In this research, a method was developed for fabricating Au–Au nanorod array substrates through the deposition of large-area Au nanostructures on an Au nanorod array using a galvanic cell reaction. The incorporation of a granular structure enhanced both the number and intensity of surface-enhanced Raman scattering (SERS) hot spots on the substrate, thereby elevating the SERS performance beyond that of substrates composed solely of an Au nanorod. Calculations using the finite difference time domain method confirmed the generation of a strong electromagnetic field around the nanoparticles. Motivated by the electromotive force, Au ions in the chloroauric acid solution were reduced to form nanostructures on the nanorod array. The size and distribution density of these granular nanostructures could be modulated by varying the reaction time and the concentration of chloroauric acid. The resulting Au–Au nanorod array substrate exhibited an active, uniform, and reproducible SERS effect. With 1,2-bis(4-pyridyl)ethylene as the probe molecule, the detection sensitivity of the Au–Au nanorod array substrate was enhanced to 10⁻¹¹ M, improving by five orders of magnitude over the substrate consisting only of an Au nanorod array. For a practical application, this substrate was utilized for the detection of pesticides, including thiram, thiabendazole, carbendazim, and phosmet, within the concentration range of 10⁻⁴ to 5 × 10⁻⁷ M. An analytical model combining a random forest and a one-dimensional convolutional neural network, referring to the important variable-one-dimensional convolutional neural network model, was developed for the precise identification of thiram. This approach demonstrated significant potential for biochemical sensing and rapid on-site identification. Full article

Two-dimensional (2D) materials have attracted great attention in the past few years and offer new opportunities for the development of high-performance and multifunctional bipolar junction transistors (BJTs). Here, a van der Waals BJT based on vertically stacked n⁺-MoS₂/WSe₂/MoS₂ was demonstrated. The electrical performance of the device was investigated under common-base and common-emitter configurations, which show relatively large current gains of α ≈ 0.98 and β ≈ 225. In addition, the breakdown characteristics of the vertically stacked n⁺-MoS₂/WSe₂/MoS₂ BJT were investigated. An open-emitter base-collector breakdown voltage (BV_CBO) of 52.9 V and an open-base collector-emitter breakdown voltage (BV_CEO) of 40.3 V were observed under a room-temperature condition. With the increase in the operating temperature, both BV_CBO and BV_CEO increased. This study demonstrates a promising way to obtain 2D-material-based BJT with high current gains and provides a deep insight into the breakdown characteristics of the device, which may promote the applications of van der Waals BJTs in the fields of integrated circuits. Full article

Flower color is a major feature of ornamental plants, and the rich flower color of plants is an important factor in determining their ornamental and economic values, so flower color is an important research target for gardening and horticulture breeders at home and abroad. Our research group collected four colors of Impatiens uliginosa (white, pink, red, and deep red) during the collection of germplasm resources in the field. In this study, we analyzed the transcriptomes of the four flower colors of I. uliginosa by using RNA-Seq technology. The transcriptomes were screened to identify candidate genes related to flower color, and the coloring mechanisms of four flower colors were revealed at the molecular level. The main findings were as follows: (1) The number of the four different transcripts ranged from 64,723 to 93,522 and contained a total of 100,705 unigenes. (2) The analysis of differentially expressed genes revealed structural genes including C4H, FLS, PAL, and ANS and transcription factors including MYB, MYB-related, AP2-EREBP, and bHLH. (3) Among the four flower colors of I. uliginosa, the C4H1 gene had the highest expression in pink flowers, and the C4H2 gene had the highest expression in red flowers. This indicated that C4H genes positively regulated the red flower color of I. uliginosa. However, FLS expression was the highest in white flowers, and with deepening flower color, FLS gene expression gradually weakened, acting as a negative regulator. The results of this study could lay the theoretical foundation for investigating the mechanism of coloration and flower color variation in I. uliginosa. Full article

