This research stems from the increasing use of hand gestures in various applications, such as sign language recognition to electronic device control. The focus is the importance of accuracy and robustness in recognizing hand gestures to avoid misinterpretation and instruction errors. However, many experiments on hand gesture recognition are conducted in limited laboratory environments, which do not fully reflect the everyday use of hand gestures. Therefore, the importance of an ideal background in hand gesture recognition, involving only the signer without any distracting background, is highlighted. In the real world, the use of hand gestures involves various unique environmental conditions, including differences in background colors, varying lighting conditions, and different hand gesture positions. However, the datasets available to train hand gesture recognition models often lack sufficient variability, thereby hindering the development of accurate and adaptable systems. This research aims to develop a robust hand gesture recognition model capable of operating effectively in diverse real-world environments. By leveraging deep learning-based image augmentation techniques, the study seeks to enhance the accuracy of hand gesture recognition by simulating various environmental conditions. Through data duplication and augmentation methods, including background, geometric, and lighting adjustments, the diversity of the primary dataset is expanded to improve the effectiveness of model training. It is important to note that the utilization of the green screen technique, combined with geometric and lighting augmentation, significantly contributes to the model’s ability to recognize hand gestures accurately. The research results show a significant improvement in accuracy, especially with implementing the proposed green screen technique, underscoring its effectiveness in adapting to various environmental contexts. Additionally, the study emphasizes the importance of adjusting augmentation techniques to the dataset’s characteristics for optimal performance. These findings provide valuable insights into the practical application of hand gesture recognition technology and pave the way for further research in tailoring techniques to datasets with varying complexities and environmental variations. Full article

Many biologically active metabolites of the essential amino acid L-tryptophan (Trp) are associated with different neurodegenerative diseases and neurological disorders. Precise and reliable methods for their determination are needed. Variability in their physicochemical properties makes the analytical process challenging. In this case, chemical modification of analyte derivatization could come into play. Here, we introduce a novel fast reversed-phase ultra-high-performance liquid chromatography (RP-UHPLC) coupled with tandem mass spectrometry (MS/MS) method for the determination of Trp and its ten metabolites in human plasma samples after derivatization with 2-bromo-4′-nitroacetophenone (BNAP). The derivatization procedure was optimized in terms of incubation time, temperature, concentration, and volume of the derivatization reagent. Method development comprises a choice of a suitable stationary phase, mobile phase composition, and gradient elution optimization. The developed method was validated according to the ICH guidelines. Results of all validation parameters were within the acceptance criteria of the guideline, i.e., intra- and inter-day precision (expressed as relative standard deviation; RSD) were in the range of 0.5–8.2% and 2.3–7.4%, accuracy was in the range of 93.3–109.7% and 94.7–110.1%, limits of detection (LODs) were in the range of 0.15–9.43 ng/mL, coefficients of determination (R2) were higher than 0.9906, and carryovers were, in all cases, less than 8.8%. The practicability of the method was evaluated using the blue applicability grade index (BAGI) with a score of 65. Finally, the developed method was used for the analysis of Alzheimer’s disease and healthy control plasma to prove its applicability. Statistical analysis revealed significant changes in picolinic acid (PA), anthranilic acid (AA), 5 hydroxyindole-3-acetic acid (5-OH IAA), and quinolinic acid (QA) concentration levels. This could serve as the basis for future studies that will be conducted with a large cohort of patients. Full article

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52). Full article

The strength–ductility mechanism of the low-carbon steels processed by laser cutting is investigated in this paper. A typical gradient-phased structure can be obtained near the laser cutting surface, which consists of a laser-remelted layer (LRL, with the microstructure of lath bainite + granular bainite) and heat-affected zone (HAZ). As the distance from the laser cutting surface increases, the content of lath martensite decreases in the HAZ, which is accompanied by a rise in the content of ferrite. Considering that the microstructures of the LRL and HAZ are completely different from the base metal (BM, ferrite + pearlite), a significant strain gradient can be inevitably generated by the remarkable microhardness differences in the gradient-phased structure. The hetero-deformation-induced strengthening and hardening will be produced, which is related to the pileups of the geometrically necessary dislocations (GNDs) that are generated to accommodate the strain gradient near interfaces. Plural phases of the HAZ can also contribute to the increment of the hetero-deformation-induced strengthening and hardening during deformation. Due to the gradient-phased structure, the low carbon steels under the process of laser cutting have a superior combination of strength and ductility as yield strength of ~487 MPa, tensile strength of ~655 MPa, and total elongation of ~32.7%. Full article

