Global optimization is widely adopted nowadays in a variety of practical and scientific problems. In this context, a group of techniques that is widely used is that of evolutionary techniques. A relatively new evolutionary technique in this direction is that of Giant Armadillo Optimization, which is based on the hunting strategy of giant armadillos. In this paper, a number of modifications to this technique are proposed, such as the periodic application of a local minimization method as well as the use of modern termination techniques based on statistical observations. The proposed modifications have been tested on a wide - series test functions, available from the relevant literature and it was compared against other evolutionary methods.

Temporal changes of population densities and species richness of three main pollinator groups: moths and butterflies (Lepidoptera), bees, wasps and sawflies (Hymenoptera) and hoverflies, horse-flies, tachninids and bee flies (Diptera) were investigated in the Carpathian Basin. Maintaining pollinator divesrity is a crucial factor for preserving our biodiversity and ecosystems, furthermore several pollinator species has strong economic role in maintaning crop and fruit cultures. Our conclusions are based on our three or four decades of faunistic surveys in various regions of the Carpathian Basin. Analysing and comparing our data with the historical data of the last 50 years, we concluded, densities of some pollinators were declined during the past decade and the half (Symphyta, hoverflies), although populations of several species of Mediterranean-origin were grown (Aculeata) and even new species were migrated from the warmer regions. In numerous cases, this decrease was dramatic: more than 90% decrease of certain butterfly species were detected. On the other hand, the composition of pollinator fauna significantly changed due to the disappearance of some montaneous or mesophyle species. The main reason of decrease of pollinator communities is partly the climatic change and partly anthropogenic factors. Our conclusion: in our region, the pollinator crisis is present, but moderate; however, there is clear sign of the gradual transition of our pollinator fauna towards the Mediterranean type.

Immunology and microbiology research in the West Indies has witnessed remarkable growth and innovation, playing a pivotal role in advancing our understanding of immune mechanisms, disease pathogenesis, and therapeutic interventions. This manuscript presents a comprehensive exploration of key areas in West Indian immunology research, spanning from the utilization of bacterial proteins as antibody reagents to the intricate realms of clinical immunology and disease management. The utilization of bacterial immunoglobulin-binding proteins (IBPs), including protein A (SpA), protein G (SpG), and protein L (SpL), has revolutionised serological diagnostics in the region. promise in early disease detection and precision medicine. Microbiological studies in the Caribbean region have shed light on antimicrobial resistance patterns, particularly the emergence of extended-spectrum beta-lactamases (ESBLs), guiding antimicrobial stewardship programs and informing therapeutic strategies. Clinical immunology research has elucidated the molecular pathways underlying immune-mediated disorders, leading to tailored management strategies for conditions such as severe combined immunodeficiency (SCID), neuropsychiatric lupus erythematosus (NPLE), and others. In summary, West Indian immunology and microbiology research has made significant contributions to global healthcare, fostering collaboration, innovation, and improved patient outcomes.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

Global public health systems have continuously faced challenges from the advent of novel infectious illnesses, necessitating quick and efficient actions to stop their spread.First discovered in Saudi Arabia in 2012, Middle East respiratory syndrome (MERS) is a viral respiratory disorder caused by the Middle East respiratory syndrome coronavirus (MERS-CoV). Since then, the nation has reported most cases and fatalities related to this sickness, with occasional outbreaks happening in other nations and regions. Severe sickness and high mortality are caused by MERS-CoV, especially in older persons and those with underlying chronic conditions. Through intimate contact, the virus can spread from people to dromedary camels and vice versa. It has sparked multiple epidemics both domestically and abroad in the Middle East, raising the possibility of fatalities and widespread transmission, especially in susceptible groups. The Riyadh region was the main target of the most current MERS-CoV outbreak in Saudi Arabia, which began in 2023 and ended on September 1, 2023, with 312 confirmed cases and ninety-seven fatalities. With the use of published studies and their contributions to the field, we hope to present a thorough overview of the MERS-CoV outbreak in Saudi Arabia in this review of the literature, including its epidemiological investigation, public health response, impact, and lessons learned. In addition to highlighting the importance and continued relevance of this problem, our work suggests ways to lower the risk of MERS-CoV transmission and avert further outbreaks.

The main source of electricity worldwide stems from fossil fuels, contributing to air pollution, global warming, and associated adverse effects. This study explores wind energy as a potential alternative. Nevertheless, the variable nature of wind introduces uncertainty in its reliability. Thus, it is necessary to identify an appropriate machine learning model capable of reliably forecasting wind speed under various environmental conditions. This research compares the effectiveness of artificial neural networks (ANN) and convolutional neural networks (CNN) in predicting wind speed across three locations in South Africa, characterised by different weather patterns. Empirical results show that CNN outperforms ANN in accurately forecasting wind speed under different weather conditions. This superiority is likely due to the inherent architectural attributes of CNNs, including feature extraction capabilities, spatial hierarchy learning, and resilience to spatial variability. These results could be useful to decision-makers in the energy sector.

Sarcopenia is associated with clinical complications, which increase mortality. Effective sarcopenia risk screening in the elderly should be grounded in a comprehensive assessment, encompassing both nutritional status and medication use. Current screening tools, while emphasizing muscular per-formance, exhibit limited sensitivity in identifying elderly individuals at risk of sarcopenia. This cross-sectional study aimed to develop a sarcopenia risk screening tool, named Sarc-Global, with enhanced sensitivity, incorporating factors pertinent to the overall health of the elderly. Utilizing the criteria established by EWGSOP2 for diagnosing sarcopenia, we evaluated 395 communi-ty-dwelling elderly individuals. This evaluation served as a reference for assessing the efficacy of two validated questionnaires, Sarc-F and Sarc-CalF, and our newly developed Sarc-Global. Con-structed through multiple logistic regression, Sarc-Global integrates variables such as sex, age, medication use, body mass index, arm circumference, and handgrip strength – all significantly associated with sarcopenia (p

Walnut trees are grown worldwide for their edible fruits of high nutritional value. To address climate change, researchers have studied walnut phenology to create cultivars adapted to warmer climates. The standardisation of walnut phenological observations is needed and the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale is the most accurate and internationally accepted for this purpose. Here, the principal growth stages (PGS) of Persian walnut (Juglans regia L.) are described using stages from a previously available alphanumerical scale. This standardised phenological scale describes Persian walnut growth from the dormant vegetative state through reproductive budding to senescence. This phenological scale is expected to increase the efficiency of walnut phenological monitoring. Fifty-seven stages were used to describe the life cycle of Persian walnut in this BBCH scale. Of these 57 stages, 3 stages are dedicated to seed germination (PGS-0), 4 stages to bud development (PGS-0), 7 stages to leaf development (PGS-1), 4 stages to stem elongation (PGS-3), 8 stages to inflorescence emergence (PGS-5), 5 stages to male flowering (PGS-6), 5 stages to female flowering (PGS-6), 5 stages to fruit development (PGS-7), 12 stages to fruit ripening (PGS-8) and 4 stages to leaf senescence (PGS-9).

The critical role of global electron density transfer (GEDT) in increasing the reaction rate of polar organic reactions has been studied within the framework of Molecular Electron Density Theory (MEDT). To this end, the series of the polar Diels-Alder (P-DA) reactions of cyclopentadiene with cyanoethylene derivatives, for which experimental kinetic data are available, have been chosen. A complete linear correlation between the computed activation Gibbs free energies and the GEDT taking place at the polar transition state structures (TSs) is found; the higher the GEDT at the TS, the lower the activation Gibbs free energy. An interacting quantum atoms (IQA) energy partitioning analysis allows establishing a complete linear correlation between the electronic stabilization of the electrophilic ethylene frameworks and the GEDT taking place at the polar TSs. This finding supports Parr’s proposal for the definition of the electrophilicity  index (J. Am. Chem. Soc., 1999, 121, 1922). The present MEDT study allows establishing the critical role of the GEDT in the acceleration of polar reactions, since the electronic stabilization of the electrophilic framework with the electron density gain is greater than the destabilization of the nucleophilic one, making a net favorable electronic contribution to the decrease of the activation energy.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

This article seeks to provide an improved and more comprehensive understanding of the concept and theories on wasteland. It achieves this by focusing on the Indian context allowing us to unpack the importance of including multiple perspectives of wasteland narratives; this means including more positive narratives of the potential of wasteland to inform and improve prospects for land policies in the global south. Wasteland is commonly recognized as an underutilized category of land that may transform into a valuable resource base with proper management measures. The term waste has multiple angles that carry different notions ranging from fallow to agroforestry land in the global south and Brownfield to green space in the global north. We conduct a narra-tive review approach to qualitatively analyze the concept wastelands studied in pre-existing lit-erature from 1970 to present. The unsystematic literature review approach incorporates multiple elements of wasteland discourse, like understanding the meaning of the term on a global scale, setting out the meaning of the term waste into multiple perspectives explicitly in the Indian con-text, along with different classes and management approaches of wasteland from a national per-spective. The multiple perspectives of wasteland not only generate misconception on land resource but spawn difficulties in land use policy particularly for Indian scenario. For sustainable land use policy, reclaiming wasteland would be the best possible way for India and other countries in Global South, which requires a comprehensive methodological overview on wasteland narrative.

A method of geoengineering/solar radiation management is proposed as global warming countermeasure. The idea is to decrease surface temperature by increasing stratospheric ozone. One-dimensional (vertical) radiative-convective equilibrium temperature calculations show that surface temperature can be lowered by increasing the amount of ozone in the stratosphere. It was the increase in altitude above about 25 km that reduced surface temperatures. The method of increasing the amount of ozone (O3 injection), the rate of decrease of surface temperature after the ozone increase, and the rate of decrease of ozone concentration by photochemical reaction and diffusion/transport after the ozone increase are discussed.

In Yaoundé, Cameroon, where walking is a leading transport mode, pedestrian safety remains an issue as they account for a fair share of road traffic casualties, partly due to the lack of walking policies and data on pedestrian facilities safety, hindering targeted intervention. This study investigated road segments using the Pedestrian Safety Index (PSI) and the Global Walkability Index (GWI) across 12 key roads frequented by diverse pedestrian groups, especially students. The indexes were graded from E to A and analyzed using description and rank correlation analysis. Only one segment (R7) achieved a grade C, while the remainder scored D or E, indicating poor pedestrian safety conditions and unpleasant walking experience. The strong correlation coefficient (0.69) between the PSI and GWI at a 99% significance level validated the safety assessment, providing confidence in the safety results. Leveraging these findings, a seven-year (2024-2030) safety strategy was developed aiming to upgrade all roads to grade B. This strategy contains interventions including engineering improvement, proven effective. The study offers evidence for city officials to improve pedestrian safety and informs walking policies development and upcoming projects implementation. Future research should include more road segments and validate indexes with crash or conflict data.