Athletes have a high risk of injury. Kinesiophobia is a condition in which an individual experiences a fear of physical movement and activity after an injury occurs. Our purpose was to systematically review the literature about Kinesiophobia in athletes. A systematic review was conducted in February 2023 using PubMed, CINAHL, SPORTDiscus, Web of Science, Cochrane Library, and Medline. Studies were included if they were peer-reviewed, in English, within the last 20 years and included athletes who had been injured and tracked Kinesiophobia. Articles were checked for quality via the modified Downs and Black checklist. Fourteen studies were included in the review and had an average “fair” quality score. Authors examined Kinesiophobia in injured athletes with mostly lower-extremity injuries. Kinesiophobia was associated with lower physical and mental outcomes. Kinesiophobia exists in athletes and can affect both physical and mental factors. The Tampa Scale of Kinesiophobia (TSK) was the most common tool used to examine Kinesiophobia. Common mental factors associated with Kinesiophobia include anxiety, low confidence, and fear avoidance. Full article

In this study spanning four decades, we explored the relationship between the Reserve Bank of India’s (RBI) interventions and the validity of Purchasing Power Parity (PPP) across two distinct exchange rate regimes: the fixed exchange rate regime (1975–1993) and the managed floating regime (1994–2015). Applying an error correction model (VECM), our analysis reveals that under the fixed exchange rate regime, the environment is conducive to PPP due to frequent interventions by the RBI. However, in the managed floating regime, selective interventions weaken the applicability of PPP. These findings align with prior research but also hint at the limitations of linear models in capturing the intricate dynamics of PPP when central banks are involved. Nonlinear models may hold the key to unraveling the relationship more effectively. Full article

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. Full article

The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care. Full article

Botrytis cinerea is a well-known pathogen that can be challenging to control in crops, such as wine grapes. To adapt to the increasing problems of climate change and strain resistance, it is important to find new methods to detect Botrytis cinerea and differentiate strains. These methods include strain differentiation and classification by simple sequence repeats (SSRs) and early detection of the fungus by qPCR. Various strains were analysed using SSR markers and either agarose gel electrophoresis or capillary sequencing via PCR. A sensitive qPCR method was refined to achieve an early detection method for the pathogen. The results demonstrate promising ways to distinguish between strains using both agarose gel electrophoresis and capillary sequencing as well as to detect infection before it becomes visible on grapes. This can be used to further understand and analyse different Botrytis cinerea strain characteristics such as laccase activity, regional or annual effects. The early detection method can be used to better prepare growers for an impending infection so that targeted efforts can be made. Full article

Allergies affect approximately 10–30% of people worldwide, with an increasing number of cases each year; however, the underlying mechanisms are still poorly understood. In recent years, extracellular vesicles (EVs) have been suggested to play a role in allergic sensitization and skew to a T helper type 2 (Th2) response. The aim of this review is to highlight the existing evidence of EV involvement in allergies. A total of 22 studies were reviewed; 12 studies showed EVs can influence a Th2 response, while 10 studies found EVs promoted a Th1 or Treg response. EVs can drive allergic sensitization through up-regulation of pro-Th2 cytokines, such as IL-4 and IL-13. In addition, EVs from MRSA can induce IgE hypersensitivity in mice towards MRSA. On the other hand, EVs can induce tolerance in the immune system; for example, pre-exposing OVA-loaded EVs prevented OVA sensitization in mice. The current literature thus suggests that EVs play an essential role in allergy. Further research utilizing human in vitro models and clinical studies is needed to give a reliable account of the role of EVs in allergy. Full article

