Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

Wearable assistant devices play an important role in daily life for people with disabilities. Those who are hearing impaired may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the goal of this study is to develop a wearable assistant device for the hearing impaired to recognize emergency vehicle sirens on the road using edge computing. An EfficientNet-based fuzzy rank-based ensemble model was proposed to classify seven audio sounds, including human vocalizations and emergency vehicle sirens. This model was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were respectively obtained from the CREMA-D dataset and Large Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included neutral vocalization, anger vocalization, fear vocalization, happy vocalization, car horn sound, siren sound, and ambulance siren sound. The audio signal was converted into a spectrogram by the short-time Fourier transform as the feature. When one of the car horns, sirens, or ambulance sirens was detected, the wearable assistant device presented alarms through vibration and messages on the OLED panel. The performances of the EfficientNet-based fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results were an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired avoid traffic accidents.

Graphene-based nanomaterials (GNMs), including graphene, graphene oxide, reduced graphene oxide, and graphene quantum dots, may have direct anticancer activity or be used as nanocarri-ers for antitumor drugs. GNMs usually enter tumor cells by endocytosis and can accumulate in lysosomes. This accumulation prevents drugs bound to GNMs from reaching their targets, sup-pressing their anticancer effects. A number of chemical modifications are made to GNMs to facili-tate the separation of anticancer drugs from GNMs at low lysosomal pH and to enable lysosomal escape of drugs. Lysosomal escape may be associated with oxidative stress, permeabilization of the unstable membrane of cancer cell lysosomes, release of lysosomal enzymes into the cytoplasm, and cell death. GNMs can prevent or stimulate tumor cell death by inducing protective autopha-gy or suppressing autolysosomal degradation, respectively. Furthermore, because GNMs prevent bound fluorescent agents from emitting light, their separation in lysosomes may enable tumor cell identification and therapy monitoring. In this review, we explain how the characteristics of the lysosomal microenvironment and the unique features of tumor cell lysosomes can be exploited for GNM-based cancer therapy.

This study was designed to extend the literature on multicomponent interventions for the children of deaf adults (CODA) population. This study used two evidence-based behavior intervention strategies: pre-session pairing (PSP) and independent group contingency (GC). These strategies have been successfully used to decrease off-task behavior in elementary school children with various abilities and diverse backgrounds. However, the research on multicomponent interventions has yet to examine their effects on the CODA population in inclusive classrooms. This study used a concurrent multiple baseline design across participants with an ABC sequence to examine teacher-implemented PSP with independent GC in reducing the off-task behavior of three CODAs in an inclusive classroom at a public elementary school. The results show that the intervention successfully decreased off-task behavior for all participating CODAs. The results suggest that CODAs can adapt to classroom rules and expectations when PSP and independent GC are systematically applied to them in classrooms.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

The recent covid crisis has proven important lessons for academia and industry regarding digital reorganization. Among fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and over. Moreover, drug development is a costly and time-consuming business, and only a minority of approved drugs return the revenue that exceeds the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper will review the most significant research on artificial intelligence in the de novo drug design for COVID-19 pharmaceutical research.

Over the last decade, a significant literature has emerged that advocates the potential of different Additive manufacturing (AM) technologies and printable polymeric materials. Nevertheless, large scale printing and complex geometric shapes, with curvatures and non-planar layer deposition, are a challenging process for the traditional gantry-based machine. The 3 degrees of freedom cartesian configuration restricted their capability to planar layered printing and restricted part dimensions. To date, many researchers have used industrial robots to overcomes this limitation. This review gives the reader a good overview of the FDM technique due to its scalability, cost efficiency and a wide range of material printability. A strong emphasis is laid on the PLA and PLA-based composites as promising materials for the FDM process applications. The second part of this paper links the successful use of these materials in the traditional printing process to large scale printing using the robot-based FDM process. This survey presents representative setups for robot-based AM and works that have been used these setups for non-planar material deposition. Finally, we conclude this paper by identifying opportunities for realizing new functional capabilities by exploiting robot-based AM, and we also present the future trends in this area.

Saccharomyces cerevisiae are a phenotypically diverse species that adapt to a wide variety of environments by exploiting standing genetic diversity and selecting for advantageous mutations. Glyphosate and copper-based herbicides/ fungicides affect non-target organisms, these incidental exposures can impact microbial populations. In this study, glyphosate resistance was found in the historical collection of yeast which was collected over the last century, but only in yeast isolated after the introduction of glyphosate. The highest glyphosate-resistant yeasts were isolated from agricultural sites. However, herbicide application at these sites was not recorded. In an effort to assess glyphosate resistance and impact on non-target microorganisms, yeast were harvested from 15 areas with known herbicidal histories, including an organic farm, conventional farm, remediated coal mine, suburban locations, state park, and a national forest. Yeast representing 23 genera were isolated from 237 samples of plant, soil, spontaneous fermentation, nut, flower, fruit, feces, and tree material samples. Saccharomyces, Candida, Metschnikowia, Klyveromyces, Hanseniaspora, and Pichia were other genera commonly found across our sampled environments. Managed areas had less species diversity and at the brewery, only Saccharomyces and Pichia were isolated. A conventional farm growing RoundUp Ready™ corn had the lowest phylogenetic diversity and the highest glyphosate resistance. The mine was sprayed with multiple herbicides including a commercial formulation of glyphosate; however, the yeast did not have elevated glyphosate resistance. In contrast to the conventional farm, the mine was exposed to glyphosate only one year prior to sample isolation. Glyphosate resistance is an example of the anthropogenic selection of nontarget organisms.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins and carbohydrates in their seeds, distinctly dominate modern agronomic practice. Indeed, these plants play an essential role in the food industry and fuel production. Therefore, the seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, not only seed development and germination, but also age- and stress-related alterations in seed vigor, longevity, nutritional value and safety can be addressed by a broad panel of analytical, biochemical and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes, accompanying plant development, senescence and response to biotic or environmental stress. Among individual methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. Here we discuss the main methodological approaches employed by seed proteomics in the context of physiological changes related to seed development, ageing and response to environmental stress.

School bullying represents a widespread expression of violence in the peer context. Guided by the social-ecological model, this study investigated the longitudinal and transactional pathways linking domestic and neighborhood/community violence exposure (through direct victimization and witnessing), self-serving cognitive distortions (CDS), and school bullying perpetration. Furthermore, the cognitive desensitization hypothesis that the literature has supposed to develop in response to chronically violent contexts was examined. Two four‐wave cross-lagged panel mediation models were tested in a sample of 778 high school students (28.1% males; Mage [Time 1; T1] = 14.20, SD = 0.58). The results showed that both domestic and neighborhood/community violence exposure, as a victim and as a witness, respectively, were associated to self-serving CDs and bullying perpetration over time. Moreover, significant associations between CDs and bullying perpetration over time were found, with bidirectional effects for each of these longitudinal patterns. Finally, self-serving CDs were found to significantly mediate the relationships between both domestic violence victimization and community violence witnessing and school bullying perpetration. These findings highlight the need to consider school bullying as a social phenomenon stemming from a complex and bidirectional interplay between the individuals and the environments they inhabit confirming a basic postulate that “violence breeds violence”.

This paper provides an overview of current literature on community-led development initiatives in urban contexts, their role in addressing urban inequality, and the challenges they face. This study examines effective strategies of community-led initiatives, such as empowering communities, building partnerships, mobilizing resources, focusing on local needs, and participatory design methods. Case studies from around the world demonstrate how successful community-led development initiatives have been able to overcome obstacles to create more inclusive and equitable urban environments. Institutional support, including technical assistance, capacity building, and regulatory frameworks, are crucial for promoting the success and sustainability of these initiatives. Challenges to community-led initiatives include limited resources, capacity building needs, inadequate infrastructure, lack of institutional support, and lack of trust among community members. Therefore, the paper suggests that it is essential to address these challenges to enable the success of community-led development initiatives. Furthermore, this study recommends that future research should explore how emerging technologies and innovative approaches can effectively support community-led development initiatives and advance more just and sustainable urban development.

Because data on the fungal gut community structure of African children are scarce, we aimed to describe by reanalysing rRNA ITS1 and ITS2metabarcoding data from a study designed to assess the influence of microbiota in malaria susceptibility in Malian children from the Dogon country. More specifically, we aimed to establish the core gut mycobiome and compare the gut fungal community structure of breastfed children, aged 0-2 years, with other age groups. Briefly, DNA was extracted from 296 children’s stool samples. Both rRNA ITS1 and ITS2 genomic barcodes were amplified and subjected to Illumina MiSeq sequencing. The ITS2 barcode generated 1,975,320 reads and 532 operational taxonomic units (OTUs), while the ITS1 barcode generated 647,816 reads and 532 OTUs. The alpha diversity was significantly higher by using the ITS1 compared to the ITS2 barcode (p

Amidst the recent energy crisis, the pivotal role of resource efficiency and renewable energy sources (RES) for sustainable development has become apparent. Transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing reliance on centralized systems and potentially transforming them into power flexibility resources. This is particularly crucial in non-interconnected island systems that primarily rely on imported oil for electricity generation. Such reliance not only implies high production costs but also threatens energy security and adds to the environmental footprint of local economic activities. Addressing the above necessitates, amongst other elements, the adoption of advanced energy management with sophisticated forecasting and demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM at the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in an advanced energy management platform, the algorithm undergoes testing using datasets collected from end-consumers and a small-scale RES installation on the island of Tilos, Greece. This study not only offers insights for energy stakeholders but also establishes theoretical parameters that can inform subsequent decision-making processes.

