A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

Type II diabetes is a major coronavirus disease comorbidity that can be prevented through a diet. Capsicum annuum (chili) consumption is often suggested, and using functional food cultivars is the most effective strategy. However, the general combining ability (GCA), specific combining ability (SCA), and heterotic effect of functional biochemicals (polyphenol content, antioxidant activities, and α-glucosidase inhibitory compounds) remain poorly known in C. annuum. This study aimed to estimate those parameters in C. annuum by using five different genotypes and their hybrid com-binations based on growth characteristics, yield, yield components, and fruit functional biochem-icals. The F1 and F1R progenies were obtained from crosses in a greenhouse and full diallel design. Each parent used in this study had a GCA advantage for each character. The hybrid combination of 074 x 005 and 435 x 367 displayed the best yield results. However, the results indicated the opposite regarding α-glucosidase inhibitory. The heterotic effect of functional biochemicals was observed for traits related to genotypes, polyphenol content, antioxidant activity, α-glucosidase inhibitory compounds, and similar properties related to yield and yield components, indicating their hybrid use in chili production.

Artificial intelligence harbors significant, unrealized promise for educational reformers, adding benefit to low-resource settings, particularly in the Least Developed Nations where access to quality learning remains most acute. The purpose of the introduction is to summarize how AI can reduce the educational gap and increase the effectiveness of education in poor resource environments. This paper, in a critical way, reviews the existing literature on the integration of AI in the education systems of developing countries concerning the benefits and challenges. This is achieved by reviewing a cross-section of literature from MDPI, Taylor & Francis, Springer, Wiley Online Library, Science Direct, and IEEE Xplore databases. Much emphasis within the selection criteria is given to those studies that had either empirically or theoretically demonstrated the influence of AI on educational practices within developing countries. Key findings: AI has the potential to greatly improve learning outcomes; it somewhat increases access to education but bodes well for the personalization agenda. These, however, will be surmounted upon adoption if it is to be effective: there exist huge challenges such as limited access to the internet, infrastructural deficit, and socio-cultural barriers. This study can, therefore, give inference to the fact that AI can change education and further enhance new equity in developing countries if there are corresponding proper policy interventions, building the capacity of teachers, and involvement of the community. More research is required to inform the long-term implications of AI and new uses being developed for AI applications in many diverse educational contexts, on which to base strategies for more equitable and more inclusive global education.

This study examines the geopolitical dynamics of water conflicts in Central Asia, with a specific focus on Uzbekistan and its neighbors. Drawing upon a mixed methods approach within the framework of political geography, the research investigates the interactions among regional nations and the implications of population growth, agricultural industrialization, and economic competition, which heavily rely on shared irrigation systems and watersheds. The study establishes clear research objectives aimed at evaluating the water crisis in the region, addressing challenges in water management, and analyzing contentious issues. By uncovering historical and contemporary factors influencing water conflicts, regional power dynamics, and the complexities of water governance, the study highlights the potential for conflict, regional instability, and disruptions to peace resulting from population growth and economic competition within shared watersheds. The study concludes by providing recommendations to enhance regional cooperation, promote sustainable water management, and foster peacebuilding efforts. These recommendations draw on “soft power theory” and advocate for collaborative approaches. The findings of this research offer valuable insights for policymakers, researchers, and stakeholders involved in water management and regional cooperation amid evolving geopolitical challenges. By contributing to informed decision-making and promoting strategies for sustainable development in the region, this study aims to support efforts toward effective water resource management and long-term stability in Central Asia. The implications of this research extend beyond the immediate region, serving as a reference for other areas facing similar water-related conflicts and providing a foundation for future studies in the field of geography.

The efficacy and cost-effectivness of Multigene Panel Next-Generation Sequencing (NGS) in directing patients towards genomically matched therapies remains uncertain. This study investigated metastatic colorectal cancer (mCRC) patients who underwent NGS analysis on formalin-fixed paraffin-embedded tumor samples. Data from 179 patients were analyzed, revealing no mutations in 39 patients (21.8%), one mutation in 83 patients (46.4%), and two or more mutations in 57 patients (31.8%). KRAS mutations were found in 87 patients (48.6%), including KRAS G12C mutations in 5 patients (2.8%), PIK3CA mutations in 40 patients (22.4%), BRAF mutations in 26 patients (14.5%). Less common mutations were identified: ERBB2 in 5 patients (2.8%) and SMO in 4 patients (2.2%). Additionally, MAP2K1, CTNNB1, and MYC were mutated in 3 patients (2.4%). Two mutations (1.1%) were observed in ERBB3, RAF1, MTOR, JAK1, and FGFR2. No significant survival differences were observed based on mutation numbers. 40% of patients had druggable molecular alterations, but only 1.1% received genomically guided treatment, suggesting limited application in standard practice. Despite this, expanded gene panel testing can identify actionable mutations, aiding personalized treatment strategies in metastatic CRC, although current eligibility for biomarker-guided trials remains limited.

Background Several vaccines have been approved for prevention of the coronavirus disease discovered on 31st December in Wuhan, China. Pakistan procured vaccines from various countries. However, the lack of knowledge and reluctance of the general population to embrace the use of the vaccines are considered to be the major determinants of the slow vaccination rate. Hence, it is necessary to evaluate the willingness of the general population about their perception of the COVID-19 vaccination. Methods A cross sectional survey based on a self-structured questionnaire comprising 18 questions was conducted (from April 21-June 21) on 400 Pakistani participants to evaluate their knowledge, attitude and perception towards the COVID-19 vaccination. Chi-square independent t-test and one-way Anova including a multiple step wise linear regression were used to draw conclusions about the results. P< 0.05 was considered significant. Results A total of 400 participants responded in the KAP survey of which 46.5% were female and 53.5% were male. The mean age of participants was 36.08 years. This survey showed a poor knowledge (50.5%), a fair attitude (75.1%) and a poor perception (58.1%) towards the COVID-19 vaccination. Higher mean knowledge and attitude scores was reported in the age group 21-40, females and unmarried urban citizens. Regression analysis showed that age, education, residence and employment status influenced the knowledge and perception score to a considerable extent. Conclusions The findings reflect an inadequate knowledge and perception on the one hand but a better attitude towards the COVID-19 vaccination. This KAP survey will help in better understanding the opinion of the general population towards vaccination and will be useful for policy makers and health care authorities aiming to increase the vaccination rate.

This study demonstrates the possibility of growing green walls in normal commercial building environments with lighting designed primarily for aesthetic reasons, rather than the promotion of plant growth. Lighting is a key resource required for the growth and maintenance of robust green walls within interior environments. The study evaluated the appearance and growth of green walls with electric lighting used primarily for aesthetic reasons. Three identical green walls with six different plants were illuminated using three different white LED light sources for a period of five months. Plant health was monitored and documented in terms of growth patterns. One hundred and six subjects appraised the appearance of the walls using questionnaires. Findings of this study indicate that it is indeed possible to grow and maintain green walls in normal commercial building environments with lighting designed for aesthetic reasons. Further, it was observed that the selection of the correct plant species for the green walls is important to ensure plant health.

The chemistry and electrochemistry basic fields have been active since the last two decades of the past century studying how surface modification of electrodes by coating with conductive films enhances their activity and performance. In the light of the development of alternative sustainable ways of energy storage and carbon dioxide conversion by electrochemical processes, these research studies have jumped in the 21st century to more applied fields such as chemical engineering, energy and environmental science and engineering. The huge amount of literature on experimental works dealing with the development of CO2 electroreduction processes addresses electrocatalyst development. Membranes can help understanding and controlling the mass transport limitations of current electrodes and reactors designs. The present bibliometric review addresses the papers published in the 21st century regarding membrane coated electrodes and electrocatalysts to enhance electrochemical reactor performance and viability with a special focus on the urgent issue of carbon dioxide capture and utilization.

Excess consumption of a high-fat diet has been identified as one of the contributing factors for obesity. Women are at higher risk for adult obesity than men, predisposing them to a different set of detrimental disease conditions. Furthermore, aging studies show that physiological decline also has a serious impact on changes in the endocrine properties of white adipose tissue. Howev-er, there is still relatively little known about the factors associated with obesity and aging and their compounding impact on women’s health. To investigate adipocytokine secretion profile changes, female C3H/HeJ mice were induced to obesity through long-term consumption of a high-fat diet. Weight gain measurements and Echo MRI analysis of fat composition showed in-creases were a due solely to the high fat content in the diet. Adipocytokine secretions were ana-lyzed in media conditioned from harvested visceral fat tissue that was organ cultured ex-vivo. Adipocytokine analysis across diets and age showed significant increases in Adiponectin and Leptin secretion in high-fat diets, accelerated increases in Resistin secretion with high-fat diets. Aging induced increased secretion of Lipcalin-2, Pentraxin-3 and Serpin E1, MCP-1 and ICAM-1, regardless of diet. Furthermore, comparisons of organoid cultured protein secretions and flash-frozen tissue samples differed greatly, suggesting the WAT organoid cultures may yield information more reflective of in situ conditions. Taken together, our results show that diet and aging significantly impact secretions from adipose tissue, that may contribute to women’s health issues.

Previous research on consumer perceptions of salmon has often neglected Indigenous rights within the Canadian salmon industry. This study acknowledges the decline in salmon farm licenses in British Columbia and aims to achieve three main objectives: (1) gain a comprehensive understanding of four consumer perceptions—environmental sustainability, economic considerations, Indigenous rights, and price increase—across diverse consumer profiles through a cross-national online survey; (2) identify factors influencing these four consumer perceptions; and (3) assess the impact of these perceptions, along with socio-demographic variables and consumer motivations, on seven purchasing behaviors related to Canadian salmon products. Data analysis employs the graded response model and cumulative link models. The results illustrate how consumer profiles influence the four perceptions and their key determinants. Additionally, the study quantifies the impact of environmental sustainability, economic considerations, Indigenous rights, and a price increase on consumer purchasing behaviors.

The COVID-19 pandemic halted progress in global vaccine coverage and disrupted routine childhood vaccination practices worldwide. While there is ample evidence of vaccination declines experienced during the pandemic, it is less clear how low-income countries were affected. We executed a systematic review to synthesize the current literature on impacts to routine childhood vaccinations in low-income countries from January 1, 2020 to February 8, 2023. We collected data using an extraction form on Covidence and assessed the quality of studies included in the review using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Effect estimates for changes in vaccination during the pandemic were reported and summarized. Factors that influenced changes were grouped into descriptive themes. Thirteen studies, encompassing 18 low-income countries and evaluating 15 vaccines at varying doses, were included in the final review. We found that routine childhood vaccinations during the COVID-19 pandemic varied considerably by vaccine type, location, and phase of the pandemic. Nine different themes were identified as factors that influenced changes in vaccination. Documenting past experiences and lessons learned is crucial for informing preparedness efforts in anticipation of future public health emergencies. Failure to effectively address these things in the next public health emergency could result in a recurrence of declining routine childhood vaccinations.

In order to reveal the impact of boundary-spanning activities of cooperative innovation teams on team innovation performance, this paper takes the panel data of 71 cooperative innovation teams from January to November 2022 as the research sample. It introduces intermediary variables (teamwork crafting and individual work crafting) to analyze the impact mechanism of boundary-spanning activities of teams on innovation performance, 71 teams were divided into 41 experimental groups and 30 control groups, and a quasi-natural experiment was conducted on the innovation performance of team boundary-spanning activities using the Double Difference Model (DID).Research has shown that boundary-spanning activities of collaborative innovation teams can promote team innovation performance. Team job crafting has a mediating effect on team innovation performance in boundary-spanning activities of collaborative innovation teams. Team job crafting and individual job crafting mediate between the boundary-spanning activities of collaborative innovation teams and team innovation performance. Further analysis using the double difference model found that compared to teams without boundary-spanning activities, teams with boundary-spanning activities can directly improve team innovation performance. When team reflection is vital, and task interdependence is high, it will promote team innovation performance. This research enriches the research on the effects of boundary-spanning activities of collaborative innovation teams, explores solutions based on quasi-nature, and provides a reference for improving the team innovation performance of collaborative innovation teams.

