenced for volatile organic compounds (VOCs), xylene, acrolein, per-/polyfluoroalkyl substances (PFAS), phthalates (including di(2-ethylhexyl)-phthalate, DEHP) and for Pb, Cd, Co, Cu, Sb and TiO₂. Of course, masks filter bacteria, dirt and plastic particles and fibers from the air we breathe and have specific indications, but according to our data they also carry risks. Depending on the application, a risk-benefit analysis is necessary. However, mask mandates during the SARS-CoV-2 pandemic have been generating an additional source of potentially harmful exposition to toxins at population level with almost zero distance to the airways.

The article focuses on the integrated environmental accounting model called 'eValue', developed for protected areas and applied in the research programme coordinated by the Italian Ministry of the Environment and aimed at implementing an environmental accounting system for Italian Marine Protected Areas (MPAs). eValue adopts a cost-benefit analysis approach. Financial accounting based on costs and revenues is integrated with environmental accounting, which reflects environmental costs and environmental revenues, i.e. environmental benefits. The environ-mental costs assess the impacts related to human activities in the MPA expressed by calculating the carbon footprint and the environmental benefits of the marine ecosystem services calculated by applying monetary valuation techniques. The values thus estimated flow into the annual flow account, where the value produced (or consumed) by the MPA is estimated by difference. The eValue model was applied to the Porto Cesareo MPA (Italy). eValue showed that the annual benefit-cost ratio reaches a value of 3.4. Furthermore, the ratio of net benefit to public funding is 3.7, completely covering the amount of public transfers and thus summarizing the MPA overall value for money.

This research experiments with a sustainability assessment based on the Sustainable Development Goals (SDGs) of Agenda 2030 through a process of their territorialisation and implementation. This process enables the development of a Spatial Decision Support System (SDSS) that can be integrated with strategic environmental assessments in urban planning. The assessment takes place on the transversality of the sustainability concept by trying to consider together the three dimensions (environmental, social and economic) into a single assessment through the Spatial Sustainability Assessment Model (SSAM) by integrating Geographic Information Systems (GIS) and multi-criteria analyses. Economic development, social equity, and ecological integrity represent three common visions for rethinking peri-urban edges.

Energy transition is a major structural change in the whole social system, and the energy system must be changed globally to replace fossil fuels. Hydropower is one of the largest renewable energy in the world. However the involuntary resettlees due to the construction of hydropower projects are suffering from being far away from their native land, losing the land cultivated for generations and the houses they live in, and losing the social relationship network based on geography and blood ties. Based on the system evaluation theory of reservoir resettlement and referring to relevant research findings, this paper constructs a comprehensive evaluation index framework for assessing the implementation effect of the PostRelocation Support (PReS) policy. The research region is located in Zhijin County, Bijie City, Guizhou Province, China. Accordingly, a combined method of a structural equation model and a fuzzy comprehensive evaluationary model is used in this paper to analyze the implementation of the PReS policy. The results show that the total score of implementing effects of the PReS policy is 4.4, with dramatic significance. The subindex scores of the resettlers’ family income, living conditions, and production conditions; the local economy; and social stability are 4.3, 4.6, 4.4, 4.6, and 4.3, respectively, with dramatic significance. This paper has analyzed and summarized the successful practices of implementing the PReS policy for reservoir resettlers in three dimensions: poverty alleviation, beautiful home construction and accelerating rural revitalization. Research shows that China's rural reservoir resettlers' PReS policy has been more effective in restoring the livelihoods of reservoir resettlers.

Abstract: The frequency and intensity of air pollution are two of most critical issues the world faces in dealing with global environmental problems. They are both important areas that need to be improved. Our previous research [20] examines the assessment of fine dust pollution in Baekryeong island by statistical reasoning through one specific example of inland city (Yanggu) in Republic of Korea; however, given that Baekryeong Island is a remote island, there has been little focus on in-depth examination of its effect on the high density of fine dust in Baekryeong Island. The purpose of this article is twofold: (i) to examine the effect of a remote island on the high density of fine dust on Baekryeong Island using statistical reasoning and to provide a more specific statistical basis for the claim that one of the non-negligible factors influencing the high density of fine dust on Baekryeong Island is the geographic proximity to China; and (ii) to present brief discussion of fundamental cause of fine dust and global air pollution issues, which are not discussed in [20].

Life Cycle Assessment (LCA) is a technique developed to measure possible environmental impacts during the manufacture and use of a given product or service. Additive Manufacturing (AM) has proven to be suitable in several industries, especially wire and arc additive manufacturing (WAAM), offering many advantages such as delivery time and reduction in terms of material waste, energy consumption and equivalent CO₂ emissions (CO₂e). This study aimed to develop a methodology to evaluate environmental indicators during the life cycle assessment, from gate-to-gate, in the production of two low-alloy carbon steel metal parts (flanges) using ER-90 wire and allowed identifying the main stages that most influence environmental indicators. The total energy consumption in the production of flanges 1 and 2 were, respectively, 10,239.0 MJ, 12,754.0 MJ and the total carbon footprint, respectively, 714.1 kgCO₂e kg^-1 and 749.3 kgCO₂e kg^-1.

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

The ISO 2859 and ISO 3951 series provide acceptance sampling procedures for lot inspection, allowing both sample size and acceptance rule to be determined, starting from a specific value either for the consumer or producer risk. However, insufficient resources often make it difficult to implement “ISO sampling plans.” In cases where the sample size is determined by external constraints, the focus shifts from determining sample size to determining consumer and producer risks. Moreover, if the sample size is very low (e.g. one single item), prior information should be included in the statistical analysis. For this reason, it makes sense to work within a Bayesian theoretical framework, such as that described in JCGM 106. Accordingly, the approach from JCGM 106 is adopted and broadened so as to allow application to lot inspection. The discussion is based on a “real-life” example of lot inspection on the basis of a single item. Starting from simple assumptions, expressions for both the prior and posterior distributions are worked out, and it is shown how the concepts from JCGM 106 can be reinterpreted in the context of lot inspection. Conceptual differences regarding the definition of consumer and producer risks in JCGM 106 and in the ISO acceptance sampling standards are elucidated and a numerical example is provided.

This paper presents the methods of estimating the mean square error of hydrological forecasts, allowing for assessment of their practical applicability. Depending upon the amount and composition of available hydrometeorological data, an appropriate method for forecast error estimation is chosen. A system of statistical tests for comparison of different forecasting methods for the same hydrologic characteristic with the same lead time is presented. These tests allow for choosing an optimal and most accurate forecasting method. Hydrological forecasting method efficiency estimation is based on comparing the forecast error with climatology or inertial (persistence) forecast error using presented tests.

