Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally， a NARX is used to obtain preliminary PV power components and residual vector features， and the PV power is predicted by combining three models of LightGBM， LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

Instep weights cause mechanical changes, modifying muscle activity and changing ground plantar support. 30 semi-professional sprinters, randomized in 3 groups [no-weight, Ascending (50, 100, 150 and 200g), Descending (200, 150, 100 and 50g)] run 6 consecutive 50-meter series at maximum speed (first and sixth without weights); partial, total times and speed were analyzed. Instep weights were safe and well tolerated. 6th series speed decreased except in men’s ascending group, who achieved a lower time in sixth compared to first series. Weights presented in ascending order in men during warm-up could improve running time. Instep weights neuromuscular effects could compensate men’s tiredness in last series; weights could be included in training methodologies. Men training weights presented in ascending order may provide better results.

Background: COVID-19 made its debut as a pandemic in 2020; since then, more than 607 million cases and at least 6.5 million deaths have been reported worldwide. While the burden of disease has been described, the long-term effects or chronic sequelae are still being described. Objective: To describe the findings of a current systematic review of the long-term effects related to post-COVID-19 sequelae. Design: A systematic review was carried out in which cohort studies, case series, clinical case reports were included, and the PubMed, Scielo, SCOPUS and Web of Science databases were ex-tracted. Information published 2020 to June 1, 2022, was sought. Results: We reviewed 300 manuscripts during the first step of the literature review process. Then 260 abstracts were analyzed. In the end, we included 32 manuscripts: 9 for pulmonary, 6 for cardiac, 2 for renal, 9 for neurological and psychiatric, and 8 for cutaneous sequelae. Conclusion: Studies show that the most common sequelae are those linked to the lungs, followed by skin, cutaneous and psychiatric alterations. Women report a higher incidence of the sequelae, as well as those with comorbidities and severer COVID-19 history. The COVID-19 pandemic has not only caused death and disease since its apparition but has also sickened millions of people around the globe who potentially suffer from serious illnesses that will continue to add to the list of health problems and further burden healthcare systems around the world.

The long-term impact of treatment of dogs with steroid-responsive enteropathy (SRE) on the fe-cal microbiome and metabolome has not been investigated. Therefore, this study aimed to evaluate the fecal microbiome and metabolome of dogs with SRE before, during, and following treatment with standard immunosuppressive therapy and an elimination diet. We retrospec-tively selected samples from 9 dogs with SRE enrolled in a previous clinical trial, which received treatment for 8 weeks, and had achieved remission as indicated by the post-treatment clinical scores. Long-term (1 year) samples were obtained from a subset (5/9) of dogs. Samples from 13 healthy dogs were included as controls (HC). We evaluated the microbiome using 16S rRNA sequencing and qPCR. To evaluate the recovery of gut function, we measured fecal metabolites using an untargeted approach. While improvement was observed for some bacterial taxa after 8 weeks of treatment, several bacterial taxa remained significantly different from HC. Seven-ty-five metabolites were altered in dogs with SRE, including increased fecal amino acids and vitamins, suggesting malabsorption as a component of SRE. One year after treatment, however, all bacterial species evaluated by qPCR and 16S rRNA gene sequencing, and all but thirteen me-tabolites were no longer different from healthy controls.

BackgroundThe novel virus COVID-19, also known as SARS-CoV‑2, is currently rapidly spreading around the globe and pushing healthcare systems to the limits of their capacity. One of the functions of predictive models is to timely act for epidemic preparedness including hospital preparedness. In Egypt, like many other countries in the world, the epidemic situation and forecasting have not yet sufficiently studied. ObjectiveThe study was carried out to develop a short-term forecast scenario for the COVID-19 epidemic situation in Egypt and predict the hospital needs to accommodate the growing number of cases.MethodsSecondary data from the COVID-2019 daily reports and the report issued 8^th of April by the Egyptian Ministry of Health and Population were used. Due to the daily changing level of knowledge and data, the article reflects the status up to 18 April 2020. The prediction was based on the exponential growth rate model. For the depiction of the situation, the full length of the epidemic timeline was analyzed (from February 14^th till April 18^th). The growth rates and their rates of decline during the period from the 22^nd of March till the 18^th of April were calculated and extrapolated in the coming 7 weeks. The predicted hospital needs were assessed against the announced allocated resources.ResultsThe epidemic curve in Egypt is on the ascending arm as of April, 18. The active cases showed exponential growth from the start of the epidemic till April, 18. At the end of this period time, the recovery rate was 23.12% and the case fatality rate (CFR) was7.39. The case fatality rate median level during the last four weeks was 6.64. The active cases are expected to reach more than 20,000 by late May then starts to decline. The allocated regular hospital beds are predicted to show shortage by the time of the release of the paper. The intensive care units (ICU) beds and ventilators are predicted to show insufficiency on May 6.Conclusions: The COVID-19 epidemic in Egypt is expected to continue on the rise for the next few weeks and expected to start to decline late in May, 2020. Our estimates should be useful in preparedness planning. Serious actions should be taken to provide ICU beds and ventilators enough for the predicted number of cases that would need them, not later than the end of April. Mitigation actions have to continue for the coming 6 weeks or until the epidemic situation is more clearly seen.

Brown adipose tissue (BAT) function may depend on its anatomical location and developmental origin. Interscapular BAT (iBAT) regulates acute macronutrient metabolism, whilst perivascular BAT (PVAT) regulates vascular function. Although phenotypically similar, whether these depots respond differently to acute nutrient excess is unclear. Given their distinct anatomical locations and developmental origins and we hypothesised that iBAT and PVAT would respond differently to brief period of nutrient excess. Sprague-Dawley rats aged 12 weeks (n = 12) were fed either a standard (10% fat, n = 6) or high fat diet (HFD: 45% fat, n = 6) for 72 h and housed at thermoneutrality. Following an assessment of whole body physiology, fat was collected from both depots for analysis of gene expression and the proteome. HFD consumption for 72 h induced rapid weight gain (c. 2.6%) and reduced serum NEFA with no change in either total adipose or depot mass. In iBAT, an upregulation of genes involved in insulin signalling and lipid metabolism was accompanied by enrichment of lipid-related processes and functions, plus glucagon and PPAR signalling pathways. In PVAT, HFD induced a pronounced down-regulation of multiple metabolic pathways which was accompanied with increased abundance of proteins involved in apoptosis (e.g. Hdgf and Ywaq) and toll-like receptor signalling (Ube2n). There was also an enrichment of DNA-related processes and functions (e.g., nucleosome assembly and histone exchange) and RNA degradation and cell adhesion pathways. In conclusion, we show that iBAT and PVAT elicit divergent responses to short-term nutrient excess highlighting early adaptations in these depots before changes in fat mass.

We propose a model for medium-term hydropower scheduling (MTHS) with variable head and uncertainty in inflow, reserve capacity, and energy price. With an increase of intermittent energy sources in the generation mix, it is expected that a flexible hydropower producer can obtain added profits by participating in other markets than just the energy market. To capture this added potential the hydropower system should be modeled with a higher level of details. In this context, we apply an algorithm based on stochastic dual dynamic programming (SDDP) to solve the nonconvex MTHS problem, and show that the use of Strengthened Benders (SB) cuts to represent the expected future profit (EFP) function provide accurate scheduling results for slightly nonconvex problems. A method to visualize the EFP function in a dynamic programming setting is provided, serving as a useful tool for a priori inspection of the EFP shape and its nonconvexity.

This paper addresses the effect of both microstructure and relative humidity on the long-term creep properties of sustainable calcium carbonate (CaCO₃) cements. Those can be prepared by mixing amorphous calcium carbonate and vaterite with water. A larger starting amount of vaterite, X_V, within the mixture design gives a higher elasticity and resistance to the specimens due to the larger overall bridging area within the newly formed calcite crystals. Regarding creep properties for a given relative humidity, the amplitude of creep strain decreases with X_V, and makes the relation between the elastic modulus, E, and hardness, H, of the samples to be linear with the contact creep modulus, C. On the other hand, for a given composition, the amplitude of creep increases with the relative humidity, making the contact creep modulus, Ci, to rise exponentially with the elastic modulus, E, and hardness, H, of the specimens. The most probable creep mechanisms for this kind of cement seem to be a combination of microcraking in the early stages and dissolution and reprecipitation of calcite in the long-term (also known as pressure solution theory). The presence of water in pores with increasing relative humidity might enhance the local dissolution of calcite, and hence the creep amplitude.

Background and Aim: Zinc plays a pivotal role in various zinc enzymes, resulting in the maintenance of liver function. Patients with chronic liver diseases (CLDs) usually have lower concentrations of zinc, which decrease further as liver fibrosis progresses. It remains unknown whether long-term zinc supplementation improves liver function and reduces the risk of hepatocellular carcinoma (HCC) development. Patients and Methods: Two hundred sixty-seven patients with CLDs who received a zinc preparation (Zn-group; 196 patients), or who did not receive zinc (no Zn-treatment group; 71 patients) were retrospectively analyzed in this study. The Zn-group was divided into 4 groups according to their serum Zn concentrations at 6 months after the start of Zn treatment. Results: Liver function significantly deteriorated in the no Zn-treatment group, while no notable change was observed in the Zn-group. The cumulative incidence rates of events and HCC at 3 years were lower in the Zn-group (9.5%, 7.6%) than in the no Zn-treatment group (24.9%, 19.2%) (p<0.001). According to the serum Zn concentrations, the cumulative incidence rates of events and HCC were significantly decreased in patients with Zn concentrations ≥ 70 µg/dl (p<0.001). Conclusion: Zinc supplementation appears to be effective at maintaining liver function and suppressing events and HCC development, especially among patients whose Zn concentration is greater than 70 µg/dl.

The optimal timing for actively discontinuing immune checkpoint inhibitor therapy in long-term responders with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains unresolved. We conducted a retrospective study of 246 patients with R/M HNSCC treated with nivolumab to determine the optimal timing to actively discontinue nivolumab therapy. We examined the point at which progression-free survival (PFS) plateaued in all cases. We compared the prognosis of 19 (7.7%) ongoing cases and 227 (92.3%) discontinued cases and analyzed treatment duration and treatment-free interval (TFI). The 6-year overall survival was 11.8% (median, 12.1), and the 6-year PFS was 15.3% (median, 3.0). The PFS curve remained stable for 3 years. The median duration of nivolumab treatment was 2.9 months (range, 0.03–81.9): Ongoing group, 41.8 (5.6–81.9); Decision group, 36.8 (4.0–70.1); Toxicity group, 30.6 (2.8–64.8); and progressive disease group, 2.0 (0.03–42.9). TFI in the Decision group was 15.1 months (0.6–61.6) and 30.6 months (2.8–64.8) in the Toxicity group. Limited number of long-term responders in R/M HNSCC cases treated with nivolumab exists. Given that PFS plateaued at 3 years, we propose that the optimal duration of treatment for this subset of patients is 3 years.