Coronavirus disease 2019 (COVID-19) has affected medical practice. More than 7,000,000 patients died worldwide after being infected with COVID-19; however, no specific laboratory markers have yet been established to predict death related to this disease. In contrast, electrocardiographic changes owing to COVID-19 include QT prolongation and ST-T changes; however, there have been no studies on ambulatory electrocardiographic markers of COVID-19. We encountered three patients diagnosed as having COVID-19 who did not have a prior history of significant structural heart diseases. All had abnormalities in ambulatory echocardiogram parameters detected by high-resolution 24-h electrocardiogram monitoring: positive late potentials (LPs) and T-wave alternans (TWA), abnormal heart rate variability (HRV), and heart rate turbulence (HRT). Case 1 involved a 78-year-old woman with a history of chronic kidney disease, Case 2 a 76-year-old man with hypertension and diabetes, and Case 3 a 67-year-old man with renal cancer, lung cancer, and diabetes. None of them had a prior history of significant structural heart disease. Although no significant consistent increases in clinical markers were observed, all three patients died, mainly because of respiratory failure with mild heart failure. LP, TWA, HRV, and HRT were all positive in three cases with no significant structural cardiac disease at the initial phase of admission. There is a possibility that LP, TWA, HRV, and HRT have the potential to be risk stratifiers of COVID-19 pneumonia in the early phase of admission. Further accumulation of data regarding ambulatory electrocardiographic markers in patients with COVID-19 is needed.

Introduction: Carcinoid heart disease is a rare cause of right-sided valvular dysfunction, an important complication in patients with carcinoid syndrome. Objectives: We aim to present our own experience regarding early myocardial dysfunction in patients with carcinoid syndrome. Methods: 64 patients (35-90 year old, 62,5% female, 37,5% male patients) with carcinoid syndrome were evaluated be-tween October 2017 and June 2021. All patients underwent two-dimensional echocardiography with additional tissue Doppler and speckle-tracking techniques. For all patients there were measured urinary level of 5- hydroxyindolacetic acid, serotonin and NT-proBNP values. Results: The following echocardiographic features: global longitudinal strain of both ventricles, mean systolic velocity of the left ventricle, systolic velocity of the free wall of right ventricle, correlated with high values of specific biomarkers: serotonin, and urinary 5-hydroxyindolacetic acid, and also with the presence of hepatic metastases and poorly differentiated tumors. Discussion: In this study we identified several patients with early myocardial dysfunction due to carcinoid syndrome, representing a predictive model of poor prognosis, requiring further investigations. Conclusion: Echocardiographic assessment based on the measurement of systolic velocities, as well as the global longitudinal strain of both ventricles, together with specific biomarkers may contribute to better recognition of carcinoid heart disease in clinical practice, but this needs validation through further studies enrolling larger number of patients.

The research develops and validates new decomposition models for DNI estimations from Southern African data. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. Furthermore, clustered models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data is limited or data is available from multiple nearby stations. The Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The overall metrics affirm the substantial advancement achieved with the Southern African model. This study focused on validating the model for hourly DNI in Southern Africa within a range of clearness index-intervals from 0.175 to 0.875. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Artificial Intelligence (AI) emerges as a powerful catalyst poised to reshape the global sustaina-bility landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile de-viation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts' collective positive perspective regarding AI's pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14 and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10 and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI's impact on peace, justice, and strong institutions as well as on partnerships for the goals respectively. This paper underscores AI as a formidable instrument poised to address humanity's most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future.

Climate change, recognized as a global crisis, is not only altering our environment but also significantly impacting human health. Particularly, its influence on non-communicable diseases (NCDs), a leading cause of global mortality, is becoming increasingly evident yet underexplored. This review aims to synthesize current research on the impact of climate change on NCDs, addressing a critical gap in our understanding of how environmental changes affect human health. We examine the complex interplay between climate change and NCDs, drawing insights from recent studies that highlight the adverse effects of climate variables on chronic diseases, such as cardiovascular and kidney diseases, and the role of food and nutrition security in this nexus. Furthermore, the review explores the disproportionate impact on vulnerable regions and populations, particularly in Pacific Island countries and territories, underscoring the urgent need for context-specific solutions and a cross-sectoral approach. By integrating findings from various disciplines, this review aims to inform policy and public health interventions, highlighting the necessity of collaborative efforts to mitigate the burgeoning burden of NCDs in a rapidly changing climate.

Background and Objectives Transpulmonary thermodilution has gained interest in recent years. It measures cardiac output (CO), stroke volume (SV), but also calculates global end diastolic indexed volume (GEDVI), and global ejection fraction (GEF). GEDVI is a static variable of cardiac preload. GEF provides information regarding cardiac function. GEF algorithm depends on SV and GEDVI. Therefore, in hypovolemic patients with decreased preload, SV or GEDVI could be decreased, and lead to altered GEF. The aim of our work was to evaluate whether impairment in GEDVI and SV may constitute a limitation for the interpretation of GEF in hypovolemic patients. A secondary objective was to determine factors independently associated with impaired GEF. Materials and Methods A retrospective study was conducted. Statistical analysis was performed by means of Mann Whitney and student t test. To determine correlation between GEF with SV, or GEF with GEDVI Pearson's coefficient was used. To determine factors independently associated with the presence of impaired GEF, binary logistic regression was performed. Results We included 113 patients with shock. Differences in mortality were detected in GEF (21[16-28] survivors versus 18 in non survivors[11-26]; p= 0.01). 38(34%) patients had hypovolemia. 44(39%) of them had depressed GEF values. Hypovolemic patients had lower GEF values than non-hypovolemic patients (16 [12-22] vs 20 [15-27]; p=0.01). Both SV and GEDVI had a statistically significant correlation with GEF (SV (Pearson's r 0.74; (p=0.00)) and GEDVI (Pearson's r -0.25; (p=0.00)). The only factors independently associated with low GEF were low SV and low GEDVI. Conclusion Hypovolemic patients frequently show low GEF values. We found correlation between SV and GEDVI with GEF. Low SV and low GEDVI were associated with low GEF. This could constitute a limitation in the interpretation of GEF and cardiac pump function. Hypovolemic patients could have low GEF without implying disorders in cardiac contractility.

This paper explores the imminent threat posed by climate change to the unique platypus species. It delves into the ecological repercussions of global temperature rise and altered climate patterns on platypus habitats. The study emphasises the multifaceted impacts, including changes in aquatic habitat quality, food sources, and breeding rituals. Climate-induced shifts in phytoplankton spring blooms and anthropogenic pressures further endanger the platypus's habitat. The article stresses the importance of understanding these effects for both species conservation and anticipating broader ecological and socio-economic consequences. Mitigating climate change impacts requires a multidimensional approach, involving habitat manipulation and restoration strategies.

Rapid increase in urbanization and industrialization due to increase in human population has led to increase in global warming resulting to climate change that affects the ecosystem. Unusual natural disasters such as cyclones, storms, floods, weather fluctuations, rising seal levels, transitional ecosystems and coastal disruptions are major adverse effects of climate change. There are huge worrisome concerns and uncertainties about the future of the planet earth due to climate change caused by high carbon emissions. Global aggressive policies in control of Carbon-dioxide (CO2) emissions from several combustion mechanisms have paved way for alternative/ clean energy sources. Green hydrogen is a promising means of clean energy sources for commercial power generation stations and other crucial systems such as hydrogen fuel cell vehicles (HFCVs) , motors, drives, backup power for buildings, and also serving as a source of electrical energy for remote locations. The goal of this study is to review the current level of Sub-Saharan African nations’ preparedness to adopt green hydrogen as a way to mitigate local energy supply issues and strengthen their individual economies.

The purpose of the study was to identify factors characterizing a decrease in LV global systolic function in patients with ST-segment elevation myocardial infarction (STEMI) after revascularization using cardiac magnetic resonance imaging (MRI)-based ischemic injury pattern and laboratory parameters sensu left ventricular global systolic function. A total of 109 STEMI patients were examined. The patients underwent contrast-enhanced cardiac MRI with a 1.5 Tesla GE SIGNA Voyager (GE HealthCare, USA) on the 7th-10th days from the onset of the disease. According to cardiac MRI analysis, the patients were divided into the following groups with regard to left ventricular ejection fraction (LVEF) values: group 1 – patients with LVEF ≥50%; group 2 – patients with mildly reduced LVEF 40-49%; group 3 – patients with low LVEF <40%. A predominance of most parameters of the ischemic injury pattern was noted in patients with mildly reduced and low LVEF versus patient group with LVEF ≥50%. Some risk factors for a decrease in LVEF <50% systolic function in STEMI patients after revascularization were revealed: male gender; time from the onset of the anginal attack to revascularization; coronary artery status; several LV parameters; ischemic injury characteristics; natriuretic peptide and troponin I levels.

Based on recent interest and on the importance of the ongoing climate change catastrophe, this article provides the basics of global carbon cycle modelling as required for the assessment of the degree of carbon neutrality of biomass energy, and its underlying dynamics. It is aimed at clari-fying the question “Are biomass fuels carbon neutral?”. The "Combined Energy and Biosphere Model" (CEBM) computes annual carbon flows including growth and decay of plants on 2.52.5° grid elements of the continents’ surface and offers detailed results on the changes of after implementation of large-scale biomass energy strategies world-wide. The main (and possibly unexpected) effect is the long-term depletion of the soil organic compartment after extraction of biomass fuels. When comparing CEBM model runs using (i) biomass energy sources and (ii) carbon-free energy sources (such as solar or wind), it becomes quantitatively clear already on the theoretical level (i.e., even without taking into account effi-ciency losses) that biomass is only “half as carbon neutral” as ideally assumed, to express a rule of thumb – mainly because of soil carbon depletion. Still, biomass energy will play an important role when fighting global warming, even if efforts to lower energy demand are preferable as a fundamental strategy.

Skin and soft-tissue infections (SSTI) are common cases of hospital-acquired infections with aetiologic agents exhibiting antimicrobial resistance (AMR). We determined the prevalence, proportion of laboratory-investigated cases, AMR-profiles, and factors associated with SSTI and multi-drug resistance (MDR). This study was based on archived data of patients suspected of SSTI from 2019-2021 at Jinja Regional Referral Hospital. The analysis involved 268 randomly selected patient reports. Prevalence of SSTI was 66.4%. Laboratory-investigated cases were 14.11%. Staphylococcus aureus (n=51) was the most isolated organism. MDR pathogens explained 47% of infections. Methicillin-resistant S. aureus was up to 44%. In addition, 61% of Gram-negatives had the potential to produce extended-spectrum beta-lactamases, while 27% were non-susceptible to carbapenems. Ward of admission was significantly associated with infection (aPR=1.78, 95% CI: 1.003-3.18, p-value=0.04). Age category (19-35) was an independent predictor for MDR infections (aPR=2.30, 95%CI:1.02-5.23, p-value=0.04). The prevalence is relatively high with MDR pathogens responsible for almost half of the infections. Routine use of culture and sensitivity testing should be done for proper infection management. Gentamicin and ciprofloxacin can be considered for empirical management of emergency SSTI suspected of S. aureus. Recognizing SSTI under the Global Antimicrobial resistance Surveillance System would lead to improved preparedness and response to AMR.

An existing simplified simulation model of global climate, population, economy, and governance is extended, through statistical and other data analysis, to include several measures of human well-being and population displacement. The revised model is used to explore (in testing to the year 2060) vicious cycles and causal cascades that, some have warned, could lead to acceleration of climate change in the coming decades. Model scenario testing addresses two uncertainties, namely the strength of the effect of climate change on governance erosion, and the strength of the “heat trap” effect from physical tipping points such as ice loss and permafrost thaw. This testing indicates that a strong heat trap effect could very well accelerate climate change, but that even a strong governance erosion effect is unlikely to do so. Governance erosion would hurt human well-being but is probably not much of a climate threat per se. These results are tentative but point to the possibility of formally modeling causal cascades beyond what the larger climate models have done to date.