Background/Objectives: The aim of this study is the operationalization of the International Classification of Functioning, Disability and Health in the rehabilitation care performed at the Institute of Cancer of the State of São Paulo through structuring a goal-oriented tool with relevant categories of evaluation and follow-up for cancer outpatients to be applied by physiatrists, physiotherapists, physical educators, occupational therapists, and psychologists. Methods: A qualitative study based on a literature review and a modified Delphi exercise, Estimate-Talk-Estimate (ETE) technique, was performed with the service’s rehabilitation experts. Consensus was obtained after four rounds under the 80% agreement threshold. Results: Forty categories were identified for neuropsychological evaluation and thirty-three for psychoaffective support, with 12 goals. Thirty-five categories with 13 goals were accounted for physiotherapy. Physical educators identified 18 categories, with 7 goals. Occupational therapists selected 54 categories, with 10 goals. A referral for personal factors was established. Functional Assessment of Cancer Therapy-General (FACT-G) was the quality of life tool chosen to be applied. Conclusions: A set of relevant ICF categories was structured, tailored by a clinically meaningful routine of assessment and follow-up of cancer outpatients based on specialists’ clinical practice and scientific literature. It will contribute for analyzable and more specific and quantifiable institutional health data. Its implementation and future discussion may promote the instrument’s refinement for its use in this population.

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

This paper deals with the environmental characterization of a large and densely populated area, with a poor reputation for contamination, considering the contribution of environmental features (air, soil, soil hydraulic and groundwater) and the potential effects on human health. The use of Geographic Information System (GIS) has made possible a georeferenced inventory and, by overlaying environmental information, an operational synthesis of comprehensive environmental conditions. The cumulative effects on environmental features were evaluated, taking into account superposition effects, by means of the spatial multicriteria decision analysis (S-MCDA). The application of the S-MCDA for converging the combination of heterogeneous factors, related to soil, land and water, deeply studied by heterogeneous groups of experts, constitutes the novelty of the paper. The results confirmed an overall higher potential of exposure to contaminants in the environment and higher mortality rates in the study area for some tumours, but hospital admissions for tumours were generally similar to the regional trend. Besides, mortality data may be strictly dependent on the poor socioeconomic conditions, quality of therapy and a lack of welfare in the area relative to the rest of Italy. Finally, as regards the possible relationship between presence of contaminants in the environment and health conditions of the population no definite conclusions can be drawn, although the present study encourages the use of the new proposed methods, that increase the possibilities for studying the combined effect of more environmental factors.

The World Health Organization estimates that every year air pollution kills seven million people worldwide. As it is expected that climate change will affect future air quality patterns, the full understanding of the links between air pollution and climate change, and how they affect human health, are challenges of future research. In this scope, a methodology to assess the air quality impacts on health was developed. The WRF-CAMx modelling framework was applied for the medium-term future climate (considering the SSP24.5 scenario) and for the recent past (considered as baseline). Following the WHO recommendations, mortality health indicators were used to estimate health impacts of long-term exposures. For that, the Aveiro Region, in Portugal, was considered as a case study. Future climate results indicate the occurrence of higher temperatures, and lower total precipitation. Despite that, improvements in the main pollutants’ concentrations, and consequently in the reduction of the related premature deaths are foreseen, mainly due to the reduction of pollutants emissions imposed by the European legislation for the upcoming years. The applied approach constitutes an added value in this research field, being crucial to anticipate the effects of climate change on air quality and evaluate their impacts on human health.

Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers like increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulated investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures to reduce heat stress. A simulation model driven by meteorological data was used to calculate heat stress impact as a projection for 2030. For a business-as-usual livestock building, the indoor climate for several adaptation measures was calculated. The weather-related value-at risk quantified the economic risks caused by global warming and the stochastic component of the weather. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980. The efficiency of other adaptation measures to reduce heat stress is distinctly lower. The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.

Partial shading on solar photovoltaic (PV) arrays is a prevalent problem in photovoltaic systems that impair the performance of PV modules and is responsible for reduced power output as compared to that in standard irradiance conditions thereby resulting in the appearance of multiple maximas on panel output power characteristics. These maxims contribute to mismatch power losses among PV modules. The mismatch losses depend on shading characteristics together with different interconnected configuration schemes of PV modules. The research presents a comparative analysis of partial shading effects on a 4 x4 PV array system connected in series(S), parallel (P), serries-parallel (SP),total-cross-tied (TCT),central-cross-tied(CCT),bridge-linked(BL),bridge-linked total cross-tied (BLTCT) ,honey-comb(HC), honey-comb total-cross-tied (HCTCT) and ladder (LD) configurations using MATLAB/Simulink. The PV module SPR-X20-250-BLK was used for modeling and simulation analysis. Each module is comprised of 72 number of PV cells and a combination of 16 PV modules was employed for the contextual analysis. Accurate mathematical modeling for the HCTCT configuration under partial shading conditions (PSCs) is provided for the first time and is verified from the simulation. The different configuration schemes were investigated under short-narrow,short-wide,long-narrow,long-wide, diagonal, entire row distribution, and entire column distribution partial shading condition patterns with mathematical implementation and simulation of passing clouds. The performance of array configurations is compared in terms of maximum power generated ), mismatch power loss (∆), relative power loss ) and the fill factor (FF). It was inferred that on average, TCT configuration yielded maximum power generation under all shading patterns among all PV modules interconnection configurations with minimum mismatch power losses followed by hybrid and conventional PV array configurations respectively.

The Internet of Things (IoT), as a technology, transforms every day, consumer devices into devices capable of collecting and transmitting data. This momentous progress highlights the need for reliable risk management frameworks that address the potential risks associated with IoT devices across all aspects of life. Traditionally, the feasibility of IoT was limited by the high costs of sensors and their connectivity. Although, recent advancements have greatly reduced costs, enabling widespread connectivity of devices to the Internet. Consequently, numerous IoT devices and networks are left vulnerable without a comprehensive framework for managing these risks effectively. This paper introduces a more systematic framework designed to counter security risks and safeguard IoT devices. This framework takes a step-by-step approach for focusing on mitigating risks arising from inadequate security designs for IoT devices. It involves procedures for risk identification, evaluation, and prioritization which is followed by comprehensive risk analysis and control measures, and because risk evaluation is tedious, we suggest using machine learning (ML) to accelerate the risk evaluation process, boost the precision and consistency of risk assessments, and provide valuable insight, which ultimately enables risk analysts to make better informed and timely decisions. Through multiple case studies, we demonstrate the practicality and efficacy of the framework for evaluating IoT device risks and guiding the implementation of appropriate controls to safeguard devices and networks. We also develop security dashboards that provide visual summaries comparing device values, threat assessments, and risk mitigation costs, aiding in clear communication and prioritization of security measures.