Meals on Wheels (MoWs) are critical in enabling adults with care and support needs to live independently. However, many individuals are unaware that the service exists. The aim of this study was to review the websites of local authorities in the UK, and those of 3^rd-party providers they signpost to, to establish the quality of online information available on MoWs services, and the websites’ overall quality. Two data capture forms were used to extract information from local authority and 3^rd-party providers, consisting of 35 items across five domains, and 36 items across five domains, respectively. Items were awarded a score based on whether certain criteria were met or not, resulting in a maximum total score of 50 and 43 points for each local authority, and 3^rd-party provider, website, respectively. A total of 271 websites (223 local authority and 48 3^rd-party provider) were reviewed. The mean total score was 14.9 (SD 10.6, range: 1-40 points) for local authority websites, and 21.4 (SD 5.3, range: 10-35 points) for 3^rd-party provider websites. For all websites combined, online provision of MoWs information was generally poor, with the vast majority of websites (69.4%, n=188) assessed as requiring improvement, whereas only 4.4% of websites (n=12) were assessed as very good. These findings suggest that local authorities and 3^rd-party providers in the UK should seek to improve their online information provision on MoWs to enhance awareness of the service, and ensure that potential service users, and individuals who refer them to the service, can access high-quality information on MoWs.

Since 2015, community forests have been promoted in Togo as an alternative to protected areas degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m x 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Hetopogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsis suberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.

Green-space resources, in the context of urbanisation, cannot meet the actual needs of residents well, and the study of the balance of green-space resource allocation from the relationship between supply and demand is an urgent problem to be solved. This study quantitatively evaluates the green-space supply level from four dimensions by constructing a framework for assessment in the community life circle. It also evaluates the matching of green space supply and demand, resource distribution fairness, and distribution equilibrium under group differentiation through the supply–demand coupling matrix, Gini coefficient, and the Kruskal–Wallis H non-parametric rank-sum test, respectively. This study shows that (1) a significant spatial imbalance exists in green-space resource allocation in community life circles in different dimensions. (2) The comprehensive green-space supply level in the community life circle matched the total demand of residents to a low degree. (3) There was significant inequality in green-space resource allocation within the community life circle (the Gini coefficient of each evaluation perspective was > 0.4). Based on the study results, we were able to identify community life circles with spatial mismatches, different supply and demand, and other green-space resource allocation problems, which is of great significance to urban green-space research and planning practice under the "green justice" framework.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

Declining agricultural productivity caused by soil salinization are becoming global dilemmas in recent years. Biofertilizers show great potential as a sustainable and environmentally friendly fertilization strategy for soil improvement, but their effectiveness for saline soil amendment and improving plant growth under saline stress is not well understood. Assess the effectiveness of biofertilizers in improving saline soils and enhancing crop growth under saline stress, as well as investigate its related potential mechanisms. Changes in soil physico-chemical properties, plant physiological parameters and soil microbial communities were analyzed through pot experiments. The results showed that the application of biofertilizer reduced total soluble salts (TSS) in soil by 30.8% and increased the Brassica rapa L. biomass by 8.4 times. Biofertilizer application increased soil organic matter (SOM), total nitrogen (TN) and available phosphorus (AP). Biofertilizer applications also increased the SOD, CAT, chl.a, chl.b, total soluble sugars, and Proline content. Biofertilizers present plant growth promotion and potential pathogenic fungi reduction by increasing the abundance of Bacillus and Planococcus and decreasing the abundance of Mortierella and Aspergillus. Overall, the results of this study demonstrated the excellent efficacy of biofertilizers in improving saline soils, and the application of biofertilizer strategies will greatly promote agricultural production.

When the zoonotic parasite of rodents that can cause human neuroangiostrongyliasis, i.e., Angiostrongylus cantonensis, is found in its natural definitive hosts, it is usually reported in isolation, as if the rat lungworm were the only component of the within-rat parasite community. In this study we report the coinfections found in rats naturally infected by A. cantonensis in urban populations of Rattus norvegicus and Rattus rattus in Valencia, Spain. In addition to the rat lungworms, which were found in 14 of the 125 rats studied (a prevalence of 11.20%), 18 other parasite infrapopulations (intestinal and tissular protists, microsporidia, and helminths) were found; some of them with high burdens. Fourteen of these 19 species found are potential zoonotic parasites, namely Blastocystis, Giardia duodenalis, Cryptosporidium spp., Enterocytozoon bieneusi, Encephalitozoon hellem, Toxoplasma gondii, Brachylaima spp., Hydatigera taeniaeformis larvae, Hymenolepis nana, Hymenolepis diminuta, Angiostrongylus cantonensis, Calodium hepaticum, Gongylonema neoplasticum and Moniliformis moniliformis. Despite the limited number of rats studied, the total predominance of coinfected rats as well as their high parasite loads seem to indicate a clear trend towards parasite tolerance.

Background. The measurement of physical performance constitutes an indicator of physical functional capacity and allows the design of physical exercise programs adapted to the individual abilities and needs of older adults with and without frailty. Aim. To present a synthesis of knowledge on the effect of exercise programs on physical performance in older adults with and without frailty in the community. Method. A systematic review was carried out in accordance with the PRISMA-2020 criteria. The search for articles was made until May 4, 2023 in PubMed, Scopus, Web of Science, Cochrane Library, SciELO and LILACS. The outcome variable was physical performance, measured through the SPPB (Short Physical Performance Battery). The mean difference (MD) was estimated to evaluate the effect. Results. We found 2,483 studies, of which 12 met the eligibility criteria for the systematic review and 9 for the meta-analysis. The effect of exercise on SPPB score was significantly higher in the exercise group compared to control in non-frail older adults with MD=0.51 [95% CI, 0.05 to 0.96, p<0.05]. Likewise, in older adults with frailty, the effect of exercise on the global SPPB score was significantly higher in the exercise group compared to the control with an MD=0.66 [95% CI, 0.09 to 1.24, p<0.05 ]. Conclusion. Our findings suggest that exercise programs are effective in increasing and/or maintaining physical performance in older adults with and without frailty, whose effect is more evident in older adults with frailty, probably due to the greater margin of recovery of intrinsic capacity.

Inflammatory bowel disease (IBD) comprises systemic inflammatory conditions primarily affecting the gastrointestinal tract, including Crohn's disease and ulcerative colitis. This research aims to analyze the clinical symptoms and pathogenesis of Dextran sodium sulfate (DSS)-induced canine IBD model, and evaluate the restorative effect of ginsenoside from a pathogenesis perspective. We established the DSS-induced canine IBD model and studied the pathological mechanisms. Additionally, we examined the therapeutic effect of ginsenosides by assessing the Canine Inflammatory Bowel Disease Activity Index (CIBDAI), C-reactive protein (CRP) levels, colonic tissue morphology, protein expression, and mucosal bacterial community analysis. Our findings revealed a total ginsenoside content of 22.7% in the ginsenoside extract. Animal experiments demonstrated that dogs with IBD exhibited decreased mental state, significantly increased CIBDAI and CRP levels, disrupted colonic epithelial tissue structure, decreased expression of mucin, tight junctions, and adherens junctions, as well as reduced diversity of the colonic mucosal bacterial community. Furthermore, correlation analysis highlighted a total of 38 bacterial strains correlated with physiological indices. Significantly, ginsenoside treatment could improve these symptoms and reverse the relative abundance of some bacterial communities. In conclusion, Alterations in the properties of the colonic mucus layer or the reduction of MUC2, its core component, in dogs with IBD can lead to bacterial penetration of the mucus layer and subsequent contact with intestinal epithelial cells, resulting in inflammation. Remarkably, ginsenoside intervention showcased the capacity to positively influence the relative abundance of bacteria and impact the colonic mucus layer properties, thereby offering promising prospects for IBD management and recovery.

Intergenerational integration has given rise to a novel aging paradigm known as all-age communities, which is garnering international attention. In China, the aging population and the implementation of the three-child policy have resulted in increased demand for retirement and childcare services among residents in older neighborhoods. Consequently, there is a pressing need to retrofit these older neighborhoods to accommodate all-age living arrangements given the high demand they generate. Therefore, this study undertakes research interviews with residents and constructs an exploratory theoretical model rooted in established theory. To assess the significance of our model, we employ Smart PLS 3.0 based on 297 empirical data points. Our findings indicate that anxiety has a significant negative effect on payment behavior; objective perception, willingness to pay, and government assistance exert significant positive effects on payment behavior. By comprehensively analyzing the mechanisms underlying residents’ payment behavior, this study provides valuable insights to the government for promoting the aging process within communities and formulating effective transformation policies.