Many people, including Horadam, have studied the numbers $W_n$, satisfying the recurrence relation $W_n=u W_{n-1}+v W_{n-2}$ ($n\ge 2$) with $W_0=0$ and $W_1=1$. In this paper, we study the $p$-numerical semigroups of the triple $(W_i,W_{i+2},W_{i+k})$ for integers $i,k(\ge 3)$. For a nonnegative integer $p$, the $p$-numerical semigroup $S_p$ is defined as the set of integers whose nonnegative integral linear combinations of given positive integers $a_1,a_2,\dots,a_\kappa$ with $\gcd(a_1,a_2,\dots,a_\kappa)=1$ are expressed in more than $p$ ways. When $p=0$, $S=S_0$ is the original numerical semigroup. The largest element and the cardinality of $\mathbb N_0\backslash S_p$ are called the $p$-Frobenius number and the $p$-genus, respectively.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of lake Nokoue in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Lake Nokoue, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Training has a significative effect on physiology of blood coagulation both in humans and in horses. Several hemostatic changes have been reported after exercise in the horse but data available are inconclusive. The aim of this study was to investigate the platelet activation and primary platelet-related hemostasis modifications in young untrained Thoroughbred racehorses in the first 4-month timeframe incremental training period in order to improve knowledge on this topic. Twenty-nine clinically healthy, untrained, 2-year-old Thoroughbred racehorses. Never trained Thoroughbreds were followed during their incremental 4-month sprint exercise schedule. Blood collection was performed once a month (T-30, T0, T30, T60, and T90). Platelet aggregation was measured by light transmission aggregometer in response to various agonists: adenosine diphosphate (ADP), collagen and calcium ionophore A23187. Platelet function was evaluated using the Platelet Function Analyzer (PFA-100®) using collagen/ADP and collagen/adrenaline cartridges. Nitrite-nitrate (NOx) plasma concentrations were measured by a colorimetric assay to assess in vivo nitric oxide bioavailability. Platelet activation was investigated also through gene expression analyses (selectin P-SELP, ectonucleotidase CD39-ENTPD1, prostaglandin I2 synthase-PTGIS, endothelial nitric oxide synthase 3-NOS3). Differences among the time points were analyzed and mean±SEM were calculated. Significant modifications were identified compared with T-30 with increase of platelet aggregation (collagen:32.6±4.8 vs 21.6±4.9%; ADP: 35.5±2.0 vs 24.5±3.1%; A23187: 30±4.7 vs 23.8±4%) and a shorter closure time of C-ADP cartridges (75.6±4.4 vs 87.7±3.4 sec) that tended to return to baseline value at T90. NOx concentrations in plasma significantly increased after 30 days of training program compared with baseline. The first long-term training period induce platelet hyperactivity already after 30 days in untrained Thoroughbreds. Regular physical training blunts the negative effects of acute efforts on platelets activation.

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

This study is part of a project designed to simulate the long-term landform equilibrium of a rehabilitated mine site. The project utilizes event fine suspended sediment (FSS) fluxes in a receiving stream following a rainfall event as an indicator of landform stability. The aim of this study was to use HEC-HMS to determine sediment and discharge quantity upstream to determine how it affects the downstream development of the catchment landform, in terms of sediment changes and geomorphology. Thus, the study focused on hydrology and sediment modelling of the upper catchment with HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) to determine daily discharge and sediment output at the catchment outlet. HEC-HMS was used to calibrate the stream discharge and FSS quantities at the catchment outlet to observed continuous discharge and FSS values. Calibration of the HEC-HMS model was done for two water years and then the same model parameters were used to validate the model for a third water year. Catchment discharge and FSS were calibrated and validated for continuous rainfall events against observed discharge and FSS data at the catchment outlet. The model was then run for a projected rainfall of 50 years. The denudation rate predicted by the model falls in the range previously determined for the region. The simulated sediment output was compared to the rainfall trends over the years. As a result, the sediment spikes following a rainfall-runoff event gradually decreased over time. Reducing FSS spikes indicate that the landform gradually attains stability. This modeling study can be used for long-term simulations to determine erosion equilibrium over the years and to quantify sediment yield in catchments for projected wet and dry rainfall scenarios.

Background: Lipoprotein lipase (LPL) deficiency is a genetic condition. Affected individuals typically develop symptoms before age 10 with severe hypertriglyceridemia. No pharmacological treatment has lowered triglyceride levels in LPL deficiency patients successfully. The purpose of this study is to report on clinical outcomes of patients with LPL deficiency who responded to dietary therapy with successful reduction of their triglyceride (TG) levels with a long-chain triglyceride (LCT)-restricted, medium-chain triglyceride (MCT)-supplemented diet. Methods: A single-center retrospective case review study of LPL deficiency patients who met the inclusion criteria and were evaluated at the Hospital of Sick Children between January 2000 to December 2022. Data collected include demographic and clinical data, biochemical profiles at diagnosis and after dietary treatment initiation, and molecular results. Results: The study included seven patients with a suspected diagnosis of LPL deficiency (hypertriglyceridemia > 20 mmol/L). Six patients had a confirmed molecular diagnosis of LPL deficiency, and one had a Glycogen storage disease 1a (GSD1a) diagnosis. After presenting with hypertriglyceridemia, an LCT-restricted and MCT-supplemented diet was initiated, resulting in immediate and sustained TG reduction in all LPL deficiency patients that adhered to the diet. Additionally, they all maintained excellent growth and development. LPL deficiency-related complications were not observed under the MCT-enriched diet, except for increased pancreatic enzymes in one patient after dietary error. As expected, the diet failed to treat hypertriglyceridemia in the GSD1 patient. Conclusions: The dietary restriction of LCT in combination with MCT supplementation was feasible and effective in the long-term management of patients with LPL deficiency. In our experience, this treatment eliminated LPL-related complications and led to good clinical outcomes.

A better understanding of background tropospheric ozone delivers multiple benefits. Robust estimates of regional background ozone assist policy makers to understand the limits to reducing ozone by controlling anthropogenic emissions alone and assist in characterising changes in the atmospheric environment and its composition. Long time series of background ozone measurements can also help quantify the influence of human activity on the global and regional atmosphere. Background tropospheric ozone measurements representative of continental air masses are however scarce in Australia, limiting our knowledge of background atmospheric composition. Here we use k-means clustering to identify a cluster of measurements from the long-term monitoring station at Oakdale, NSW which are likely to be representative of background air. The cluster is associated with NOx-limited air masses of continental origin. From this analysis we estimate background ozone representative of Eastern Australia. We find recent (2017-2022) mean ozone mixing ratios of 28.5 ppb and identify a statistically significant (α=0.05) trend in the mean of +1.8 (1.0 – 2.8) ppb/decade. Our methods demonstrate that some long-term monitoring stations within or near urban areas can provide suitable conditions and datasets for regional Global Atmosphere Watch monitoring.

Immersion in forest environments was shown to produce beneficial effects to human health, in particular psychophysical relaxation, so much that this practice is increasingly recognized as a form of integrative medicine. Limited evidence exists about both statistical significance and size of the effects conditioned on personal characteristics, as well as on the main external variables. The primary purpose of this study was to substantiate the very concept of forest therapy by means of the quantification and significance of the psychological effects, stratified by gender, age groups and place of residence. A preliminary qualitative analysis of the main determinants, in particular the method of conducting, the meteorological comfort and the concentration of volatile organic compounds in the forest atmosphere, was afforded. Seven forest therapy sessions were performed in late summer though early fall, resulting in 150 psychological self-assessment questionnaires administered before and after each session. The results were comparable or even better than others reported in the international literature. Moreover, preliminary evidence arose about different functionality towards specific psychological indexes conditioned at least on gender and age groups, as well as meteorological comfort, structured programs and, possibly, volatile organic compounds showed an impact on the outcomes.

Thermal variations affect tri-trophic interactions involving plants, herbivores, and parasitoids, causing a mismatch between their phenological cycles. In the current environmental context of climate change, climatic factors can undergo profound and sudden changes, such as sudden hot or cold snaps. Here we show that episodes of short but sustained low temperatures have increased, mainly in the month of May, in the last two decades. We subjected galls induced by Asian chestnut gall wasp (ACGW) Dryocosmus kuriphilus to cold stress to assess if and how the pest and its parasitoids were affected. Over the course of two years seasonal parasitism, parasitism rates, the relative abundance of each parasitoid species, and mortality of ACGW were calculated. We found that the cold treatment affected both pest and parasitoids, resulting in a reduction in emerging ACGWs and differing ratios of species within the parasitoid community. The striking example was the change in the relative frequency of three species of Eupelmus spp. and of Mesopolobus tibialis, which doubled in thermally-stressed galls in all chestnut fields. The thermal effects on the development of the host and the direct effects of cold temperatures on the surface of galls (i.e., humidity or hardness of the galls) warrant further research in this direction.

In this paper, we investigate the dynamics of following higher order difference equation x_{n+1}=A+B((x_{n})/(x_{n-m}²)) with A,B and initial conditions are positive numbers, and m∈{2,3,⋯}. Especially we study the boundedness, periodicity, semi-cycles, global asymptotically stability and rate of convergence of solutions of related higher order difference equations.

Farm activities continued sand winning operations and the allocation of plots of land to prospective developers in Ghana pose a serious threat to the forest covers and lifespan of the Forest and game reserves. With all the positive add ups to the country from forests, Ghana has lost more than 33.7%(equivalent to 2,500,000 hectares) of its forest, since the early 1990s between 2005 and 2010, the rate of deforestation in Ghana was estimated at 2.19% per annum; the sixth highest deforestation rate globally for that period. This shows how important forest monitoring can be to the forestry commission in Ghana. Despite the frameworks which have been developed to help Ghana to protect and restore its forest resources, inadequate monitoring systems remain a barrier to effective implementation. In this study, Google earth engine was used to map and analyze the structural changes of forest cover using JavaScript to query and compute Landsat, MODIS and NOAA AVHRR satellite imageries of the study area (Ghana) with spatial resolutions 30m, 250m and 7km respectively. A supervised classification was performed on three multi-temporal satellite imageries and a total of six major land use and land cover classes were identified and mapped. By using random Forest-classification technique, from 1985 to 2018 recorded by NOAA AVHRR, forest cover has decreased by 66% and 2000 to 2018 recorded by Landsat and MODIS 61% and 47% respectively. A decrease in the forest has been as a result of anthropogenic activities in Ghana. A change detection analysis was performed on these images and it was noted that Ghana is losing forest reserves in every 5years. Overlay of the reserved forest of the 2000 and the classified map of 2018 shows vegetation changed during 2000-2018 remarkably. Therefore, forest-related institutions like the Forestry Commission can employ and use this monitoring system on Google Earth Engine for processing satellite images particularly Landsat, MODIS and NOAA AVHRR for forest cover monitoring and analysis for fast, efficient and reliable results.

The current generation of nuclear reactors are evolutionary in design, mostly based on the technology originally designed to power submarines, and dominated by Light Water Reactors. The aims of the GenIV consortium are driven by sustainability, safety and reliability, economics, and proliferation resistance. The aims are extended here to encompass the ultimate and universal vision for strategic development of energy production, the ‘perpetuum mobile’ – at least as close as possible. We propose to rethink nuclear reactor design with the mission to develop a system which uses no fresh resources and produces no fresh waste during operation as well as generates power safe and reliably in economic way. The results of the innovative simulations presented here demonstrate that, from a theoretical perspective, it is feasible to fulfil the mission through the reuse of spent nuclear fuel from currently operating reactors as the fuel for a new reactor. The produced waste is less burdensome than current spent nuclear fuel which is used as feed to the system. However, safety, reliability and operational economics will need to be demonstrated to create the basis for the long term success of nuclear reactors as a major carbon free, sustainable, and applied highly reliable energy source.

This study demonstrates a global, non-parametric, non-iterative optimization of time-mean value of a kind of index vibrated by time-varying forcing. It is based on the fact that the (steady) forced vibration of non-autonomous ordinary differential equation systems is well approximated by an analytical solution when the amplitude of forcing is sufficiently small and its base state without forcing is linearly stable and steady. It is applied to optimize a time-averaged heat-transfer rate on a two-dimensional thermal convection field in a square cavity with horizontal temperature difference, and the globally optimal way of vibrational forcing, i.e. the globally optimal, spatial distribution of vibrational heat and vorticity sources, is first obtained. The maximized vibrational thermal convection corresponds well to the state of internal gravity wave resonance. In contrast, the minimized thermal convection is weak, keeping the boundary layers on both sidewalls thick.

Great Himalayan range with regions namely, Jammu & Kashmir, Ladakh and high altitude parts of north eastern states inherently experience high solar exposure, cold, arid climate and significantly challenging geography. With a scarcity of affordable public housing projects in these regions a grid enabled low energy residential prototype is proposed having the capability to operate on nearly zero consumption of heating and cooling energy with the application of indigenous and contemporary energy efficient passive design techniques. Preliminary study, development of design and reading of simulation results were applied for the performance and benefit analysis of the low energy building prototype. Analysis of the climatic zone further classified and narrowed the design criterion by confirming upon beneficial architectural components proven to be energy efficient. These architectural components include, optimization of direct solar gain and orientation of building envelope, modified trombe wall system, traditional sunrooms, solar chimney with openable vents, efficient spatial planning and compliance of relevant parameters such as window-wall-ratio (WWR). The proposed low energy housing for the economically weaker section is estimated to save nearly 50% of active fuel based household heating energy. Site has been planned in such a way that about 100% of required electricity can be generated from on site renewable energy for the proposed infrastructure, it is proven more beneficial if additional electricity is generated to offset the power supply and result in lower or subsidized electricity bills. Design is compliant to different codes relevant to energy conservation and low-income housing with an embodied carbon of about 1.2 kgCO2/m2 per dwelling unit giving ‘A’ grade in cradle to gate impacts. The findings and performance of this prototype can serve as a guideline for energy efficient design in low income group housing situated in severe cold and arid regions of India such as Ladakh (U.T.).