In principle, weak sustainability, WS, requires that inclusive wealth, IW, be non-decreasing. WS provides a more complete account of the sustainability of a geographical entity (e.g., the world, a nation, or a sub-national region) than sustainability indicators, SIs, on one hand, and conventional economic accounts (e.g., gross domestic product, GDP) on the other. We briefly summarize WS theory, noting that it was developed, implicitly, for a geographical space without internal boundaries. Empirical WS assessment was developed by international bodies (the World Bank and the UN Environmental Program) to shine light upon national governments that might be maintaining the current welfare of their citizens, but investing insufficiently in national assets to maintain intertemporal wealth. In so doing, the need arose to address a suite of issues stemming from national boundaries that may limit the mobility of people, resources, goods and services. WS assessment at sub-national levels (states and counties, in the case of the US) has similar motivations – to provide more complete accountability for regional officials who may be maintaining welfare by consuming assets – but the implications of cross-boundary flows are much greater given the porosity of regional boundaries. WS scholars and practitioners have arrived at a consensus that the appropriate criterion for WS status is IW/capita, i.e. the welfare of people should be sustained. Regional economists and economic geographers tend to associate regional success with population growth, which may increase total IW at some cost to individual welfare. We consider this issue in some depth – what is at issue, and how any conflict might be resolved – ultimately concluding that IW/capita is the appropriate criterion for WS assessment, while leaving broader questions of population ethics unresolved. We summarize recent research [1] that starts with the Arrow et al framework [2] and improves methods of assessing IW, especially its health capital component, and assesses IW/capita for all states and counties in the US. These improved methods reveal substantially more not-WS states and counties than the Arrow et al framework [2]. The not-WS counties exhibit a distinct rural bias, consistent with the hypotheses and empirical findings of regional scientists. Our work demonstrates that regional WS assessment is feasible, produces results that are consistent with prior expectations based on reasoning and empirical research, and has the potential to provide fresh insights into longstanding questions of regional development.

Life Cycle Assessment (LCA) methodology is increasingly prevalent in the quantification of environmental status in the building sector, following new legislative frameworks. Countries need to quantify and assess their environmental impacts as a previous base for regulation and decision making. In this context, standardised national data sets are being developed, based on consensus with manufacturers: sectoral Environmental Product Declarations. This study focuses on assessing these EPDs with the aim of providing relevant information for global decision-making, focusing on consistency and pointing out shortcomings. An assessment has been carried out both at national level, considering all sectoral EPDs and declared products, and at international level, with three common building materials (brick, steel and concrete). The results highlight the lack of traceability in the development and assessment of uncertainty, resulting in substantial discrepancies in reported impacts, especially in the later stages of the life cycle (up to 90% difference). Meanwhile, impacts such as Global Warming Potential or total renewable and non-renewable primary energy use show homogeneous values in the cradle-to-gate stages with differences generally less than 10%. The analysis of sectoral EPDs should be a design guide with environmental sustainability criteria, as the last section of this study attempts to focus on.

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports, able to reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to inform the delivery of ecological momentary interventions (EMIs of just-in-time interventions) that prevent such events from occurring. To enable this work, this narrative review provides examples of scales and single-item questions used in EMA studies, makes concrete recommendations for researchers seeking to employ EMAs in their research, and discusses limitations of EMA methods. In doing so, we aim to encourage the use of these methods in research given that, when used carefully, EMA methods are well-poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

Among the explored solutions to reduce the environmental impact of the transport sector, Vehicle Integrated Photovoltaics. Thus, we developed a simulation tool of the distance covered by VIPV. It considers various usage patterns and vehicle types, several characteristics of the PV system and all the losses that may decrease energy yield. Focusing on passenger car, simulations indicate the order of influence of the parameters on the outputs of the model: geographic locality, shading, thresholds due to extra-consumption to charge the vehicle battery from PV and frequency of recharge with the grid. With projections of the technology in 2030, with 30 % shading, VIPV cover up to 1444 km yearly distance. This represents up to 12 % of the driven mileage. For the best month, it can get up to 14 km/day. For average Europe and worst-case conditions, VIPV cover only 293 km per year. Life Cycle Assessment (LCA) of solarized passenger car shows negative balance for low-carbon electricity mix and average solar irradiance. In favorable conditions, the carbon footprint is up to 489 kg CO₂-equivalent avoided emissions on 13 years lifespan. Beyond km and LCA focus, VIPV may provide useful functions in non-interconnected zones and for resilience in disaster zones.

Nepal is one of the most seismically active regions in the world, as highlighted by the recent devastating 2015, Mw7.8 Gorkha earthquake and a robust assessment of seismic hazard is paramount for the design of earthquake-resistant structures. In this study we present a new probabilistic seismic hazard assessment (PSHA) model for Nepal. We considered data and findings from recent scientific publications, which allowed us to develop a unified homogenized seismicity catalogue, propose alternative Seismic Source Characterization (SSC) models including up-to-date parameters of major thrust faults like Main Frontal Thrust – MFT and Main Boundary Thrust – MBT, while also considering existing SSC models and various hazard modelling strategies within a logic tree framework. The sensitivity analyses show the hazard levels are generally higher for SSC models integrating the major thrust faults, followed by homogenous volume sources and smoothed seismicity approach. The hazard maps covering entire Nepal is presented as well as the Uniform Hazard Spectra (UHS) for 5 selected locations (Kathmandu, Pokhara, Biratnagar, Nepalganj and Dipayal) at return periods of 475- and 2,475-years considering Vs,30=760 m/s. The results obtained are generally consistent with most recent studies. However, a notable variability in hazard levels and several discrepancies with respect to the Nepal Building Code NBC105: 2020 [1] and Global Hazard Model, GEM [2] are noted and possible causes are discussed.