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment. Full article

Farm crop diversity is often overlooked, predominantly indigenous crops’ role in this diversity. The main concentration has been on the contribution or role of exotic crops to household crop diversification. At the same time, the role played by both types of crops in household food security has only been aggregated, failing to show how indigenous crops play a key role in household food security. This research paper uses Tanzanian Panel data from waves 4 and 5 to study the factors influencing indigenous and exotic crop diversification and the role of this diversity in household food security. Using a random effect model, the author found that various factors are crucial in determining household crop diversification. Gender, household size, marital status, and expected harvest quantity are among the key factors influencing indigenous crop diversification. On the other hand, age, education, access to markets, access to irrigation services, and soil quality are the primary factors that affect the diversification of exotic crops. Moreover, the findings show that indigenous and exotic crop diversity significantly influences household food consumption. Thus, policies to increase the production of indigenous crops in order to improve household food consumption should be considered. Full article

In living cells, some reactions can be conducted by more than one enzyme and sometimes it is difficult to establish which enzyme is responsible. Such is the case with proteins from the TET family, capable of converting 5-methyl-2’-deoxycytidine (5-$mdC$) in DNA to 5-(hydroxymethyl)-2’-deoxycytidine (5-$hmdC$) and further to 5-formyl-2’-deoxycytidine (5-$fdC$) and 5-carboxy-2’-deoxycytidine (5-$cadC$). The estimation of the efficiency of particular TETs in particular oxidative reactions and different cell types is important but experimentally difficult. Here, we propose an approach with mathematical modeling in which methylation and known deoxycytidine modification pathways are presented by 343 possible model versions with assumed different combinations of TET1, 2, and 3 activities in different pathways. Model parameters were calculated on the basis of 5-$mdC$, 5-$hmdC$, 5-$fdC$, 5-$cadC$, and 5-$hmdU$ levels experimentally assessed in five human cultured cell lines and previously published. Selection of the model versions that give in simulations the best average fit to experimental data suggested that not all TET proteins participate in all modification reactions and that TET3 activity may be especially important in the reaction of 5-$fdC$ removal. Full article

Foam concrete is a typical cement-based porous material; its special microstructure endows it with excellent properties, such as light weight, energy efficiency, thermal insulation, and fire resistance. Therefore, it is widely used as a thermal insulation material for buildings. The heat transfer modes of foam concrete include conduction, convection, and radiation. However, previous studies considered conduction to be the dominant mode, often neglecting the effects of convection and radiation. In this study, a stochastic numerical model of the foam concrete microstructure is established based on the statistical parameters of the pore structure. With this model, the heat transfer mechanism of foam concrete is analyzed at the mesoscopic level, and the equivalent thermal conductivity is calculated. By comparing four different working conditions, the influence of conduction, convection, and radiation on the heat transfer of foam concrete is analyzed, and the specific contribution rates of conduction, convection, and radiation are calculated. The results show that the convection effect is weak due to the pore size being smaller than 1 ; so, the influence of convection can be neglected in the heat transfer analysis of foam concrete. The contribution of radiation increases with the decrease in foam concrete density and the increase in temperature difference. When the temperature difference is 40 and the density is 300 , the contribution of radiation exceeds 20. Therefore, for low-density and high-temperature difference situations, the influence of radiation cannot be ignored. The heat transfer in foam concrete is mainly through conduction, but with the decrease in density and the increase in temperature difference, the contribution of conduction shows a downward trend. Nevertheless, the contribution of conduction is still much larger than that of radiation and convection. Full article

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let us delve into the mathematical intricacies of these sequences. We have previously devised a mathematical approach for defining a “healthy” sequence. This sequence is characterized by having at most four distinct nucleotides (denoted as $nt\le 4$). It serves as the generator of a group denoted as $f_p$. The desired properties of this sequence are as follows: $f_p$ should be close to a free group of rank $nt-1$, it must be aperiodic, and $f_p$ should not have isolated singularities within its $S{L}_2\left(\mathbb{C}\right)$ character variety (specifically within the corresponding Groebner basis). Now, let us explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as $S_2^4$. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI Equation. In certain cases, $S_2^4$ degenerates, in the sense that two punctures collapse, resulting in a “wild” dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find many applications including cancer research and the investigation of neurodegenative diseases. Full article