The paper is based on the research project P2Endure funded within the H2020-EE-2016-2017 call. The main aim of the research is to propose innovative solutions for deep renovation that are applicable and replicable for the widest range of building typologies, from public to residential and historic buildings. In this paper, the methodology adopted to perform the energy analyses and to verify the achievement of the expected targets in terms of savings is defined, based on a range of real case studies. The main aim is to promote a guideline for designers showing specific instruments to perform towards the attainment of the 60% threshold of the energy savings giving advice on how to further improve the energy performance of the buildings, basing on the overview of the energy analyses run for similar cases.

Within financial technology, Blockchain is a topic that is receiving a lot of attention and discussion. There has been a meteoric rise in engagement and recent studies on this technology. The mainframe, personal computers, the Internet, and mobile applications follow the breakthrough in the history of computing. In qualitative research, the investigation was synthesized utilizing the documentary approach, including content analysis for a review article. The results reveal that Blockchain Technology is getting a foothold not only in the financial industries but is also likely to disrupt other areas like healthcare, real estate, the legal industry, security, government, education, rentals and ride-sharing, charities and aid organizations, and politics. Currently, several projects are researching the possibility of implementing the Blockchain as a structure to support them; as a result, we will soon see that it truly becomes the technology of the future. Furthermore, qualitative interviews or focus group discussions could give more insight into details. In addition, a quantitative approach for further research could provide a generalized explanation.

In this work, the first phytochemical analysis ever performed on the whole cone of Araucaria bidwillii Hook is presented. This was carried out by means of column chromatography, NMR spectroscopy and MS spectrometry on the female cone, evidencing the presence of forty metabolites, thirty-three of which are primary metabolites whilst the remaining are secondary metabolites. A chemophenetic evaluation was also performed reporting seven new compounds for the species. These results are important since they provide more information on the phytochemistry of this species and also underline the need to study more of its populations.

Background and Objectives: The existing recommendations for female pelvic magnetic susceptibility weighted imaging MRI (FP-SWI) have not been optimized to capture the disease characteristics of the female pelvis. Therefore, it is imperative to explore acquisition parameters before conducting larger-scale studies. To establish optimized flip angle (FA) for acquiring FP-SWI to improve the image quality of female pelvic lesion sites. Materials and Methods: To evaluate signal quality and lesion conspicuity, regions of interest (ROIs) were manually drawn within the lesion on SWI sequences. Signal intensity was measured within the ROIs. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for SWI sequences acquired at three FA: 10°, 15°, and 20°. Finally, a qualitative assessment of anatomical detail visualization, geometric distortion extent, artifacts, and lesion clarity was performed using a four-point scale. The Wilcoxon rank sum test was employed to compare the qualitative evaluation parameters. The intra-class correlation coefficient (ICC) was calculated to determine the consistency of subjective ratings between two observers. Results: FA=10° showed significantly higher SNR than FA=15° and 20° in most anatomical regions (P&lt;0.05), while the CNR was significantly higher than FA=15° and 20° (P&lt;0.05). Compared with FA=15° and 20°, the image quality of FA=10° was significantly higher, with less distortion and ghosting, and better image contrast (P&lt;0.05). Lesions with FA=10° exhibited higher clarity compared to FA=15° and 20° (P&lt;0.05). Conclusions: The image quality of FP-SWI images at FA=10° exceeds that at FA=15° and 20°. We recommend that when conducting SWI examination of the female pelvic cavity, the FA value should be set at 10° to stabilize the SWI sequence and improve image quality.

Background: Hemangiomas are an aberrant proliferation of blood vessels and are the most frequent benign pediatric soft tissue tumors. Despite their being common, genital localisation is rare. This study aimed to assist doctors in the diagnosis, management, and treatment of paediatric vulvovaginal hemangiomas by conducting a review of the literature and also gathering some cases of patients from our clinic.Methods: We conducted a literature review including papers published between October 2021 and April 2023, and we described the cases of 3 patients who referred to our clinic with vulvovaginal hemangioma.Results: While most of hemangiomas are usually indolent and resolve with time, in some cases, especially vaginal and cervical ones, they might present with severe symptoms like heavy bleeding and need further instrumental investigation for diagnosis, like CT or MRI. As for the treatment many options are available with medical therapy or expectant management being the first choice.Conclusions: Vulvovaginal pediatric hemangiomas are rare and, so, more research should be done to how to detect and manage this lesions, especially the symptomatic and the psychologically impacting ones. For the time being treatment should personalized based also on the patient situation and clinician’s expertise.

The Encyrtidae encompass large group of insects widely used in the biocontrol programs of scale insects (Hemiptera: Coccoidea). Here we investigated antennal sensory system organization of Anagyrus vladimiri females, a species widely used as a biocontrol agent of Planococcus spp. and Pseudococcus spp. Ultrastructural investigations with scanning (SEM) and transmission (TEM) electron microscopy revealed filiform antennae made of nine segments. The scape was enlarged and paddle-like, compared to the other antennomeres. The club was mono-segmented and housed the highest number of sensilla. On the antennae, eight morphologically different types of sensilla were recorded; sensilla trichoidea I, trichoidea II, chaetica I, chaetica II, grooved peg sensilla, campaniform sensilla, multiporous plate sensilla, multiporous basiconic sensilla. The sensilla belonged to the major types sensilla types (olfactory, gustatory, thermos-hygroreceptor). The sensilla trichoidea I and multiporous plate sensilla were the most abundant types. The potential functional role of each sensillum was discussed.

Additive manufacturing (AM) or 3D printing of metals is gaining popularity due to its flexibility in fabricating parts with highly complex designs, as well as simplifying manufacturing steps and optimizing process times. In this investigation, 17-4 PH stainless steel was additively manufactured using Laser-Powder Bed Fusion (L-PBF), followed by functionalization through a DUPLEX treatment. This treatment involved a plasma-assisted nitriding process, followed by deposition of an arc-PVD c-Al0.7Cr0.3N hard coating. The microstructural modifications resulting from plasma nitriding (such as the formation of Fe2,3N and Fe4N, and the αN or expanded martensite phases), and the surface improvements with the c-Al0.7Cr0.3N coating on the 3D-printed 17-4 PH steel, are evaluated in comparison to conventionally manufactured 17-4 PH steel. These microstructural characteristics are correlated with the mechanical response of the treated surfaces. As a result of the plasma nitriding process, the hardness of the 3D-printed 17-4 PH SS increased by approximately 260%. The wear, measured through dynamic and static scratch-testing, was reduced by approximately 31%. This improvement was attributed to the modification of adhesive failure mechanisms, leading to a reduction in wear volume, improved coating adhesion, and enhanced scratch resistance.

The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations, reflections, and the anti-symmetry for the phase swapping have been considered to reduce the number of input parameters. The neural network, establishing a correlation between curvature and height function values, can efficiently predict the local interface curvature. We compare the trained neural network to the standard Height Function method to assess its performance and robustness.

The sexual health needs of female immigrant adolescents in Canada have been largely unmet and have increased in magnitude over the last few years. Evidence suggests that immigrant adolescents lack sexual and reproductive health knowledge and use fewer sexual health-related services and sex education resources than do non-immigrant youth. In Pakistani immigrant adolescents, this difference appears to be associated with socio-cultural and religious practices. This paper aims to describe how first-or-second generation Pakistani-descent female adolescents, living in Canada, describe their perspectives on developing sexuality and well-being. In addition, this paper explicates female adolescents’ perceptions of their needs to support their sexuality while going through the adolescence stage. Using a qualitative interpretive descriptive design, individual interviews were conducted in combination with drawing timelines. A purposive sample of 21 female adolescents who were first- or second-generation Pakistani-descent was obtained. A thematic analysis approach was used for data analysis. Findings suggest that immigrant female adolescents encounter mental health concerns as a result of confusing messages they received from their parents related to sexuality. Also, discrimination, exclusion from sex education classes, and lack of knowledge on sexual health can result in social exclusion, avoidance of health care, and poor mental health outcomes such as depression and anxiety. These participants related experiences are potentially contributed by the lack of communication with parents about sexuality and lack of health care providers who can understand and speak to their needs and realities as immigrant individuals. Female adolescents expressed their need to break the silence around the topic of sexuality, to have a non-judgemental and blame-free attitude from adults, and for open, honest, and stigma-free conversations. This study provides preliminary qualitative evidence of the interplay between experiences of developing sexuality and overall well-being, particularly psychological wellbeing in female adolescent immigrants. It is crucial to involve, listen to, and incorporate adolescent girls’ voices when planning and implementing interventions to support healthy sexuality among immigrant adolescents.

Discovery of novel variants from data derived from local population provides confident targets for developing biomarkers for personalized medicine. Biomarker discovery would be an important tool in advancing and utilizing the concept of precision and personalized medicine in the clinic. We identified the need to generate high quality sequencing data from local population and understand the pattern of occurrence of variants in colorectal cancer patients. In this report, we used archived samples from Saudi Arabia and used Ampliseq Comprehensive Cancer panel to identify novel somatic variants. We report a comprehensive analysis of next generation sequencing results with a coverage of >300X. We identified 466 novel variants which were previously unreported in COSMIC and ICGC databases. We analyzed the genes associated with these variants in terms of their frequency of occurrence, probable pathogenicity and clinicopathological features. Among pathogenic somatic variants, 174 were identified for the first time in large intestine. APC, RET and EGFR genes were most frequently mutated. Higher number of variants were identified in left colon. Occurrence of variants in ERBB2 was significantly correlated with those of EGFR and ATR genes. Network analyses of the identified genes provide functional perspective of the identified genes and suggest affected pathways and probable biomarker candidates. This report lays the ground work for biomarker discovery and identification of driver gene mutations in local population.

This research aims to study the ultimate limit state (ULS) behaviour of stiffened panel under longitudinal compression by non-linear finite element method (NLFEM). There are different types of stiffeners being used in shipbuilding i.e. T-bar, flat-bar and angle-bar. However, this research focuses on the ultimate compressive strength behaviour of flat-bar stiffened panel. A total of 420 of reliable scenarios of flat-bar stiffened panel are selected for numerical simulation by ANSYS NLFEM. The ultimate strength behaviours obtained were used as data for the development of closed form shape empirical formulation. Recently, Kim et al. [1] proposed for advanced empirical formulation for T-bar stiffened panel and the applicability of the proposed formulation to flat-bar stiffened panel will be confirmed by this study. The accuracy of the empirical formulation obtained for flat-bar stiffened panel has been validated by FE simulation results of statistical analysis (R2 = 0.9435). The outcome obtained will be useful for ship structural designers in predicting the ultimate strength performance of flat-bar type stiffened panel under longitudinal compression.

A new lateral force-resisting wall panel is presented for applications in mid-rise prefabricated lightweight steel construction in seismic prone regions. This panelised system is composed of a hot-rolled frame and cold-formed studs and tracks, which works as a lateral force resisting system in lightweight steel framing. The proposed hybrid panel system exhibits proper ductility and energy dissipation behaviour. The hysteretic responses of full-scale panel experiments demonstrate that the system can safely resist high cyclic loads. The hot-rolled steel part, on the other hand, can significantly improve the initial lateral stiffness of the total system that will control the maximum allowable drift of multi-story buildings. Finally, in a case study, by applying the proposed system to the design of a mid-rise building in a high seismic area, the performance of the hybrid panels, are compared with those of fully hot-rolled systems (moment resisting frames) and fully cold formed systems. The findings indicate that applying hybrid panels will result in lower weights and better performance in seismic prone regions.

It is vital to analyze ship collision risk for preventing collisions and improving safety at sea. This paper takes Ningbo-Zhoushan Port, a typical complex navigable water, as the research object, propose a probabilistic conflict detection method to estimate the potential collision risk by using a dynamic domain model driven by AIS data. Combined with the algorithm of fast modularity optimization and spectral clustering, a ship group extraction method from the perspective of water navigation management was proposed. Aiming at ship traffic characteristics in complex navigable waters, conduct dynamic calculation of ship collision risk from individual, regional and local multi-scale perspectives. The results show that the proposed method can detect the collision risk in a timely, reliable and effective manner under complex dynamic conditions. As such, they provide valuable insights for ship collision risk prediction and the development of risk mitigation measures.