Pandemic scenario has significantly changed several factors of life, including teaching, and learning at university. The development of suitable teaching materials to support university studies is mandatory to overcome distance learning difficulties and improve traditional teaching methodologies. This work explains a novel method for the preparation of teaching materials for medical sciences, but also plausible for other learning fields. An encephalon was extracted and prepared by using this methodology for teaching purposes. More than 200 students evaluated several factors of the material prepared this way, indicating a better understanding (up to 80%) of theoretical contents related to this human section, together with a high usability and good physical appearance

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Nutrient depletion in Tanzanian sisal production has led to yield decreases over time. We use nutrient mass balances embedded within a life cycle assessment to quantify the extent of nutrient depletion for different production systems, then used circular economy principles to identify potential cosubstrates from within the Tanzanian economy to anaerobically digest with sisal wastes. The biogas produced is then used to generate bioelectricity and the digestate residual can be used as a fertilizer to address the nutrient depletion. If no current beneficial use of the cosubstrate was assumed, then beef manure and marine fish processing waste were the best cosubstrates. If agricultural wastes were assumed to have a current beneficial use as fertilizer, then marine fish processing waste and human urine were the best cosubstrates. The largest reduction in environmental impacts resulted from bioelectricity replacing electricity from fossil fuels in the national electricity grid and improved onsite waste management practices. There is significant potential to revitalize Tanzanian sisal production by applying circular economy principles to sisal waste management and bioenergy production.

Estimating the remaining useful life (RUL) of components is a crucial task to enhance the reliability, safety, productivity, and to reduce maintenance cost. In general, predicting the RUL of a component includes constructing a health indicator ( ) to infer the current condition of the component, and modelling the degradation process, to estimate the future behavior. Although many signal processing and data-driven based methods were proposed to construct the , most of the existing methods are based on manual feature extraction techniques, and need the prior knowledge of experts, or rely on a large amount of failure data. Therefore, in this study, a new data-driven method based on the convolutional autoencoder (CAE) is presented to construct the . For this purpose, the continuous wavelet transform (CWT) technique is used to convert the raw acquired vibrational signals into a two-dimensional image; then, the CAE model is trained by the healthy operation dataset. Finally, the Mahalanobis Distance (MD) between the healthy and failure stages is measured as the . The proposed method is tested on a benchmark bearing dataset and compared with several other traditional construction models. Experimental results indicate that the constructed exhibits a monotonically increasing degradation trend and has a good performance to detect incipient faults.

The storm of November 12th-13th, 2019 provoked the displacements of boulders in a central Mediterranean rocky coast; with reference to a selected area, prone to the boulder production and geomorphologically monitored for years, a field-oriented study approach was applied for the phenomenon, by collating data concerning pre-storm locations and kinematics of these boulders. The number of displaced boulders is 11, that is, in terms of morphological imprint of a specific storm, one of the major study cases for the Mediterranean. In addition, based on widely used hydrodynamic equations, the minimum wave height required to displace the boulders is assessed. The values conform with the expected values for the wave climate dominating during the causative meteorological event and give a measure of the energy of the storm slamming the coast. Boulder dislodgements usually have plays a key role in determining the rate of the coastal recession, likely also in the investigated area. In view of an adverse climate evolution with a possible increase of energy and frequency of severe storms, the results deriving from the study of this morphodynamics should be considered for the hazard assessment and coastal management.

A new model for risk assessment of urban rainstorm disasters, based on information diffusion method and variable fuzzy sets (IDM-VFS) was proposed. In addition, an integrated index system of urban rainstorm risk was established. In the proposed model, IDM was employed to calculate the classification standards of urban rainstorm risk levels, then the VFS was adopted to assess the dangerousness, sensitivity, vulnerability and comprehensive risk of urban rainstorm disasters. In the present study, the urban rainstorm risk of Jiangsu province was evaluated with the proposed model. The results show that Wuxi, Changzhou, Nanjing and Suzhou have higher dangerousness, due to sustained rainfall and strong rainfall intensity in short duration; Wuxi, Changzhou and Nanjing have higher sensitivity because of lower disaster resistance ability; and Wuxi and Suzhou have higher vulnerability because these cities have higher potential losses in face of urban rainstorm disasters. The comprehensive risk zoning map of urban rainstorm shows apparent regional characteristics: the northwestern cities have lower risk than the southern cities. Moreover, most cities of the Jiangsu province are of the moderate urban rainstorm risk level. The results are consistent with the actual situation of Jiangsu province, and the study can provide some decision-making references for the urban rainstorm management.

In this paper we present an understanding of cyber risks in the Internet of Things (IoT), we explain why it is important to understand what IoT cyber risks are and how we can use risk assessment and risk management approaches to deal with these challenges. We introduce the most effective ways of doing Risk assessment and Risk Management of IoT risk. As part of our research, we also developed methodologies to assess and manage risk in this emerging environment. This paper will take you through our research and we will explain: what we mean by the IoT; what we mean by risk and risk in the IoT; why risk assessment and risk management are important; the IoT risk management for incident response and recovery; what open questions on IoT risk assessment and risk management remain.

Our socio-demographic status is determined by socio-demographic position and success. In the theoretical frame works, little was known about the theory, quantification and evolutionary mechanism of socio-demographic status of individual(s). This paper proposed theoretical assumptions for the evolution of socio-demographically unique population. Seven (7) factors (the driving forces) and eight (8) conditions responsible for the evolution of socio-demographically unique population were identified and explained. The evolutionary transitions were organized into six (6) socio-demographic ranks. The paper also identifies the basic features that clarify our understanding about the conception of socio-demographic status of an individual(s) as common, universal and desirable positions, achievements, and advantages every individual strives and competes to attain, achieve and maintain which are determined by biological and social environments, driven and regulated by a continuum of competitions. The paper presents a new dimension for the quantitative assessment of an individuals’ socio-demographic status and the established hierarchical transitions.

Environmental assessment is a concept that has been designed to facilitate the present generation to meet their needs without compromising the ability of future generations to meet their own needs as well. Thus, this concept has drawn significant attention from various scholars, researchers and industrial practitioners around the world over the past three decades. Life Cycle Environmental Assessment (LCEA) is a widely metric used to assess the potential ecological impacts, which can be caused by electricity generating supply systems or by other systems than power production plants. However, the current LCEA model is biased and ineffective. Because, its omits factors that are increasingly contributing to the ecological degradation. This study has identified the omitted factors through a critical analysis of a set of previous journal articles conducted in the energy sector. In light of this, this study has developed a novel LCEA framework addressing those blind spots. The framework developed in this study is holistic in nature including all the life cycle stages of a power supply system such as Extraction of the Raw Material (ERM), Transport of Raw Material (TRM), Conversion of Raw into Electricity (CRE), and Transmission and Distribution of Electricity (TDE) to the end users. The novel developed LCEA model has been tested and applied to nine power generation plants such as coal, gas, nuclear, biomass, geothermal, hydro, solar thermal, wind onshore and wind offshore. The results have demonstrated that of conventional technologies including coal, gas, and nuclear, coal energy generating source has got the highest life cycle greenhouse gas Grid Emission Factor (GEF) of 2866 kg CO2e/MWh, followed by gas with 728 kg CO2e/MWh, and nuclear has got the least GEF of 35 kg CO2e/MWh. Whereas of renewable energy sources biomass has got the highest GEF of 1508 kg CO2e/MWh, followed by solar thermal with 46.6 kg CO2e/MWh, hydro 39 kg CO2e/MWh, wind offshore 25.25 kg CO2e/MWh, wind onshore 10.1 kg CO2e/MWh, and geothermal closes the ranking with 6.23 kg CO2e/MWh.