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling. Full article

The burgeoning interest in intelligent transportation systems (ITS) and the widespread adoption of in-vehicle amenities like infotainment have spurred a heightened fascination with vehicular ad-hoc networks (VANETs). Multi-hop routing protocols are pivotal in actualizing these in-vehicle services, such as infotainment, wirelessly. This study presents a novel protocol called multiple junction-based traffic-aware routing (MJTAR) for VANET vehicles operating in urban environments. MJTAR represents an advancement over the improved greedy traffic-aware routing (GyTAR) protocol. MJTAR introduces a distributed mechanism capable of recognizing vehicle traffic and computing curve metric distances based on two-hop junctions. Additionally, it employs a technique to dynamically select the most optimal multiple junctions between source and destination using the ant colony optimization (ACO) algorithm. We implemented the proposed protocol using the network simulator 3 (NS-3) and simulation of urban mobility (SUMO) simulators and conducted performance evaluations by comparing it with GSR and GyTAR. Our evaluation demonstrates that the proposed protocol surpasses GSR and GyTAR by over 20% in terms of packet delivery ratio, with the end-to-end delay reduced to less than 1.3 s on average. Full article

The current investigation aims to underline the impact of salicylic acid or calcium chloride seed pre-treatments on mineral status and oxidative stress markers, namely levels of electrolyte leakage (EL) and lipid peroxidation levels, measured as thiobarbituric reactive substances (TBARS), and the activity of some antioxidant enzymes in roots and leaves of plants in two barley species grown under various salt treatments. Overall, our results revealed that salinity inhibits essential nutrient absorption such as iron, calcium, magnesium and potassium and stimulates the absorption of sodium. Also, this environmental constraint induced oxidative stress in plants in comparison with the control conditions. This state of oxidative stress is reflected by an increase in TBARS content as well as the stimulation of EL values. In addition, salinity induced disturbances in the activity of antioxidant enzymes, which were mainly dependent on the applied salt concentration and the species. In addition, Hordeum marinum maintained high antioxidant enzyme activity and low levels of oxidative stress parameters, which reinforces its salt-tolerant character. Importantly, salicylic acid or calcium chloride seed priming alleviated the mineral imbalance and the oxidative damage induced by salinity. Moreover, seed priming improves iron, calcium magnesium and potassium content and limitsthe accumulation of sodium. Also, both treatments not only decrease TBARS levels and limit EL, but they also stimulate the antioxidant enzyme activities in the leaves and roots of the stressed plants as compared with stressed plants grown from non-primed seeds. Interestingly, the beneficial effects of the mentioned treatments were more notable on Hordeum vulgare species. Full article

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere. Full article

Almond hull, a substantial byproduct comprising more than half of almond fresh weight, has recently gained attention due to its functionality and sustainability benefits. Despite heightened interest, information regarding its toxicity remains limited. In order to assess its genotoxic potential, we conducted Good Laboratory Practice-compliant in vitro and in vivo studies following Organization for Economic Co-operation and Development (OECD) guidelines. No evidence of toxicity or mutagenicity was observed in a bacterial reverse mutation assay using five tester strains, evaluating almond hull at concentrations up to 5 mg/plate, with or without metabolic activation. Almond hull did not induce chromosome structural damage in a chromosome aberration assay using Chinese hamster ovary cells, nor did it cause any spermatogonial chromosomal aberration in tested male BALB/c mice. To evaluate its ability to induce DNA damage in rodents, a combined micronucleus assay was conducted in KM mice of both sexes. Almond hull was administered at doses of 1250, 2500, and 5000 mg/kg/day via gavage once daily for 2 days. No adverse effects of almond hull were observed in the micronucleus assay. Our results indicate no evidence of the genotoxic potential of almond hull administered up to the maximum concentrations of 5 g/kg, as recommended by OECD guidelines. Full article

Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the improved Soil and Water Assessment Tool with a salinity module (SWAT-Salt) model was used to establish the distributed water and salt transport model for the watershed in this study. The results demonstrated that the modified model could more accurately represent the process of water and salt changes in the HID. The coefficient of determination (R²) in the simulation of streamflow and discharge salt loading was 0.83 and 0.86, respectively, and the Nash–Sutcliffe efficiency (NSE) was 0.80 and 0.74, respectively. Based on this, different hydrological processes (surface runoff, lateral flow, groundwater, soil seepage) as well as spatial–temporal distribution characteristics of water salinity in groundwater and soil were analyzed in the HID. Differences in groundwater and soil salinity in different land uses and soil types were also compared. Of these, surface runoff and lateral flow salt discharge loading are concentrated in the southwestern portion of the basin, while groundwater salt discharge loading is concentrated in the eastern as well as southwestern portions of the basin. The salt discharge loading from groundwater accounts for about 98.7% of the total salt discharge loading from all hydrological pathways and is the major contributing part of salt discharge from the irrigation area. Soil salinity increases gradually from west to east. Groundwater salinity (2946 mg/L) and soil water electrical conductivity (0.309 dS/m) were minimized in the cropland. Meanwhile, rational allocation of irrigation water can appropriately increase the amount of salt discharge loading. In conclusion, the model could provide a reference for the investigation of soil salinization and water–salt management measures in irrigation areas. Full article

Berberis amurensis var. latifolia Nakai is a plant native to the Ulleung Island in Korea. In this study, we aimed to identify seed dormancy-breaking and germination requirements of this species using water imbibition experiments, gibberellic acid (GA₃) treatment (0, 10, 100, or 1000 mg/L), cold stratification (0, 2, 4, 8, or 12 weeks at 5 °C), move-along experiments, and phenological studies. In the water imbibition experiment, the seed weight increased by more than 120% after 24 h. Analysis of the internal morphological characteristics of the seeds revealed that the embryo in freshly matured seeds was fully grown and did not grow thereafter. The final germination percentages after 12 weeks of cold stratification at 5 °C were 49 ± 6.4% and 63 ± 3.4% under light and dark conditions, respectively. In move-along and phenological studies, a longer cold stratification treatment period resulted in a higher germination percentage; however, the warm stratification treatment did not affect germination significantly. The GA₃ treatment had little effect on seed germination. Therefore, we concluded that B. amurensis var. latifolia seeds have intermediate physiological dormancy, and pre-treatment with cold stratification for 12 weeks and incubation in the dark are required for effective seed propagation. Full article

Once-weekly semaglutide is a widely used glucagon-like peptide-1 receptor agonist (GLP-1RA) used for the treatment of type 2 diabetes (T2D). In clinical trials, semaglutide improved glycemic control and obesity, and reduced major cardiovascular events. However, the reports are limited on its real-world efficacy relating to various metabolic factors such as dyslipidemia or metabolic dysfunction-associated steatotic liver disease (MASLD) in Asian patients with T2D. In our retrospective longitudinal study, we selected patients with T2D who were given once-weekly semaglutide and compared metabolic parameters before and after the start of semaglutide. Seventy-five patients were eligible. HbA1c decreased significantly, by 0.7–0.9%, and body weight by 1.4–1.7 kg during the semaglutide treatment. Non-HDL cholesterol decreased significantly at 3, 6 and 12 months after the initiation of semaglutide; LDL cholesterol decreased at 3 and 6 months; and HDL cholesterol increased at 12 months. The effects on body weight, HbA1c and lipid profile were pronounced in patients who were given semaglutide as a first GLP-1RA (GLP-1R naïve), whereas improvements in HbA1c were also observed in patients who were given semaglutide after being switched from other GLP-1RAs. During a 12-month semaglutide treatment, the hepatic steatosis index (HSI) tended to decrease. Moreover, a significant decrease in the AST-to-platelet ratio index (APRI) was observed in GLP-1RA naïve patients. Our real-world study confirmed the beneficial effects of once-weekly semaglutide, namely, improved body weight, glycemic control and atherogenic lipid profile. The beneficial effects on MASLD were also suggested. Full article

