This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

Designing motion representations for the problem of 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio-temporal patterns of skeletal movements and how to learn their discriminative features for classification task. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.

Results-Based Funding (RBF) for Reducing Emissions from Deforestation and Forest Degradation (REDD+) has become an important instrument for channeling financial resources to forest conservation activities. At the same time, much literature on conservation funding is ambiguous about the effectiveness of existing RBF schemes. Many effectiveness evaluations follow a simplified version of the principal-agent model, although in practice the relation between aid providers and funding recipients is much more complex. As a consequence, intermediary steps of conservation funding are often not accounted for effectiveness studies. This research paper aims to provide a nuanced understanding of conservation funding by analyzing the allocation of financial resources for one of the largest RBF schemes for REDD+ in the world: the Brazilian Amazon Fund. As part of this analysis, this study has built a dataset of information on Amazon Fund projects at unprecedented detail in order to accurately reconstruct the allocation of financial resources across different stakeholders (i.e. governments, NGOs, research institutions), geographies and activities. The results show that stakeholders seem to hold preferences with respect to the type of activities that they support, thereby suggesting that project owners exert much influence on how deforestation reduction is to be attained. There are evidences that governmental organizations lack financial additionality of their projects, which renders the growing share of funding to this type of stakeholder particularly worrisome. By contrast, the geographical distribution of financial resources seemed to follow a more focused rationale as financial support tends to concentrate in areas where deforestation threats are highest. Overall, the allocation of the financial resources from the Amazon Fund reflects an arbitrary support of different projects that adopt very diverging theories of change that are not primarily concerned with attaining further deforestation reductions. As projects owners exert influence on funding effectiveness to some extent, the Amazon Fund may either seek to regulate the allocation of financial resources more actively or adopt funding effectiveness evaluations that account for this influence more comprehensively.

This paper presents the latest developments in microCT, both globally and locally, for supporting the additive manufacturing industry. There are a number of recently developed capabilities which are especially relevant to the non-destructive quality inspection of additive manufactured parts; and also for advanced process optimization. These new capabilities are all locally available but not yet utilized to their full potential, most likely due to a lack of knowledge of these capabilities. The aim of this paper is therefore to fill this gap and provide an overview of these latest capabilities, showcasing numerous local examples.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

In the second decade of the 21st century, social media changed the nature of communication and cooperation between participants of the culture services market. They became, among other things, an important marketing instrument in the area of contact with the customers of the cultural offer. However, despite their growing importance in various areas of activity of organisations in the cultural sector, the issue of building the cultural institution's brand equity by social media users is relatively seldom raised. Research on the impact of online consumer activity on brand equity is at an early stage of development. Therefore, this article is an attempt to fill the research gap in this area. The article presents the results of a survey conducted in 2018 on a group of 1021 consumers of cultural services, who at the same time regularly used social media. The statistical analysis carried out and the research results obtained prove that the 3C sustainable system developed by the authors, concerning the activity of consumers of cultural services in social media, stimulates the consumer-based brand equity (CBBE). Statistically significant relations have been observed in particular for CBBE components related to the awareness of a cultural institution's brand and for the relationship related to the perception of its quality. The study opens a review of literature on social media and consumer-based brand equity (CBBE). Next, based on the COBRA model (consumer's online brand-related activities), a proposal of the 3C sustainable system, concerning the activity of consumers of cultural services in social media, has been presented. The further part of the article presents research hypotheses, a conceptual model, research methodology as well as results and conclusions. The last part of the article discusses the results obtained and indicates the existing management implications.

The early-mid Paleocene Farewell Formation is stratigraphically distributed across the southern Taranaki Basin (STB) which is also encountered within the Maui Gas Field. Using available 3D seismic and well log data, a challenging task to delineate the spatial distribution and geobody patterns of the potential reservoir sands of the formation was performed. Object based modeling coupled with sequential indicator simulation were used to analyze the spatial distribution of facies configuration and a conceptual model was developed based on the outputs from the structurally- modeled grids. The facies modeling followed a hierarchical object-based mechanism which was set to perform with constraints like channel geometry and heterogeneity within the formation. The resultant 3D geobody model showed that the distributary channels, mainly braided geobodies flowed from northeast cutting through several regional normal-fault systems to the southwest. Overbank facies was adhered to the fringe of the channels whereas the floodplain facies was at the periphery of the model. Meandering channel-sand facies were mostly observed at the center of the model flowing in a more random manner, occupying major flow directions of northwest to southwest and southeast to northwest within the model.

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several dierent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho,  and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

In the current study, the failure behavior of retrofitted steel structures was studied experimentally and theoretically with steel/CFRP double strap joints (DSJs) under quasi-static tensile loading. A series of DSJs with different bonding lengths are also considered and examined to experimentally assess the effective bond length. To predict the failure load values of the tested specimens, a new stress-based criterion, namely the point stress (PS) criterion is proposed. Although some theoretical predictive modelling for the strength between steel/CFRP joints under various loading conditions has been presented, in this work by using the new proposed approach, one can calculate rapidly and conveniently the failure loads of the steel/CFRP specimens. Furthermore, to assess the validity of the new proposed criterion, further experimental data on steel/CFRP DSJs available in the open literature are predicted using the PS criterion. Finally, it was found that a good agreement exists between the experimental results and the theoretical predictions based on the PS criterion.

the intermittency and variability of these permeated Distributed Generators (DGs) could critically cause many security and economy risks to distribution systems. This paper applied a certain mathematical distribution to imitate the output variability and uncertainty of DGs. And then, four risk indices EENS, PLC, EFLC and SI were established to reflect the system risk level of distribution system. For the certain mathematical distribution of the DGs' output power, a developed PEM (point estimate method)-based method was proposed to calculate these four system risk indices. In this developed PEM-based method, enumeration method was used to list the states of distribution systems, an improved PEM was presented to deal with the uncertainties of DGs and the value of load curtailment in distribution systems was calculated by an optimal power flow algorithm. Finally, the effectiveness and advantages of this proposed PEM-based method for distribution system assessment were verified by the tests of a modified IEEE 30-bus system. Simulation results have shown that this proposed PEM-based method has a high computational accuracy and highly reduced computational costs compared with other risk assessment methods and is very effective for risk assessments.

In this study, we aim to enhance the accuracy of product quality inspection and counting in the manufacturing process by integrating image processing and human body detection algorithms. We employed the SIFT algorithm combined with traditional image comparison metrics such as SSIM, PSNR, and MSE to develop a defect detection system that is robust against variations in rotation and scale. Additionally, the YOLO v8 Pose algorithm was used to detect and correct errors in product counting caused by human interference on the load cell in real-time. By applying the image differencing technique, we accurately calculated the unit weight of products and determined their total count. In our experiments conducted on products weighing over 1kg, we achieved a high accuracy of 99.268%. The integration of our algorithms with the load cell-based counting system demonstrates reliable real-time quality inspection and automated counting in manufacturing environments.

Antimicrobial resistance (AMR) is a growing global threat, affecting the effectiveness of antimicrobials and posing significant challenges in the treatment of bacterial and viral infections. The emergence of the coronavirus in 2019 and the ongoing menace of infectious diseases such as HIV/AIDS, tuberculosis, and hepatitis remind us of the impact infections have on economic stability and global health, and therefore, the need for a more holistic and long-term solution as opposed to antibiotics - measures that were previously relied on. This review explores recent advances in immunotherapeutic strategies, particularly cytokine-based therapies, adoptive cell therapy, monoclonal antibodies, and immune checkpoint inhibitors, in the control of AMR. It focuses on pathogens with high clinical relevance, including Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Mycobacterium tuberculosis, and viral threats such as HIV and SARS-CoV-2. New inventive approaches such as CAR T cell therapy and MAIT cells were discussed in the context of bacterial and viral infections, highlighting promising results from clinical trials and addressing the challenges of toxicity, immune evasion, and therapy resistance inherent in these diseases. Future priorities include optimizing combination therapies and exploring new immunomodulatory targets to improve these interventions' effectiveness in treating AMR and other infectious diseases.

Abstract: New paradigms have emerged in mapping since the 20th century due to theoretical and practical advances in computer technology, communications, and cartography. For the design of the Expert-Topographic Map Service in Mexico, we adopted a theoretical framework of Cybercartography and expanded to incorporate expert systems and eventually other artificial intelligence resources. Using a transdisciplinary approach, our efforts could focus on the interconnection between knowledge and geotechnology to offer solutions to societal complex problems. This paper explores the theoretical pillars supporting the design of an Expert-Topographic Map Service, including cybercartography, knowledge-based systems and surface mathematical modeling. For the first time, cybercartography is applied in the realm of topographic mapping. As shown, a map conceived solely as an information system has been transformed, in the digital era, into a container of knowledge through the abstract representation of features like relief via the Triangular Irregular Network Model. Thereby, we can bridge from digital topographic maps to other knowledge-based models such as expert systems. The societal demand and empirical context for this research demonstrate that topographic maps and services are still at the core of cartographic initiatives in the 21st century.

Breathing in fine particulate matter with diameters less than 2.5 µm (PM_2.5) has greatly increased an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to rise, exacerbating air pollution. The 2023 Canadian wildfires highlighted the growing threat of PM_2.5 as smoke spread across U.S. cities like New York, Philadelphia, and Washington D.C. This research investigates the application of data augmentation techniques to improve the accuracy of PM_2.5 concentration forecasts in these urban environments. Models trained on imbalanced datasets often struggle to capture extreme pollution events, underestimating high PM_2.5 levels due to the model’s focus on more frequent, low-value samples. To address this, we implemented cluster-based undersampling and trained transformer models using various cutoff thresholds (12.1 µg/m³ and 35.5 µg/m³) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m³ threshold, coupled with a 20/80 partial sampling ratio, provides the best performance regarding RMSE and R², particularly in capturing high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These insights significantly contribute to a better understanding of PM_2.5 pollution with the hopes of more informed public health and environmental policies.

This study aimed to develop fermented cashew nut cheese alternative supplemented with Chondrus crispus and Porphyra sp. for enhancing their nutritional and sensory characteristics, as well as familiarizing consumers with seaweeds consumption. The impact of seaweeds supplementation was evaluated through analysis of some physicochemical, microbiological and organoleptic properties of the developed food products. The total lipids content decreased with the supplementation with seaweeds. Crude protein content also slightly decreased, while elemental analysis showed that mineral and trace elements (Ca, K, Mg, Na, Fe, I, Se, and Zn) content increased when C. crispus was added to the paste. The analyses of color and textural (TPA) attributes showed that theese were significantly influenced by adding seaweeds to the cashew paste. Generally, the microbiological results comply with the different European guidelines for assessing the microbiological safety of ready-to-eat foods placed on the market, except for aerobic mesophilic bacteria and marine agar counts. Flash Profile analysis al-lowed to distinguish samples attributes, showing an increased flavor complexity of plant-based cheese alterna-tives supplemented with seaweeds. Overall, the study indicates that seaweed enrichment mainly influenced the physicochemical and sensory characteristics of plant-based cheese alternatives.

