Obesity is invariably accompanied by autonomic dysfunction, although data in pediatric populations are conflicting. We conducted a systematic review and meta-analysis of 12 studies (totaling 1102 participants) comparing obese and normal-weight subjects (5-18 years of age), defined as body mass index >95th or <85th percentile, respectively. Using a random-effects model, we report the standardized mean differences (SMD) of sympathetic and vagal indices, calculated by heart rate variability. Autonomic dysfunction was present in the obese group, based on the average SMD in the standard deviation of sinus intervals (at -0.5340), and on the ratio of low (LF) to high (HF) frequency spectra (at 0.5735). There was no difference in sympathetic activity, but the heterogeneity among the relevant studies weakens this result. SMD in HF (at 0.5876), in the root mean square of successive differences between intervals (at -0.6333), and in the number of times successive intervals exceeded 50ms divided by the total number of intervals (at -0.5867) indicated lower vagal activity in the obesity group. Thus, autonomic dysfunction is present in obese children and adolescents, attributed to lower vagal activity. Further studies on autonomic function are needed in various pediatric cohorts, placing emphasis on sympathetic activity.

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and quantile generalized additive regression to evaluate relationships with lower quantiles, such as low yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as Conditional Tail Expectation, Certainty Equivalence of Revenue, and Mean Root Square Loss. The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. Additionally, our study suggests that this type of insurance could also apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

The molecular entity that catalyzes ferredoxin (Fd)-dependent cyclic electron flow around photosystem I (PSI) (Fd-CEF) remains unknown. To elucidate the in vivo molecular mechanism of Fd-CEF, the evaluation of Fd reduction‒oxidation kinetics is a good indicator of Fd-CEF activity. Recent research showed that the expression of Fd-CEF activity requires the oxidation of plastoquinone, and furthermore, chloroplast NAD(P)H dehydrogenase does not catalyze Fd-CEF in Arabidopsis thaliana. In the present research, the effect of reduced Fd on Fd-CEF activity was analyzed by comparing wild-type and pgr5-deficient mutants (pgr5hope1). PGR5 has been proposed to be the mediator of Fd-CEF, and pgr5hope1 showed the same CO2 assimilation rate and the same reduction‒oxidation level of PQ as WT, but P700 oxidation with Fd was highly reduced, unlike WT. As expected, the activity of Fd-CEF was enhanced in pgr5hope1 compared to the wild type, and its activity was also enhanced with the oxidation of PQ by the increase in the CO2 assimilation rate. The present in vivo research clearly shows that the expression of Fd-CEF activity requires reduced Fd in addition to oxidized PQ, and PGR5 does not catalyze Fd-CEF.

Single-cell green microalga Nannochloropsis oceanica is potentially considered to be the bioresource for biofuel and pharmaceutical production. Urea is a kind of nutrient component for photosyn-thetic N. oceanica. Meanwhile, urea could induce to accumulate some substances such as lipid and affect its growth and physiology. However, how marine microalga N. oceanica respond and adapt to urea addition remains elusive. Here, acclimation of the metabolic reprogramming to changes in the nutrient environment was studied by high-throughput mRNA sequencing in N. oceanica under Ct vs U, there were 2104 genes displayed differential expression (including 1354 upregulation and 750 downregulation, respectively) and some pathways including carbon/nitrogen metabolism, photosynthesis, and lipid metabolism would be reprogrammed in N. oceanica. The results showed that genes related to photosynthesis in N. oceanica were significantly down-regulated, especially those related to light harvesting proteins. Interestingly, urea absorption and transport may de-pend not only on specialized transport mechanisms such as urease, but also on alternative transport channels such as the ABC transporter family, CLC protein family. In addition, urea causes specific changes in carbon and lipid metabolism. Genes associated with the Calvin cycle and carbon concentration mechanisms were significantly upregulated. In lipid metabolism, genes associated with lipases, or polyunsaturated fatty acid enzymes, are highly activated. In addition, expression is enhanced in the tricarboxylic acid cycle and folate metabolism, making important contributions to energy supply and synthesis and modification of genes or macromolecules. These phenomena indicate that N. oceanica actively and dynamically regulates the redistribution of carbon and nitrogen after urea addition., which will provide references for further research on the effects of urea on N. oceanica.

Due to the nonlinear stiffness and fractional-order damping characteristics of some real suspension systems, coupled with the presence of viscoelastic materials, fractional-order nonlinear suspension system modeling has become a focus of research in recent years. In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. A fractional order PID algorithm is used to control the motor's rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. Notably, the designed fractional order PID control circuit for the motor shows better control results compared to the conventional PID control circuit. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems.

Neurocutaneous disorders, also known as phakomatoses, are congenital and acquired syndromes resulting in simultaneous neurologic and cutaneous involvement. In several of these conditions, the genetic phenomenon is understood, providing a pivotal role in the development of therapeutic options. This review encompasses the discussion of the genetic and clinical involvement of neurocutaneous disorders and examines clinical management and treatment options. With the current advances in genetics, the role of precision medicine and targeted therapy plays a substantial role in addressing management of these conditions. The interconnectedness between therapeutic options highlights the importance of precision medicine in treating each disorder’s unique molecular pathway. This review provides an extensive synthesis of ongoing and current therapeutics in the management of such clinically unique and challenging conditions.

A system to evaluate several facets of hand function for neuroprostheses-mediated tasks is proposed. It includes a platform that serves as a test bench, sensors for dynamometry, electromyography, sensorized glove, and interactive objects. The system methodically locates and assesses the motor points along the forearm, evaluates stimulation-induced fatigue, measures digit motion and forces exerted during ADL grasps, and evaluates nerve excitability to advance electrode designs. While the system is exclusively designed for neuroprostheses-mediated grasp, it can also be extended to generic grasp and object manipulation-based research.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring Volatile Organic Compounds (VOCs) using a Photoionization Detector (PID). This paper analyzes the reasons for the low accuracy of the traditional Support Vector Machine (SVM) regression method. To address the issue, the PID signal is subjected to feature extraction and Principal Component Analysis (PCA) to reduce the data dimensionality. Moreover, the optimal SVM parameters are selected using a Genetic Algorithm (GA), and a combined approach of SVM regression with PCA and GA is utilized for PID signal regression analysis. And the effectiveness of the method is validated through extensive experiments and simulations. Furthermore, the influence of the sample quantity on the regression accuracy is analyzed, enabling accurate monitoring of VOCs concentration in a moist environment.

This study aimed to use biomineralization technology to improve the bond strength of fiber-reinforced lightweight aggregate concrete (LWAC) after exposure to temperatures of 300 °C and 500 °C. The specimens of a control group (Group A) and two experimental groups (Group B and Group C) were prepared. These specimens healed themselves in different ways after exposure to high temperatures and pull-out tests. The self-healing method of Group A involved placing the specimen into an incubator. The self-healing method of Group B was the same as that of Group A. The self-healing method of Group C involved immersing the specimen in a mixed solution of urea and calcium acetate for two days, then taking it out and placing it in an incubator for two days. A cycle of four days was undertaken until the desired age was reached. After the specimens were healed to the planned age, the first and secondary pull-out tests and repair compound identification were carried out. After exposure to a temperature of 300 °C and self-healing for 90 days, compared with the specimens in Group A, the relative bond strength ratios of the secondary pull-out tests of the specimens in Group C increased by 20.3%. This indicates that biomineralization can effectively improve the bond strength of LWAC after high-temperature damage.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

This study investigated the impact of chitosan (CH, 1%) and aloe vera gel (AL, 30%) edible coatings on the preservation of blue honeysuckle quality during a 28-day storage at -1°C. Coating with CH, AL, and CH+AL led to notable enhancements in several key attributes. These included increased firmness, total soluble solids, acidity, pH, and antioxidant capacity (measured through DPPH, ABTS, and FRAP assays), as well as the preservation of primary (ascorbic acid) and secondary metabolites (TPC, TAC, and TFC). These coatings also resulted in reduced weight loss, respiration rate, color, abscisic acid, ethylene production, and malondialdehyde content. Notably, the CH+AL treatment excelled in preserving secondary metabolites and elevating FRAP-reducing power, demonstrating a remarkable 1.43-fold increase compared to the control after 28 days. Overall, CH+AL exhibited superior effects compared to CH or AL treatment alone, offering a promising strategy for extending shelf life and preserving quality of blue honeysuckle during storage.

Light is an electromagnetic radiation that has visible and invisible wavelength spectrum. The visible light can only be detected by the eyes through the optic pathways. With the presence of scalp, cranium and meninges, the brain is seen as being protected from direct exposure to light. For that reason, the brain can be viewed as a black body lying inside a black box. In physics, a black body tends to be in thermal equilibrium with its environment and can tightly regulate its temperature via thermodynamics principles. Therefore, a healthy brain inside a black box should not get exposed to light. On the contrary, photobiomodulation, a form of light therapy to the brain has been shown to have beneficial effects for some neurological conditions. The proposed underlying mechanisms are multiple. Herein we present our intraoperative findings of rapid electrocorticographic brainwave changes when the brain was shone directly with different wavelengths of light during awake brain surgery. Our findings provide literature proof on light’s ability to influence human brain energy and function. Our proposed mechanism for these rapid changes is the presence of plasma-like energy inside the brain which causes fast brain activities that are akin to lightning strikes.

Lightning is responsible for most annually burned area by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms. Here, we describe the development and verification of a probabilistic lightning-strike algorithm designed to run on a uniform 20-km grid over the Continental USA and Alaska. The algorithm consists of a large set of logistic equations parameterized via logistic regression using long-term data records of observed lightning strikes and meteorological reanalysis fields from NOAA. Principal Component Analysis was employed to extract 13 Principal Components (strong predictors) from a list of 611 potential predictors. Our statistical analysis revealed that the occurrence of cloud-to-ground lightning strikes primarily depends on three factors: horizontal temperature distribution by pressure levels, amount of low-level atmospheric moisture, and wind vectors. These physical variables impact the vertical separation of electric charges in the lower troposphere during storms causing the voltage potential between ground and the cloud deck to increase to a level that triggers electrical discharges. Results from a forecast verification using independent data showed excellent model skill, thus making this algorithm suitable for inclusion into software systems forecasting the chance of wildfire ignitions.

Burkholderia are a group of Gram-negative bacteria that can cause a variety of diseases in at-risk populations. B. pseudomallei and B. mallei, the etiological agents of melioidosis and glanders, respectively, are the two clinically relevant members of the B. pseudomallei complex (Bpc). The development of vaccines against Bpc species has been accelerated in recent years, resulting in numerous promising subunits and glycoconjugate vaccines incorporating a variety of antigens. However, a second group of pathogenic Burkholderia species exists known as the Burkholderia cepacia complex (Bcc), a group of opportunistic bacteria which tend to affect individuals with weakened immunity or cystic fibrosis. To date there have been few attempts to develop vaccines to Bcc species. Therefore, the primary goal of this review is to provide a broad overview of the various subunit antigens that have been tested in Bpc species, their protective efficacy, study limitations, and known or suspected mechanisms of protection. Then, we assess the reviewed Bpc antigens for their amino acid sequence conservation to homologous proteins found in Bcc species. We propose that protective Bpc antigens with a high degree of Bpc-to-Bcc sequence conservation could serve as components of a pan-Burkholderia vaccine capable of protecting against both disease-causing groups.

This study aimed to investigate the coloring ingredient potential of liver homogenates that form Zn-protoporphyrin (ZnPP), a natural red pigment, after anaerobic incubation. The liver homogenates were used to develop nitrite-free sterile pork liver pâtés. These liver homogenates can be applied in the formulation of pâtés directly or as a centrifuged insoluble fraction that is highly concentrated in ZnPP. Both the whole homogenate and its insoluble fraction were adjusted to pH 7.5 before their use in the formulation of pâtés with and without antioxidant (0.5% ascorbate plus 0.1% tocopherol) addition. Pâtés formulated with the whole homogenate showed color and texture characteristics similar to those of the positive control with nitrite. However, high levels of the insoluble fraction also led to pâtés with improved color characteristics but with a two-fold softened texture. Therefore, the form and amount of ZnPP added played a role in the final appearance of the product. The ZnPP pigment was more stable than heme was in the sterilization treatment, and antioxidant addition proved to be unnecessary. The ZnPP-rich ingredients allowed for the preparation of nitrite-free cooked liver pâtés with a stable red color and could be potentially applied in other uncured cooked meat products.