The clustering methods of Software Defined Networking (SDN) gained popularity due to their ability to offer improved scalability, consistency, dependability, and load balancing within overlay networks and SDN partitions. This paper delved into the effects of increasing the number of OpenFlow-enabled southbound devices on the establishment and coordination of SDN-Controller clusters. Specifically, we examined the volume of east-west cluster packet communication concerning the number of southbound devices within the topology. Many research studies focused on bandwidth and the number of bytes in East-West communication. While bandwidth referred to the maximum rate at which data could be transferred, and the number of bytes reflected the volume of data being transmitted, the number of packet communications directly influenced the efficiency and responsiveness of network operations. Our investigation encompassed the impact of SDN controllerto- controller communication within the cluster concerning the rising number of OpenFlow switches connected to various topologies, including tree (star-bus network), linear, and torus configurations. This study presented data on communication patterns within Open Network Operating Systems (ONOS) and OpenDaylight (ODL) clusters, revealing differing levels of controller communication with southbound network expansions. We evaluated the scalability of ODL and ONOS controllers by scrutinising the effect of increasing the number of southbound devices on control communication volume. Our analysis revealed varied communication patterns within ONOS and ODL clusters, resulting in different volumes of control communication with southbound expansions. The findings indicated that in small to medium-sized SDNs, ODL outperformed ONOS, notably with faster cluster discovery. Conversely, ONOS demonstrated greater efficiency in larger networks owing to its centralised communication architecture. Finally, we provided recommendations for selecting the most suitable controllers based on the size of southbound networks, aiming to provide practical guidelines for optimal network performance.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

In the present paper, we efficiently solve the two-body problem for extreme cases such as those with high eccentricities. The use of numerical methods, with the usual variables, cannot maintain the perihelion passage accurately. In previous articles, we have verified that this problem is treated more adequately through temporal reparametrizations related to the mean anomaly through the partition function. The biparametric family of anomalies, with an appropriate partition function, allows a systematic study of these transformations. In the present work, we consider the elliptical orbit as a meridian section of the ellipsoid of revolution, and the partition function depends on two variables raised to specific parameters. One of the variables is the mean radius of the ellipsoid at the secondary, and the other is the distance to the primary. One parameter regulates the concentration of points in the apoapsis region, and the other produces a symmetrical displacement between the polar and equatorial regions. The three most used geodesy latitude variables are also studied, resulting in one not belonging to the biparametric family. However, it is in the one introduced now, which implies an extension of the biparametric method. The results obtained by the method presented here now allow a causal interpretation of the operation of numerous reparametrizations used in the study of orbital motion.

Background: Diagnosis of acute kidney injury (AKI) relies on serum creatinine (SCr) changes. This study investigated if urinary aminopeptidases are early and predictive biomarkers of cardiac surgery associated-AKI (CSA-AKI). Methods: Glutamyl aminopeptidase (GluAp), alanyl aminopeptidase (AlaAp), dipeptidyl peptidase-4 (DPP4), proteinuria, albuminuria, N-acetyl-β-D-glucosaminidase (NAG) and neutrophile gelatinase-associated lipocalin (NGAL) were measured in urine samples from 44 patients at arrival in Intensity Care Unit (ICU) after cardiac surgery. Sensitivity, specificity, positive and negative predictive value for diagnosis of stages 1, 2 and 3 of AKI were analyzed for the highest quartile of each marker. We also studied the relationship with SCr after surgery, 6- and 12-months glomerular filtration rate (GFR) and other long-term events over the next 5 years. Results: GluAp diagnosed the maximal number of patients that developed stage 2 or 3 of AKI, increasing diagnostic sensitivity from 0 to 75 %. Besides, GluAp and DPP4 was related to the decrease in GFR at 6- or 12-months after surgery. Conclusions: Urinary aminopeptidases are a potential tool for the early diagnosis of CSA-AKI, being GluAp the most effective marker for diagnosing stages 2 or 3 of AKI at ICU admission. GluAp and DPP4 serve as predictive biomarkers for the decrease in GFR.

This 16-month study aimed to evaluate the extended effects of administering direct-fed microbials (DFM) on milk yield and components in 150 dairy cows. The cows were randomly divided into two groups: control (n=75) and DFM (n=75) based on parity and days in milk. Throughout the study, the two groups of cows were housed separately in a free stall yard, and each group had their own feeding area. The DFM group received 10 ml/cow of the Lacticaseibacillus- and Lentilactobacillus-based DFM via top dressing of the feed during lactation and drying off. Milk yield and liveweight were recorded daily. Milk samples were collected every two months for milk components analysis. Both groups produced less milk in the second production season due to adverse weather conditions. DFM cows gained more liveweight across the study (19.40 kg, 95% CI 0.44; 38.30) compared to controls. In the second season, DFM cows lost weight at a slower rate (-6.06, 95% CI -10.49; -1.61) during the postpartum period and produced more milk (0.39 L/d 95% CI 0.10; 0.89). Over a full lactation, DFM cows yielded at least 258L (95% CI 252L; 265L) more milk than controls. No significant differences were found in fat yield, protein yield, or somatic cell counts. The study suggests that prolonged DFM administration positively impacted milk production, but further research is needed to understand the underlying mechanism.

Methamphetamine (METH) has the potential to disrupt the activities of neurotransmitters in the Central Nervous System (CNS) and cause neurotoxicity through various pathways. These pathways include increased production of reactive nitrogen and oxygen species, hypothermia, and induction of mitochondrial apoptosis. In this study, we investigated the long-term effects of METH addiction on the structural changes in the amygdala of postmortem human brains, as well as the involvement of the CREB/BDNF and Akt-1/GSK3 signaling pathways. We examined ten male postmortem brains, comparing control subjects with chronic METH users, using immunohistochemistry, Real-time PCR (to measure levels of CREB, BDNF, Akt-1, GSK3, and TNF-α), Tunnel assay, stereology, and assays for ROS, GSSG, and GPX. The findings revealed that METH significantly reduced the expression of BDNF, CREB, Akt-1, and GPX, while increasing the levels of GSSG, ROS, RIPK3, GSK3, and TNF-α. Furthermore, METH was found to induce inflammation and neurodegeneration in the amygdala, with ROS production mediated by the CREB/BDNF and Akt-1/GSK3 signaling pathways.

Obesity represents a worldwide epidemic disorder, increasing the overall morbidity and mortality rate. In this study we investigated the impact of obesity on peri-operative and long-term functional outcomes of Robotic assisted simple prostatectomy (RASP). Baseline flowmetry parameters and validated questionnaires’ scores were prospectively recorded. Follow-up assessments included Flowmetry and validated questionnaires. Composite outcomes (Trifecta) was defined as combination of: post-operative Q-max>15 ml/sec, IPSS score<8 and absence of complications. Pentafecta included also post-operative ejaculation persistence (MSHQ score>0) and the Erectile function maintenance (∆IEEF<6). Data were stratified by BMI (<30 or ≥30). 81 patients underwent RASP in our Institution. Baseline demographics and clinical features, questionnaires score and baseline flowmetry results were comparable between obese and non-obese cohorts. At follow-up, obese patients reported lower subjective improvement in IPSS (p=0.02) and OABQ scores (p<0.001) and higher incidence of stress incontinence requiring Duloxetin (p<0.001). Flowmetry outcomes, were also worse in this cohort (p=0.02 and p=0.03, respectively). At comprehensive outcomes assessment, obese patients had comparable Trifecta (67% vs 54%, p=0.39) and pentafecta achievement rate (p=0.76). Our prelimirary results proved that obesity is associated with worse functional outcomes (storage LUTS and incontinence rate) after RASP, but doesn’t affect trifecta and pentafecta achievement rate.

This study investigates the cyclic load application impact on fly ash-based geopolymer composites that are reinforced with a low amount of fibre reinforcement. For reinforcement purposes, PVA and steel fibres are used. For testing purposes, four geopolymer composite mixes were made, 3 of which had fibre reinforcement. Simultaneously specimens were tested for shrinkage, static load-induced creep, and cyclic load-induced creep. For static and cyclic creep testing, specimens were loaded with 20% of their strength. For cyclic creep testing, load application and release cycles were seven days long. When each cycle was introduced, the load was added in steps. In 5 minutes, by 25% steps of the necessary load, the specimens were loaded or unloaded. Only plain specimens show that static creep strains are within cyclic creep strains. For all the other specimens, the static load is higher than the cyclic load-induced creep amplitude. Also, 1% PVA fibre-reinforced specimens show the most elastic characteristics under cyclic load, and 1% steel fibre-reinforced specimens appear to be the most resistant to the cyclic load introduction.

Accurately predicting electrical signals of three-phase Direct Torque Control (DTC) induction motors is crucial for optimal motor performance and effective condition monitoring. However, multiple DTC motors’ complexity and operating conditions’ variations pose challenges for traditional prediction methods. To overcome these challenges, we propose an innovative approach that combines Fast Fourier Transform (FFT) to transform electrical motor simulation data and a Bi-directional Long Short-Term Memory (Bi-LSTM) network to forecast processed motor data. By transforming the DTC induction motor signals from the time domain to the frequency domain, we enhance the capability of learning models to capture subtle differences and generate various input features. The Bi-LSTM model effectively captures both forward and backward dependencies in time series data, enabling accurate prediction of electrical signals. We evaluate the proposed approach using our simulated dataset and compare the proposed method to a state-of-the-art model, the Gated Recurrent Unit (GRU), demonstrating its effectiveness in improving the accuracy and reliability of induction motor signals forecasting. The finding provides valuable insights for advancing motor control and operation in industrial applications.

Feline immunodeficiency virus (FIV) is a lentivirus in the family Retroviridae that infects domestic cats resulting in an immunodeficiency disease featuring a progressive and profound decline in multiple sets of peripheral lymphocytes. Despite compelling evidence of FIV-associated immunopathology, there are conflicting data concerning the clinical effects of FIV infection on host morbidity and mortality. To explore FIV-associated immunopathogenesis and clinical disease, we experimentally inoculated a cohort of 4 specific pathogen free kittens with a biological isolate of FIV clade C and continuously monitored these animals along with 2 uninfected control animals for more than 13 years, from the time of inoculation to the humane euthanasia endpoint. Here we report the results obtained during the late asymptomatic and terminal phases of FIV infection in this group of experimentally FIV-infected cats.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

Overweight and Obesity (Ow/Ob) is a risk factor for developing cardiometabolic disease, and metabolic dysfunction. Dietary interventions, such as caloric restriction (CR), have been explored but little is known about the acute effects of CR and often such diets are not standardized. PURPOSE: To assess the impact of a standardized 3-day CR diet (~590kcal/d) on cardiometabolic health and fat metabolism in Ow/Ob individuals. METHODS: Fifteen Ow/Ob men and women were assessed pre-post 3-day CR using standardized diet; specifically, body weight/composition (% body fat, visceral fat score (Vfs), waist-hip circumferences, blood pressure and vascular stiffness, resting energy expenditure (REE), substrate utilization (respiratory quotient, RQ), and blood glucose/lipid profile. RESULTS: CR induced changes in body weight (93.1±15.2 to 90.67±14.4 kg, p&lt;0.001, d=1.9), body mass index (32.9±4.0 to 32.0±3.7 kg/m2, p&lt;0.001, d=1.4), body fat (37.2±7.5 to 35.8±7.5%, p=0.002, d=1.1) and Vfs (13.1±4.5 to 12.2±3.9 a.u., p=0.002, d=1.1), but not body water (46.3±3.6 to 46.0±3.6%, p=0.29, d=0.3). CR had no effect on peripheral (86±5 to 89±5 mmHg, p=0.23, d=0.3) or central mean arterial pressure (86±5 to 87±6 mmHg, p=0.44, d=0.2), but lowered augmentation index (29.8±17.5 to 21.5±14.5%, p=0.05, d=0.6). Blood glucose (86±7 to 84±11 mg/dl, p=0.33, d=0.3) and blood lipids (total cholesterol (196±49 to 203±54 mg/dl, p=0.16, d=0.4) and TC/HDL (4.9±2.4 to 6.1±4.7, p=0.13, d=0.4)) were unchanged. RQ decreased with CR (0.84±0.01 to 0.76±0.00, p&lt;0.001, d=1.9), though REE was unchanged (1718±274 to 1722±317 kcal/d, p=0.83, d=0.1). CONCLUSION: The 3-day CR significantly improved body weight and composition and fat metabolism, with minimal cardiovascular effects.

