The dynamic landscape of 3D printing has experienced a notable surge in the development of diverse filaments tailored for a wide range of applications in medicine. This experimental study thoroughly examines the mechanical properties of nine popular 3D printer filaments, namely PLA, ABS, HIPS, PETG, PA, Carbon Fiber, Ceramic, PMMA, and Metal composite. Focusing on key aspects such as tensile strength and compressive tests, the study delves into the intricate mechanical characteristics of these 3D printer filaments. In addition to the mechanical properties, the study provides a comprehensive exploration of essential filament attributes, including melting point, bed temperature requirements, and density of nine different filament properties. Furthermore, the study addresses crucial considerations in material selection for different applications. Through insightful comparisons, the article illuminates the advantages and limitations of various available filaments within the realm of applications. This study serves as a valuable resource, offering nuanced insights for researchers, engineers, and medical professionals seeking a deeper understanding of the mechanical characteristics of 3D printer filaments and their potential applications.

The search for active cellulolytic consortia among soil microorganisms is of significant applied interest, while the dynamics of the formation of such communities remain insufficiently studied. To gain insight into the formation of an active cellulolytic community, the experiment was designed to study the colonization of a sterile substrate (cellulose) by microorganisms from two soil types: sod-podzolic and chernozem. To achieve this, the substrate was placed in the soil and incubated for six months. To assess microbiome dynamics, the experiment employed sequencing of 16S rRNA and ITS2 amplicon libraries at four-time points. It was demonstrated that from the second month of the experiment, the prokaryotic component of the communities reached a state of stability, with a community composition that was specific to each soil type. The results demonstrated that there was no relationship between changes in community diversity and soil respiration. There also was no significant shift in community diversity throughout the chronosequence. Furthermore, the taxonomic composition of the community shifted towards a decrease in the proportion of Proteobacteria and an increase in representatives of the Bacteroidota, Bacillota, and Verrucomicrobiota phyla. The network analysis of the community demonstrated that, in contrast to sod-podzolic soil, chernozem is distinguished by a higher modularity, with the formation of taxon-specific groups of microorganisms at each stage of the chronoseries. These differences are attributed to alterations in the eukaryotic component of the community, particularly in the prevalence of nematodes and predatory fungi, which in turn influenced the cellulolytic community.

An all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. The product is based on clear-sky LST retrieved from MSG/SEVIRI infrared (IR) measurements, complemented by LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a Microwave (MW) based LST product derived from AMSR-E measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.

Climate change, growing world population and increasing food demand require higher yields and adaptability of crops, including spring bread wheat. Modern plant breeding based on achievements of genetics has a significant impact on plant resistance to diseases, climate variability and other factors. Genome wide association studies (GWAS) is one of genetics tools for mining of genome loci associated with economically important traits. In the current study, we applied GWAS to reveal QTL for spike productivity and grain quality using a panel of spring varieties of Triticum aestivum L. bred in different decades of the last century in Russia and Germany. The study has enabled to find new genetic markers associated with grain yield and quality. Studying genetic basis of grain yield and quality by comparison of wheat varieties from different breeding periods, including landraces preserved as the source of valuable genetic diversity for breeding and genetic studies, makes it possible not only to understand selection processes in the past, but also to develop strategies for future breeding work.

3D Printing /Additive manufacturing holds a promising future in healthcare as it aids in visualizing things and making 3D objects which is very important in different areas of medicine. The concept of one size fits all has failed the quality of care delivered by us in healthcare which demands customised needs for patient care to create 3D printed tissues, make personalized prosthetics, implants and physical models of the human body. The materials used for printing models and other healthcare devices for medical education, patient needs, and prototyping are increasing. The application of different materials for use in healthcare is not studied. New materials are also available for the various applications in healthcare industry. This experimental study was conducted to review the nine distinct 3D printing filament materials like PETG, PA, ABS, PLA, HIPS, PMMA, Ceramic, Carbon Fiber, and (PLA + Metal Composite) with the help of a 3D printed physical model of T-12 thoracic vertebrae using all the abovementioned materials. The evaluation of materials was based on diverse mechanical properties, applications across medical fields, performance metrics, costs, accuracy, and quality. The assessment This study also focuses on comparison of mechanical properties such as tensile strength, compressive strength of the abovementioned materials with human bone. The study also reviews the 3D printing process and explores its utilization in biomedical applications. The Insights about the application of various materials holds a promising future for development of newer materials for healthcare industry.

Analysis of 49 strains of F. langsethiae originating from northern Europe (Russia, Finland, Sweden, UK, Norway, and Latvia) revealed the presence of spontaneous auxotrophic mutants that reflect natural intraspecific diversity. Our investigations detected that 49.0% of F. langsethiae strains were auxotrophic mutants for biotin, and 8.2% of the strains required thiamine as a growth factor. They failed to grow on vitamin-free media. For both prototrophic and auxotrophic strains, no growth defect was observed in rich organic media. Without essential vitamins, a significant reduction in the growth of the auxotrophic strains results in a decrease of the formation of T-2 toxin and diacetoxyscirpenol. In addition, all analysed F. langsethiae strains were distinguished into two subgroups based on PCR product sizes. According to our results, 26 and 23 strains of F. langsethiae belong to subgroups I and II respectively. We determined that the deletion in the IGS region of the rDNA of F. langsethiae belonging to subgroup II is linked with temperature sensitivity and causes a decrease in strain growth at 30 °C. Four thiamine auxotrophic strains were found in subgroup I, while 21 biotin auxotrophic strains were detected in subgroups II. To the best of our knowledge, the spontaneous mutations in F. langsethiae observed in the present work have not been previously reported.

Hypoxia is one of the common abiotic stresses that negatively affects the development and productivity of agricultural crops. Quercetin is used to protect plants from oxidative stress when exposed to environmental stressors. O2 deficiency leads to impaired development and morphometric parameters in wheat varieties Orenburgskaya 22 (Triticum aestivum) and varieties Zolotaya (Triticum durum). Cytological analysis revealed various types of changes in the cytoplasm under conditions of hypoxia and treatment with quercetin. The most critical changes in the cytoplasm occur in the Zolotaya variety during pretreatment with quercetin followed by hypoxia, and in the Orenburgskaya 22 variety during hypoxia. Quercetin has a protective effect only on the Orenburgskaya 22 variety, and also promotes more effective recovery after exposure to low O2 content. Hypoxia causes an increase in ROS and activates the antioxidant system. It has been shown that the most active components of the antioxidant system in the Orenburgskaya 22 variety are MnSOD and Cu/ZnSOD, and in the Zolotaya variety GSH. We have shown that quercetin provides resistance only to the wheat genotype Orenburgskaya 22, as a protective agent against abiotic stress, which indicates the need for a comprehensive study of the effects of exogenous protectors before use in agriculture.

Hemoglobin S (Hb S) is genetically inherited variant of hemoglobin affecting around 300 000 infants are born annually. Facilitates for universal population testing of carrier state are available only in developed countries. The screening tests for Hb S have their respective limitations and the confirmatory tests are costly and require experts for conduct and interpretation. The use of two tests will enhance the overall diagnostic accuracy at the level of screening. We have considered two scenarios for population screening for SCD – community based in health camp mode / house to house survey and facility-based mode at health center. The combination of lateral flow immunoassay based POCT with sickling test can be used in absence of HPLC for population screening. However, for antenatal women portable microelectrophoresis based test is recommended as it flags the presence other hemoglobin variants along with Hb S.

Fourteen Russian potato cultivars (cvs.) grown for three years in field plots of VIR (Northwestern region of the Russian Federation) were evaluated for productivity and a number of biochemical parameters of tubers. The 12 parameters that have been evaluated in tubers annually one month after harvesting and after cold storage (at 2 - 4°C) include the content of dry matter, starch, protein, ascorbic acid, carotenoids and β-carotene, anthocyanins, total sugars and monosaccharides (reducing sugars), phenolic compounds, as well as total acidity, and antioxidant activity. The cultivars differed significantly in productivity and biochemical composition of tubers. Dry matter content ranged from 16.40% in cv. ʻKumachʼ to 27.28% in ʻNayadaʼ, starch content ranged from 8.96 % in cvs. ʻFioletovyjʼ, ʻKumachʼ to 20.08% in ‘Sokur’, the total sugar content reached 2.46% in cv. ʻSiverskij’ whereas ‘Sokur’ has the smallest content of reducing sugars in post-harvest tubers. Protein content ranged from 1.02% to 2.77% with the highest accumulation in cv. ʻSokurʼ. Content of ascorbic acid (vitamin C) ranged from 6.3 mg/100 g FW in ʻKrasa Meŝeryʼ to 26.6 mg/100 g FW in cv. ʻKrasavchikʼ. Carotenoids content ranged from 0.157 mg/100 g FW in cv. ‘Nayada’ to 0.645 mg/100 g FW in cv. ʻGrandʼ. Content of anthocyanins in potato ranged from 1.04 mg/100g FW (not colored tubers of cv. ʻKrasa Meŝeryʼ) to 283.04 mg/100g FW (colored tubers of cv. ʻFioletovyiʼ). The nutritional value of tubers (content of dry matter, sugars, starch and protein) was determined to a greater extent (38-49%) by varietal characteristics, rather than by meteorological conditions in vegetation period. The content of vitamin C, carotenoids, protein and anthocyanins varied depending on the year. The highest content of dry matter, starch and carotenoids was noted in tubers harvested in 2019, marked by low temperatures and precipitation deficit in growing season. The conditions of 2020 with low temperatures and excess precipitation were the most favorable for anthocyanin’s accumulation in potato tubers while not for other biochemical parameters. Of the cultivars, ʻSokurʼ had the highest content of dry matter, protein and starch. Cv. ʻFioletovyiʼ had small starch content and high level of anthocyanins. Cv. ʻPlamyaʼ can be recommended for potato products manufacturing due to dry matter content above 20% and reducing sugars content 0.24%..