The term sustainable energy is very familiar to all of us yet its exact definition or meaning has so far been vague. To date the widely adopted definition of sustainable energy has been inspired by the definition of sustainable development formulated more than 30 years ago in the Our Common Future - the UN’s Report of the World Commission on Environment and Development. The current definition conceals the true spirit of the Report and this has some ramifications. It raises the question as to whether we have put too much emphasis on attaining the future dimension of sustainability in the middle of the situation where the energy survivability is in fact the real issue in many parts of the world. In this paper the role of energy in sustaining livelihoods of low income communities is discussed. Existing definition of sustainable energy that the whole world seems to have embraced is looked at critically and its downsides exposed. The identified pillars of sustainable energy and the associated issues is discussed and the need for an additional pillar, i.e. affordability is discussed and called for. This paper will argue that global definition of sustainable energy seems inappropriate and should be jettisoned and a more realistic definition should be formulated, adopted and embraced at local (i.e. national or regional) level taking into account local attributes and factors. The current push for a transition from the reliance on conventional energy sources to ‘cleaner’ energy sources (generally associated with renewable energy) has further exacerbated the issue of energy affordability that has made the conditions even more dire for so many groups of the world population. In the midst of this situation, those who push for a rushed energy transition seem to be those who also produce unsustainably high carbon footprint but who can easily offset their carbon foot-print through the ‘net-zero’ concept.

Background: Evaluation of left ventricular(LV) functions in patients with idiopathic premature ventricular contraction(PVC) with preserved LV Ejection Fraction(LVEF), especially in the subclinical stage, may be of great importance in terms of directing early treatment. Methods: A total of 122 patients, retrospectively recruited, were divided into three groups according to PVC burden: Group 1; 5%PVC<10%, Group 2; 10%PVC<15%, and Group 3; 15% PVC. Transthoracic echocardiography(TTE) was performed to evaluate LV parameters. Results: LV-GLS was found to be significantly lower in groups 2 and 3 with high PVC burden (18.89 ± 1.4, 17.55 ± 2.1, 16.26 ± 1.3; p<0.001 respectively). Correlation analysis revealed a positive and significant correlation between PVC burden with deterioration in LV-GLS (r:0.536; p<0.001). In addition, PVC burden was found to be an independent predictor of LV-GLS deterioration in multiple linear regression analysis(:0.525, p<0.001). The ROC curve analysis demonstrated that a PVC burden cut-off value of 8.4% was associated with a LV-GLS deterioration greater than -18, with a specificity of 75.4% and a sensitivity of 75.4% (AUC: 0.81 [0.73-0.88] 95% CI; p<0.001). Conclusions: PVC burden was an independent predictor of deterioration in LV-GLS. In the presence of subclinical LV injury associated with PVC burden, early catheter ablation strategies may be therapeutically beneficial.

Atrial fibrillation (AF), a complex cardiac arrhythmia, necessitates a shift in management paradigms, emphasizing an integrative approach that incorporates lifestyle modifications. Investigations into the role of interleukins and inflammatory mediators are underway, exploring their potential as diagnostic markers and predictors of cardiovascular events in AF. Despite the identification of biomarkers related to cardiac remodeling, their predictive ability for post-cardioversion outcomes remains controversial. Computational models are being developed to understand proarrhythmic contributions and identify anti-AF targets. Addressing the rising global incidence of AF, linked to stroke and heart failure, presents a critical health challenge. As risk factors like obesity prevail, AF becomes a pervasive public health concern, expected to double in prevalence over the next 50 years. The impact on mortality and morbidity, especially through hospitalizations, underscores the need for early detection and effective clinical management. Identifying individuals at increased AF risk becomes crucial for screening and prevention strategies. Lifestyle modifications, including weight management and physical activity, emerge as influential factors in AF management, offering the potential to alleviate symptoms and modify disease progression. The paper concludes by emphasizing the need for safer antiarrhythmic therapies based on AF mechanisms, promising improvement in global AF management. The multifaceted nature of AF etiology requires a broader perspective beyond traditional medical interventions, acknowledging the importance of integrated care to address this significant public health challenge.

In our increasingly urbanized world, precise and up-to-date maps of human settlements are essential for sustainable urban development policies. The availability of open-access Sentinel-2 data from the Copernicus program presents an opportunity to create a comprehensive global map of human settlements, offering a detailed view of built areas on a large scale. This study estimates large-scale built-up fractions using encoder-decoder deep learning architectures like U-net, Res-U-net, and Attention-U-net in the large and complex urban area of Delhi, India. Openly available datasets like Open Street Map (OSM) and Microsoft building footprint datasets are used to derive built-up fractions at 10×10m resolution cells for over 34,000 km2. Our results show that Attention-U-net with the Huber loss function performs the best in different built-up densities (i.e., urban, semi-urban or rural) with an R2 score of 0.631, while Res-U-net and U-net obtained an R2 score of 0.623 and 0.612, respectively. The investigated networks significantly improve the accuracy over the latest Global Human Settlement Layer product (GHSL-S2), which uses a deep-CNN and reaches an R2 of 0.387 in our case study area. The result of this study yields a valuable spatial layer for examining the spatial distribution of human settlements across the entire spectrum from rural to urban areas.

This article delves into the complex tension between national self-rule and international involvement, in the context of global politics, examining the inherent challenges of upholding both principles when confronted with human rights abuses and the pursuit of global peace. The introduction presents the fundamental dilemma: self-governance versus moral obligations. The problem statement calls for a nuanced approach to address the intricate complexities of sovereignty and intervention, acknowledging the potential misuse by oppressive regimes and the ongoing challenges of determining when and how intervention is warranted. The article advocates for a more responsible and effective approach to global conflicts and human rights violations, balancing national sovereignty with judicious intervention when required. The methodology employs both literature review and case analysis to construct a framework for objectively assessing when international intervention is justified in instances of human rights abuses. The conclusion stresses the criticality of objective intervention criteria, transcending cultural, political, and economic biases, and advocates for a globally accepted approach that celebrates diversity while promoting global stability.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Background: In chronic obstructive pulmonary disease (COPD), there are two known classifications for assessing what is called disease severity. One is the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, which is based on the post-bronchodilator value of FEV1 (% reference). The other is the STaging of Airflow obstruction by Ratio (STAR). Methods: We reanalyzed data from our outpatient cohort study, which included 141 participants with COPD from 2015 to 2023. We compared mortality and COPD-specific health status between the GOLD 1 to 4 groups and the STAR 1 to 4 groups. Results: By simple calculation, GOLD and STAR severity classes coincided in 75 participants (53.2%). The weighted Bangdiwala B value with linear weights was 0.775. The participants were observed for up to 95 months, with a median of 54 months. Death was confirmed in 29 participants (20.5%). In univariate Cox proportional hazards analyses, there was a significant difference in mortality between the GOLD 1 and GOLD 3+4 groups, with the GOLD 1 group used as the reference [Hazard Ratio 4.222 (95% CI 1.298 - 13.733), p=0.017]. However, there was no statistically significant predictive relationship between STAR 1 and STAR 2, or between STAR 1 and STAR 3+4. The St. George's Respiratory Questionnaire (SGRQ) Total and COPD Assessment Test (CAT) scores were significantly different between all GOLD groups, except for the CAT score between GOLD 1 and GOLD 2. The SGRQ Total and CAT scores were significantly different between STAR 1 and STAR 3+4, but not between STAR 1 and STAR 2. Conclusion: From the perspective of all-cause mortality and COPD-specific health status, the GOLD classification is more discriminative than STAR.

Abstract: Recent years demonstrate the increased attention to radon from two scientific directions. After neglecting radon as earthquake precursors in 1990-th it becomes again the subject of earthquake-forecast papers discussions due to growing networks of the radon monitoring in different countries, especially, the technologies of real-time radon measurements where the gamma-spectrometers become the leader of interest as the sources of 222Rn identification. The second fast developing direction is including the radon in the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) models as a source of the boundary layer ionization. And here we encounter with second direction which is not connected with the earthquake forecast problems. It is the role of air ionization by radon as a source of the Global Electric Circuit (GEC) modification. In this publication we try to unite all these problems to present more complex view on radon as important element of our environment.

Background: Parkinson's disease (PD) is a global disease. However, there is a lack of research specifically focused on the AfrAbia population. Climate change, driven mainly by global warming, is linked to detrimental health impacts, including an increased frequency and intensity of heat waves. Neurological diseases, including neurodegenerative syndromes like Alzheimer's disease, Parkinson's disease, and motor neuron illnesses, have been increasingly seen and recorded in scientific literature. Although there is much literature demonstrating the growth in neurodegenerative disorders, the relationship between environmental variables such as global warming, air pollution, heavy metals, and pesticides and the higher occurrence of these illnesses is still unknown. Objective: The current article primarily aims to examine the relationship between climatic changes, air pollution, pesticides, heavy metals, and the occurrence of PD. Initially, we evaluate the influence of exposure to these variables on the cause or origin of Parkinson's disease. In addition, we will investigate how these variables disrupt processes such as excitotoxicity, oxidative stress, and neuroinflammation, all of which are strongly linked to Parkinson's disease (PD). Conclusions: This research presents a plausible rationale for the adverse effects of environmental influences on the brain, which are pivotal in the progression or onset of Parkinson's disease from an AfrAbian standpoint.

This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993-2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

Climate change presents an urgent and impending threat to virtually all global biological systems. In recent years, there has been a surge in research endeavors aimed at investigating the potential relationship between changes in climate patterns and the spread of infectious diseases. This scoping review included studies on global warming and infectious diseases from 2019 to 2023, The studies included in this research were obtained from reputable academic databases such as Web of Science and google scholar. A systematic keyword search was conducted to identify relevant studies, and a predefined set of inclusion criteria was applied to review and select the appropriate studies for analysis. This review found that climate change has a remarkable impact on the dissemination and incidence of many infectious diseases, it increases microbial resistance and may increase the prevalence the arthropods-borne diseases with mild effect on fungal infections, also, the low to middle income countries may be affected more than other countries from these virulent pathogens, this study concluded that a future research in the field of climate change and infectious diseases should involve a thorough examination of the transmission of diseases, considering both direct and indirect modes of transmission

Cultures in Mediterranean climate zones (MCZs) around the world have long been reliant on groundwater and springs as freshwater sources. While their ecology and cultural sustainability are recognized as critically important, inter-relationships between springs and culture in MCZs has received little attention. Here we augmented a global literature review with case studies in MCZ cultural landscapes to examine the intensity of cultural and socio-economic impacts on springs ecohydrogeology. MCZs are often oriented on the west and southern coasts in tectonically active landscapes, which control aquifer structure, the prevalence of westerly winds, and aridity, and generally expose associated habitats to harsh, afternoon sunlight. Cultural appreciation and appropriation of springs ranges widely, from their use as subsitence supplies to profound traditions, such as Greco-Roman nymphs, and Asian and Abrahamic spiritual cleansing and baptism. Commoditization of water resources for agriculture, industry (e.g., mining, water bottling, geothermal resorts), and urban needs is placing ever-increasing, non-sustainable demands on aquifers and springs. When economic values approach or exceed cultural values, these irreplaceable aquatic resources are degraded and lost. Sustainable stewardship of springs and the aquifers that support them are a little-recognized, but central conservation challenge for modern society facing global climate changes.