Periodontal disease is very common in pregnant women. Paraprobiotics are a subset of probiotics. They can be defined as inactivated microbial cells providing health benefits to the host and are considered particularly safe. The aim of this study was to compare the periodontal health of pregnant women and puerperae after 6 months of home use of paraprobiotics. A total of 30 pregnant women were enrolled and divided into two groups: the test group, who had to use a paraprobiotic-based toothpaste (Biorepair Peribioma Pro, Coswell S.p.A., Funo di Argelato, BO, Italy) and mousse (Mousse Mouthwash Biorepair Peribioma, Coswell S.p.A.) twice a day, and the control group, who had to use only the paraprobiotic-based toothpaste. The time frames of the study were: 1 month (T1), 3 months (T2) and 6 months (T3), and data were collected during pregnancy and in the period immediately following delivery. The following indices were evaluated at T0, T1, T2 and T3: clinical attachment loss (CAL), probing pocket depth (PPD), bleeding on probing (BOP), plaque control record (PCR), modified marginal gingival index (mMGI), papillary marginal gingival index (PMGI) and recessions (R). All data were subjected to statistical analysis. PCR decreased significantly from T0 to T1 in the control group and from T0 to T2 and from T0 to T3 in the test group. BOP tended to decrease in both groups, but a significant reduction was observed only in the test group. CAL, PPD, PMGI and mMGI tended to decrease gradually in both groups without significant differences between or within groups. The combination of the paraprobiotic-based toothpaste and the paraprobiotic-based mousse significantly reduced BoP and plaque control over time, although there were no significant differences with the use of the paraprobiotic-based toothpaste alone. In addition, the combination of the two products promoted a trend towards the better stabilization of recessions. Full article

Background: Acute kidney injury (AKI) is a sudden episode of kidney failure which is frequently observed at intensive care units and related to high morbidity/mortality. Although AKI can have many different causes, ischemia–reperfusion (IR) injury is the main cause of AKI. Mechanistically, NADPH oxidases (NOXs) are involved in the pathophysiology contributing to oxidative stress following IR. Previous reports have indicated that knockout of NOX4 may offer protection in cardiac and brain IR, but there is currently less knowledge about how this could be exploited therapeutically and whether this could have significant protection in IR-induced AKI. Aim: To investigate the hypothesis that a novel and specific NOX4 inhibitor (GLX7013114) may have therapeutic potential on kidney and mitochondrial function in a mouse model of IR-induced AKI. Methods: Kidneys of male C57BL/6J mice were clamped for 20 min, and the NOX4 inhibitor (GLX7013114) was administered via osmotic minipump during reperfusion. Following 3 days of reperfusion, kidney function (i.e., glomerular filtration rate, GFR) was calculated from FITC-inulin clearance and mitochondrial function was assessed by high-resolution respirometry. Renal histopathological evaluations (i.e., hematoxylin–eosin) and TUNEL staining were performed for apoptotic evaluation. Results: NOX4 inhibition during reperfusion significantly improved kidney function, as evidenced by a better-maintained GFR (p < 0.05) and lower levels of blood urea nitrogen (p < 0.05) compared to untreated IR animals. Moreover, IR caused significant tubular injuries that were attenuated by simultaneous NOX4 inhibition (p < 0.01). In addition, the level of renal apoptosis was significantly reduced in IR animals with NOX4 inhibition (p < 0.05). These favorable effects of the NOX4 inhibitor were accompanied by enhanced Nrf2 Ser40 phosphorylation and conserved mitochondrial function, as evidenced by the better-preserved activity of all mitochondrial complexes. Conclusion: Specific NOX4 inhibition, at the time of reperfusion, significantly preserves mitochondrial and kidney function. These novel findings may have clinical implications for future treatments aimed at preventing AKI and related adverse events, especially in high-risk hospitalized patients. Full article