Metered Dose Inhalers (MDIs) play a crucial role in managing respiratory diseases, but their effectiveness depends on whether the intended dose is delivered to the target, which can be influenced by various factors. Accurate assessment of MDI performance is crucial for optimizing MDI delivery and ensuring drug efficacy. This study numerically examined the role of evaporation dynamics and dosimetry methods in assessing MDI delivery efficiency to different regions in a mouth-lung model extending to G11. Experimentally determined spray exit speed, applied dose, and droplet size distribution were implemented as the initial/boundary conditions. Large eddy simulations (LES) were used to resolve the transient inhalation flows, and a chemical species model was applied to simulate vapor and temperature variations in the airflow. A multi-component model was used to consider the heat and mass transfer between the droplets and airflow. The model was validated against literature data and applied to evaluate the impact of evaporation on pulmonary drug delivery using MDI, in comparison to inert particles. Three methods were used to quantify deposition: based on droplet count, droplet mass, and the drug carried by the droplets. Results demonstrate that evaporation notably alters the spray droplet size distribution and subsequent deposition patterns. Compared to inert particles, evaporation led to significantly more droplets ranging from 1–5 µm entering the pulmonary region. For a given region, large discrepancies were observed in deposition fraction (DF) using different dosimetry methods. In the lower lung, the count-based DF (33.9%) and mass-based DF (2.4%) differed by more than one order of magnitude, while the drug-based DF fell between them (20.5%). This large difference highlights the need to include evaporation in predictive dosimetry, as well as to use the appropriate method to quantify the delivery efficiency of evaporating droplets.

Microalgae and cyanobacteria are photosynthetic microorganisms with significant biotechnological potential for the production of bioactive compounds, making them a promising resource for diverse industrial applications. This study presents the development and validation of a modular, droplet-based microfluidic photobioreactor (µ-PBR) designed for high-throughput screening and cultivation under controlled light conditions. The µPBR, based on PTFE tubing and a 4-channel LED illumination system, enables precise modulation of light intensity, wavelength, and photoperiod, facilitating dose-response experiments. Synechococcus elongatus UTEX 2973 and Chlorella vulgaris were used to demonstrate the system's capacity to support photosynthetic growth under various conditions. The results indicate that continuous illumination, particularly under blue and mixed blue-red light, promotes higher autofluorescence and chlorophyll-a content in cyanobacteria Synechococcus elongatus UTEX2973, while Chlorella vulgaris achieved optimal growth under a 16:8 light-dark cycle with moderate light intensity. This µ-PBR offers not only a flexible, scalable platform for optimizing growth parameters but also investigating highly resolved dose response screenings of environmental stressors such as salinity. The findings highlight its potential for advancing microalgal biotechnology research and applications.

The importance of continuously emerging new distribution is a mandate to understand the world and environment surrounding us. In this paper, the author will discuss a new distribution defined on the interval (0,1) as regards the methodology of deducing its PDF, some of its properties and related functions. A simulation and real data analysis will be highlighted.

Habitat fragmentation is often assumed to negatively impact species diversity because smaller, more isolated populations on smaller habitat patches are at a higher extinction risk. However, some empirical and theoretical studies suggest that landscapes with numerous small habitat patches may support higher species richness, although the circumstances remain elusive. We used an agent-based metacommunity model to investigate this and simulate landscapes of the same total area but diverse patch sizes. Our model, as generic and unbiased by specific assumptions as possible, aimed to explore which circumstances may be more conducive to supporting higher biodiversity. To this end, most parameters and behaviors were random. The model included generalized species traits, dispersal, and interactions to explore species richness dynamics in fragmented landscapes of distinct patch sizes. Our results show that landscapes with many small patches maintain higher species richness than those with fewer large patches. Also, the relationship between patch connectivity and species richness is more pronounced in landscapes with smaller patches. High connectivity in these landscapes may support species diversity by preventing local extinctions and facilitating recolonization. In contrast, connectivity is less significant in large-patch landscapes, where generalist species dominate. The findings highlight the complex interplay between patch size quality, connectivity, species traits, and diverse interactions among species in determining species richness. We suggest the patterns produced by the model represent null predictions and may be useful as a reference for a diversity of more specialized questions and predictions. These insights may also have specific implications for conservation strategies, suggesting that maintaining a mosaic of small, well-connected patches could enhance biodiversity in fragmented landscapes.

In this work, an attempt is made to the theoretically substantiate the experimentally known facts of the influence of halogen concentration on the catalytic properties of the neodymium-based Ziegler–Natta system. Based on the structural and thermochemical data obtained using modern methods of quantum chemistry, the process of the 1,3-butadiene cis-1,4-polymerization under the model active centers of the neodymium Ziegler-Natta catalysts with different content of chloride ions was studied. Results are presented that explain the increase in the cis-stereospecificity and activity of the polymerization system with an increase in the content of the chloride ions in the neodymium catalytic system. There were established the reasons for the decrease in the concentration of active centers relative to the introduced Nd(III) with an excess of chloride ions and the occurrence of the anti-syn isomerization as a source of the formation of the trans-1,4-structures in the cis-1,4-polybutadiene.

Burnout in healthcare affects the quality of patient care. In 2020, Mayo Clinic implemented Advanced Care at Home (ACH), a new hospital-at-home program. We aimed to assess burnout levels among ACH providers, analyzing data from quarterly surveys. Physicians and nurse practitioners registered in ACH completed a 16-question anonymous survey distributed via email. Ten questions from the original survey used the Maslach Burnout Inventory to measure the three domains associated with burnout: depersonalization, emotional exhaustion, and personal achievement. We conducted a descriptive analysis using frequency distribution and percentages. A total of 27 providers participated, with response rates of 38%, 23%, and 66% in the first, second, and third quarters. Over 75% of the participants responded satisfactorily to nine of the ten questions associated with the risk of burnout. Moreover, 77% of providers felt joyful at work "always or most of the time." Of the 74% of participants who responded to the question about feeling burned out from work, 48% of the participants "strongly” or “somewhat” disagreed, while only 26% "strongly” or “somewhat” agreed. Mayo Clinic's ACH has demonstrated low levels of burnout among providers, positively impacting the well-being of healthcare workers and contributing to a high standard of patient care.

The significance of digital sustainability in donation-based crowdfunding platforms lies in its ability to encourage environmentally aware and socially responsible practices in the sphere of online philanthropy. Fundraising has been revolutionized by the emergence of donation-based crowdfunding platforms. This study explores the various factors influencing individuals' attitudes towards these platforms, particularly focusing on perceived usefulness, ease of use, credibility, and self-efficacy. The study examines how these factors affect donation intentions and electronic word-of-mouth intentions, utilizing data from 326 college students. The findings confirm that perceived usefulness, ease of use, credibility, and self-efficacy play crucial roles in shaping attitudes. Furthermore, these attitudes significantly impact both donation intentions and electronic word-of-mouth intentions. Adjustments to the structural model highlight the interconnectedness of these factors, improving the model's accuracy. The results provide practical guidance for platform administrators, campaign creators, and stakeholders involved in online philanthropy, emphasizing the need for user-friendly designs, trust-building, and fostering user confidence for successful crowdfunding efforts.

Conventional biochemical methods for studying cellular signaling cascades have relied on destructive cell disruption. In contrast, live cell imaging of fluorescent-tagged transfected proteins offers a non-invasive approach to understanding signal transduction events. One strategy involves monitoring the phosphorylation-dependent shuttling of a fluorescent-labeled kinase between the nucleus and cytoplasm using nuclear localization, export signals, or both. This paper introduces a simple method to visualize intracellular signal transduction in live cells by exploring the translocation properties of PKC from the cytoplasm to the membrane. We fused bait protein to PKC, allowing the bait (RFP-labeled) and target (GFP-labeled) proteins to co-translocate from the cytoplasm to the membrane. However, in non-interacting protein pairs, only the bait protein was translocated to the plasma membrane. To verify our approach, we examined the Raf–MEK–ERK signaling cascade (ERK pathway) and directly observed the Raf1/MEK2 interaction. We also identified ternary complexes containing the KSR1 scaffold protein. Additionally, the MEK/ERK interaction was dependent on the exogenous expression of KSR1.

Based on the perspective of complex directed weighted network analysis, this paper constructs a static resilience evaluation framework for trade networks from two dimensions: structural resilience and node resilience. It studies the evolution of static resilience in the upstream, midstream, downstream, and recycling trade networks of global wood forest products from 2002 to 2021. The study finds that the total trade volume of global wood forest products has increased by 58.0%, 77.3%, 71.2%, and 77.2% in the upstream, midstream, downstream, and recycling sectors, respectively. The downstream trade network exhibits efficient information transmission and resource mobility capabilities. All four types of trade networks exhibit scale-free characteristics, with core countries such as China, the United States, and Germany playing a dominant role in the network and playing a crucial role in network robustness, but also increasing network vulnerability. In terms of unweighted assortativity, all four networks exhibit the characteristic of "small countries relying on hubs," while the weighted assortativity of the downstream network is positive, indicating enhanced network resilience. In terms of node resilience, countries exhibit significant differences and dynamic changes. China demonstrates strong resilience and adaptability, while the United States maintains its core position. China's influence in the downstream network has significantly increased, and India has rapidly emerged in the recycling network. To enhance network resilience, it is necessary to increase diversity and redundancy, establish effective emergency response mechanisms, and promote the diversification and green transformation of the supply chain.

Localizing faults in a software is a tedious process. The manual approach is becoming impractical because of the large size and complexity of contemporary computer systems as well as their logs, which are often the primary source of information about the fault. Log-based Fault Localization (LBFL) is a popular method applied for this purpose. However, in real-world scenarios, this method is vulnerable to a large number of previously unseen log lines. In this paper, we propose a novel method that can guide programmers to the location of a fault by creating a hierarchy of log lines with the highest rank, selected by the traditional LBFL method. We use the intuition that the symptoms of faults are in the context of normal behavior, whereas suspicious log lines grouped together are from new or additional functionalities turned on during faulty execution. To obtain this context, we used unsupervised log sequence segmentation, which has been previously used to segment log sequences into meaningful segments. Experiments on real-life examples show that our method reduces the effort to find the most crucial logs by up to 64% compared to the traditional timestamp approach. We demonstrated that context is highly useful in advancing fault localization, showing the possibility of further speeding up the process.