Let Vn(d) denote the least number such that every collection of nd-cubes with total volume 1 in d-dimensional (Euclidean) space can be packed parallelly into some d-box of volume Vn(d). We show that V3(d)=r1−dd if d≥11 and V3(d)=1r+1rd+1r−rd+1 if 2≤d≤10, where r is the only solution of the equation 2(d−1)kd+dkd−1=1 on 22,1 and (k+1)d(1−k)d−1dk2+d+k−1=kddkd+1+dkd+kd+1 on 22,1, respectively. The maximum volume is achieved by hypercubes with edges x, y, z such that x=2rd+1−1/d, y=z=rx if d≥11, and x=rd+(1r−r)d+1−1/d, y=rx, z=(1r−r)x if 2≤d≤10. We also proved that only for dimensions less than 11 there are two different maximum packings, and for all dimensions greater than 10 the maximum packing has the two smallest cubes the same.

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

Selected glasses with 10 Na₂O + (90-x) P₂O₅ + x BCD where bypass cement dust (BCD) x value =10, 20, 30 in mol% were synthesized by recognizable melting annealing technique. Cooperative characterization of the prepared glasses were carried out through FTIR and SEM analysis before and after immersed in simulated body fluid (SBF) solution for 13 and 23 days at 37 ^oC. After immersion in SBF, apatite layer is produced on the glass surface after 13 day and increase after 23 day, showing good bioactivity after immersion in the SBF especially for bioglass sample with 30% BCD. A porous hydroxy apatite layer produced on the surface of SBF-glass composite and this layer became denser after more soaking time, periods were extended from 13 to 23 days. Atomic absorption spectroscopy explained the early period of soaking that cause release of both Si and Ca ions through the glass beside decreasing of phosphrous ions. Bioglass (BCD-30) is studied as shielding for gamma, protons and alphas by using Phy-X software, SRIM Monte Carlo simulation code and its subroutine TRIM. The gamma shielding parameters, mass stopping power (MSP), range for both proton (H- ions) and alpha (He- ions) in bioglass- BCD-30 and human bone tissue have estimated. Also, comparison between them is calculated in predicting the radiation damage and atomic displacements per atom (dpa).

Deployments of real-world object-detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, a conditioning method is investigated. The method aims to guide the training loop of thermal object detection systems by leveraging an auxiliary branch to predict the weather, while directly or indirectly conditioning the baseline detection system. Leveraging such an approach to train detection networks does not necessarily improve the performance of native architectures, however, it can be observed that conditioned networks manage to extract a signal from thermal images that guides the network to detect objects that baseline models miss. As the extracted signal appears to be quite noisy and very challenging to regress accurately, further work is needed to identify an ideal optimization vector.

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.

A binary biomass-based solid electrolyte film (LSE) was successfully synthesized through the incorporation of lignin nanoparticles (LNP) with sodium alginate (SA). The impact of the mass ratio of SA to LNP on the microstructure, porosity, electrolyte absorption capacity, ionic conductivity, and electrochemical properties of the LSE was thoroughly investigated. The results indicated that as the proportion of SA increased from 5% to 15% LNP, the pore structure of the LSE became increasingly uniform and abundant. Consequently, enhancements were observed in porosity, liquid absorption capacity, ionic conductivity, and overall electrochemical performance. Notably, at a SA amount of 15% LNP, the ionic conductivity of resultant LSE-15 was recorded at 14.10 mS cm1, with porosity and liquid absorption rates reaching 308% and 58.4%, respectively. LSE-15 was employed as a solid electrolyte, while LNP-based carbon aerogel (LCA) served as the two electrodes in the construction of a symmetric all-solid-state supercapacitor (SSC). The SSC device demonstrated exceptional electrochemical storage capacity, achieving a specific capacitance of 197 F g1 at 0.5 A g1, along with maximum energy and power density of 27.33 W h kg1 and 4998 W kg1, respectively. Furthermore, the SSC device exhibited highly stable electrochemical performance under extreme conditions, including compression, bending, and both series and parallel connections. Therefore, the development and application of the binary biomass-based solid electrolyte film in supercapacitors represent a promising strategy for harnessing high-value biomass resources in the field of energy storage.

The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

Background: Neuroinflammation has long been implicated in the progression of amyloid beta (Aβ) accumulation and the decline of cognitive function in Alzheimer's disease (AD). Currently, many transcription factors, downstream signaling pathways, and molecular mechanisms that regulate the polarization of microglia have been explored. Furthermore, microglia may also exert a complex role in AD through the transformation of Aβ plaques or debris clearance, reflected in Aβ phagocytosis. One of the mediators of neuroinflammation in AD is the activated microglia. Therefore, the regulation of microglial function may be the key to successfully treating AD. Methods: This is a review article. PubMed, Embase, Scopus, and research meeting abstracts were searched up to 2024 for studies of microglia and neuroinflammation in Alzheimer's Disease. Systematic information retrieval was performed and appropriate studies were isolated based on important information available in the studies. The information from each of the articles was un-derstood and extracted to form a database. Results: The similar neuropathological results between several animals and AD cases show the possibility to implement microglia-related changes as an earlier diagnostic marker for AD in humans. The gene sets identified in various transcriptomic studies further foster this avenue of research by offering potential targets for therapeutic development. Multiple studies suggest that microglia-associated neuroinflammation at a special stage could also be protective, and there-fore, intervention should be delicate so that a beneficial response is retained. Conclusion: The phenotype balance between A1 (toxic) and A2 (safe) microglial phenotypes to toxic illness in AD has become a hot research topic at present. Substantial evidence, both in vitro and in vivo, has suggested that the loss of the normal A2 phenotype and the activation of toxic A1 microglia contribute to neurodegeneration in AD. Promoting or restoring the polarization of mi-croglia towards the A2 phenotype may thus represent an effective therapeutic strategy for ame-liorating neuroinflammation and progressive neurocognitive impairments.

We describe a forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to operationally forecast conditions in Northern and Central California. It was established in 2014 and has been periodically improved. In the current configuration, WRF forecasts are routinely performed multiple times each day on a 2-km grid, and the results are used as input to wildfire fuel moisture, fire probability, and wildfire spread models. Components of the system include a stochastically perturbed ensemble; an annually updated reanalysis covering more than 30 years; and validation against PG&E’s extensive mesonet and other observing networks. We describe the development and validation of the next operational edition. Configurations tested include irrigation triggered by crop growing season and an ensemble-based approach with intelligently sub-selected Global Ensemble Forecast System (GEFS) members. The latter approach allows for enhanced uncertainty sampling in WRF forecasts and can greatly improve WRF forecasts when calibrated by observations.

Background: Both hypertension and carotid atherosclerosis are independent risk factors for cardiovascular disease. Understanding their combined impact could enhance risk assessment and management strategies. Hypothesis: We hypothesized that the synergistic effects of hypertension and carotid plaques would increase the risk of both cardiovascular events and all-cause mortality. Methods: A follow-up study was conducted at the Preventive Cardiology department of Vilnius University Hospital Santaros klinikos between 2012 and 2021. The study recruited participants aged 40-60 who did not have overt CVD and were part of the Lithuanian High Cardiovascular Risk primary preventive program. The study collected demographic and clinical data, including an ultrasound assessment of carotid plaque. The participants were monitored for 4-10 years for CVD events and all-cause mortality. Results: Among 6,138 participants, 954 (16%) experienced cardiovascular disease (CVD) events. The presence of carotid plaque on both sides was significantly associated with CVD events, myocardial infarction, and all-cause mortality. However, the combination of hypertension and carotid plaque did not significantly increase the risk for CVD events, stroke, or all-cause mortality. Conclusion: The combination of hypertension and carotid plaque did not significantly increase the risk of CVD events or all-cause mortality. CV events depend on the spread of the atherosclerosis process in the carotid vessels.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

The socio-economic benefits of globalization cannot be overemphasized. However, the global spread of infectious diseases has so followed a parallel course. The recent wave of pandemics with emerging new diseases has shown that no part of the world is immune from the spread of infectious diseases. COVID-19 like previous pandemics has exposed inadequacies of our healthcare systems and global capacity for prevention, early detection, and response in pandemic outbreaks. Given the highly interconnected global economy and the high potential for passim spread of infections, every nation must not only come up with concerted efforts to prevent and better prepare for pandemics, international solidarity and engagements will also be critical to ensure a long-lasting and sustainable solution. We are only as strong as our weakest health system, global support to strengthen all health systems around the world is the most effective way to be pandemic – ready. ‘All for one and one for all’.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Abstract: Lung cancer, characterized by its heterogeneity, presents a significant challenge in ther-apeutic management, primarily due to the development of resistance to conventional drugs. This resistance is often compounded by the tumour’s ability to reprogram its metabolic pathways, a survival strategy that enables cancer cells to thrive in adverse conditions. This review article ex-plores the complex link between drug resistance and metabolic reprogramming in lung cancer, offering a detailed analysis of the molecular mechanisms and treatment strategies. It emphasizes the interplay between drug resistance and changes in metabolic pathways, crucial for developing effective lung cancer therapies. The review examines the impact of current treatments on metabolic pathways and the significance of considering metabolic factors to combat drug resistance. It highlights the different challenges and metabolic alterations in non-small cell lung cancer and small cell lung cancer, underlining the need for subtype-specific treatments. Key signalling pathways, including PI3K/AKT/mTOR, MAPK, and AMPK, have been discussed for their roles in promoting drug resistance and metabolic changes, alongside the complex regulatory networks involved. The article evaluates emerging treatments targeting metabolism, such as metabolic inhibitors, dietary management, and combination therapies, assessing their potential and challenges. It concludes with insights into the role of precision medicine and metabolic biomarkers in crafting personalized lung cancer treatments, advocating for metabolic targeting as a promising approach to enhance treatment efficacy and overcome drug resistance. The review underscores ongoing advancements and hurdles in integrating metabolic considerations into lung cancer therapy strategies.