Efficient beam steerable high-gain antennas enable high-speed data rates over long-distance networks, including wireless backhaul, satellite communications (SATCOM), and SATCOM On-the-Move. These characteristics are essential for advancing contemporary wireless communication networks, particularly within 5G and beyond. Various beam-steering solutions have been proposed in the literature, with passive beam-steering mechanisms employing planar metasurfaces emerging as cost-effective, power-efficient, and compact options. These attributes make them well-suited for use in confined spaces, large-scale production and widespread distribution to meet the demands of the mass market. Utilizing a dual-band antenna terminal setup is often advantageous for full duplex communication in wireless systems. Therefore, this article presents a comprehensive review of the dual-band beam steering techniques for enabling full-duplex communication in modern wireless systems, highlighting their design methodologies, scanning mechanisms, physical characteristics, and constraints. Despite the advantages of planar metasurface-based beam steering solutions, literature on dual-band beam steering antennas supporting full duplex communication is limited. This review article identifies research gaps and outlines future directions for developing economically feasible passive dual-band beam-steering solutions for mass deployment.

Verticillium wilt is a soil borne disease caused by distinct vegetative compatibility groups (VCG) of the fungus Verticillium dahliae. Two V. dahliae isolates were recovered from symptomatic cot-ton plants in Australian cotton fields and were assigned to two distinct vegetative compatibility groups (VCGs). One corresponding to the defoliating (VCG 1A) and the other non-defoliating (VCG 2A) pathotypes of Verticillium, have been transformed with Red and Green Fluorescent protein genes, respectively. The transformants maintained their ability to infect cotton and both strains were observed to colonise the xylem vessels of cotton plants. Moreover, we observed that the cotton V. dahliae strains could also infect some sampled non-Gossypium species found in the Australian landscape. The fluorescently labelled strains of V. dahliae will allow us to gain a thorough understanding of the infection processes of this important pathogen.

Riparian zones represent ecotones between terrestrial and aquatic ecosystems and are of utmost importance to biodiversity and ecosystem functions. Modelling/mapping of these valuable and fragile areas is needed for an improved ecosystem management, based on an accounting of changes and on monitoring of their functioning in time. In Europe, the main legislative driver behind this goal is the European Commission’s Biodiversity Strategy to 2020, on one hand aiming at reducing biodiversity loss, on the other hand enhancing ecosystem services by 2020, and restoring them as far as feasible. A model, based on Earth Observation data, including Digital Elevation Models, hydrological, soil, land cover/land use data, and vegetation indices is employed in a multi-modular and stratified approach, based on fuzzy logic and object based image analysis, to delineate potential, observed and actual riparian zones. The approach is designed in an open modular way, allowing future modifications and repeatability. The results represent a first step of a future monitoring and assessment campaign for European riparian zones and their implications on biodiversity and on ecosystem functions and services. Considering the complexity and the enormous extent of the area, covering 39 European countries, including Turkey, the level of detail is unprecedented. Depending on the accounting modus, 0.95%–1.19% of the study area can be attributed as actual riparian area (considering Strahler’s stream orders 3-8, based on the Copernicus EU-Hydro dataset), corresponding to 55,558–69.128 km². Similarly depending on the accounting approach, the potential riparian zones are accounted for about 3-5 times larger. Land cover/land use in detected riparian areas was mainly of semi-natural characteristics, while the potential riparian areas are predominately covered by agriculture, followed by semi-natural and urban areas.

In the paper, the author takes stock of the conceptual reflection and empirical studies described in the current scientific literature on responsible innovation in the context of the emergence of Responsible Research and Innovation (RRI) concept. RRI has been promoted in the European Union as a part of the Europe 2020 strategy with the objective of making research and innovation more sustainable and inclusive. As more than half of the EU’s firms declare conducting innovation activities RRI problematic becomes more relevant than ever. There remain many open questions, unresolved dilemmas and empirical white spots that call for more research in this field. This paper’s main focus is the problem of RRI acceptance as a global framework for responsible innovation and the scarcity of suitable instruments that may help industry understand and adopt this concept. The main contribution of this paper are: the critical analysis of the RRI concept and its implications for industry, proposing a concept of RRI index for innovating enterprises.

The extent of access to energy of displaced populations in settlements and camps in Africa is largely 14 unknown. A multitude of contextual factors, such as the location and the characteristics of housing, 15 the legal status, the socio-cultural background, and the availability of humanitarian and public ser- 16 vices impact the living conditions and the needed energy services. Limitations in accessing energy 17 services have direct, multilayered, and far-reaching implications, including impacts on health, nu- 18 trition, education, protection, and livelihood. The objective of this article is to contribute to a more 19 comprehensive overview of the current state of energy access in displacement contexts on the Afri- 20 can continent by identifying and utilizing existing data. After a screening of the vast and various 21 available information, setting up of a database, consolidating the gathered data as well as assessing 22 the quality through a quality assessment method, the currently available information is visualized 23 and discussed. Considerable differences in the access to electricity for displaced persons across the 24 countries are found. For both electricity and clean cooking, the availability for displaced persons 25 ranges from nearly no access at all up to an access rate of 100%. More strikingly, the results also 26 show that besides South Africa and countries in the Maghreb region, the access to both clean cook- 27 ing and electricity for displaced persons is very low. At the same time, the fragmented data availa- 28 bility, the poor data quality, and the questionable expediency of available data allows neither solid 29 theoretical conclusions nor planning of effective practical implementation measures. Novel inter- 30 disciplinary research, conceptual frameworks and indicators are needed for the purpose of compa- 31 rability and consistency. Future research has the potential to more comprehensively capture the 32 current state of access to energy in displacements contexts and in a next step to examine how energy 33 is interwoven in the lives of displaced people to derive a set of more detailed context-sensitive en- 34 ergy indicators. It is essential that the concerned persons, the displaced persons themselves are in- 35 cluded in the research in a meaningful way.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

Abstract This study investigates the assessment literacy, perceived practice, and observed practice of CBA practices among TVET teachers and assessors in private TVET colleges in South Gonder Zone, Ethiopia. The participants included 49 teachers and 14 assessors. Data collection utilized a modified four-option multiple-choice test, the Teacher Assessment Competency Inventory Questionnaire, a five-point Likert scale for perceived and observed practices, a semi-structured observation checklist, and interviews. Descriptive analysis, including mean scores, standard deviation, one-sample t-test, and proportions (%), was employed to analyze the data. Pearson correlation test was used to explore relationships between teacher assessment test scores, observed practices, and perceived practices. Findings revealed that private TVET teachers in the Amhara National Regional State lacked competence in assessing students' competence, falling short of overall and specific standards in both the assessment competence test and real classroom practice. Despite this, teachers modestly perceived their assessment practices across competency domains. Consequently, there were significant differences between teachers' actual competence and practice in educational assessments and their self-perceived practice. Qualitative approaches, employing theme analysis of assessment practice from semi-structured observations and interviews, highlighted poor performance among teachers on most assessment criteria. A small percentage demonstrated competence in specific concepts. Overall, the majority of teachers exhibited low levels of assessment literacy, suggesting a need for professional support in educational assessment approaches to enhance competence among TVET teachers.

COVID-19 pandemic in first seven months has led to more than 15 million confirmed infected cases and 600,000 deaths. SARS-CoV-2, the causative agent for COVID-19 has proved a great challenge for its ability to spread in asymptomatic stages and a diverse disease spectrum it has generated. This has created a challenge of unimaginable magnitude not only affecting human health and life but also potentially generating a long-lasting socioeconomic impact. Both medical sciences and biomedical research have also been challenged consequently leading to a large number of clinical trials and vaccine initiatives. While known proteins of pathobiological importance are targets for these therapeutic approaches, it is imperative to explore other factors of viral significance. Accessory proteins are one such trait that have diverse roles in coronavirus pathobiology. Here we analyze certain genomic characteristics of SARS-CoV-2 accessory protein ORF8, predict upon its protein features and review current available literature regarding its function. We have also undertaken review of ORF8 homolog ORF8ab from SARS-CoV with a purpose of developing holistic understanding of these proteins for reason that coronaviruses have been infecting humans repeatedly and might continue to do so. Despite low nucleotide and protein identity and differentiating genome level characteristics, there appears to be significant structural integrity and functional proximity between these proteins pointing towards their high significance. There is further need for comprehensive genomics and structural-functional studies to lead towards definitive conclusions regarding their criticality and that can eventually define their relevance to therapeutics development.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

Machine Learning is increasingly and ubiquitously being used in the medical domain. Evaluation metrics like accuracy, precision, and recall may indicate the performance of the models but not necessarily the reliability of their outcomes. This paper assesses the effectiveness of a number of machine learning algorithms applied to an important dataset in the medical domain, specifically, mental health, by employing explainability methodologies. Using multiple machine learning algorithms and model explainability techniques, the project provides insights into the model workings to help determine the reliability of the machine learning algorithm predictions. The results are not intuitive. It was found that the models were focusing significantly on less relevant features and at times, unsound ranking of the features to make the predictions. The paper therefore argues that it is important for research in applied machine learning to provide insights into explainability of the models in addition to other performance metrics like accuracy. This is particularly important for applications in critical domains such as healthcare. A future direction is to investigate methods to quantify the effectiveness of the machine learning models in terms of the insights from their explainability.

The Asian hornet, Vespa velutina, is an invasive species that has not only expanded its range in Asia but also invaded European countries, incurring significant costs on the local honey and bee industry. This phylogeographic study aims to trace the evolutionary trajectory of V. velutina and its close relatives, aiming to identify features that characterize an invasive species. The last successful invasion of Vespa velutina into France occurred in 2002.40, and into South Korea in 2002.77, estimated by fitting a logistic equation to the number of observations over time. The instantaneous rate of increase is 1.3667 for V. velutina in France and 0.2812 in South Korea, consistent with the interpretation of little competition in France and strong competition from local hornet species in South Korea. The invasive potential of two sister lineages can be compared by their distribution area when proper statistical adjustments are made to account for differences in sample size. V. velutina has a greater invasive potential than its sister lineage. The ancestor of V. velutina split into two lineages, one found in Indonesia/Malaysia and the other colonizing the Asian continent. The second lineage split into a sedentary clade inhabiting Pakistan and India, and an invasive lineage colonizing much of Southeast Asia. This latter lineage gave rise to subspecies V. v. nigrithorax that invaded France, South Korea, and Japan. My software PGT, which generates geophylogenies and computes geographic areas for individual taxa, is useful for understanding biogeography in general and invasive species in particular. I discussed the conceptual formulation of an index of invasiveness for comparison between sister lineages.

The isolation and characterization of chitin (CHI) obtained from Vespa velutina (CHIVV) is described. Moreover, a trapping procedure is presented to selectively catch the invasive species. The chitin contents of dry Vespa velutina was observed to be 11.7 %. The physicochemical properties of CHIVV was characterized by Fourier transform infrared spectroscopy (FTIR), solid-state NMR (ssNMR), elemental analysis (EA), scanning electron microscopy (SEM) and thermogravimetric analysis (TG). Obtained CHIVV is close to pure (43, 47% C, 6.94% H, and 6.85% N) and full acetylated with a value of 95.44%. Also, lifetime and kinetic parameters such as activation E and the frequency factor A using model-free and model-fitting methods, were determined. For CHIVV the solid state mechanism that follows the thermodegradation is of type F2 (Random nucleation around two nuclei). Vespa velutina chitin should not be used at temperatures above 60ºC, since its half-life would be only one year, and from an industrial point of view it would not be profitable. Based on certain factors such as the current and probable continued abundance of Vespa velutina and the quality of the product obtained, the invasive Asian hornet is a promising alternative source of chitin.

We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensemble (NODE) and Conditional Continuous Normalizing Flows (CNF). It offers improved modeling capabilities for arbitrary probabilistic distributions, addressing the limitations of traditional parametric approaches. In NodeFlow, the NODE captures complex relationships in tabular data through a tree-like structure, while the conditional CNF utilizes the NODE’s output space as a conditioning factor. The training process of NodeFlow employs standard gradient-based learning, facilitating end-to-end optimization of the NODEs and CNF-based density estimation. This approach ensures outstanding performance, ease of implementation, and scalability, making NodeFlow an appealing choice for practitioners and researchers. Comprehensive assessments on benchmark datasets underscore NodeFlow’s efficacy, revealing its achievement of state-of-the-art outcomes in multivariate probabilistic regression setup and its strong performance in univariate regression tasks. Furthermore, ablation studies are conducted to justify the design choices of NodeFlow. In conclusion, NodeFlow’s end-to-end training process and strong performance make it a compelling solution for practitioners and researchers. Additionally, it opens new avenues for research and application in the field of probabilistic regression on tabular data.