This study aims at highlighting and illustrating innovative actions for achieving and/or improving accurate medical coding within a DRG system. Already grouped DRG cases recorded in the first DRG implementation year in the region of Crete were examined. A sample of 130,000, from a total of 200,000, cases were analyzed and audited, through a process consisting of three stages: i) Digitalization, ii) Auditor training, and iii) Control & consultation. The results indicated that a significant proportion of DRG coding, with a length of stay exceeding one day, were reclassified into different DRG categories. This was not primarily due to errors in the selection of the principal diagnosis, but resulted from coding errors, including omission of secondary diagnoses coding, medical procedures that should have been included and utilization of less specific codes. The findings indicate that medical coding control, consulting services and the adoption of targeted actions can be instrumental for proper implementation of the DRG coding system, and for ensuring transparent allocation of resources within hospitals. Ultimately, this shortens the coding learning curve, from four years typically reported in the international literature, to a more efficient duration of two years, leading to rational and documented clinical and financial management of hospitals.

Nuʻupia Ponds, a traditional Hawaiian fishpond system, are located at Marine Corps Base Hawaii (MCBH) and part of the Nuʻupia Ponds Wildlife Management Area, a wetland refuge for native, endangered, and protected birds and Hawaiian green sea turtles, as well as many native fish species. Currently, there is uncertainty regarding the ecological status and condition of the fishponds following prior modification of wetland habitats in and around the ponds. This study examines circulation dynamics and characterizes water exchange, pond volume, and residence time across the full tidal spectrum at Nuʻupia fishponds. Our results indicate a general west to east gradient in current flow; with higher flushing rates and lower residence times of fishponds in the western ponds of the Nu’upia system compared to the eastern ponds. We further found low flushing rates at several sites causing limited water exchange with Kāne’ohe Bay, as well as within Nu’upia Pond system. Sufficient water circulation plays a fundamental role in maintaining a healthy balance of fishpond flora and fauna and preserve ecosystem health. The results from this study provide a baseline of current physical water circulation dynamics and implications for ecosystem health, as well as inform science-based conservation and management strategies moving forward.

Stroke causes disability and significantly affects patient quality of life. Post-stroke rehabilitation of upper limb function is crucial, as it affects daily activities and individual autonomy. Traditional rehabilitation methods often require frequent visits to specialized centers, which can be costly and challenging. This study investigated the effectiveness of a home-based self-training device, "Kenko Yusuri," in improving upper limb function in patients with chronic stroke. This multicenter prospective intervention study used a quasi-randomized controlled trial design. Ninety-three outpatients from five hospitals in Japan were enrolled and assigned to either the intervention group who used the "Kenko Yusuri" device or the control group who underwent conventional rehabilitation. All patients received botulinum toxin type A (BoNT-A) injections to treat upper limb spasticity. The primary outcome measure was the Fugl-Meyer Assessment (FMA) of upper-extremity motor function. Secondary outcomes included the modified Ashworth scale (MAS) score, range of motion (ROM), and chronic pain assessments. The intervention group demonstrated significantly greater improvements in FMA total scores than the control group. Patients with moderate paralysis experienced the greatest benefits from the intervention. There were no significant between-group differences in MAS scores or ROM measurements. Pain and wrist ROM showed improvement in the intervention group. Home-based training with a shaking device significantly improved upper limb function in patients with moderate chronic post-stroke paralysis. This approach aligns with the principles of use-dependent plasticity and offers a feasible and cost-effective alternative to traditional rehabilitation methods.

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations are ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a specific patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program, called GeneFinder, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, GeneFinder identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targets 100 highly prevalent gene mutations in the individual patient. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures which are freely available fo download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes which were also found in the reference study where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide vaccine using human patient data to identify gene mutations associated with the specific tumor of interest.

Reducing the carbon footprint of energy production is one of the most pressing challenges facing humanity today. Lignocellulosic biomass residues from fruit production industries show promise as a viable energy source. This paper presents a study of the Italian context concerning the utilization of orchard lignocellulosic residues for energy production as electricity or bioethanol. The potential of various orchard residues was assessed through chemical and physical analysis, and an equivalent electrical energy of about 6441.62 GWh or an amount of 0.48 Mt/y of bioethanol was obtained based on the average annual dry residue mass availability of about 3.04 Mt/y. These data represent the 9.30% of the national electrical energy production from renewable sources, as well as the 6.21% of the Italian demand for gasoline in 2022. Electricity generation from these residues shown its potential as a reliable and sustainable baseload power source, as well as source of renewable transportation fuel. The studied process could be a valuable reference to expand these concepts on global scale to achieve a greener and more sustainable energy future.

Urban air pollution is a significant threat to both the environment and public health. Primarily originating from traffic emissions, particulate matter is identified as the most prevalent and toxic pollutant in cities. In recent years, street vegetation has emerged as a nature-based solution to improve urban air quality. However, to achieve this goal, proper placement of vegetation is crucial. This study critically investigates how different street tree placement strategies affect the local air quality, based on a model calibrated using existing air quality measurements from one of the most polluted streets in Glasgow. We then test the local air quality effects of tree orientation, spacing, shape, porosity, size, species, and wind directions. The findings suggest that trees effectively enhance air quality, particularly when implemented on the leeward side of the street. To achieve optimal effectiveness, the spacing of trees require careful alignment with the aspect ratio, as haphazard planting could lead to unfavorable results. The insights on improving air quality in Glasgow's street canyons, complements the objectives of the ‘Glasgow Avenues Programme’ and can be included in its Low Emission Zones to co-benefit the environment and the wellbeing of the local community.

Artificial intelligence (AI), deep learning (DL) or machine learning (ML) is the ability of computer systems, machines and enginery to work devise procedures like humans. These technologies provide opportunities and possibilities to advance diagnostics and in planning also in the field of human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI, as well as to highlight its uses in maxillofacial surgery. The primary inclusion criterion for this publication was an original paper written in English focusing on the use of AI, DL or ML in maxillofacial surgery. The sources of information were PubMed, Scopus and Web of Science, and the queries were made on 31st of December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery” and “deep learning maxillofacial surgery”. Following the removal of duplicates, all remaining publications that were returned by the searches and were screened by three independent operators to minimize the risk of bias. The analysis of publications from 1992 to 2023 identified certain records, of which 324 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) of R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, artificial intelligence and machine learning have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the set of published papers significantly increased to the number of 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These tools have been found useful for diagnosis, treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and in oral pathology. In orthognathic surgery, they have been utilised for diagnosis, treatment planning, assessment of treatment needs, cephalometric analyses, and orthognathic surgeries, among other applications. The review confirms that the current use of artificial intelligence and machine learning in maxillofacial surgery is focused mainly on the evaluation of digital diagnostic methods especially radiology, treatment plans and postoperative results. However, as these technologies are integrated in maxillofacial surgery and robotic surgery in head and neck region, it is expected that in the future they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

As the world attempts to decarbonise the food industry and limit greenhouse gas (GHG) emissions, plant-based meat analogues (PBMAs) have emerged as a sustainable alternative to traditional meat. This study implemented a life cycle assessment (LCA) to rigorously compare the environmental impacts of a beef burger (BB), produced with British beef, against a meat analogue (MA) equivalent with a publicly available recipe. For the LCA, a cradle-to-fork system boundary was utilised, alongside a functional unit of a single 100 g burger patty. Results from the LCA revealed that the BB patty had more than double the total environmental impact than the MA equivalent, as well as possessing a global warming impact that was 62% higher. A simple extrapolation illustrated that if the UK population switched from beef to meat analogue patties, 3 million tonnes of CO2e could be saved annually, corresponding to 0.74% of the country’s yearly territorial GHG emissions. Scenario analyses displayed how the environmental impact of the MA patty remained stable regardless of changes in exportation, ingredient origin or soy protein sourcing. Moreover, sensitivity analysis conducted with an alternative characterisation method corroborated initial findings, whilst uncertainty analysis ensured that nearly all conclusions generated from the original comparison were robust. Future studies should conduct peer-reviewed LCAs on PBMA patties with commercial recipes based on other plant-based proteins, as well as fully understanding any potential health implications of long-term PBMA consumption.

This research study focuses on addressing practical optimization challenges in audio-visual content using advanced computational intelligence methods, with a specific emphasis on privacy-preserving issues. The research will be presented as a proposal and can be conducted based on the provided ideas. Computational intelligence encompasses various approaches, including evolutionary algorithms, learning methods, fuzzy systems, and neural networks, which have become essential tools for tackling complex engineering optimization problems due to computational expenses, dimensional complexities, and interdisciplinary integration. The research objectives include a thorough investigation of optimization challenges in audio-visual content, the delineation of problem characteristics, and the development of relevant methodologies based on these specifications. The main goals of the study, titled "Advanced Computational Intelligence Assisted Less-Expensive Privacy-Preserving Optimization," encompass the development of a metamodel-based learning algorithm integrated with evolutionary computation for privacy-preserving optimization, less-expensive signal watermarking optimization using hybrid metamodeling and evolutionary algorithms, and a closed-loop robust intelligent PID control system applied in real-time dynamic signal watermarking. Additional objectives may be defined as the research progresses and further background review is conducted.

Wastewater-Based Epidemiology (WBE) is currently used to monitor not only the spread of the pandemic virus SARS-CoV-2 but also for other viruses in endemic conditions, particularly in the absence of syndromic surveillance. Continuous monitoring of sewage requires high costs and significant time investment, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Italy, targeting Human Adenovirus (HAdV), Norovirus Genogroup II (NoVggII), Enterovirus (EV), and SARS-CoV-2. Samples collected at the entrance of treatment plants were concentrated using PEG/NaCl precipitation and viral nucleic acids were extracted and detected by real-time (RT-)qPCR. NoVggII resulted the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. SARS-CoV-2 results compared with data previously collected in the same study area (February 2021 to September 2021) revealed a shift from an epidemic to a pandemic status, likely due to the evolution of variants. In conclusion, WBE, using standardized methods and an efficient monitoring strategy, proves valuable for virus surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decision.

The recent green manufacturing demands have boosted the development of new biodegradable lubricants to replace petroleum-based lubricants. In this regard, water-based lubricants have been at the forefront of recent research for a wide range of industrial applications, including metalworking fluids (MWFs). In this work, we present an experimental investigation on the performance of novel green MWFs based on aqueous nopal mucilage solutions. For this, fully biodegradable solutions with different mucilage concentrations were evaluated in terms of rheological, tribological, thermal stability and turning (minimum quantity lubrication) performance, and compared to a commercial semisynthetic oil-based MWF (Cimstar 60). Mucilage solutions exhibited viscoelastic shear-thinning behavior, which was enhanced along with mucilage concentration. The solution with the highest mucilage content studied resulted in the lowest wear, friction and temperature in comparison to the other solutions and neat water in extreme pressure four-ball tests, and a similar level of lubricity as compared to the commercial MWF in cutting tests. This performance is associated to the enhanced viscosity and elasticity of the solution, as well as to the contents of lipids with fatty acids in the mucilage. Overall, the present results reveal the relevance of the viscoelastic behavior of the lubricant, elasticity in particular, in lubrication processes, and points to nopal mucilage as an effective green additive to produce innocuous MWFs.