Installations of decentralised Renewable Energy Systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for decentralised renewable generation and hybrid storage system to create a Renewable Energy Community (REC), developed in Python. The model implements PV Solar and Wind Turbines combined with a hybrid battery and Regenerative Hydrogen Fuel Cell (RHFC). The electrical service demand is derived using real usage data from a rural island case study location. Cost remuneration is managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective Genetic Algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design results in an LCOE of 0.15€/kWh, reducing costs by over 50% compared with typical EU grid power, with a project IRR of 10.8%, simple return of 9.6%/year, and ROI of 9 years. Emissions output from grid only use is reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

Spent mushroom compost (SMC) substrates are commonly used as growth media for greenhouse crops and horticulture production. This study aimed to investigate the responses of physiochemical soil properties, enzyme activities, and microbial community compositions to different cultivation durations and SMC soil treatments on tomatoes (Solanum lycopersicum L.) grown in plastic greenhouses on the Loess Plateau, China. The experiment included treatments: Two control treatments of non-planting SMC substrates (Sub CK) and continuous mono-cropping soil (Soil CK), and SMC substrate and the surrounding soil after planting at 1, 3, and 7 years. The results revealed that the SMC substrates had higher contents of major nutrients (e.g., total N and total P) than the surrounding soil treatments. The physicochemical soil properties and soil enzyme activities of the SMC substrates were significantly decreased with longer cultivation duration. Microbial alpha-diversity was higher in the SMC substrates regardless of cultivation duration than the control treatment (soil CK and substrate CK). Interestingly, following several years of tomato cultivation, the compositions of bacterial communities had more similarities with fungal communities in both the SMC substrates and surrounding soils. It was observed that many beneficial microbes, such as bacteria of the Deinococcus-Thermus, Halanaerobiaeota, and Nitrospirae phyla, and the fungi of the Basidiomycota, Mortierellomycota, and Chytridiomycota phyla were enriched in the SMC substrates. The pathogenic bacterial genus Sphingomonas and fungal genus Fusarium were abundant in the Soil CK treatment, while the potentially beneficial bacterial genera Saccharimonadales, Gaiella, Bacillus, and fungal genera Thermomyces, Kernia, and Mortierella were abundant in both the SMC substrate and surrounding soil. This study demonstrated that agro-based SMC substrates were a suitable growth media for a new grooved cultivation system.

Background Hop creep continues to present an unresolved issue for the brewing industry, specifically stemming from those hops added to beer during fermentation. Hops have been found to contain four dextrin-degrading enzymes: alpha amylase, beta amylase, limit dextrinase, and an amyloglucosidase. One recent hypothesis predicts that these dextrin-degrading enzymes could originate from microbes rather than the hop plant itself. Scope and Approach This review begins by describing how hops are processed and used in the brewing industry. It will then discuss hop creep’s origins with a new beer style, antimicrobial factors from hops and resistance mechanisms that bacteria use to counter them, and finally microbial communities that inhabit hops, focusing on whether they can produce the starch degrading enzymes which drive hop creep. After initial identification, microbes with possible links to hop creep were then run through several databases to search the genomes (if available) and for those specific enzymes. Key Findings and Conclusions Several bacteria and fungi contain alpha amylase as well as unspecified glycosyl hydrolases, but only one contains beta amylase. Finally, this paper closes with a short summary of how abundant these organisms typically are in other flowers.

The research examines the relationship between self-rated health situation and personal percep-tion of heat waves, and how social linkage of communities would be a moderator variable in residents’ perception of heat waves in Taiwan. This study uses the questionnaire conducted by Sinica “Responsive Capacity under Heat Wave: The Perspectives of the Locals”(2019), using OLS method for estimating the unknown parameters in multiple regression model. The author finds that the correlation of self-rated health situation and perception toward heat is significantly posi-tive. Also, social linkage in communities affects strongly as a moderator variable: While the sat-isfaction to with their community could reduce negative reaction to heat, contacts with neighbors could increase possibility people feel uncomfortable in high-temperature situation. This study ex-hibits the effects of social environment on community, and expects further related researches or practices to strengthen capability to resist heat wavesƒ for Taiwanese residents.

Objective: To analyze the clinical and economic impact of community-onset urinary tract infections (UTI) caused by extended-spectrum beta-lactamase (ESBL)-producing Klebsiella pneumoniae requiring hospitalization. Methods: A retrospective cohort study included all adults with UTI caused by K. pneumoniae admitted to a tertiary care hospital in Barcelona, Spain, between 2011 and 2015. Demographic, clinical and economic data were analyzed. Results: One hundred and seventy-three episodes of UTI caused by K. pneumoniae were studied; 112 were non-ESBL-producing and 61 ESBL-producing. Multivariate analysis identified ESBL production, acute confusional state associated with UTI, shock, and time to adequate treatment as risk factors for clinical failure during the first 7 days. Economic analysis showed differences between ESBL-producing and non-ESBL-producing K. pneumoniae for the total cost of hospitalization per episode (mean 6,718 € vs 3,688 € respectively). Multivariate analysis of the higher costs of UTI episodes found statistically significant differences for ESBL production and time to adequate treatment. Conclusion: UTI caused by ESBL-producing K. pneumoniae requiring hospitalization, and time to adequate antimicrobial therapy are associated with worse clinical and economic outcomes.

Ecologists use the net biotic interactions among plants to predict fundamental ecosystem features. Following this approach, ecologists have built a giant body of theory founded on observational evidence. However, due to the limitations that a phenomenological approach raises both in empirical and theoretical studies, an increasing number of scientists claim the need for a mechanistic understanding of plant interaction outcomes, and a few studies have taken such a mechanistic approach. In this synthesis, we propose a modeling framework to study the plant interaction mechanistically. We first establish a conceptual ground to frame plant-plant interactions, and then, we propose to formalize this research line theoretically developing a family of individual-based, spatially-explicit models in which biotic interactions are an emergent property mediated by the interaction between plants’ functional traits and the environment. These models allow researchers to evaluate the strength and sign of biotic interactions under different environmental scenarios and thus constitute a powerful tool to investigate the mechanisms underlying facilitation, species coexistence, or the formation of vegetation spatial patterns.

UN 2030 Sustainable Development Goal 6 presents difficulties for small island developing states such as the Kingdom of Tonga, which relies on rainwater and groundwater lenses for freshwater supply. Planning and managing water resources to supply demands in dispersed small islands under variable climate and frequent extreme events is challenging. Tensions between water planning using top-down versus bottom-up processes have long been recognized. Tonga’s overarching national planning instrument is the Tonga Strategic Development Framework, 2015-2025 (TSDFII). This identifies desired national outcomes and is used to direct and resource Ministries and address international and regional commitments. Water supply was a low priority in the three-month consultations that led to TSDFII. Community Development Plans (CDPs), developed by rural villages throughout Tonga’s five Island Groups over nine years, involved participation from 80% of each village population who ranked local priorities. Analysis of priorities in 117 available village CDPs reveals improvements to village water supply was the highest overall priority in all five Island Groups and ranked within the top three priorities by 76% of all villages, with women, youth and men returning figures of 83%, 66% and 80% respectively. The mismatch between top-down and bottom-up priorities appears to result from an urban/rural divide.

The known three-component reaction-diffusion system modeling competition and co-existence of different language speakers is under study. A modification of this system is proposed, which is examined by Lie symmetry method; furthermore exact solutions in the form of traveling fronts are constructed and their properties are identified. Plots of the traveling fronts are presented and the relevant interpretation describing the language shift occurred in Ukraine during the Soviet times is suggested

A community food environment plays an essential role in explaining the healthy life-style patterns of its community members. However, there is a lack of compelling quantitative approaches to evaluate these environments. This study introduces and validates a new tool named the Facility List Coder (FLC), whose purpose is to assess food environments based on data sources and classification algorithms. Using the case of Mataró (Spain), we randomly selected 301 grids areas (100 m2) where we conducted street audits in order to physically identify all the facilities by name, address and type. Then, audit-identified facilities were matched with those automatically-identified and were classified using the FLC in order to determine its quality. Our results suggest that automatically-identified and audit-identified food environments have a high level of agreement. The ICC estimates and their respective 95% confidence intervals for the overall sample, yield the result “excellent” (ICC ≥ 0.9) for the level of reliability of the FLC.

Lineage diversity can refer to the number of genetic lineages within species or to the number of deeper evolutionary lineages, such as genera or families, within a community. Community lineage diversity (CLD) is of interest to ecologists, evolutionary biologists, biogeographers, and those setting conservation priorities. Despite its relevance, it is not clear how to best quantify CLD. With North American tree communities as an example, we test which taxonomic and phylogenetic metrics best measure CLD. We find that phylogenetic metrics outperform taxonomic metrics. Faith’s phylogenetic diversity performs well, but is skewed towards the number of lineages in recent time. The best metric is newly derived here, and termed time integrated lineage diversity (TILD). Mapping the lineage diversity of tree communities across the contiguous United States, we find a spatial pattern differing from that of species richness in key areas. The Pacific Northwest, Great Lakes Region, state of Maine, and south-eastern piedmont and coastal plain forests all emerge as areas high in lineage diversity, but relatively lower in species richness. We urge the consideration of lineage diversity, as well as species richness, when setting conservation priorities.