This study introduces a neural network-based approach to predict dust emission, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), the research predicts PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. The model is trained on extensive field data from the almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a Mean Squared Error (MSE) of 0.02 and a Mean Absolute Error (MAE) of 0.01, indicating a high degree of precision in forecasting PM2.5 levels. The study also finds a strong correlation between certain operational parameters and PM2.5 emissions, highlighting specific areas for optimization in harvesting techniques. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

The purpose of this study was to compare the long-term clinical outcomes and quality of life measures for two fixation methods in the setting of displaced femoral neck fractures. The two groups included fixation with multiple cancellous screws (group 1) and telescopic femoral neck screws and small locking plate device (Targon FN) (group 2). Patients underwent reduction and internal fixation with either multiple cancellous screws or Targon FN device from March 2000 to January 2012. Failure endpoints included nonunion, osteonecrosis of the femoral head, and revision surgery. Patient reported outcome measures included chronic pain, ability to ambulate, and the use of ambulation assistive devices. Statistical analysis demonstrated a statistically significant lower rate of non-union and overall complication in the Targon FN group (p value < 0.001 and p value = 0.005 respectively). Logistic regression analysis showed that operative fixation with the Targon FN device decreased the odds ratio for overall complication by a factor of 0.34 (P = 0.02). There were no statistically significant differences between groups 1 and 2 in patient reported outcomes (chronic pain (p-0.21), ability to ambulate (p- 0.07), and the use of ambulation assistive device (p-0.07)). When compared to traditional cancellous screw fixation of femoral neck fractures, the Targon FN device has significantly lower complications rates and equivalent patient reported outcomes.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

(1) Background: Pseudotumor of the hip is a sterile, non-neoplastic soft tissue mass associated with total hip arthroplasties. Pseudotumors may mimic soft tissue tumors, or infections, so the differential diagnosis is crucial and biopsy is recommended. The purpose of this study was to compare complications and functional results between one-stage and two-stage procedures. (2) Methods: We retrospectively analyzed 21 patients surgically treated at our Institution with “pseudotumor” associated with hip prosthesis (8 male,13 female; mean age 69 years). One-stage revision was performed in 10 cases, two-stage in 10, excision only in one case. Complications were classified as major and minor and functional results assessed using the Harris Hip Score (HHS). (3) Results: Five patients (24%) reported major complications. Survival rate to all com-plications was 75%. Overall survival was 95% at five years. Mean HHS went from 35 pre-operative to 75 post-operative, highlighting improved functional results in all cases. We rec-orded no differences in complications and functional outcome between one and two-stage proce-dures. (4) Conclusions: In our experience, two-stage surgical approach is preferable in cases with major bone defects and larger pseudotumor size. The use of custom-made 3D-printed prostheses is increasing and is a further reason to prefer two-stage revision.

In view of the inability to directly and accurately obtain the athlete's aerodynamic force during take-off phase through wind tunnel test, the athlete's aerodynamic force and surrounding flow field form under different take-off postures are obtained through numerical simulation research, and the effects of different take-off modes on the aerodynamic characteristics during take-off in ski jumping are discussed. The multi-body system composed of the athlete and skis was selected as the research objects. By using partially averaged Navier-Stokes (PANS) turbulence model and 3D numerical simulation of computational fluid dynamics (CFD), the aerodynamic characteristics of the athlete under different take-off postures were predicted. Take-off modes include knee-push-hip (KPH) mode and hip-drive-knee (HDK) mode, and the hip joint angle of HDK mode is significantly greater than KPH mode. First, the aerodynamic force ratio of the athlete’s torso and legs is obviously large. Although the aerodynamic force of arms themselves is not obvious, they have a great impact on the overall aerodynamic characteristics of the athlete, so the posture of arms cannot be ignored. Then, the total drag and moment of HDK mode are significantly higher than that of KPH mode, and the lift-to-drag ratio of HDK mode is significantly lower than that of KPH mode. At first the total lift of HDK mode is higher than that of KPH mode, but in the last attitude the total lift of HDK mode does not rise but fall, and finally the total lift of HDK mode is lower than KPH mode. The aerodynamic characteristics change dramatically during take-off phase, and the aerodynamic characteristics of the two take-off modes are quite different, and these changes and differences are difficult to be observed in real training and competition site. KPH mode has obvious aerodynamic advantage over HDK mode. During the take-off process, the athlete should increasethe force generated by the knee joint extension and appropriately reduce the speed of hip joint extension, and control the using force order of the lower limb joints, and push hip joint extension by knee joint extension, in order to avoid issues such as the hip joint angle is too large, the hip joint extension angle is too fast, the center of gravity is to back and other problems. Studying the aerodynamic characteristics during the take-off phase provides valuable insights for athletes to achieve favorable flight postures after take-off, offering scientific guidance to improve training strategies and enhance competitive performance.

The fate of critically sick patients with infections in intensive care units (ICUs) is significantly influenced by antimicrobial resistance, which has become a serious concern in developing nations. This observational study was conducted at the IBN Sina Specialised Hospital in Dhaka, Bangladesh, over a 12-month period from January 2021 to December 2021, in order to track the pattern of antibiotic resistance among the patients admitted to the intensive care unit (ICU). Aspiration pneumonia (29%) and diabetes mellitus (24%), among 200 patients, were the most common main diagnoses. 65 samples (or 26%) of the 250 samples had 85 bacteria identified from them. Urine (14.12%) and tracheal aspirate (64.71%) were the most frequently found sites of infection. In the sample from the tracheal aspirate, Pseudomonas spp. (30.59%), Escherichia coli (24.71%), Acinetobacter spp. (20%), and Klebsiella spp. (14.12) were the most frequently isolated microorganisms. The following antibiotics were shown to have the highest overall patterns of resistance: levofloxacin (68.24), amikacin (64.71), meropenem (49.41), ceftazidime (75.29), ciprofloxacin (78.82), and gentamicin (82.35). The most effective antibiotic against Klebsiella species was meropenem (64.61%), while Acinetobacter species were mostly susceptible to cotrimoxazole (64.67%) and piperacillin + tazobactam (60.50%). Most susceptible to netilmicin (70.48%) and meropenem (49.32%) were Escherichia coli, while Pseudomonas spp. were mostly responsive to colistin (55.14%) and netilmicin (52.25%). Antibiotic resistance is common among intensive care unit (ICU) patients, and most isolated microbes have resistance to traditional medicines.

Abstract: (1) Background: No consensus has been reached on indicators and protocols for monitoring drug concentrations in organ transplant recipients for the immunosuppressant mycophenolic acid (MPA) in clinical therapy.In this study, we propose to establish a Ultra Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS) for quantitative determination of MPA and its metabolites ( mycophenolic acid carboxybutoxyether, MPAG and mycophenolic acid carboxyglucuronide, AcMPAG) concentrations in peripheral blood mononuclear cells (PBMCs) and to assess the clinical application of PBMCs concentration detection of MPA and its metabolites in infection prediction in Chinese kidney transplant recipients(KTRs); (2) Methods: PBMCs were isolated with Ficoll-Paque solution, and cells were counted. Acetonitrile precipitated proteins to extract the target compounds.Waters ACQUITY UPLC BEH C18 column of 1.7μm (2.1 mm*50 mm) and an ACQUITY-Xevo TQ-S UPLC-MS/MS system were used for the detection with mobile phases: water and methanol (containing 2 mmol/L NH4Ac + 0.1% formic acid); flow rate: 0.3 mL/min. After gradient elution, the samples were positively ionized by an electrospray positive ion source (ESI) and then quantitatively analyzed by a triple quadrupole tandem mass spectrometer in multiple reaction monitoring (MRM) mode. The performance of the established method was evaluated through a series of validation experiments according to the requirements of the relevant guidelines. We enrolled 7 KTRs with stable conditions and 8 KTRs with infection from August to November 2022, who were treated at the West China Hospital of Sichuan University.Their peripheral blood samples were collected and detected MPA, MPAG, and AcMPAG concentrations in PBMCs using the methods mentioned above and evaluated for their clinical application in the prediction and monitoring of infections in KTRs; (3) Results: After validation of the methodology, the UPLC-MS/MS method established in this study for the determination of MPA, MPAG, and AcMPAG concentrations in PBMCs showed good linearity in the quantitative ranges of 0.0625-50 ng/ml, 0.5-50 ng/ml and 0.1-20 ng/ml, and the selectivity, specificity, matrix effect, carry-over contamination, reproducibility, precision and accuracy all satisfied the requirements.AcMPAG in PBMCs was poorly stabilized and showed significant degradation after -80°C storage for 24 h or 3 cycles of freeze-thaw.There was a large variability in MPA and MPAG concentrations in PBMCs of KTRs and it correlated poorly with plasma MPA concentrations (rs=0.206,p=0.117).Subgroup analysis showed that the correlation between MPA in plasma and PBMCs was still poor in the infected group(rs=0.201,p=0.116), but significant correlation in the stable group (rs=0.643,p=0.001). A significantly higher level of MPA in PBMCs was observed in the infected group than those in the stable group at 2h and 4h after drug dosing (p&lt;0.05).AcMPAG concentrations in PBMCs of KTRs were extremely low and virtually undetectable.ROC curve analysis (area under the curve, AUC) was performed for infection after renal transplantation with indicators of significant difference showed that the area AUC for PBMCs-MPA-C4 was 0.928 ,for PBMCs-MPA-C2 was 0.875,and for Plasma-MPA-C4 was 0.803; (4) Conclusions: In this study, a method based on UPLC-MS/MS was developed and validated for the simultaneous determination of MPA, MPAG, and AcMPAG concentrations in PBMCs.With easy operability and good stability, this method provides a reliable analytical method for the clinical implementation of the detection of MPA, MPAG, and AcMPAG concentrations in PBMCs.The concentrations of MPA and MPAG in PBMCs from KTRs were highly variable and were closely associated with the development of infections after renal transplantation.Monitoring MPA and MPAG in PBMCs may be useful indicators for mycophenolic acid therapeutic drug concentration monitoring (TDM) in KTRs.

Despite being one of the most abundant elements in soil, phosphorus (P) often becomes a limiting macronutrient for plants due to its low bioavailability, primarily locked away in insoluble organic and inorganic forms. Phosphate solubilizing and mineralizing bacteria, also called phosphobacteria, isolated from P-deficient soils have emerged as a promising biofertilizer alternative, capable of converting these recalcitrant P forms into plant-available phosphates. Three such phosphobacteria strains - Serratia sp. RJAL6, Klebsiella sp. RCJ4, and Enterobacter sp. 198 - previously demonstrated their particular strength as plant growth promoters for wheat, ryegrass, or avocado under abiotic stresses and P deficiency. Comparative genomic analysis of their draft genomes revealed several genes encoding key functionalities, including alkaline phosphatases, isonitrile secondary metabolites, enterobactin biosynthesis and genes associated to the production of indole-3-acetic acid (IAA) and gluconic acid. Moreover, overall genome relatedness indexes (OGRIs) revealed substantial divergence between Serratia sp. RJAL6 and its closest phylogenetic neighbors, Serratia nematodiphila and Serratia bockelmanii. This compelling evidence suggests that RJAL6 merits classification as a novel species. This in silico genomic analysis provides vital insights into the plant growth-promoting capabilities and provenance of these promising PSRB strains. Notably, it paves the way for further characterization and potential application of the newly identified Serratia species as a powerful bioinoculant in future agricultural settings.

The MQTT IoT connection technology is widely used, becoming the primary means for IoT devices to communicate with servers amid the vigorous development of IoT technology. On the OneNET cloud platform, MQTT protocol connection and data communication, message sending, and subscription are established, one-to-many message transmission is solved, and the reverse control from server to device is realized in big data environment. This technology can provide fast and secure messaging services for remote devices with a small code capacity and limited bandwidth. Experiments indicated that the communication mode has low cost, small bandwidth, and slight delay, which meets the requirements of device data acquisition and control in a large area and has broad application prospects and reference significance in the field of mobile applications.