The main purpose of this work is to contribute to understanding the mechanism of oxidation of the polymeric components of common disposable masks used during the COVID-19 pandemic to offer the chemical basis to predict their long-term behaviour under typical environmental conditions. Artificial ageing of representative mask layers under isothermal conditions (110℃) or accelerated photoageing showed that all the PP-made components underwent a fast oxidation process, following the typical hydrocarbon oxidation mechanism. In particular, yellowing and the melting temperature drop are early indicators of their diffusion-limited oxidation. Morphology changes also induced a loss of mechanical properties, observable as embrittlement of the fabric fibres. Results were validated through preliminary outdoor ageing of masks, which allows us to predict they will suffer fast and extensive oxidation only in the case of contemporary exposure to sunlight and relatively high environmental temperature, leading to their extensive breakdown in the form of microfiber fragments, i.e., microplastics.

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the Long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029% and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.

Conclusive evidence that specic long-term memory formation relies on den- dritic growth and structural synaptic changes has proven elusive. Connec- tionist models of memory based on this hypothesis are confronted with the so-called plasticity stability dilemma or catastrophic interference. Other fun- damental limitations of these models are the feature binding problem, the speed of learning, the capacity of the memory, the localisation in time of an event and the problem of spatio-temporal pattern generation. This paper suggests that the generalisation and long-term memory mechanisms are not correlated. Only the development and the improvement of the feature ex- tractors in the cortex involves structural synaptic changes. We suggest the long-term memory has a separate mechanism which involves protein synthe- sis to encode the information into the structure of these proteins. A model of memory should be capable of explaining the dierence between memorisation and learning. Learning has in our approach two dierent mechanisms. The generalisation in the brain is handled by the proper development of the links between neurons via synapses. The Hebbian learning rule could be applied only for this part of learning. Storing an internal ring pattern involves, in our approach, a new mechanism which puts the information regarding this ring pattern into the structure of special proteins in such a way that it can be retrieved later. The hypotheses introduced in this article includes a physiological assumption which has not been yet verified because it is not currently experimentally accessible. Keywords: Waves, Protein Synthesis, Resonance, Long Term Memory Preprint submitted to Neural Networks

Long-term changes in nanoparticles of copper-silver bimetallic systems on natural clinoptilolite obtained by ion exchange of Cu2+ and Ag+, and then reduced at different temperatures, have been studied. Even after storage under ambient conditions, XRD and UV-Vis diffuse reflectance spectra indicate the presence of nanospecies of reduced copper and silver. Scanning Electron Microscopy of aged bimetallic samples, reduced at the highest temperature (450oC) and the primary samples for their preparation, also aged, showed the presence of silver nanoparticles with a size of about 100 nm. They are formed in the initial ion-exchanged sample (without reduction) due to the degradation of Ag+ ions. The nanoparticles in the reduced sample are larger; in both samples they are evenly distributed over the surface. The presence of silver affects the stability and the mechanism of decomposition/oxidation of reduced copper nanospecies, and this stability is higher in bimetallic systems. The decomposition pattern of recently reduced species includes the formation of smaller nanoparticles and few-atomic clusters. This can occur, preceding the complete oxidation of Cu to ions. Quasi-colloidal silver, which is present in fresh bimetallic samples reduced at lower temperatures, transforms after aging into Ag8 clusters, which indicates the stability of these nanospecies on natural clinoptilolite.

Background: There are anecdotal reports on reversible QTc prolongation during therapeutic hypothermia (TH) for moderate to severe neonatal encephalopathy after asphyxia. As the QTc interval is a relevant biomarker to assess safety during medication development, a structured search and review on published neonatal QTc values to generate reference values is warranted to facilate medication development in this specific population. Methods: A structured search and literature assessment (PubMed, Embase, Google Scholar) with ‘Newborn/Infant, QT and hypothermia’ was conducted (October 2021). Retrieved individual values were converted to QTc (Bazett) over postnatal age (day 1-7). Results: We retrieved 94 QTc intervals [during TH (n=50, until day 3) or subsequent normothermia (n=44, day 4-7)] in 33 neonates from 6 publications. The median (range) of QTc intervals during TH was 508 (430-678), and 410 (317-540) ms afterwards (difference 98 ms, or +28 ms/°C decrease). Four additional cohorts (without individual QTc intervals) confirmed the pattern and magnitude of the effect of body temperature on the QTc interval. Conclusions: We added a relevant non-maturational covariate (TH, °C dependent) and generated reference values for the QTc interval in this specific neonatal subpopulation. This knowledge on QTc during TH should be considered and integrated in neonatal medication development.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

Multidrug resistant bacteria pose a significant threat to global health, particularly in healthcare settings. This study aimed to explore the potential of aminoglycoside consumption and resistance data to correlate with multidrug resistance in Acinetobacter baumannii and Klebsiella pneumoniae. Data on aminoglycoside consumption and antibiotic resistance indices (ARI) were collected from 2010 to 2023. Descriptive statistics and Pearson correlation analyzes were performed to examine both immediate and delayed (one-year delay) correlations between aminoglycoside use (amikacin, tobramycin, gentamicin) and resistance patterns. The results indicated that the consumption of amikacin was consistently high and correlated with resistance in both bacterial species. Tobramycin and gentamicin showed variable resistance patterns, with negative correlations in some cases. Delayed effect analysis revealed that amikacin and tobramycin consumption had a slight delayed positive impact on multidrug resistance in A. baumannii, while gentamicin use was associated with a delayed reduction in resistance. In K. pneumoniae, gentamicin showed a strong delayed positive correlation with multidrug resistance, while amikacin and tobramycin had weaker associations. These findings highlight the complex and species-specific relationships between aminoglycoside consumption and resistance development, emphasizing the need for customized antibiotic management strategies that account for the immediate and delayed effects of antibiotic use.

California has experienced a surge in wildfires, prompting research into contributing factors, including weather and climate conditions. This study investigates the complex, multiscale interactions between large-scale climate patterns, such as the Boreal Summer Intraseasonal Oscillation (BSISO), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO), and their influence on moisture and temperature fluctuations, and wildfire dynamics in California. The combined impacts of PDO and BSISO on intraseasonal fire weather changes, the interplay between Fire Weather Index (FWI), relative humidity, vapor pressure deficit (VPD), and temperature in assessing wildfire risks, and geographical variations in the relationship between FWI and climatic factors within California are examined. The study employs a multi-pronged approach, analyzing wildfire frequency and burned areas alongside climate patterns and atmospheric conditions. Findings reveal significant variability in wildfire activity across different climate conditions, with heightened risks during specific BSISO phases, La-Niña, and cool PDO. The influence of BSISO varies depending on its interaction with PDO. Temperature, relative humidity, and VPD show strong predictive significance for wildfire risks, with significant relationships between FWI and temperature in elevated regions (correlation, r &gt; 0.7, p ≤ 0.05) and FWI and relative humidity along the Sierra Nevada Mountains (r ≤ -0.7, p ≤ 0.05).

Intrauterine growth restriction (IUGR) and being born small for gestational age (SGA) are two distinct conditions with different implications for child development. IUGR is associated with greater perinatal morbidity and mortality and can be identified by additional abnormalities (pathological Doppler sonography, oligohydramnios, lack of growth in the interval, estimated weight <3rd percentile). IUGR may also be present in neonates with birth weights above the 10th percentile. Recognizing fetuses being “at risk” of a higher perinatal mortality and morbidity rate in order to monitor them accordingly and deliver them in good time is still challenging. This review article summarizes approaches to increase the diagnostic accuracy of IUGR and presents current concepts of pathophysiology with a focus on oxidative stress and consecutive inflammatory and metabolic changes. Since prenatal influences affect the risk of later diseases, we also discuss the need for interdisciplinary follow-up strategies during childhood and look at future scientific challenges. It should be emphasized that prenatal and postnatal care of IUGR neonates should be regarded as a continuum.

In this study, long-term mortality effects associated with exposure to PM10, PM2.5, BC (black carbon), and NOx were analyzed in a cohort in southern Sweden during the period from 1991‒2016. Participants (those residing in Malmö, Sweden, born between 1923‒1950) were randomly recruited from 1991‒1996. At enrollment, 30,438 participants underwent a health screening, which consisted of questionnaires about lifestyle and diet, a clinical examination, and blood sampling. Mortality data were retrieved from the Swedish national cause of death register. The modeled concentrations of PM10 (particles with an aerodynamic diameter smaller than or equal to 10 µm), PM2.5 (particles with an aerodynamic diameter smaller than or equal to 2.5 µm), BC (black carbon), and NOx (nitrogen oxides) at the cohort participants' home addresses were used to assess air pollution exposure. Cox proportional hazard models were used to estimate the associations between long-term exposure to PM10, PM2.5, BC, and NOx and the time until death among the participants during the period from 1991‒2016. The hazard ratios (HRs) associated with an interquartile range (IQR) increase in each air pollutant were calculated based on the exposure lag windows of the same year (lag0), 1‒5 years (lag1‒5), and 6‒10 years (lag6‒10). Three models were used with varying adjustments for possible confounders including both single-pollutant estimates and two-pollutant estimates. With adjustments for all covariates, the HRs for PM10, PM2.5, BC, and NOx in the single-pollutant models at lag1‒5 were 1.06 (95% CI: 1.02‒1.11), 1.01 (95% CI: 0.95‒1.08), 1.07 (95% CI: 1.04‒1.11), and 1.11 (95% CI: 1.07‒1.16) per IQR increase, respectively. The HRs were in most cases decreased by the inclusion of a larger number of covariates in the models. The most robust associations were shown for NOx, with statistically significant positive HRs in all models. An overall conclusion is that road traffic-related pollutants had a significant association with mortality in the cohort.

Background: Severe eosinophilic asthma (SEA) represents a substantial global health burden. Benralizumab has been shown to restore good control of asthma. Robust data on benralizumab effectiveness over periods longer than 2 years are still scarce. Methods: This retrospective multicentric study was conducted on 108 Italian SEA patients treated with benralizumab for up to 36 months. Clinical outcomes were evaluated at 6, 12, 24 and 36 months. Partial and complete clinical remission (CR) were also assessed. Data were analysed either with descriptive statistics or using linear, logistic, and negative binomial mixed-effect regression models. Results: Benralizumab induced a persistent reduction in exacerbation rates (p

Lithium sulfur (Li–S) batteries are expected to be very useful for next-generation transportation and grid storage because of their high energy density and low cost. However, their low active material utilization and poor cycle life limit their practical application. The use of a carbon-coated separator in these batteries serves to inhibit the migration of the lithium polysulfide intermediate and increases the recyclability. We report the extent to which the electrochemical performance of Li–S battery systems depends on the characteristics of the carbon coating of the separator. Carbon-coated separators containing different ratios of carbon black (Super-P) and vapor-grown-carbon-fibers (VGCF) were prepared and evaluated in Li–S batteries. The results showed that larger amounts of Super-P on the carbon-coated separator enhanced the electrochemical performance of Li–S batteries; for instance, the pure Super-P coating exhibited the highest discharge capacity (602.1 mAh g-1 at 150 cycles) with a Coulombic efficiency exceeding 95%. Furthermore, the separators with the pure Super-P coating had a smaller pore structure, and hence limited polysulfide migration, compared to separators containing Super-P/VGCF mixtures. These results indicate that it is necessary to control the porosity of the porous membrane to control the movement of the lithium polysulfide.