Substance abuse is a significant public health issue both globally and nationally. Substance abuse involves the detrimental or risky consumption of psychoactive substances, posing a significant global issue, particularly among the youth. In India, cannabis abuse is a major concern. Cannabis is consumed legally in many states as Bhang, also known as marijuana or weed, while Charas and Ganja are illegal under both international drug conventions and the Indian NDPS Act of 1985. The Cannabis users are at great risk of developing Cannabis use disorder which is a pattern of problematic cannabis use leading to clinically significant impairment or distress. It is the most widely used illegal substance worldwide, showing a high prevalence in India.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Spontaneous tumours in higher plants can develop depending on plant genotype without any pathogen invasion. Spontaneous tumour formation on the taproots is consistently observed in certain inbred lines of radish (Raphanus sativus var. radicula Pers.). In this work, we have sequenced the genomes of two closely related radish inbred lines that differ in their ability to spontaneously form tumours. We have identified numerous single nucleotide variants (amino acid substitutions, insertions or deletions) that are likely to be associated with the spontaneous tumour formation. Furthermore, we performed the identification of CLE and WOX genes the genomic sequences of the radish inbred lines and, as a result, identified two unique radish CLE genes which probably encode proteins with multiple CLE domains. The results obtained provide a basis for investigating the mechanisms of plant tumour formation and also for future genetic and genomic studies of radish.

Job characteristics shape organizational sustainability, conferring tangible benefits to employees. Job characteristics shape attitudes toward work, such as satisfaction and engagement, and are related to the well-being and health of employees. The aim of the study was to determine and describe the role of work engagement and support at work in the relation between selected job characteristics (organizational justice, job control, and role clarity) and self-rated health. The study was conducted on a sample of 368 employees representing various industries. The study used: Utrecht Work Engagement Scale (UWES), Organizational Justice Survey, Coworker Support Scale, Supervisor Support Scale, Role Clarity Scale, Job Control Scale. Cantril ladder was used to measure self-rated health. The presented study established the importance of lower scores on the UWES subscale: dedication as a significant moderator of the examined correlations. In relation to the correlation: justice and its dimensions, and self-rated health, support from supervisors showed a greater effect than that from coworkers. The conducted study expands knowledge on the formation of employees' self-rated health through the sense of justice experienced within the organization. The results obtained are applicable to organizational practice. This finding highlights the importance of supporting organizational justice, which contributes to employee well-being (particularly when combined with high levels of work engagement and supervisor support) and ultimately increases organizational sustainability. Further research can be continued taking into account the limitations of the previous procedure of selecting people for the study and the measurement tools used.

Precision metagenomic approaches using Oxford Nanopore Technology (ONT) sequencing has been shown to allow recovery of complete genomes of Escherichia coli O157:H7 from overnight enrichments of agricultural waters. This study tests the applicability of a similar approach for Cronobacter genome recovery from powdered infant formula (PIF) overnight enrichments, where Cronobacter typically dominates the overall microbiome (>90%). In this study, we aimed to test whether using ONT sequencing of overnight PIF enrichments could recover a completely closed Cronobacter genome for further genomic characterization. Ten PIF samples, each inoculated with different Cronobacter strains, covering Cronobacter sakazakii, C. muytjensii, C. dublinensis, C. turicensis, and C. universalis, were processed according to the Bacteriological Analytical Manual (BAM) protocol. qPCR was used for initial screening (detection and quantification) of the overnight enrichments and confirmed that the spiked PIF samples after the overnight enrichment have high levels of Cronobacter (107 to 109 CFU/mL). DNA from overnight PIF enrichments was extracted from the enrichment broth and sequenced using ONT. Results showed that ONT sequencing could accurately identify, characterize, and close the genomes of Cronobacter strains from overnight PIF enrichments in 3 days, much faster than the nearly two weeks required by the current BAM method. Complete genome recovery and species differentiation were achieved. This suggests that combining qPCR with ONT sequencing provides a rapid, cost-effective alternative for detecting and characterizing Cronobacter in PIF, enabling timely corrective actions during outbreaks.

Considering the unquestionable role of education in economic development, it has an impact at a macroeconomic level—contributing to enhancing productivity and innovation, reducing poverty and promoting social cohesion—and at a personal level—ensuring the improvement of knowledge, skills and individual capabilities and promoting social values like empowerment and equality. Notably, quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study used a fuzzy set qualitative comparative analysis to assess the conditions for attaining higher levels of quality education based on the different indicators used to measure SDG 4. The analysis will allow comparisons of results among different countries and provide information about the initiatives that could be relevant to increasing quality education worldwide and for policymakers.

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M7.8 and M7.5 Kahramanmaras earthquake sequence on February 6, 2023, the closed strong magnetic storm occurred on November 7, 2022, SYM/H=-117 nT. Storm started at 08:04 UT. At this time, the high latitudinal part of Turkey's longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth's environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from some days to some months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

In every economy, Stock markets are part of the key elements the build it up. A few decades ago, there has been a significant change in Ghana stock market returns (GSE). Our study examines the statistical and economic significance of investor sentiment, based on weather conditions/changes, on stock market returns. OLS models, assisted by unit root tests were employed in analyzing the data obtained from the Ghana stock exchange platform from 2000 to 2017. From our literature review, we discovered that investors’ perceptions play a central role in finalizing the direction of stock market returns. Regarding our empirical results, we tested whether weather variations influence the investment decisions of investors; we discovered that temperature and cloud cover significantly influences stock market returns. This is because of mood changes is associated with weather conditions variations. However, sunshine per our regression coefficient shows a statistically insignificant impact on investors’ investment choices. Precipitation to a large extend influence stock market activities further affecting its results negatively as our regression results depicted. We concluded stock brokerage firms, companies, and investors (foreign/local) must incorporate weather changes/effects when strategizing about their investment outcomes.

We consider an alternative hypothesis of crater formation -- through an electric discharge machining (EDM) of the celestial bodies' surfaces. The source of the energy and the current itself may lie in the plasma infalling on a celestial body (from an extremely strong solar outburst or a nearby supernova). This mechanism may have been even more important than impact cratering in the early Solar System, with potential implications to the history of the dynamics of the System itself and solar activity in particular. In this light, the conclusion that shocked and metamorphosed minerals found within crater sites are evidence of hypervelocity impacts might be reconsidered. There are ambiguities that remain among sources of mineralogical formations. This study is ongoing with details of investigations supporting a new approach to crater formation not yet exhausted. Apart from mineralogical analysis and theoretical considerations regarding the large scale crater formation, we present a description of experimental studies of EDM acting at low pressures. Experimental results are made with crude setups and could easily be replicated on a larger scale with valuable research potential, as there is a well understood relation between the crater size and the energy needed for its production.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Previous studies have acknowledged the impact of weather changes on retail uncertainty. They primarily focus on understanding how weather conditions affect offline consumer behavior and aiming to develop effective marketing strategies. However, there is little research on the complex impact of weather on online shopping behavior. To bridge this gap, we conduct a study with a sample of 261 consumers from China with shopping experience in Community Retail Shops (CRSs). We utilize the S-O-R model and theories including meteorological emotional effect theory, emotional coherence and meteorological psychology to propose and validate models elucidating the relationship between weather and consumers' online shopping behavior in CRS. Our findings reveal that weather conditions affect consumers' spending patterns and purchase diversity, mediating by consumers' emotion and risk aversion, when they comfortably shop online at home. Furthermore, employing the fsQCA model, we identify the critical path through which weather conditions and consumer types influence risk aversion awareness. The results provide management implications for retailers to develop online marketing strategies for different consumer types.

Weather-related risks in aviation are one of the main causes of accidents worldwide. This article aims to analyze aviation risks resulting from adverse weather conditions, using the case study of the Pucallpa accident that occurred on August 23, 2005, in eastern Peru. In this analysis, we have determined the relationship between meteorological information and aviation risks, presented a matrix of risks associated with the most predominant meteorological variables at Pucallpa airport, and drawn up three maps of meteorological risks indicating the month of the year in which their incidence is most relevant. Finally, we conclude that it is essential to emphasize the importance of considering the prevailing weather conditions at Pucallpa airport to help mitigate the potential risks that may occur during different phases of the flight.