We propose Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes the 3×3 convolution in bottleneck of HRNet with Contextual Transformer (CoT) block, while reducing the complexity of the network. Specifically, CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on the query and contextualization keys, the dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for the multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module(AFAM). Technically, given an intermediate input, AFAM successively deduces attention maps along channel and spatial dimensions. Then, Adaptive refinement module(ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above studies, our lightweight network provides a powerful clue for detection of keypoints. Experiments are implemented on the COCO and MPII datasets. The model achieves 76.2 AP on the COCO val2017. Compared to other methods with the CNN as the backbone, CaFTNet reduces the number of parameters by 72.9 %. On the MPII, our method uses only 60.7% of the number of parameters, acquiring semblable results to other methods with the CNN as the backbone.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform path characterizes this error. In response to this challenge, we propose a linear, parameter-free model, which uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established from the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation with substantial pose differences and efficient pose optimization in larger circuits.

The procedures used to create modern cars require extensive thought in various relevant scientific domains. Arguably, the most challenging obstacle facing the automobile sector is managing production facilities by integrating software production lines and CI/CD. All this is determined by market demands, the engine of a vehicle, and the complexity of assembling the entire car and installing its corresponding embedded software. As a result, concerns about various types of global change have grown, as well as the lack of the ability to use fossil fuels, creating a substantial impact on the purchase and sale of modern automobiles. The research foundation is reflected in covering strategies for the deployment and administration of software, as well as opportunities for business improvement in particular production processes. This article strives to provide a summary of a scientific investigation of original equipment manufacturers, market segmentation, and the effects of global market changes on automotive manufacturing by examining the correlation between certain changes in the purchase of a specific brand and the powertrain of a vehicle. The research tries to examine numerous datasets from the United States of America and Washington State, on the basis of which we may estimate possible future changes in the automotive industry’s sales.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

On February 24, 2022 Russia started the full-scale invasion of Ukraine, which created an unprecedented number of refugees. To estimate the influence of this humanitarian disaster on the COVID-19 pandemic dynamics, the averaged daily numbers of cases and reproduction numbers in Ukraine, the UK, Poland, Germany, the Republic of Moldova, and in the whole world were calculated for the period February-April 2022. The registered numbers of cases were compared with ones calculated with the use of the generalized SIR-model and corresponding parameter identification procedure for the previous epidemic waves in Ukraine, Poland, Germany, and the world. Since before February 24, 2022 the estimation of the number of infectious persons per capita in Ukraine 3.6 times exceeded the global figure, the increase of the number the new cases and the pandemic duration was expected. In March 2022 the increase of the averaged number of new cases in the UK, Germany, and worldwide was visible. A simple formula to estimate the effective reproduction number based on the smoothed accumulated numbers of cases is proposed. The results of calculations agree with the figures presented by John Hopkins University and demonstrate a short-term growth of the reproduction number in the UK, Poland, Germany, Moldova, and worldwide in March 2022.

Heavy metal pollution poses a significant threat to global populations, particularly impacting human health. Developing countries, characterized by chemical engineering and mining industrial activities, often contribute to diverse heavy metal contaminants. Consumption of water contaminated with heavy metals, prevalent in such regions, can lead to severe health risks, including gastric cancer. Addressing the urgent issue of heavy metal pollution, this paper employs a bibliographic approach to explore the latest literature on heavy metal pollution. The study discusses recent research findings and potential methods for the treatment and mitigation of heavy metal pollution, emphasizing the pressing need for effective solutions to safeguard public health.

Complaint of transient impairment or loss of memory as an attendant feature in some migraine attacks has long been recognised. In some cases, migraine may be a trigger or precipitating factor for the syndrome of transient global amnesia (TGA). However, the exact frequency of this concurrence is unknown, perhaps related to the absence from the International Classification of Headache Disorders (ICHD3) of amnesia or memory loss symptoms in association with migraine, unlike the situation with epileptic seizure (migralepsy) or stroke (migrainous infarction). Similarly, headache has generally been regarded as an incidental feature of TGA, reported in about 10% of cases. We present further examples of TGA in the context of migraine headache; consider possible reasons why this concurrence might be under-recognised, such as the retrograde amnesia characteristic of TGA; review possible shared pathogenetic mechanisms; and suggest a new terminology, “migramnesia”, which may encourage clinicians to address the possible significance of migraine in the context of an episode of TGA.

In the transition towards sustainable energy sources, hydrogen has emerged as the predominant alternative after fossil fuels. This comprehensive literature review delves into the evolving landscape of hydrogen launching strategies within the energy market. Numerous countries have strategically initiated efforts to introduce products based on hydrogen, underscoring the global acknowledgment of the potential inherent in hydrogen. Despite witnessing significant growth in the adoption of hydrogen energy worldwide, impediments to its widespread commercialization persist. This review identifies key barriers, including cost structures, technical complexities, and marketing challenges, hindering the establishment of a robust hydrogen energy market. The article critically examines the actions already undertaken to harness hydrogen energy for commercial purposes, leveraging scientific insights and statistical data. By carefully examining the current state of hydrogen energy initiatives, the review highlights gaps in existing strategies that require thoughtful consideration. Insights derived from scientific and statistical analyses inform the identification of barriers and contribute to a nuanced understanding of the challenges faced in the journey towards hydrogen energy commercialization. Ultimately, the article presents a strategic proposal outlining marketing actions geared towards expediting the commercialization and widespread adoption of hydrogen energy. Grounded in a thorough review of existing literature and supported by scientific and statistical evidence, this comprehensive analysis offers valuable insights for stakeholders seeking to navigate the intricate landscape of hydrogen energy in the global energy market.

In this paper, we propose a method for assessing the effectiveness of entropy features using several chaotic mappings. We anlyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) on four discrete mappings: the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn is shown to have improved global efficiency (GEFMCC) in the classification task compared to NNetEn. At the same time, there are local areas of the time series dynamics in which the classification efficiency NNetEn is higher than FuzzyEn. The results of using horizontal visibility graphs (HVG) instead of the raw time series are also shown. GEFMCC decreases after HVG time series transformation, but there are local areas of the time series dynamics in which the classification efficiency increases after including the HVG. The scientific community can use the results to explore the efficiency of entropy-based classification of time series.

Global health is significantly impacted by antimicrobial resistance. Antibiotic misuse and overuse have resulted in the emergence of drug-resistant bacteria, viruses, parasites and fungi, which makes ordinary infections more difficult to cure and accelerates the spread of disease. The World Health Organisation has declared that antimicrobial resistance could result in up to 10 million deaths yearly by 2050, making it a worldwide health emergency. One of the greatest challenges to global health, food security, and development today, according to the World Health Organisation is the emergence of antibiotic resistance. Even though antibiotic resistance can arise naturally, improper usage of these medications and many other factors are accelerating its global spread. Antimicrobial resistance is frequently referred to as the "Silent Pandemic" and is a problem that requires current action and should be managed more effectively rather than considered as a future situation. This paper address a number of antimicrobial classes, global health, Onehealth and how drug resistance affects human health, and related risk factors. This review will also provide background information on the causes and processes of antimicrobial resistance in brief by identifying these characteristics, targeted interventions can be developed to lower the risk of illnesses that are resistant to antibiotics. It will also offer insights into new and emerging solutions that are being studied and may be created in the future to address the advancement of antimicrobial resistance.

More than 13.5 billion COVID-19 vaccine doses have so far been delivered between 2021 and 2023 through a mix of delivery platforms, with mass vaccination campaigns being the main approach. In 2022, with the most likely scenario of continued circulation of SARS-CoV-2 and the need for periodic booster doses, there was a need for countries to plan for more sustainable approaches to provide COVID-19 vaccination. In this context of uncertainty, a global tool for integrating COVID-19 vaccines into immunization programmes and primary health care was published jointly by WHO and UNICEF to respond to country needs. The guidance leveraged existing health system frameworks, proposed four steps for the COVID-19 integration journey, and identified investment areas. The development process maximized robust global stakeholder and country engagement, and the timeframe was aligned with donor funding windows to support the integration of COVID-19 vaccination. The rapid dissemination of guidance allowed its use by countries to ascertain their readiness for integrating COVID-19 vaccination and inform the development of national plans and funding applications. While progress has been made in specific areas (e.g., optimizing cold chain and logistics leveraging COVID-19 vaccination), in the context of decreasing demand for COVID-19 vaccines, reaching adult COVID-19 vaccine high-priority-use groups and engaging and coordinating with other health programmes (beyond immunization) remains a challenge. We share the learning that despite the uncertainties of a pandemic, guidance documents can be developed and used within a short timeframe. Working in partnership with stakeholders beyond immunization towards a common objective is powerful and can allow progress to be made on integrating health services and preparing better for future pandemics.

The important topic of climate change is discussed in this article, with a focus on the historical background and present effects. It draws attention to the differences between climate change and global warming and attributes the faster pace of climate change to human activity, specifically the release of greenhouse gases into the atmosphere. Agricultural practices, deforestation, and the combustion of fossil fuels are examined in conjunction with natural contributors like volcanic emissions. There is discussion of the far-reaching effects of climate change, such as increased temperatures, rising sea levels, harsh weather, and effects on water supplies. The article emphasizes how crucial it is to mitigate and adapt to the effects of climate change by putting in place climate-resilient infrastructure, switching to renewable energy sources, and lowering greenhouse gas emissions. While recognizing the difficulties involved, it examines several approaches, including nuclear energy, wind power, carbon capture and storage, and green building techniques. To successfully address the global challenge of climate change, the conclusion emphasizes the urgent need for joint efforts by scientists, environmentalists, communities, and governments.

Microplastic contamination, defined as plastic particles less than 5 mm in size, has become a major global environmental concern. This study synthesizes findings from carefully chosen studies undertaken in a variety of geographical areas and ecosystems, offering light on the multifaceted issues posed by microplastic pollution. These articles show the global prevalence of microplastics in both land and marine ecosystems. Seasonal fluctuations in microplastic abundance highlight the cyclical nature of this pollution, with larger amounts being reported during the summer months. Tourism, industrialization, and urbanization have all been highlighted as significant contributors to microplastic contamination, stressing the impact of man-made influences. Wastewater treatment plants (WWTPs) have been identified as hotspots for microplastic release into aquatic habitats, emphasizing the need for enhanced treatment procedures. Because microplastics can build up in the food chain, it underlines the possible health problems connected with microplastic pollution for both aquatic creatures and human consumers. Longitudinal studies, geographical variety, in-depth health and ecological impact evaluations, standardization, and effective mitigation methods should be prioritized in future studies. To address this worldwide issue, interdisciplinary collaboration among scientists, health professionals, politicians, and industrial stakeholders is required.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the GNSS data collected from 12 stations situated in Yunnan, which are part of Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) from 2011 to 2022. The retrieved PWV at Station KMIN were evaluated by the nearby radiosonde data, and the result shows that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the station heights and latitudes. Differences between average PWV in wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. Amplitudes of the PWV annual cycles are 9.75-20.94 mm. The spatial variation of these amplitudes is similar to that of average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

Recent years have experienced an increase in construction and demolition waste (C&amp;DW), which is the most significant solid waste stream. C&amp;DW is contributing to exceeding all planetary boundaries and presents a range of other issues. In order to better understand the existing research on C&amp;DW, a global bibliographic analysis was undertaken through seven groups of keyword searches and the results visualised using VOCviewer. The results show that C&amp;DW receives only a modest research attention compared to other areas of waste, and this is despite an exponential increase in C&amp;DW research since 2016. The analyses also show that concrete is the most researched material in terms of C&amp;DW, and that reuse, recycling, and circular economy are so far attracting only proportionally modest research attention. This signals a need for further acceleration of the C&amp;DW research, and specifically for more research on reuse, recycling, and circular economy for materials other than concrete.