Atherosclerosis is associated with vascular diseases, diabetes, and cancers. The Jumonji C-domain-containing 6 (JMJD6) gene plays an important role in diverse biological processes. In addition, autoantibodies are reportedly useful in the early diagnosis and treatment of many diseases. Here, JMJD6 was identified by serological identification of antigens by recombinant cDNA expression cloning as an antigen recognized by serum IgG from patients with unstable angina pectoris. Then, we performed amplified luminescent proximity homogeneous assay-linked immunosorbent assay using purified recombinant antigens and found elevated levels of serum anti-JMJD6 antibodies (s-JMJD6-Abs) in patients with atherosclerosis-related diseases including ischemic stroke, acute myocardial infarction (AMI), diabetes mellitus (DM), and cancers including esophageal cancer (EC), gastric cancer, lung cancer, and mammary cancer, compared with those in healthy donors. The s-JMJD6-Ab levels were closely associated with hypertension, habitual smoking, and the intima-media thickness, an atherosclerosis index. The better postoperative survival status was shown in patients with EC from the JMJD6-Ab-positive group than the negative group, especially from JMJD6-Ab-positive/PD-L1-negative group and JMJD6-Ab-positive/p53-Ab-negative group than JMJD6-Ab-negative/PD-L1-positive group and JMJD6-Ab-negative/p53-Ab-positive group, respectively. Hence, JMJD6 autoantibodies may reflect atherosclerosis severity, thereby a potential biomarker for atherosclerosis-related diseases including ischemic stroke, AMI, DM, and cancers and for predicting EC prognosis.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Heat-stressed lactating dairy cattle exhibit unique metabolic symptoms; many of which are undoubtedly involved in heat-induced subfertility. Because of its known systemic effects, we hypothesized that γ-aminobutyric acid (GABA) participates in the regulation of insulin and progesterone during heat stress. Multiparous lactating Holstein cows (n=6) were studied during four experimental periods: 1) thermoneutral (TN; d 1-5), 2) TN + hyperinsulinemic-hypoglycemic clamp (d 6-10), 3) heat stress (HS; d 16-20), and 4) HS + euglycemic clamp (d 21-25). Blood samples were collected once daily via coccygeal venipuncture into heparinized evacuated tubes. Analysis of GABA concentrations from all four treatment periods yielded no differences. In direct comparison to TN concentrations, plasma GABA tended to be decreased during HS (16.57±2.64 vs 13.87±2.28 ng/ml, respectively, P = 0.06). Both milk production and plasma insulin were moderately correlated with plasma GABA (r=0.35, P<0.01; r = -0.32, P<0.01). Plasma progesterone was correlated with plasma GABA concentrations during TN but not HS. These results are the first to indicate that peripheral GABA could be involved in regulating aspects of production and reproduction during heat stress. More research is needed to determine its precise role(s).

To meet the calls of the International Labor Organization and National Institutes of Occupational Safety and Health, this paper discusses how to build Occupational, Health, Safety (OSH), and Well-Being competence by expanding education to non-traditional undergraduate disciplines. The authors provide (1) theoretical background on why this would benefit society as a whole, (2) learning theory strategies for teaching OHS and Well-being outside of traditional majors, (3) case studies of how this can occur in undergraduate classes and (4) opportunities and challenges for the future.

All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS-reflectometry (GNSS-R) using observations from NASA’s Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites, and double difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalogue for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.

Chronic pancreatitis (CP) is an irreversible and progressive inflammation of the pancreas that can involve both pancreatic parenchyma and pancreatic duct. CP results in morphological change in the gland in the form of fibrosis and calcification along with functional impairment in the form of exocrine and endocrine insufficiency. Studies on the natural history of CP reveal the irreversibility of the condition and the resultant plethora of complications of which pancreatic adenocarcinoma is the most dreaded one. (1,2) In Japanese population-based studies by Otsuki and Fuzino et al, CP was clearly shown to reduce lifespan among males and females by 10.5 years and 16 years, respectively. (3) Most of these patients had occult pancreatic malignancies which were diagnosed late and were detected to be the commonest cause of mortality among these patients with a standardized mortality rate of 7.33. (3) This dismal prognosis is superadded with significant morbidity due to pain and poor quality of life rendering a significant burden on health and health-related infrastructure. These factors have led researchers to conceptualize early CP which theoretically is a reversible stage in the disease spectrum characterized by ongoing pancreatic injury with the presence of clinical symptoms and absence of classical imaging features of CP. Subsequently, the disease is thought to go through a compensated stage, a transitional stage, and culminating into decompensated stage with florid evidence of functional impairment of the gland. (4) In this focused review, we will discuss the definition and concept of early CP, risk factors and natural history of the development of CP, and the role of various modalities of EUS in the timely diagnosis of early CP.

Bioprospecting native Australian plants offers potential discovery of latent and novel bioactive compounds. Promising cytotoxic and antibacterial activity of methanolic extracts of Pittosporum angustifolium and Terminalia ferdinandiana led to further fractionation and isolation using our laboratory’s bioassay guided fractionation protocol. Hence, the aim of this study was to further evaluate the bioactivity of the fractions and subfractions and characterize bioactive compounds using liquid chromatography mass spectroscopy (LC-MS/MS) and gas chromatography MS (GC-MS). Compounds tentatively identified in P. angustifolium Fraction 1 using LC-ESI-QTOF-MS/MS were chlorogenic acid and/or neochlorogenic acid, bergapten, berberine, 8’-epitanegool and rosmarinic acid. GC-MS analysis data showed the presence of around 100 compounds, mainly comprising of carboxylic acids, sugars, sugar alcohols, amino acids, and monoalkylglycerols. Furthermore, the fractions obtained from T. ferdinandiana flesh extracts showed no cytotoxicity, except against HT29 cell lines, and only Fraction 2 exhibited some antibacterial activity. The reduced bioactivity observed in the T. ferdinandiana fractions could be attributed to the potential loss of synergy as compounds become separated within the fractions. As a result, further fractionation, and separation of compounds in these samples were not pursued. However, additional dose-dependent studies are warranted to validate the bioactivity of T. ferdinandiana flesh fractions, particularly since this is an understudied species. Moreover, LC-MS/GC-MS studies confirm the presence of bioactive compounds in P. angustifolium Fraction 1/subfractions which helps to explain the significant acute anti-cancer activity of this plant. The screening process designed in this study has the potential to pave the way for developing scientifically validated phytochemical/bioactivity information on ethnomedicinal plants, thereby facilitating further bioprospecting efforts, and supporting the discovery of novel drugs in modern medicine.

The growing importance of sustainability in organizational success, particularly in the pharma-ceutical industry, underscores the need for leveraging technologies like blockchain to enhance sustainability indicators across environmental, social, and economic pillars. This study aims to identify and understand the challenges hindering the adoption of blockchain in the pharmaceuti-cal sector for improving sustainability performance, addressing two research questions: the spe-cific challenges faced by blockchain adoption in this context and the interdependencies among these challenges. Employing a two-step approach, the study compiles challenges through a litera-ture review, refines them via expert opinions, and establishes their interrelationships using methodologies like Fuzzy Interpretive Structural Modeling (FISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). The research contributes to unraveling the complex relationships and dependencies within the system, providing a structured framework for improved decision-making and strategic planning. It fills a literature gap by being the first attempt to outline driving and dependent factors related to the challenges of adopting blockchain technology for sustainability enhancement in the pharmaceutical sector, offering insights that can significantly impact brand image, company perception, and consumer value.

Transcutaneous nerve stimulation has several neuromodulatory applications, including pain management, sensory restoration, and motor recovery. Transcutaneous stimulation delivered via electrode arrays offers ease of use and reconfigurability through non-invasive means. Also, employing electrode arrays allows regulation of the permeated electric fields for greater control over the stimulation performance. Field shaping involves the regulation of permeated electric fields and is a well-established practice for deep brain stimulation; however, its applicability to peripheral nerve stimulation still needs exploration. Adopting such techniques can help to overcome current limitations with transcutaneous stimulation, like poor targetability, early onset of fatigue, and perceived discomfort. This article provides a perspective on the applicability of field-shaping techniques in improving the overall stimulation outcome.

This paper offers a novel geometric and visual approach to the renowned Four Color Map Theorem and K5 non-planarity problem while unveiling their profound connection to the "kissing number" problem in 2D. We represent planar graphs through circles and tangents, simplifying complex structures and shedding light on these classic problems. Our proofs by contradiction, rooted in the kissing number concept, reveal that both the Four Color Map Theorem and K5 non-planarity are fundamentally linked, as they pivot around the concept of coloring. This study bridges the realms of geometry and graph theory, providing fresh insights and emphasizing the significance of the kissing number problem in various fields.

Although cardiac resynchronization therapy (CRT) reduces morbidity and mortality and reverses left ventricular (LV) remodeling in heart failure patients with LV electrical dyssynchrony, induced proarrhythmia has been reported. The mechanism of CRT-induced proarrhythmia remains under debate. In this case report, a description of how a LV pacing induced polymorphic ventricular tachycardia immediately after the initiation of CRT has been reported. Changing the pacing configuration using a multipoint pacing stimulation, we can assume that VTs induced are related to the reentry mechanism facilitated by the unidirectional block. As a result, MPP pacing configuration near the scar area avoid the onset of a unidirectional block with the establishment of the reentry phenomenon thus avoiding induced VTs.

Seafood, especially from the ocean, is now seen as a greener and more sustainable source of protein causing an increase in its demand. This has also led to people making choices towards seafood as a replacement of carbon intensive protein sources. As a result, the demand for seafood is growing, and the Aquaculture industry is required to increase their produce while keeping the produce safe and sustainable. There are many challenges faced by the aquaculture industry in meeting these increased demands. One such challenge is the presence of harmful algal blooms (HAB) in the ocean which can have a major impact on aquatic life. In this paper we look at the impact of this challenge on aquaculture and the mitigating strategies. We will focus on Abagold Limited, a land-based marine aquaculture business that specializes in large scale production of abalone (Haliotis midae) based in Hermanus, South Africa. HABs are considered a threat to commercial scale abalone farming along the South-African coastline and requires continuous monitoring. The most recent HAB was in February-April 2019, the area experienced a severe red-tide event with blooms of predominantly Lingulodinium polyedrum. We present some of the mitigation strategies employing digital technologies for future proofing the industry.

In order to the effect of hydrated phase change materials on suppressing coal spontaneous combustion, the thermogravimetric experiment and reaction activation energy analysis experiment were used to explore the changes of combustion characteristic, parameters characteristic temperature and activating energy of gas coal, long flame coal, meagre coal, and lean coal before and after adding hydrated phase change materials. The research results showed that hydrated phase change materials increased the coal samples’characteristic temperature point and have effective inhibitory effects on different stages of oxidation process.However, the effect was best at low temperatures, as hydrated phase change materials undergo phase change and absorb heat when heated at low temperatures, isolating coal from contact with oxygen. The activating energy increased by 1.138 KJmol-1～23.048 KJmol-1 and the mass loss reduced by 1.6%～9.3% after the inhibition of the coal sample,indicating that the oxidation rate of various coal samples can be slowed down and the spontaneous combustion can be suppressed by using hydrated phase change materials.At the same time, this material can also reduce the combustibility index of meagre coal and lean coal, as well as the comprehensive combustion index and maximum loss rate of long flame coal and gas coal.

Gamma-aminobutyric acid (GABA) is thought to have beneficial effects on human health and well-being. GABA enrichment by lactic acid bacteria (LAB) could be used in the development of functional fermented foods. In this work, four GABA-producing strains each of Lactococcus lactis and Streptococcus thermophilus were isolated from cowʼs milk, and their phenotypic, technological, and safety profiles were determined. Genome analysis provided genetic support for the majority of the analyzed traits. The operon harboring the glutamate decarboxylase gene (gadB) was chromosomally encoded in all strains and showed the same gene content and gene order as that reported, respectively, for L. lactis and S. thermophilus. In the latter species, the operon was flanked (as in most strains of this species) by complete or truncated copies of insertion sequences (IS), suggesting recent acquisition by horizontal gene transfer. The genomes of three L. lactis and two S. thermophilus strains showed a gene encoding a caseinolytic proteinase (PrtP in L. lactis and PrtS in S. thermophilus). These strains grew in milk forming a coagulum of good appearance and an appealing acidic flavor and taste. They also produced GABA from monosodium glutamate in this medium. Two L. lactis strains were identified as belonging to the biovar. diacetylactis, utilized citrate from milk and produced significant amounts of acetoin. None of the strains showed any noticeable antibiotic resistance, nor did their genomes harbor transferable antibiotic resistance genes, genes involved in toxicity, virulence, or pathogenicity. Altogether these results suggest all eight strains may be considered candidates for use as starter or components of mixed LAB cultures for the manufacture of GABA-enriched fermented dairy products.