Traditional resistance spot welding (RSW) has been unsuccessful in forming quality composite joints between steel– or aluminum–polymer-based composites. This has led to the development of spot welding variants such as friction stir spot welding (FFSW), ultrasonic spot welding (USW), and laser spot welding (LSW). The paper reviewed the differences in the bonding mechanisms, spot weld characteristics, and challenges involved in using these spot welding variants. Variants of RSW use series electrode arrangement, co-axial electrodes, metallic inserts, interlayers, or external energy to produce composite joints. FFSW and USW use nanoparticles, interlayers, or energy directors to create composite spot welds. Mechanical interlocking is the common composite joint mechanism for all variants. Each spot welding variant has different sets of weld parameters and distinct spot weld morphologies. FFSW is the most expensive variant but is commonly used for composite spot weld joints. USW has a shorter welding cycle compared to RSW and FFSW but can only be used for small components. LSW is faster than the other variants, but limited work was found on its use in composite spot weld joining. The use of interlayers in FFSW and USW to form composite joints is a potential research area recommended in this review. Full article

Students’ and teachers’ acceptance of the COVID-19 vaccination may help boost the uptake of the vaccines in the general population because teachers and students serve as a source of information and campaign mechanisation for vaccination. This review aimed to map evidence on the prevalence and predictors of COVID-19 vaccine acceptance and hesitancy among teachers and students. After removing duplicates, a search in several databases (Dimensions, PubMed Central, JSTOR, Google Scholar, Google, the WHO Library, and HINARI) produced 2060 records. Through screening based on the inclusion criteria, 27 records were used for this review. A relatively high prevalence of vaccine hesitancy was found among teachers and students. Teachers and students in countries such as China, Egypt, the USA, and India however, reported relatively low levels of COVID-19 vaccine acceptance. Vaccine hesitancy depends on perceived adverse effects, safety, efficacy, and benefits among teachers and students, with male teachers and male students being more likely to accept the COVID-19 vaccine than their female counterparts. Moreover, we found that vaccine acceptance could result from trust in the healthcare system and pharmaceutical companies, sources of COVID-19 information, and trust in healthcare providers. Public health experts, academics, other scientists, and health practitioners are required to take a more distinctive, multidisciplinary, and structured approach that focused on communicating effective evidence-based information to combat misinformation concerning COVID-19 vaccines. Full article

As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model’s accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R²) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents. Full article

Cryo-rolled aluminum alloys have a much higher strength-to-weight ratio than cold-rolled alloys, which makes them invaluable in the aerospace and automotive industries. However, this strength gain is frequently accompanied by a formability loss. When uniformly applied to the blank surface, hydroforming provides a solution by generating geometries with constant thickness, making it possible to produce complex structures with “near-net dimensions”, which are difficult to achieve with conventional approaches. This study delves into the cavity die sheet hydroforming (CDSHF) process for high-strength cryo-rolled AA5083 aluminum alloy, focusing on two primary research questions. Firstly, we explored the utilization of a nonlinear 3D finite-element (FE) model to understand its impact on the dimensional accuracy of hydroformed components within the CDSHF process. Specifically, we investigated how decreasing fluid pressure and increasing the holding time of peak fluid pressure can be quantitatively assessed. Secondly, we delved into the optimization of process parameters—fluid pressure (FP), blank holding force (BHF), coefficient of friction (CoF), and flange radius (FR)—to achieve dimensional accuracy in hydroformed square cups through the CDSHF process. Our findings reveal that our efforts, such as reducing peak fluid pressure to 22 MPa, implementing a 30 s holding period, and utilizing an unloading path, enhanced component quality. We demonstrated this with a 35 mm deep square cup exhibiting a 16.1 mm corner radius and reduced material thinning to 5.5%. Leveraging a sophisticated nonlinear 3D FE model coupled with response surface methodology (RSM) and multi-objective optimization techniques, we systematically identified the optimal process configurations, accounting for parameter interactions. Our results underscore the quantitative efficacy of these optimization strategies, as the optimized RSM model closely aligns with finite-element (FE) simulation results, predicting a thinning percentage of 5.27 and a corner radius of 18.64 mm. Overall, our study provides valuable insights into enhancing dimensional accuracy and process optimization in CDSHF, with far-reaching implications for advancing metal-forming technologies. Full article