The extension of virology beyond its traditional medical, veterinary or agricultural applications, now called environmental virology, has shown that viruses are both the most numerous and diverse biological entities on earth. In particular, virus isolation studies involving unicellular eukaryotic hosts (heterotrophic and photosynthetic protozoans) revealed numerous viral types previously unexpected in terms of virion structure and morphology, genome size and gene content, or mode of replication. Complemented by large-scale metagenomic analyzes, these discoveries have rekindled interest in the enigma of the evolutionary origin of viruses, for which no simple definition encompassing all of their diversity is still unanimous. Several laboratories have repeatedly tackled the deep reconstruction of the evolutionary history of viruses, using various methods of molecular phylogeny applied to the few shared genes detected in certain virus groups (e.g. the Nucleocytoviricota). Beyond the practical difficulties of establishing reliable homology relationships from extremely divergent sequences, I present here purely conceptual arguments highlighting several fundamental limitations plaguing the reconstruction of the deep evolutionary history of viruses, and even more the identification of their unique of multiple origin (s). Those limitations are direct consequences of the particularly random mechanisms which govern the reductive evolution of obligate intracellular parasites.

The susceptibility of seven species of chestnuts to the orchid leafminer was studied in the arboretum of the Main Botanical Garden of the Russian Academy of Sciences (MBG RAS), taking into account their interspecific characteristics. Using pheromone delta traps, the highest number of moths was shown for A. hippocstanuam and the lowest for A. chinensis. A number of anatomical parameters of leaves were investigated, such as the thickness of the epidermal cell wall and the thickness of the palisade and spongy parenchyma layers. As a result, it was shown that the most infected chestnut species had a greater thickness of the nutritious parenchyma tissue. No de-pendence was found between the degree of susceptibility to the Ohrid leaf miner and such indi-cators as the content of chlorophyll a + b and carotenoids in the leaves of seven species of chestnuts. Nevertheless, resistance of different species of the genus Aesculus to C. ohridella under increased tannin content in leaves has been shown. Evaluation of phenolic compounds and flavonoids has not established their reliable role as repellents. The high levels of carbohydrates found during the study contributed to increased susceptibility to the Ohrid leaf miner.

Background and Objectives: Cancer as the second leading cause of death in the United States poses a huge healthcare burden. Barriers to access to advanced therapies influence the outcome of cancer treatment. In this study, we examined whether insurance types affect the quality of cancer clinical care regarding access to an important monoclonal antibody, ramucirumab. Materials and Methods: Data for 13,340 cancer patients with Purchased or Medicaid insurance from the All of Us Database were collected for this study. The Chi-square test of proportions was employed to determine the significance of patient cohort characteristics and ramucirumab usage between Purchased and Medicaid insurance groups. The independent t-test was utilized to assess the influences of other determinants on insurance types. Results: Cancer patients who are African American, with lower socioeconomic status, or with lower educational attainment are more likely to be insured by Medicaid. Analysis of survey questions demonstrated the relationship between income and education level with insurance type as Medicaid cancer patients were less likely to receive primary care and specialist physician access and more likely to request lower cost medications. In addition, those with Medicaid insurance were identified to have poorer access to expensive therapeutics like ramucirumab compared to those with Purchased insurance.  Conclusions: The inequities of the US healthcare system are observed for cancer patient care; access to physicians and therapeutics are highly varied and dependent on insurance types. Socioeconomic status determines insurance type, which unfortunately generates a significant impact on cancer treatment and disease outcome.

This paper studies pandemic viruses that spread during the period (1759–2020) according to solar activity cycles. Our findings and results include the following: (1) The severity of a pandemic correlates negatively with the strength of solar activity; (2) Pandemic viruses are classified into three types based on their compatibility with solar activity associations. Most of them spread through the quiet Sun, where viruses survive better in cold and rainy weather, and in stable geomagnetic fields without strong disturbances; (3) The emergence of new strains of influenza viruses was manifested in two ways. First, the annual epidemics due to antigenic drift. Second, pandemics recur every 1–12 solar cycles (about 11–120 years) due to viral reassortment of new subtypes, which results in antigenic shifts; (4) Pandemic viruses have two groups according to their recurring period: first, recurring in nine solar cycles; second, recurring in twelve solar cycles. Furthermore, we reassort pandemic viruses from their previous spread in the same periodic classification. Moreover, we derive a periodicity formula for each subtype of the pandemic virus as a spread date.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Living organisms with developed endocrine systems react in a complex way to environmental changes. Changing atmospheric pressure causes different blood pressures or hormone levels; carcinogenic radiation modifies the structure of DNA. Charged particles and ions act as neurotransmitters and block certain types of protein channels and receptors. A high concentration of carbon dioxide has an indirect effect on both blood pressure and neuron activity. The bioelectric nature of living tissues highlights the complexity of the connection between the dynamic physical environment and biological systems. Recent results from studies on the interactions and connections mentioned above are reviewed in this paper.

In the past two decades the world experienced an exceptional number of unprecedented extreme weather events, some causing major human suffering and economic damage, such as the March 2012 heat event, which was called “Meteorological March Madness.” From the beginning of space era a correlation of solar flares with pressure changes in atmosphere within 2–3 days or even less was reported. In this study we wanted to test the possible relation of highly warm weather events in North-East America with Solar Energetic Particle (SEP) events. For this reason we compared ground temperatures T_M in Madison, Wisconsin, with energetic particle fluxes P measured by the EPAM instrument onboard the ACE spacecraft. In particular, we elaborated case events and the results of a statistical study of the SEP events related with the largest (Dst ≤ −150nT) Coronal Mass Ejection (CME)-induced geomagnetic storms, between with the years 1997–2015. The most striking result of our statistical analysis is a very significant positive correlation between the highest temperature increase_. ΔT_M and the time duration of the temperature increase ΔT_M (r = 0.8, p <0.001) at “winter times” ( r = 0.5, p , 0.01 for the whole sample of 26 SEP examined events). The time response of T_M to P was found to be in general short (a few days), but in the case of March 2015, during a gradual P8 increase, a cross-correlation test indicated highest c.c. within 1 day (p < 0.05). The March 2012 “meteorological anomaly” was elaborated in the case of South-East Europe, where, beside a period of strong winds and rainfall (6-13.3.2012), intense precipitation in North-East Greece (Alexandroupoli) were found to be correlated with distinct high energy flux enhancements. A rough theoretical interpretation is discussed for the space—atmospheric extreme weather relationship we found. However, much work should be done to achieve early warning of space weather dependent extreme meteorological events. Such future advances in understanding the relationships between space weather and extreme atmospheric events would improve atmospheric models and help people’s safety, health and life.

Information is physically measurable as a selection from a set of possibilities, the domain of information. This defines the term "information". The domain of the information must be known together reproducibly beforehand. As a practical consequence, digital information exchange can be made globally efficient, interoperable and searchable to a large extent by online definition of application-optimized domains of information. There are even more far-reaching consequences for physics. The purpose of this article is to present prerequisites and possibilities for a physical approach that is consistent with the precise definition of information. This concerns not only the discretization of the sets of possible experimental results, but also the order of their definition along time. The access to or comparison with the domain of information is the more frequent, the earlier it was defined. The geometrical appearance of our space is apparently a delayed statistical consequence of a very frequent connection with the common primary domain of information.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

Dengue virus is a global health problem most prevalent in tropical and subtropical regions. However, after the approval of two vaccines, Dengvaxia, TV003/TV005 of Takeda, for its prevention, problems were reported, like enhanced risks of infection or less efficiency of protection. Since then, more struggles have been in demand for the development of better vaccines. Here, we conducted a new design through an in-silico strategy. Initially, epitopes were chosen based on their antigenicity, immunogenicity, and binding affinity with MHC molecule, while excluded with allergenicity, toxicity and potential risk of antibody dependent enhancement. Subsequently, a core antigen was constructed with selected epitopes and linked with distinct adjuvant proteins to achieve three candidate vaccines, PSDV-1~3. PSDV-2 was chosen for further validation based on their advantage in physicochemical and structural properties. With a panel of simulations, this artificial protein showed tight binding with pattern recognition receptors, good stability, robust immune induction, and ultimately was confirmed as a high quality vaccine candidate. The plasmid for its recombinant expression was then designed accordingly. With our new design, one more choice was obtained for the effective protection from Dengue virus. Further experimental validations are still require to confirm it protection capacity and safety.

To date, the patent foramen ovale (PFO) does not get the deserved medical attention. The PFO poses a serious threat to health and even life of mankind. The first respective case report in the medical literature dates back to the 19th century. It led to death. The fact that a PFO is present in roughly 25% of people underscores its overall potential to do harm. Yet at the same time by the sheer number, it discourages the medical community from screening for it and from treating it. About 5% of the population have particularly dangerous forms of PFOs. Such PFOs portray a high enough risk for clinical events, the likes of death, stroke, myocardial infarction, or ocular, visceral, and peripheral embolism, to justify screening for them. Highly significant health incidents being at stake, it appears obvious that PFO closure should be used for primary prevention. This is supported by the fact that closing a PFO is the simplest intervention in cardiology, with presumably the highest clinical yield. Being mainly a preventive measure, PFO closure represents a mechanical vaccination. When closing PFOs for one of the rarer therapeutic indications (migraine, platypnea orthodeoxia, etc.), patients automatically profit from the collateral benefit of getting at the same time mechanically vaccinated for life against paradoxical embolism. Vice versa, closing a PFO for prevention of paradoxical embolism not infrequently betters or cures migraine or exercise dyspnea, thereby improving quality of life as a collateral benefit.