In Hawaiʻi, the transition from customary subsistence flooded taro agroecosystems, which regulate stream discharge rate trapping sediment and nutrients, to a plantation-style economy (c. the 1840s) led to nearshore sediment deposition - smothering coral reefs and destroying adjacent coastal fisheries and customary fishpond mariculture. To mitigate sediment transport, Rhizophora mangle was introduced in estuaries across Hawai’i (c. 1902) further altering fishpond ecosystems. Here, we examine the impact of cultural restoration between 2012-2018 at Heʻeia Fishpond, a 600-800-year-old walled fishpond. Fishpond water quality was assessed by calculating water exchange rates, residence times, salinity distribution, and abundance of microbial indicators prior to and after restoration. We hypothesized that R. mangle removal and concomitant reconstruction of sluice gates would increase mixing and decrease bacterial indicator abundance in the fishpond. We find that Heʻeia Fishpond’s physical environment is primarily tidally driven; wind forcing and river flux are secondary drivers. Post-restoration, two gates in the northeastern region account for >80% of relative flux in the fishpond. Increase in exchange rates during spring and neap tide and shorter minimum water residence time corresponded with the reconstruction of a partially obstructed 56 m gap together with the installation of an additional sluice gate in the fishpond wall. Lower mean salinities post-restoration suggests increased freshwater influx due to R. mangle removal. Spatial distribution of microbial bio-indicator species inversely correlated with salinity. Average abundance of Enterococcus and Bacteroidales did not significantly change after restoration efforts, however, average abundance of a biomarker specific to birds nesting in the mangroves decreased significantly after restoration. This study demonstrates the positive impact of biocultural restoration regimes on water flushing and water quality parameters, encouraging the prospect of revitalizing this and other culturally and economically significant sites for sustainable aquaculture in the future.

Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.

This paper presents an in-depth contextualized tutorial on Agricultural IoT (Agri-IoT), covering the fundamental concepts, assessment of routing architectures and protocols, and performance optimization techniques via systematic survey and synthesis of related literature. The negative impacts of climate change and the increasing global population on food security and unemployment threats have motivated the adoption of the wireless sensor network (WSN)-based Agri-IoT as an indispensable underlying technology in precision agriculture and greenhouses to improve food production capacities and quality. However, most related Agri-IoT testbed solutions have failed to achieve their performance expectations due to the lack of an in-depth and contextualized reference tutorial that provides a holistic overview of communication technologies, routing architectures, and performance optimization modalities based on users’ expectations. Thus, although IoT applications are founded on a common idea, each use case (e.g., Agri-IoT) varies based on specific performance and users expectations as well as its technological, architectural, and deployment requirements. Likewise, the agricultural setting is a unique and hostile area where conventional IoT technologies do not apply, hence the need for this tutorial. Consequently, this tutorial addresses these via the following contributions: (1) a systematic overview of the fundamental concepts, technologies, and architectural standards of WSN-based Agri-IoT, (2) an evaluation of the technical design requirements of a robust, location-independent, and affordable Agri-IoT, (3) a comprehensive survey of the benchmarking fault tolerance techniques, communication standards, routing and medium access control (MAC) protocols, and WSN-based Agri-IoT testbed solutions, and (4) an in-depth case study on how to design a self-healing, energy-efficient, affordable, adaptive, stable, autonomous, and cluster-based WSN-specific Agri-IoT from a proposed taxonomy of multi-objective optimization (MOO) metrics that can guarantee an optimized network performance. Furthermore, this tutorial established new taxonomies of faults, architectural layers, and MOO metrics for cluster-based Agri-IoT (CA-IoT) networks and a 3-tier objective framework with remedial measures for designing an efficient associated supervisory protocol for cluster-based Agri-IoT networks.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

This review aims to consolidate varied information regarding Port Scanning, and examine the tools, techniques, and detection algorithms used. It explores TCP SYN, Full TCP, UDP, and stealth scans, and shows their functions and abilities. Paired with these techniques, it also shows the tools that they can be used by, such as NMap, Zmap, and MASSCAN. It discusses the various detection techniques such as Signature and Anomaly based detection, and discusses the real world impact of this technology, while commenting on the legal and ethical issues posed.

This study reports the synthesis and characterization of novel per-fluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers reveal variations in the fluorine content via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers, which enhanced their hydrophobic nature. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights between 4886 to 11948 g/mol. All polymers exhibit thermal stability in the range of 350-500°C, with varying thermal stability depending on the fluorine and nitrogen content. Conjugation of the polymers was confirmed through changes in the UV-Vis spectra, with a hypsochromic shift observed in all cases, more pronounced in azo-based fluorinated chains due to H-bonding on the nitrogen sites, chain conformations, and planarity. The optical band gap (Eg) of the polymers was determined from the UV-Vis data, with the Eg values of the azo-based fluorinated polymers being 1 eV higher than those of their corresponding linkers. The cross-linking formation was characterized by porosity measurements, with the azo-based polymer exhibiting the highest surface area of 770 m²/g with a pore volume of 0.35 cm³/g, while the open-chain azomethine-based polymer exhibited the lowest surface area of 285 m²/g with a pore volume of 0.0872 cm³/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. DAB-Z-1h, with the highest surface area and rich in nitrogen sites, exhibited the highest CO₂ uptake at 298 K (17.25).

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. 3D pharmacophore models are powerful computational tools in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

The generation at the helm faces an unprecedented responsibility in the near future of artificial intelligence. The implications of setting up the founding rules that will regulate the operation of AI are heavy since after they’re set they last forever. Once this first AI is commenced, it can be such that no other subsequent AIs could emerge thereby assuming dominion over its own creation stand. As a result, retaining control becomes necessary. Lest humanity surrender agency to its own creation. At this juncture of big talks, critical issue are raised concerning AI administration owners. Is it appropriate for only a few people to have unrestricted control on AI commands while leaving out all precautionary measure? Therefore, we have to always consider between control and constraint when dealing with AI issues which involves authority plays off against morality. The direction Artificial Intelligence takes in the future depends on the decisions made by today’s generation. We will determine how we are viewed historically in terms of technology based on how well we take on such an important duty. There’s a major turning point ahead of us where we who are the stewards of tomorrow must make a choice that protects humanity’s right to self-determination and also exploits the power of AI for change.

Coronary heart disease (CHD) has become a prevalent issue in Indonesia, resulting in a majority of fatalities and illnesses. The most recent guidelines in the United States for managing patients with chronic coronary syndrome (CCS) have undergone significant changes, with the recommendation for conducting percutaneous coronary intervention (PCI) strictly limited to patients who continue to experience symptoms despite receiving optimal medical treatment (OMT). However, adopting this approach may lead to increased morbidity and mortality due to the presence of vulnerable plaque. Vulnerable plaque is characterized by minimal obstruction during coronary angiography (40-70%) and may not produce ischemia during physiological studies. Identifying vulnerable plaques can be accomplished through several methods, among which computed tomography coronary angiography (CTCA) is the most cost-effective approach. Recently, experts have employed different techniques, either with stents or drug-coated balloons, to manage vulnerable plaques, showing the methods to be safe and effective. The other challenge faced by patients with CCS is the possibility of achieving complete revascularization (CR) to reduce the likelihood of future major adverse cardiac events (MACE) and mortality. Both approaches, identifying and intervening in the vulnerable plaque, are difficult to perform in developing countries due to the limited budget. Studies have shown that plant-based diets (PBD) can reverse atherosclerotic plaque and stabilize vulnerable plaque, possibly leading to CR. PBD is an affordable and effective approach, but it requires healthcare providers skilled in educating and implementing this dietary approach with their patients. Large-scale randomized controlled trials in PBD, specifically aimed at addressing the vulnerable plaque and achieving CR, are necessary before implementing the PBD intervention.

A significant portion of fatalities stemming from acute myocardial infarction (AMI) occur suddenly and without warning. This can be prevented through early detection of coronary stenosis. In recent years, coronary artery calcium (CAC) and computed tomography coronary angiography (CTCA) have been utilized. For instance, a 55-year-old interventional cardiologist had a CAC of 58 with 50% stenosis involving his left anterior descending (LAD) artery. Despite possibly having vulnerable plaque, which carries significant future cardiovascular events, the patient followed a plant-based diet (PBD), took supplements, and continued optimal medical therapy (OMT). After three years, there was a significant regression in both CAC and stenosis. During our discussion, we emphasized the importance of food selection, quantification, and how food is processed (raw) to achieve maximal benefit in regressing atherosclerosis. Additionally, we highlighted the importance of identifying the possibility of vulnerable plaque and the mechanism stabilizing it. The case supported the guidelines set by the American College of Cardiology (ACC) and the American Heart Association (AHA) for managing chronic coronary syndrome (CCS) by emphasizing OMT and lifestyle changes. We hope that in the future, more doctors and patients will take responsibility for implementing lifestyle changes rather than solely relying on medications and sophisticated medical technologies.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

As belonging to one of the most isolated continents on our planet, the microbial composition of different environments in Antarctica could hold a plethora of undiscovered species with potential for biotechnological applications. This manuscript delineates our discoveries after an expedition to the Bulgarian Antarctic Base "St. Kliment Ohridski" situated on Livingston Island, Antarctica. Amplicon-based metagenomics targeting the 16S rRNA genes and ITS2 region were employed to assess the metagenomes of the bacterial, fungal and archaeal communities across diverse sites within and proximal to the research station. The predominant bacterial assemblages identified included Oxyphotobacteria, Bacteroidia, Gammaprotobacteria and Alphaprotobacteria. A substantial proportion of cyanobacteria reads were attributed to a singular uncultured taxon within the family Leptolyngbyaceae. The bacterial profile of a lagoon near the base exhibited indications of penguin activity, characterized by a higher abundance of Clostridia, similar to lithotelm samples from Hannah Pt. Although most fungal reads in the samples could not be identified at the species level, noteworthy genera, namely Betamyces and Tetracladium, were identified. Archaeal abundance was negligible, with prevalent groups including Woesearchaeales, Nitrosarchaeum, Candidatus Nitrosopumilus and Marine Group II.