There is a deep link existing between symmetry and nature that may be best apprehended by using group theory. In physics, giving a symmetry group is entirely equivalent to fix natural laws (Noether’s theorem). Within such a framework, it follows that all fundamental mechanical laws may be given by defining a group of symmetry operations belonging to the Gal(3,1) symmetry group having ten generators (3 + 3 + 3 + 1 = 10). The move to special relativity amounts simply to substitute another group ISO(3,1) to Gal(3,1) and having the same number of generators. In both cases, one immediately perceives that we live in universe with 3 dimensions of space and 1 dimension of time. A further progress could be made by looking for the symmetry group that leaves Maxwell’s equations invariant. The answer to this fundamental question is the group ISO(4,2)⊗U(2)⊗U(2) characterized by 23 generators : 15 = 10 + 5 for the conformal group ISO(4,2), 4 for U(2) and hence 8 = 4 + 4 for the compact part U(2)⊗U(2) that could be related to the existence of quantum physics besides relativity. As ISO(3,1) is a sub-group of ISO(4,2), the two numbers 4 and 2 suggests that, taking into account the existence of light besides matter, we live in an universe having 4 spacelike dimensions and 2 time-like dimensions. Here, it is proposed that, as music is characterized by a scale-invariant symmetry operation (transposition by octaves) and that as time may be perceived differently in shamanic trance for instance, the two “new” dimensions of our universe may be related to music and consciousness. Related to the fifth dimension of music, the notion of diapason linking physical frequencies to a musical note is revisited. It is thus proposed to adopt a new A4 = 429.62 Hz value based on universal physical constants of our universe and on the mass of the water molecule H₂O, the most abundant molecule in the universe. Demonstration is done that this new diapason is superior to other diapasons based on frequencies of 440 Hz or even 432 Hz

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are po-tentially applicable in artificial and spiking neural networks, memory arrays, and many other de-vices and circuits for artificial intelligence. In this paper, a complete electronic realization of ana-log circuit model of modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an im-portant step towards the forthcoming design of complex memristor-based neural networks for ar-tificial intelligence, for implementation in very high-density integrated circuits and chips.

The operation of transformers suffers from natural or man-made disasters, subjecting the equipment to prolonged exposure to temperature, lightning strikes, transient over-voltages, and over-current, gradually producing insulation aging phenomenon, which results in partial discharges within the transformer. Without dealing with it beforehand properly, it may lead to insulation breakdown. Nowadays, with the mature development of microchip control technology and wireless communication technology, which is helpful for realizing the research for online real-time and long-term monitoring capabilities for power equipment. A transformer operating condition monitoring and diagnostic system that can rapidly and effectively prevent the breakdown of transformer insulation, which can impact the overall power supply system is developed in this paper. The system is divided into two parts based on its function. The first part is the operating condition monitoring and real-time alert unit for the transformer, primarily composed of an embedded computing module, a sensor module, and a transmission module. Its work content is to capture real-time online partial discharge ultrasonic signals and high frequency current comparator pulse signals, and analyze and determine the condition of the internal insulation and operational status whether it is fault or not. The second part is the central control unit, consisting mainly of an industrial computer and a transmission module. Its work content is to store the ultrasonic and high frequency current comparator pulse signals from the transformer operation monitoring and real-time alert unit. It uses the software to eliminate signal noise and interference, and then utilizes artificial neural networks to identify the type of fault. In this paper, the research for the phenomenon of insulation degradation in transformer equipment aims to establish aging trend profiles through the dual malfunctioning alert and aging process analysis, as well as the long-term measurement of ultrasonic signals from partial discharges and high-frequency current comparator pulse signals. This approach provides crucial reference data and a foundation for disposable on-site measurement, assessing the phenomenon of insulation aging stages, defects and damage of the transformers effectively. It allows for timely replacement or maintenance in advance, achieving the prevention of malfunctioning and predictive diagnosis of aging.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

Background: In antibiotic naïve populations, there is a strong association between the use of an antimicrobial and resistance to this antimicrobial. Less evidence is available as to whether this relationship is weakened in populations highly exposed to antimicrobials. Individuals taking HIV PrEP have a high intake of antimicrobials. We previously found that there was no difference in the prevalence of pheno- and genotypic antimicrobial resistance between two groups of PrEP clients who had, and had not, taken antimicrobials in the prior 6-months. Both groups did, however, have a higher prevalence of resistance than a sample of the general population. Methods: In the current study, we used zero-inflated negative binomial regression models to evaluate if there was an individual level association between the consumption of antimicrobials and 1. the minimum inhibitory susceptibilities of oral Neisseria subflava and 2. the abundance of antimicrobial resistance genes in the oropharynges of these individuals. Results: We found no evidence of an association between the consumption of antimicrobials and the minimum inhibitory susceptibilities of oral Neisseria subflava or the abundance of antimicrobial resistance genes in these individuals. Conclusions: We conclude that in high antimicrobial consumption populations, the association between antimicrobial consumption and resistance may be attenuated.

Little is known about how the degree of mixing of forest-forming species affects forest floor hydrology. We evaluated the water storage capacity of the resulting litter layer by mixing litterfall of Scots pine and sessile oak and studying their decomposition time. We prepared 90 artificial samples containing pure pine litter, pure oak litter, and mixed pine-oak litter with varying shares of pine needles. These samples were subjected to 15 months of decomposition in soil. After every three months of decay, some samples were removed from the soil, and their water storage capacity, bulk density, and C:N ratio were evaluated. Our findings indicate that samples with the greatest water storage capacity had a low C:N ratio and a predominant share of oak leaves. Conversely, samples with a high C:N ratio and a predominant share of pine needles had the lowest water storage capacity. After 12 and 15 months of decomposition, the water storage capacity increased by more than 52% compared to the initial water capacity of the samples. The highest increase in water storage capacity (>40%) was observed in samples with a predominant share of oak leaves, while the lowest (approximately 28%) was recorded in samples with 80 and 100% of pine needles.

Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique, that has been playing an indispensable role in the field of science, technology, and biomedical imaging. The technique is competent for the imaging and analysis of label-free living cells and the investigation of reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages with several advantages including less-vibration sensitive to external perturbations due to common-path configuration, compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability of phase imaging, is presented. The applications of the proposed system are demonstrated by quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (microlens array and living plant cells). The proposed system could find its applications in the investigations of several biological specimens and optical metrology of micro-surfaces.

The role of the databases is to allow persisting data, no matter if they are of the SQL or NoSQL type. In SQL databases, data is structured in a set of tables in the relational database model, grouped in rows and columns. The CRUD operations (create, read, update, and delete) are used to manage the information contained in the relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application and why. A comparison between the most used relational database management systems, mixed with the most used frameworks should give us some guidance about when to use what. The evaluation is done on timing for each CRUD operation, from thousands to millions of entries, using the possible combinations between the relational database system and the framework. The experiments included the possibilities to warm up the Java Virtual Machine before the execution of the queries. Also, the research investigated the time spent inside different methods of the code, to determine the critical regions. Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite of decisions considering the database type, the framework in use, the type of operation, and supporting architects and developers for their technological decisions and for improving the speed of their programs.

Background: COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a recurrent endemic disease affecting the whole world. Since November 2021, Omicron and its subvariants are dominating. In order to prevent severe courses of disease, vaccines are needed to boost and maintain antibody levels capable of neutralizing Omicron. Recently we produced and characterized a SARS-CoV-2 vaccine based on a recombinant fusion protein consisting of hepatitis B virus (HBV)-derived PreS and two SARS-CoV-2 wild-type RBDs. Objectives: To develop a PreS-RBD vaccine which induces high levels of Omicron-specific neutralizing antibodies. Methods: We designed, produced, characterized and compared strain-specific (wild-type: W-PreS-W; Omicron: O-PreS-O), bivalent (mix of W-PreS-W and O-PreS-O) and chimeric (i.e., W-PreS-O) SARS-CoV-2 protein subunit vaccines. Immunogens were characterized in vitro by protein chemical methods, mass-spectrometry, circular dichroism in combination with thermal denaturation and immunological methods. In addition, BALB/c mice were immunized with aluminum hydroxide-adsorbed proteins and aluminum hydroxide alone (i.e., placebo) to study specific antibody and cytokine responses, safety and Omicron neutralization. Results: Defined and pure immunogens could be produced in large amounts as secreted and folded proteins in mammalian cells. Antibodies induced after vaccination with different doses of strain-specific, bivalent and chimeric PreS-RBD fusion proteins reacted with wild-type and Omicron RBD in a dose-dependent manner and resulted in a mixed Th1/Th2 immune response. Interestingly, RBD-specific IgG levels induced by the different vaccines were comparable but the W-PreS-O-induced virus neutralization titers against Omicron (median VNT50: 5000) were 7- and 2-fold higher than the W-PreS-W- and O-PreS-O-specific ones, respectively and 6-fold higher than those of the bivalent vaccine. Conclusion: Among the tested immunogens, the chimeric PreS-RBD subunit vaccine, W-PreS-O, induced the highest neutralizing antibody titers against Omicron. Thus, W-PreS-O seems to be a highly promising COVID-19 vaccine candidate for further preclinical and clinical evaluation.

.Background: Negative outcomes associated with medications (NOM) and drug-related problems (DRP) significantly impact individuals with kidney replacement therapy (KRT) given the complexities of managing kidney disease and associated comorbidities. The present study aims to assess the frequency of NOMs/DRPs among KRT patients and identify contributing factors. Methods: A cross-sectional study was conducted at the Virgen de las Nieves University Hospital (Granada, Spain), involving 117 outpatient adults with KRT. Data were collected from February 2021 to July 2023 using electronic records, semi-structured interviews (Dáder Method), and discussions with nephrology specialists. NOMs/DRPs were identified following treatment guidelines. Binary logistic regression determined associated factors (p-value < 0.05). Results: Across 117 patients, 2,436 NOMs and 3,303 DRPs were identified, averaging 20.82 NOMs and 28.23 DRPs per patient. Prevalent NOMs included untreated conditions (58.95%), quantitative ineffectiveness (35.43%), and non-quantitative safety problems (5.13%). Dominant DRPs were undertreated conditions (37.63%), wrong dose/posology/length (33.00%), risk of adverse drug reactions (ADR) (16.14%), and non-adherence (6.87%). Patients with ADR, undertreated condi-tions, and anemia were associated with quantitative ineffectiveness. Risk of ADR and vitamin D deficiency/insufficiency correlated with non-quantitative safety problems. Conclusions: KRT pa-tients exhibited a substantial prevalence of NOMs/DRPs. Further research is needed to deepen our understanding of these complexities for improved patient care.

Background: A careful work-up before Transcatheter aortic valve replacement (TAVR) consists not only of measuring anatomical conditions but also assessment of frailty. Previous studies have evaluated body composition parameters, primarily fat and muscle mass. Few data are available on other frailty parameters, which, however, can be easily determined by pre-TAVR computed tomography (CT). Objectives: The primary objective was to investigate the impact of total muscle area (TMA) and visceral adipose tissue (VAT), as frailty parameters, on 5-year all-cause mortality in patients undergoing TAVR. Methods: Between 01/2017 and 12/2018, consecutive TAVR patients undergoing CT-angiography scan enabling TMA and VAT measurement were included. A single axial image was acquired at the level of the middle third lumbar vertebra to perform TMA and VAT measurements using dedicated slicing software (Sliceomatic, TomoVision). Results: A total of 500 patients qualified for TMA analysis. Age was not associated with a higher risk of 5-year mortality HR 1.02 (95% CI: 0.998 - 1.049; p=0.069). Body surface area normalized TMA (nTMA) was significantly associated with 5-year all-cause mortality (HR 0.927, 95% CI: 0.927-0.997; p= 0.033), while VAT had no effect (HR 1.002, 95% CI: 0.99-1.015; p= 0.7). The effect of nTMA on 5- year all-cause mortality was gender-dependent: the protective effect of higher nTMA was found in male patients (pinteraction: sex * nTMA = 0.007). The combination of normalized TMA and VAT was not associated with higher mortality rates. Conclusion: Normalized TMA derived from routine CT before TAVR complements frailty assessment in patients undergoing TAVR.