The article suggests a Value Proposition (VP) framework that enables the analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP development activities. To develop such a framework, we examined existing management research publications to identify and extract assertions that could be used as a source of actionable insights for new growth-oriented companies. The extracted assertions were assembled into a corpus of texts that were subjected to topic modelling analysis – a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modeling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modeling results led to the identification of seven topics: 1) Value created; 2) Stakeholder value propositions; 3) Foreign market entry; 4) Customer base; 5) Continuous improvement; 6) cross-border operations; and 7) Company image. The uniqueness of the adopted topic modeling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e. in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance emerging the four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, Foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the VP development of new growth-oriented companies.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

During the last decade, Bangladesh has been experienced a significant climatic anomalies which lead to an increase in magnitude and frequency of various climate relate extreme events. Climate scientists substantiate that global temperature and precipitation pattern is expected to change, which may result in consequential impact on agriculture, livelihood, and ecosystem. Bangladesh is therefore likely to face substantial challenges in the coming decades. In order to adequately understand this complex, dynamic phenomena, Analyzing historical Climate change scenarios as well as projecting its future trends is a great concern for researcher. This study aims to analyzes/ historical climatic data from (1901-2020), and predict future temperature and precipitation patterns in Bangladesh using CMIP6 data. Data used in this study (Observed data is from CRU TS 4.05 and future data is from CMIP6) have been acquired from WorldClim v2.1. Various methods including correlation, regression, standard deviation, correlation matrix, percentiles, cell statistics, and IDW interpolation were performed to analyze the trends, variability and spatial patterns of temperature and rainfall. This study revealed that Over the historical study period (1901-2020) Bangladesh has been experienced a significant warming trend with an average increase in temperature 2°C and with annual decline of the total precipitation 607.26 mm along with a shift towards drier conditions despite weak correlation with hotter years. Projected climate models represents that Bangladesh minimum temperature is expected to increase from 1°C to 4.4°C as well as maximum temperatures from 1°C to 4.1°C by 2100. Moreover, projected precipitation is expected to increase by 480.38 mm, with the biggest rises during monsoon months. Regional variations in temperature and precipitation are again expected, with the Southeast (SE) likely experiencing the most significant warming and the Northeast (NE) seeing the most substantial increase in precipitation.

The reagents and raw materials used, as well as the products obtained (bio-oil, reaction condensates, polyols and sugar phases) were characterized by elemental analysis, infrared spectroscopy, thermogravimetry and high performance liquid chromatography with mass spectrometry. The heating value of the bio-oils is higher than that of the original biomass (higher heating value of Eucalyptus sawdust bio-oil 29 MJ/kg versus 19.5 MJ/kg of the original Eucalyptus sawdust). The analyses of the bio-oils allowed to identify the presence of high added-value compounds, such as levulinic acid and furfural. Finally, a study of the accelerated aging of the liquefied biomass showed that the biofuel density increases with the storage time due to the occurrence of repolymerization reactions.

Significant problems have arisen in the last years, such as climate change, global warming, and hunger. These complications are correlated with the depletion and exploitation of natural resources and environmental contamination. Due to overcrowding, the list of challenges for the next few years is growing. A comprehensive approach was made to the agro-industrial production of Avocado (Persea americana) and the management of all its biomass waste. So, bioprocesses and biorefinery can be used to produce high added-value products. A large number of residues are composed of lignin and cellulose. They have many potentials to be exploited sustainably for chemical and biological conversion; physical, chemical, and natural treatments improve the following operations. There are some applications to many fields such as pharmaceutical, medical, material engineering, and environmental remediation. Possible pathways are mentioned to take advantage of Avocado as biofuels, drugs, bioplastics, and even in the environmental part and emerging technologies such as nanotechnology using bioprocesses and biotech. In conclusion, Avocado and its waste could be transformed into high value-added products in industries above to mitigate global warming and save non-renewable energy.

In the last years, we have witnessed a remarkable progress of algorithms solving Boolean satisfiability (SAT). The success of these algorithms has been especially relevant in a large number of industrial or real-world applications, for which these SAT solvers are nowadays an essential core part of their solving processes. Interestingly enough, these applications include a very diverse and heterogeneous range of domains, such as hardware verification, planning, and cryptography, among others. Unfortunately, the reasons of the good performance of these solvers on this variety of industrial benchmarks are not completely understood yet. Since SAT solvers’ efficiency is fundamental in various domains, obtaining a better understanding of these algorithms and the reasons of their good performance is crucial. In order to shed light on this question, SAT solvers are often viewed as complex systems with many interconnected components (e.g., conflict analysis and learning mechanism, database management, search restarts) interacting in many unpredictable ways. There is the common belief that the resulting emergent behavior of these complex systems takes advantage of a certain underlying structure of the SAT formula, which is shared by the majority of these industrial problems regardless the domain they come from. Recently, there have been some attempts of characterizing this structure under the lens of complex networks, with the purpose of better understanding the success of the solvers, and potentially improving them. In this paper, we analyze the structure of industrial SAT instances under the lens of self-similarity, and study how the execution of SAT solvers affect that structure. Many real-world graphs exhibit self-similar structure (with small fractal dimension), which means that after rescaling (replacing groups of nodes by a single node), the same kind of structure can be observed. In our analysis, in which we represent SAT instances as graphs, we observe that many industrial SAT formulas exhibit the same kind of structure. Moreover, we analyze how this structure evolves by effects of learning new clauses during the search. In particular, we observe that learned clauses usually contain variables that are close in the graph representation of the formula. This is, the learning mechanism tends to work locally. On the contrary, this learning mechanism on random SAT formulas –which do not exhibit any structure at all– is unable to generate these local clauses. This difference contributes to explain the success of modern SAT solvers on industrial problems.

Generic object detection is one of the most important and flourishing branches of computer vision and has real-life applications in our day to day life. With the exponential development of deep learning-based techniques for object detection, the performance has enhanced considerably over the last 2 decades. However, due to the data-hungry nature of deep models, they don't perform well on tasks which have very limited labeled dataset available. To handle this problem, we proposed a transfer learning-based deep learning approach for detecting multiple pigs in the indoor farm setting. The approach is based on YOLO-v2 and the initial parameters are used as the optimal starting values for train-ing the network. Compared to the original YOLO-v2, we transformed the detector to detect only one class of objects i.e. pigs and the back-ground. For training the network, the farm-specific data is annotated with the bounding boxes enclosing pigs in the top view. Experiments are performed on a different configuration of the pen in the farm and convincing results have been achieved while using a few hundred annotated frames for fine-tuning the network.

The coronavirus 2019 (COVID-19) pandemic was caused by a positive sense single-stranded RNA (ssRNA) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, other human coronaviruses (hCoVs) exist, of which Middle East Respiratory Syndrome (MERS) and SARS-CoV (SARS) showed higher mortality rates without causing a pandemic. As of December 2022, SARS-CoV-2 has resulted in over 6.6 million deaths worldwide through an array of acute to chronic pathologies. Historical pandemics include smallpox and influenza with efficacious therapeutics utilized to reduce overall disease burden. Therefore, immune system process analysis is required to compare innate and adaptive immune system interactions. Lymphatic system organs include bone marrow and thymus using a network of nodes throughout which white blood cells traverse glycolipid membranes utilizing cytokines and chemokine gradients that affect cell development, differentiation, proliferation, and migration processes as well as genetic factors affecting cell receptor expression. Innate processes involve antigen-presenting cells and B lymphocyte cellular responses to pathogens relevant to other viral and bacterial infections but also in oncogenic diseases. Such processes utilize cluster of differentiation (CD) marker expression, major histocompatibility complexes (MHC), pleiotropic interleukins (IL) and chemokines. The adaptive immune system consists of Natural Killer (NK) and T cells. Other viruses are also contributory to cancer including human papillomavirus (cervical carcinoma ), Epstein-Barr virus (EBV) ( lymphoma), hepatitis B and C (hepatocellular carcinoma) and human T cell leukemia virus-1 (adult T-cell leukemia). Bacterial infections also increase the risk of developing cancer( e.g. H. pylori). Therefore, as the above factors can cause both morbidity and mortality along-side being transmitted within clinical and community settings, it is appropriate to now examine advances in single cell sequencing, FACS analysis and many other laboratory techniques that allow insights into discoveries of newer cell types. These developments offer improved clarity and understanding that over-lap with known autoimmune conditions that could be affected by innate B cell or T cell responses to SARS-CoV-2 infection. Thus, this review quantifies and outlines the nature of specific receptors and proteins relevant to clinical laboratories and medical research by documenting both innate and adaptive immune system cells within current coronavirus immunology case study data and other pathologies to date.

In this paper, a distributed algorithm for reaching average consensus is proposed for multi-agent systems with tree communication graph, when the edge weight distribution is unbalanced. First, the problem is introduced as a key topic of core algorithms for several modern scenarios. Then, the relative solution is proposed as a finite-time algorithm, which can be included in any application as a preliminary setup routine, and it is well-suited to be integrated with other adaptive setup routines, thus making the proposed solution useful in several practical applications. A special focus is devoted to the integration of the proposed method with a recent Laplacian eigenvalue allocation algorithm, and the implementation of the overall approach in a wireless sensor network framework. Finally, a worked example is provided, showing the significance of this approach for reaching a more precise average consensus in uncertain scenarios.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection And Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, with each stem further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. The study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees.

Mediterranean countries are a cauldron of cultural exchange, with a strong emphasis on wild plants in cuisine traditions. Many of these plants belong to the family Apiaceae. The common climate determines the common range of distribution. While many plants have wide distribution, the range of distribution of others is restricted to Western Mediterranean or North Africa. This review investigates wild plants from the family Apiaceae traditionally used as food in 13 study sites - 11 countries in the Mediterranean and adjacent territories - the mainland and 3 islands. The aim is to trace patterns of native distribution versus consumption. As a result, 81 wild umbellifers are listed, traditionally used as food. Their consumption and distribution patterns are described and discussed. Interestingly in 8 of the 13 study sites (61.5%) are recorded 50 plants (66.7% of all wild umbellifers, traditionally used as food) which are consumed in only one particular country. These are as follows: 8 taxa in Morocco, 9 taxa in Spain, 2 taxa in Sicily, 3 taxa in Bulgaria 3 taxa in Crete, 8 taxa in Armenia, 14 taxa in Turkey, and 3 taxa in Jordan. However, these 50 restrictedly consumed plants are distributed in more than one country (except 15 taxa, which are endemics). Many of the plants that are used in certain countries are not consumed by the neighboring people. The results of the two statistical tests, namely Jaccard index and heatmap clustering (double dendrogram) are discussed. The presence of an outlier, such as Bulgaria, which shares borders, history, and culture with Greece and Turkey, emphasizes the importance of local climate on plant distribution and consumption over cultural interactions. The same was observed for some pairs of countries, such as Spain and Morrocco and Turkey and Armenia, although they had the highest number of common plants, that are both distributed and consumed as food.