Suicidal behaviour remains difficult to predict and prevent, even for experienced mental health care professionals. The known distal risk factors for suicidal behaviour are not sufficiently specific to fully understand the complex dynamic processes that precede a suicide attempt. Real-time mobile monitoring data can be used to analyse proximal risk mechanisms within the suicidal process. At the same time smartphone-based safety planning and self-monitoring may enhance a patient’s self-management skills thereby increasing their capacity to respond to a suicidal crisis and to become more aware of crisis symptoms. The current paper describes the theoretical and conceptual rationale for the CASPAR study which applies an innovative approach to the study of suicidal processes. It uses basic science approaches to inform the implementation of an innovative suicide prevention intervention. We aim to develop and implement mobile safety plan in conjunction with real-time monitoring in order to both directly implement suicide prevention interventions and to study the ongoing dynamics of individual suicidal behaviour by applying network analysis.

The health of the landing gear retraction and extension hydraulic system may be assessed using fuzzy comprehensive evaluation (FCE), however the traditional FCE method depends solely on human assessment by specialists, which is excessively subjective. To address the issue of excessive human subjective variables in the assessment, an improved FCE model based on enhanced risk coefficient is provided, which includes four consideration indexes: failure probability, failure severity, failure detection difficulty, and failure repair difficulty. To reduce subjective human judgment errors entirely due to expert experience, the improved FCE takes into account the likelihood of failure using a statistical method, the severity of failure using a fault simulation analysis based on the LMS Imagine.Lab AMESim simulation platform, and the difficulty of fault detection and repair using the aircraft manufacturer's professional maintenance information. As part of the evaluation model, the range of health assessment values and accompanying treatment methods are included, making it easier to implement on a daily basis in aircraft maintenance. As a final step, the simulation is evaluated and the simulated faults are calculated.

The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owing to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging, and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.

Digitalization is globally transforming the world with profound implications. It has enormous potential to foster progress toward sustainability. However, in its current form, digitalization also continues to enable and encourage practices with numerous unsustainable impacts affecting our environment, ingraining inequality, and degrading quality of life. There is an urgent need to identify such multifaceted impacts holistically. Impact assessment of digital interventions (DIs) leading to digitalization is important specifically for Sustainable Development Goals(SDGs). Action is required to understand the pursuit of short-term gains toward achieving long-term value-driven sustainable development. We need to understand the impact of DIs on various actors and in diverse contexts. A holistic understanding of the impact it creates will help us align it with visions of sustainable development and identify potential measures to mitigate negative short and long-term impacts. The recently developed Digitainability Assessment Framework (DAF) unveils the impact of DIs with an in-depth context-aware assessment and offers an evidence-based impact profile of SDGs at the indicator level. We performed the impact assessment of diverse technologies using DAF. This paper summarizes the insights from the Digitainable Spring School 2022 on "Sustainability with Digitalization and Artificial Intelligence," one of whose goals was to operationalize the DAF as a tool in the action learning process with diverse professionals in the field of digitalization and sustainability. The DAF guides a holistic context-aware process formulation for a given DI. An evidence-based evaluation within the DAF protocol benchmarks a specific DI’s impact against the SDG indicators framework. The operationalization of the DAF was carried out by looking at four different DIs: smart home technologies (SHT) for energy efficiency, blockchain for food security, artificial intelligence for land use cover and changes (LUCC), and big data for international law. Each of the four studies addresses different DIs for digitainability assessment using different techniques for a diverse group of indicators, demonstrating the potential of the DAF but also outlining the existing data gaps that limit a comprehensive analysis.

Mediterranean climate is marked by arid climate conditions in summer, therefore, crop irrigation is crucial to sustain plant growth and productivity in this season. If groundwater is utilized for irrigation, an impressive water pumping is needed to satisfy crop water requirements at catchment scale. Consequently, irrigation water quality gets worse, specifically considering groundwater salinization near the coastal areas due to seawater intrusion, also triggering soil salinization. With reference to an agricultural coastal area in the Mediterranean basin (Southern Italy), close to the Adriatic sea, an assessment of soil salinization risk due to processing tomato cultivation was carried out. A simulation model was arranged to perform, on daily basis, a water and salt balance along the soil profile. Long-term weather data and soil physical parameters representative of the considered area were utilized in applying the model, also considering three salinity levels of irrigation water. Based on the climatic analysis performed and the model outputs, the probability of soil salinity came out very high, such as to seriously threaten tomato yield. Autumn-winter rainfall resulted frequently insufficient to leach excess salts away from the soil profile and reach sustainable conditions of tomato cultivation. Therefore, alternative cropping strategies were prospected.

Currently, the most important challenge in any assessment of state-of-the-art sensor technology and its reliability is to achieve road traffic safety targets. The research reported in this paper is focused on the design of a procedure for evaluating the reliability of Internet-of-Things (IoT) sensors and the use of a Cyber-Physical System (CPS) for the implementation of that evaluation procedure to gauge reliability. An important requirement for the generation of real critical situations under safety conditions is the capability of managing a co-simulation environment, in which both real and virtual data sensory information can be processed. An IoT case study that consists of a LiDAR-based collaborative map is then proposed, in which both real and virtual computing nodes with their corresponding sensors exchange information. Specifically, the sensor chosen for this study is a Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. Implementation is through an artificial-intelligence-based modeling library for sensor data-prediction error, at a local level, and a self-learning-based decision-making model supported on a Q-learning method, at a global level. Its aim is to determine the best model behavior and to trigger the updating procedure, if required. Finally, an experimental evaluation of this framework is also performed using simulated and real data

The epistemology of the traditional risk assessment examined has a taxonomy based upon deterministic, behaviouristic and compatibilist methodologies which are likely driven by neo-liberal market related requirements for bigger, faster cheaper wind-turbines. Analysis of available risk assessments suggested that there is no conscious effort to deceive the reader although the intended audience is unclear. The language, and scoring mechanisms utilized indicates the presence of conformity bias, confirmation bias, and numerical inconsistency leading to data ossification. By reverse engineering risk assessment content, Pragmatic and Social Psychological aspects can be investigated, and the adequacy of the document can be evaluated using evidence-based models.