This study introduces a fruit harvesting mechanism powered by a single motor, designed for integration with unmanned aerial vehicles (UAVs). The mechanism performs reciprocating motion by converting linear motion into rotational motion. Consequently, the end-effector can execute multi-dimensional kinematic trajectories, including biaxial and rotational movements, synchronized with the motor's position. These axial and rotational motions facilitate the gripper's ability to reach, retrieve, and detach fruit from branches during the harvesting process. Notably, a critical consideration in designing this fruit harvesting mechanism is to generate the necessary torque at the end-effector while minimizing reaction forces and torque that could destabilize the UAV during flight. With these considerations in mind, this preliminary study aimed to harvest a Fuji apple and conducted dynamic analysis. We constructed a prototype of the single motor-driven fruit harvesting mechanism using a suitable servo motor. To assess its mechanical performance and evaluate its impact on the hexacopter, we developed both a specific test platform featuring a six-spherical-prismatic-spherical parallel structure and a virtual environmental flight simulator. Overall, the results demonstrate the successful harvesting of a Fuji apple weighing approximately 300 g by the single motor-driven fruit harvesting mechanism, with no adverse effects observed on the hexacopter's operation.

Background: Autonomic neuropathy commonly arises as a long-term complication in end stage kidney disease (ESKD) and can be diagnosed from heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. There is limited data about HRV using real-time ECG to predict HD efficiency in patients with ESKD who are routinely doing HD in real-world practice. Methods: Total of 50 patients (62.1 ± 10.7 years) with ESKD underwent continuous real-time ECG monitor (237.4 ± 15.3 min) during HD for HRV using remote monitoring system and checked electrolyte levels before/after HD. And we compared HRV according to electrolyte levels. Results: During the monitor, we checked total 2374 times of ECG and electrolyte level simultaneously of all patients. Both time and frequency domain HRV were higher when the patients had lower K+ level (&lt;0.5 mEq/L) and P+ level change (&lt;2 mEq/L) before/after HD as compared with those with higher K+ level (≥0.5 mEq/L) and P+ level (≥2 mEq/L) change respectively. In addition, there were higher incidence of arrhythmic events including atrial/ventricular premature complexes, even though no difference of mean heart rate in patients with lower K+ and P+ level change group (p &lt; 0.001). Conclusions: Higher HRV was independently associated with poor controlled K+ and P+ level during hemodialysis in patients with ESKD. This is further substantiated by independent continuous associations between real-time measures of higher HRV and K+ and P+ level. These data strongly suggest that cardiac autonomic dysfunction can be caused by lower change of electrolytes before/after hemodialysis alone.

Ocular pathologies present significant challenges in achieving effective therapeutic re-sults due to various anatomical and physiological barriers. Natural products, such as flavonoids, alone or in association with allopathic drugs, present many therapeutical actions, including anti-cancer, anti-inflammatory, and antibacterial action. However, their clinical employment is chal-lenging for scientists due to their low water solubility. In this study, we designed a liquid formu-lation based on rutin/sulfobutylether-β-cyclodextrin (RTN/SBE-β-CD) inclusion complex for treating ocular infections. The correct stoichiometry and the accurate binding constant were de-termined by employing the SupraFit software on the Uv-vis titration experiment. A deep physical-chemical characterization of the RTN/SBE-β-CD inclusion complex was also performed; it con-firmed the predominant formation of a stable complex (Kc, 9660 M−1) in a 1:1 molar ratio, with high water solubility of 20 times (2.5 mg/mL) higher than the free molecule (0.125 mg/mL), per-mitting the dissolution of the solid complex within 30 min. NMR studies revealed the involve-ment of the bicyclic flavonoid moiety in the complexation, which was also confirmed by molecu-lar modeling studies. In vitro, the antibacterial and antibiofilm activity of the formulation was assayed against Staphylococcus aureus and Pseudomonas aeruginosa strains demonstrating a sig-nificant activity of the formulation to the free components.

Cs2NaInCl6 double perovskites, which have excellent photoelectric conversion properties and are non-toxic and lead-free, have recently attracted much attention. In particular, double perovskite quantum dots (QDs) are considered to be a promising material for optoelectronic device applications. Ligands such as oleic acid (OA) and oleylamine (OAm) are essential for the synthesis of perovskite QDs, but their roles on double perovskite QDs are not yet clear. In this study, we have investigated the binding of OA and OAm to Cs2NaInCl6 QDs through FTIR and NMR and their effects on the surface defect reduction and stability improvement for Cs2NaInCl6 QDs. We found that only OAm was bound to the QD surfaces while OA was not. The OAm has a great effect on photoluminescence quantum yield (PLQY) improvement by passivate the QD surface defects. The stability of the QDs was also investigated, and it was shown that OA played a significant role in the stability of the QDs. Our findings provide critical insights into the roles of the ligands for photophysical properties and stability of lead-free double perovskite QDs.

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the motor's weight. This can result in decreased both the ride comfort and driving stability. To resolve this issue, this study aims to apply an optimal switching controller with a semi-active n actuator; magnetorheological (MR) damper. For the implementation of the optimal switching controller, a road type classification is also carried out. An acceleration sensor is used for road type classification, and control logics include a ride comfort controller; the linear quadratic regulator (LQR _Paved Road) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both LQR_ Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are undertaken to evaluate vibration control performance, including the ride comfort and driving stability at various road profiles.

The study of angular correlations of heavy-flavor particles in hadronic collisions can provide crucial insight into the heavy-quark production, showering and hadronization processes. The comparison with model predictions allows us to discriminate among different approaches for heavy-quark production and hadronization, as well as different treatments of the underlying event employed by the models to reproduce correlation observables. In ultra-relativistic heavy-ion collisions, where a deconfined state of matter, the quark-gluon plasma (QGP), is created, heavy-flavor correlations can shed light on the modification of the heavy-quark fragmentation due to interaction of charm and beauty quarks with the QGP constituents, as well as characterize their energy loss processes while traversing the medium. Insight into possible emergence of collective-like mechanisms in smaller systems, resembling those observed in heavy-ion collisions, can also be obtained by performing correlation studies in high-multiplicity proton-proton and proton-nucleus collisions. In this review, the most recent and relevant measurements of heavy-flavor correlations performed in all collision systems at the LHC and RHIC will be presented, and the new understandings that they provide will be discussed.

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

We consider that the relationship between entropy in statistical mechanics, which is the Boltzmann principle, and the complex velocity potential in complex fluid dynamics. We define the generalized complex entropy which expanded entropy from real space to complex space. We show that the complex entropy can be expressed by the composition of sources, sinks and laminar flows of the complex velocity potential. Therefore, the complex entropy is considered a special case of the complex fluid dynamics,that is, the complex velocity potential. In other words, we show that the complex entropy is expressed by the complex velocity potential. Moreover, we show that a complex number is expressed by the complex entropy. Thus, we show that the complex velocity potential is expressed by the complex entropy. Namely, we will expand entropy to complex space and see that the complex entropy is expressed by the complex velocity potential. Furthermore, we examine the possibility that it is an expansion of Boltzmann's principle and Planck distribution.

The impacts of spice (sumac, cumin, black pepper, and red pepper) extract on Gram-positive (S. aureus and E. faecalis) and Gram-negative (K. pneumonia, P. aureginosa, C. jejuni, A. hydrophila, S. Paratyphi A, and Y. enterocolitica) food-borne pathogen bacterial strains (FBP) for their antibacterial activity and accumulation of ammonia (AMN), trimethylamine (TMA), and biogenic amines (BAs) in tyrosine decarboxylase broth (TDB) were investigated. Sumac extract exhibited the highest an-tibacterial potential against all FBP followed by cumin and peppers. Ammonia (AMN) production (844.24-543 mg/L) was strongly inhibited by sumac extract (55.10 mg/L), whilst suppression of trimethylamine (TMA) production (53.66-3.21 mg/L) was observed by sumac (1.28 mg/L), followed by red pepper (0.91 mg/L) extract. Cumin extracts stimulated the production of cadaverine, spermidine, tryptamine, and 2-phenylethylamine. Furthermore, sumac extracts inhibited hista-mine and tyramine production in the majority of FBP. This research suggests the application of sumac extracts as natural preservatives for inhibiting the growth of FBP and inhibiting the pro-duction of AMN, TMA, and BAs.

The current economic development paradigm, which is based on steadily rising resource consumption and pollution emissions, is no longer viable in a world with limited resources and ecological capacity. The "green economy" idea has presented this context with a chance to alter how society handles the interplay between the environmental and economic spheres. The related concept of "green nanotechnology" aims to use nano-innovations within the fields of materials science and engineering to generate products and processes that are economically and ecologically sustainable, enabling society to establish and preserve a green economy. Many different economic sectors are anticipated to be impacted by these applications, including those related to corrosion inhibitor nano fertilizers, nano remediation, biodegradation, heavy metal detection, biofuel, insecticides & pesticides, and catalytic CO2 reduction. These innovations might make it possible to use non-traditional water sources safely and to create construction materials that are enabled by nanotechnology, improving living and ecological conditions. Therefore, our aim is to highlight how nanotechnology is being used in the green economy and to present promises for nano applications in this domain. Additionally, we want to critically examine the practical difficulties these applications raise, especially in consideration of any possible implications for the health and safety of those employed within this innovative sector. In the end, it emphasizes how critical it is to attain a really sustainable advancement in nanotechnology.

The envelope (façade) of a building is the first barrier between the exterior and the interior of the building and withstands the highest variation of temperature and solar radiation. Trombe walls are used to take advantage of that and contribute to the heating of interior air, helping the heating system. In this study, a new Trombe wall design is presented with the aim to contribute significantly to the ventilation of residential buildings. For this an exterior wall equipped with a proposed Trombe system was studied in a numerical simulation analysis. The proposed systems consist of two parts, the air cavities on the exterior of the wall, covered with silica glass, and another layer used for heat transfer, on the interior, comprised of a MPCLBs wall of 115mm. Two cities in Romania were considered to represent the coldest and hottest climate in Romania. Brașov representing the cold climate and Constanța representing the hot climate. In the investigation, both the presence and absence of solar radiation were taken into account. In total 4 cases were established. For all four cases monthly research was made using monthly means temperature, direct and diffuse solar radiation. The results are promising and illustrate that the system works the best during the transitional seasons of spring and autumn. The lower the outdoor temperature the higher the temperature can be increased. Overall, solar radiation accounted for an average of 2 °C increase. The temperature increase varied between 3.4 °C to 15.99 °C for Brașov and between 6.42 °C to 12.07 °C in Constanța.

Air pollution poses a significant risk to public health and ecosystems. The use of sophisticated models to simulate the transformation and dispersion of pollutants in the atmosphere is a recognized method to assess air pollution levels and its sources and inform policy decisions for effective mitigation strategies in urban and industrial environments. The reliability of models depends, in part, on the sensitivity to the variance of input parameters, which can introduce uncertainty into the model's predictions. This uncertainty is especially challenging when the input parameters exhibit inherent variability. In this study, we analyse the uncertainty of an integrated emission/dispersion model by evaluating the relationship between input parameters and model outcomes. We uncover new aspects of this relationship while clarifying some previously known dependencies. Wind speed was found to have the most significant effect on calculated concentrations, with small differences leading to substantial concentration variations. The time of day, which reflects prevailing vertical turbulence conditions, was also found to have a notable impact on concentrations, whereas ambient temperature can also significantly influence model’s results. Based on these findings, we offer specific recommendations for regulators and scientists involved in environmental impact assessment to improve the accuracy and reliability of air pollution modelling.

The loads generated inside agricultural silos under static and dynamic conditions depend on the mechanical properties of the materials stored inside them. The silo calculation methodologies are based on these mechanical properties. Although it is known that moisture content greatly influences the values reached by these mechanical properties, just few studies have been conducted to determine them. The present work determines the angle of internal friction, the apparent cohesion, the dilatancy angle and the apparent specific weight of wheat when subjected to different moisture contents. Direct shear and oedometer assay devices were used. In addition, a climatic chamber was used to moisten the wheat samples used in this work. The moisture content applied to the samples ranged from 7.5% to 15.5%. From the different assays carried out it could be observed that the values of the angle of internal friction and apparent cohesion obtained from direct shear assays, as well as those related to the apparent specific weight obtained from oedometer assays were like those found in the literature. The apparent specific weight increased as moisture content increased, reaching a peak value at a moisture content of 13.4%, followed by a drop at a moisture content of 15.5%. The values here provided can be used in silo load calculations involving numerical methods.