Background: Oral squamous cell carcinoma (OSCC), with poor prognosis and high mortality rates, is one of the most diagnosed head and neck cancers. Cancer stem cells (CSCs) - epithelial-to-mesenchymal transition (EMT) loop is increasingly implicated in the therapy-resistance, relapse, and metastasis of OSCC patients. Accumulating evidence indicate that aberrantly expressed CD47 is associated with cell-death evasion, invasion and cancer metastasis; however, the role of CD47 in the modulation of CSCs-like phenotypes, including therapy-resistance and metastasis, with its underlying mechanism in OSCC remains largely underexplored. Methods: This study investigated the CSCs- modulating potential of CD47 in OSCC cell lines SAS, TW2.6, HSC-3 and FaDu using bioinformatics approach, immunoblotting, immunofluorescence staining, migration, invasion, colony and orosphere formation, as well as radiosensitivity assays. Results: We demonstrated that the characteristic ectopic expression of CD47 in OSCC patients was associated with ~ 20% 2-year survival disadvantage (p = 0.01) and positively correlated with the expression of pluripotency factors; while shRNA silencing of CD47 significantly suppressed cell viability and markedly inhibited orosphere formation, resulting in smaller and fewer orospheres, and downregulated CD133, SOX2, OCT4 and c-Myc mRNA and protein expression levels. We also showed that CD47 downregulation attenuates EMT, migration and clonogenicity of OSCC cells, with associated E-cadherin upregulation and suppression of Vimentin, Slug, Snail, and N-cadherin expression. Conclusion: Of therapeutic relevance, combined with radiotherapy, CD47 knockdown enhanced the anti-OSCC effect of radiotherapy. Thus, we demonstrate the therapeutic feasibility of a CD47-mediated anti-CSCs strategy, and suggest a role for CD47 suppression in potentiating the therapeutic efficacy of radiation therapy in OSCC patients.

Abstract: Chloroplast is a new hotspot in the field of plant transformation system of plant genetic engineering. Initially developed in Chlamydomonas and tobacco, it is now feasible in a broad range of species. They exploit the homologous recombination and segregation pathways acting on chloroplast genomes and are based on direct repeats, transient co-integration or co-transformation and segregation of trait and marker genes. Foreign site-specific recombinases and their target sites provide an alternative and effective method for removing marker genes from plastids.Chloroplast genetic engineering has many advantages over nuclear genetic enginering, especially site-specific introduction of foreign genes ,leading to the absence of gene siliency and positon effect,which providing the available to explore the regulation and mechanism of chloroplast genes`expression in vitro .It also can identify the structure and function of the chloroplast genome , the expression of chloroplast affect the nulear genome. In this paper, the basic methodology of chloroplast transformation, the current techniques and applications, and the future possibilities for Chloroplast genetic engineering was reviewed[1-3].

.Background: Prophylactic hyperthermic intraperitoneal chemotherapy (HIPEC) showed promising results in patients with colorectal carcinoma at high risk of recurrence but still without clinically and radiologically evident signs of peritoneal spread. This study aims to analyze the feasibility of this proactive, early phase, multimodality approach. Methods: A mono-institutional, prospective, parallel, two-stage phase II trial enrolled 49 patients to standard surgery or surgery plus intraoperative HIPEC. Before the procedure and during surgery, patients received intravenous fluorouracil (and leucovorin to potentiate oxaliplatin activity. Data analysis included length of hospital stay, surgery duration, type of surgery and chemotherapy-related complications risk score. Results: No significant difference was seen in the median time spent in the hospital with a median stay of 7 days in both groups (p=0.5720). The surgical procedure's median duration was longer in the HIPEC group than in the control one. Side-effects and surgical complications did not cross at any time the Pocock-type boundary for side/effect monitoring (p=0.80, N.S.). Conclusions: The present prospective study results demonstrate the feasibility and safety of the colorectal surgery plus HIPEC treatment in patients with colorectal cancer patients at high-risk for peritoneal invasion, although clinically and radiologically undetectable.

To investigate whether the ech42 gene in Clonostachys rosea can improve the biocontrol efficacy of Bacillus amyloliquefaciens and its molecular mechanism.Compared to the wild type, the B. amyloliquefaciens transformed with the ech42 gene exhibited a higher chitinase activity. The B. amyloliquefaciens-ech42 also showed a significantly higher biocontrol efficiency against B. cinerea when tomato plants were pre-treated with amyloliquefaciens-ech42. No significant difference of control efficiency was observed between the wild type and amyloliquefaciens-ech42 when tomato plants were first infected by B. cinerea. In addition, the activity of the defense-related enzyme polyphenol oxidase, but not superoxide dismutase was significantly higher in amyloliquefaciens-ech42 than in the wild type.The ech42 enhances the Bacillus amyloliquefaciens biocontrol efficiency by increasing the capacity of protection/prevention to plants, rather treating/killing the pathogens.

Background: Diabetic retinopathy (DR) is one of the most prominent pathological microvascular complications in diabetes. A series of studies reported that vitamin D deficiency was associated with increased prevalence of retinopathy in diabetic patients but the results were inconsistent. In this study we focused on evaluating the relationship between vitamin D deficiency and DR by conducting a meta-analysis of observational studies. Methods: Systematic computerized searches were performed in PubMed, MEDLINE, and the Cochrane Library for relevant original articles till November 20, 2016. The pooled odds ratios (ORs) with corresponding confidence intervals (CIs) were calculated to assess the associated value of vitamin D deficiency to the risk of DR. 9 studies including 6332 participants were subjected to final analysis. Results: The results indicated that vitamin D deficiency increases the risk of DR (OR = 1.57, 95% CI 1.32-1.87) with a little heterogeneity (I2 = 23%). In addition, the subgroup analysis demonstrated that there were obvious heterogeneities in T2DM (I2 = 47.8%). Sensitivity analysis showed that the results were relatively stable and reliable. Conclusion: our meta-analysis demonstrated that vitamin D deficiency could increase the risk of DR.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

Mortality rates in sub-Saharan Africa remains unacceptably high, and disproportionately affecting economically disadvantaged communities. And healthcare delivery which is considered a major contributor to this is challenged by the limited resources and high service costs, prompting many patients to opt for outpatient department services. This study addresses the need for a data-driven approach to healthcare policy formulation by proposing a model for the implementation of a data warehousing system that integrates outpatients into the national healthcare framework. By leveraging data warehousing, healthcare delivery can be made more efficient through a robust repository that supports decision-making and policy formulation. The research focuses on the outpatient department in resource-scarce settings, proposing a novel multidimensional data model and practical framework implementation. The benefits include improved patient experiences, cost-effective service delivery, and enhanced patient management through electronic health records. Additionally, mobile apps are explored for patient navigation and reminders to enhance treatment adherence. The study’s results indicate that data warehousing can significantly improve healthcare delivery by consolidating critical data and facilitating better planning, resource allocation, and policy formulation. Ultimately, this approach offers valuable insights for optimizing healthcare systems in resource-constrained environments, enabling data-driven policies, and improving health outcomes across sub-Saharan Africa.

Onychomycosis is a common fungal nail infection for which new antibiotics are needed to overcome antifungal resistance and other limitations of conventional treatments. This study reports the development of antifungal nail lacquers containing oregano essential oil (OEO), rosemary essential oil (REO), and/or biogenic silver nanoparticles (bioAgNP). The formulations (F) were tested against dermatophytes by agar diffusion, ex vivo nail infection, and scanning electron microscopy techniques. They were evaluated pharmacotechnically and by FTIR-PAS to access permeation across the nail plate. F-OEO and F-OEO/Ag were promising for the final nail lacquer formulation, they permeated through the nail plate and showed antifungal efficacy against dermatophytes-contaminated nails after 5 days of treatment. The treated nails had decreased hyphae and spores compared to untreated control; the hyphae were atypically flattened which indicates loss of cytoplasmic content due to damage to cytoplasmic membrane. Formulations were stable after centrifugation and thermal stress, maintaining organoleptic and physicochemical characteristics. F-OEO and F-OEO/Ag had pH compatible with the nail, and drying time (59-90 s) within the reference for nail lacquer. For the first time, OEO and bioAgNP were incorporated in a nail lacquer, resulting in a natural and nanotechnological antifungal product for onychomycosis that may combat microbial resistance.

This research aims to understand the Cambodian green economy transition and analyze its strengths, weaknesses, opportunities, and threats (SWOT). The research showed that Cambodia has been significantly transitioning its green economy since 2009 as evidenced by the National Green Growth Roadmap. Cambodia also established the National Council for Green Growth in 2012 and upgraded it to the National Council for Sustainable Development in 2015 in alignment with 2030 Agenda for Sustainable Development of the United Nations. The members of the Council are from all the government’s ministries and relevant inter-ministerial councils/committees, which are significant for policymaking. All governors of the capital and provincial administrations are also its members, which are significant for supporting and implementing the works at the subnational levels. Under the structure of the Council’s Secretariat, Department of Green Economy is playing an important role in promoting green economic development in Cambodia by looking at national policies and strategic plans, undertaking projects, and mobilizing resources. Through coordination between this Department and UNIDO, Cambodia in 2022 became a Member of the United Nations Partnership for Action on Green Economy. The findings showed that Cambodia is transitioning its green economy at the right time, especially the new government’s Pentagonal Strategy strongly supported this green economy transition with a clear and strong political commitment. The national inter-ministerial council for sustainable development is also very significant for cross sectoral policy-making and implementing levels, inclusively for promoting green economic development.

The Green Deal for Europe aims to achieve climate neutrality by 2050 and sets a binding EU target of a 55% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. The contributions of EU Member States to this target will be partly determined by the market through the EU Emissions Trading Scheme. For the remaining part of greenhouse gas emissions outside the EU Emissions Trading Scheme, the Effort Sharing Regulation sets binding national reduction targets. The paper applies an energy optimization model to analyze the ability of the Czech Republic to meet the climate targets. Given the high level of GHG emissions in 1990 and the significant reduction of GHG emissions in the 1990s, Czechia could achieve a 55% reduction by 2030. However, further decarbonization will be quite challenging.

A Pacific brittle star Ophiura sarsii has previously been shown to produce a chlorin (3S,4S)-14-Ethyl-9-(hydroxymethyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (ETPA) (1) with potent phototoxic activities making it applicable to photodynamic therapy. Using extensive LC-MS metabolite profiling, molecular network analysis and targeted isolation with de novo NMR structure elucidation, we here identify five additional chlorin compounds from O. sarsii and its deep-sea relative O. ooplax: 10S-Hydroxypheophorbide a (2), Pheophorbide a (3), Pyropheophorbide a (4), (3S,4S,21R)-14-Ethyl-9-(hydroxymethyl)-21-(methoxycarbonyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (5), and (3S,4S,21R)-14-Ethyl-21-hydroxy-9-(hydroxymethyl)-4,8,13,18-tetramethyl-20-oxo-3-phorbinepropanoic acid (6). Chlorins 5 and 6 have not been previously reported in natural sources. Interestingly, low amounts of chlorins 1-4 and 6 could also be identified in a distant species, the basket star Gorgonocephalus cf. eucnemis, demonstrating that chlorins are produced by a wide spectrum of marine invertebrates of the class Ophiuroidea. Following purification of these major Ophiura chlorin metabolites, we discovered the high singlet oxygen quantum yield upon their photoinduction and their strong phototoxicity against a panel of cancer cell lines. These studies identify an arsenal of brittle star chlorins as powerful natural photosensitizers with potential photodynamic therapy applications.

Following the rapid spread of COVID-19 across the globe, the intense response that was demanded of diagnostic centers and research laboratories prompted the use of numerous products and protocols for the management of SARS-CoV-2 specimens. In these settings, proper handling of such infectious specimen is necessary to ensure the safety of personnel and to reduce the risk of active transmission. Our aim was to evaluate the inactivation efficacy of different inactivating methods, notably from commercial lysis buffers available in diagnostic kits. Heat and sodium dodecyl sulfate detergent were also included in our investigations. A cell culture-based assay was used, and supported by molecular qRT-PCR detection, to show in vitro infectivity reduction after inactivation treatment. Overall, all the investigated methods were successful in inactivating SARS-CoV-2. Ten minutes of contact with the commercial buffers completely stopped in vitro SARS-CoV-2 infectivity. Fifteen minutes at 68°C and 30 minutes at 56°C as well as one hour with sodium dodecyl sulfate detergent at 2, 1, 0.5, and 0.1% yielded the same results. These findings demonstrate the reliability of these protocols with regards to biosafety. Inactivation by heat and sodium dodecyl sulfate detergent are rather simple and can be readily available methods for rendering an infectious SARS-CoV-2 specimen inactive, especially in settings where commercial buffers are not available.