Mucosal healing (MH) is the main treatment target in ulcerative colitis and Crohn’s disease, and it is defined by a combination of complete endoscopic and histologic remission. Indeed, the complete resolution of mucosal inflammation should be confirmed at histology. Neutrophil infiltration represents the unique histological marker in discriminating the active vs quiescent phase of disease, also including crypt injuries (cryptitis and crypt abscesses), erosions and ulcerations. Basal plasmacytosis do not qualify by itself the remission in inflammatory bowel diseases (IBD), but it represents a diagnostic clue, mostly at onset. Several histological scoring systems have been developed to evaluate therapeutic efficacy, even though they often resulted tricky and subjective. Moreover, these scores listed a heterogeneous group of histological features as parameters. The recently proposed simplified histological score on MH was based on neutrophils and their distribution in the gut mucosa was also considered, aiming to provide a simple, time-sparing and reproducible tool to be applied to the routinary diagnostic practice. The artificial intelligence could be a promising tool both in evaluating and standardizing the histological assessment of disease activity in IBD. Moreover, novel molecules involved in the inflammatory dynamics in the gut could be employed in supporting the diagnostic practice in IBD.

In Eucalyptus plantations, coppice rotations often yield less than initial rotations. The TECHS project (Tolerance of Eucalyptus Clones to Hydric, Thermal and Biotic Stresses) studied short rotation coppicing across a 3,000 km gradient. The main objective of this work was to compare the survival, sprouting and initial growth of Eucalyptus clones managed, and to examine factors that might influence the productivity of the coppice rotation: climate, genotypes and stocking. Eight of the TECHS sites spread from latitudes 6o S to 30o S were included in the coppice study, with 17 genotypes at each site. The initial rotation had been planted at a 3 x 3 m spacing, and also in a spacing trial at densities from 450 to 14,000 trees ha-1. Six months after harvesting the initial Eucalyptus rotation, average survival was 88%, with tropical clones showing over twice the sprouting biomass (6.7 vs 2.9 Mg ha-1) and four times the woody biomass compared to subtropical clones (4.7 vs 1.1 Mg ha-1). Greater initial water deficits had stronger sprouting and growth. Clones with higher belowground carbon allocation in the initial rotation performed better in coppicing, and precipitation became more influential after 12 months. Drought and spacing trial significantly affected growth.

A 3-month (June-August) regime of the year 2002 summer rainfall (JJA2002) was simulated with 30 physics combinations using the Weather Research and Forecasting (WRF) model at 12-km horizontal grid resolution. The objective is to examine summer rainfall sensitivity to parameterization of microphysical, convective, and boundary layer processes and identify the best possible combination of parameterization options that perform relatively better in simulating the spatial and temporal distribution of summer rains over Ethiopia. The WRF simulated rainfall was evaluated against station data and satellite rainfall products (CHIRPS and ENACTS) using mean absolute error, Pearson and Pattern correlation coefficients (PCC), pattern correlation, and error in a number of rainy days as evaluation metrics. Summer rainfall is found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All simulations captured the spatial distribution of mean seasonal precipitation with PCC ranging from 0.89-0.94. However, all simulations overestimated precipitation amount and number of rainy days. Out of the 30, the simulations that use a combination of Grell-3D cumulus scheme, ACM2 boundary layer, Lin Microphysics, Dudhai shortwave radiation, and RRTM longwave radiation scheme ranked the top and provided the most realistic simulation in terms of amount and spatio-temporal distribution of summer rainfall.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Garcinia indica (Thouars) Choisy (commonly known as Kokum) is an important medicinal plant in Ayurvedic medicine and belonging to the Clusiaceae family. Kokum is a highly traded medicinal plant species of India, dried rind of fruits and butter from seeds are traded in the markets. This study aimed to assemble the complete chloroplast (cp) genome of G. indica and compare it with the previously published cp genome within the Garcinia taxa to identify the potential species-specific barcode marker for Kokum authentication. The assembled cp genome, with a size of 1,56,891 bp, exhibits a typical quadripartite structure. The large single-copy (LSC) region spans 85,580 bp, and the small single-copy (SSC) region spans 17,181 bp, together comprising 64.5% of the genome. The pair of inverted repeats (IRA and IRB) are each 27,065 bp, covering the remaining 35.5% of the genome. A total of 126 unique genes were identified, including 86 protein-coding genes, 32 tRNA genes, and 8 rRNA genes. Phylogenetic analysis using complete cp genomes from 12 species in the Clusiaceae family indicated that 10 Garcinia species form a sister clade. Notably, the ycf1 gene in the LSC region was more divergent within the Garcinia species. This study presents the first report on the chloroplast genome of G. indica. This chloroplast genome resource provides a basis for identifying new DNA barcode marker as well as species-specific marker for herbal drug authentication of Kokum, and species delineation within the Garcinia genus.

Shared e-scooters are gaining popularity as a solution for first and last-mile connectivity in urban areas. This study conducted in Doha, Qatar, aimed to understand how weather and land use patterns affect e-scooter usage, utilizing various datasets. Given the novelty of micromobility systems in Qatar and their sensitivity to the local hot and humid climate, the study employed a comprehensive analytical approach. Analysis revealed that e-scooter demand spikes on weekends, particularly in the afternoon. Influential land use factors include proximity to universities and employment centers, while a higher presence of Qatari females in an area correlates with reduced usage, pointing to cultural influences. Weather conditions, especially humidity and extreme temperatures, significantly impact e-scooter demand. The study employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for trip clustering and the Random Forest (RF) algorithm to model trip counts, considering temperature and humidity. Insights showed humidity as a critical predictor of hourly e-scooter trip counts. These findings underscore the importance of considering weather, land use, the relationship between land use characteristics and weather variations, and finally cultural factors in optimizing e-scooter services. By integrating data analysis and machine learning, the study offers valuable insights for enhancing urban mobility and transportation planning in GCC countries, promoting sustainable urban mobility.

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect and analyze meteorological measurements. This paper presents the results of our analysis, demonstrating the effectiveness of citizen science in generating valuable environmental data. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. Our findings underscore the potential of citizen science to augment traditional monitoring efforts and inform decision-making processes in environmental research and management. The paper discusses the methodology employed, including data collection and statistical analysis techniques. Results highlight the reliability and accuracy of citizen-generated data, as well as the strengths of the MeteoTracker platform. The implications of our findings for environmental research, policy development, and public engagement are discussed, along with recommendations for enhancing the effectiveness of citizen science initiatives in environmental monitoring.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

Universal education is an elusive goal in many countries, especially for disabled children. Nonetheless determined efforts around the globe have shown that it can become a reality once existing systems were re-imagined by practitioners who arguably have been to the fore more so than academic researchers. Their efforts have identified new ways of thinking about children’s disabilities, the introduction of new practices in schools, forging partnerships between teachers and parents, and mobilising community resources. Societal change is both a consequence of, and a support to these local systems. The complexity of creating education for all may be daunting but it is achievable when driven by committed, creative and inspirational leadership from practitioners as is evident from the examples provided in this paper which have been further validated by research and evaluation into their efforts.

The climate crisis is developing to a life-changing event on a global level. Health promotion with the aim to increase the health status of individuals, independent from the present health status has been developed on scientific basis at least for the last eight decades and follows. There are some basic principles, which are prerequisites for both, health promotion and climate protection. Those principles include (1) sustainability, (2) orientation on determinants, and (3) requirement of in-dividual as well as community approaches. People are generally aiming to protect their lifestyle habits (e.g. traveling, consumer habits) and personal properties (e.g. car, house) by easy solution with as little effort as possible, and this can affect both, health and climate. To reduce the emission of greenhouse gases and to protect our environment, changes towards a sustainable lifestyle have to be embed into everybody’s mind. Examples for domains, that need to be addressed in health promotion as well as in climate protection include education/ knowledge/ (health) literacy, physical activity, nutrition and dietary habits, mental health, and social capital. If health promo-tion fails to tackle those domains, this will continue to drive climate crisis. And climate change, in turn, will affect health. On the other hand, developing and promoting health resources in the domains mentioned could again help to mitigate the health-damaging effects of climate change.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

This article focuses on the analysis of dynamics emerging in a Network of Hodgkin-Huxley Reaction-Diffusion equations. The network is forced with three external periodic stimulus input. A synchronization phenomenon is observed.

Lead-free copper halide perovskite nanocrystals (NCs) are emerging materials with excellent photoelectric properties. Herein, we present a colloidal synthesis route of orthorhombic Cs2CuCl4 NCs with well-defined cubic shape and an average diameter of 24 ± 2.1 nm. The Cs2CuCl4 NCs exhibit bright deep blue photoluminescence, which is attributed to the Cu(II) defects. In addition, passivating the Cs2CuCl4 NCs by Ag+ can effectively improve the photoluminescence quantum yield (PLQY) and environmental stability.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

By framing the problem of determining the state of Schrodinger’s cat as a problem of searching for Hidden Markov Model (HMM), this work shows that Schrodinger’s cat is dead with stationary probabilities P(Cat = Dead) = 1 and P(Cat = Alive) = 0 consistently in various observation-based modelling of the HMM.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

The COVID-19 pandemics has had an unprecedented global impact, and the COVID-19 mass vaccination campaign has been commonly regarded as crucial to overcome the pandemics. Since all-cause mortality is the best way to measure the consequences of a health intervention, the present study was devised to analyze the all-cause mortality data of the United Kingdom (UK), which are made publicly available broken down by vaccination status. Data from April 2021 to May 2023 were retrospectively collected and analyzed according to age groups and vaccination status and the Standardized Mortality Ratio (SMR) for all-cause mortality and for non-COVID-19 were calculated in comparison to the corresponding unvaccinated groups. Results show that, since an age group dependent date, the all-cause mortality SMRs were increasing in any of the age groups considered. Initially, the all-causes death SMRs were less than 1 for all age groups, but due to their growth, for the age groups 18-39, 80-89 and 90+ years by a certain date they exceeded the reference value. For the other age groups, as long as the trend is maintained, it is possible to predict the date in which the SMR would reach the value 1. Non-COVID-19 SMR values show a very similar trend. Their initial values much lower than 1 suggest the presence of significant biases in the ONS dataset which lead to an underestimation of the risks for those vaccinated, as it is not plausible that the vaccine protects from causes other than COVID-19. The finding that all-cause mortality SMRs in vaccinated in comparison to unvaccinated people increases over time requires careful examination to understand the underlying factors. Meanwhile, the other major countries should undertake a systematic collection of all-causes mortality broken down by vaccination status, and should be implemented a moratorium of the promotion of mass vaccination campaigns.