Machine learning has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of machine learning models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9,584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg-1, which favor enhanced organic matter in the soil. Different models identified different impacts of nutrients on the organic matter level in the soil. The SHAP method is practical for assessing whether different machine learning models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.

Modern power systems are confronted with a spread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load frequency control (LFC) scheme based on a hydrogen based energy-storage unit and a parameter tuning strategy for fractional-order proportional-integral-derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, gas power generator, and hydrogen based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme are tested through load disturbance and renewable energy fluctuations test scenarios, and provides comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of IPS and has excellent robustness.

Recent development for synthesis of bio-based long chain aliphatic polyesters by acyclic diene metathesis (ADMET) polymerization of α,ω-dienes, derived from plant oils and bio-based chemicals, like bis(10-undecenoate) with isosorbide, using ruthenium-carbene catalysts have been reviewed. Development of subsequent (one-pot) tandem hydrogenation afforded saturated polyesters under mild conditions. The polymerizations under bulk (without solvent, 80-90 ºC) or in ionic liquids (50 ºC) under vacuum conditions enabled synthesis of high molar mass polymers (Mn >30,000 g/mol). The polymerization by molybdenum-alkylidene catalyst afforded the highest molecular weight polyesters (44000‒49400 g/mol, in toluene at 25 ºC) exhibiting promising tensile properties (strength and elongation at break) beyond polyethylene, polypropylene. Depolymerizations of these polyesters including closed loop chemical recycling were also demonstrated. Catalyst developments (more active, under mild conditions) play a key role for the efficient synthesis.

In this work, electrochemical deposition (galvanostatic and potentiostatic) techniques were used to obtain of new composite polymer: 3-methylpyrrole - sodium dodecyl sulfate/poly 2-methythiophene (P3MPY-SDS/P2MT) coatings on cobalt -based alloy sample for anticorrosion protection. The sodium dodecyl sulfate as a dopant ion used in electropolymerization practice can have a relevant effect at the anticorrosion protection of the composite polymer layer by blocking the entry of aggressive ions. The cobalt -based alloy substrate had an important influence on the electrochemical performance of the composite coating and this together with the presence of the polymeric layer was achieved by simultaneously formation of a complex oxides film and polymeric layers. The polymeric coatings were analyzed by cyclic voltammetry, Fourier transform infrared (FT-IR) spectroscopy and scanning electron microscopy (SEM) methods. The corrosion protection of P3MPY-SDS/P2MT coated cobalt based alloy has been explored by potentiostatic and potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) procedures in1 M HCl medium. The corrosion establishment of P3MPY-SDS/P2MT covered cobalt based alloy was observed to be ~10 times less than that uncoated specimen and the effectiveness of these composite layers of this coating is above than 90%. The superior efficacy is achieved by the electrochemical polymerization of P3MPY-SDS/P2MT obtained at 0.5 mA/cm2 and 1mA/cm2 current densities and at 0.9V and 1.0V potential applied and in molar ratio 5:1. Corrosion tests result indicated that P3MPY-SDS/P2MT coatings provide good corrosion protection of cobalt -based alloy in aggressive environments.

Process mining offers significant services in the development of digital threads and digital twin applications for healthcare. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes is an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. To address this, we present the DIAG approach that consists of a meta-model and an algorithm that enables diagnosing in process mining through an ontology-driven approach. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of the DIAG approach.

There is a great need to develop research-based models for supporting collaboration between educational institutions. Collaboration models are needed, for example, to assist the transition from lower educational level to higher education or from higher education to work life. In this research, collaboration was conducted through a joint upper-secondary school and university course that addressed global challenges by using a project-based learning approach. There is a shortage of research exploring students’ perceptions of collaboration. Therefore, the main purpose of this qualitative case study is to explore the relevance of the joint course for students and university students. The research was guided by the following research question: What kind of relevance did the students experience in the joint project-based learning course? The data was gathered using questionnaires, interviews, and reflective study reports. The data was analyzed via theory-based content analysis where research-oriented relevance model was used as the analysis framework. The analysis framework enabled categorizing experienced relevance on individual, vocational and societal dimensions. Altogether, the analysis produced 7 relevance categories for upper-secondary students and 9 for university students. According to the analysis, upper-secondary school students felt that the course offered the most on an individual relevance dimension whereas university students experienced the vocational relevance dimension most diverse. As the main conclusion, this research produced new insights on the experienced relevance of upper-secondary level and higher education cooperation from the students’ point of view. The acquired knowledge can be useful for everyone developing new cross-institutional cooperation models.

In the paper we present a new test for comparison of the means of multivariate samples with unknown distributions. The test is based on the comparison of the distributions of the distances between the samples’ elements and their means using univariate two-sample Kolmogorov-Smirnov test. The activity of the suggested method is illustrated by numerical analysis of the real-world and simulated data.

The prevalent use of synthetic chemical rodenticides (both 1st or 2nd generation anticoagulants and acute rodenticides) is the primary method for managing rodents in sub-Saharan Africa. However, this practice poses substantial health and environmental risks and often fails to yield significant, sustainable reductions in rodent pest populations. In this paper, the second one of a two-part series, we advocate for a more responsible and sustainable approach to rodent management in urban and agricultural settings in sub-Saharan Africa. We propose a shift towards environmentally friendly rodent management strategies, specifically emphasizing the adoption of Ecologically-Based Rodent Management (EBRM) as a viable alternative to synthetic chemical rodenticides. EBRM relies on a comprehensive understanding of pest rodent biology, ecology, and behavior, along with the implementation of a range of community-based interventions. These actions are designed to ensure the consistent reduction of rodent pest populations to economically and hygienically acceptable levels. By embracing EBRM, we not only anticipate a reduction in the social impacts of pest rodents in a cost-effective manner but also a significant decrease in the risks posed by rodents and synthetic chemical rodenticides to human health and the environment. This paradigm shift towards EBRM promises a more sustainable and responsible approach to rodent management in sub-Saharan Africa.

A bridged 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) derivative (PN-DOPO) in combination with aluminum phosphates coated sepiolite (Sep@AlPO4) was used to improve the flame retardancy, thermal stability and mechanical properties of poly (ethylene oxide) (PEO)/poly (butylene adipate-co-terephthalate) (PBAT) blends. The synergistic effects of PN-DOPO and Sep@AlPO4 on the fire resistance, thermal decomposition behavior and mechanical properties of PEO/PBAT composites were investigated systematically. The results indicated that 5 wt % Sep@AlPO4 combined with 10 wt% PN-DOPO into PEO/PBAT achieved a V-1 rating in UL-94 test and the LOI value increased to 23.7%, meanwhile, the peak-heat release rate (p-HRR), av-HRR and total heat release (THR) values of PEO/PBAT/PN10%/Sep5% composites decreased by 35.6%, 11.0% and 23.0% compared with those of PEO/PBAT, respectively. The thermogravimetric analysis (TGA) results confirmed that PN-DOPO/Sep@AlPO4 could enhance the initial thermal stability and char yield of PEO/PBAT matrix, and TGA/Fourier transform infrared spectrometry (TGA-FTIR) results revealed that the composites exhibited the absorption peaks of phosphorous-containing groups and an increase in gas-phase volatiles during the thermal decomposition process. The digital photographs and morphological structures of char residues demonstrated that PN-DOPO and Sep@AlPO4 mixtures benefited to the formation of a more continuous and dense carbon layer on the composites surface during combustion. Rheological behavior revealed that a higher storage modulus, loss modulus and complex viscosity of PEO/PBAT/PN-DOPO/Sep@AlPO4 composites could also promote the cross-linking structure during burning. Furthermore, the PEO/PBAT/PN-DOPO/Sep@AlPO4 composites exhibited excellent mechanical properties in terms of elongation at break and flexural performance than the PEO/PBAT system. All the results demonstrated that PEO/PBAT system modified by the combination of PN-DOPO and Sep@AlPO4 not only exhibited excellent synergistic effect on the flame retardance, but also had good improvement on thermal stability and mechanical properties, indicating the potential application in areas requiring fire safety.

Persistent human papillomavirus (HPV) infection is responsible for practically all cervical and a high proportion of anogenital and oropharyngeal cancers. Therapeutic HPV vaccines in clinical development show great promise in improving outcomes for patients who mount an anti-HPV T cell response, but far from all patients elicit a sufficient immunological response. This witnesses a translational gap between animal models and human patients. Here, we investigated the potential of a new assay consisting of co-culturing vaccine-transduced dendritic cells (DCs) with syngeneic healthy human peripheral blood mononuclear cells (PBMCs) to mimic a human in vivo immunization. This new promising human ex vivo PBMC assay was evaluated using an innovative therapeutic adenovirus (Adv)-based HPV vaccine encoding the E1, E2, E6, and E7 HPV16 genes. This new method allowed us to show that vaccine-transduced DCs yielded functional effector T cells and unveiled information on immunohierarchy showing E1-specific T cell immunodominance over time. We suggest that this assay can be a valuable translational tool to complement the known animal models, not only for HPV therapeutic vaccines, and supports the use of E1 as an immunotherapeutic target. Nevertheless, the findings reported here need to be validated in a larger number of donors, and preferably in patient samples.

With the engineering system becoming more and more complex, the uncertainty factors have a more and more profound influence on the reliability and security of the engineering system. In recent years, Reliability-Based Design Optimization (RBDO) has been studied extensively in complex engineering system design. The research on reliability optimization design considering stochastic uncertainty has been comprehensive and widely used. However, the reliability optimization design considering mixed uncertainty has the disadvantages of large computation and imprecise convergence. This study proposes an RBDO method based on Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to overcome this challenge. In the method, PSO is used to solve the most probable point, while SA has excellent global optimization capability to acquire the global optimal solution. Finally, three examples are given to illustrate the advantages of the proposed method.