This systematic literature review meticulously explores the complexities of technological innovation and entrepreneurship, with a particular focus on the strategies and management practices involved in new product development from an interdisciplinary standpoint. By conducting an extensive analysis of 323 key articles from 11 top-tier journals published between 2020 and 2023, the review delineates the current research trajectories within these fields, underscoring significant scholarly interest and development. The study particularly emphasizes the emergence of artificial intelligence-generated content technology since 2022 and critically assesses its influence on innovation processes and product development practices among both nascent entrepreneurs and well-established corporations. This emergent technology's role in shaping the competitive landscape and its potential to drive significant shifts in market dynamics are thoroughly examined.

Under the background that the international technological innovation environment VUCA has been intensifying and the innovation situation of Chinese manufacturing enterprises has been impacted, the innovation dilemma faced by enterprises has attracted increasing attention, while the innovation dilemma is also one of the important factors affecting the operation efficiency of enterprises. Based on the micro-data of China’s A-share manufacturing listed enterprises, this paper empirically analyzes the impact of innovation dilemmas faced by enterprises on operational efficiency (innovation dilemmas are measured by the difference when actual innovation performance is lower than expected innovation performance). The results show that the innovation dilemma of manufacturing enterprises significantly inhibits the operational efficiency of manufacturing enterprises by reducing their risk tolerance, development ability expectation and profitability. After using a series of tests, such as instrumental variable method, propensity score matching method, DID (Differences-in-Differences) and changing variable measure method, the conclusion is still robust. In addition, the inhibitory effect of innovation dilemma on enterprise operation efficiency is more obvious for private enterprises, small and medium-sized enterprises, enterprises in the ascend or decline phase, high-tech enterprises and enterprises in the eastern region. Therefore, the government should strengthen the policy support for the manufacturing enterprises in the innovation dilemma, help the enterprises to overcome the innovation bottleneck. Manufacturing enterprises themselves should enhance their innovation capabilities and strive to get rid of the innovation dilemma in order to maintain a stable footing in the fierce market competition and improve the high-quality development of China’s manufacturing industry.

Revenue-sharing (RS) contracts are a common approach to incentivize innovation of upstream suppliers by addressing the uneven profit distribution between upstream and downstream firms. Considering the possible overconfidence characterizing decision makers in the supply chain, we investigate the effect of the RS contract and the tendency of overconfidence of supply chain members on the investment in R&D of key components of products in the context of an upstream supplier that is the leader in the R&D and production of key components. We find that regardless of the bargaining power of either party, a RS contract can increase the R&D investment in key components. Regarding the effects of overconfidence of either downstream manufacturer or up-stream supplier, it can increase the R&D investment in key components. Supplier (manufacturer) overconfidence can harm their own profits but increase the profits of the manufacturer (supplier), and when the level of overconfidence is below a certain threshold, the damage to their own profits is less than the increase in each other's profits, thus benefiting the whole supply chain. In addition, we also find a joint effect of RS contracts and overconfidence: when the bargaining power of the supplier is low, the RS contract has a certain compensatory effect on the loss of their own profits caused by overconfidence.

Lucid dreaming is a dream in which one is aware that they are dreaming while dreaming as well as being able to influence the dream. It is utilized in scientific research to explore dreaming, in clinical context to treat nightmares, and in private settings to have fun, solve problems, and to train various skills. In this paper, based on an interview with a lucid dreamer, a fascinating, yet hardly described way to use lucid dreaming in sports and martial arts is presented. The dreamer used his lucid dreams to explore new movements and new possibilities in martial arts. For the interview, the guide from Schädlich and Erlacher [1] was used. The interviewee’s lucid dream experience is compared to interviews of other lucid dreamers and their experience and to contemporary research on lucid dreams. While the potential of lucid dreaming is explored, the interviewee also mentions the possible shortcomings of lucid dreaming.

Innovation and technology are important tools of minerals sector efficiency and productivity im-provement. The uptake of technologies has proven important levelrs to improve the productivity of mining sector. This paper provides a comprehensive analysis of mine-level productivity by using a global data of copper, gold, and platinum from 1991-2020. Various drivers of proioductivity have been analysed to draw policy insights. Empirical findings reveal significant disparities in technical efficiency and productivity across mines and regions. A further decomposition of TFP into its different components suggest that the adoption of innovative practices and investment in technology adoption could improve the overall productivity of these commodities. The findings also suggest that an appropriate input mix and optimal scale of production could boost platinum mining productivity. Regional disparities in productivity in different commodities (e.g., South Africa vs. Zimbabwe) give policymakers with insights into how to support production scale and productivity through appropriate input mixtures.

With the rapid development of the economy and the transformation of social contradictions, the economic development model in China has also changed, and a high-quality economic development model has been proposed. This paper uses the entropy method to measure the level of innovation and high-quality economic development in nine prefecture-level cities in Fujian Province from 2011 to 2020, and finds that the overall level shows an increasing trend year by year, but there are obvious regional differences. By constructing a grey correlation degree model, it is found that the innovation subsystem and the high-quality economic development are strongly correlated, the development level of innovation and the subsystem of high-quality economic development are both strongly correlated, the grey correlation degree from high to low is green, coordinated, shared, and livelihood, indicating that innovation has a significant impact on high-quality economic development. Based on the research results, countermeasures and suggestions are proposed to promote the coordinated development of innovation and high-quality economy.

Climate change-induced events amplify existing social, political, economic, infrastructural and environmental concerns in many Global South cities, and perhaps no city is more vulnerable than Bangladesh’s capital of Dhaka. Climate-induced rural-urban migration is a profound concern, and Dhaka’s political leaders have embraced technology-based innovation as a solution pathway. This article explores the societal impact of Dhaka’s innovation environment strategies for climate change adaptation and mitigation. Employing a case study qualitative methodology, our three findings expand knowledge about innovation-urban climate mitigation as understood by Dhaka-based entrepreneurs: First, the most effective innovations were not the most technologically advanced, but those with the highest degree of participant ownership. Second, gaps between recipient, corporate and governmental understandings of effective mitigation and adaptation harmed projects, and were driven by different definitions of risk and competing understandings of vulnerability. Third, even the most technical climate adaptation measures were inherently political in their application. We discuss how to better position urban climate innovation infrastructures in Bangladesh and beyond, including developing a better recognition of innovation lifecycles for urban climate adaptation and widening our definitions of ‘innovation’ to better incorporate more effective and inclusive climate adaptation solutions.

HIV incidence remains alarmingly high among female sex workers (FSWs) in low- and middle-income countries, with Uganda being no exception. The elevated risk of HIV transmission among this vulnerable population can be attributed to a complex interplay of factors, including engaging in sexual activities with multiple partners, inconsistent condom usage, substance use, and structural barriers, such as legal criminalization and pervasive stigmatization by healthcare providers. To combat this epidemic, various HIV prevention interventions have been devised, targeting these multi-faceted challenges. This study involved conducting focus groups among FSWs and healthcare providers in Uganda, with the overarching objective of informing the development of a tailored HIV prevention intervention aimed squarely at FSWs. Through these discussions, valuable insights were gained into FSWs’ perceptions of their HIV risk, their attitudes regarding HIV prevention strategies, and their recommendations regarding the content and delivery of future interventions.

Oxytocin (OXT), a pro-social peptide, is increasingly recognized as a potential protective substance against drug addiction. In the context of ethanol, previous researches have shown OXT properties to reduce self-administration, alleviate motor impairment in rodents, and reduce craving in humans. However, its role in behavioral sensitization, a neuroadaptive response resulting from repeated drug exposure linked to increased drug incentive, remains unexplored. OXT is also associated with modulating the Hypothalamic-Pituitary-Adrenal (HPA) axis, with corticosterone well-known as a significant factor in behavioral sensitization development. This study aimed to investigate the effects of Carbetocin (CBT), an analogue of OXT, on the expression of behavioral sensitization to ethanol and concurrent alterations in plasma corticosterone levels in male and female Swiss mice. We also aimed to confirm previous studies on OXT impact on ethanol consumption in male mice, but with a focus on CBT, using the two-bottle choice model and drinking in the dark (DID) methodology. For the sensitization study, mice received either ethanol (1.8 g/kg, ip) or saline treatments daily for 15 consecutive days, followed by treatment with carbetocin (0.64 mg/kg, ip) or a vehicle for 6 days. Subsequently, on day 22, all animals underwent an ethanol challenge to assess the expression of behavioral sensitization. Plasma corticosterone levels were measured on days 21 and 22. CBT effectively prevented the expression of ethanol-induced behavioral sensitization in both male and female subjects, with no alterations detected in corticosterone levels. In the ethanol consumption study, following an initial phase of ethanol acquisition, mice underwent a 6-day treatment with CBT i.p or saline before being reexposed to ethanol. We also found a reduction in ethanol consumption due to CBT treatment. In conclusion, carbetocin emerges as a promising and effective intervention in mitigating ethanol-induced behavioral sensitization and reducing ethanol intake, highlighting its potential significance in alcohol addiction treatment.

The giant anteater (Myrmecophaga tridactyla) is a vulnerable specie from Central and South America and is considered extinct in Belize, Guatemala, El Salvador, and Uruguay. Therefore, studies describing the reproductive characteristics of this species are pivotal for its conservation. Thus, this study aimed to provide a morphological description of their female reproductive tissues. We collected tissue samples from six female giant anteaters, and performed gross, morphological, and histochemical analyses. Five adult and one young subjects were enrolled in the study. In the ovary, classifications were made according to the follicle and oocyte sizes: primordial, primary, secondary, early antral, or antral. Typical follicles with a single oocyte surrounded by a simple or stratified layer of cubic epithelium, atretic follicles, corpora lutea, corpora albicans, and ovarian cysts were also observed. No ovarian lesions were observed. By contrast, endometritis, metritis, mucometra, and endometrial cysts were identified in the uterus. Uterine alterations in these subjects were frequent and could affect reproduction.

The study presents a novel approach of programing pain inhibition in chronic pain patients based on the hypothesis that pain perception is modulated by dysfunctional dorsal medial nucleus tractus solitarii (dmNTS) reflex arcs that produce diminished baroreflex sensitivity (BRS) resulting from a conditioned response. This study tested whether administration of noxious and non-noxious electrical stimuli synchronized with the cardiac cycle resets BRS, reestablishing pain inhibition. 30 pain-free normotensives controls (NC) and 32 normotensives fibromyalgia (FM) patients received two, ≈8 minute-epochs of cardiac-gated, peripheral electrical stimuli. Non-painful and painful electrical stimuli were synchronized to the cardiac cycle as the neuromodulation experimental protocol (EP) with 2 control conditions (CC1, CC2). BRS, heart-rate-variability (HRV), pain threshold and tolerance, and clinical pain intensity were assessed.Reduced BRS in FM at baseline increased by 41% during two, ≈8 minute-epochs of stimulation. Thresholds in FM increased significantly during the experimental protocol (all Ps<0.001) as did HRV. FM levels of clinical pain significantly decreased by 35.52% during the experimental protocol but not during control stimulations (P<0.001). Baroreceptor training may reduce FM pain by BRS-mediated effects on intrinsic pain regulatory systems and autonomic pain modulation. These processes seem to be linked by classical and operant conditioning.

Climate change poses a major threat to development in most low and middle-income countries, especially the sub – Saharan Africa. Wurompo is a small farming community in the Wenchi Municipality of the Brong-Ahafo region of Ghana that depends on rain-fed agriculture activities for livelihood. In recent years, droughts, unpredictable rainfall pattern and crop failure have become common in the area. The study assessed knowledge and awareness, effects of climate change on female farmers, and their adaptation strategies. A case study in design, qualitative methods were used to collect data from 50 purposefully selected participants. Data were analyzed using themes and sub-themes generated from the research questions. Findings showed lack of adequate information and knowledge on climate change and its effects. Climate change has impacted negatively on these farmers stemming from decline in crop production and unavailability of adequate water supply in due season. Challenges to climate change adaptation are poverty, poor basic infrastructure, and modern farming practices. Farmers must be educated on climate change and its effects, with training on the necessary adaptation strategies to build their resilience. Policies that target rural farmers to adapt to climate change, and device modern agricultural techniques and practices are also necessary.