The aim of this work is to provide a full description of how air temperature and solar radiation induce changes in the land cover over an Antarctic site. We use shortwave broadband albedo (albedo integrated in the range 300-3000 nm) from a spaceborne sensor and from field surveys to calculate the monthly relative abundance of landscape units. Field albedo data were collected using a portable albedometer over seven landscape units: clean fresh snow; clean old snow; rugged landscape composed of dirty snow with disperse pyroclasts and rocky outcrops; dirty snow; stripes of bare soil and snow; shallow snow with small bare soil patches; bare soil. MODIS MCD43A3 daily albedo product was downloaded using the Google Earth Engine API from the 2000-2001 season to the 2020-2021 season. Each landscape unit was characterized by an albedo normal distribution. The monthly relative abundance of the landscape units were calculated by fitting a linear combination of the normal distributions to the histogram of the MODIS monthly mean albedo. The monthly relative abundance of the landscape unit consisting of rugged landscape composed of dirty snow, with dispersed clasts and small rocky outcrops exhibits a high linear positive correlation with the monthly mean albedo (R2=0.87) and a high linear negative correlation with the monthly mean air temperature (R2= 0.69). The increase of the solar radiation energy flux from September to December coincides with the decrease of the relative abundance of the landscape unit composed of dirty snow with dispersed clasts and small rocky outcrops. We propose a mechanism to describe the evolution of the landscape: uncovered pyroclasts act as melting centres favouring the melting of surrounding snow. Ash does not play a decisive role in the melting of the snow. The results also explain the observed decreasing of the thaw depth of the permafrost on the island in the period 2006-2014, resulting from an increase in the snow cover over the whole island.

The recent outbreak of Nipah virus in India in 2023 had affected the Kozhikode district in Kerala, with a total of 6 confirmed cases and 2 deaths. The source of the outbreak was traced to fruit bats that contaminated date palm sap, a common drink in the region. The outbreak was contained by rapid detection, isolation, and contact tracing of the cases, as well as public health education and social distancing measures. It also highlighted the need for improved surveillance, diagnostics, and therapeutics for Nipah virus. This letter highlights the latest insights gained from the epidemiological, clinical, and laboratory investigations of the outbreak.

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. This data covers significant fuels such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic, influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision-making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trend for electricity generation using data from 2015 to 2022. Several time-series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For Natural Gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5,936.203. The seasonal Naïve (SNaïve) model outperforms others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models' performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from the study help policymakers strategically allocate resources, plan infrastructure development and support economic growth.

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on December 30, 2022 and January 5, 2023, covering heavily polluted areas of east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points.

The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore the damaged central nervous system function in patients. The patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot assisted torque which focuses on the evaluation only of the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer, which deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limbs. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner loop position control module for the pneumatic swing cylinder in the joint space. An upper limb endpoint stiffness estimation model is constructed and a parameter identification algorithm is designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements is obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then the motor needs of the affected limb when completing the same movement are quantified based on the performance of the healthy limb. A stiffness mapping algorithm is designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method can effectively estimate the patient's movement needs and achieve progressive rehabilitation training. The rehabilitation training robot that simulates the movement characteristics of the patient's healthy limbs drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb use habits, and also beneficial for the consistency of bilateral limb movements.

In Chuanming Zong’s program to attack Hadwiger’s covering conjecture, which is a longstanding open problem from Convex and Discrete Geometry, it is essential to estimate covering functionals of convex bodies effectively. Recently, He et al. [J. Optim. Theory Appl. 2023, 196, 1036–1055.] and Yu et al. [Mathematics, 2023, 11, 2000] provided two deterministic global optimization algorithms having high computational complexity for this purpose. Since satisfactory estimations of covering functionals will be sufficient in Zong’s program, we propose a stochastic global optimization algorithm based on CUDA and provide an error estimation for the algorithm. The accuracy of our algorithm is tested by comparing numerical and exact values of covering functionals of convex bodies including the Euclidean unit disc, the three-dimensional Euclidean unit ball, the regular tetrahedron, and the regular octahedron. We also present estimations of covering functionals for the regular dodecahedron and the regular icosahedron.

Nonmedical prescription stimulant use (NPSU) is an ongoing public health crisis in the United States. There exists, however, a dearth of research investigating specific childhood risk factors that may contribute to this illicit use. Adverse childhood experi-ences (ACEs) may be a significant risk factor to address in the prevention and treatment of NPSU, as research has provided evi-dence for increased substance use, potentially including prescription stimulant misuse, among individuals who have experienced trauma. A systematic review of the literature was conducted to gather and synthesize research articles specifically examining the relationship between nonmedical prescription stimulant use in adults and the experience of adverse childhood experiences (ACEs). Four studies met all inclusion criteria and were included in the review. The results demonstrated that nonmedical use of prescription stimulants in adults is significantly associated with ACEs, with a higher numerical count of ACEs associated with a greater likelihood of nonmedical prescription stimulant use. Gaps in the literature were identified, specifically noting a lack of information available regarding the relationship between ACEs and NPSU within gender- and racially-diverse populations. The findings have implications for informing interventions related to ACEs and the misuse of prescription stimulant medication in adults.

Proton exchange membrane (PEM) fuel cells have emerged as a viable alternative energy production source for stationary and transportation applications. Reliable and sustainable fuel cell operation requires effective water management. Membrane water content can vary along the stack during transients which can lead to losses in fuel cell performance. To control these variations a model that predicts the internal humidity dynamics of the stack is needed. In this study, a control-oriented model for predicting membrane water content variation was developed and implemented in MATLAB/Simulink. A lumped parameter model was initially developed and then further discretized into smaller control volumes to track humidity distribution along the stack. To validate the model’s predictions, the predicted results were compared to computer simulation results from GT-Suite. The root mean square error (RMSE) between the model’s prediction and GT-Suite’s simulation results was found to be within 1.5 membrane water content for all cases, demonstrating the model’s capability to capture the variation of membrane water content along the stack. The developed model will be useful for real-time control of membrane water content variation in PEM fuel cells.

The studied Khanbogd pluton of peralkaline granite is located in southern Mongolia and occupies an area of ~1000 sq. km. Previously, two ring-shaped bodies were constructed in a flattened shape pluton. However, this shape was not used for the genesis of this pluton. A recent study of Landsat image showed a several elliptical intrusions in two bodies. Chemistry interpretation shows that each intrusion differs in ASI, CIPW norms, 87Sr/86Sri and δ18O isotopes. Elliptical intrusions, uplifted country rocks between them, and roof pendants suggest magma penetration along fractured country rocks. Furthermore, two adjacent intrusive bodies and undeformed host rocks at the front part of the pluton are consistent with underplating magmatic factors during duplex-type thrusting. This magma event is also supported by low of Sr, Ba, Eu and high of Th, U, Pb anomalies and a few amount of garnet crystals in the granites, implying vapor absent melt crystallization associated with underplating. Multiple melt sources, as well as from mantle and recycled sea floor rocks, are identified by ratio of 87Sr /86Sri and δ18O values. The low isotope values, the thrust nature and 295–286 Ma ages of the alkali granites, indicate an underplate magma evidenced during Permian rifting.

An 18-month-old neutered male domestic shorthair cat was presented for an emergency consultation after falling from the second floor. The cat sustained minor traumatic injuries but did not exhibit any dyspnea. Routine radiographic examination raised suspicion of a diaphragmatic hernia, but the circumscribed nature of the soft tissues visible in the thorax was atypical for a classic traumatic diaphragmatic hernia. A positive contrast peritoneography highlighted the likely presence of a hernial sac, which strongly suggested a “true diaphragmatic hernia” also known as “pleuroperitoneal hernia”. This diagnosis was confirmed during laparotomy, which allowed the visualization of a 3 cm radial diaphragmatic defect in the right ventral quadrant of the pars sternalis. The diaphragm's edges were rounded. A portion of the falciform ligament and a part of the omentum were protruding through the defect and were contained within a hernial sac. Herniorrhaphy was performed. The cat recovered without complications. Given its presentation and location, ventrally and to the right, this anomaly is analogous to what is described in humans as “Morgagni hernia”. We believe that 6 other cases of Morgagni hernias have been reported in cats but were not identified as such. This case underscores the utility of peritoneography, a straightforward technique usefull for diagnosing diaphragmatic hernias, which enables differentiation between acquired traumatic forms and congenital forms, particularly pericardoperitoneal hernias and pleuroperitoneal hernias. True diaphragmatic hernias are almost always serendipitous discoveries.

Prime period sequences can serve as the fundamental tool to construct arbitrary composite period sequences. This is a review study of prime period perfect Gaussian integer sequence (PGIS). When cyclic group {1,2,…,N−1} can be partitioned into k cosets, where N=kf+1 is an odd prime number, the construction of a degree-(k+1) PGIS can be derived from either matching the flat magnitude spectrum criterion or making the sequence with ideal periodic autocorrelation function (PACF). This is a systematic approach of prime period N=kf+1 PGIS construction, and is applied to construct PGISs with degrees 1, 2, 3 and 5. However, for degrees larger than 3, matching either the flat magnitude spectrum or achieving the ideal PACF encounters a great challenge of solving a system of nonlinear constraint equations. To deal with this problem, the correlation and convolution operations can be applied upon PGIS of lower degrees to generate new PGIS with degree-4 and other higher degrees, e.g., 6, 7, 10, 11, 12, 14, 20 and 21 in this paper. In this convolution based scheme, both degree and pattern of a PGIS vary and can be indeterminant, which is rather nonsystematic compared with the systematic approach. The combination of systematic and nonsystematic schemes contributes the great efficiency for constructing abundant PGISs with various degrees and patterns for the associated applications.

Respiratory distress syndrome (RDS) is a major respiratory morbidity associated with prematurity. In this case-control study, we prospectively evaluated whether targeted metabolomic analysis (gas chromatography–mass spectrometry) of the gastric fluid obtained from very preterm neonates (< 32 weeks’ gestation) during the first hour after birth could predict the need for exogenous surfactant for the treatment of RDS. 43 infants with RDS necessitating surfactant (cases) were compared with 30 infants with mild or no RDS (controls) who were not treated with surfactant. Perinatal and neonatal characteristics were recorded. Univariate analysis showed significant differences between the two groups in gastric fluid metabolites (L-glycine and acetyl-L-serine) and clinical parameters (gestational age, 1- and 5-minute Apgar scores, and intubation in the delivery room). Moreover, multivariate analysis revealed intubation in the delivery room as the most potent clinical predictor of surfactant administration [AUC of 0.69 (95% CI 0.57-0.81)]. The combination of metabolomic and clinical data allowed for the construction of a model with good accuracy [AUC 0.76 (95% CI 0.64-0.86)] in predicting surfactant replacement therapy. Further research is required to explore the role of gastric fluid metabolomics in the prediction of RDS and other neonatal outcomes.

Ischemic heart disease is the leading cause of mortality worldwide. There is evidence of the participation of Aβ in thrombosis and clinical manifestations of acute coronary syndromes. In this study, we investigated the correlation of Aβ peptides with myocardial injury and inflammation biomarkers in acute coronary syndrome. This is a single-center, cross-sectional, observational, and correlation study that included 65 patients within the first 12 hours after symptom onset (chest pain, shoulder pain, or chest discomfort) with ST-elevation and non-ST elevation myocardial infarction who were admitted in the Coronary Care Unit, biochemical parameters of Aβ peptides levels were evaluated. Our results show that NSTEMI patients had a higher prevalence of hypertension, diabetes, smoking, and prior myocardial infarction when compared to STEMI patients. We observed a higher level of Aβ-42 in NSTEMI, but no difference in Aβ-40 levels. We also found a correlation between age and NT-proBNP with both Aβ peptides (Aβ-40, Aβ-42) in both groups. In this analysis, NSTEMI patients had higher prevalence of cardiovascular risk factors. Considering the toxic properties of Aβ-42 in coronary endothelial cells and cardiomyocytes, this peptide may be useful in risk stratification scores in patients with NSTEMI.