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.

The inherited ‘EIA’ process at Brexit is based on a ‘Project’ period from Inception tothe start of construction works. The ‘Incompleteness’ of the process covers andidentifies stages that will impact on the environmental cycle: construction period,operational life, de-commissioning and a return to acceptable environmental footprint.A master environment structure-plan framework is required to commence with theupgrading of the complete ‘EIA’ process, embracing separate limbs for the UK’spriority environmental targets for climate change, and other major adverse effectswhich result in serious ‘harm’ to the UK environment. The identification of the processfor ‘incompleteness’ requires effective sustainability, and inclusion of effectivemanagement tools, for the essential ‘monitoring/control/recording’ proceduresthroughout the project life and the natural environmental cycle. This will allow theopportunity for a new approach to a structured ‘Suite of EIAR Directives’, to enable a‘EIAR’ regime that ‘will remain fit and proper for UK purpose’.

Diet is considered a possible cofactor that affects the immune system and potentially could cause dysregulation of intestinal homeostasis and inflammation. This study aimed to review the evidence on specific diet composition effects on some immune-mediated inflammatory diseases (IMID) -rheumatoid arthritis (RA), psoriatic arthritis, spondyloarthritis, multiple sclerosis (MS), Inflammatory bowel disease (IBD) [remission maintenance of Crohn´s disease and ulcerative colitis] and psoriasis- symptoms in adult patients. We conducted a rapid review of meta-analyses and Cochrane systematic reviews using PubMed, EMBASE, from inception to September 2024, and Google Scholar. The methodological quality of meta-analyses was assessed using AMSTAR 2 rating. Three COCHRANE systematic reviews and eight meta-analyses Some specific composition diets have been shown to reduce symptoms in RA, IBD, and MS; and improve some activity parameters in IBD and RA; with critically low, or low levels of evidence. The reduction in inflammatory biomarker levels is unclear. This review updates the global dietary evidence to improve IMID symptoms, discusses the influences of dietary mechanisms, clarifies the weaknesses of clinical trials and dietary meta-analyses, with critically low, or low levels of evidence, and shows the need to use indexes such as DII, that allow diets to be classified for their content of pro-inflammatory or anti-inflammatory foods, to better compare diet groups in clinical trials. The difficulty in obtaining high-level evidence in dietary studies is apparent, which may delay the application of the results.

In response to a threefold increase in osteoporotic vertebral fractures (OVF) at our institution over the past decade and the subsequent development of new fractures within a year in a third of cases, we evaluated the effectiveness of STIR MRI scans in conjunction with CT scans for initial OVF diagnosis. This study, conducted over five months, involved full spine sagittal STIR MRI scans alongside CT scans for patients over 50 with minor injury-related vertebral fractures. The independent assessments by radiologists and neurosurgeons of 64 patients revealed that while CT scans identified 161 fresh fractures, of which only 68 were previously reported, MRI detected 266 fractures, 1.65 times more than CT. CT scans showed single fractures in 27% of patients and multiple fractures in 73%, whereas MRI identified single fractures in 9% and multiple fractures in 91% of patients. Additionally, MRI demonstrated a 1.5-fold increase in adjacent fracture detection. The use of MRI influenced treatment plans in 67% of cases. Given these findings, we advocate for including a STIR MRI of the entire spine alongside CT scans in OVF cases, as it significantly enhances fracture detection and can alter treatment strategies.

Website defacement is an illegal electronic act of changing a website. In this paper, robust machine learning classifiers’ capabilities were exploited to select the best input feature set for evaluating a website’s defacement risk. Zone-H, a private organization, offered us a defacement mining dataset. A sample of 93644 datapoints was concisely pre-processed and used for modelling purposes. Considering multidimensional features as input, reason and hackmode variables were chosen as outputs. Massive machine learning models were examined; however, decision tree (DT), k-nearest neighbour (k-NN), and random forest (RF) were the most powerful algorithms used to predict the target model. The input variables 'domain', 'system', 'web_server','redefacement', 'type', 'def_grade', and 'reason/hackmode' were tested and used to shape the final model. Using the cross-validation (CV) technique, the model’s key performance factors were calculated and reported. After calculating the average scores for the hyperbolic metrics (i.e., max-depth, min-sample-leaf, weight, max-features, and CV), both targets were evaluated, and the learning algorithms were ranked as RF, DT, and k-NN. The reason and hackmode variables were thoroughly analysed. The average score accuracies for the reason and hackmode targets were 0.85 and 0.585, respectively. The results showed a significant development in terms of modelling and optimizing website defacement risk. The study also successfully addresses the main cybersecurity concerns, in particular website defacement.

The nine plateau lake watersheds in Yunnan are important ecological security barriers for southwest China, and the prevention and control of landslides are important considerations in the management of these watersheds. Taking Dianchi Lake watershed as a typical study area, this paper puts forward an improved comprehensive process of landslide susceptibility evalua-tion, and discusses the model’s sensitivity regarding susceptibility to landslides. The compre-hensive process is based on the weight of evidence method (WoE) and integrates many analyti-cal techniques, such as cross-validation, multi-quantile cumulative Student’s comprehensive weight analysis, independence tests, step-by-step modeling, ROC analysis and ROC-based sus-ceptibility zoning. This paper established fourteen models, with high accuracy and validity AUC, reaching 0.83-0.87 and 0.85-0.88, respectively. Moreover, according to the susceptibility zoning map compiled using the optimal model, 80% of landslides can be predicted in the very high and high susceptibility zones, which only account for 19.58% of the study area. Finally, the strategies for geological disaster prevention and ecological restoration deployment were put forward, providing great guiding significance for deeply understanding the landslide susceptibility of lake watersheds (Dianchi Lake) in Yunnan Plateau and guiding the planning and deployment of landslide prevention and mitigation activities and ecological restoration in the watershed.