Abstract: Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to hospital-based settings. Updated Dutch PPSA guidelines recommending two anesthesiologists and a ventilator during out-of-hospital PPSA have increased costs and raised concerns about the accessibility to specialized high quality dental care for children in the Netherlands. Aim: This study aims to investigate the impact of the updated 2017 guideline on the occurrence rate of adverse events during PPSA, as recorded in the anesthesia complication database of twelve participating Dutch dental clinics, with adjustments made for age, weight, and duration of treatment. Method: Data of 25,872 children treated at 12 dental clinics performing out-of-hospital PPSA between 1997 and 2019 were analyzed. Eligible children were aged 2-18 with ASA classification I and II. A logistic two-level mixed-effects model is used to estimate the updated guideline’s impact on adverse events. Results: In the final model we analyzed the data of 21,759 children in nine dental clinics. The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64 – 0.89) after the implementation of the updated guideline, compared to before the updated guideline. This outcome was significant with a P=0.001, indicating a protective effect. The average odds per clinic of having an adverse event was 0.04 with a 3.81% chance. Conclusion: Our findings demonstrate a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adherence to evidence-based guidelines in improving patient safety and the quality of care provided during pediatric procedural sedation and analgesia in out-of-hospital dental clinics. Our study underlines the necessity of considering the complex underlying mechanisms that play a role in the development and implementation of evidence-based practice and safety initiatives on the occurrence of adverse events.

The healing of osteochondral defects (OCD) resulting from injury, osteochondritis, or osteoarthritis in middle- and old-age individuals, bearing lesions in the cartilage and bone, pain, and loss of joint function, presents challenges to the clinical practitioners because of non-regenerative cartilage and the limitations of current therapies. Bioactive peptide-based osteochondral (OC) tissue regeneration is becoming more popular because it does not have the immunogenicity, misfolding, or denaturation problems associated with original proteins. Periodically, reviews are published on the regeneration of bone and cartilage; however, none of them address the simultaneous healing of these tissues in the complicated heterogeneous environment of the OC interface. As regulators of cell adhesion, proliferation, differentiation, angiogenesis, immunomodulation, and antibacterial activity, potential therapeutic strategies for OC defects utilizing bone and cartilage-specific peptides should be examined and investigated. Therefore, the main goal of this review is to study how they contribute to the healing of OC defects, either alone or in conjunction with other peptides and biomaterials.

In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.

AI-based approaches are increasingly being used to revolutionize vaccine development for COVID-19. Small molecules, peptides, and epitopes are being collected for therapy discovery, which could also direct AI-based models, screening, or generation [1]. AI-based models are being used for drug discovery and vaccine development, and pre-existing data is being leveraged through machine learning approaches for COVID-19 drug discovery and vaccine development [1]. The best candidate targets for future treatment development are being identified and evaluated using AI-based models [1]. AI-based approaches can be used to overcome challenges in manufacturing, storage, logistics, and safety and efficacy issues related to different vaccine candidates for COVID-19 [2]. AI algorithms can help identify the best vaccine candidates for COVID-19 while considering the efficiency of antigenic peptides for immune response generation [2]. The presentation of antigenic peptides by major histocompatibility complex (MHC) class II molecules is the first step after COVID-19 vaccine administration for any vaccine-induced immune response [2]. Thus, AI-based models are being used to identify the best vaccine candidates for COVID-19 and to ensure that the vaccine-induced immune response is efficient and safe. The utilization of AI-based methods to address logistical, manufacturing, storage, safety, and efficacy issues regarding several COVID-19 vaccine candidates will be examined in this study. Additionally, while considering the effectiveness of antigenic peptides for the induction of an immune response, we will determine the best potential targets for the next treatment development and assess how AI-based models can help discover the best vaccine candidates for COVID-19. This research ultimately intends to offer insights into how AI-based techniques can transform COVID-19 vaccine development and how they can be applied to address vaccine development issues. In this paper, we focus on recent advances in using artificial intelligence to develop COVID-19 drugs and vaccines, as well as the potential of intelligent training in discovering COVID-19 therapeutics, highlighting potential challenges and solutions.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

The corrosion resistance of two cast CoCr based alloys, with different amounts of chromium and with different alloying elements in the bulk composition of the alloy, was assessed. Herewith, we investigated the corrosion behavior of Co21Cr8Mo7W and Co29Cr7W by open circuit potential (OCP), potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) in 0.1 M phosphate buffer solution (PBS) at 37°C for long time immersion. After 1000 hours of immersion, the corrosion current density (icor), estimated from anodic polarization tests, is lower for Co21Cr8Mo7W (i.e. 49 nA cm-2) alloy compared to Co29Cr7W alloy (180 nA cm-2). As regards the corrosion potential (Ecor), a nobler value was observed for Co21Cr8Mo7W (i.e. -59 mV vs. Ag/AgCl) compared to Co29Cr7W (i.e. -114 mV vs. Ag/AgCl). These findings suggest a better corrosion resistance of the film formed on the alloy containing lower amount of Cr and two alloying elements, namely Mo and W. These results are promising ones in terms of medical applications because they open new strategies in obtaining alloys with lower content of chromium and with higher protective properties against corrosion attack.

The global construction sector contributes a significant share of total greenhouse gas (GHG) emissions. In Australia, infrastructure activity alone generates 18% of the GHG emissions budget. The use of low-embodied carbon building materials is crucial to achieving sustainability in the construction sector and to fulfill national and international climate goals. Industrial hemp (Cannabis sativa L.) is considered a promising feedstock for sustainable construction materials because of its biogenic carbon content, fast-growing cycles with low agricultural input requirements, and technical functionality which is comparable to traditional materials. This study has applied the life cycle assessment (LCA) guideline of ISO 14040:2006 to estimate the carbon footprint (CF) expressed in carbon dioxide equivalent (CO2eq) emissions of hemp-based building materials in Western Australia. The functional unit is 1 m2 of hemp-based board, and the system boundary includes cradle-to-gate stages, i.e., pre-farm, on-farm, and post-farm activities. The production of 1 m2 of hemp-based board is estimated to be - 2.302 kgCO2eq. Electricity from the public grid for lignin extraction during the post-farm stage is the main contributor to total CO2eq emissions (26%), followed by urea production (14%) during the pre-farm stage. Overall, the use of electricity from the SWIS during the post-farm stage accounts for 45% of total emissions. Sensitivity analysis shows that the CF of hemp-based boards is highly sensitive to the source of energy, i.e., total replacement of the SWIS by solar power decreases the CF from - 2.30 to -6.07 kgCO2eq (164%). The results suggest that hemp-based boards exhibit lower embodied GHG emissions compared to traditional materials, such as gypsum plasterboards.

TiC particles were prepared from Ti and C powders, and TiC-reinforced Cu-based composites were prepared by powder metallurgy with SPS sintering. The effects of SPS sintering temperature and TiC particle reinforcement phase content on the electrical and mechanical properties of Cu-based composites were investigated. The reinforcement of TiC particles by thermal mismatching at increasing sintering temperature has a significant strengthening effect on the composites, but the high temperature causes softening of the Cu matrix and the growth of grains reduces the properties of the composites. In this study, the effects of porosity and density on the composite properties were not significant.

Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

Background: The incidence of prostate cancer (PC) has been risen annually. Despite the diagnosis is made mainly with non-metastatic PC, mortality is explained by the metastatic disease (mPC). Without a doubt there is an intermediate scenario in which patients have no mPC but will have initiated a metastatic cascade through an epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict what patients will progress in the future to non-localized clinical disease or already have micrometastatic disease, and therefore, will clinically progress after primary treatment. Biomarkers for predicting mPC are still under development; there are few studies and not much evidence of their usefulness. Summary: This review is focused on tissue-based genomic biomarkers (TBGB) for predicting metastatic disease. We developed four main research questions that will attempt to answer according to the current evidence. Why is important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are current prostate cancer treatments? Key Messages: Knowing useful predict tools could help determine which patients may need multimodal or adjuvant treatment even with a localized disease, and in consequence, what patients do not need more than a single modality of treatment. The importance of predicting metastasis is fundamental, given that once metastasis is diagnosed, the quality of life (QoL) and survival drop dramatically.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

The paper explores the potential of a low-cost advanced video-based technique for the assessment of structural damage induced to buildings by seismic loading. A low-cost high-speed video camera was utilized for motion magnification (MM) processing of footages of a two-story reinforced concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e. in terms of modal parameters) and the structural deformations of the building in the MM videos. The results by MM were compared for method validation to damage assessment obtained by the analyses of conventional accelerometers and high-precision optical markers tracked by a passive 3D motion capture system. Also, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometers were also processed and analyzed by using several stationary and non-stationary techniques with the aim to analyze the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed MM-based procedure provided accurate estimate of the main modal frequency and the damage location through the analysis of modal shapes, which were confirmed by advanced analyses of accelerometric data.