Empowering teachers with Critical Thinking and understanding of sustainability is vital for guiding students’ informed decisions and actions in today’s world. Higher education needs innovative, student-centered methods like case-based teaching in order to promote Critical Thinking and the principles of Education for Sustainable Development. The aim of the study was to explore and evaluate pre-service teachers' Critical Thinking when engaged with case studies in a practicum preparation course centered on Education for Sustainable Development. Nine undergraduate students in Teacher Education participated in the study and answered four case studies. The case studies’ responses were analyzed through a bottom-up content analysis revealing eight higher-order categories. While approaching the case studies, student-teachers followed thinking processes similar to the processes proposed by the iterative models of Critical Thinking as perceived by Garrison (1991) and/or Brookfield (1987). Nevertheless, in some cases, pre-service teachers’ disengagement was evident. The rubric assessment highlighted that the responses fell under three profiles, namely the “Deficient Critical Thinker”, the “Emerging Critical Thinker” and the “Competent Critical Thinker”. The findings emphasize the need for targeted and individualized teaching interventions in teacher higher education programs aiming at Education for Sustainable Development complying with the learning needs of student-teachers’ Critical Thinking profiles.

A clinical simulation web-based program, known as FIRST2ACT (Feedback Incorporating Review and Simulation Techniques to Act on Clinical Trends) is designed to increase the efficacy of cli-nicians’ actions in the recognition and immediate response to a patient’s deterioration. This study, which was nested in a larger mixed method project, used ten focus groups (n=65) of graduate, enrolled, registered nurses, Associate Nurse Unit Managers, and general manag-ers/educators/coordinators from four different institutions to investigate whether nurses felt their practice was influenced by participating in either a face-to-face or web-based simulation educa-tional programme about patient deterioration. Results indicate individuals who were less ‘tech-savvy’ appreciated the flexibility of web-based learning, which increased their confidence. Face-to-face students appreciated self-reflection through performance evaluation. While face-to-face simulations were unable to completely duplicate symptoms, they did show nurses' adaptability. Both interventions enhanced clinical practice by improving documentation and re-plies while also boosting confidence and competence. Web learners initially experienced tech-related anxiety, which gradually subsided, demonstrating healthcare professionals' resilience to new learning approaches. Overall, the study highlighted the advantages and challenges of web-based and face-to-face education in clinical practice, emphasising the importance of adapta-bility and reflective learning for healthcare professionals. Further exploration of specific topics is required to improve practice, encourage knowledge sharing among colleagues, and improve early detection of patient deterioration.

Cellular organisms possess intricate DNA damage repair and tolerance pathways to manage various DNA lesions arising from endogenous or exogenous sources. Dysregulation of these pathways is associated with cancer development and progression. Synthetic lethality (SL), a promising cancer therapy concept, involves exploiting the simultaneous functional loss of two genes for selective cell death. PARP inhibitors (PARPis) have demonstrated success in BRCA-deficient tumors. Cisplatin (CPT), a widely used chemotherapy agent, forms DNA adducts and crosslinks, rendering it effective against various cancers but less so for prostate cancer (PCa) due to resistance and toxicity. Here, we explore the therapeutic potential of TLK1, a kinase upregulated in androgen-insensitive PCa cells, as a target for enhancing CPT-based therapy. TLK1 phosphorylates key homologous recombination repair (HRR) proteins RAD54L and RAD54B, critical for HRR alongside RAD51. The combination of CPT with TLK1 inhibitor J54 exhibits SL in androgen-insensitive PCa cells. The formation of double-strand break intermediates during inter-strand crosslink processing necessitates HRR for effective repair. Therefore, targeting TLK1 with J54 enhances the SL of CPT by impeding HRR, leading to increased sensitivity in PCa cells. These findings suggest a promising approach for improving CPT-based therapies in PCa, particularly in androgen-insensitive cases. By elucidating the role of TLK1 in CPT resistance, this study provides valuable insights into potential therapeutic targets to overcome PCa resistance to CPT chemotherapy. Further investigations into TLK1 inhibition in combination with other DNA-damaging agents may pave the way for more effective and targeted treatments for PCa and other cancers that exhibit resistance to traditional chemotherapy agents.

In order to create a smart mine, it is necessary to digitize the existing emulsion pumping station. Existing research does not achieve this task, this paper designs and manufactures a high-pressure, high-flow, high-water-based mining digital controlled unloading valve (abbreviated as HLHMDCUV), which achieves good results in both theoretical analysis and experimental testing. In order to obtain the accurate dynamic characteristics, this valve was analyzed by simulation using AMESim. Based on the dynamic performance analysis and the flow field analysis inside the valve body, the structure has been optimized to improve the performance of HLHMDCUV. The results show that the HLHMDCUV has a better dynamic characteristics, which were also demonstrated in the experiment. At the same time, the HLHMDCUV is easy to disassemble and maintain. This paper provides a theoretical basis for the design of such valves, which is conducive to the subsequent promotion of intelligent mine transformation.

Detecting, especially locating local degradations at an incipient stage, is very important for mission-critical high voltage rotating machines. One particular challenge of the existing testing techniques is that the characteristic of a local incipient defect is not prominent due to various factors such as averaging with the healthy remainder, attenuation in signal propagation, interference, and varied operating conditions. This paper proposes and investigates the frequency domain reflectometry (FDR) technique based on scattering parameter measurement. The FDR result presents the object length, wave impedance, and reflections due to impedance discontinuity along the measured windings. Experiments were performed on two commercial coils with artificially made defects. These defects include turn-to-turn short, surface creepage, loose coils, and local overheating, which are commonly seen in practice. Two practical water pumps in the field were also selected for investigation. The study outcome shows that the FDR can identify and locate both structure and insulation degradations of both shielded and unshielded objects with good sensitivity. This makes the FDR a comprehensive tool for fault diagnosis and aging assessment.

We assessed the use of Evidence-Based Practice (EBP) among pharmacists working in community pharmacies in France, and the factors linked to this practice. During 3 months in 2018 an online survey was sent to over 7,000 active pharmacists, and posted on pharmacist social media sites. The 40-item questionnaire described four clinical cases reflecting typical situations (conventional med-icine and Complementary and Alternative Medicine) encountered daily by community pharma-cists. Multiple-choice responses were proposed and scored according to whether they reflected EBP. A high total score indicated behavior in line with EBP. Univariate and multivariate analyses were used to evaluate factors that might explain adherence to EPB (pharmacy’s characteristics, the pharmacist’s status, mode of continuing education and sources of information). 595 pharmacists completed the questionnaire. The majority relied on pharmaceutical industry and other biased and/or non-evidence-based sources, particularly concerning information on homeopathic products. The consultation of independent reviews, health agency recommendations, and peer-reviewed scientific journals, was associated with evidence-based decisions. In contrast, reliance on phar-maceutical industry documents, personal experience and informal handbooks was linked to lower EBP scores. The level of EBP use by French community pharmacists needs to be improved to ensure that good quality scientifically-based advice is given to customers.

Due to the non-degradability of petroleum-based packaging materials, serious environmental pollution and the depletion of non-renewable resources have become pressing issues. In order to actively promote green production and address these concerns, there is an urgent need for new packaging materials to replace traditional plastic products. Starch-based packaging materials, composed of starch, fiber, and plasticizers, offer a degradable and environmentally friendly alternative. Starch alone cannot undergo thermoplastic processing due to its inherent structural characteristics. Therefore, researchers have explored modifications by incorporating plasticizers to create processable thermoplastic starch (TPS). However, there are challenges related to the high crystallinity and poor compatibility between TPS and fibers, resulting in decreased mechanical properties. To address these challenges, a novel approach combining plasticizer optimization and response surface method (RSM) optimization has been proposed to enhance the mechanical properties of starch-based packaging materials. This method leverages the advantages of composite plasticizers and process parameters. The findings demonstrate that the composite plasticizer effectively disrupt the hydrogen bonding and granule morphology of starch, leading to a significant reduction in crystallinity. Based on these findings, the optimal process parameters are determined using the RSM, resulting in a forming temperature of 198 °C, forming time of 5.4 minutes, and AC content of 0.84 grams. Compared with the non-optimized values, the tensile strength increases by 12.2% and the rebound rate increases by 8.1%.

Abstract: Filipendula ulmaria, commonly known as meadowsweet, is a wild herbaceous flowering plant widely distributed in Europe. A range of salicylic acid derivatives and flavonol glycosides have been previously associated with the antirheumatic and diuretic properties of F. ulmaria. In the present work, an hydroalcoholic extract of F. ulmaria aerial parts was extensively profiled using an efficient NMR-based dereplication strategy. The approach involves fractionation of the crude extract by centrifugal partition chromatography (CPC), ¹³C NMR analysis of the fractions, 2D-cluster mapping of the whole NMR dataset and finally, structure elucidation using a natural metabolite database, validated by 2D NMR data interpretation and liquid chromatography coupled to mass spectrometry. The chemodiversity of the aerial parts was quite broad, with 28 compounds unambiguously identified, belonging to various biosynthetic classes. The F.ulmaria extract and CPC fractions were screened for their potential to improve skin epidermal barrier function and also skin renewal properties using in vitro assays performed on Normal Human Epidermal Keratinocytes. Fractions containing quercetin, kaempferol glycosides, ursolic acid, pomolic acid, naringenin, β-sitosterol, and tellimagrandins I and II were found to be responsible for the up-regulation of genes related to skin barrier function, epidermal renewal and stress responses. This research is significant as it could provide a natural solution for improving hydration and skin renewal properties.

Background and Objectives: Elders’ needs are rarely examined beforehand, and thus, although technology-based tools can enhance self-management, acceptability rates are still low. This study aimed to examine and compare self-reported needs, priorities, and preferences of elders with heart failure (HF), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) toward technology use to enhance self-management. Materials and Methods: A convenience sample of 473 participants over 60s (60.5% females), diagnosed with HF (N=156), DM (N=164), or COPD (N=153) was recruited. They were administered a questionnaire about the usefulness of technology in general and in specific areas of disease management. Results: Most participants (84.7%) admitted that technology is needed for better disease management. This was equally recognized across the three groups both for the overall and specific areas of disease management (in order of priority: ‘Information’, ‘Communication with Physicians & Caregivers’, and ‘Quality of Life & Wellbeing’). Sociodemographic differences were found. Cell phones and PCs were the devices of preference. The four common features prioritized by all three groups were related to ‘information about disease management’ (i.e., monitoring symptoms, reminders for medication intake, management and prevention of complications), whereas the fifth one was related to ‘communication with physicians and caregivers (i.e., in case of abnormal or critical signs). The top disease-specific feature was also monitoring systems (of respiratory rate or blood sugar or blood pressure, oxygen), whereas other disease-specific features followed (i.e., maintaining normal weight for HF patients, adjusting insulin dose for DM patients, and training on breathing exercises for COPD patients). Conclusions: Elders in these three samples seem receptive to technology in disease management. mHealth tools, incorporating both common and disease-specific features and addressing different chronic patients, and being personalized at the same time, could be cost-saving and useful adjuncts in routine clinical care to improve self-management.