(1) Background: donor-derived circulating free DNA (dd-cfDNA) is an innovative biomarker with great potential for the early identification and prevention of graft damage; (2) Methods: Prospective study to analyze dd-cfDNA plasma levels in 30 kidney transplant patients during their post-transplant follow-up (15 days, 3, 6 and 9 months), to determine if the result could be of interest in the identification of possible adverse events, specially allograft rejection. In addition, the aim is also to verify the obtained data on sensitivity, specificity, NPV and PPV; (3) Results: Plasma levels of dd-cfDNA could be considered a novel biomarker of graft rejection after first term post-transplant up to several months before its clinical presentation, mainly in TCMR or ABMR. In this study, it appears to have no positive predictive value for borderline rejection or TCMR IA. Our results show a low NPV of the technique because other pathologies or biological post-transplant complications can also affect dd-cfDNA levels and they must be considered in the interpretation of the result.; (4) Conclusions: dd cfDNA could allow, together with other additional biomarkers in the context of a future score, avoid invasive biopsies in most cases, mainly in the case of suspected TCMR or ABMR.

We employed the Riemannian Face Manifold (RFM) as an alternative approach to the conventional linear Euclidean space for explaining the atypicality bias in face likeliness judgments. The RFM posits that the mental representation of faces is better captured as a manifold of stable states, accounting for the nonlinearity in the physical properties of faces. To examine the relationship between physical and psychological distance of morph and typical/atypical faces, we manipulated the parameter k and incorporated a weight function in the Riemannian metric. Our results indicate that the psychological distance between the morph and typical face was longer than that between the morph and atypical face, consistent with prior research on the atypicality bias in perceptual similarity. The RFM approach provides mathematical support and is a powerful tool for studying face perception and recognition, offering potential implications for explaining the other-race effect. Future research can utilize the RFM to investigate individual differences in face recognition abilities and expertise, and how they impact psychological distance and face processing.

Stock price prediction has long been a critical area of research in financial modeling. The inherent complexity of financial markets, characterized by both short-term fluctuations and long-term trends, poses significant challenges in accurately capturing underlying patterns. While Long Short-Term Memory (LSTM) networks have shown strong performance in short-term stock price prediction, they struggle with effectively modeling long-term dependencies. In this paper, we propose an advanced stock price prediction model based on the Extended Long Short-Term Memory (xLSTM) algorithm, designed to enhance predictive accuracy over both short and long-term periods. We conduct extensive experiments by building and evaluating models based on xLSTM and LSTM architectures for multiple stocks. Our results demonstrate that the xLSTM model consistently outperforms the LSTM model across all stocks and time horizons, with the performance gap widening as the prediction period extends. The observations underscore the superior capability of the xLSTM-based model to capture long-term patterns in financial data, offering a promising approach for more accurate and reliable stock price predictions.

Short-term energy consumption forecasting crucial for the operation of new electrical en-ergy grids, not only due to their own characteristics, but also due to the new elements pre-sent in the energy matrix. Many existing models are limited by their methodologies or by considering only a narrow set of factors influencing energy consumption. This study in-troduces a pipeline for developing an energy forecasting model and evaluates the signifi-cance of factors affecting energy consumption and, consequently, forecast accuracy. The study utilizes a dataset from ISO NE (Independent System Operator New England), span-ning the total electric load of various cities in New England from January 2017 to December 2019. This dataset comprises 23 independent variables, including weather data, economic indicators, and market information. The results outline the steps involved in constructing energy forecast models using time series analysis. By carefully selecting variables and rep-resenting external factors, the study demonstrates the feasibility of generating more accu-rate predictions with reduced computational resources.

In this work, we consider the properties of the two-term Machin-like formula and develop an algorithm for computing digits of π by using its rational approximation. In this approximation, both terms are constructed by using a representation of 1/π in the binary form. This approach provides the squared convergence in computing digits of π without any trigonometric functions and surd numbers. The Mathematica codes showing some examples are presented.

The work is devoted to the development of methods for constructing asymptotic formulas as x→∞ of a fundamental system of solutions of linear differential equations generated by a symmetric two-term differential expression of odd order. The coefficients of the differential expression belong to classes of functions that allow oscillation (for example, those that do not satisfy the classical Titchmarsh-Levitan regularity conditions). As a model equation, the 5th order equation i2p(x)y′′′′′+p(x)y′′′′′+q(x)y=λy, for which various cases of behavior of the coefficients p(x),q(x), is investigated. New asymptotic formulas are obtained for the case when the function h(x)=−1+p−1/2(x)∉L1[1,∞) significantly influences the asymptotics of solutions to the equation. The case when the equation contains a nontrivial bifurcation parameter is studied.

This paper reports modelling outcomes for improvements to building energy performance in Indonesia. Long-term climate effects due to building energy demands including carbon emissions are also considered. The global change assessment model (GCAM) was used to generate the related end-user building energy data, including socioeconomics for urban areas of Indonesia. As a comprehensive study, the total life cycle of carbon in the building sector and the concept of zero-carbon buildings, including energy efficiency, zero-emissions electricity and fuel switching options were considered. Building shell conductance (u-value) of building envelope, floor area ratio (FAR), air conditioner (AC) efficiency, electrical appliances (APLs) efficiency, rooftop photovoltaic (PV) performance and ground source heat pump (GSHP) systems were considered as parameters to mitigate carbon emissions under the operational energy category in GCAM. Carbon mitigation associated with the cement production process was considered in the raw material category. Urban population and labour productivity in Indonesia were used as base inputs with projected growth rates to 2050 determined from available literature. Low growth rate ‘LowRate’ and high growth rate ‘HighRate’ were considered as variable inputs for u-value, FAR, AC efficiency, APLs efficiency, and PV capacity factor to model emissions mitigation. The energy consumption of the GSHP was compared to the conventional reverse cycle ACs to identify the potential of the GSHP as a fuel-switching option. Only base input data were used for cement production process parameters without applying any variable inputs. GCAM base scenarios based on input data only (without modifying variable data) for the residential building sector in Indonesia were considered the benchmark for this study. Total potential carbon emissions mitigation was found to be 432 Mt CO2-e for the residential building sector in Indonesia over 2020-2050. It was found that an average of 24% carbon emissions mitigation could be achieved by 2020-30 and 76% in 2031-2050.

Innate immune pathways play a crucial role in the development of atherosclerosis, from sensing initial danger signals to the long-term reprogramming of immune cells. Despite the success of lipid-lowering therapy, anti-hypertensive medications, and other measures in reducing complications associated with atherosclerosis, cardiovascular disease (CVD) remains the leading cause of death worldwide. Consequently, there is an urgent need to devise innovative preventive and therapeutic strategies to alleviate the global burden of CVD. Extensive experimental research and epidemiological studies have demonstrated the dominant role of innate immune mechanisms in the progression of atherosclerosis. Recently, landmark trials including CANTOS, COLCOT, and LoDoCo2 have provided solid evidence, demonstrating that targeting innate immune pathways can effectively reduce the risk of CVD. These groundbreaking trials mark a significant paradigm shift in the field and open new avenues for atheroprotective treatments. It is therefore crucial to comprehend the intricate interplay between innate immune pathways and atherosclerosis for the development of targeted therapeutic interventions. Additionally, unraveling the mechanisms underlying the long-term reprogramming may offer novel strategies to reverse the pro-inflammatory phenotype of immune cells and restore immune homeostasis in atherosclerosis. In this Review, we present an overview of the innate immune pathways implicated in atherosclerosis, with a specific focus on the signaling pathways driving chronic inflammation in atherosclerosis and the long-term reprogramming of immune cells within the atherosclerotic plaque. Elucidating the molecular mechanisms governing these processes presents exciting opportunities for the development of a new class of immunotherapeutic approaches aimed at reducing inflammation and promoting plaque stability. By addressing these aspects, we can potentially revolutionize the management of atherosclerosis and its associated cardiovascular complications.

Landform Evolution Modelling (LEM) provides an avenue for simulating how a landscape may evolve over extended time periods of thousands of years. The CAESAR-Lisflood LEM that includes a hydrologic model (TOPMODEL) and a hydraulic model (Lisflood) can be used to assess the proposed final landform morphology of a mine site by simulating how the mine landform and the landscape would evolve over a 1,000-year period. The accuracy of future simulations depends on the calibration and validation of the model to the past and present events. Calibration and validation of the model involves finding a combination of parameters of the model which when applied and simulated gives model outputs similar to those observed for the real site scenario for corresponding input data. Calibrating the sediment output of the CAESAR-Lisflood model at the catchment level and using it for studying the equilibrium conditions of the landform is an area yet to be explored. Therefore, the aim of this study was to calibrate the CAESAR-Lisflood model and then validate it. To achieve this, the model was run for a rainfall event with a set of parameters, plus discharge and sediment data for the input point of the catchment, to analyze how similar the model output would behave when compared with the discharge and sediment data for the output point of the catchment. The model parameters were then adjusted until the model closely approximate the real site values of the catchment. It was then validated by running the model for a different set of events and checking that the model gave similar results to the real site values. The outcomes demonstrated that while the model can be calibrated to a greater extent for hydrology (discharge output) throughout the year, the sediment output calibration may be slightly improved by having the ability to change parameters to take into account the seasonal vegetation growth during the start and end of the wet season. This study is important for designing and testing post-mining rehabilitated landscape systems that assess hydrology and sediment movement in seasonal biomes.

As the name implies, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus and a member of the corona virus family, primarily affecting the upper respiratory system and the lungs. Like many other respiratory viruses, SARS-CoV-2 can spread to other organ systems. Apart from causing diarrhea, another most common but debilitating complication caused by the SARS-CoV-2 is neurological symptoms and cognitive difficulties, which occur in up to two thirds of hospitalized covid patients and ranging from shortness of concentration, overall declined cognitive speed to executive or memory function impairment. Neuro-cognitive dysfunction and “brain fog” are frequently present in COVID-19 cases, which can last several months after the infection, leading to disruption of daily life. Cumulative evidence suggests that SARS-CoV-2 affects vasculature in the extra pulmonary systems directly or indirectly, leading to impairment of endothelial function and even multi-organ damage. The post COVID-19 long-lasting neurocognitive impairments have not been studied fully; and the underlying mechanism remains elusive. In this review, we summarize the current understanding of the effects of COVID-19 on vascular dysfunction and how vascular dysfunction leads to cognitive impairment in patients.

During most of the lifespan of the Venetian republic, doges or presidents were elected for life. However, a long tenure was a rare event, which effectively resulted in term limits, as has already been deported by several authors. In this paper we examine these tenures and its evolution during the existence of the Venetian republic, trying to prove Smith et al. thesis, but also the event or events that effectively caused that limitation by design. Finally, we will discuss for the causes of this limitation and its effective consequences.

The electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way, failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes to perform a failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The Long Short-Term Memory (LSTM) model will be evaluated to obtain a forecast result that can be used by the electric power utility to organize the maintenance teams. The Wavelet transform shows to be promising in improving the predictive ability of the LSTM, making the Wavelet LSTM model suitable for the study at hand. The results show that the proposed approach has better results regarding the evaluation of the error in prediction and has robustness when a statistical analysis is performed.

Retinal pigment epithelium (RPE) replacement therapy is evolving as a feasible approach to treat age-related macular degeneration (AMD). In majority of the preclinical studies, RPE cells are transplanted as cell suspension into immunosuppressed animal eyes and transplant effects were monitored only short-term. We investigated long-term effects of human iPSC derived RPE transplants in immunodeficient Royal College of Surgeons (RCS) rat model, in which RPE dysfunction lead to photoreceptor degeneration. iPSC-RPE cultured as polarized monolayer on nanoengineered ultrathin parylene C scaffold was transplanted into the subretinal space of 28-day old immunodeficient RCS rat pups and evaluated after 1, 4 and 11 months. Assessment at early time points showed good iPSC-RPE survival. The transplants remained as a monolayer, expressed RPE specific markers, performed phagocytic function and contributed to vision preservation. At 11-month post-implantation, RPE survival was observed only in 50% of the eyes that were concomitant with vision preservation. Loss of RPE monolayer characteristics at the 11month time point was associated with peri-membrane fibrosis, immune reaction through activation of macrophages (CD 68 expression) and transition of cell fate (expression of mesenchymal markers). The overall study outcome supports the therapeutic potential of RPE grafts despite the loss of some transplant benefits during long-term observations.