The present study analyzes students' weather-related hazard and risk literacy in Greece, a climate change hotspot region in the Eastern Mediterranean. In this context, we examine the students' level in two core literacy variables, namely knowledge and competency. In addition, we explore how knowledge, attitudes and socio-demographic variables influence students' competencies related to weather and climate risk assessment and adaptability. A questionnaire-based survey was conducted on 474 students aged 12-16. The regression results showed that knowledge significantly affects the level of competency. Self-belief and confidence in science were the most influential among the attitudinal variables. We conclude by discussing educational and behavioural issues highlighted as essential to address with targeted policies and measures in formal education complemented by non-formal educational activities.

The objective of this study was to identify the factors determining a company’s corporate governance related to climate change. We analyzed the effect of various sustainability corporate governance variables on the disclosure level of climate change governance. These variables included facts such as having a dedicated sustainability executive and board committee, the mediating effect of female representation on the board of directors, number of reporting years according to TCFD, membership in a sustainability index, MSCI ESG rating, the existence of a corporate climate transition plan, a mention of the UN Global Compact and GRI, company location, as well as company size and profitability. By adopting a multi-theoretical framework that included stakeholder theory as well the legitimacy and agency theory, the underlying research study used a sample of 100 of the largest global companies by market capitalization and their reporting for the year 2020. Based on 1,400 observations for fiscal year 2020 and using correlation analysis, univariate and linear multiple regressions, we find a positive association between having a climate transition plan in place, being a leader in sustainability according to MSCI ratings, and being a DJSI constituent and the propensity to disclose information on governance for climate change. In addition, we find a company with a dedicated sustainability executive show an increased tendency to be transparent on climate governance issues. Furthermore, having a company location in a developed country is significantly and positively associated with climate change governance. Surprisingly, gender diversity in the corporate board or having a sustainability board committee did not show any significant correlation between a higher climate change governance level. The same was true for companies being active in either the extractive or non-extractive sector. Companies referring to the Global Reporting Initiative (GRI) or UN Global Compact also did not score higher in climate change governance. Neither did corporate profitability or size play a significant role. Our results are robust to variations and provide valuable insights for researchers, academics, executives, practitioners as well as regulators. As more and more companies are shifting towards a climate change reporting framework, it is of paramount importance that we are able to determine the contributing variables that lead to effective climate change corporate governance. Our results are inconsistent with stakeholder theory and are strongly suggesting that a diversified board and the existence of a sustainability committee that meets often/sufficiently may not necessarily lead to a higher level of transparency/quality regarding climate change. While more research is needed, knowing that a dedicated sustainability executive as well as having a climate plan in place can make a difference in climate change reporting, can be very beneficial to many corporate stakeholders. Given the current urgent climate change situation and the crucial role that corporation play in it, dedicated sustainability positions and committees need to be established. The findings could be useful for managers as well as governmental standards setter and regulators who are interested in improving corporate practices dealing with climate change. This study applies STATA software with various regression models to empirically test the relationship between CG and other variables and corporate climate change reporting.

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. An electric-bus (e-bus) transportation system favors reducing pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was lower than that of the battery-swapping e-bus system.

Suppose for n∈ ℕ, set A⊆ℝⁿ and function f∶A→ℝⁿ. If set A, using the Hausdorff outer measure, is measurable in the sense of Carathèodory; we want to find an extension of the expected value, w.r.t the Hausdorff measure, that's unique, finite and "natural" (defined on §2.3 & §2.4) for all f in a prevalent subset of ℝᴬ. The issue is current extensions of the expected value are finite for all functions in only a shy subset of ℝᴬ. The reason this issue wasn't resolved is mathematicians have not thought of the problem, focusing on application rather than generalization. Despite the lack of potential use, we'll attempt to solve the problem by defining a choice function---this shall choose a unique set of equivalent sequences of sets (Fₖ***) , where the set-theoretic limit of Fₖ*** is the graph of f; the measure Hʰ is the ℎ-Hausdorff measure, such for each k∈ℕ, 0 < Hʰ(Fₖ***) < +∞; and (fₖ*) is a sequence of functions where {(x,fₖ*(x))∶x∈ dom(Fₖ***)}=Fₖ***. Thus, the extended expected value of or E**[f,Fₖ***] is: ∀(ε>0)∃(N∈ℕ)∀(k∈N)(k≥N⇒1/Hʰ(dom(Fₖ***))∫dom(Fₖ*** )fₖ* dHʰ - E**[f,Fₖ***]<ε ) which should be unique, finite, and "natural" (defined on §2.3 & §2.4) for all f in a prevalent subset of ℝᴬ. Note we guessed the choice function using computer programming but we don’t use mathematical proofs due to the lack of expertise in the subject matter. Despite this, the biggest use of this research is the extension of the expected value is finite for "almost all" functions: this is easier to use in application when finding the "average" of functions covering an infinite expanse of space.

Cardiovascular (CV) morbidity and mortality are markedly increased in non-dialysis patients with chronic kidney disease (CKD). Thus, the precise management of CV risk factors involved in CKD is crucial to improving outcomes. Serum phosphate (Pi) and FGF-23 levels have been linked with a higher risk of CV events in CKD. However, the exact thresholds of Pi and FGF-23, at which the risk of adverse events increases, remain unknown. We evaluated the expression of intact FGF-23 (iFGF-23) and Pi in a non-dialysis CKD patient population (n=82; 42M:40F; median age 61 years) and investigated their association with CV and renal outcomes, in a 5-year follow-up period. At baseline, the median estimated glomerular filtration rate (eGFR), iFGF-23, and Pi were 45 ml/min/1.73m² (IQ 26.6-73.1), 69.9 µg/ml (IQ 33-117) and 3.4 mg/dL (IQ 3.3-3.9), respectively. Univariate analysis showed a strong association of both iFGF-23 and Pi with age, Charlson Comorbidity Index, hypertension, and diabetes. In addition, iFGF-23 and Pi were both associated with the composite outcome (major CV and cerebrovascular events – MACCEs, hospitalizations, and all-cause mortality) during follow-up. Moreover, Pi was independently associated with all-cause mortality during follow-up. The segmentation of the population in terciles, according to Pi (<3mg/dL; 3-3.6 mg/dL; ≥3.7 mg/dL) within reference serum levels, showed a distribution of the fatality of 0%, 20% and 80% (p =0.034), respectively. Our results reinforce the association of both iFGF-23 and Pi with composite CV outcomes in non-dialysis CKD patients and further suggest that Pi, within current reference levels, may behave as an independent risk factor for mortality in this population. It is suggested that a reassessment of Pi reference levels for early therapeutic intervention in this population may be justified.

Kyrgyzstan, a landlocked nation in Central Asia, is characterized by its rugged mountainous terrain, which covers approximately 90% of its land area. This unique geography poses specific challenges related to climate vulnerability. To address these challenges, we propose a comprehensive approach that involves gathering meteorological data and making it accessible to decision-makers. By leveraging LoRaWAN communication technology, which efficiently transmits sparse and low-speed data over long distances while minimizing power consumption, we can enhance climate resilience. The Internet Society Kyrgyz Chapter, in collaboration with the International Centre for Theoretical Physics (ICTP) and the Central Asia Institute for Applied Geosciences (CAIAG), has initiated the installation of meteorological sensors and disaster mitigation devices, including river water level sensors, terrain moisture sensors, and tilt detectors. These sensors collect critical data, which is stored within the country on an ad hoc server. Stakeholders can access this data according to their specific requirements. This paper outlines the criteria for selecting the deployed equipment and provides details on the installation process at pilot sites, along with the challenges encountered during project execution.

Following the coronavirus disease 2019 (COVID-19) pandemic, several studies have examined the possibility of correlating the virulence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, to the climatic conditions of the involved sites; however, inconclusive results have been generally obtained. Although either air temperature or humidity cannot be independently correlated with virus viability, a strong relationship between SARS-CoV-2 virulence and the specific enthalpy of moist air appears to exist, as confirmed by extensive data analysis. Given this framework, the present study involves a detailed investigation based on the first 20–30 days of the epidemic before public health interventions in 30 selected Italian provinces with rather different climates, here assumed as being representative of what happened in the country from North to South, of the relationship between COVID-19 distributions and the climatic conditions recorded at each site before the pandemic outbreak. Accordingly, a correlating equation between the incidence rate of the pandemic and the average specific enthalpy of atmospheric air was developed, and an enthalpy-based seasonal virulence risk scale was proposed as a tool to predict the potential danger of COVID-19 spread due to the persistence of weather conditions favorable to SARS-CoV-2 viability. For practical applications, a conclusive risk chart expressed in terms of coupled temperatures and relative humidity (RH) values was provided, showing that safer conditions occur in case of higher RH at the highest temperatures, and of lower RH at the lowest temperatures. The proposed risk scale was in agreement with the available infectivity data in the literature for a number of cities around the world.