Background: Conflicting data exist on the occurrence and outcome of infective endocarditis (IE) after pulmonary valve implantation. Objectives: This study sought to assess the differences between transcatheter pulmonary valve implantation (TPVI) and surgical pulmonary valve replacement (SPVR). Methods: All patients ≥ 4 years who underwent isolated pulmonary valve replacement between 2005 and 2018 were analyzed based on the data of a major German health insurer (≈9.2 million insured subjects representative of the German population). The primary endpoint was a composite of IE occurrence and all-cause death. Results: Of 461 interventions (cases) in 413 patients (58.4% male, median age 18.9 years [IQR 12.3–33.4]), 34.4% underwent TPVI and 65.5% SPVR. IE was diagnosed in 8.0% of cases during a median follow-up of 3.5 years. Risk for IE and all-cause death was increased in patients with prior IE (p < 0.001), but not associated with age (p = 0.50), sex (p = 0.67) or complexity of disease (p = 0.59). While there was no difference in events over the entire observational time period (p = 0.22), the time dynamics varied between TPVI and SPVR: Within the first year, the risk for IE and all-cause death was lower after TPVI (Hazard Ratio (HR) 95% CI 0.19 (0.06–0.63; p = 0.006) but increased over time and exceeded that of SPVR in the long term (HR 10.07 (95% CI 3.41–29.76; p < 0.001). Conclusions: Patients with TPVI appear to be at lower risk for early but higher risk for late IE, resulting in no significant difference in the overall event rate compared to SPVR. The results highlight the importance of long-term specialized care and preventive measures after both interventions. Full article

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey Model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which could provide powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Results show an improvement in the performance of more than 50% in terms of Root Mean Squared Error (RMSE) when the Markov chain is integrated in the analysis. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability. Full article

Despite many advances in surgical repair during the past few decades, the majority of tetralogy of Fallot patients continue to experience residual hemodynamic and electrophysiological abnormalities. The actual issue, which has yet to be solved, is understanding how this disease evolves in each individual patient and, as a result, who is truly at risk of sudden death, as well as the proper timing of pulmonary valve replacement (PVR). Our responsibility should be to select the most appropriate time for each patient, going above and beyond imaging criteria used up to now to make such a clinically crucial decision. Despite several studies on timing, indications, procedures, and outcomes of PVR, there is still much uncertainty about whether PVR reduces arrhythmia burden or improves survival in these patients and how to appropriately manage this population. This review summarizes the most recent research on the evolution of repaired tetralogy of Fallot (from adolescence onwards) and risk factor variables that may favor or delay PVR. Full article

Since we aim to test new options to find medication for cognitive disorders, we have begun to assess the effect of semaglutide and to conduct a review gathering studies that have attempted this purpose. This systematic review focuses on the cognitive effects of semaglutide, a glucagon-like peptide 1 receptor agonist (GLP-1 RA), in the context of neurological and cognitive impairment. Semaglutide, a synthetic GLP-1 analog, showcased neuroprotective effects beyond metabolic regulation. It mitigated apoptosis and improved cognitive dysfunction in cerebrovascular disease, suggesting broader implications for neurological well-being. Also, studies highlighted GLP-1 RAs’ positive impact on olfactory function in obese individuals with type 2 diabetes, on neurodegenerative disorders, multiple sclerosis, and endotoxemia. In order to analyze current studies that assess the impact of semaglutide on cognitive function, a literature search was conducted up to February 2024 on two online databases, MEDLINE (via PubMed) and Web of Science Core Collection, as well as various websites. Fifteen studies on mice populations and two studies on cell lines were included, analyzed, and assessed with bias-specific tools. The neuroprotective and anti-apoptotic properties of GLP-1 and its analogs were emphasized, with animal models and cell line studies demonstrating enhanced cognitive function. While promising, limitations include fewer studies, highlighting the need for extensive research, particularly in the human population. Even though this medication seems promising, there are significant limitations, one of which is the lack of studies on human subjects. Therefore, this review aims to gather current evidence. Full article

This paper presents a first-order noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC) with a process, (supply) voltage, and temperature (PVT)-insensitive closed-loop integrator and data-weighted averaging (DWA). The use of a cascode floating inverter amplifier (FIA)-type dynamic amplifier with high gain enables an aggressive noise transfer function while minimizing the power consumption associated with the use of an active filter. In the proposed ADC, the residue is generated by a capacitive digital-to-analog converter (CDAC) employing DWA, which is made possible by employing a second CDAC, which operates after the SAR operation is completed. The proposed ADC is designed with a 28 nm CMOS process with 1 V power supply. The simulation results show that the ADC achieves the SNDR of 71.2 dB and power consumption of 228 μW when operated with a sampling rate of 80 MS/s and oversampling ratio (OSR) of 10. The Schreier figure-of-merit (FoM) is 173.6 dB, and Walden FoM is 9.6 fJ/conversion-step. Full article