Wildlife tourism and wildlife trade may appear juxtaposed, but are two, potentially aligning, income generators that could benefit conservation in developing countries. Utilising data sets collated from Madagascar’s Ministère du Tourisme and CITES respectively for the period 2007 to 2018, this study estimated levels of income from wildlife tourism and wildlife trade for Madagascar. Between 2007-2018, tourism reported yearly incomes ranging from a low of US $1.4 million up to a high of US $15.7 million. However, it was unclear what percentage of this figure flowed to benefit local communities. Alternatively, using reported networks for the live wildlife trade, the estimated economic value reaching collectors and/or intermediaries on Madagascar was US $72,299.80 for the period 2007 to 2018. Both revenue generators operated within different geographical areas, with tourism opportunities presenting themselves to communities adjacent NPs, while wildlife trade networks were not restricted to NPs and operated sporadically across Madagascar. Hence, the economic benefits reached different Malagasy participants across the country. The management of both activities needs great care to ensure that environmental impacts and sustainability are core measures on any such activities. Whilst this study shines light on economic values and novel perspectives regarding these two trade types, it also highlighted knowledge gaps. Thus, indicating where much greater research attentions was required to allow better understanding of the specific benefits and risks from engaging with both trade types for local Malagasy people and their environments.

In this study, an extensive data set was based on existing literature records in order to enable the suitability of several predictive models, from Multiple Linear Regression (MLR) to Neural Networks (NN). The main objective was to, through regression analyses, generate model computations to correlate tensile properties (UTS- Ultimate Tensile Strength, YTS – Yield Tensile Strength and EF – Elongation-to-Fracture) to a given alloy composition and microstructural spacing. This investigation led to positive results, as the highest accuracies of the trained modules (in 80% of the database) were found to be above ~82% (UTS and EF) and a maximum of ~98% (YTS), when analyzing the results to a test data set. Later, these models were used to define trends for possible next solder alloy commercial compositions. Overall, using the standard model’s setup, the Random Forest and Decision Tree models showed the highest accuracy results, with 0.958 for YTS as opposed to 0.907 for MLR. Moreover, Multilayer Perceptron (MLP)-optimized models yielded the best results for each variable, with the highest increases in accuracy associated with the YTS and EF. The present contribution might imply an important milestone towards alloy design research based on data science guidelines to unlock the full potential of former experiments and their extensive set of results.

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes a novel approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats’ thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB-imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for continuous and objective assessment of animal welfare and hereby guiding the way to modern and bioethical research.

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 60s have been adapted and improved upon over the years. Generating management alternatives for forest management planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. Climate change has made forest management calculations even more complex, requiring flexibility, diverse parameters, models, and methods. Despite this complexity, consistent concepts can be applied in developing management alternatives for forest management planning. This work describes iGen, a flexible forest prescription generator that applies the AI technique Rule-Based System (AI-RBS). iGen projects the state and associated inputs and outputs for a set of management units using rules from its knowledge base. An Inference Engine uses the rules to simulate a set of prescriptions in a tree-like graph structure. Without needing IT specialists, forest managers can describe the potential development of their forest through variables, rules, formulas, functions, and procedures. A key feature of iGen is that it is not limited to, adapted to, or focused on any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. This article introduces iGen, explaining its concepts, structure, and algorithms through two FEM problems: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/…/iGen.

The goal of this lab is to look at how the characteristics of cementitious materials are affected by bending loads combined with dry and wet cycles of sulfate. In this paper, three sets of specimens with different water–cement ratios were designed. This study applied 20% and 40% of the ultimate bending load as a continuous bending load to the cementitious material specimens for a sulfate erosion test. It was discovered that the specimens' porosity, flexural strength, and compressive strength all tended to improve initially before declining as the cementitious material was degraded in the sulfate solution aged. The flexural strength of the cementitious materials declined as the bending load increased, and their internal sulfate ion concentration rose. A transport model of sulfate ions in cementitious materials under the coupling effect of bending load and sulfate erosion was created by combining the improved version of Fick’s second law and chemical reaction kinetics. The simulation results show that the ion transport properties of sulfate ions in cementitious materials are in accordance with the experimental laws. These research results formed the basis of the unified standards for the coupling of load dry and wet cycle systems, as well as evaluating the durability of cementitious materials under corrosive environments.

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of Lake Water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations used for the two lakes; Lake Koka and Lake Ziway. Sampling stations were selected from sites infested with water hyacinth (low, medium and high) and non-water hyacinth aquatic plants during wet and dry seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties namely phosphate, nitrate, pH, electrical conductivity (EC), Biological oxygen demand (BOD5), temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu). These water quality variables were compared by means of ANOVA. Despite the COD of Lake Ziway, this study found no significant (p > 0.05) variation in the concentrations of Cu, EC, pH and temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD, COD, nitrate, phosphate) between low, medium and high levels of water hyacinth were significant in both lakes (p<0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites.

Nowadays, the European Union and the governments of the different countries have focused on the development of the Sustainable Development Goals (SDG) and the 2030 agenda, something that has been translated into education itself. Video Games, Gamification, and Game Based Learning have become different strategies and tools to enhance the learning process and some of the growing approaches used by teachers to develop sustainable education in the classrooms. This research aims to analyze the characteristics to promote sustainability in education using games and technology, specifically its learning benefits for Higher Education. A systematic review of the literature was conducted following the PRISMA methodology. At first, 2025 documents were found which, after the filtering phases, the number of articles has been reduced to nine, which subsequently were analyzed in depth. The results indicated that among the benefits of the use of games mediated by technologies are the following: it favors education for sustainability and it promotes the educational inclusion and the work of various social skills such as collaborative and cooperative work. Also, showed an increase of the number of publications between 2019 and 2023, reflecting the growing interest in the topic. However, there are some research gap in this field.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

Nowadays, in low voltage (LV) electric distribution networks (EDNs), the Distribution Network Operators (DNOs) are encountering a high number of connected small-scale distributed generation units, mainly photovoltaic (PV) prosumers. The irregular behavior of the prosumers, together with the uncertainty degree of the requested and injected powers associated with all end-users from the LV EDN, can cause voltage variations with violations of the allowable limits. In this context, the paper developed an efficient and resilient expert system integrating the rule-based reasoning applied to the On-load Tap Changer (OLTC)-fitted transformer to improve the efficiency of the voltage control in the PV-rich LV EDNs. An in-depth analysis based on 75 scenarios, resulting from the combinations of three indicators: the penetration degree of the PV prosumers, consumption evolution associated with the consumers, and energy production of the PV systems, has been performed to demonstrate the efficiency of the proposed expert system in an LV EDN from a rural area belonging to a Romanian DNO. The success rate of the expert system has been 86.7% (65 out of 75 scenarios didn't have voltage issues, all values being between the allowable limits in 100% of the time slots associated with the analyzed period). For the other scenarios (representing 13.3%), the values have been inside the range [-10%, +10%] at least 95% of the time slots, according to the European Power Quality Standard, and for the rest of the time slots up to 100% being very close to the upper and lower limits of the range, with highly mitigating of the voltage variations.

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

The threat posed by climate change, the progressive erosion of natural resources, the polluting effects related to private motorization, energy waste, excessive consumption of agricultural and natural soils and their progressive waterproofing and reduction in vegetation cover, with the subsequent environmental degradation, have highlighted the need for a sustainable management of natural resources, in terms of an equitable and ethical development, to build inclusive communities able to adapt to the negative effects of emergency phenomena, through new capacities of economic, cultural and social resilience. This contribution presents the results of the activities conducted in the RivEr/Generation_LAB, a project organized by three CIVIS members (Sapienza University of Rome, Universitè libre de Bruxelles, Universidad Autonoma de Madrid) as a part of the CIVIS Project "RivEr/Generation_LAB. Linking resilience with inclusiveness in the urban built environment of Rome, Brussels, and Madrid", financed into the Hub4 Cities, Territories & Mobilities’ Call for proposals 2021. The project proposes a methodology of intervention in the Flaminio district, in particular on the 'Olympic Village' and its relationship with the Tiber River, in line with the Sustainable Development Goals, the Millennium Ecosystem Assessment and the New European Bauhaus, to establish new relationships between cities and the natural environment, favouring sustainable and inclusive public spaces.

This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose Motif Convolution Module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better in capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.

Student attendance serves many other important purposes aside from monitoring. In certain universities, the attendance of students in a course is also used as one of the requirements for students to be allowed to sit for the final examination. Traditionally, among most Malaysian Institutions of Higher Learning (IHL), attendance recording is usually done using pen and paper, or uses simple web-based system that is time consuming and difficult for faculty periodic monitoring. To address the identified drawbacks, this research aims to develop a Smart Attendance for Faculty Monitoring System using the Bluetooth Low Energy (BLE) technology to assist faculty in recording, managing and monitoring students’ attendance and class schedules effectively. The system is developed for Android-based devices using an agile methodology consists of iteration and incremental approaches. Thus, to evaluate the effectiveness of the system, a survey was conducted on 140 respondents involving lecturers and students of Kolej Universiti Poly-Tech MARA (KUPTM). Respondents were selected using purposive sampling. The descriptive analysis showed that 87.9% of the respondents strongly agreed that the system is effective in assisting lecturers to record attendance, manage class schedules and student attendance as well as to assist faculty in monitoring students’ absenteeism.

Passive and forced drinking harm was prevalent but less recognized in Chinese adolescents. We educated adolescents on such harm to reduce their intention to drink. Students (n=1244) from 7 secondary schools in Hong Kong participated in a video-based health talk on passive and forced drinking harm. Paired t-test was used to assess their change in knowledge of passive and forced drinking, health and social harm of drinking after the health talk. McNemar's chi-squared test and adjusted multivariable logistic regression (AOR) were used to assess their change in intention to drink and intention to quit. Students were less likely to drink (OR 0.29, 95% CI 0.19-0.42) and more likely to quit drinking (OR 3.50, 1.10-14.6) after the health talk. Increased knowledge of passive drinking was associated with less intention to drink (AOR 0.93, 0.90-0.97), increased knowledge of health harm (adjusted b 0.06, 0.05-0.08), and social harm of drinking (adjusted b 0.12, 0.10-0.16). Similar associations were observed in forced drinking (intention to drink: AOR 0.87, 0.79-0.96; health harm: adjusted b 0.16, 0.12-0.19; social harm: adjusted b 0.36, 0.28-0.43). We showed preliminary evidence that the health talk on passive and forced drinking reduced the intention to drink in adolescents.