Deforestation is an important environmental problem in México and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in the Municipality of Tapalpa, Jalisco, México between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA); which represent more than 88% of the regional territory (1066.03 km2). We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction, in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 0.57% of the regional surface albedo in comparison to 2014 scenario. From 2014 to 2021, the albedo for CF, OGW, and AA, increased significantly (p&lt;0.001; Mann-Whitney U Test) by 79, 12, and 9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p&lt;0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), minimum temperature (Tmin), and diurnal thermal range (DTR). The decrease in the regional values of relative humidity in 2021, occurred even when in that year the annual precipitation levels were higher than those of 2014, hence, reinforcing the fact of a possible climatic effect of the regional albedo changes. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture caused albedo increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

We propose a novel approach to quantum theory construction that involves solving a maximization problem on the Shannon entropy of all possible measurements of a system, relative to its initial preparation. This maximization problem is additionally constrained by a phase condition that vanishes under measurements. Specifically, enforcing a vanishing U(1)-valued phase constraint leads to standard quantum mechanics, while a vanishing Spin^c(3,1)-valued phase constraint extends the theory to relativistic quantum mechanics and to quantum gravity. The latter scenario is found to incorporate the SU(3)xSU(2)xU(1) symmetries of the Standard Model as invariants of the probability measure itself, and to construct the metric tensor as an operator via a double-copy mechanism applied to Dirac currents. Significantly, this solution is consistent exclusively with a 3+1-dimensional spacetime configuration---all other dimensional settings are shown to lead to fundamental obstructions. This framework seamlessly integrates fundamental concepts from quantum mechanics, relativistic quantum mechanics, quantum gravity, the dimensional specificity of spacetime, and particle physics symmetries as the solution to a simple entropy maximization problem.

The global photovoltaic (PV) installed capacity, vital for the electric sector decarbonation, has reached 1,552.3 GWp in 2023. In France, the capacity stood in April 2024 at 19.9 GWp. The growth of the PV installed capacity over a year was nearly 32% worldwide and 15.7% in France. However, integrating PV electricity into grids is hindered by poor knowledge of rooftop PV systems, constituting 20% of France's installed capacity, and the lack of measurements of the production stemming from these systems. This problem of lack of measurements of the rooftop PV power production is referred to as the lack of observability. Using ground truth measurements of individual PV systems, available at an unprecedented temporal and spatial scale, we show that estimating the PV power production of an individual rooftop system by combining solar irradiance and temperature data, the characteristics of the PV system inferred from remote sensing methods and an irradiation-to-electric power conversion model provides accurate estimations of the PV power production. Our study shows that we can improve rooftop PV observability, and thus its integration into the electric grid, using little information on these systems, a simple model of the PV system and weather data.

Recently, NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in the numerical weather prediction system, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CDWP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire data products for weather and climate applications. Spire RO data are collected from commercial CubeSats through careful comparison with the data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), ERA-5, and high-quality radiosonde data. The results demonstrated that although with lower Signal-Noise-Ratio (SNR) in general, the pattern of the lowest penetration height for Spire is similar to those for COSMIC-2. The Spire and COSMIC-2 penetrate heights are between 0.6 and 0.8 km altitude at the tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRASP receivers is of the same quality as those of COSMIC-2 Global Positioning System - GPS, GALILEO, and GLObal NAvigation Satellite System – GLONASS (TGRS) receivers. The retrieval accuracy from Spire is very compatible with those from COSMIC-2. We validated Spire temperature and water vapor profiles by comparing them with collocated radiosonde data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well with temperature biases &lt; 0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~-0.034 K with RS41 RAOB being warmer. We also estimated the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. Results showed that the COSMIC-2 estimated error covariance values are slightly more significant over the oceans at the mid-latitudes (45oN-30oN and 30oS-45oS), which may also be owing to COSMIC-2 SNR being lower at those latitudinal zones.

This article presents a forecast model that uses a hybrid architecture of recurrent neural networks (RNN) with surface neural networks (ANN), based on historical records of exported active energy (EAE) and weather data. Two types of models were developed: the first type includes six models that use EAE records and weather variables as inputs, while the second type includes eight models that use only weather variables. Different metrics were applied to assess the performance of these models, and the best model of each type was selected. Finally, a comparison of the performance between the selected models of both types is presented, and they are validated with real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type has an RMSE of 0.19, MSE of 0.03, MAE of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower precision in the metrics (RMSE = 0.24, MSE = 0.06, MAE = 0.10, Corr. Coef. = 0.95, and Det. Coef. = 0.90). Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy production of the solar plant.

Physiological storage disorders continue to cause sizable economic losses in a range of commercially important pomefruit cultivars. Given similar storage regimes, the incidence and severity of browning disorders in the apple cultivar ‘Braeburn’ can vary in different years in a way that can be explained by the interaction of preharvest seasonal and orchard factors. Over a three-year period (2016 to 2019) at the Kompetenzzentrum Obstbau-Bodensee (KOB) in Southwest Germany a range of orchard and storage treatments were conducted for: air temperature during cell division for three weeks post petalfall or during four weeks preharvest, calcium orchard sprays, crop load and harvest timings. Following controlled atmosphere (CA) storage, the disorder incidence for internal browning and cavity formation varied markedly over the three different growing seasons. Crop load treatments strongly influenced the expression of browning disorders in all years. Differences in air temperatures (△ +/- 2 °C compared to ambient) during the cell division period showed little effect on browning incidence. Warm night temperatures (>10 °C) prior to harvest can reduce internal browning in ‘Braeburn’ apples during CA storage and shelf-life.

We investigate the impact of air quality and weather on the equity returns of the Shenzhen Exchange. To capture the air quality and weather effects, we use dummy variables created by employing a moving average and moving standard deviation. The important results are as follows. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative influence on the equity returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative impacts on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the weather effect of the abnormal temperature on the stock returns is greater in severe bearish market. Whereas the effect of the air pollution on the stock returns is greater in the bullish market. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analyzing these effects in a very volatile emerging market such as the Shenzhen stock market.

Background: The current coronavirus disease 2019 (COVID-19) is spreading globally at an accelerated rate. There is some previous evidence that weather may influence the incidence of COVID-19 infection. We assessed the role of meteorological factors including temperature (T) and relative humidity (RH) considering the concentrations of two air pollutants, inhalable coarse particles (PM10) and nitrogen dioxide (NO2) in the incidence of COVID-19 infections in Finland, located in arctic-subarctic climatic zone. Methods: We retrieved daily counts of COVID-19 in Finland from Jan 1 to May 31, 2020, nationwide and separately for all 21 hospital districts across the country. The meteorological and air quality data were from the monitoring stations nearest to the central district hospital. A quasi-Poisson generalized additional model (GAM) was fitted to estimate the associations between district-specific meteorological factors and the daily counts of COVID-19 during the study period. Sensitivity analyses were conducted to test the robustness of the results. Results: The incidence rate of COVID-19 gradually increased until a peak around April 6 and then decreased. There were no associations between daily temperature and incidence rate of COVID-19. Daily average RH was negatively associated with daily incidence rate of COVID-19 in two hospital districts located inland. No such association was found nationwide. The sensitivity analyses indicate the results are robust. Conclusions: Weather conditions, such as air temperature and relative humidity, may not be important factors affecting the COVID-19 incidence in the arctic and subarctic winter and spring. More evidence is needed on the associations between weather and COVID-19 during different seasons.

Additive manufacturing, commonly referred to as 3D printing, is an exciting and versatile manufacturing technology that is gaining traction and interest in both academic and industrial settings. Polymeric materials are essential components in a majority of the “inks” used across the various 3D printing technologies. As the environmental ramifications of sole or primary reliance on petrochemicals as a resource for industrial polymers continue to manifest themselves at a global scale, a transition to more sustainable bioderived alternatives could offer solutions. In particular, cellulose is promising due to its global abundance, biodegradability, excellent thermal and mechanical properties, and ability to be chemically modified to suit various applications. Thus far, cellulose has typically been incorporated in additive manufacturing applications as a substrate, filler, or reinforcement for other materials. However, due to its versatility and desirable properties, the exploration of all-cellulose 3D printing materials and associated methodologies is increasing. This review will discuss the progress and status of all-cellulose 3D printing approaches, associated challenges, and their potential as a key player in a more sustainable industrial future.

The scheme to realize high speed (~250Gb/s) all-optical Boolean logic gates using semiconductor optica amplifiers with quantum-dot (QD-SOA) is introduced and analyzed in this review. Numerical simulation method was presented by solving the rate equation and taking into account nonlinear dynamics including carrier heating and spectral hole-burning. Binary phase shift keyed (BPSK) signal and on-off keyed signal are used to generate high speed all-optical logic gates. The applications based on all-optical logic gates such as, all-optical latches, pseudo random bit sequence (PRBS) generation and all-optical encryption, are also discussed in this review. Results show that the scheme based on QD-SOA is a promising method for the realization of high speed all-optical communication system in the future.

The aim of this technical note is to describe the Cycle 46 reference configuration of the HARMONIE-AROME convection-permitting numerical weather prediction (NWP) model. HARMONIE-AROME is a configuration of the shared ALADIN-HIRLAM system, which is developed, maintained, and validated in the ACCORD consortium, a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. This technical note describes updates to the physical parametrizations, both upper-air and surface, configuration choices such as lateral boundary conditions, model levels, horizontal resolution, model time step and databases associated with the model, such as for physiography and aerosols. An outlook on upcoming developments is also included.