Phosphorus is required by laying hens for a variety of biological functions. It is usually provided in excess, in a readily digestible inorganic form in diet formulations. However, this does not represent an efficient use of a finite resource and can result in water pollution. Plant bound phosphorus in phytate, is common in commercial poultry diets, and represents an alternate source of phosphorus, however poultry lacks the enzyme to digest it. By adding enzymes to their diets, the availability of this phytate-bound phosphorus can be increased allowing for the reduced inclusion of inorganic P in the diet formulation. Therefore, this study was conducted to examine the effect of a natural enzyme complex that contains phytase and xylanase in layer diets. Novogen Brown Classic layers (n=314) were fed one of two diets: (T1) control diet or (T2) control diet plus a natural mixed-enzyme product with both, phytase and xylanase. Experimental diets were provided for 12 days after which the excreta from each pen were collected for an additional three days to determine total retention and ileal digestibility of phosphorus and gross energy. At the end of the trial the birds were euthanized, and their ileal contents were collected. Productivity parameters were also recorded over the course of the study. The amount of phosphorus detected in the excreta of the control group was significantly higher than the amount detected in the enzyme-supplemented group (P<0.05). Both phosphorus retention and phosphorus ileal digestibility were significantly higher in the enzyme-supplemented birds (P<0.001). Gross energy digestibility was significantly higher in the enzyme-supplemented birds for both the total tract and ileal samples (P<0.001). There were only numerical improvements, not significant, in the layer productivity parameters. This study showed that the enzyme supplement can improve total retention and ileal digestibility of phosphorus and gross energy in older layers.

The solar heat pump system based on photovoltaic photothermal (PV/T) modules is a new technology that integrates photovoltaics and heat reutilization. It can improve the efficiency of photovoltaic conversion and recovery the remaining heat generated during the photoelectric conversion process, thereby improving the comprehensive efficiency of the whole system. Simulink software is a visual simulation tool, which has the advantages of strong applicability, clear simulation structure and calculation process. The design of its graphical programming greatly simplifies the user modeling process and allows for direct viewing of results, therefore improving the simulation efficiency. At present, there is little research on simulation of solar heat pump systems with PV/T modules based on Simulink software. In this paper, the mathematical models of each component contained in the PV/T solar heat pump system were established based on the system working principle, energy conservation equations and some empirical formulas, and then the operation of the system was simulated by the Simulink software. Due to the operation characteristic of PV/T modules is the key influencing factor for the whole system performance, the model and simulation process for PV/T modules were introduced in detail. The results showed that the surface temperature of PV/T modules under different operating conditions was significantly lower than that of ordinary photovoltaic panels, and the temperature change amplitude was lower. As results, the power generation and photovoltaic conversion efficiency were significantly improved.

Currently, one of the main focuses of attention in academia is microgrids (MGs) and their ability to maintain stable, resilient, reliable and high–power quality operations. A crucial component in MGs is power converters (PC), which facilitate the decentralization of power generation. This decentralization, however, poses challenges in terms of power quality. This paper presents a microgrid model based on the IEEE 14–bus distribution system and aims to investigate the power quality when the MG operates while connected to the conventional power grid. The MG model has been developed using MATLAB–Simulink®, a specialized tool for electrical engineering simulations. The results obtained are subjected to rigorous analysis and compared with the compatibility levels established by the IEEE–519 standard. This approach allows an accurate assessment of the MGs’ ability to maintain power quality at acceptable levels while interconnected to the conventional power grid. Finally, this study provides a valuable contribution to the MGs’ field of microgrids by providing a detailed and quantitative assessment of power quality, which will aid in the design and optimization of MGs’ for effective implementation in real electric power systems.

Portugal’s EPAC silo network, initially planned in the 1930s but constructed and utilized primarily in the 1970s, consisted of 31 silos with a total capacity of 841,100 t. The network’s usage declined, however, due to market liberalization and Portugal’s accession to the European Economic Community in 1985. This study focuses on adapting a methodology to inventory and analyse the 31 silos in the EPAC network considering their general features, construction, technological facilities, and socioeconomic aspects. The silos are situated in 30 cities and towns, predominantly in the country’s key grain-growing regions, particularly the Alentejo region. While there are variations in design and construction, most EPAC silos contain two or three rows of circular reinforced-concrete cells and use the spaces between cells for storage. Their capacities range from 6,000 to 35,000 t. Some are inland grain reception and storage silos, while others are larger-capacity port silos designed to unload grain rapidly onto ships using mechanical or pneumatic unloaders. These structures are a significant part of Portugal’s agro-industrial heritage and have in some cases been repurposed as museums or event venues. Compared to other agro-industrial buildings, silos pose unique conversion challenges due to their height and design complexities. Examples of successful reuse in countries like Spain and Italy may provide insights for potential silo projects in Portugal. However, analysis suggests that such proposals and similar initiatives may be viable only in the more highly populated towns.

Background: Atopic dermatitis (AD), a common dermatological condition, is often associated with significant economic and social burdens. Despite extensive studies globally, there is a gap in understanding the comprehensive impact of this condition in Romania. Objectives:This study evaluated the economic burden and quality of life impact of AD in Romania, considering both direct and indirect costs. Materials & Methods: A mixed methods cost of illness study was conducted from April to October 2023. A patient survey, expert reviews, and national health data analysis were used to assess direct medical costs (treatments, medical services), direct non-medical costs (transportation, accommodation), and indirect costs (productivity losses). Quality of life was evaluated using the EuroQol EQ-5D-5L instrument. Results: In 2022, the total cost of atopic dermatitis in Romania was 32.983.498 EUR for adults and 31.573.968 EUR for children. Adult costs included 24.534.603 EUR in direct and 8.429.884 EUR in indirect medical costs, with an average annual cost per patient of 447 EUR. Pediatric costs involved 15.026.781 EUR in direct and 16.516.410 EUR in indirect costs, averaging 290 EUR per patient annually. Conclusion: Care expenditure with AD management in Romania is substantially lower than in other countries, posing a significant economic burden, primarily driven by indirect costs. This study provides a foundation for future policy interventions and highlights the need for targeted management strategies for AD.

Ultrasound A-scan is an important tool for quantitative assessment of ocular lesions. However, its usability is limited by difficulty to accurately localize the ultrasound probe to a lesion of interest. In this study, a transparent LiNbO3 single crystal ultrasound transducer was fabricated, and integrated with a widefield fundus camera to guide the ultrasound local position. The novel fundus camera guided ultrasound probe was validated for in vivo measurement of rat eyes. Anterior and posterior segments of the rat eye could be unambiguously differentiated with the fundus photography guided ultrasound measurement. A model eye was also used to verify the clinical potential of the prototype device.

Background: Traumatic events adversely affect the clinical course of obsessive-compulsive disorder (OCD). Our investigation explores the correlation between prolonged interpersonal trauma and the severity of symptoms related to obsessive-compulsive and anxiety disorders. Methods: The study follows a cross-sectional and observational design, employing the International Trauma Questionnaire (ITQ) to examine areas linked to interpersonal trauma, the Hamilton Anxiety Rating Scale (HAM-A), and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) to assess anxious and obsessive-compulsive symptoms, respectively. Descriptive analysis, analysis of variance (ANOVA), and logistic regression analyses were conducted. Results: We recruited 107 OCD-diagnosed patients, categorizing them into subgroups based on the presence or absence of complex post-traumatic stress disorder (cPTSD). The ANOVA revealed statistically significant differences between the two groups in the onset age of OCD (p=0.083), psychiatric familial history (p=0.023), HAM-A, and Y-BOCS (p=0.000). Logistic regression indicated a statistically significant association between the presence of cPTSD and Y-BOCS scores (p=0.000). Conclusion: The coexistence of cPTSD in OCD exacerbates obsessive-compulsive symptoms and increases the burden of anxiety. Further advancements in this field are crucial for mitigating the impact of early trauma on the trajectory of OCD and associated anxious symptoms.

A homogenization of billets from Al-Cu-Li-Mg-Sc-Zr alloys should be accomplished at high annealing temperatures exceeding 500 °C. This type of aluminum alloy is susceptible to the depletion of surface layers from Li. Therefore, choosing a suitable homogenization temperature and duration is a crucial step in assuring a homogeneous distribution of alloying elements and optimal exploitation of the potential of the alloy. In-situ heating in an electron microscope was performed on a twin-roll cast Al-Cu-Li-Mg-Sc-Zr alloy to understand the peculiarities of the homogenization process. Four types of primary phase particles rich in Cu, Li, Mg, and Fe were identified in the as-cast material. They appear as coarse particles at the boundaries of eutectic cells. Their partial dissolution occurs at temperatures above 450 °C. They are almost fully dissolved at 550 °C, except for complex phases containing Fe and Cu. Small dimensions of eutectic cells in the range of 10 µm assure a homogeneous distribution of main alloying elements within the matrix after 20 min of annealing. Direct comparison with the same material prepared by mold-casting indicates that such short annealing times result in the dissolution of main primary phase particles but do not assure a homogeneous distribution of alloying elements in the whole volume of the specimen.

Background: Children's environmental health encompasses a wide range of factors that impact the well-being of children, including physical, chemical, biological, and social elements in their immediate environment. Safeguarding children from harmful substances is the crucial role of nursing students. Nursing students play a vital role as valuable resources for individuals, families, communities, and policymakers. This study aims to assess the knowledge and skills of nursing students concerning children's environmental health at academic institutions delivering nursing program in Nekemte town, Ethiopia. Methods: An institutional-based cross-sectional study design was carried out from August 1–30, 2023, among nursing students in Nekemte Town, and 634 study participants were selected through simple random sampling. Data was entered into Epinfo version 6.4 and exported to STATA version 14 for analysis, and multicollinearity was checked. Finally, from the bivariate analysis, variables of < 0.2 were included in the multivariate analysis; an adjusted odd ratio with a 95% CI was computed; and variables with a P-value <0.05 in the multivariable analysis were taken as significant associated factors. Results: The overall nursing students' level of knowledge about children's environmental health was insufficient (56%). More than one-third (34%) demonstrate their skill towards children's environmental health insufficiently, and one-fourth (25%) and 27% of the learners conduct their craft to prevent children from ecological hazards to a good and better level, respectively. While gender of the participant, age of the participant, year of the study, training history of the participants, and field of study of the students were significantly associated variables with their knowledge of children's environmental health. Conclusion: The study reveals that nursing students' knowledge of children's environmental health is limited, with factors such as gender, age, training history, and field of study significantly influencing their understanding. Therefore, female students should be considered for more tutorials to be effectively knowledgeable regarding children’s environmental health.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

Breeding for low hydrogen cyanide (HCN) varieties is a major objective of programmes targeting boiled cassava food product. To enhance breeding for low HCN, knowledge on genetic variation and trait heritability is essential. In this study, 64 cassava clones established across four locations and evaluated for HCN using three HCN assessment methods; 1 to 9 scale, 0 ppm to 800 ppm scale and a quantitative assay based on spectrophotometer readings (HCN_Spec). Data were also collected on the weather variables precipitation, relative humidity, and temperature. Significant differences were observed among clones (P &lt; 0.001) and locations (p &lt; 0.001). There were also significant (p &lt; 0. 001) clone by environment interactions. Locations Arua and Serere posted higher HCN scores among clones and were associated with significantly higher (p &lt; 0.001) mean daily temperature (K) and lower relative humidity (%) across 12-hour and 18-hour intervals. Within locations HCN broad sense heritability estimates ranged 0.22 – 0.64 while combined location heritability estimates ranged 0.14 to 0.32. Relationships between the methods were positive and strong (R2 = 0.75 – 0.92). It is expected that the information herein will accelerate efforts towards breeding for low HCN cassava varieties.

Chondroitin is a glycosaminoglycan that has gained widespread use in nutraceuticals and pharmaceuticals, mainly for treating osteoarthritis. Traditionally, it has been extracted from animal cartilage but recently, biotechnological processes have emerged as a commercial alternative to avoid the risk of viral or prion contamination and offer a vegan-friendly source. Typically, these methods involve producing the chondroitin backbone using pathogenic bacteria and then modifying it enzymatically through the action of sulfotransferases. Despite the challenges of expressing active sulfotransferases in bacteria, the use of eukaryotic microorganisms is still limited to a few works using Pichia pastoris. To create a safer and efficient biotechnological platform, we have constructed a biosynthetic pathway for chondroitin production in S. cerevisiae as a proof-of-concept. Up to 125 mg/L and 200 mg/L of intracellular and extracellular chondroitin were produced, respectively. Furthermore, as genome-scale models are valuable tools for identifying novel targets for metabolic engineering, a stoichiometric model of chondroitin-producing S. cerevisiae has been developed and used in optimization algorithms. Our research has yielded several novel targets, such as uridine diphosphate(UDP)-N-acetylglucosamine pyrophosphorylase (QRI1), glucosamine-6-phosphate acetyltransferase (GNA1) or N-acetylglucosamine-phosphate mutase (PCM1) overexpression that might enhance chondroitin production, which will guide future experimental research to develop more efficient host organisms for the biotechnological production process.