Coastal areas are highly vulnerable to climate change hazards (e.g., sea-level rise, flooding, coastal erosion), which can lead to significant impacts at the ecosystem and societal level. Interest in Ecosystem-based Adaptation (EbA) is gaining importance due to its potential multiple benefits, including social and environmental aspects, when compared to more traditional approaches such as hard engineering interventions. When assessing EbA strategies, further understanding of the nature-society functions, processes, values, and benefits is needed to increase its application. This study contributes to a better knowledge of EbA by developing a systematic literature review of studies performing socio-economic assessments of climate change adaptation in coastal areas. The analysis of 54 publications revealed that most of the studies assessed adaptation solutions through cost-benefit analysis, followed by multi-criteria analysis, and other techniques. Hybrid adaptation strategies based on different combinations of hard, soft and EbA interventions were considered as potential optimal solutions in a significant part of the assessments. This study suggests the potential co-benefits of EbA in the form of ecosystem services, livelihood diversification or biodiversity conservation, but also stresses the need for further research on this topic, as well as on evaluating how EbA perform in the long-term under climate changing conditions scenarios.

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predicting the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

Quadrotor drones have rapidly gained interest recently. Numerous studies are underway for the commercial use of autonomous drones, and especially the distribution businesses are taking serious reviews on drone delivery services. However, there are still many concerns about urban drone operations. The risk of failures and accidents makes it difficult to provide drone-based services in the real world with ease. There have been many studies that introduced supplementary methods to handle drone failures and emergencies. However, we discovered the limitation of the existing methods. The majority of approaches were improving PID-based control algorithms which is the dominant drone control method. This type of low-level approach lacks situation awareness and the ability to handle unexpected situations. This study introduces an event-based control methodology that takes a high-level diagnosing approach that can implement situation awareness via time-window. While leaving the low-level controller to involve in operating the drone for most of the time in normal situations, our controller operates at a higher level and detects unexpected behaviors and abnormal situations of the drone. We tested our method with real-time 3D computer simulation environments with Unreal Engine[15] and AirSim[31]. We were able to verify that our approach can provide enhanced double safety and better ensure safe drone operations. We hope our discovery to possibly contribute to the advance of real-world drone services in the near future.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

In this work, we have developed a non-ordinary state-based peridynamic model for multiple crack initiation and propagation due to compression-compression fatigue load. In each loading cycle, the fatigue loading is redistributed among the peridynamic solid body, leading to the progressive fatigue damage initiation and propagation in an autonomous fashion. The proposed fatigue model parameters are firstly validated by 3D numerical benchmark tests, and then it is applied to simulate widespread fatigue damage evolution of the aircraft wing corner box. The modified constitutive damage model has been implemented into the peridynamics framework at finite strain. Furthermore, the criterion algorithm from multiple initiation to propagation is discussed. It is shown that the numerical results obtained from peridynamics simulations are in general agreement with those from the experiment data. The comparison of experimental and numerical results indicates that the proposed non-ordinary state-based peridynamics fatigue model has the ability to capture the multiple crack initiation and propagation and other features of the aluminium alloy material.

The acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are only a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, new novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual-focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available at https://figshare.com/s/ceb58d58e8f5585ce67e.

Telehealth is the delivery of many health care services and technologies to individuals at different geographical areas and is categorized as asynchronously or synchronously. The coronavirus disease 2019 (COVID-19) pandemic has caused major disruptions in health care delivery to breast cancer (BCa) patients and there is increasing demand for telehealth services. Globally, telehealth has become an essential means of communication between patient and health care provider. The application of telehealth to the treatment of BCa patients is evolving and increasingly research has demonstrated its feasibility and effectiveness in improving clinical, psychological and social outcomes. Two areas of telehealth that have significantly grown in the past decade and particularly since the beginning of the COVID-19 pandemic are telerehabilitation and teleoncology. There two technological systems provides opportunities at every stage of the cancer care continuum for BCa patients. We conducted a systematic literature review that examined the use of telehealth services via its various modes of delivery among BCa patients particularly in areas of screening, diagnosis, treatment modalities, as well as satisfaction among patients and health care professionals. The advantages of telehealth models of service and delivery challenges in delivery to patients in remote arears are discussed.

During the COVID-19 outbreak, most university courses have been offered on online platforms. A sudden shift from face-to-face classroom learning to online formats could influence the learning effectiveness of classes. This study aims to investigate differences in the learning effectiveness of online and face-to-face lecture courses. It also explores factors that impact the effectiveness of online instruction. These factors include interactions among learners, interactions between learners and the instructor, the quality of online platforms, learners’ ability to use devices and follow instructions, and learners’ situational challenges. The study participants were 261 university students at King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. All participants were enrolled in at least one lecture course, such as history, humans and the environment, the environment and development, or general philosophy, during the 2019 academic year. A questionnaire was distributed to participants after they completed these courses in May 2020. Paired simple t-test analyses were used to compare the effectiveness of online and face-to-face classes, and a multiple regression analysis was used to identify factors that impact the learning effectiveness of online classes. The results show that online classes are less effective than face-to-face courses. The multiple regression analysis also revealed that the effectiveness of online learning was significantly impacted by learners’ ability to interact with classmates during class, their ability to interact with instructors after the class, the quality of online platforms, and disturbances or distractions in learners’ environments.

In the last five years, European research and innovation programmes have prioritised the development of online catalogues and tools (handbooks, models, etc.) to facilitate the implementation and monitoring of Nature Based Solutions (NBS). However, only a few catalogues and toolkits within European programmes are directly related to mainstreaming of NBS for food production (i.e., edible NBS). Therefore, the main aim of this paper is to present existing NBS tools through the eyes of productive urban landscapes. We reviewed 32 projects related to NBS and 50 tools were identified and characterised. Then, the 6 tools already available, and providing indicators, were further analysed in terms of their format and knowledge domains. Our main conclusion demonstrates that there is a lack of tools capable of supporting users for planning and implementing edible NBS, calculating the food potential of the city and/or of individual edible NBS, including the needed resources for implementation and operation (water, nutrients, energy), and assessing their urban design value, environmental and socio-economic impacts. And when they do exist, there is a resistance to share the models and equations behind the tools to allow other projects to reuse or validate them, fact which is contrary to Open Science principles stood up by many research public agencies.

BODIPY dyes have recently raised attention as potential photosensitizers. In this work, commercial and novel photosensitizers (PSs) based on BODIPY chromophores (haloBODIPYs and orthogonal dimers strategically designed with intense bands in the blue, green or red region of the Visible spectra and high singlet oxygen production) were covalently linked to mesoporous silica nanoparticles (MSNs) further functionalized with PEG and folic acid (FA). MSNs of approximately 50 nm in size with different functional groups were synthesized to allow multiple alternatives of PS-PEG-FA decoration of their external surface. Different combinations varying the type of PS (commercial Rose Bengal, Thionine and Chlorine e6 or custom-made BODIPY-based), the linkage design and the length of PEG are detailed. All the nanosystems were physicochemically characterized (morphology, diameter, size distribution and PS loaded amount) and photophysically studied (absorption capacity, fluorescence efficiency, and singlet oxygen production) in suspension. For the most promising PS-PEG-FA silica nanoplatforms, the biocompatibility in dark conditions and the phototoxicity under suitable irradiation wavelengths (blue, green, or red) at regulated light doses (10-15 J/cm2) were compared with PSs free in solution in HeLa cells in vitro.

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modelled ICT supply chain system.

In 2019, Chinese government implemented the first round of National Centralized Drug Procurement (NCDP) pilot (so-called "4+7" policy) in mainland China, achieved a prominent price reduction of 52% on average for 25 bidding winning products. Under cross-price elasticity theory, the price behavior of pharmaceutical enterprises for policy-related drugs might change. This study used drug purchasing data from the Centralized Drug Procurement Survey in Shenzhen 2019, and applied single-group Interruption Time Series (ITS) design to examine the impact of "4+7" policy on the drug price index (DPI) of policy-related drugs. The ITS analysis showed that the DPI of winning (-0.183 per month, p<0.0001) and non-winning (-0.034 per month, p=0.046) products significantly decreased after the implementation of "4+7" policy. No significant difference was found for the immediate change of DPI for alternative drugs (p=0.537), while a significant decrease in change trend was detected in the post-"4+7" policy period (-0.003 per month, p=0.014). The DPI of the overall policy-related drugs significantly decreased (-0.261 per month, p<0.0001) after "4+7" policy. These findings indicate that the price behavior of pharmaceutical enterprises changed under NCDP policy, while the price linkage effect is still limited. It is necessary to further expand the scope of centralized purchased drugs and strengthen the monitoring of related drugs regarding price change and consumption structure.

Estimating the economic risks of climate shocks and climate stressors on spatially heterogeneous cities over time remain highly challenging. The purpose of this paper is to present a practical methodology to assess the economic risks of climate change in developing cities to inform spatially sensitive municipal climate response strategies. Building on a capital-based framework (CBF), spatially disaggregated baseline and future scenario scores for economic wealth and its exposure to climate change are developed for six different classes of capital and across 77 major suburbs in Cape Town, South Africa. Capital-at-risk was calculated by combining relative exposure and capital scores across different scenarios and with population impacted plotted against the major suburbs and the city’s 8 main planning districts. The economic risk assessment presented here provides a generic approach to assist investment planning and the implementation of adaptation options through an enhanced understanding of relative levels of capital endowment vis-à-vis relative levels of exposure to climate-related hazards over time. An informed climate response strategy in spatially heterogeneous cities need to include spatially sensitive estimates on capital-at-risk and populations disproportionally impacted by climate exposure over time. The economic risk assessment approach presented here helps in advancing to such a goal.

Bacterial cellulose (BC) is receiving great attention due to its unique properties such as high purity, water retention capacity, high mechanical strength, and biocompatibility. However, the production of BC has been limited because of high cost and low productivity. In this light, the isolation of new BC producing bacteria and selection of high productive strains became a promising issue. Kombucha tea is a fermented beverage in which the bacteria fraction of the microbial community is composed mostly by strains belonging to the genus Komagataeibacter. In this study Kombucha tea production trials were performed starting from a previous batch, and bacterial isolation was conducted along cultivation time. From the whole microbial pool, 46 isolates were tested for their ability in producing BC. The obtained BC yield ranged from 0.59 g/L, for the isolate K2G36, to 23 g/L for K2G30 used as the reference strain. The genetic intraspecific diversity of the 46 isolates was investigated using two repetitive-sequence-based PCR typing methods, which are the enterobacterial repetitive intergenic consensus (ERIC) elements and the (GTG)5 sequences, respectively. The results obtained using two different approaches revealed the suitability of the fingerprints techniques, showing a discrimination power, calculated as D index, of 0.94 for (GTG)5 rep-PCR and 0.95 for ERIC rep-PCR. In order to improve the sensitivity of the applied method, a combined model from the two genotyping experiments was performed, allowing to discriminate among strains.