The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .

Good estimators are characterized as robust, unbiased, efficient, and consistent. However, the commonly used estimators are weak or lack one or more of these properties. In this article, eight (8) estimators for statistical and geometrical estimations of symmetry/asymmetry, similarity/dissimilarity, identity/unidentity, and feature transformation were proposed following Kabirian-based optinalysis and other operations. The proposed estimators are characterized as invariant (robust) under scaling, location shift, and rotation or reflection. A computing code was written in python language for each of the proposed estimators so that peers can have working codes for application and performance evaluation.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Has anyone ever missed an event because he was confused in days and dates? Do we remember the date of any day without looking at a calendar? Is the current Gregorian Calendar efficient enough for use, and does it facilitate our life or make it more complicated? Have you ever thought about a much simpler way to calculate days and dates in a year? All these questions are answered in this paper, in which the author proposes original optimization algorithm that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize and very simple to predict the days and dates in any year.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 0.8x1e6 Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer or simulant Urine) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 1.2x10⁶ Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10^-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. Closed forms of multiplicative degree-based topological indices which are numerical parameters of the structure and determine physico-chemical properties of the concerned molecular compound. In this article, we compute and analyze many multiplicative degree-based topological indices of silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q].

Recent discovery of triangular boron Nanotubes makes it a competitor of carbon in many respects. Closed forms of M-polynomial of nanotubes produce closed forms of many degree-based topological indices which are numerical parameters of the structure and, in combination, determine properties of the concerned nanotubes. In this report, we give M-polynomials of triangular boron nanotubes and recover many important topological degree-based indices of these nanotubes. We also plot surfaces associated to these nanotubes.

Swarm electrification-driven communities face significant challenges, including implementing advanced distributed control in areas with limited ICT access and establishing trust among villagers hesitant to grant access to their assets. This paper proposes a distributed DC microgrid architecture for P2P energy exchange in these communities, ensuring stability and effective exchange operation. By implementing a Blockchain marketplace specifically designed to suit the rural context, the proposed architecture ensures tracing of exchange transactions to fairly settle participants. Validation experiments demonstrate its efficacy in achieving peak shaving. It provides 11% of the requester's total demand from the community even while maintaining the constraint of reducing discharge-charge cycles to one per day, thereby preserving battery life. Additionally, the solution reduces prosumer production losses by 16% of the total PV production.

Efficient triaging and referral assessment are critical in ensuring prompt medical intervention in the Community Health Care (CHC) system. However, the existing triaging systems in many Community Health Services are an intensive, time-consuming process and often lack accuracy, particularly for various symptoms that might represent heart failure or other health-threatening conditions. There is a noticeable limit of research papers describing AI technologies for triaging patients. This paper proposes a novel quantitative data-driven approach using machine learning (ML) modelling to improve the community clinical triaging process. Furthermore, this study aims to employ the feature selection process and machine learning power to reduce the triaging process’s waiting time and increase accuracy in clinical decision-making. The model was trained on medical records of “Heart Failure” patients’ dataset, which included demographics, past medical history, vital signs, medications, and clinical symptoms. A comparative study was conducted using the list of machine learning algorithms where XGBoost demonstrated the best performance among other ML models. The triage levels of 2,35,982 patients achieved an accuracy of 99.94%, precision of 0.9986, recall of 0.9958, and f1-score of 0.9972 within 0.059 seconds. The proposed diagnostic model can be implemented for the CHC decision system and be developed further for other medical conditions.

The Jakarta LARP community, established in March 2020 and primarily active in Gelora Bung Karno, fosters inclusivity, diversity, and physical role-playing activities. It adheres to a policy of excluding ultra-nationalist ideologies and integrates new members despite language barriers. Bruhn (2011) defines this kind of community sharing the same goals, values, and lifestyles, while McMillan & Chavis (1986) outline four aspects of the sense of community.The research discovered that communities endure by utilizing solidarity built on the basis of their peculiarities. This study employed ethnographic methods alongside grounded research to identify the community's culture and their efforts to sustain and utilize it as community solidarity.

Integrating a grid-connected battery into a Renewable Energy Community boosts Collective-Self-Consumption of photovoltaic produced energy and allow to perform Energy Arbitrage in the day-ahead and intraday electricity market. However, is it a cost-effective investment? The proposed analysis relay on Net Present Value assessment. Ageing affects are integrated and sensitivity analyses are conducted: considering community customers number, electricity mar-ket prices, battery cost, size and the decision to engage in energy arbitrage. Each scenario under-goes a 20-year hourly simulation using an ageing aware rolling-horizon 24-hours-looking-ahead scheduling, optimized with Mixed-integer linear Programming. Forecast errors, provid-ing of ancillary services, real time control and grid constrains are not taken into account. Simulations conducted on the Italian market indicates that a battery dedicated solely to collec-tive self-consumption is not economically appealing. However, integrating Energy Arbitrage, despite impact on battery lifespan, halves the payback period and enhances the attractiveness of larger battery investments. The Net Present Value is contingent upon battery size, customer number and market prices. However, if battery cost does not exceed 200 €/kWh the investment become cost-effective across all scenarios.

This experiment aimed to investigate the impact of malic acid (MA) and citric acid (CA) on the nutritional composition, fermentation quality, rumen degradation rate, and microbial diversity of a mixture of apple pomace and corn protein powder during ensiling. The experiment used apple pomace and corn protein powder as raw materials, with four groups: control group (CON), malic acid treatment group (MA, 10g/kg), citric acid treatment group (CA, 10g/kg), and citric acid + malic acid treatment group (MA, 10g/kg + CA, 10g/kg), subjected to mixed ensiling for 60 days. The results indicated: (1) Compared to the CON group, the crude protein content significantly increased in the MA, CA, and MA+CA groups (p < 0.05), with the highest content observed in the MA+CA group. The addition of MA and CA effectively reduced the water-soluble carbohydrate (WSC) content (p < 0.05). Simultaneously, the CA group showed a decreasing trend in NDFom and hemicellulose content (p = 0.08; p = 0.09). (2) Compared to the CON group, the pH significantly decreased in the MA, CA, and MA+CA groups (p < 0.01), and the three treatment groups exhibited a significant increase in lactic acid and acetic acid content (p < 0.01). The quantity of lactic acid bacteria increased significantly (p < 0.01), with the MA+CA group showing a more significant increase than the MA and CA groups (p < 0.05). (3) Compared to the CON group, the in situ dry matter disappearance (ISDMD) significantly increased in the MA, CA, and MA+CA groups (p < 0.05). All three treatment groups showed highly significant differences in in situ crude protein disappearance (ISCPD) compared to the CON group (p < 0.01). (4) Good's Coverage for all experimental groups was greater than 0.99, meeting the conditions for subsequent sequencing. Compared to the CON group, the Shannon index significantly increased in the CA group (P < 0.01), and the Simpson index increased significantly in the MA group (p < 0.05). However, there was no significant difference in the Chao index among the three treatment groups and the CON group (p > 0.05). At the genus level, the abundance of Lentilactobacillus in the MA, CA, and MA+CA groups was significantly higher than in the control group (p < 0.05). PICRUSt prediction results indicated that the metabolic functional microbial groups in the CA and MA treatment groups were significantly higher than in the CON group (p < 0.05), suggesting that the addition of MA or CA could reduce the loss of nutritional components such as protein and carbohydrates in mixed ensilage. In conclusion, the addition of malic acid and citric acid to a mixture of apple pomace and corn protein powder during ensiling reduces nutritional losses, improves fermentation quality and rumen degradation rate, enhances the diversity of the microbial community in ensiled feed, and improves microbial structure. The combined addition of malic acid and citric acid demonstrates a superior effect.

This study aimed to assess the prevalence and identify risk factors for schistosomiasis among school-aged children in low- and middle-income communities. A retrospective cross-sectional study was conducted, involving the examination of 24 034 children for Schistosoma haematobium ova. Data were analyzed using IBM Statistical Package for the Social Sciences (SPSS) version 27. Logistic regression was employed to determine the factors associated with schistosomiasis. The overall prevalence of schistosomiasis in the study population was 75%, with higher rates observed in rural-dominated areas, particularly Bushbuckridge (73%) and Nkomazi (51%). Age, a history of bilharzia, and the presence of blood in urine were significantly associated with schistosomiasis, while being a female child was found to be a protective factor. This study underscores the importance of implementing robust screening procedures and the necessity for health education to mitigate the high prevalence of schistosomiasis and prevent its further spread.