After a survey on polymer plasticization theories and conventional criteria to evaluate polymer-plasticizer compatibility through the solubility parameter, an attempt to create a polymer-plasticizers polarity scale through solvatochromic dyes has been made. Since the Reichardt’s ET(30) dye is insoluble in rubber hydrocarbon polymers like polyisoprene, polybutadiene and styrene-butadiene copolymers and is not also useful for the evaluation of the hydrocarbons and ester plasticizers, the Nile Red solvatochromic dye was instead used extensively and successfully for this class of compounds. A total of 53 different compounds were evaluated with the Nile Red dye and wherever possible also with the ET(33) Reichardt’s dye. A very good correlation was then found between the Nile Red scale E(NR) and the Reichardt’s ET(30) scale for this class of compounds focusing on diene rubbers and their typical hydrocarbons and new esters plasticizers. Furthermore, the E(NR) scale shows also a reasonable correlation with the total solubility parameter calculated according to the Van Krevelen method. Based on the above results, some conclusion was made about the compatibility between the diene rubbers and the conventional plasticizers as well as some new and green plasticizer proposed for the rubber compounds.

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients Length of Stay (LOS). This study aims to develop a machine learning prediction model that may aid to predict LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients undergoing elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization has been classified as “Short LOS” if it was less than or equal to 6 days and “Long LOS” if it was greater than 7 days. Clinical data coming from pre-operative laboratory analysis, vital parameters, demographic characteristics of patients were screened. Final data have been used to train a Logistic Regression model with the aim of predicting short or long LOS. Results: Final dataset was composed of 1517 patients (795 “LONG”, 722 “SHORT”, p = 0.3196) with a total of 1541 admissions (729 “LONG”, 812 “SHORT”, p &lt; 0.000). Complete model had a prediction efficacy of 78,99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice predicting which patients are suitable for a shorter LOS, from those with a longer LOS in which a cautious approach could be recommended.

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value at Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning from January 1st, 2017, to October 25th, 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

Opioid use disorder has increased in the world in recent years and Iran ranks first in terms of opium smoking in the world. In fact, opium smoking by traditional methods, in addition to dependence on morphine, is a behavioral addiction in Iran. The body position during opium smoking is usually non-standard and non-ergonomic. Over time, changes in muscle tissue, tendons and ligaments, and the connective tissue go from elasticity to neuromuscular plasticity. This study aims to compare sway back posture and musculoskeletal problems between opium smokers and non-drug users. In this comparative and cross-sectional study, 80 opium smokers were compared with 74 non-drug users in terms of sway angle (sway back posture), and musculoskeletal complaints (NORDIC questionnaire) and the association of possible risk factors was investigated. Data were analyzed by Kolmogorov-Smirnov, Correlation Coefficient and paired-t tests using SPSS version 23. There were significant differences between two groups in terms of sway back posture (P=0.007), and neck (<0.001), upper back (<0.001) and lower back (0.006) musculoskeletal complaints. Homelessness, the lifetime duration of opium smoking (in months), and the duration of daily opium smoking (in minutes) had significant correlation with sway back posture and musculoskeletal complaints. Homelessness was the strongest predictor and had strongest correlation. Overall, an increase in the duration of sitting in non-ergonomic positions could lead to increase in sway angle and sway back posture and increase in musculoskeletal complaints of neck, upper back and lower back due to the non-neutral posture of opium smoking. It seems that plasticity and structural and functional adaptations of the posture occurs due to opium smoking and further studies are recommended.

Introduction: Overuse of antibiotics has been increasing in recent years and is currently the most prescribed drug in the paediatric age. It is estimated that 72% of patients in the neonatal intensive care unit (NICU) are treated with antibiotics. One of the effects observed with their use is the alteration of the microbiota, which is associated with metabolic disorders including obesity. In this study, the aim was to determine in newborn rats, whether most commonly prescribed antibiotics in Mexican NICUs have a differential effect on weight gain, development of adiposity, and modification of carbohydrate and lipid metabolism. Methods: This is an experimental study in newborn rats, comparing Ampicillin/Meropenem (Access/Watch groups) 100 mg/g every 12 h /10 mg/g every 12 h (each of these two antibiotics was adjusted to the required dosages based on weight); Cefotaxime 200 mg/g every 24 h; (Watch group) and Amikacin 15 mg/g every 24 h (Access group) versus Saline Solution as Control. Each antibiotic was intraperitoneal administered daily for 5 days in males (to avoid gender differences) to 14 newborn rats in each group. A comparison of anthropometric and biochemical parameters registered on day 28 was made using ANOVA analysis. Differences were considered if p < 0.05.Results: Amikacin was the antibiotic with the greatest effect on anthropometric measurements and LDL-cholesterol; as well as Cefotaxime on Glucose and triglycerides, while Meropenem plus Ampicillin showed the weakest effect on measured parameters. Conclusion: The administration of antibiotics in the neonatal stage has repercussions on the body composition of adult rats, as well as on lipids and carbohydrates serum levels. Future studies might evaluate the toxicity of antibiotics on the immature neonatal organs.

The COVID-19 pandemic and tuberculosis have epidemiological similarities, being transmitted airborne, favored by direct contact, crowded environments and vulnerable biological status. We performed a retrospective study of 45 cases of pulmonary tuberculosis associated with COVID-19 (TB+COV+) compared to 45 cases with tuberculous monoinfection (TB+COV-), hospitalized during 2021-2022. The demographic characteristics were similar in the two groups, predominating men, a median age of 51 years, living in rural areas, medium level of education and smoking. Common symptoms of the two groups were cough, weight loss, profuse sweating, loss of appetite and hemoptysis, while fever, headache, myo-arthralgias and digestive symptoms characterized the TB+COV+ forms. The scores of radiological lesions in the TB+COV+ compared to TB+COV- group were significantly higher and persistent, revealing more frequent bilateral extensive lung lesions. There were no significant differences in the biological parameters between the two groups. Mortality was 2.2%, regardless of the association of COVID-19. The frequency of infections with Clostridioides difficile was higher in TB+COV+ cases. In conclusion, the co-infection of COVID-19 had an impact on the clinical and biological expression of tuberculosis diagnosed in a pandemic context.

Agarwood, a highly valuable fragrant wood of Aquilaria spp. has been widely used in perfumes, traditional medicine, religious rites and cultural activities. Due to the presence of aromatic and medicinal properties, the Agarwood has gained its economic importance in recent years. It has been providing livelihood and employment facilities to many people who are cultivating those. Owing to high demand and profitability, the people of Assam has started to cultivate Agarwood in a commercial manner in recent days. This study was conducted to examine the economic viability of Agarwood cultivation in Assam and to understand the problems of the cultivators to consider Agarwood cultivation as the primary source of income. The primary data were collected during the months of May and June, 2022 from 210 households using structured questionnaire. The cost-benefit analysis revealed that INR 100 of initial investment yields a net benefit of INR 623 which indicates that the return from the Agarwood cultivation among the cultivators are very high. Thus, the present study argues that the government should encourage the cultivators by providing the necessary support in the form of a legalized market, credit requirement, creating the awareness about the demand of Agarwood and making provision of training.

A novel reinforcement learning deep deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in the attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the studied quadrotor is derived and the attitude control problem is described by formulas. Second, a sliding mode controller including its sliding mode surface and reaching law is selected for the nonlinear dynamic system, and the stability of the designed SMC system is supported by Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adjust the switching control gain adaptively. During the training process, the input signals of agent are the actual and desired attitude angles, and the output action is the time-varying control gain. Finally, the above trained agent is applied to the SMC as a parameter regulator, to implement the adaptive adjustment of the switching control gain related to reaching law, and the simulation results verify the robustness and effectiveness of the proposed DDPG-SMC method.

Three different populations of sulfated polysaccharides can be found in the cell wall of the red alga Botryocladia occidentalis. In a previous work, the structures of the two more sulfated polysaccharides were revised. In this work, NMR-based structural analysis was performed on the least sulfated polysaccharide and its chemically modified derivatives. Results have revealed the presence of both 4-linked α- and 3-linked β-galactose units having the following chemical features: more than half of the total galactose units are not sulfated, the α-units occur primarily as 3,6-anhydro-galactose units either 2-O-methylated or 2-O-sulfated, and the β-galactose units can be 4-O-sulfated or 2,4-O-disulfated. SPR-based results indicated weaker binding of the least sulfated galactan to thrombin, factor Xa, and antithrombin, but stronger binding to heparin cofactor II than unfractionated heparin. This report together with our previous publication completes the structural characterization of the three polysaccharides found in the cell wall of the red alga B. occidentalis and correlates the impact of their composing chemical groups with the levels of interaction with the blood co-factors.

Skeletal muscles are crucial for voluntary movements, and impairments in their function significantly impact our activity. Dysferlin, a protein predominantly present in skeletal muscle, is a focal point of extensive research due to its vital role in muscle repair and maintenance. This study investigates Dysferlin's levels and function in skeletal muscles, emphasizing its significance and the potential effects of its dysregulation on fast and slow muscle fibers. We employed Western blotting on two different rat muscles. The results revealed distinct expressions of Dysferlin in fast and slow fibers, as demonstrated by Western blotting. We propose that Dysferlin may have a metabolic role in addition to its involvement in the repair mechanism.

Introduction Our aim was to determine the impact of antimicrobial stewardship tools (AST) and COVID-19 pandemic on antibiotic consumption (AC). Methods We used the national software Consores to determine AC in DDD / 1000 days of hospitalization from 2017 to 2022 in voluntary private hospitals in France. The AST considered were 1/ internal guidelines 2/ list of antibiotics with restricted access 3/ presence of an antibiotic referent or 4/ an ID specialist 5/ proof of annual meeting on antimicrobial resistance. Institutions with dedicated units for COVID-19 patients were specified. Results In 30 institutions total AC varied from (means) 390 to 405 DDD/1000 DH from 2017 to 2022. Fluoroquinolones and amoxicillin / clavulanate consumption decreased from 50 to 36 (p = 0.003) and from 112 to 77 (p = 0.025) respectively, but consumption of piperacillin / tazobactam increased from 9 to 21 (p < 0.001). Over the study period, 10 institutions with ≤ 2 AST had lower AC compared to 20 institutions with ≥ 3 AST (p < 0.01). COVID-19 units opened in 10 institutions were associated with a trend toward higher macrolide consumption from 15 to 25 from 2017 to 2020 (p = 0.065) and with an acceleration of piperacillin / tazobactam consumption from 2020 to 2022 (p ≤ 0.003). Conclusion Antibiotic consumption in 30 private hospitals in France was inversely related to the number of AST. The COVID-19 pandemic was associated with limited impact on AC but special attention should be paid on piperacillin / tazobactam consumption.

Background. IgA nephropathy (IgAN) is associated with chronic inflammation. Platelet-related parameters, such as platelet (PLT) count, platelet-to-albumin ratio (PAR) and platelet-to-lymphocyte ratio (PLR), were examined as potential prognostic indicators for renal and cardiovascular (CV) outcomes in IgAN. Methods. 150 IgAN patients were devided into two groups based on the cut-off value of PAR. All-cause mortality, major CV events and end-stage renal disease were the primary combined endpoints. Secondary endpoints, such as CV or renal endpoints, were also analyzed separately. Results. The patient’s mean age was 43.7 ± 13.5 years, and the follow-up time was 124 ± 67 months. The K-M curve showed that PLR, PAR and PLT were strongly associated with primary combined (p = 0.002, p = 0.004, p = 0.001) and renal outcomes (p < 0.001, p < 0.001, p < 0.001), but not with CV outcomes in IgAN. However, when combined with left ventricular hypertrophy (LVH) or metabolic syndrome (MetS) PAR was found to be a significant predictor of both primary (p < 0.001, p < 0.001) and secondary outcomes (p = 0.001 and p = 0.038; p = 0.001 and p = 0.015). Additionally, PLR correlated with microalbuminuria (r = -0.165, p = 0.033) and LVH (r = -0.178, p = 0.025), while PLT correlated with eGFR (r = 0.158, p = 0.040). Conclusion. Elevated PAR and PLR may predict progression to end-stage kidney disease, but in combination with LVH and MetS, they showed CV events also in IgAN. The determination of PAR and PLR can be useful and cost-effective parameters for assessing both cardiovascular and renal risks in CKD.