Systemic lupus erythematosus (SLE) is an autoimmune disorder characterized by a broad array of clinical signs. In this study, we aimed to use intravenous immunoglobulins (IVIG), called intacglobin, as a monotherapy to manage SLE in three patients. Laboratory investigations for SLE diagnosis were performed, including the detection of anti-nuclear antibodies (ANA) and SLE confirmation by detecting high titers of anti-dsDNA antibodies. C3 and C4 serum levels were assessed, as well as the determination of immunoglobulins. The SLEDAI score was measured to determine whether a significant degree of disease activity existed and as a prognostic value. The evaluation of any chest infection was performed by chest-X-ray. The patients were treated with five–ten g/day of IVIG for six consecutive days, followed by five–ten g/month. Immunological evaluation demonstrated that patients presented with a flare of SLE with high titers of ANA and anti-dsDNA antibodies, low C3 and C4, and elevated immunoglobulin levels. The SLEDAI score falls from 10 to below 3, and chest infections in some patients are cleared up. The postulated mechanisms of action of IVIG demonstrated that it could be used as an immunosuppressor, immunomodulator, and antimicrobial agent in patients with SLE.

Tubulin and heat shock protein 27 (HSP27) are up-regulated in cancer cells, and play a critical role in cell division, and proliferation. Therefore, they are targets for discovery of anticancer therapy. The objective of this study is to design, characterize, and biologically evaluate the nimesulide analogues to combat female cancer such as ovarian cancer, and breast cancer. Herein, the nimesulide analogues are designed to target both tubulin and HSP27 functions. Ovarian cancer (SKOV3) and breast cancer (SKBR3) cell lines were used as surrogate models to test the nimesulide analogs biological activities using MTT assay. In the present study, four nimesulide analogues were designed, synthesized and the chemical structures were with the biological evaluation were studied. The synthesized agents were characterized by 1H-NMR, 13C-NMR, the molecular weight was confirmed using GC-MS technique, and melting point. Besides, the agent L4 structure was confirmed using X-ray crystallographic analysis. The present data revealed that nimesulide analogs have potent anticancer activity against SKOV3and SKBR3 cell lines. The IC50 values for both SKOV3 and SKBR3 cell lines treated with the agents showed a potent cell growth inhibition range of 0.23-2.02 µM and 0.50-3.73 µM respectively. In conclusion, the designed nimesulide analogues can target both tubulins, and HSP27 concurrently, and they are promising agents as future chemotherapy female cancers.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid-solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics - Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The paper details the experimental design, video analysis using Particle Tracking, and a novel method for deriving volumetric concentrations and flowrate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying, which could be used to enhance the efficiency of DED processes.

Existing implants used with Total Knee Arthroplasty (TKA), Total Hip Arthroplasty (THA), and other joint reconstruction treatments, have displayed premature failures and frequent needs for revision surgery in recent years, particularly with young active patients who represent more than 55% of all joint reconstruction patients. Bone cement and stress shielding have been identified as the major reasons for premature joint failures. A breakdown of the cement may happen, and revision surgery may be needed because of the aseptic loosening. The significant mismatch of stiffness properties of patient trabecular bones and metallic implant materials in joint reconstruction surgery results in the stress shielding phenomenon. This could lead to significant bone resorption and increased risk of bone fracture and the aseptic loosening of implants. The present project introduces an approach for development of customized cellular structures to match the mechanical properties and architecture of human trabecular bone. It consists of four modules:1- characterization of geometry, microarchitecture, and stiffness properties of patient’s bones; 2- developing customized lattice structures tailored to mimic properties established in module 1; 3 - integration of developed lattice structure into full scale implant design and manufacturing; 4- implementation of the developed technology for use in implants that would overcome most of the issues related to aseptic loosening phenomenon. The present work aims at fulfilling the objectives of module 2 by exploring new designs of customized lattice structures and texture tailored to mimic closely patients’ bone anisotropic properties and that can incorporate an engineered biological press-fit fixation technique. The effects of various lattice design variables on mechanical performance of the structure are examined through systematic experimental plan using statistical design of experiments technique and analysis of variance method. All tested lattice designs were explored under realistic geometrical, biological, and manufacturing constraints. Of the four design factors examined in this study, strut thickness was found to have the highest percent contribution (41%) regarding the structure stiffness, followed by unit cell type, and cell size. Srut shape was found to have the lowest effect with only 11% contribution. The introduced solution offers lattice structure designs that can be adjusted to match bone stiffness distribution and promote bone ingrowth and hence eliminating the phenomenon of stress shielding while incorporating biological press-fit fixation technique.

An adequate representation of the water infiltration process in the soil allows improving the efficiency in application and the uniformity in surface irrigation. The Green and Ampt model has shown a good representation of the process, and researchers from the United States Department of Agriculture (USDA) determined the values of their parameters for soils of that country, which are shown in tables or through functional relationships and this information is used as reference in several parts of the world, although there is no certainty that they are representative of the soils in Mexico. In this study, the parameters of the Green & Ampt equation were determined and evaluated in some soils of agricultural importance in Mexico. The parameters were obtained in four ways: one of them applied a methodology adapted from Brooks and Corey to quantify the wetting front capillary pressure head and used an permeameter under constant hydraulic head to determine the saturated hydraulic conductivity, and the other three consisted in taking them from three studies reported by the USDA. The values of the parameters suggested in Mexico drastically underestimated the results with relative errors (RE) in a range of -49.0 to -94.0% and the most representative were those obtained with the methodology proposed in this research with RE of -15.0 to 6.0%.

Background: Breastfeeding is essential for optimal infant health, offering numerous benefits such as infection prevention and enhanced bonding. Despite its importance, challenges exist, particularly for mothers of preterm and very low birth weight (VLBW) infants, necessitating effective lactation support strategies. Initiatives like the Baby Friendly Hospital Initiative (BFHI) and Kangaroo Mother Care (KMC) aim to promote breastfeeding success, while donor human milk (DHM) serves as an alternative bridge while the mother is supported to build up her supply. Adequate lactation support, counseling, and education are vital antenatally, during NICU stays and after discharge to improve breastfeeding rates and infant health outcomes, ultimately contributing to global efforts to reduce neonatal mortality and achieve sustainable development goals. This study explores existing research on factors influencing mothers’ ability to produce breast milk and sustain breastfeeding, with the aim of enhancing lactation support and improving infant health outcomes.Methods/Design: A systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Searches will be performed in the following databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE Ovid via EBSCOhost, Google Scholar, and WILEY online Library) to identify and incorporate primary research articles related to the topic published between 2014 and 2024. Authors will search peer-reviewed articles, gray literature sources, and reference lists to identify eligible studies. Independent abstract and full-text screening by two reviewers will follow the title screening. The analysis of data will be carried out using Review Manager software (version 5.3.5). The I² test will be used to quantify the amount of unexplained heterogeneity. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger’s test, Begg’s test, and Trim and Fill analysis.Discussion: Our review represents a singular exploration into exclusive breastfeeding among very low birth weight (VLBW) infants in low- and middle-income countries (LMICs), focusing on breastfeeding assistance methods. With VLBW defined as less than 1500 g, and most low birth weight (LBW) babies born in LMICs, there is a critical need to address feeding challenges and promote optimal nutrition. Despite updated WHO guidelines in 2022, there is still a lack of comprehensive advice for this vulnerable population. Peer support interventions and health worker counseling have shown promise in increasing breastfeeding continuation rates in LMICs, but challenges persist due to workforce shortages and inadequate documentation. Promoting early postpartum milk expression is crucial for milk production and infant health. Our review aims to fill the gap in understanding VLBW mothers’ breastfeeding experiences in LMICs, shedding light on the effectiveness of breastfeeding support methods and identifying areas for further research. Systematic review registration: CRD42024547864

Due to their remarkable intrinsic physical properties, carbon nanotubes (CNTs) can enhance mechanical properties and confer electrical and thermal conductivity to polymers currently investigated for use in advanced applications based on thermal management. An epoxy resin filled with varying concentrations of CNTs (up to 3 wt.%) was produced and experimentally characterized. The electrical percolation curve identified two critical filler concentrations: 0.5 wt%, near the electrical percolation threshold, suitable for exploring mechanical and piezoresistive properties, and 3 wt% for investigating thermo-electric properties due to the Joule effect with applied voltages ranging from 70V to 200V. Numerical models were developed using multiphysics simulation software. Once validated with experimental data, these models were used to investigate additional physical properties of the composites. Furthermore, a statistical approach based on the design of experiments (DoE) was employed to examine the influence of certain thermal parameters on the final performance of the materials. The purpose of this research is to promote the use of contemporary statistical and computational techniques alongside experimental methods to enhance understanding of materials science. New materials can be identified through these integrated approaches, or existing ones can be more thoroughly examined.

Buttonweed (Cotula coronopifolia) is native to South Africa but invasive in wetlands in Europe, North America, and Australasia, where it excludes native plants. Despite being dry-fruited, field studies suggest migratory waterbirds can disperse its seeds via gut passage (endozoochory), aiding its expansion. To explore the potential for endozoochory in different regions and habitats, we collected seeds from six populations in Spain, Sweden, and the UK. Germination was tested under different salinity levels (0, 5, 10, 15 g/L) and simulated gut passage treatments: scarification, acidification, or both. No germination occurred at 15 g/L. Higher salinity reduced and delayed germination, but full gut passage treatment increased germinability and accelerated germination. Scarification or acid treatment alone resulted in intermediate germination patterns. There were significant salinity x population and gut passage x population interactions on germinability. The acceleration effect of gut passage on germination was stronger at 5-10 g/L than at 0 g/L. This study highlights how migratory birds can facilitate the spread of alien plants introduced by humans. Non-classical endozoochory by waterbirds is an understudied mechanism for the long-distance dispersal of dry-fruited alien plants. Further research on C. coronopifolia, including population genetics, is necessary to understand dispersal mechanisms and facilitate management strategies.

Infective endocarditis remains an illness that carries a significant burden to healthcare resources. In recent times, there has been a shift from Streptococcus sp to Staphylococcus sp as the primary organism of interest. This has significant consequences given the virulence of Staphylococcus and its propensity to form a biofilm, rendering non-surgical therapy ineffective. In addition, antibiotic resistance has affected treatment of this organism. The cohorts at most risk for Staphylococcal endocarditis are the elderly patients with multiple comorbidities. The innovation of transcatheter technologies alongside other cardiac interventions such as implantable devices have contributed to the increased risk attributable to this cohort. We examine the role of the heart team for diagnosis and treatment of this condition. In addition, we examine the determinants of virulence of Staphylococcus aureus, the interaction with hosts immunity and the discovery and emergence of a potential vaccine. We also examine the potential role of prophylactic antibiotics during dental procedures. With increasing rates of transcatheter device implantations, there is a projected increment of endocarditis especially in this high-risk group. A high index of suspicion is needed alongside early initiation of therapy and referral to the heart time to improve outcomes.

Antibiotics are in excessive use that has extensively increased antimicrobial resistance worldwide which has become the major public concern among the countries. To control this threat proper monitoring of the antimicrobial usage along with the increasing rate of antimicrobial resistance (AMR) is required. Further, surveillance of both the parameters is highly recommended for comparing the differences in distinct countries. Moreover, alternatives for antibiotics are also surveyed and are being researched for quick use in the near future. AMR is an issue that needs immense attention from various sectors. Thus, intervention of multisector is highly encouraged for better outcomes. One Health is one of the approaches that play a vital role in resolving this issue. In this research paper, six different European countries are discussed in terms of antimicrobial usage and AMR in the human and livestock sectors with the help of literature study and various reports published by different organizations. Data study has been conducted to collect the data for comparison study. Data sources of AMR and antimicrobial usage are analyzed and a thorough comparison of both antimicrobial use and AMR are conducted. Also, the application of One Health is studied for a balanced system. This article provides about various surveillance systems that are formed only to keep a track on the upcoming situation of AMR and the consumption of antimicrobials by the humans as well as animals. The article does not provide about all the details required to monitor the AMR issue but firmly allow the readers to get acknowledged with the broad information about the antimicrobial resistance across the six countries of Europe. The regular data collected by the different organizations play a vital role in monitoring the status of AMR and antimicrobial usage by humans and in live stocks. These annual reports have highly helped the government to decide for alternatives and have focused in many training activities to combat the AMR situation globally. AMR prevention is linked to the One Health concept. As antibiotic resistance genes persist on an interface between environment and animal and animal health, an approach is required in all three areas that stress the concept of 'One Approach to Health.'