Accurate forecasts of ocean waves energy can not only reduce costs for investment but it is also essential for management and operation of electrical power. This paper presents an innovative approach based on the Long Short Term Memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analyzing is provided by collecting the experimental data from another study and the exerted data from numerical simulation of searaser. The simulation is done with Flow-3D software which has high capability in analyzing the fluid solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study the wind speed and output power are related with a LSTM method. Moreover, it can be inferred that the LSTM Network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement and the root mean square is 0.49 in the mean value related to the accuracy of LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of LSTM method.

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network(NN) along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and Mean Squared Error (MSE) of 0.1168.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

Accurate and fast traffic flow forecasting is vital in intelligent transportation system because many of the advanced features in intelligent transportation systems are based on it. However, existing methods have poor performance regarding accuracy and computational efficiency in long-term traffic flow forecasting under big data. Hence, we propose an improved Long short-term memory (LSTM) Network and its cluster computing implementation in this paper to address the above challenge. We propose a singular point probability LSTM (SDLSTM) algorithm. The method discards the units of the network according to the singular point probability during the training process and amends the SDLSTM by Autoregressive Integrated Moving Average Model (ARIMA) to achieve the accurate prediction of 24-hour traffic flow data. Furthermore, the paper designs a scheme for implementing this method through cluster computing to shorten the calculation time and improve the system's operating speed. Theoretical analysis and experimental results show that SDLSTM gains a higher accuracy rate and better stability in the long-term traffic flow forecasting compared with previous methods.

Purpose: Testicular cancer (TC) is a rare cancer but due to early age at diagnosis and excellent cure rates, there is a big cohort of survivors. Recent studies have highlighted the late side effects of treatments of TC especially cisplatin based chemotherapy. These complications make the survivorship care challenging with detrimental effects on health and prognosis of TC survivors. In this study we provide a snapshot of common late side effects in TCS and a possible care pathway with nurse-led specialised clinic. Methods: We invited TCS in one of the cancer centre in Ireland for a comprehensive screening using questionnaires, examination and blood tests. Further investigations were performed as indicated. Mortality was assessed through retrospective chart reviews. Results: We recruited 78 TCS to participate in study. 3 patients who had died in survivorship phase were assessed through retrospective chart review. Second malignant neoplasms accounted for all three mortalities. Most common conditions in TCS after 5 years of diagnosis were hypertension (40%), dyslipidaemia (55.6%), hypogonadism (~45%) and high body mass index (52%). Majority of underlying conditions were diagnosed during screening in our clinic including two cases of coronary artery disease and one case of transient ischaemic attacks. Chemotherapy and age more than 30 years at the time of TC diagnosis were associated with a higher incidence of the late side effects. Conclusions: TC is a highly curable cancer but survivorship phase is marred by a range of late side effects. As we lack clear guidelines and care pathways for survivorship, this remains a challenge for patients and healthcare workers. A nurse-led, specialised, screening clinic can improve the care and prognosis of TC survivors.

A cross-flow ultrafiltration harvesting system of a pre-concentrated microalgae culture was tested in an innovative anaerobic-based WRRF. The microalgae culture was cultivated in a membrane photobioreactor fed with the effluent from an anaerobic membrane bioreactor treating sewage. These harvested microalgae biomass was then anaerobically co-digested with primary and secondary sludge from the water line. Depending on the needs of this anaerobic co-digestion, the filtration harvesting process was evaluated intermittently over a period of 212 days for different operating conditions, mainly the total amount of microalgae biomass harvested and the desired final total solids concentration (up to 15.9 g·L1 with an average of 9.7 g·L1). Concentration ratios of 15-27 were obtained with average transmembrane fluxes ranged from 5 to 28 L·m2·h1. Regarding membrane cleaning, both backflushing and chemical cleaning resulted in transmembrane flux recoveries that were, on average, 21% higher than those achieved with backflushing alone. The carbon footprint assessment shows promising results as the GHG emissions associated with the cross-flow ultrafiltration harvesting process could be less than the emissions savings associated with the energy recovered from the biogas production from the anaerobic valorisation of the harvested microalgae.

: The primary use of the potato is direct consumption. The classification of potatoes into consumption type is made on the basis of consumption evaluation of hydrothermally treated tubers. Nutrient deficiency in the soil, including Mg, contributes to the inhibition of potato growth and development and negatively affects the cooking characteristics of the tubers. Magnesium and biostimulants are responsible for plant nitrogen management. Thus, it is necessary to control the content of harmful nitrates and toxic nitrites in tubers. In potato production, it is also important to maintain appropriate conditions during storage. The purpose of this study was to determine the effect of mineral fertilization with magnesium and a biostimulant preparation during cultivation of edible potato and long-term storage on the traits determining utility-consumption type and the content of harmful nitrogen compounds in tubers. The study was conducted using the early potato variety Satina. In a three-year (2015-2017) three-factor experiment: evaluation date (immediately after harvest and after six months of storage), soil fertilization with mineral magnesium (0, 30, 60, 90 kg MgO ha-1), application of an amino acid biostimulant (0, 1.5, 3.0 l ha-1), five consumption traits of tubers after cooking determining the utility-consumption type of potato were determined. In addition, the consumer safety of the potato was determined based on the nitrate and nitrite content of the tubers. The tendency to overcook tubers significantly depended on all the factors used during potato cultivation. In contrast, the texture and structure of tuber flesh after cooking depended only on mineral fertilization with magnesium. The flesh of the tubers after cooking was most tender (1.0 pt) and firm (1.4 pt) after the application of 60 and 90 kg MgO ha-1, respectively. The moisture level of the flesh after cooking was significantly influenced by the interaction of magnesium fertilization with the biostimulant preparation. The factors of the experiment did not modify the utility-consumption type (B/A), while there was a change in the type in relation to that specified by the grower - general utility (B). The applied cultivation technology and long-term storage did not deteriorate the consumer safety of tubers (&lt;200 mg kg-1) in terms of NO3 (max. 112.1 mg kg-1) and NO2 (max. 1.08 mg kg-1) contents. Organoleptic traits: mealiness, moisture and flesh structure were significantly positively related to nitrate and nitrite content of tubers immediately after harvesting and to nitrite concentration after long-term storage.

The aim of this study was to create a novel machine learning (ML) algorithm for predicting the post-pubertal mandibular length and Y-axis in females. Cephalometric data from 176 females with Angle Class I occlusion were used to train and test 7 ML algorithms. For all ML methods tested, the mean absolute errors (MAEs) for the 2-year prediction ranged from 2.78-5.40 mm and 0.88-1.48 degrees, respectively. For the 4-year prediction, MAEs of mandibular length and Y-axis ranged from 3.21-4.00 mm and 1.19-5.12 degrees, respectively. The most predictive factors for post-pubertal mandibular length were mandibular length at previous timepoints, age, sagittal positions of the maxillary and mandibular skeletal bases, mandibular plane angle, and anterior and posterior face heights. The most predictive factors for post-pubertal Y-axis were Y-axis at previous timepoints, mandibular plane angle, and sagittal positions of the maxillary and mandibular skeletal bases. ML methods were identified as capable of predicting mandibular length within 3 mm, and Y-axis within 1 degree. Compared to each other, all of the ML algorithms were similarly accurate, with the exception of MLP regressor.

To compare concentrations of endogenous N-acylethanolamines (NAEs) lipid mediators pal-mitoylethanolamide (PEA), oleoylethanolamide (OEA), and anandamide (AEA) in fresh, decon-taminated, cryopreserved and freeze-dried amniotic membrane (AM) allografts. Thereby deter-mining whether AM's analgesic and anti-inflammatory efficiency related to NAEs persists during storage. The concentrations of NAEs were measured using ultra-high-performance liquid chro-matography-tandem mass spectrometry. Indirect fluorescent immunohistochemistry was used to detect the PEA PPARα receptor. The concentrations of PEA, OEA, and AEA were significantly higher after decontamination. A significant decrease was found in cryopreserved AM compared to decontaminated tissue for PEA but not for OEA and AEA. However, significantly higher val-ues for all NAEs were detected in cryopreserved samples compared to fresh tissue before decon-tamination. The freeze-dried AM had similar values to decontaminated AM with no statistically significant difference. The nuclear staining of PPARα receptor was clearly visible in all specimens. The stability of NAEs in AM after cryopreservation was demonstrated under tissue bank storage conditions. However, a significant decrease, but still higher concentration of PEA compared to fresh not decontaminated tissue was found in cryopreserved, but not frieze-dried AM. Results in-dicate that NAEs persist during storage in levels sufficient for the analgesic and anti-inflammatory effects of AM allografts.

We present an attempt to estimate the long-term changes of Relative Sea Level (RSL) and the different factors contributing to such trends on a local and regional scale using a statistical linear model. The time series analysed correspond to 17 tide-gauges grouped in three different areas: the northern and western Atlantic coasts of the Iberian Peninsula, the Canary Islands, and the southern and eastern coasts of the Iberian Peninsula and Balearic Islands. The analysis was performed for two periods: 1948-2019, using tide-gauge data, and 1993-2019, using both tide-gauge and altimetry data for comparison. The trends for the period 1948-2019 ranged between 1.09 0.14 (Canary Islands) and 2.05 0.21 mm/yr, for the northern and western Atlantic Iberian Peninsula. Altimetry data during the period 1993-2019 yielded quite homogeneous results for all the locations and regions, ranging between 2.7 0.4 and 3.0 0.3 mm/yr. On the contrary, the results obtained from tide-gauge data for this recent period showed a large dispersion, very likely due to local effects or even levelling or instrumental errors. Nevertheless, when the results were averaged for each area, the observed trends were comparable to the altimetry results, with values of 2.3 0.8, 2.7 0.5 and 2.8 0.8 mm/yr for the three regions of study. A stepwise forward linear regression has been used to relate the observed RSL variability to the atmospheric forcing and the thermosteric and halosteric components of the sea level. Surprisingly, the thermosteric and halosteric contributions were not significantly correlated to the observed RSL in many cases, and consequently the steric, the total addition of mass, the mass of salt, and the freshwater contributions to the observed sea level trends could not be reliably estimated. This result seems to be the consequence of the scarcity of temperature and salinity data. This hypothesis is confirmed by the exception of L’Estartit tide-gauge. This location is close to a well sampled region. In this case, the atmospheric variables and the thermosteric and halosteric terms explained the 80 % of the observed RSL variance and the contributions of these terms could be estimated. The freshwater contribution for this location was between 1.3 and 1.4 mm/yr, consistent with recent estimations of the contributions of glaciers and Greenland and Antarctica Ice Sheets. These results highlight the importance of monitoring programs and routine sampling for the determination of the different factors contributing to the sea level variability.

Soil-borne oomycetes include devastating plant pathogens that cause substantial losses in the agricultural sector. To better manage this important group of pathogens, it is critical to understand how they respond to common agricultural practices, such as tillage and crop rotation. Here, a long-term field experiment was established using a split-plot design with tillage as the main plot factor (conventional tillage [CT] vs. no till [NT], 2 levels) and rotation as the subplot factor (monocultures of soybean, corn, or wheat, and corn-soybean-wheat rotation, 4 levels). Post-harvest soil oomycete communities were characterized over three consecutive years (2016-2018) by metabarcoding the Internal Transcribed Spacer 1 (ITS1) region. The community contained 292 Amplicon Sequence Variants (ASVs) and was dominated by Globisporangium spp. (85.1% in abundance, 203 ASV) and Pythium spp. (10.4%, 51 ASV). NT decreased diversity and community compositional structure heterogeneity, while crop rotation only affected the community structure under CT. The interaction effects of tillage and rotation on most oomycetes species accentuated the complexity of managing these pathogens. Soil and crop health represented by soybean seedling vitality was lowest in soils under CT cultivating soybean or corn, while grain yield of the three crops responded differently to tillage and crop rotation regimes.