Most human emotion recognition methods largely depend on classifying stereotypical facial expressions that represent emotions. However, such facial expressions do not necessarily correspond to actual emotional states and may correspond to communicative intentions. In other cases, emotions are hidden, cannot be expressed, or may have lower arousal manifested by less pronounced facial expressions, as may occur during passive video viewing. This study improves an emotion classification approach developed in a previous study, which classifies emotions remotely without relying on stereotypical facial expressions or contact-based methods, using short facial video data. In this approach, we desire to remotely sense transdermal cardiovascular spatiotemporal facial patterns associated with different emotional states and analyze this data via machine learning. In this paper, we propose several improvements, which include a better remote heart rate estimation via a preliminary skin segmentation, improvement of the heartbeat peaks and troughs detection process, and obtaining a better emotion classification accuracy by employing an appropriate deep learning classifier using an RGB camera input only with data. We used the dataset obtained in the previous study, which contains facial videos of 110 participants who passively viewed 150 short videos that elicited the following five emotion types: amusement, disgust, fear, sexual arousal, and no emotion, while three cameras with different wavelength sensitivities (visible spectrum, near-infrared, and longwave infrared) recorded them simultaneously. From the short facial videos, we extracted unique high-resolution spatiotemporal, physiologically affected features and examined them as input features with different deep-learning approaches. An EfficientNet-B0 model type was able to classify participants’ emotional states with an overall average accuracy of 47.36% using a single input spatiotemporal feature map obtained from a regular RGB camera. Full article

We present double-perovskite molybdate with the formula of Ba₂MgMoO₆ doped with Sm³⁺ ions as a potential red phosphor to improve the color characteristics of white-light-emitting dioded (wLEDs). The new orange–red phosphor was synthesized using the co-precipitation (CP) method, and then its structural and spectroscopic properties were determined. Red emission at 642.6 nm dominates, which results from the electric dipole (ED) transition of the ⁴G_5/2 → ⁶H_9/2 type, and the materials are characterized by short luminescence decay times. BMM:Sm³⁺ is, to our best knowledge, the clearest example of dominant red emission of Sm³⁺ resulting from the location of the dopant in octahedral sites of high-symmetry cubic structure. In the sample containing 0.1% Sm³⁺, Sm³⁺ ions are located in both Mg²⁺ and Ba²⁺ sites, while at higher concentrations the Ba²⁺ site is less preferable for doping, as a result of which the emission becomes more uniform and single-site. The relative sensitivity calculated from FIR has a maximum of 2.7% K⁻¹ at −30 °C and another local maximum of 1.6% K⁻¹ at 75 °C. Such value is, to the best of our knowledge, one of the highest achieved for luminescent thermometry performed using only Sm³⁺ ions. To sum up, the obtained materials are good candidates as red phosphor to improve the color characteristics of wLEDs, obtaining a color-rendering index (CRI) of 91 and coordinated color temperature (CCT) of 2943 K, constituting a warm white emission. In addition to this, a promising precedent for temperature sensing using high-symmetry perovskite materials is the high sensitivity achieved, which results from the high symmetry of the BMM host. Full article

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

River conditions are complex and affected by human activities. Various hydraulic structures change the longitudinal slope and cross-sectional shape of the riverbed, which has a significant impact on the simulation of water-head evolution. With continuous population growth, the hydrological characteristics of the Yongding River Basin have undergone significant changes. Too little or too much water discharge may be insufficient to meet downstream ecological needs or lead to the wastage of water resources, respectively. It is necessary to consider whether the total flow in each key section can achieve the expected value under different discharge flows. Therefore, a reliable computer model is needed to simulate the evolution of the water head and changes in the water level and flow under different flow rates to achieve efficient water resource allocation. A one-dimensional hydrodynamic coupling model based on the Saint-Venant equations was established for the Yongding River Basin. Different coupling methods were employed to calibrate the coupling model parameters, using centralised water replenishment data for the autumn of 2022, and the simulation results were verified using centralised water replenishment data for the spring of 2023. The maximum error of the water-head arrival time between different river sections was 4 h, and the maximum error of the water-head arrival time from the Guanting Reservoir to each key cross-section was 6 h. The maximum flow error was less than 5 m³/s, and the changing trend of the flow over time was consistent with the measured data. The model effectively solved the problem of low accuracy of the water level and flow calculation results when using the traditional one-dimensional hydrodynamic model to simulate the flow movement of complex river channels in the Yongding River. The output results of the model include the time when the water head arrives at the key section, the change process of the water level and flow of each section, the change process of the water storage of lakes and gravel pits, and the change process of the total flow and water surface area of the key section. This paper reports data that support the development of an ecological water compensation scheme for the Yongding River. Full article