In this study, a computational method is proposed that employs Recurrent Convolutional 1 Neural Networks, utilizing meteorological radar images to forecast storm movement and intensity up 2 to 3 hours ahead, a process known as nowcasting. For this purpose, images from a radar situated in 3 southern Brazil were used. These data are publicly accessible on the website of the National Institute 4 for Space Research (INPE) in Brazil. The approach involves evaluating a spatiotemporal learning 5 recurrent convolutional neural network called PredRNN++. The results were validated through 6 case studies of storms within the radar’s coverage area. To evaluate the performance of the neural 7 network, both visual assessments and metrics such as RMSE and SSIM were employed. The findings 8 indicate that PredRNN++ was effective in simulating the shape and location of the meteorological 9 system.

Abstract: The effect of some weather parameters (rainfall and temperature) on the production of oil palm in Peninsular Malaysia was investigated. Data were analysed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, time series analysis, and multiple linear regression (MLR) carried out. SPSS and Microsoft Excel 2010 were used to analyse the results. The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature and rainfall (independent variables). The regression output returned three components; regression coefficients, regression statistics and ANOVA. The findings of the study revealed medium to high rainfall variability at the rate of 0.0008. This implies that rainfall is increasing over time with variations in its amount and intensity. As rainfall increases oil palm FFB production is predicted to increase at a slow rate of 0.0009. The estimation of average annual temperature indicated an increase of 5.6℃ at the rate of 0.0357℃ per year with a temperature maximum of 32.01℃ and minimum of 25.45℃. The result also revealed an increase in oil palm yield at the rate of 0.2581 per year with a mean value of 176247.6. Overall, there is a significant difference in the impact of rainfall and temperature on oil palm yield. This signifies that rainfall has a significant impact on oil palm yield (FFB) compared to temperature.

Puccinia graminis f. sp. tritici (Pgt), the causal agent of wheat stem rust, poses a significant threat to global wheat production. Genetic resistance offers a cost-effective and sustainable solution. The durum wheat landrace PI 94701 was previously hypothesized to carry two stem rust resistance (Sr) genes, but their chromosomal locations were unknown. In this study, we mapped and characterized an all-stage Sr gene in PI 94701, temporarily designated as SrPI94701. In seedling tests, SrPI94701 was effective against all six Pgt races tested. Using a large segregating population (2,302 gametes), we mapped SrPI94701 within a 0.17-cM region flanked by markers pku69124 and pku69228, corresponding to 1.04- and 2.15-Mb genomic regions in the Svevo and Chinese Spring reference genomes. Within the candidate region, eight genes exhibited differential expression between the Pgt-inoculated resistant and susceptible plants. Among them, two NLR genes, TraesCS5B03G1334700 and TraesCS5B03G1335100, showed high polymorphism between parental lines, with upregulation observed in the resistant F4 line compared to its susceptible sister line. However, the flanking and completely linked markers developed in this study could not accurately predict the presence of SrPI94701 in uncharacterized wheat genotypes. SrPI94701 holds promise as a valuable component in breeding programs aimed at enhancing resistance against Pgt.

Current Li battery technology employs graphite anode and flammable organic liquid electrolytes. Thus, current Li battery is always facing the problems of low energy density and safety. Additionally, sustainable supply of Li due to scarce abundance of Li sources is another problem. All-solid-state Mg battery is expected to solve the problems owing to non-flammable solid-state electrolytes, high capacity/safety of divalent Mg metal anode and high abundance of Mg sources. Therefore, solid-state electrolytes and all-solid-state Mg battery have been researched intensively last two decades. However, realization of all-solid-state Mg battery is still far. In this article, we review recent research progress on all-solid-state Mg battery so that researchers can pursue recent research trend of all-solid-state Mg battery. At first, solid-state electrolyte research is described briefly in the categories of inorganic, organic and inorganic/organic composite electrolytes. After that, the recent research progress of all-solid-state Mg battery is summarized and analyzed. To help readers, we tabulate electrode materials, experimental conditions and performances of all-solid-state Mg battery so that the readers can find necessary information at a glance. In the last, challenges to realize the all-solid-state Mg batteries are visited.

Convolution, recurrent and attention-based deep learning techniques have produced the most recent state-of-the-art results in multiple sensor-based human activity recognition (HAR) datasets. However, these techniques have high computing costs, restricting their use in low-powered devices. Different methods have been employed to increase the efficiency of these techniques; however, this often results in worse performance. Recently, pure MLP architectures have demonstrated competitive performance in vision-based tasks with lower computation costs than other deep-learning techniques. The MLP-Mixer is a pioneering pure MLP architecture that produces competitive results with state-of-the-art models in computer vision tasks. This paper shows the viability of the MLP-Mixer in sensor-based HAR. Furthermore, experiments are performed to gain insight into the Mixer modules essential for HAR, and a visual analysis of the Mixer’s weights is provided, validating the Mixer’s learning capabilities. As a result, the Mixer achieves an F1 score of 97%, 84.2%, 91.2% and 90% on the PAMAP2, Daphnet Gait, Opportunity Gestures and Opportunity Locomotion datasets, respectively, outperforming state-of-the-art models in all datasets except Opportunity Gestures.

We successfully created a composite photonic structure out of porous silicon (PSi) microcavities doped by the photochromic protein, photoactive yellow protein (PYP). Massive incorporation of the protein molecules into the pores was substantiated by a 30-nm shift of the resonance dip upon functionalization, and light-induced reflectance changes of the device due to the protein photocycle were recorded. Model calculations for the photonic properties of the device were consistent with earlier results on the nonlinear optical properties of the protein, whose degree of incorporation into the PSi structure was also estimated. The successful proof-of-concept results are discussed in light of possible practical applications in the future.

Authorities define cities – or human settlements in general – through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations – all building footprints of America (mainland) or their centroids more precisely – to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities – in contrast to government defined city boundaries – constitute a valuable data source for city-related research.

Korenaga and coworkers present evidence to suggest that 4.3 billion years ago the Earth’s mantle was dry and water filled the ocean to twice its present volume.[2] CO₂ was constantly exhaled during the mafic to ultramafic volcanic activity associated with magmatic plumes that produced the thick, dense and relatively stable oceanic crust. In that setting two distinct major types of sub-marine hydrothermal vents were active: ~400 °C acidic springs whose effluents bore vast quantities of iron into the ocean, and ~120 °C, highly alkaline and reduced vents exhaling from the cooler, serpentinizing crust at some distance from the heads of the plumes. When encountering the alkaline effluents, the iron from the plume head vents precipitated out forming mounds likely surrounded by voluminous exhalative deposits similar to the banded iron formations known from the Archean. These mounds and the surrounding sediments likely comprising nanocrysts of the variable valence FeII/FeIII oxyhydroxide, green rust. The precipitation of green rust, along with subsidiary iron sulfides and minor concentrations of Ni, Co and Mo in the environment at the alkaline springs may have established both the key bio-syntonic disequilibria, and the means to properly make use of them – those needed to drive the essential inanimate-to-animate transitions that launched life. In the submarine alkaline vent model for the emergence of life specifically it is first suggested that the redox-flexible green rust microcrysts spontaneously formed precipitated barriers to the complete mixing of carbonic ocean and alkaline hydrothermal fluids, barriers that created and maintained steep ionic disequilibria; and second, that the hydrous interlayers of green rust acted as 'engines' that were powered by those ionic disequilibria and drove essential endergonic reactions. There, aided by sulfides and trace elements acting as catalytic promoters and electron transfer agents, nitrate could be reduced to ammonia and carbon dioxide to formate, while methane may have been oxidized to methyl and formyl groups. Acetate and higher carboxylic acids could then have been produced from these C1 molecules and aminated to amino acids, and thence oligomerized to offer peptide nests to phosphate and iron sulfides and secreted to form primitive amyloid-bounded structures, leading conceivably to protocells.

The fractal dimension of geomagnetic variations at the Baigazan magnetic station at Russian Altay for 2011-2013 was calculated using the Higuchi method. The daily variation of Higuchi Fractal Dimension (HFD) for the D,H,Z-components of the geomagnetic field has been investigated, its contribution to the variability of HFD is from 30 to 40 percent of the total variance. A correlation analysis of the fractal dimension of the variations of the D,H,Z-components with Auroral Electrojet (AE) index and solar wind characteristics was carried out. A negative correlations with logarithms of AE-index, interplanetary magnetic field (IMF) strength and solar wind velocity were found. About 25 percent of the fractal dimension variance is controlled by the variability of these characteristics. Pair and partial correlation coefficients for these parameters were calculated for every month of 2011-2013.

We investigate the impact of air quality and weather on the stock market returns of the Shenzhen Exchange. To capture the air quality and weather effects, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides several interesting results. First, in the whole sample period (2005–2019), we find that high air pollution and extremely high temperature have significant and negative effects on the Shenzhen stock returns. In the sub-period I (2005–2012), the 11-day model and 31-day model show that high air pollution have significant and negative effects on the Shenzhen stock returns. Second, the results of the quantile regression show that high air pollution have significant and negative effects during bullish market phase, and extremely high temperature have significant and negative effects during bearish market phase. This implies that the air quality and weather effects are asymmetric. Third, the more the Shenzhen stock returns drop, the greater the effect of the abnormal temperature is. Whereas, the more the Shenzhen stock returns increase, the greater the effect of the abnormal air quality is. Fourth, the least squares method underestimates the air quality and weather effects compared to the quantile regression method, suggesting that the quantile regression method is more suitable in analysing these effects in a very volatile emerging market such as the Shenzhen stock market.