The COVID-19 pandemic has been challenging for society, especially for those residing in long-term care facilities (LTCF). This study aimed to describe rates of infection, hospitalization, and death due to COVID-19 among older people and staff of LTCF in a state of Southeastern Brazil and identify strategies to prevent and control the disease spread. This cross-sectional study was conducted with 164 LTCF (6,017 older people). Among the studied LTCF, 48.7% confirmed COVID-19 infection in older people, resulting in 39.6% hospitalization and 32.3% death among infected. Moreover, 68.9% of LTCF confirmed COVID-19 infection in the staff, with 7.3% hospitalization and 1.2% death. Preventive measures were identified and classified as organizational, infrastructure, hygiene items and personal protective equipment, and staff training against COVID-19. These measures showed strategies and barriers experienced in the daily routine of LTCF during the pandemic. LTCF in Brazil experienced challenges similar to observed worldwide.

With real estate cycles being a subset of business cycles, contraction stages are often perceived as short-term, after which a revival of the property market sets in. The worsening Nigerian economy has further complicated the issues of housing supply and affordability in economic nerves like Lagos. This study examined the fundamental and associated factors contributing to rental housing affordability issues between 2019-2023 in Lagos. This article is produced with the intention of providing a market data update on rental housing price movements for both the mainland and island zones of Lagos, as well as comparing their affordability convenience for income-earners or households based on the rent-income ratio. The study adopted a mixed research design and various data sources were consulted in analyzing the fundamentals, rent and affordability trends. The study discovered that affordability convenience ratios for the various income bands had deteriorated further between 2019 and 2023 in Lagos mainland (8%) and island (9%). 67% and 80% of income earners are paying above the optimal 30% of their personal or household income in meeting their rental demand in Lagos mainland and island respectively. The paper suggested the application of fundamental, institutional and environmental strategies to combating the current phenomenon.

Present experimental study assesses the mechanical properties of glass/carbon/glass hybrid composite laminates after exposure to moisture and elevated temperatures for extended periods. The top and bottom layers of the hybrid laminates are glass fiber reinforced and the middle layer is carbon fiber reinforced with the matrix specified as a polymer material. The hybrid laminates were manufactured using the resin transfer molding method and their compressive and tensile properties were determined using a tensile testing machine. The material parameters such as the storage modulus, the loss modulus, and the damping factors of the laminates were identified using dynamic mechanical analysis as a function of the temperature and the vibration frequency. The experimental results on compressive and tensile properties revealed slight variations when the hybrid laminates were kept at low temperatures for extended periods. This might occur due to the increasing molecular cross-linking of the polymer network. As the temperature increased, compressive, tensile, storage modules, loss modulus, and damping factors decreased. This might occur due to the increasing mobility of the binder material. Particularly, the highest stiffness parameters were obtained on -80°C/GCG (Glass/Carbon/Glass) laminates due to the presence of beta transition on the glassy region. The relationships between the glass transitions and the targeted frequencies were characterized. The values of the glass transition shift towards higher temperatures as the frequency increases. This might occur due to a reduction in the gaps between the crosslinking of the epoxy network when the frequency increases. The accuracy of the storage modulus results was compared with the empirical models. The model based on the Arrhenius law provided the closest correlation. Meanwhile, another model was observed that was not accurate enough to predict when gamma and beta relaxations occur under a glassy state.

Soil tillage intensity influences the distribution of nutrients, and soil’s physical and me-chanical properties, as well as gas flows. The impact of reduced tillage on these indices in spring barley cultivation is still insufficient and requires more analysis on a global scale. This study was carried out at Vytautas Magnus University, Agriculture Academy (Lithu-ania) in 2022–2023. The aim of the investigation was to determine the effect of the tillage systems on the soil temperature, moisture content, CO2 respiration and concentration in spring barley cultivation limited by the semi-humid subarctic climate. Based on a long-term tillage experiment, five tillage systems were tested: deep and shallow mold-board ploughing, deep cultivation-chiselling, shallow cultivation-chiselling, and no-tillage. Shallow ploughing technology was found to be better at conserving soil moisture and maintaining a higher temperature. Deep cultivation had a lower moisture content and lower soil temperature. Shallow cultivation fields in most cases increased CO2 emissions and CO2 concentration. The results show that in direct sowing fields, most cases had a positive effect on crop density. Direct sowing fields resulted in significantly lower grain yields of spring barley in the years studied.

In cutting processes tool condition affects the quality of the manufactured parts. As such an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of ware beyond which behavior is unreliable. In this paper the tool condition is being monitored by analyzing the electric current on the main spindle via an artificial intelligence model utilizing a LSTM neural network. In the current study the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminum alloy with a custom polycrystalline diamond tool, for the purposes of monitoring the ware of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on a LSTM neural network is utilized for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool ware range in real time. The neural network was designed and trained to notice significant char-acteristics of the captured current signal. The conducted research serves as a proof of concept for use of a LSTM neural network-based model as a method of monitoring the condition of cutting tools.

Coffee production around the world is under threat due to climate variability and change, and drought stress will increase in many coffee regions, nutrient management practices could enhance the adaptation capacity of coffee production. Considering that coffee is a sensitive crop to boron (B) deficiency, this research investigated how B nutrition improves resistance to drought stress in coffee under controlled conditions as well how with interaction with calcium (Ca+2) influences productivity, seed compositions, and soil fertility during a production cycle of 5 year. Under controlled conditions for seven months, coffee plants were submitted to water stress with and without B nutrition. In the field, coffee plantation was submitted to two B rates (0,6 and 1,1 kg. ha-1-year-1) with fixed calcium (Ca+2) rate and, a control without Ca+2 and B. After 9 months of growth and seven months of water deficit, under controlled conditions, the application of B significantly (p&lt;0,05) reduced the negative effect of water stress on coffee growth. Under field conditions, after a production cycle of 5 years, the application of 0,6 kg B and 77 kg CaO. ha-1. year-1 yielded 14% more than the control without B and Ca+2. An increase of the B rate to 1.1 kg.ha-1.year-1 with the same Ca+2 rate increased the productivity significantly (p&lt;0,05) by 37% compared to the control. B application also improved significantly (p&lt;0,05) the B content in the soil at 0-30 cm. The B treatments influenced the biochemical composition of the green coffee beans, with a significant (p&lt;0,05) inverse correlation between the B content of the green coffee beans with caffeine, and unsaturated fatty acids (UFAs) mainly, oleic, linoleic, and linolenic acids which are considered negative descriptors of the coffee quality.

The rapid ageing of populations around the World is creating complex challenges for national governments. The establishment of sustainable and equitable long-term care systems for old and dependent people is one of the main issues of social policy in developed countries. The aim of this work is to define a cost model for residential and day care centres for dependent persons in Cantabria (Spain). The cost model will make it possible to establish the theoretical cost of attending to the needs of the different types of dependent persons in the different types of care centres, and the methodology used could be extrapolated to other regions. The daily cost per user for elderly residential care is €53.72. The cost per user in elderly day centres (5 days) is 32.56 euros. In residential centres for people with disabilities, the values range between €47.41 and €75.25 depending on the category of the centre. In three categories of centres the public price is not enough to cover the cost (physical disability, intellectual disability, mental illness – low care), and therefore the administration should reconsider their public prices for these kind of centres if they want to really contribute to the sustainability of these residential care centres. This research will have important implications for policy-makers in a context of fulfilment of SDGs and where better support for old and disabled people and their carers, as well as fair and efficient financing of social care services, are essential to address the current and future challenges of dependency.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

Neuroinflammation is one disease hallmark on the road to neurodegeneration in primary tauopathies. Thus, immunomodulation might be a suitable treatment strategy to delay or even prevent the occurrence of symptoms and thus relieve the burden for patients and caregivers. In the last years, the peroxisome proliferator-activated receptor γ (PPARγ) received increasing attention as it is immediately involved in the regulation of the immune system and can be targeted by the anti-diabetic drug pioglitazone. Previous studies have shown significant immunomodulation in amyloid-β (Aβ) mouse models by pioglitazone. In this study, we performed long-term treatment over six months in P301S mice as a tauopathy model with either pioglitazone or placebo. We performed serial 18 kDa translocator protein positron-emission-tomography (TSPO-PET) imaging and terminal immunohistochemistry to assess microglial activation during treatment. Tau pathology was quantified via immunohistochemistry at the end of the study. Long-term pioglitazone treatment had no significant effect on TSPO-PET, immunohistochemistry read-outs of microglial activation, or tau pathology levels in P301S mice. Thus, we conclude that pioglitazone modifies the time course of Aβ-dependent microglial activation, but does not significantly modulate microglial activation in response to tau pathology.

Carbon fiber insole (CFI), which is lightweight and stiff to reduce energy loss and help wearers perform better in sports, has recently been introduced. However, there are scarce reports on the effects of CFI on sports performance, muscle fatigue and wearing comfort. This study investigat-ed the short-term effects of CFI on sports performance, lower extremity muscle activity, and sub-jective comfort. Thirty young healthy males performed various sports tasks and treadmill runs with wearable sensors under two experimental insole conditions (benchmark insole as a baseline, CFI). The results showed that compared to the benchmark insole, CFI significantly improved sports performance in terms of power generation and agility. However, it activated more of the Tibialis Anterior and Gastrocnemius Medialis muscles and was perceived stiffer, and less com-fortable. These findings suggested that CFI can improve sports performance, but it could cause more lower extremity muscle fatigue and subjective discomfort.

In a changing climate, extreme weather events such as heat waves will be more frequent and could affect grain weight and the quality of crops such as wheat, one of the most significant crops in terms of global food security. In this work, we characterized the response of Triticum turgidum spp. durum wheat to a short-term heat-stress (HS) treatment at transcriptomic and physiological levels during early grain filling in glasshouse experiments. We found a significant reduction in grain weight and size from HS treatment. Grain quality was also affected, showing a decrease in starch content in addition to increments in grain protein levels. Moreover, an RNA-seq analysis of durum wheat grains allowed us to identify 1590 differentially expressed genes related to photosynthesis, response to heat, and carbohydrate metabolic process. A gene regulatory network analysis of HS-responsive genes uncovered novel transcription factors (TFs) controlling the expression of genes involved in abiotic stress response and grain quality, such as a member of the DOF family predicted to regulate glycogen and starch biosynthetic processes in response to HS in grains. In summary, our results provide new insights into the extensive transcriptome reprogramming that occurs during short-term HS in durum wheat grains.

Recent preclinical studies have shown the potential benefits of short-term calorie reduction (SCR) on cancer treatment. In this integrative review, we aimed to identify and synthesize current evidence regarding the feasibility, process, and effects of SCR in cancer patients receiving chemotherapy. PubMed, Cumulative Index to Nursing and Allied Health Literature, Ovid Medline, PsychINFO, and Embase were searched for original research articles using various combinations of Medical Subject Heading terms. Among the 311 articles identified, seven studies met the inclusion criteria. The majority of the reviewed studies was small randomized controlled trials or cohort study with fair quality. The results suggest that SCR is safe and feasible. SCR is typically arranged around the chemotherapy with the duration ranging from 24 to 96 hours. Most studies examined the protective effects of SCR on normal cells during chemotherapy. The evidence supports that SCR had the potential to enhance both physical and psychological wellbeing of patients during chemotherapy. SCR is a cost-effective intervention with great potential. Future well-controlled studies with sufficient sample sizes are needed to examine the full and long-term effects of SCR and its mechanism of action.

COVID-19 patients can recover with a median SARS-CoV-2 clearance of 20 days post initial symptoms (PIS). However, we observed some COVID-19 patients with existing SARS-CoV-2 for more than 50 days PIS. This study aimed to investigate the cause of viral clearance delay and the infectivity in these patients. Demographic data and clinical characteristics of 22 long-term COVID-19 patients were collected. SARS-CoV-2 nucleic acid, peripheral lymphocyte count, and functionality were assessed. SARS-CoV-2-specific and neutralization antibodies were detected, followed by virus isolation and genome sequencing. The median age of the studied cohort was 59.83±12.94 years. All patients were clinically cured after long-term SARS-CoV-2 infection ranging from 53 to 112 days PIS. Peripheral lymphocytes counts were normal. Interferon gamma (IFN-ƴ)-generated CD4+ and CD8+ cells were normal as 24.68±9.60% and 66.41±14.87%. However, the number of IFN-ƴ-generated NK cells diminished (58.03±11.78%). All patients presented detectable IgG, which positively correlated with mild neutralizing activity (ID50=157.2, P=0.05). SARS-CoV-2 was not isolated, and a cytopathic effect was lacking. Only three synonymous variants were identified in spike protein coding regions. In conclusion, decreased IFN-γ production by NK cells and low neutralizing antibodies might favor SARS-CoV-2 long-term existence. Further, low viral load and weak viral pathogenicity was observed in COVID-19 patients with long-term SARS-CoV-2 infection.