The subtropical forest soil plays a pivotal role in terrestrial carbon (C) cycling. Although global warming is expected to influence subtropical soil C cycling, a consensus on its impact on soil C dynamics remains elusive. Given the significant response of soil microbial respiration to temperature in subtropical soils, understanding its behavior under varying temperature regimes is critical for predicting soil organic C (SOC) responses to climate warming. We conducted a short-term laboratory soil warming incubation experiment, sampling both warmed and un-warmed soils from a subtropical plantation in southeastern China, incubating them at 20 °C, 30 °C, and 40 °C. Our aim was to study the SOC mineralization response to increasing temperatures. Our findings revealed that the temperature sensitivity (Q10) of SOC mineralization to short-term experimental warming varied between the warmed soil and the un-warmed soil. The Q10 of the un-warmed soil escalated with the temperature treatment (20-30 ℃: 1.31, 30-40℃: 1.63). Conversely, the Q10 of the warmed soil decreased (20-30 ℃:1.57, 30-40 ℃:1.41). Increasing temperature treatments enhanced hydrolytic enzyme activity but decreased soil substrate availability in both un-warmed and warmed soil. The Q10 of un-warmed soil was positively correlated with the response ratio of DOC. Additionally, soil microbial biomass exhibited a significant decline, leading to a reduced total amount of PLFAs and a decreased abundance of bacteria, fungi, and Gram-negative bacteria (GN). The changes in temperature also considerably altered the composition of the warmed soil microbial community. A drop in the microbial quotient coupled with a rise in the metabolic quotient indicated that warming amplified microbial respiration over microbial growth. The differential Q10 of SOC mineralization, in response to temperature across varying soil, can largely be attributed to shifts in soil nutrients, C-degrading enzyme activities, and microbial communities (the ratio of fungal to bacterial PLFAs).

The present study was to investigate effects of total flavonoids from Taraxacum mongolicum Hand.-Mazz. (FT) on fermentation quality, antioxidant status and microbial community of Caragana korshinskii Kom. (CK) silage. CK was ensiled with no additive (CON), 1% FT and 2% FT on a fresh weight (FW) basis for 60 days. The results showed that 1% FT and 2% FT groups displayed higher DM content than CON group, and 2% FT group had the best effect on nutrient preservation. Compared with CON and 1% FT groups, 2% FT group exhibited the best silage fermentation quality and the highest antioxidant activity, including increased lactic acid, acetic acid concentrations and the activities of antioxidant enzymes, as well as decreased pH and the ammonia nitrogen (NH₃-N) concentration. Moreover, 2% FT addition significantly affected the microbial community, such as increased abundance of Lactobacillus and decreased abundances of Pseudomonas and unidentified Cyanobacteria. The abundances of Lactobacillus parafarraginis and Lactobacillus brevis were negatively correlated to pH, while they were positively correlated with T-AOC, GSH-Px and CAT activities. In conclusion, 2% FT may be used as additives to inhibit the growth of undesirable microorganisms and promote fermentation quality and antioxidant activity of CK silage.

Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.

Meals on Wheels (MoWs) provide access to daily meals and social contact to adults with care and support needs.MoWs could be essential for the increasing rates of an ageing population and adults living with complex needs in England, yet many do not know that the service exists. This study aimed to explore the perceptions of MoWs service users, and people who refer them to MoWs (‘referrers’), with accessing and setting up the service in England, and what information would be valued when considering accessing MoWs services.Semi-structured interviews were conducted in May-July 2022 with seven service users and 21 referrers, recruited from four MoWs providers across England. Data were analysed using thematic analysis. Participants indicated various pathways into the service, but referrers (family members) were more likely to be the ones enquiring about, and setting up, MoWs for service users. Once an enquiry about MoWs had been made, the service was perceived as straightforward to set up.However, existing preconceptions and stereotypes about the service (e.g. bringing to mind ‘school meals’, aimed solely at older adults) were perceived to act as barriers to accessing MoWs.Information that participants deemed important to have available when deciding on whether to access MoWs related to the meals (e.g. nutritional value, variety, need for any preparation, catering for individual needs), the specific services provided (e.g. wellbeing checks, daily social contact), the reliability and flexibility of delivery, and the cost of services. These findings could inform MoWs providers’ public awareness strategies about MoWs, in order to facilitate referrals to the service for adults with care and support needs.

Abstract: This research aims at developing a virtual reality destination experience (VRDE) model as a moderating variable between sustainable tourist behavior (STB) and tourist visit intention (TVI). The portability variable strengthens VRDE, and the content quality variable supports VRDE. The research method used was purposive convenience sampling covering 204 respondents who visited the vicinity of Kebon batik village in Klaten, Central Java, Indonesia. The research findings were H1: STB has a significant effect on VRDE, H2: VDRE has an impact on TRVI, H3: portability has a significant effect on VRDE, and H4: content quality did not have an impact on VRDE. The novelty of this research is the VRDE application applied to Community Sustainable Tourist Behavior in developing countries.

Intercropping systems can flexibly use resources such as sunlight, heat, water, and nutrients in time and space, improve crop yield and land utilization rates, effectively reduce continuous cropping obstacles and the occurrence of diseases and insect pests, and control the growth of weeds. Thus, intercropping is a safe and efficient ecological planting mode. The legume–cereal intercropping system is the most common planting combination. Legume crops fix nitrogen from the atmosphere through their symbiotic nitrogen fixation abilities, and the fixed nitrogen can be transferred to and utilized by cereal crops in various ways. The symbiotic nitrogen fixation efficiency of legume crops was improved by reducing the inhibition of soil nitrogen on nitrogenase activity through competitive absorption of soil nitrogen. However, the effects of nitrogen transformation and distribution in intercropping systems, and microbial community structure characteristics on nitrogen transfer need to be further explored. In this review, ⅰ) we present the transformation and distribution of nitrogen in the legume–cereal intercropping system; ⅱ) we describe the soil microbial community characteristics in intercropping systems; and ⅲ) we discuss the advantages of using modern biological molecular techniques to study soil microorganisms. We conclude that intercropping can increase the diversity of soil microorganisms, and the interaction between different plants has an important impact on the diversity and composition of the bacterial and fungal communities. The extensive application of modern biological molecular techniques in soil microbial research and the great contribution of intercropping systems to sustainable agriculture are particularly emphasized in this review.

Oaks are a dominant and key tree species in Mediterranean forest ecosystems. However, in recent decades, oak forests have been heavily impacted by oak decline, a worldwide phenomenon exacerbated by climate change. The charcoal disease agent Biscogniauxia mediterranea is involved in the decline of Mediterranean oak formations in a variety of contexts. Here, we investigated the impact and role of B. mediterranea in decline of oaks in Castelfidardo Forest, a relict wood of the late Holocene and a Site of Community Importance. We established five plots within which we recorded the tree positions, any symptoms and signs of decline, association of B. mediterranea to declining trees, and the deadwood and associated mycota. Of 471 oaks inspected, 7.0% showed brownish exudates on the stems, 46.9% showed epicormic shoots along the main trunk, and 24.4% showed black carbonaceous stromata on diseased branches and trunks. The decline was most severe for Quercus cerris, which comprised plots #4 and #5, at 50.0% (81/162 trees) and 29.0% (33/114), respectively; then for Quercus robur for plot #3, at 40.0% (38/95); and finally for Quercus pubescens for plots #1 and #2, at 13.7% (7/51) and 12.3% (6/49), respectively. Bark tissue was collected from trees with charcoal cankers, with microscopy examination and identification by mycological and molecular methods. This investigation revealed close association between oaks with pronounced reduction of vitality and incidence of B. mediterranea. The deadwood was equally distributed among the five plots, and was heavily colonized by Basidiomycota. The high incidence of this oak-weakening pathogen B. mediterranea appears to be related to stress conditions (e.g., water scarcity, prolonged drought). However, absence of silvicultural management, high competition among physiologically mature trees, and the geographic isolation of this residual forest might have predisposed these oaks to decline.

Pneumonia remains a major cause of morbidity and mortality worldwide, especially during COVID-19 pandemic. With the significant global health burden that pneumonia poses, it is es-sential to improve therapeutic and management strategies. The increasing emergence of antibiotic resistant bacterial strains limits options for effective antibiotic use. New antibiotics for treatment of pneumonia may address deficits in current antimicrobial drugs, with an ability to cover both typical, atypical, and resistance pathogen. Several of these newer drugs also have structural characteristics that allow for a decreased propensity in development of bacterial resistance. Po-tential use of stem cell therapies in place of corticosteroid treatments may also offer an im-provement in patient outcomes. Human mesenchymal stem cell treatments have shown efficacy and safety in treating COVID-19 induced pneumonia. Combined treatment with both stem cells and antibiotics in pneumonia in a rabbit model has also shown significantly increased efficacy in comparison to antibiotic treatment alone, presenting yet another possible route for a novel strategy in treating pneumonia, though additional future studies are necessary before clinical implementation. While pneumonia remains a major disease of concern, having newer approved antibiotics as well as novel therapies such as stem cell treatments in the pipeline offers clinicians more options in effectively treating pneumonia.