This article develops duality principles, a related convex dual formulation and primal dual formulations suitable for the local and global optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation. Other applications include primal dual variational formulations for a Burger's type equation and a Navier-Stokes system. We emphasize the novelty here is that the first dual variational formulation developed is convex for a primal formulation which is originally non-convex. Finally, we also highlight the primal dual variational formulations presented have a large region of convexity around any of their critical points.

Background: We aimed to evaluate whether baseline GLS and NT-proBNP and their changes after cardiac resynchronization therapy (CRT) can predict the long-term clinical outcomes and the echocardiographic-based response to CRT (defined by 15% relative reduction in left ventricular end-systolic volume). Methods: We enrolled 143 patients with stable, ischemic heart failure (HF) undergoing CRT-D implantation. NT-proBNP and echocardiogram were obtained before and 6 months after. The patients were followed-up (median: 58 months) for HF-related deaths and/or HF hospitalizations (primary endpoint) or HF-related deaths (secondary endpoint) Results: 84 patients achieved the primary and 53 the secondary endpoint, while 104 patients were considered as CRT-responders and 39 as non-responders. At baseline, event-free patients had higher absolute GLS values (p&lt;0.001) and lower NT-proBNP serum levels (p&lt;0001) than those achieving primary endpoint. A similar pattern was observed in favor of CRT-responders vs non-responders. In Cox regression analysis, lower baseline absolute GLS value (HR=0.77;95%CI, 0.51-1.91; p=0.002), higher baseline NT-proBNP levels (HR=1.55;95%CI, 1.43-2.01;p=0.002) and diabetes (HR=1.27;95%CI, 1.12-1.98;p=0.003) were associated with lower incidence of primary endpoint. Conclusions: In HF patients undergoing CRT-D, baseline GLS and NT-proBNP concentrations may serve as prognostic factors, while they may predict the echocardiographic-based response to CRT.

Post-training quantization is pivotal in deploying convolutional neural networks for mobile applications. Block-wise reconstruction with adaptive rounding, as employed in prior works like BrecQ and Qdrop, facilitates acceptable 4-bit quantization accuracy. However, adaptive rounding is time-intensive, and its constraint on weight optimization space curtails the potential for quantization performance. The optimality of block-wise reconstruction hinges on the quantization status of subsequent network blocks. In this investigation, we delve into the theoretical underpinnings of the limitations inherent in adaptive rounding and block-wise reconstruction. Our exploration leads to the development of a post-training quantization methodology, designated as AE-Qdrop. This algorithm operates in two distinct phases: block-wise reconstruction and global fine-tuning. The block-wise reconstruction phase introduces a progressive optimization strategy, superseding adaptive rounding, which not only augments quantization precision but also significantly improves quantization efficiency. To mitigate the risk of overfitting, we introduce a random weighted quantized activation mechanism. During the global fine-tuning phase, we account for interdependencies among quantized network blocks. The weight of each network block will be corrected with logit matching and feature matching. Extensive experiments validate that AE-Qdrop achieves high-precision and efficient quantization. For instance, in the case of 2-bit MobileNetV2, AE-Qdrop outperforms Qdrop by achieving a 6.26% enhancement in quantization accuracy and quintupling the quantization efficiency.

We consider boundary value problems for a nonlinear mass transfer model, which generalizes the classical Boussinesq approximation, under inhomogeneous Dirichlet boundary conditions for the velocity and the substance’s concentration. It is assumed that the leading coefficients of viscosity and diffusion and the buoyancy force in the model equations depend on concentration. We develop mathematical apparatus of studying inhomogeneous boundary value problems under consideration. It is based on using of a weak solution of the boundary value problem and construction of liftings of the inhomogeneous boundary data. They remove the inhomogeneity of the data and reduce original problems to equivalent homogeneous boundary value problems. Based on this apparatus we will prove the theorem of the global existence of a weak solution to boundary value problem under study and establish important properties of the solution. In particular, we will prove the validity of the maximum principle for the substance’s concentration. We will also establish sufficient conditions for the problem data, ensuring the local uniqueness of weak solutions.

This work explores five falling film evaporator (FFE) simulation approaches combined with energy consumption minimization strategies, namely Mechanical Vapor Recompression and Thermal Vapor Recompression (MVR and TVR respectively). Global system analysis and advanced dynamic optimization strategies are then investigated to minimize steam consumption, the cost of steam, the total annualized cost and maximizing product yield. The results indicate that a higher TVR discharge pressure, or MVR compression ratios, along with higher feed temperatures enhance evaporation with but increase operational costs. The most economic option includes three evaporator effects with TVR for achieving 50% product dry mass content. However, for a 35% dry mass content, MVR becomes cost-effective with an 11% reduction in unit electricity prices or a simultaneous 7% drop in electricity prices and a 5% increase in gas-based steam prices. Furthermore, switching from milk powder production to milk concentrates, an annual cost reduction ranging from 10.75 to 44% is achieved. Also, a forecasted 20% (or more) reduction of biomass-based steam cost can lead to lower annual expenditure comparing with the nominal NG-based steam Case. Regarding the total annualized cost, for a new plant design, optimization strategies lead to a 9 - 45% reduction in the total cost depending on the Case under consideration.

Worldwide, among older adults, infection is the leading mortality cause; chronic nutritional deficits confer significant mortality risk and increase healthcare costs. The association of the Global Leadership Initiative on Malnutrition (GLIM)-defined malnutrition with infection-related mortality, especially with renal dysfunction in older patients, remains unevaluated. In older patients hospitalized with infection, stratified by renal function, we investigated the association of malnutrition severity with mortality. This single-center, retrospective cohort study of 1220 participants (age ≥65 years) admitted to Aichi Medical University from 2019 to 2020 used a multivariate Cox proportional hazards model to determine the association of GLIM-defined nutritional status (no, moderate, or severe malnutrition) with all-cause mortality. Compared to no malnutrition, moderate and severe malnutrition conferred significantly higher mortality (adjusted hazard ratio [confidence interval ]: 1.00 [reference], 1.76 [1.28-2.42], and 2.44 [1.78-3.33], respectively), with the highest all-cause mortality in malnutrition with a low estimated glomerular filtration rate (eGFR &lt;30 mL/min/1.73m2; 7.35 [4.57-11.8]) compared to no malnutrition and normal eGFR (≥60 mL/min/1.73m2). GLIM-defined malnutrition confers higher all-cause mortality risk in older adults with infection, especially those with renal dysfunction. This information enables the identification of high-risk older patients with infection and targeted clinical management of those with malnutrition, renal dysfunction, and infection.

Previous studies have suggested that digitization of finance leads to growth in the economy in several middle- and high-income countries, but research in low- and lower-middle-income countries (LLMICs) remains limited. This research aimed to examine the trends, barriers, relationships, and prospects of finance digitization in LLMICs. This study used analytical and descriptive cross-sectional study designs. Sixty-six countries were purposively selected based on the available data on the Global Findex and the World Development Indicators databases. Descriptive analysis was used to identify the trends in the finance digitization indicators, correlation analysis was used to assess the nature of the association between finance digitization and the economy, and exponential smoothing was used to predict future outcomes in digital finance. The results indicated that the adoption and utilization of finance digitization increased in these countries, but the growth rates varied by region and income group. Low-income countries reported the highest rates of subscriptions to digital finance accounts and storing money in digital accounts, while lower-middle-income countries (LMICs) reported higher rates of inbound/outbound digital transactions, borrowings, and savings using digital channels. Sub-Saharan Africa reported the highest rates of digital finance account subscriptions, digital savings, and digital borrowings, while Europe and Central Asia reported the highest rates of in/out digital transactions. Barriers to digitization in these countries included poor access to the internet, high cost of digital finance products, and lack of a mobile phone, with low-income countries facing more challenges. The analysis revealed a negative significant relationship between finance digitization and Gross National Income growth and a positive significant correlation between finance digitization and other economic growth indicators (gross savings and Gross Domestic Product). Finance digitization was predicted to continue to rise in these countries for the next decade. The study recommended that policymakers should promote the adoption of digital finance and address the barriers and risks of finance digitization in LLMICs, as it had positive effects on economic growth.

Currently, many researchers have an interest in the investigation of the electric field in the fair-weather electric environment, along with its diurnal and seasonal variations across all regions of the world. However, a similar study in the southern part of Siberia has not yet been carried out. In this regard, the study aims to estimate the mean values of the electric field and their variations in the mountain and steppe landscapes using measurement data from the Khakass-Tyva expedition in 2022. The maximum values of positive ion density were noted at the site in the Iyussko-Shirinsky steppe between Belyo and Tus salt lakes in the Khakass-Minusinsk ba-sin. The maximum values of negative ion density were observed at site in the Shol tract in the center part of the Tyva depression. The potential gradient tends to increase with altitude and reaches a maximum in the highlands. The maximum values of the potential gradient were noted in the highlands plateau near the Mongun-Taiga mountain massif and Khindiktig-Khol lake. The diurnal cycles of potential gradient at different observation sites were divided into two groups: 1) a diurnal cycle in the form of a double wave; 2) a daily cycle with a more complex course due to the strong influence of local factors.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

This paper introduces new improvements to the modified version of the BIRECT (BI secting RECTangles) algorithm referred to as BIRECTv. We explore various approaches, by first including a grouping strategy for hyper-rectangles having almost the same sizes by categorizing them into different classes. Therefore constraining them not to exceed a certain pre-defined threshold (a small positive value to define the tolerance level). This approach allows for more efficient computation and can be particularly useful when dealing with a large number of hyper-rectangles with varying sizes. To avoid over-sampling, and preventing redundant or excessive sampling, at some shared vertices in descendant subregions, a particular vertex database is used to limit the number of samples taken within each subregion to two. The experimental investigation shows that these improvements have a positive impact on the performance of the BIRECTv(imp.) algorithm and the proposal is a promising global optimization algorithm compared to the original BIRECTv algorithm and its variants. Additionally, the BIRECTv(imp.) algorithm showed particular efficacy in solving high-dimensional problems.

In the context of globalization One Health enables and concurs to the achievement of Global Health. One Health approach involves wide knowledge and understanding of its critical factors, next to an adequate implementation, also in farm animal sector. Moreover, animal farming and breeding shows particularities depending on country. Therefore, well-defined and specific strategies are still needed to be developed and adapted to the current conditions found in various countries for ensuring a well implementation, thus driving to better and healthier global outcomes and security. This paper will explore the One Health status in Romania, highlighting the current progress registered until now and the future perspectives based on its potential, in the context of the livestock farming framework and challenges. Our findings may help to disclose the challenges of putting in practice One Health in Romania, alongside to the pathways showing potential to enhance sustainability in livestock farming.