The biomass fraction of processed municipal and industrial wastes added to soil can maintain, and in some case improve, the soil’s organic fertility. One of the main constraints in the agricultural use of the sewage sludge is its content of heavy metals. In the long term, soil administration of sewage sludge in agriculture could result in a risk of environmental impact. The aim of this research was to evaluate the effects of medium-term fertilization with sewage sludge diversely processed on the soil’s organic carbon content and humification – mineralization soil’s processes and on the physical and mechanical properties of soil. Furthermore, the heavy metals accumulation in soil, in their total and available form, has been investigated. After eight years of administration to soil, the use of sewage sludge as an agricultural soil amendment has contributed to maintaining the soil’s organic fertility. An increase in concentrations of total Ni and Zn was detected in soil. For bioavailable form (DTPA-extractable) this trend was evidenced for all heavy metals analysed. However, the concentrations of total and available heavy metals in the soil did not exceed the legal threshold established by Italian law for unpolluted soils.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

Developing short-term forecasting model for global atmospheric temperature and wind in the near space is crucial for understanding atmospheric dynamics and supporting human activities in this region. While numerical models have been extensively developed, deep learning techniques have recently shown promise in improving atmospheric forecasting accuracy. In this study, convolutional long short-term memory (ConvLSTM) and convolutional gated recurrent unit (ConvGRU) neural networks were applied to build for short-term global-scale forecasting model of atmospheric temperature and wind in the `near space based on the MERRA-2 reanalysis dataset from 2010-2022. The model results showed that the ConvGRU model outperforms the ConvLSTM model in the short-term forecast results. The ConvGRU model achieved a root mean square error in the first three hours of approximately 1.8 K for temperature predictions, and errors of 4.2 m/s and 3.8 m/s for eastward and northward wind predictions, respectively.

In the post-pandemic era, evaluating long-term immunity against COVID-19 has become increasingly critical, particularly in light of continuous SARS-CoV-2 mutations. This study aimed to assess long-term immunogenicity by analyzing booster shots’ impact and sera infection history collected in Makassar, Indonesia. We measured anti-RBD IgG levels and neutralization capacity (NC) against both the wild-type (WT) Wuhan-Hu and Omicron XBB 1.5 variants across groups of COVID-19 vaccinated individuals with no booster (NB), single booster (SB), and double booster (DB). The mean durations since the last vaccination were 25.11 months, 19.24 months, and 16.9 months for the NB, SB, and DB group, respectively. Additionally, we evaluated the effect of breakthrough infection (BTI) history, with a mean duration since the last confirmed infection of 21.72 months. Our findings indicate fair long-term WT antibody (Ab) titers, with the DB group showing significantly higher level than other groups. Similarly, the DB group demonstrated higher anti-Omicron XBB 1.Ab titer, which was insignificantly different from the other groups. Although the level of anti-WT Ab was moderate, we observed near-complete (96-97%) long-term neutralization against the WT pseudo-virus for all groups. There was a slight decrease in NC against Omicron XBB 1.5 compared to the WT among all groups; DB group, SB group, and NB group showed 80.71±3.9%, 74.29±6.7%, and 67.2±6.3% neutralization activity, respectively. Breakdown analysis based on infection and vaccine status showed that booster shots increase the NC against XBB 1.5. Individuals with BTI demonstrate a better NC compared to their counterpart uninfected individuals with the same number of booster shots. Our findings suggest that long-term immunity against SARS-CoV-2 persists and is effective against the mutant variant. Booster shots enhance the NC especially among uninfected individuals .

COVID-19 pandemic-related pressures on primary care may have driven inappropriate continuation of antibiotic prescriptions. Yet prescribing modality (repeat/non-repeat) has not previously been investigated in a pandemic context. Using the OpenSAFELY-TPP analytics platform, we analysed repeat/non-repeat prescribing frequency in monthly patient cohorts between Jan 2020–2022. In-depth analysis was conducted on Jan 2020 (“pre-pandemic”) and Jan 2021 (“pandemic”) cohorts. Per-patient prescribing and clinical conditions were determined by searching primary care record data using clinical code lists. Repeat prescribing was defined as >=3 prescriptions in a 6 month lookback period. Associations between demographics (e.g. age, sex, geography) and repeat/non-repeat prescribing were explored using unadjusted risk ratios. Antibiotic prescribing declined from May 2020; non-repeat prescribing declined more strongly than repeat prescribing (maximum declines -26% vs -11%, respectively). In the pandemic cohort, older patients were at higher risk of prescribing (especially repeat prescribing). The most common clinical conditions for repeat prescribing were COPD comorbidity and urinary tract infection. Comorbidities were more common among repeat vs non-repeat prescribed patients. In the pandemic cohort, vs pre-pandemic, repeat and non-repeat prescribing for comorbidities generally declined less compared with shorter-term conditions (infections, including COPD exacerbation/lower respiratory tract infection). Our findings inform ongoing development of stewardship interventions in England..

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

Climate change is expected to increase the frequency and severity of abiotic stresses that lead to loss of crop yield. We investigated the effect of salinity (S), short episodes of high temperature (HS) and combination of S+HS at the reproductive phase on dead pericarps properties and yield of the crop plant Brassica juncea. Three intervals of HS resulted in massive seed abortion; seeds from salt-treated plants germinated poorly. Pericarp extracts of salt-treated plants reduced seed germination of B. juncea; all pericarp extracts completely inhibited seed germination of tomato and Arabidopsis; removal of pericarp extracts restored seed germination. HS reduced all metabolites accumulated in dead pericarps, except for upregulation of isomaltose and cellobiose. Salt induced alteration in metabolite levels including increase in proline, reduction in TCA intermediates and changes in phytohormone levels. Proteome analysis revealed hundreds of proteins stored in dead pericarps whose levels and composition were altered under salt stress. The integration of metabolic and proteomic data showed that changes in metabolites were highly correlated with changes in proteins involved in their biosynthetic pathways. Thus, besides providing a physical shield for seed/embryo protection dead pericarps store beneficial substances whose levels, composition and biological function are altered under stress, further highlighting the elaborated function of dead organs enclosing embryos in seed biology and ecology. The detrimental effect of HS on crop production might have implications for global food security in the face of climate change.

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.

Objectives: Minimally invasive direct coronary artery bypass (MIDCAB) is an alternative for revascularisation of isolated left anterior descending (LAD) or as multi-vessel (MV) procedure for diagonal branch (RD) or circumflex artery (RCX) region. Methods: From 2021 to 2022 91 patients underwent MIDCAB procedure in our heartcenter. 80% were male. Eighty percent of the patients had two or three vessel coronary artery disease. Mean age was 65±10 years. The left internal mammary artery (LIMA) was anastomosed to the left anterior descending via left minithoracotomy approach in all patients. Fourteen patients received multi-vessel MIDCAB (15%) with two or three anastomoses a saphenous vein graft (as T-graft to LIMA) was anastomosed to RD or RCX. Results: Mean operation time was 2.6±0.8 hours. The 30-day mortality was 0. Mean required packed red blood cells (pRBC) was 0.4±1.2 unit. The intensive care unit stay (ICU) was 1.5±1.6 days mean. We observed two patients (2%) with wound infections postoperatively. Four patients underwent rethoracotomy because of bleeding. Mean follow-up time was 1.5±0.5 years. Seventy-eight patients (86%) were in New York Heart Association (NYHA) class 1, 11 patients (12%) were in NYHA class 2. One patient received percutaneous coronary intervention due to de novo stenosis of the RCA. Late mortality was 2%. The Kaplan-Meier survival rate was 98.8% at 1 and 2 years. Conclusion: MIDCAB is technically demanding. Our postoperative and short-term clinical results demonstrate that this procedure is safe and feasible. Optimal patient selection and an experienced surgical team are mandatory.

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

Background: Despite scientific advances, the management of procedural pain in neonates remains suboptimal. Applying adequate therapy to control pain during the neonatal period is a moral and ethical act. In recent decades, ample evidence has accumulated regarding the risks, associated with both untreated pain and the use of more aggressive analgesic therapy. Thus, the emphasis in neonatal clinical practice is on non-aggressive pain management techniques, including homeopathic agents. The efficacy and safety of homeopathic agents for reducing procedural pain in neonates is the subject of the present study. Materials and Methods: Healthy full-term newborn babies with an average age of 72-84 hours were included. They were divided into three study groups: group (A) – not anesthetised, /n= 67/, group (B) – anesthetised with Arnica D30 /n=40/ and group (C)- anesthetised with Sol.Glucose 25% /n=40/. The severity of the pain was assessed using the multimodal - Neonatal Infant Pain Scale (NIPS) and the unimodal - Neonatal Facial Coding System (NFCS) scale. Assessments were done 30s before, 30 seconds after, and 5 minutes after the painful stimulus caused by the heel prick. Heart rate, transcutaneous oxygen saturation, respiratory rate and blood pressure were examined at the same intervals. The results obtained were processed statistically by descriptive analysis and ANOVA at a significance level of p&lt; 0.05. Results: 5 minutes after heel prick both scales showed a statistically significant lower score in those given Arnica D30, compared to those who received Sol.Glucose 25%. When monitoring the physiological indicators, we found a significant increase in the heart rate 30 seconds after the procedure.5 minutes after the heel prick we recorded significantly respiratory rate and systolic blood pressure in all three groups compared to pre-procedural values. Conclusions: Arnica D30 has better analgesic effects compared to Sol.Glucose 25% for newborn babies after heel prick tests.

In patients with prior Takotsubo syndrome (TTS), long lasting functional cardiac limitations were described as compared with normal subjects. Emotions-triggered Takotsubo syndrome (E-TTS) has more favorable outcomes than TTS preceded by a physical trigger or by no identifiable fac-tors. The aim of the present study was to assess long-term cardiac functional limitations in a co-hort of asymptomatic E-TTS patients. We enrolled n=39 asymptomatic patients with a diagnosis of E-TTS. Cardiopulmonary exercise tests (CPET) were performed at 30 [12-40] months median fol-low-up from the acute event. A cohort of n=39 individuals matched for age, sex, body mass index and comorbidities served as control. Despite recovery of left ventricular ejection fraction, patients with prior E-TTS had lower peak VO2 and percentage of predicted peak VO2 (17.8 ± 3.6 vs 22.5 ± 6.5; P < 0.001 and 75.2 ± 14.1 % vs 100.6 ± 17.1%, P <0.001), VO2 at anaerobic threshold (AT) (11.1 [10.1-12.9] vs 14.4 [12.5-18.7]; P <0.001), peak O2 pulse (9.7 ± 2.5 vs 13.1 ± 3.5; P <0.001) and higher VE/VCO2 slope (30.4 ± 3.7 vs 27.2 ± 3.5; P <0.001) compared with matched controls. We found no statistically significant differences in heart rate reserve (HRR), respiratory equivalent ratio (RER), mean blood pressure and peak PetCO2 between patients and controls. Despite its favorable out-come, patients with E-TTS in our population were found to have subclinical long-term functional cardiac limitations as compared with a control cohort.

In a super-aged society, maintaining healthy aging, preventing death, and enabling a continua-tion of economic activities are crucial. This study sought to develop a model for predicting the survival time in community-dwelling older individuals by using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older in-dividuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9,462 (5.0%) died. Using deep learning based models (C statistics = 0.7011), we identified Charlson’s comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habit as factors impacting survival. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Older individuals should rec-ognize modifiable risk factors that affect survival period in order to manage their own health.

The collective considerations presented here lead to a crucial question: What truly constitutes the primary public health challenge posed by SARSCoV-2 infection and its variants?The findings of Bowen et al.(1) and Noé et al.(4) have offered us new insights into the actual repercussions of SARSCoV-2 virus infections on public health. They unmistakably point to the late sequelae and complications arising as secondary effects of the viral infection, causing severe and long-term damage to global public health. Rather than the acute infection which has a very low mortality rate in the general population, as initially and commonly believed.