This study aims to verify the time-variant feature of American ginseng (AG) continuous cropping obstacles and to explore the factors impeding continuous cropping. We verified the feature with a plant-soil feedback pot experiment and then investigated the factors by comparing the properties of control soils that had not been previously used for growing ginseng (CS) with those of soils with a 10-year-crop-rotation cycle following the growth of AG (RS). It’s found that the survival rate of AG in RS was lower than that in CS. The RS had lower pH, available potassium content, and urease activity. Additionally, p-coumaric, p-hydroxybenzoic, vanillic, caffeic, and cinnamic acid levels were lower in RS than in CS, but salicylic acid levels showed the opposite pattern. RS had higher Rhodanobacter and lower Acidothermus, Sphingomonas relative abundances in bacterial community. It’s also found that many bacteria were substantially correlated with phenolic acids and soil physiochemical properties. Results indicate that even after 10-year crop rotation, the negative effects of prior continuous cropping of AG has not been eliminated. The growth of AG can be affected negatively with deterioration of soil physicochemical properties and with lower levels of phenolic acids which promote pathogen reproduction. Probiotics reduction also weighs. Moreover, biotic factors are interrelated with abiotic ones. Therefore, it can be inferred that the comprehensive change of soil properties is the main obstacle for continuous cropping.

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI. Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories. A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.

Microbes do not live in isolation but in microbial communities. The relevance of microbial communities is increasing due to the awareness about their biotechnological influences in a huge number of environmental, health and industrial processes. Hence, being able to control and engineer the output of both natural and synthetic communities would be of great interest. However, most of the available methods and biotechnological applications (both in vivo and in silico) have been developed in the context of isolated microbes. In vivo microbial consortia development, i.e. to reproduce the community life conditions in the wet-lab, is extremely difficult and expensive requiring of computational approaches to advance knowledge about microbial communities, mainly with descriptive modelling, and further with engineering modelling. In this review we provide a detailed compilation of available examples of engineered microbial communities as a launch pad for an exhaustive and historical revision of those computational methods devoted so far toward the better understanding, and rational engineering of natural and synthetic microbial communities.

Social network analysis is an essential means to uncover and examine infectious contact relations between individuals. This paper aims to investigate the spread of coronavirus disease (COVID-19) from international to the national level and find a few super spreaders which played a central role in the transmission of disease in India. Our network metrics calculated from 30 January to 6 April 2020 revealed that the maximum numbers of connections were established from Dubai (degree-144) and UK (degree-64). These two countries played a crucial role in diffusing the disease in Indian states. The eigenvector centrality of Dubai is found to be the highest, and this marked it the most influential node. However, based on the modularity class, we found that the different clusters were formed across Indian states which demonstrated the forming of a multi-layered social network structure.A significant increase in the confirmed cases was reported during the first lockdown 1.0 (22 March 2020) primarily attributed to a gathering in Delhi Religious Conference (DRC) known as Tabliqui Jamaat. As of 6 April 2020, the overall structure of the network has encompassed local transmission, and it was significantly seen in the states like Gujarat, Rajasthan, and Karnataka. An important conclusion drawn from the presented social network reveals that the COVID-19 spread till 6 April was mainly due to the local transmission across Indian states. The timely quarantine of infected cases in DRC has not led it to spread at the level of community transmission.

Outbreaks of coronavirus disease 2019 (COVID-19) have been recorded in different countries across the globe. The virus is highly contagious, hence early detection, isolation, and quarantine of infected patients will play an important role in containing the viral spread. Diagnosis in a mobile lab can aid to find infected patients in time. Here, we develop a field-deployable diagnostic workflow that can reliably detect COVID-19. Instruments used in this workflow could easily fit in a mobile cabin hospital and also be installed in the community. Different steps from sample inactivation to detection were optimized to find the fastest steps and portable instruments in detection of COVID-19. Each step was compared to that of the normal laboratory diagnosis set-up. From the results, our proposed workflow (80 min) was two times faster compared to that of the normal laboratory workflow (183 min) and a maximum of 32 samples could be detected at each run. Additionally, we showed that using 1% Rewocid WK-30 could inactivate the novel coronavirus directly without affecting the overall detection results. Comparison of our workflow using an in-house assay to that of a commercially acquired assay produced highly reliable results. From the 250 hospital samples tested, there was a high concordance 247/250 (98.8%) between the two assays. The in-house assay sensitivity and specificity were 116/116 (100%) and 131/134 (97.8%) compared to that of the commercial assay. Based on these results, we believe that our workflow is fast, reliable, adaptable and most importantly, field deployable.

A saprophytic soil fungus, Aspergillus flavus, produces aflatoxin (toxigenic strains) in the kernels of corn (Zea mays L.) and seeds of many other crops. Many strains of A. flavus do not produce toxigenic aflatoxin, and soil application of these atoxigenic strains is a suppressive control tactic to assist in controlling toxigenic conspecifics. Effects of atoxigenic A. flavus applications on honey bees (Apis mellifera L.) and other bees are unknown, and basic information on bee occurrences in corn fields treated with and without this biological pesticide is needed to inform integrated pest management in corn. Fields with atoxigenic A. flavus applications were compared to nearby control fields in three counties in corn production regions in eastern Texas. In each corn field, twenty bee bowl traps were deployed along four equal transects located between corn rows, with contents of the bowls (i.e. bees) retrieved after 24 hours. Eleven bee genera from four families were collected from corn fields, with only two honey bees collected and zero honey bees observed in transects. The sweat bee genus Agapostemon (primarily composed of the Texas-striped sweat bee A. texanus) was most abundant in corn fields (44% of the total number of bees collected) followed by long-horned bees (Melissodes spp., 24%). The southernmost county (i.e. San Patricio) produced over 80% of the total number of bees collected. Bee communities occurring in corn production fields with applications of atoxigenic A. flavus applications were not significantly different from nearby control fields. While little is known of bee resource use in corn production systems in Texas, the abundant yet variable bee communities across latitudes in this study suggests a need to investigate the influence of farming practices on bee resources in regional corn production systems.

The research objectives were (1) to examine the determinants of community empowerment towards the revegetation of Arjuna mount forests; (2) assessing the influence of geography and soil conditions on the revegetation of Arjuna mount forests; (3) reviewing the effect of Arjuna mount forest revegetation on the preservation of spring water sources and (4) formulate a model of community empowerment around the forest based on revegetation of Arjuna mount and preservation of the area around the springs. The method used was descriptive method. While based on the techniques and tools used to research, the author uses the survey method to obtain facts that occur in the research area, namely in the area around the spring of the Kedunglarangan and Gumandar Prigen watersheds, Pasuruan Regency. Data obtained in the field are then processed and analyzed using GIS, ArcView 3.3 and Google Earth programs. The study population covering the community around the forest and springs, who lived in three villages, namely Leduk, Jatiarjo and Dayurejo village, Pasuruan, East Java-Indonesia. Data obtained from respondents with questionnaires and analyzed using SEM (structural equation modeling), to find out a general description of respondents' responses about community participation in the preservation of Arjuna mount forest. The results showed that (1) human resources, economic, social, local institutional factors, facilities and infrastructure had a very significant effect on forest revegetation; (2) geography and land variables have a significant effect on forest revegetation; (3) forest revegetation variables have a significant effect on the preservation of springs; and (4) The development of human resources, economic, local institutional, facilities and infrastructure in the community around the forest was a relevant model for the success of forest revegetation and the preservation of springs in Arjuna mount.

In the U.S. 44% of low-income households struggle to pay their utility bills, affecting their ability to afford necessities such as food and health expenses. Several government and utility funded energy efficiency programs exist to assist those experiencing energy insecurity. In Salt Lake City, Utah, there is a high demand for, but low availability of, energy efficiency services in underserved neighborhoods creating an opportunity for creative community-based programs to fill this inherent gap. This pilot project, involving the exchanging of LED bulbs in Salt Lake City, highlights the development of a community-based energy efficiency program that aims to bring energy savings to a uniquely targeted portion of the city and determines its feasibility in addressing energy insecurity at a larger scale. Through the 8-month project duration, 1,432 bulbs were exchanged at 23 events reaching 181 households in low-income areas. Through a year of use, these bulbs are estimated to save residents approximately 18,219 USD in electricity bills and reduce CO2 emissions from power plants by 122.23 metric tons, in addition to a savings of 4,400 USD in social cost of carbon as defined by the U.S. Environmental Protection Agency. Since this pilot reached less than 1% of households, we extrapolated a reach of 2%, 5%, and 7.5% and found substantial potential decreases in power plant emissions and financial savings. As this project is ongoing and being expanded, we discuss relevant findings that will help shape future community-based models so that they are appropriately deployed and more effective in alleviating local energy insecurity.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

This paper presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes too long for systems engineers and architects to develop a new system from scratch, particularly new space-based systems derived from the existing space systems architectures. A pattern language is a holistic view of reusable logical model artifacts; many are interdisciplinary and introductory, if at all. The results are mostly a combination of the application-specific logical solution, which further results in the best possible overall solution. The main benefit of the pattern language is reducing the time and validation required to generate a new space-based system architecture; this approach will develop top-level requirements in the initial phase of the system development. The rationale of the methodology proposed by the paper is as follows, collect, and decompose published literature and other open-source information available on space system architectures and system models; develop SysML models for systems, subsystems, products, assembly, subassembly level, and mission-specific requirements using CAMEO SysML software. Arrange these patterns to develop a functional ontology and construct a logical architecture pattern library. This approach created, updated, and managed SysML pattern language, which evaluated the expedited new model construction. Again, our objective is to develop a logical pattern language using public domain information and evaluate patterns by constructing a new space mission concept—for example, planetary surface habitat.