AbstractBackground: The World Health Organization (WHO) said the situation in India was a "devastating reminder" of what the coronavirus could do. COVID-19 cases suddenly spiked across India. Union Health Minister Harsh Vardhan has said that one of the major reasons for the spike in coronavirus cases was people not following COVID-appropriate behaviour. The Union minister noted that the sudden rise in cases is largely or maybe event-driven comprising local body elections, grand weddings, and farmers' protest. These events may cause asymptomatic covid-19 carriers to spread wide covid-19 to others. Malaysia is also facing a surge in Covid-19 may due to the spread of covid-19 by asymptomatic covid-19 carriers. Therefore, it is important to develop an application that can publicize information on asymptomatic covid-19 carriers. The purpose of this application is to provide sufficient information and scientific research evidence to ensure that prevention strategies for asymptomatic covid-19 carriers must be implemented. The app is also open to anyone who uses it to educate others so that information can be shared more quickly to prevent other countries from becoming "Second India or Malaysia".Method: The homepage of the app shows that asymptomatic covid-19 carriers may have a lower viral load, the same viral load, or a higher viral load than symptomatic covid-19 carriers. When the user app is pressed by each category, they will see sufficient information and scientifically based research evidence about each category. These apps also show the evidence that on January 13, 2021 - Malaysian Health Department Director Dr Noor Hisham Abdullah instructs test Only those Close Contacts With Symptoms and The Malaysian Medical Association (MMA) has urged the Health Ministry to urgently improve the management of mild Covid-19 cases and revert to its policy of testing all close contacts. In addition, These apps also show App raise public awareness of the importance of COVID-19 vaccination(version 4) [Peter Chew, 2021] can intuitively see that countries with high vaccination rates can solve the problem of asymptomatic transmission of covid-19 carriers.Result: This application displays sufficient information and scientifically based research evidence to prove asymptomatic covid-19 carriers are the main key to the outbreak of covid-19. Some countries are using covid-19 symptom prevention strategies. They are only testing the symptomatic closed contact of covid-19 patients, because they may think that asymptomatic covid-19 carrier is just a low viral load and a low transmission rate, which is wrong. Some asymptomatic covid-19 carriers of covid-19 have high viral loads. The accumulation of asymptomatic covid-19 carriers with high viral load is the main cause of the covid-19 outbreak. Conclusion: Three apps have been developed to educate the public about the importance of asymptomatic covid-19 carriers. The asymptomatic covid-19 carrier education app (1) will provide sufficient information and scientific research evidence to educate citizens of any country to ensure that preventive strategies must be implemented for asymptomatic carriers to prevent the country’s Covid-19 outbreak. App, Game Base Learning to Prevent Infection from COVID-19 (version 3) [Peter Chew, 2020 ]. The app allows anyone to intuitively see that when the second wave covid-19 arrives, the accumulation of a large number of asymptomatic carriers in some countries has led to the high infection rate of covid-19. This is what is happening in India now. App raise public awareness of the importance of COVID-19 vaccination(version 4) can intuitively see that countries with high vaccination rates can solve the problem of asymptomatic transmission of covid-19 carriers. This is what is happening in Israel now.

Growing food autonomously on Mars is challenging due to the Martian soil's low nutrient content and high salinity. Understanding how plants adapt and evaluating their nutritional attributes are pivotal for sustained Mars missions. This research delved into the regeneration, stress tolerance, and dietary metrics of sweet potato (Ipomoea batatas) across different Mars Global Simulant (MGS-1) concentrations (0, 25, 50, and 75%). In our greenhouse experiment, 75% MGS-1 concentration most significantly inhibited sweet potato growth, storage root biomass, and chlorophyll content. This concentration also elevated plant tissues' H2O2, proline, and ascorbic acid levels. Higher MGS-1 exposures (50 and 75%) notably boosted vital amino acids and sugar groups in the plant's storage roots. Yet, increased MGS-1 concentrations notably diminished the total C: N ratio and elemental composition in both vines and storage roots. In summary, sweet potato exhibited optimal growth, antioxidant properties, yield, and nutrient profiles at 25% MGS-1 exposure, compared to higher concentrations. This study underscores the need for future interventions, like nutrient enhancement and controlled metal accessibility, to render it a suitable plant for space-based studies.

Dietary guidance to recommend food patterns to deliver nutrients has been well accepted for the past century. Foods to deliver essential nutrients and energy are needed across the life cycle and are dependent upon local industries and accepted cultural practices to deliver essential nutrients to prevent nutrient deficiency diseases. Since the 1980s, dietary guidelines to prevent chronic diseases have relied on epidemiological research to predict what dietary patterns are linked to reduced risk of chronic disease or links to health outcomes. Dietary guidelines have been broad, typically recommending avoiding “too much sugar”, “too much saturated fat”, and “too much sodium”. Efforts to fine tune these recommendations have met with limited scientific data to support more specific recommendations across the life cycle. Consumers have become skeptical of dietary guidelines as media coverage of new studies is often in conflict with accepted nutrition dogma. We discuss whether we really have a science-based databank to support dietary guidelines, based on a scientific session at the 10th International Conference on Nutrition and Growth.

Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.

We propose a silviculture with a purely biological logic that takes soil into account. The article is divided into three parts: 1) presentation of the issue in the context of biodiversity and forest pro-tection; 2) impact of the soil functioning on forestry; 3) possible implementation of soil functioning in forestry practice, to support and protect a non-or minimally anthropized evolution of the forest. The first chapter illustrates the importance of the forest for the future of the planet and our human species, referring to recently published works on the topic of climate warming and biodiversity. The second illustrates the aspects that link soil dynamics to the silvogenetic cycle, focusing on the humipedon (organic and organo-mineral surface horizons, approximately the first 30 cm of forest soil), and more significant carbon storage in old forests since up to 2/3 is found in the soil. In the third chapter we consider the possibility of lengthening the forestry cycle, to allow the return of nutrients to the soil. Theoretical graphs and distribution models of the number of trees as a func-tion of diameter and age are reported, with examples of logging in uneven Alpine forests, with reference to Swiss fir-beech forests published on the Pro-Silva website.

The stability and optimal control for an SIS epidemic model with demographics and heterogeneous control strategies (immunization and quarantine) on complex networks are investigated. The epidemic threshold, disease-free and endemic equilibria are derived. Then, global asymptotical stability about the two equilibria are studied based on different methods. Furthermore, the optimal control issue about the system is also considered. We obtain the existence and uniqueness of the optimal control and optimal control tactics. Some numerical simulations were conducted to illustrate and supplement the theoretical results. Meanwhile, the effects of the combination of various immunization and quarantine schemes are studied and also compared with the optimal control strategies.

Natural montane peatlands generally are not a significant source of nitrous oxide (N₂O) due to environment limitation including phosphorus (P) scarcity and temperature lowness. Phosphorus enrichment and warming caused by global change are altering this limitation, likely to increase the source function of N₂O. However, the combined effects of P addition and warming on N₂O fluxes and biotic/abiotic factor in peatlands are still uncertain. To address this, we investigated the long-term (12 yrs) effects of P addition (5 and 10 kg ha^-1 yr^-1) and its interaction with warming on N₂O fluxes in a peatland. The results showed that although long-term P addition did not significantly affect the source/sink function of N₂O in the peatland, it stimulated enzyme activities, promoted peat decomposition. However, warming amplified the effect of P addition to increase N₂O emissions by stimulating enzyme activities and changing soil stoichiometry, even turned the peatland into a significant source of N₂O with an emission of approximate 100 g m^-2 during the growing season. Our study suggests that P enrichment under the current background of global warming will enhance the possibility of strong N₂O emissions in montane peatlands, which may increase the risk to further aggravate global warming.

Human violence recognition is an area of great interest in the scientific community, given its broad spectrum of applications, especially in video surveillance systems, since detecting violence in real-time could prevent criminal acts and save lives. Despite the number of existing proposals and research, most focus on the precision of results, leaving aside efficiency and its practical implementation. Thus, this work proposes a model that is effective and efficient in recognizing human violence in real-time. The proposed model consists of three modules: a first module called Spatial Motion Extractor (SME), in charge of extracting regions of interest from a frame; a second module called Short Temporal Extractor (STC), whose function is to extract temporal characteristics of rapid movements, finally the Global Temporal Extractor (GET) module, responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated regarding efficiency, effectiveness, and ability to operate in real-time. The results obtained on Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to other alternatives. A VioPeru dataset was created to validate real-time applicability with violent and non-violent videos captured by real video surveillance cameras in Peru. The effectiveness results in this dataset outperformed the best existing proposal. Therefore, our proposal has contributions in efficiency, effectiveness, and real-time.

This case study explores the transformative effects of emergent digital technologies, particularly augmented reality (AR), on literacy engagement and reading for pleasure. The United Arab Emirates Ministry of Education implemented the UAE Storytime programme, utilising AR to create an interactive literacy experience for young learners. The programme incorporated bilingual episodes, featuring stories based on local and global contexts, with 3D objects and interactive games to enhance understanding. The results indicate a positive correlation between the use of AR and literacy engagement. A high proportion of learners scanned the QR codes to access the AR objects, which enhanced their engagement and understanding of the stories. The programme fostered a learner agency, engaged family members in the learning process, and established a sense of global community. The study recommends the integration of similar initiatives into the curriculum to promote collaboration, creativity, and holistic learning experiences.

The Global Hunger Index (GHI) is a vital tool for assessing and understanding the state of hunger and food security across the world. In this review article, we examine the GHI scores of various countries in the years 2000, 2008, 2015, and 2023. We aim to explore the changes in GHI scores over this period, identify trends, and discuss the implications for global food security. The GHI provides a comprehensive snapshot of the world's progress in reducing hunger, and this analysis sheds light on the successes and challenges faced by different countries.

Background: Cervical cancer prevention in regions with limited access to screening and HPV vaccination necessitates innovative approaches. This study explored the potential of a test-and-treat strategy using mRNA HPV tests to impact cervical cancer prevention in a high-prevalence HIV population. Methods: A cervical screening study was conducted at three South African hospitals involving 710 under-screened, non-pregnant women (25 to 65 years) without known cervical diseases. Cytology, HPV testing, colposcopy, and biopsies were performed concurrently. Histopathologists determined final histological diagnoses based on biopsy and LLETZ histology. mRNA-HPV-genotyping for 3 (16, 18, 45) to 8 (16, 18, 31, 33, 35, 45, 52, 58) high-risk types was performed on leftover liquid-based cytology material. The preventive potential of the test-and-treat approach was estimated based on published data, reporting the causative HPV types in cervical cancer tissue from South African women. Treatment was provided as needed. Results: HPV positivity rate more than doubled from 3-type (15.2%; 95% CI: 12.6-17.8) to 8-type mRNA (31.5%; 95% CI: 28.8-34.9) combinations, significantly higher among HIV-positive women. CIN3+ prevalence among HIV-positive women (26.4%) was double that of HIV-negative women (12.9%) (p&lt;0.01). The 6-type combination showed the best balance of sensitivity, specificity and treatment group size, and effectiveness to prevent cervical cancer. A 4-type combination (16, 18, 35, 45) could potentially prevent 77.6% (95% CI: 71.2-84.0) of cervical cancer burden by treating 20% and detecting 41.1% of CIN3 cases in the study group. Similarly, a 6-type combination (16, 18, 31, 33, 35, 45), treating 25% and including 62% of CIN3 cases, might prevent 85% of cervical cancer cases (95% CI: 79.6-90.6) among HIV-positive and negative women. Conclusion: Employing mRNA HPV tests within a test-and-treat approach holds huge promise for targeted cervical cancer prevention in under-screened populations. Testing for mRNA of the 6 highest-risk HPV types in this population and treating them all is projected to effectively prevent progression from CIN3 to invasive cervical cancer while reducing overtreatment in resource-constrained settings.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

This article presents the development of an innovative Maximum Power Point Tracking (MPPT) strategy, utilizing a Particle Swarm Optimization (PSO) algorithm to improve the effectiveness of PV systems and expedite convergence. The new MPPT method incorporated a unique Swarm Self-Reinforcement Mechanism (SSRM) within the PSO algorithm, targeting quick convergence and excellent tracking accuracy. This approach enables the PSO to eliminate the fitness function that has the lowest value and subsequently reinforce it in the next iteration, revolving around the global maximum power point (GMPP). By applying this novel PSO-based method, the MPPT performance of PV systems was significantly improved, facilitating the algorithm to proficiently navigate through the solution space and quickly locate the GMPP, even in rapidly changing environmental conditions. The outcomes derived from this novel approach were contrasted with other algorithmic optimization methods, validating its superior convergence speed and tracking accuracy. Different swarm sizes were examined using SSRM, and the optimal swarm size for the system employing MPPT was identified to achieve the lowest convergence time (CT). The results showcased the impressive performance capabilities of this novel strategy, resulting in a time con-traction of up to 28% compared to the conventional PSO technique, where the optimal swarm size was found to be 5. This achievement marks a significant milestone in the evolution of PSO-based MPPT techniques, and paves the way for future advancements in this exciting field.