Type two diabetes mellitus (T2DM) represents a chronic condition with increasing prevalence worldwide among the older population. T2DM condition increases the risk of micro and macro-vascular complications as well as the risk of geriatric syndromes as falls, fractures and cognitive impairment. The management of T2DM in the older population represents a challenge for the cli-nician, and a Comprehensive Geriatric Assessment should always be prioritized, in order to tailor the glycate haemoglobin target according to functional and cognitive status comorbidities, life ex-pectancy and type of therapy. According to the most recent guidelines, older adults with T2DM should be cathegorized in three groups: healthy patients with good functional status, patients with complications and reduced functionality and patients at the end of life; for each group the target for the glycemic control is different, also according to the type of treatment drug. The therapeutic ap-proach should always begin with lifestyle changes; after that, several lines of therapies are available, with different mechanism of action and potential effect other than glucose level reduction. Partic-ular interest is growing around sodium-glucose cotransporter-2 inhibitors, due to their effect on the cardiovascular system. In this review we evaluate the therapeutic options available for the treat-ment of older diabetic patients, to ensure a correct treatment approach

Hip fractures are an important socio-economic problem in western countries. Over the past 60 years orthogeriatric care has improved the management of older patients admitted to hospital after suffering hip fractures. Orthogeriatric co-management units have increased quality of care, reducing adverse events during acute admission, length of stay, both in-hospital and mid-term mortality, as well as healthcare and social costs. Nevertheless, a large number of areas of controversy regarding the clinical management of older adults admitted due to hip fracture remain to be clarified. This narrative review, centered in the last 5 years, combined the search terms “hip fracture”, “geriatric assessment”, “second hip fracture”, “surgery”, “perioperative management” and “orthogeriatric care”, in order to summarise the state of the art of some questions such as the optimum analgesic protocol, the best approach for treating anemia, the surgical options recommendable for each type of fracture and the efficiency of orthogeriatric co-management and functional recovery.

Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with UAV imagery as an example. We compared the performance of an open-source AutoML framework, AutoKeras in image classification and regression tasks to transfer learning using modern convolutional neural network (CNN) architectures. For image classification which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved best classification accuracy of 93.2%, whereas Autokeras had 92.4% accuracy. For image regression which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2=0.8303, RMSE=9.55, MAE=7.03, MAPE=12.54%), followed closely by AutoKeras (R2=0.8273, RMSE=10.65, MAE=8.24, MAPE=13.87%). Interestingly, in both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. The merits and drawbacks of AutoML compared to transfer learning for image-based plant phenotyping are discussed.

To assess the presence of organochlorine pollutants (OCP) in Alaskan sub-Arctic latitudes, we analyzed ice core and meltwater samples from Jarvis Glacier, a polythermal glacier in Interior Alaska. Jarvis Glacier is receding as atmospheric warming continues throughout the region, increasing opportunity for OCP transport both englacially and into the proglacial watershed. Across all meltwater and ice core samples we identify the pesticides DDT, DDE and DDD, α- HCH and ϒ-HCH. OCP concentrations in ice core samples were highest at the 7-14 m depth (0.51 ng/L of DDT) and decreased gradually approaching the bedrock at 79m. Meltwater concentrations from the proglacial creek slightly exceeded concentrations found in the ice core, potentially indicating aggregate OCP glacial loss, with peak OCP concentration (1.12 ng/L of DDD) taken in July and potentially associated to peak melt. Ongoing use of DDT to fight Malaria in Asia, and the extended atmospheric range of HCH may account for concentrations in near-surface ice, correlating with use and atmospheric transport. The opportunity for biota bioaccumulation of OCPs, or human uptake of OCPs from glacial meltwater, may increase as glacial melt continues.

There is increasing evidence and recognition that Lyme borreliosis, and other associated tick-borne diseases (LB/TBD) cause mental symptoms. Data was drawn from databases, search engines and clinical experience to review current information on LB/TBD. LB/TBD infections cause immune and metabolic effects that result in a gradually developing spectrum of neuropsychiatric symptoms, usually presenting with significant comorbidity and may include developmental disorders, autism spectrum disorders, schizoaffective disorders, bipolar disorder, depression, anxiety disorders (panic disorder, social anxiety disorder, generalized anxiety disorder, posttraumatic stress disorder, intrusive symptoms), eating disorders, decreased libido, sleep disorders, addiction, opioid addiction, cognitive impairments, dementia, seizure disorders, suicide, violence, anhedonia, depersonalization, dissociative episodes, derealization and other impairments. Screening assessment followed by a comprehensive psychiatric clinical exam relevant to patient’s complaints and findings with a thorough history, mental status exam, review of systems, neurological exam, physical exam, a knowledgeable interpretation of laboratory findings, pattern recognition and clinical judgment facilitate diagnosis. Psychotropics and antibiotics may help improve functioning and prevent further disease progression. Awareness of the association between LB/TBD and neuropsychiatric impairments and studies of their prevalence in neuropsychiatric conditions can improve understanding of the causes of mental illness and violence and result in more effective prevention, diagnosis and treatment.

The number of individuals meeting criteria for genetic counseling and testing for hereditary cancer syndromes (HCS) is far less than the number that actually receive it. To facilitate identification of patients at risk for HCS, Counsyl developed a digital identification tool (digital ID tool) to match personal and family cancer history to National Comprehensive Cancer Network (NCCN) BRCA-related Hereditary Breast and Ovarian Cancer (HBOC), Lynch syndrome, and polyposis testing criteria in one-to-one, automated fashion. The purpose of this study was to validate the ability of the digital ID tool to accurately identify histories that do and do not meet NCCN testing criteria. Methods: Third-party recorded three-generation pedigrees were retrospectively reviewed by a certified genetic counselor (CGC) to determine if independent events included in pedigree histories met NCCN guidelines, and were then sorted into groups: high risk events (meets criteria) and low risk events (does not meet criteria). Events were entered into the digital ID tool to determine the extent of its concordance with events sorted by CGC review. Statistical tests of accuracy were calculated at a 95% confidence interval (CI). Results: 197 pedigrees were reviewed consecutively representing 765 independent events for analysis across groups. 382/382 (100%) high risk events identified by the digital ID tool and 381/383 (99.47%) low risk events identified by the digital ID tool were concordant with CGC sorting. The digital ID tool had a sensitivity of 100% (99.04-100% CI) and specificity of 99.48% (98.13-99.94% CI). The overall accuracy of the digital ID tool was estimated to be 99.74% (99.06-99.97% CI), reflecting the rate at which the digital ID tool reached the same conclusion as that of CGC review of pedigree events for the recommendation of genetic testing for individuals at risk for HCS. Conclusions: The digital ID tool accurately matches NCCN criteria in one-to-one fashion to identify at-risk individuals for HCS and may be useful in clinical practice, specifically for BRCA-related HBOC and Lynch Syndrome.