Three kinds of Ni based alloy cladding coatings were prepared on 316L stainless steel at different power. The microstructure of the cladding layer was observed and analyzed by XRD, metallographic microscope and SEM. The hardness of the cladding layer was measured, and the wear resistance of the cladding layer was tested by friction instrument. The results show that the effect of laser cladding is good and the cladding layer has a good metallurgical bonding with the substrate. Different microstructures such as dendritic and equiaxed grains can be observed in the cladding layer. With the increase of laser power, more equiaxed and columnar dendrites can be observed. The phase composition of the cladding layer is mainly composed of γ - Ni solid solution and some intermetallic compounds such as Ni3B, Cr5B3 and Ni17Si3. The results of EDS show that there are some differences in the distribution of C and Si between dendrites. The hardness of the cladding layer is about 600 HV0.2, which is about three times of the substrate (~ 200 HV0.2). Through the analysis of the wear morphology, the substrate wear is serious, there are serious shedding, mainly adhesive wear and abrasive wear. However, the wear of cladding layer is slight, which is abrasive wear, and there are some grooves on the surface.

Nature Based Solutions (NBS) are becoming increasingly important in both the EU and individual countries' political agendas, as a sustainable means to reduce the risk posed by hydrometeorological hazards. However, as the use of NBS is increasing, a number of barriers regarding their practical implementation also becomes apparent. A number of review studies have summarized and classified barriers, mainly in urban settings. PHUSICOS is a H2020 Innovation Action to demonstrate the use of NBS in rural and mountain landscapes. Large scale demonstrator case sites with several sub-projects are established in Italy, Norway and in the French and Spanish Pyrenees. The present paper describes the project's NBS measures, and their experienced barriers, some of which have resulted in full cancellation of the planned interventions. Many of the barriers experienced in rural settings have the same root causes as the ones described from urban areas, and the main barrier-creating mechanisms are institutional factors, resistance among stakeholders and technical and economic issues. The key element, however, is lack of knowledge about the ability of NBS to deliver a series of co-benefits in addition to their risk-reducing effects, and that long-term thinking is required to see the effect of many of these co-benefits.

Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

COVID-19 has emerged as deadly pandemic worldwide with no vaccine or suitable antiviral drugs to prevent or cure the disease. Because of the time-consuming process to develop new vaccines or antiviral agents, there has been a growing interest in repurposing some existing drugs to combat SARS-CoV-2. Vitamin D is known to be protective against acute respiratory distress syndrome (ARDS), pneumonia and cytokine storm. Recently it has been used as a repurposed drug for the treatment of H5N1 virus-induced lung injury. Circumstantial evidences indicate that people with low level of vitamin D are more susceptible to SARS-CoV-2. Although, vitamin D was suggested to interfere with viral replication, its interaction with any SARS-CoV-2 protein is unexplored yet. Beside this, ivermectin, a well-known anti-parasitic agent, exhibits potent anti-viral activities in vitro against viruses such as HIV-1 and dengue. Very recently, ivermectin has been found to reduce viral load of SARS-CoV-2 in vitro. We have analyzed available structures of SARS-CoV-2 proteins to identify probable binding partner(s) of vitamin D and ivermectin through knowledge-based docking studies and figured out possible implication of their binding in SARS-CoV-2 infection. Our observations suggest that the non-structural protein nsp7 possesses a potential site to house 25-hydroxyvitamin D3 (VDY) or the active form of Vitamin D, calcitrol. Binding of vitamin D with nsp7 likely to hamper the formation of nsp7-nsp8 complex which is required to bind with RNA dependent RNA polymerase (RdRP), nsp12 for optimal function. On the other hand, potential binding site of ivermectin has been identified in the S2 subunit of trimeric spike(S) glycoprotein of SARS-CoV-2. We propose that deeply inserted mode of ivermectin binding at three inter-subunit junctions may restrict large scale conformational changes of S2 helices which is necessary for efficient fusion of viral and host membrane. Our study, therefore, opens up avenues for further investigations to consider vitamin D and ivermectin as potential drugs against SARS-CoV-2.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

There are guidelines on lactation following maternal analgo-sedative exposure, but these do not consider the effect of maternal fasting, nor fluid abstention on human milk macronutrient composition. We therefore performed a structured search (PubMed) on ‘human milk composition’ and screened title, abstract and full paper on ‘fasting’ or ‘abstention’ and ‘macronutrient composition’ (lactose, protein, fat, solids, triglycerides, cholesterol). This resulted in 6 papers and one abstract related to religious fasting (n=129 women) and observational studies in lactating women (n=23, healthy volunteers, fasting). These data reflect two different ‘fasting’ patterns: an acute (18-25h) model in 71 (healthy volunteers, Yom Kippur/Ninth of Av) women and a chronic fasting (Ramadan) model in 81 women. Changes were most related to electrolytes and were moderate, with almost no changes in macronutrients during acute fasting. We therefor conclude that neither short term fasting nor fluid abstention (18-25h) affect human milk macronutrient composition, so that women can be reassured when this topic were raised during consulting. Besides the nutritional relevance, this also matters as clinical research samples – especially to estimate analgo-sedative exposure by lactation - are commonly collected after maternal procedural sedation, associated with maternal fasting and physiology-based pharmacokinetic (PBPK) models assume stable human milk composition.

The importance of antibodies in health care and the biotechnology research and development demands not only knowledge of their experimental structures at high resolution, but also practical implementation of this knowledge for both effective and efficient design and production of antibody for its use in both medical and research applications. While the experimental wet-lab approach is usually costly, laborious and time-consuming, computational (dry-lab) approaches, in spite of their intrinsic limitations in comparison with its experimental (wet-lab) counterpart, provide a cheaper and faster alternative option. For the first time, this article reports a comprehensive set of structural electrostatic features extracted from experimentally determined antigen-antibody-related structures, including especially those structural electrostatic features at the interfaces of all experimentally determined antigen-antibody complex structures as of February 29, 2020, to facilitate effective and efficient machine learning-based computational antibody design using currently available experimental structures inside Protein Data Bank.

Incorporating source-side linguistic knowledge into the neural machine translation (NMT) model has recently achieved impressive performance on machine translation tasks. One popular method is to generalize the word embedding layer of the encoder to encode each word and its linguistic features. The other method is to change the architecture of the encoder to encode syntactic information. However, the former cannot explicitly balance the contribution from the word and its linguistic features. The latter cannot flexibly utilize various types of linguistic information. Focusing on the above issues, this paper proposes a novel NMT approach that models the words in parallel to the linguistic knowledge by using two separate encoders. Compared with the single encoder based NMT model, the proposed approach additionally employs the knowledge-based encoder to specially encode linguistic features. Moreover, it shares parameters across encoders to enhance the model representation ability of the source-side language. Extensive experiments show that the approach achieves significant improvements of up to 2.4 and 1.1 BLEU points on Turkish→English and English→Turkish machine translation tasks, respectively, which indicates that it is capable of better utilizing the external linguistic knowledge and effective improving the machine translation quality.

Over the last few decades there has been an exponential growth in IT, motivating IT professionals and scientists to explore new dimensions resulting in the advancement of artificial intelligence and its subcategories like computer vision, deep learning and augmented reality. AR is comparatively a new area which was initially explored for gaming but recently a lot of work has been done in education using AR. Most of this focuses on improving students understanding and motivation. Like any other project, the performance of an AR based project is determined by the customer satisfaction which is usually affected by the theory of triple constraints; cost, time and scope. many studies have shown that most of the projects are under development because they are unable to overcome these constraints and meet project objectives. We were unable to find any notable work done regarding project management for augmented reality systems and application. Therefore, in this paper, we propose a system for management of AR applications which mainly focuses on catering triple constraints to meet desired objectives. Each variable is further divided into subprocesses and by following these processes successful completion of the project can be achieved.

The transition to reproduction is a crucial step in the life cycle of any organism. In Arabidopsis thaliana the establishment of reproductive growth can be divided into two phases: Firstly, cauline leaves with axillary meristems are formed and internode elongation begins. Secondly, lateral meristems develop into flowers with defined organs. Floral shoots are usually determinate and suppress the development of lateral shoots. Here, we describe a transposon insertion mutant in the Nossen accession with defects in floral development and growth. Most strikingly is the outgrowth of stems from the axillary bracts of the primary flower carrying secondary flowers. Therefore, we named this mutant flower-in-flower (fif). However, the transposon insertion in the annotated gene is not the cause for the fif phenotype. By means of classical and genome sequencing-based mapping, the mutation responsible for the fif phenotype was found to be in the LEAFY gene. The mutation, a G-to-A exchange in the second exon of LEAFY, creates a novel lfy allele and results in a cysteine-to-tyrosine exchange in the α1-helix of LEAFY´s DNA-binding domain. This exchange abolishes target DNA-binding, whereas subcellular localization and homomerization are not affected. To explain the strong fif phenotype against this molecular findings, several hypotheses are discussed.

The Sustainable Development Goals, while complex at first sight, express a simple narrative about the relationships between people and nature. This paper illustrates this in the context of a coral reef land or seascape supporting coastal people. Coral reefs, their health described by measures of coral and fish diversity and abundance, provide key services and benefits to people. These services directly support 10s of millions of jobs in multiple economic sectors in coastal and distant states, protect and harbor communities and cities across tropical coastlines, sustain use of living and non-living resources, provide transport infrastructure and valuable natural products, and in future may provide energy solutions. Through these multiple benefits, coral reefs contribute to reducing hunger and poverty, thus improving health, and potentially strengthening gender and social equality. However, access and use result in pressures that may drive decline in coral reef health. Broader land and seascape factors also affect reef health and therefore delivery of benefits, including land-use change and altered freshwater flows, as well as climate change. Managing this complex system requires appropriate awareness and knowledge, governance mechanisms and investments by stakeholders. This ‘SDG narrative’ can be used from local to global levels, motivating actions and policy at and across these scales to sustain ecosystem function and use, for the oceans what is also increasingly called a blue economy.

As time progresses and technology improves, biological data sets are continuously increasing in size. New methods and new implementations of existing methods are needed to keep pace with this increase. In this paper, we present a high performance computing(HPC)-capable implementation of Iterative Random Forest (iRF). This new implementation enables the explainable-AI eQTL analysis of SNP sets with over a million SNPs. Using this implementation we also present a new method, iRF Leave One Out Prediction (iRF-LOOP), for the creation of Predictive Expression Networks on the order of 40,000 genes or more. We compare the new implementation of iRF with the previous R version and analyze its time to completion on two of the world's fastest supercomputers Summit and Titan. We also show iRF-LOOP's ability to capture biologically significant results when creating Predictive Expression Networks. This new implementation of iRF will enable the analysis of biological data sets at scales that were previously not possible.