Quantum computing is an emerging field that have given a significant impact on optimization. Among the diverse quantum algorithm, the quantum gradient descent has become a prominent technique for solving unconstrained optimization problems. In this paper, we propose a quantum-spectral Polak-Ribi{\'e}re-Polyak (PRP) conjugate gradient approach. The technique is considered as a generalization of the spectral PRP method which employs a $q$-gradient that approximate the classical gradient with quadratically better dependence on the quantum variable $q$. Additionally, the proposed method reduces to the classical variant as quantum variable $q$ gets closer to $1$. The quantum search direction always satisfies the sufficient descent condition and does not depend on any line search. This approach is globally convergent with the standard Wolfe line search without any convexity assumption. Numerical experiments are conducted and compared with the existing approach to demonstrate the improvement of the proposed strategy.

Myocardial involvement among critical patients with coronavirus disease 2019 (COVID-19) has a worse outcome. The occurrence of an imbalance in oxygen supply results in the release of pro-inflammatory cytokines which leads to increased ventilation support requirement and increase risk of mortality. Our aim was to evaluate the association between hs-Troponin I level and global longitudinal strain as evidence of myocardial involvement among critical COVID-19 patients. Methods. We performed prospective cohort study from February 1st – July 31st, 2021 at Dr. Soetomo General Hospital, Surabaya as referral center for COVID-19 care. Of the 65 critical COVID-19 included, 41 (63.1%) were men, with a median (interquartile range) age of 51.0 years (20.0-75.0).Subjects were recruited according to WHO criteria for severe COVID-19 and CDC criteria for myocarditis. All subjects recruited were examined using echocardiography to measure global longitudinal strain (GLS) and blood samples were taken to measure hs-Troponin. Subjects were then followed to assess their needs for mechanical ventilation and in-hospital mortality. Severe COVID-19 subjects with cardiac injury were associated with an increased need for intubation (78.5%) and an increased incidence of myocarditis (50.8%). There was an association between the use of intubation and the risk of mortality in patients (66.7% vs. 33.3%, p-value &lt;0.001). Both reduced GLS and increased hs-troponin are associated with increased myocarditis (p-value &lt;0.001 and 0.004 respectively). Reduced GLS is associated with higher mechanical ventilation needs (12.17 + 4.79 vs.15.65 + 4.90, p-value 0.02) and a higher mortality rate (11.36 + 4.64 vs. 14.74 + 4.82; p-value 0.005). Increased hs-troponin is associated with higher mechanical ventilation needs (25.33% vs. 3.56%, p-value 0.002) and a higher mortality rate (34.57% vs. 5.76%, p-value 0.002). Compared with contemporary controls, critical COVID-19 patients with myocardial involvement and elevated cardiac troponin level are associated with higher mechanical ventilation needs and a higher mortality rate.

This study assesses the feasibility of hepatitis B and C elimination, using an analysis of trends of epidemiology data (1990-2019) from the global burden of disease. Joinpoint regression analysis was used to identify significant changing points in trends of Age-standardizes Prevalence Rate (ASPR) and Age-standardizes Mortality Rate (ASMR) and to estimate the annual percentage changes (APC) and the average annual percentage changes (AAPC) for the period. Sociodemographic Index (SDI) was used to analyze trends between countries. The total percentage change of the ASPR (2019/1990) was -31.4% and -12.8% for HBV and HCV worldwide respectively, the rate ratio (HBV/HCV) was 2.5. Mortality has decreased for HBV but not for HCV. The total percentage change for the ASMR (2019/1990) was -26.7% and 10.0%, for HBV and HCV respectively. While ASMR of HBV decreased, HCV increased during this period. The percentage change in ASMR of HBV was highest in countries with high-middle SDI and lowest in countries with high SDI. For HCV, the percentage change in ASMR was highest in countries with High SDI (increase) and only in countries with low SDI decreased. The global HBV and HCV rates have fallen with different AAPC associated with the SDI. Despite the advances, there is still a long way forward to achieve the 2030 elimination goals. An important challenge is related to finding a way to speed up the yearly rate at which the decline is happening.

Sustainable energy development (SED) is a crucial component of the Sustainable Development Goals (SDG), aiming to maintain economic and social progress while protecting the environment and mitigating climate change’s effects. SED serves as a transition paradigm for sustainable development, providing a blueprint for energy peace and prosperity for people and all uses. This article presents the history of SED and then uses a critical discourse approach to summarize existing review studies in SED. Ten interlinked themes of SED are identified, with two of them considered to be among the least studied in existing SED reviews and in the current global discussion around climate change. This study explores these two themes, which include energy financing and the need for 100% renewable energy (RE), a sub-theme of decarbonization strategy working towards the 1.5–2.0 °C scenario. The study suggests that the current G20 countries’ contributions, if maintained continuously per annum, in addition to 80% more funding from private investment compared to the amount in the 1.5 °C scenario financial requirements for clean energy, are sufficient to limit global warming. In addition to the present drive for 100% RE, the article also discusses emerging issues, such as energy storage options with an indication of hydrogen as the most promising, other energy-related development agendas, and the need for regional security stability to prevent energy wars. Selected SED decarbonization strategies are presented across the power, transport, building, and industrial sectors. The study concludes with progress and directions for future research, mainly the need for re-defining nationally determined contribution (NDC) through an emissions budgeting and centralized global or regional emissions stock-taking strategy working towards the 1.5 °C scenario.

Nowadays, higher education students play an active role in the realization of sustainable development goals (SDGs) for global citizenship. However, some important issues such as how much students know about these goals can play a role in achieving these goals are not yet clear. The aim of this study is to establish a fundamental study for determining the level of knowledge on the higher education students about SDGs. An open-ended questionnaire was conducted with the qualitative research methods on 54 higher education students after pandemic of COVID-19. As a result of the research, the goals of quality education, gender equality, health and quality of life, reduction of inequalities and peace, justice and strong institutions are the most frequently mentioned by the students. In addition, the opinions of the students on the subject of “sustainable cities and communities” are sequentially; the themes include ensuring socio-economic development, preserving cultural heritage identity, safe environment and durable buildings, as well as modern irrigation and waste methods, and reducing air pollution. On the other hand, they agreed on the need for global citizenship to have universal qualifications. In conclusion, global citizenship contributes directly to the SDGs and human-oriented production should be given more importance.

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

We report on ground-based measurements of the atmospheric electric field (Ez= -Potential Gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is the Emilio Segre cosmic ray station located on Mt. Hermon (34.45 N, 2020 m AMSL) located in northern Israel near the Syrian-Lebanon border and at the other at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18 N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruption to the Global Electric Circuit are often observed following strong solar events. The first case study (St. Patrick Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 Sep 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed) which were investigated as well.

The Global Burden of Disease Study (GBD) 2019 reveals an increasing prevalence of Type 2 Diabetes Mellitus (T2DM) from 1990 to 2019. This study delves into the role of dietary risk factors across different demographic and socioeconomic groups. Utilizing data from the GBD 2019, it analyzes age-adjusted T2DM metrics—death counts, Disability-Adjusted Life Years (DALYs), and Age-Standardized Rates (ASR)—stratified by age, sex, and region. The study employed Estimated Annual Percentage Changes (EAPCs) to track trends over time. Results show that in 2019, 26.07% of T2DM mortality and 27.08% of T2DM DALYs were attributable to poor diets, particularly those low in fruits and high in red and processed meats. Geographic variations in T2DM trends were significant, with regions like Southern Sub-Saharan Africa and Central Asia experiencing the most substantial increases in Age-Standardized Mortality Rate (ASMR) and Age-Standardized DALY Rate (ASDR). A positive correlation was noted between Socio-Demographic Index (SDI) and T2DM burden due to dietary risk factors. The study concludes that targeted public health initiatives promoting dietary changes could substantially reduce the global T2DM burden.

Cardiac surgery is a modern science in the history of medicine. The impact in cardiac disease, in term of treatment and prognosis, has made this discipline indispensable in the global health. In recent decades, the greatest investment has been dispensed in technological and material improvement, for increase the life expectancy. This surgery must address different epidemiological aspects dictated by the geography and economic-social conditions of the global populations. For this reason, it is progressively important to address the cardiac surgery accessibility disparity. Many scientific paper and international meetings have studied how cardiac surgery can increase accessibility in various countries around the world. In this review we analyze all the challenges, solutions and suggestions to make this surgery accessible to the entire global population, with the purpose to reducing the disparity across the all seven continents. For a long time, high-income countries have invested in technological capabilities and experimental advancement, not caring about unequal access in the rest of the world. We believe that it is time to reverse the growth trajectory, placing – as a priority – the accessibility and distribution of a surgical science, too significant for the right to health of all the people of the world. This is the real new challenge in cardiosurgery.

The oncogenicity of the human cytomegalovirus (CMV) is debated to this day. In recent years, mounting evidence addresses an anti-cancer effect via T cell-mediated CMV-targeted tumor de-struction. However, the data mostly comes from single-center studies and in vitro experiments. Broad geographic coverage is required to offer a global perspective. This study examined the correlation between country-specific CMV seroprevalence (n=73) and age-standardized inci-dence rates for 34 tumors using data obtained from the International Agency for Research on Cancer of the World Health Organization. The association between CMV and cancer incidences in 10-year age increments was also analyzed. The study revealed a worldwide inverse correlation between CMV seroprevalence and incidences of 88.2% tumors. Notably, this inverse link persists for all cancers combined (Spearman’s ρ= -0.732, p= 0.001). An antithetical and significant correlation is also observed in particular age groups for the vast majority of tumors. Our results corroborate the conclusions of previous studies and indicate that this phenomenon holds true on a global scale. It applies to a wide spectrum of cancer histologies, suggesting a common under-lying mechanism – CMV-stimulated T cell tumor targeting. Although these results further advance the notion of CMV-based therapies, further in-depth investigation of host-virus interactions is warranted.

This chapter explores the pivotal role of maize (Zea mays L.) in global nutrition, emphasizing its status as a staple food worldwide. It discusses maize's complex nutritional profile, including macronutrients, micronutrients, and dietary fiber. The chapter also covers biofortification efforts to enhance maize with essential nutrients, addresses nutritional concerns related to maize-centric diets, and explores genetic modification and agronomic strategies to boost nutrient-rich maize yields. Additionally, it delves into maize processing traditions, nutrient retention techniques, and the importance of promoting nutrient-rich maize varieties. The chapter outlines post-harvest strategies to combat nutrient loss and prevent contamination while emphasizing the significance of consumer education and diverse diets. Case studies illustrate the impact of improved maize nutrition and provide replicable strategies. The chapter concludes by considering emerging research and technology, identifying challenges, and proposing solutions to enhance maize's nutritional value in the future.