Auricular keloids pose significant aesthetic and functional challenges, and traditional treatments often fall short in ad-dressing these. Our study presents an innovative combined approach of ablative CO2 and dye laser therapy for improved keloid management. This treatment protocol was applied to 15 patients with auricular keloids after an initial multispec-tral analysis to assess keloid composition. The laser sequence was tailored per patient based on this analysis. Evalua-tions using the Vancouver Scar Scale and Patient and Observer Scar Assessment Scale were carried out at baseline and at 3-week intervals post-treatment. The results showed a significant reduction in these scores at the final follow-up (p<0.05), suggesting improvements in keloid color, texture, and pliability, with minimal adverse events. Additionally, no recur-rence of keloids was observed. Our findings indicate that this novel methodology of multispectral analysis followed by tailored laser therapy may offer a safe and effective solution for auricular keloids, promising enhanced keloid treatment and prevention of recurrence. However, further investigations, including randomized controlled trials, are needed to confirm and optimize this treatment protocol.

China is facing the development requirements of the dual carbon goal and the construction needs of resilient cities. Currently, high-strength composite structural systems are a feasible solution, but their research and application in the construction field are insufficient, especially for the combination forms of concrete above C100 and steel above Q550. In response to this issue, this paper proposes a new high-performance structural system, namely the composite frame - high-strength steel plate wall core tube resilient structure system, which includes high-strength steel plate (Q550) - ultra-high strength concrete (C100) shear walls, replaceable energy dissipation coupling beams, and composite frames. Taking a 200 meter building as an example, this paper designs and establishes calculation models for a conventional reinforced concrete frame-core tube structure and a high-performance structure based on PKPM software, and establishes elastoplastic analysis models for both based on SAUSAGE software, and then conducts dynamic elastoplastic time history analysis and seismic resilience assessment of structures under design basis earthquakes (DBEs), maximum considered earthquake (MCEs) and extremely rare earthquakes (EREs). Research has shown that compared to conventional structures, new high-performance structures can effectively reduce the size of shear walls, reduce the self-weight of the structure, and enhance the space for building use; The story drift ratio has higher redundancy than specification limits, lower plastic damage and overall stiffness degradation of the structure, and better seismic performance; The seismic resilience has significantly improved, especially in the case of casualties, which can better ensure the safety of people's lives and property.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

Target 12.3 of the United Nations Sustainable Development Goals (SDGs) calls for halving per capita global food waste at the retail and consumer levels, by 2030. The Food Waste Index is suggested as a methodology for grasping the situation. This paper focuses on the consumer level (household food waste). We argue that in order for generating useful information for devising and implementing effective measures for reducing food waste, it should be measured at Level 3 of the Food Waste Index, based on sorting analysis of generated waste, making a distinction between avoidable and non-avoidable food waste. Furthermore, a breakdown by sub-categories that reflect the flow of food in the household could help identify target behaviours. We have developed a categorisation scheme that is internationally agreeable and adoptable, and 1) generates useful information for policy-making and for tackling with reduction of food waste, 2) makes clear the concept of avoidable food waste, and 3) is practical and does not overcomplicate the work of grasping the situation of food wastage. Results of workshops regarding this scheme suggest that the scheme satisfies the criteria. This scheme has been applied to a few sorting analyses of household food waste in Japan, and their results are compared.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

Understanding behavioral factors associated with obesity is of importance in addressing this issue. This study examined the association between cognitive restraint, uncontrolled eating, emotional eating and body mass index (BMI) and amount of food plated, consumed, leftovers, and leftover food thrown into the trash (food wasted) in early adolescent girls nine to 13 years in O’ahu, Hawai’i (n = 93). Food plated, consumed, leftovers, and food wasted were estimated using a three-day mobile food record (mFR). Weight and height were measured to compute BMI (kg/m2). The three-factor eating questionnaire provided a score from 0 to 100 for cognitive restraint, uncontrolled eating, and emotional eating. Higher scores are indicative of greater cognitive restraint, uncontrolled eating, and emotional eating. Pearson’s correlation and general linear models were computed to examine the relationship between three factor eating scores, BMI, and food plated, consumed, leftovers, and food wasted. There was no clinically significant association between cognitive restraint and amount of food wasted. Cognitive restraint was positively correlated with BMI (r=0.36, p<0.001) and with BMI z score (r=0.40, p<0.001). Uncontrolled eating and emotional eating were positively correlated with amount of leftover food at dinner (r=0.30, p=0.006; r=0.33, p=0.003, respectively). Emotional eating was positively associated with percentage of leftover food at dinner (r=0.24, p=0.30). Additional research should examine the specific roles of cognitive restraint, uncontrolled eating, emotional eating and food waste in the development of obesity in adolescents.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

Natural Fibre Composites (NFC) are eco-friendly, sustainable alternatives to synthetic polymers. However, some intrinsic natural fillers´ properties hinder their widespread implementation as re-inforcement in polymeric matrices and require further investigation. In the scope of this study, the thermal, rheologic, mechanical (tension and flexural modes), morphological properties, water ab-sorption and dimensional stability of the NF polypropylene (PP) based injection moulded compo-sites with the rice husk (rh) and olive pits (op) of 20 wt% and 30% wt%, were investigated. The results suggest that the higher content of the rice husk and olive pits led to a similar reduction of the Melt Flow Index (MFI), independent of the additive type compared to virgin polypropylene (PPv). The melting and crystallization temperatures of the PPrh and PPop composites did not change with statistical significance. The composites are stiffer than the PP matrix by up to 49% and possess higher mechanical strength in what concerns the tension mode at the expense of de-creased ductility. PPrh and PPop have superior flexural modulus in the bending mode, while the flexural strength improvement was accomplished for the PP30%rh. The dimensional stability of the composites was improved as the linear shrinkage in the flow direction was decreased by 49% for PPrh and 30% for PPop, positively correlating with an increase in the filler content and stiff-ness. PPop was less susceptible to water sorption than PPrh due to fibres' composition and larger surface-to-area volume ratios.

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

3D measurement is a high-throughput method that can record a large amount of information. In this study, we have developed new methods that could be used for morphological analysis of plants from the information contained in 3D data. Specifically, we investigated characteristics that can be measured by scale (dimension) and/or visual assessment by humans. The characteristics that can be measured on a scale-related dimension were tested based on the bounding box, convex hull, column solid, and voxel. Furthermore, for characteristics that can be evaluated by visual assessment, we propose a new method using normal vectors and local curvature (LC) data. For these examinations, we used our highly accurate all-around 3D plant modelling system. The correlation coefficients between manual measurements and the scale-related methods were all above 0.9. In particular, the differences in LC calculated from the normal vector data allowed us to visualize and quantify the concavity and convexity of leaves. Furthermore, we also found a difference in the time point at which leaf blistering began to develop among the cultivars. The precise 3D model made it possible to perform quantitative measurements of lettuce size and morphological characteristics. In addition, the newly proposed LC-based analysis method made it possible to quantify the characteristics that rely on visual assessment.