This paper is about energy as viewed through an integrated model that links energy with environment, technology and urbanisation as related areas. Our goal is to empirically investigate the (in)efficient energy use across 30 developed OECD member states during the period from 2001 to 2018. For that purpose, we set up an output-oriented BCC data envelopment analysis that employs a set of input variables with non-negative values to calculate the efficiency scores on minimising energy use and losses as well as environmental emissions. We develop a couple of baseline models for primary energy and secondary energy (electricity) in which we find that countries have mean inefficiency margins of 16.1 per cent for primary energy and from 10.8 to 13.5 per cent for electricity. Then, we extend the baseline models by adding environment as an important closely related concept and confirm the consistency of the baseline findings. In the context of this analysis, however, the inefficiency scores, on the one hand, point out to a mismatch in the utilisation of the inputs to produce efficiency but, on the other hand, they uncover a hidden potential to increasy efficiency through re-allocation under constant inputs.

An evaluation of emerging issues in One Health (OH) in Sub-Saharan Africa was undertaken to map the existing OH initiatives in Sub-Saharan African (SSA) countries. Desk review, expert opinions survey, limited interviews and wider consultations with selected OH stakeholders were conducted. The strengths, weaknesses, opportunities and threats to OH initiatives were identified. OH influence, interest and impacts were evaluated. One Health is transiting from multidisciplinary to transdisciplinary concepts and OH viewpoint should move from ‘proxy for zoonoses’, to include issues of climate change, nutrition and food safety, social sciences, geography, policy and planning, economics, welfare and well-being, antimicrobial resistance (AMR), vector-borne diseases, toxicosis and pesticides issues. While the identified major strengths should be boosted, the weaknesses should be addressed.OH Networks in SSA were spatially and temporally spread across SSA and stakeholders were classified as key, latent, marginal and OH defenders. Imbalance in stakeholders’ representation led to hesitation in buying-in from stakeholders who are outside the main networks. Theory of change, monitoring and evaluation frameworks, and tools to standardized evaluation of OH policies is needed for sustained future of OH and the future OH engagement should be outputs and outcomes-driven and not activity-driven.National roadmap for OH implementation and institutionalization is necessary and proofs of concepts in OH should be verified and scaled-up. Dependence on external funding is unsustainable and must be addressed. Necessary policy and legal instrument to support OH nationally and sub-nationally should be implemented taking cognizance of contemporary issues like urbanization, endemic poverty and other emerging issues. Utilizing current technologies and OH approach to address ongoing pandemic of COVID-19 and other emerging diseases is desirable. Finally, OH implementation should be anticipatory and not reactive to significantly benefit budgeting and contain disease outbreaks in animal sources before the risk of spillover to human can be envisaged.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.

This research aimed at examining how social enterprises mitigate climate change in Ghana, examining their strategies, impacts on the environment that affect climate change, and barriers. The research adopted a cross-sectional survey design, descriptive and involved 185 social enterprises. Questionnaires were administered online and data was analyzed using descriptive statistics and regression analysis. The findings revealed that the most used strategies include the development of new funding structures and advocacy for sustainable farming. It also highlights numerous environmental concerns such as the judicious use of hazardous materials and pollution. Nevertheless, results from regression analysis showed that the relationship between the number of social entrepreneurship activities and the impact on the environment is relatively low and the role of other factors may be more significant. Some of the main barriers that were found include competition from large organizations, cultural perspectives towards climate change, policy instability and lack of adequate technical education. Accordingly, the recommendations proposed are developing specialized funding programs for climate-focused social enterprises; climate change education and awareness campaigns; and capacity building to upgrade technical competencies in clean technologies and climate resilience design.

This research investigates the challenges and opportunities associated with supply chain transparency and traceability in the renewable energy sector. Using a qualitative methodology, the study involved semi-structured interviews with key stakeholders, including industry experts, supply chain managers, regulatory officials, and NGO representatives. The findings reveal that the complexity and global span of renewable energy supply chains pose significant hurdles for ensuring transparency and traceability. Variability in the maturity of traceability systems across companies further complicates the landscape, with some companies benefiting from advanced systems while others struggle with cost and technical barriers. Technological advancements, especially blockchain, offer promising solutions but require standardization and scalability to be effective. Regulatory frameworks play a crucial role, yet inconsistencies across jurisdictions create compliance challenges, underscoring the need for harmonized standards. Collaboration and partnerships are essential, providing platforms for sharing best practices and developing common standards. Enhanced transparency can lead to operational efficiencies, improved consumer trust, and stronger market positioning. Integrating sustainability into supply chain practices is fundamental, aligning with the increasing emphasis on ESG criteria among investors and stakeholders. Education and capacity-building, particularly for suppliers in developing countries, are vital for promoting transparency. Leadership and corporate culture are critical in driving these efforts, with strong commitment from the top necessary to implement effective practices. This study contributes valuable insights for stakeholders in the renewable energy sector, offering a foundation for future research and policy development aimed at advancing sustainable and ethical supply chain practices. The findings highlight the need for a holistic approach that integrates transparency and traceability into broader sustainability and business strategies.

Ecuador has 4000 Km2 of volcanic nanoclay reserves that could be industrialized. An immediate application is to modify the local AC-20 asphalt with this aggregate. This resource was characterized with FTIR, XRD, FRX, SEM, BET, Laser Granulometry, SPECMIN, TSG, TGA, DTG, DSC and Atmospheric Adsorption. FTIR showed vibration peaks Si and Al. X-ray diffraction indicated the presence of silicates and alumina, BET measured particle size (30 to 250 nm), and the SSA property measured the specific surface area (280.38 m2/g). Chip rock (natural clay) was treated with H2O2 or NaOH or NH3 and evaluated with SPECMIN -TSG obtaining spectra close to vermiculite, allophane and bentonite minerals. At 1000 °C additional samples were calcined in Thermogravimetry (TGA), Differential Thermogravimetry (DTG), Differential Thermal Analysis (DCS), with mass loss around 40%, corresponding to organic matter, hydroxyl (OH) and destruction of its structure with spicule formation at 956 °C. With the DSC there were endothermic and exothermic manifestations. The Si/Al layers would have micro and meso porosity, it was also found to be a nanoparticulate material, with high adsorption capacity. At temperatures above 350 °C, its structure collapsed. Thus, this synthesized nanoclay will be used as an additive to form new materials.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

In today’s Industrial IoT (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities, the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions against these attacks, as they have been proposed in the most recent literature.

Substantial resources are invested in conservation of marine biodiversity globally. Fishing is the primary threat to many marine species and is one we can manage. However, threatened marine species are legally caught in industrial fisheries. To determine the magnitude and extent of this problem, we analysed global fisheries catch and import data and found reported catch records of 91 globally threatened species, thirteen of which are traded internationally. Seventy-three species targeted in industrial fisheries account for 99% of threatened species catch volume and value. Our results are a conservative estimate of threatened species catch and trade because we only consider species-level data, excluding group records; for example, we omit ‘sharks and rays,’ which represents over 200 threatened species. Although most fishing countries are involved in catch or trade of threatened species, it is driven largely by European nations. On land and for charismatic marine animals (e.g., whales), industrial-scale harvest of species at risk of extinction is controversial and usually highly regulated. In contrast, fishing for endangered fish and invertebrates is widespread but poorly documented. Given the development of new fisheries monitoring technologies and the current push for stronger international mechanisms for biodiversity management, industrial fishing of threatened fish and invertebrates should no longer be neglected in conservation and sustainability commitments.

Background: Endometriosis is a female chronic inflammatory disease in which endometrial tissue develops outside the uterine cavity. It is a complex pathology, that significantly contributes to morbidity in premenopausal women, leading to chronic pain, infertility, and subfertility negatively impacting the physical, and emotional well-being and overall quality of life. The public health burden of endometriosis remains elusive and challenging to determine, and this uncertainty can lead to inadequate healthcare services and treatments. Objective: To estimate the incidence and prevalence of endometriosis in Italy using the hospital discharge records database. Population-based retrospective study. Nationwide between 2011 and 2020. Methods: From the National Hospital Discharge Database were selected all admissions with a diagnosis of endometriosis (ICD-9-CM, codes 617.x), supported by the presence of a procedure code of laparoscopy or any other surgical procedure allowing direct visualisation of the lesions. Main outcomes measures Incidence and prevalence of endometriosis were estimated for the entire 2010-2020 period and by individual year, analysing the time trend and variability in different geographical areas of Italy. Results: A total of 134,667,646 women aged 15–50 years with one or more hospitalisations for endometriosis in all Italian hospitals. The incidence of endometriosis in Italy during this period was 0.839 per 1000 women (CI95% 0.834-0.844), exhibiting a statistically significant decreasing trend over the years. A discernible north-south gradient was observed, with higher rates documented in the northern regions. The prevalence rate stood at 14.0 per 1000 during the same period, and a similar north-south geographical gradient was identifiable in the prevalence rates as well. Conclusions: The utilization of national-level hospital data enables the generation of incidence and prevalence data for endometriosis without variations in methods and definitions, facilitating the evaluation of temporal trends and regional comparisons. Understanding and quantifying this phenomenon is essential for appropriate healthcare planning in various Italian regions.

This study explores the use of remote ultrasound vibration to enhance welding strength. It examines the effects of 20 kHz, and 40 kHz High Power Ultrasound Transducers (HPUT) in contact and contactless modes during ultrasonic assistance laser welding. Employing shear tests and microscopy for assessment, the research evaluates ultrasound's impact on welding strength and identifies the optimal transducer. Theoretical and numerical analyses investigate vibration transmission into the welding area, particularly examining the vibration distribution and cavitation initiation within the molten pool area. Results indicate that ultrasound-assisted welding increases pull test performance, including the resistance force and strain, and improves microscopic structural integrity. Numerical simulations suggest that narrow-band vibration intensifies pressure in specific welding zones. Contactless ultrasound application also shows enhanced welding quality, offering a promising alternative to current methods. This work contributes to electric car technology by suggesting improvements in welding techniques for power battery systems.

A typical halogen-free flame-retardant (HFFR) formulation for electric cables may contain polymers, various additives, and fire-retardant fillers. In this study, composites are prepared by mixing natural magnesium hydroxide (n-MDH) with linear low-density polyethylene (LLDPE) and a few types of ethylene-octene copolymers (C₈-POE). Depending on the content of LLDPE and C₈-POE, we obtained composites with different crystallinity that affected the final mechanical properties. The nucleation effect of the n-MDH and the variations in crystallinity caused by the blending of C₈-POE/LLDPE/n-MDH were investigated. In the C₈-POE/LLDPE blend, we found a decrease in the crystallization temperature of LLPDE compared to pure LLDPE and an increase in the crystallization temperature of C₈-POE compared to pure C₈-POE. On the contrary, the addition of n-MDH led to an increase in the crystallization temperature of LLDPE. As expected, the increase in the crystallinity of the polyolefin matrix of composites led to higher elastic modulus, higher tensile strength, and lower elongation at break. Overall, these results show how to obtain the required mechanical features for halogen-free flame-retardant compounds for electric cable applications depending on the quantities of the two miscible components in the final blend.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

New ways of generating energy are currently being discussed, with a transition from traditional primary sources to more environmentally friendly options, particularly renewables. Energy storage is also closely related to this transition. Battery storage currently dominates this area. However, flywheel energy storage system (FESS) technology offers an alternative that uses stored kinetic energy to be transformed into mechanical energy and, using a motor-generator, electrical energy. FESS technology is thus flexible and can be applied in different industrial applications. The management of the technology of recycling W-MMC waste material from other products and the subsequent trial production of high-strength W-MMC material with a density of more than 17 500 kg/m3 from recycled powders allowed to test the limits of the so-called "heavy" flywheels used in rotor production. Materials with densities ≥ 7 800 kg/m3, and operating at lower to medium speeds up to 20 000 rpm. The results achieved lead to the conclusion that the developed recycled materials of the W-MMC type with a density ≥ 17 500 kg/m3, with a yield strength of 1 200 - 1 700 MPa depending on the production method, can be used as a substitute for the structural steels used today, without an enforced reduction of the maximum allowed rotor speed due to exceeding the maximum allowed stress.