This scholarly investigation conducts a rigorous exploration of Bangladesh's textile sector spanning the years 2011 to 2022, centering its attention primarily on the development of an intricate econometric framework. The study unveils profound insights into the sector's growth trajectories, the intricate dynamics of global demand, the undulating fluctuations of interest rates, and other pivotal economic gauges. The core component of this research, the econometric model, adeptly prognosticates textile exports through the incorporation of multifarious variables, encompassing the count of garment establishments, the magnitude of the workforce, market penetration, worldwide demand patterns, currency exchange rates, and interest rate fluctuations. Notably, the model attains a lofty degree of explicative potency, with an R-squared coefficient approximating 0.756, thereby attesting to its remarkable capacity to elucidate variances in textile export values. These discoveries carry substantial consequences for policymakers and stakeholders within the industry, as they bestow upon them a potent instrument for judicious decision-making and strategic blueprinting within Bangladesh's textile domain. The model accentuates the paramount significance of global demand and market share, concurrently accentuating the latent repercussions posed by fluctuations in interest rates.This research provides valuable insights into promoting the industry's sustainable growth, diversification, and resilience in the face of economic challenges.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

This research delves into the intricate dynamics underlying the impact of investments on global output, em-ploying the Leontief matrix as a robust analytical framework. Investments wield a profound influence on economies worldwide, with varying effects contingent upon investment types, development levels of coun-tries, and external factors such as trade conflicts and global shocks. The diverse range of investment forms, including physical capital, human capital, R&D, and technological investments, engenders distinct implica-tions for productivity, innovation, and efficiency. Developing and developed economies navigate unique tra-jectories, with investments playing a pivotal role in bridging infrastructure gaps, improving technology, and spurring growth. However, external disruptions, such as trade wars and global shocks, introduce an element of complexity, reshaping investment patterns and altering global output trajectories. This study centers on harnessing the Leontief matrix's prowess to evaluate the interplay of investments and global output, focusing on the Romanian economy. By analyzing input-output tables, encompassing 105 branches aggregated into 10 sectors, the research captures the intricate connections between economic segments. No-tably, the Romanian context reveals the volatility of the matrix coefficients', an outcome of ongoing transi-tional processes, technological advancements, and fluctuating relative prices. In unraveling the intricate threads weaving investments and global output, this study contributes to a nuanced comprehension of these multifaceted interactions. The findings underscore the significance of tailoring investment strategies to specific economic contexts and advocate for robust frameworks, such as the Leontief matrix, to inform pol-icy decisions and drive sustainable growth in an increasingly complex global economy.

This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.

This comprehensive review examines the critical issue of antimicrobial resistance (AMR), a growing global concern with profound societal implications. This article presents an overview of the causes of AMR, including the misuse and overuse of antibiotics in healthcare, agriculture, and veterinary practices. Various conventional and advanced detection methodologies have been explored, highlighting the importance of the accurate and timely identification of resistant strains. The challenges associated with tackling AMR, encompassing scientific, economic, and regulatory aspects have been discussed in depth. In addition, this article sheds light on the far-reaching societal implications of AMR, including increased healthcare costs, treatment complexities, and potential disruptions to healthcare systems. This emphasizes the urgency of adopting proactive strategies to combat AMR and underscores the need for global cooperation, policy interventions and public awareness campaigns. By unraveling the multifaceted dimensions of AMR, this review aims to enhance our understanding, stimulate further research, and promote concerted efforts to mitigate the threat of antimicrobial resistance.

This research highlights the importance of error-correcting codes in ensuring secure and efficient data transmission over noisy channels. It discusses the application of Convolutional codes and the Viterbi algorithm for error detecting and correcting in applications where the data rate is high and the error probability is relatively low. This research extends beyond previous studies by conducting a comparative analysis of Convolutional codes, the Viterbi algorithm, and alternative coding methods utilized in satellite communication systems, by evaluating their effectiveness and efficiency relative to other coding approaches like BCH, LDPC, and turbo codes. The evaluation encompasses key performance metrics including error correction capabilities, computational complexity, and robustness against various error types. Additionally, practical implementation factors such as power consumption, computational requirements, and compatibility with existing hardware are considered. The analysis sheds light on the advantages and disadvantages of each coding method, offering insights into why convolutional codes and the Viterbi algorithm are particularly suitable for GPS systems.

Methane and carbon dioxide are the main contributors to global warming, being the methane effect twenty-five times more powerful than carbon dioxide. Although the sources of methane are diverse, it is a very volatile and explosive gas. One way to store the energy content of methane is its conversion to methanol. Methanol is liquid under ambient conditions, easy to transport and, apart from its use as an energy source, it is a chemical platform that can serve as a starting material for the production of various higher value-added products. Accordingly, the transformation of methane to methanol has been extensively treated in the literature, using traditional catalysts as different types of zeolites. However, in the last years, a new generation of catalysts have emerged to carry out this transformation with higher conversion and selectivity, and more importantly, under mild temperature and pressure conditions. These new catalysts typically involve the use of a highly porous supporting material such as zeolite, or more recently, metal-organic frameworks (MOFs) and graphene, and metallic nanoparticles or a combination of different types of nanoparticles that are the core of the catalytic process. In this review, the characteristics, the catalytic mechanisms, reactors, and the main results of these catalysts are presented as a way to overcome the challenges found in traditional catalysts.

Global climate change, dominated by climate warming, is seriously affecting the balance of global ecosystems, but the risk of species extinction is particularly high in low-altitude mountain areas. To clarify the response of the endemic and critically endangered species Ilex nanchuanensis to climate change, this study used the MaxEnt model to simulate and predict the potential habitat of I. nanchuanensis during the Last Interglacial, Last Glacial Maximum, the current period, and two future periods (the 2050s and 2070s). The results showed that the hottest monthly minimum temperature is the most important climatic factor affecting the geographical distribution of I. nanchuanensis. Furthermore, I. nanchuanensis will be at risk of population shrinkage and extinction in the future, with the center of mass moving further northwest as concentrations of greenhouse gases increase, especially in the 2070s, when its geographical distribution shrinks the most under the RCP6 scenario. Global warming is causing habitat degradation for many species and will continue to migrate to higher altitudes and latitudes in the future, seriously affecting the global ecosystem balance. Therefore, to actively respond to the impacts of climate change, protected areas should be established around the geographical distribution centers of species, and core, buffer, and experimental areas should be scientifically and rationally delineated for the conservation and cultivation of germplasm resources.

Syphilis continues to be a significant global health concern, and understanding the latest advancements in immunology and pharmacological treatment is crucial for effective disease management. This review article provides an in-depth exploration of these advancements, aiming to contribute to the growing body of knowledge on syphilis research. The epidemiology of syphilis sets the stage for this review, highlighting the persistent burden of the disease worldwide. Current trends and challenges in disease control are discussed, shedding light on the need for comprehensive prevention strategies and improved surveillance systems. The immunological aspects of syphilis are the focus of this review. The intricate interplay between the host immune response and the evasive strategies employed by Treponema pallidum, the causative agent of syphilis, are explored. These include the mechanisms of immune evasion, the role of adaptive and innate immunity, and the development of immune-mediated tissue damage. The article also highlights advancements in diagnostic methods, with emphasis on the importance of accurate and timely detection. From traditional serological tests to the emergence of novel molecular and point-of-care assays, these developments have significantly improved the efficiency and accuracy of syphilis diagnosis. In terms of pharmacological treatment, the review covers the current recommendations and emerging strategies for managing syphilis infections. The challenges posed by antimicrobial resistance are addressed, emphasizing the importance of appropriate treatment regimens and close monitoring of therapeutic efficacy. Looking to the future, this review highlights the potential of vaccine development as a preventive measure against syphilis. Promising vaccine candidates and ongoing research efforts are discussed, offering hope for an effective means of disease prevention. In conclusion, this comprehensive review explores the advancements in syphilis research, from the understanding of immunological complexities to the development of innovative treatment strategies. By collectively advancing our knowledge, we can strive towards better disease management and minimize the impact of syphilis on global public health.

Cyrene was evaluated for the first time as a potential sustainable mobile phase solvent in re-versed-phase chromatography. As a benign biodegradable solvent, Cyrene is an attractive re-placement to classical non-green organic chromatographic solvents such as acetonitrile and a modifier, co-eluent to known green solvents such as ethanol. Compared to ethanol, Cyrene is less toxic, non-flammable, bio-based, biodegradable and cheaper solvent. A fire safety spider Chart was generated to compare the properties of Cyrene to ethanol and show its superiority as a greener solvent. Cyrene's behaviour, advantages, and drawbacks in reversed-phase chromatography, including cut-off value, elution power, selectivity, and effect on the column, were investigated using a model drug mixture of moxifloxacin and metronidazole. A monolithic C18 (10 x 4.6 mm) column was used as a stationary phase. Different ratios of Cyrene: ethanol with an aqueous portion of sodium acetate buffer mobile phases were tested. A mobile phase consisting of Cyrene: ethanol: 0.1 M sodium acetate buffer pH 4.25 (8:13:79, v/v) was selected as most suitable mobile phase system for separating and simultaneously determining metronidazole and moxifloxacin. The greenness and whiteness of the method were evaluated using the qualitative green assessment tool AGREE and the white analytical chemistry assessment tool RGB12. Further potentials of Cyrene as a solvent or modifier in normal phase chromatography, liquid chromatography-mass spectrometry, and supercritical fluid chromatography are discussed.

With enhanced life expectancy and ageing global populations, the prevalence of multimorbidity continues to increase. However, there is a dearth of evidence linked to multimorbidity diagnoses, treatments and health outcomes which remains a concern for future proofing optimal healthcare services. Generating evidence is critical to managing multimorbidity, promoting public health and minimizing health inequalities via effective healthcare policies that improve quality of life for vulnerable populations. This study assessed meta-epidemiology of multimorbidity to report the gaps in scientific knowledge and clinical practice. A systematic methodology was designed and published in PROSPERO (CRD42022347308) to report meta-epidemiology analyses using databases including PubMed, Web of Science, ScienceDirect, EMBASE, The Cochrane Gynaecology and Fertility Group Specialised Register of Controlled Trials and MEDLINE for studies published between the 1^st of January 1980 - 31^st December 2022. A random-effects model was used to estimate the pooled proportion of multimorbidity in adults. Forest plots, pooled odds ratios and statistical heterogeneity metrics were used to assess the association between multimorbidity and investigated factors. Funnel plots and Egger’s regression were used to detect and correct for publication bias. Our findings identified women to be 0.32 times more likely to have multimorbidity in comparison to males. In regard to ethnicity, white people were 0.47 times less likely to develop comorbidities than black people. People who identified as a drinker or unmarried were more likely to develop comorbidities than those who are non-drinkers or married, respectively. Regardless of smoking status, people were equally likely to have comorbidity. In terms of environmental influences, people in rural areas were found to be 0.2 times less likely to have comorbidity in comparison to those living in urban areas. Interestingly, people with a higher education level were 0.57 times more likely to develop comorbidities than those with only a high school education. It is evident that multimorbidity has a significant burden globally and impacts the provision of care necessitated across populations given its association with several social determinants of health. Robust research and healthcare policies are required to better manage multimorbidity in patients. An example of such intervention includes employing prevention programs to reduce risk and incidence of multimorbidity within at-risk populations.