Myocardial ischemia/reperfusion injury is reduced by cardioprotective adaptations such as local or remote ischemic conditioning. The cardioprotective stimuli activate signaling cascades, which converge on mitochondria and maintain the function of the organelles, which is critical for cell survival. The signaling cascades include not only extracellular molecules that activate sarcolemmal receptor-dependent or -independent protein kinases that signal at the plasma membrane or in the cytosol, but also involve kinases, which are located to or within mitochondria, phosphorylate mitochondrial target proteins, and thereby modify, e.g., respiration, the generation of reactive oxygen species, calcium handling, mitochondrial dynamics, mitophagy, or apoptosis. In the present review, we give a personal and opinionated overview of selected protein kinases, localized to/within myocardial mitochondria, and summarize the available data on their role in myocardial ischemia/reperfusion injury and protection from it. We highlight the regulation of mitochondrial function by these mitochondrial protein kinases. Full article

Heart failure (HF) is a growing issue in developed countries; it is often the result of underlying processes such as ischemia, hypertension, infiltrative diseases or even genetic abnormalities. The great majority of the affected patients present a reduced ejection fraction (≤40%), thereby falling under the name of “heart failure with reduced ejection fraction” (HFrEF). This condition represents a major threat for patients: it significantly affects life quality and carries an enormous burden on the whole healthcare system due to its high management costs. In the last decade, new medical treatments and devices have been developed in order to reduce HF hospitalizations and improve prognosis while reducing the overall mortality rate. Pharmacological therapy has significantly changed our perspective of this disease thanks to its ability of restoring ventricular function and reducing symptom severity, even in some dramatic contexts with an extensively diseased myocardium. Notably, medical therapy can sometimes be ineffective, and a tailored integration with device technologies is of pivotal importance. Not by chance, in recent years, cardiac implantable devices witnessed a significant improvement, thereby providing an irreplaceable resource for the management of HF. Some devices have the ability of assessing (CardioMEMS) or treating (ultrafiltration) fluid retention, while others recognize and treat life-threatening arrhythmias, even for a limited time frame (wearable cardioverter defibrillator). The present review article gives a comprehensive overview of the most recent and important findings that need to be considered in patients affected by HFrEF. Both novel medical treatments and devices are presented and discussed. Full article

The latest convolutional neural network (CNN) models for object detection include complex layered connections to process inference data. Each layer utilizes different types of kernel modes, so the hardware needs to support all kernel modes at an optimized speed. In this paper, we propose a high-speed and optimized CNN accelerator with flexible diagonal cyclic arrays (FDCA) that supports the acceleration of CNN networks with various kernel sizes and significantly reduces the time required for inference processing. The accelerator uses four FDCAs to simultaneously calculate 16 input channels and 8 output channels. Each FDCA features a 4 × 8 systolic array that contains a 3 × 3 processing element (PE) array and is designed to handle the most commonly used kernel sizes. To evaluate the proposed CNN accelerator, we mapped the widely used YOLOv5 CNN model and evaluated the performance of its implementation on the Zynq UltraScale+ MPSoC ZCU102 FPGA. The design consumes 249,357 logic cells, 2304 DSP blocks, and only 567 KB BRAM. In our evaluation, the YOLOv5n model achieves an accuracy of 43.1% ([email protected]). A prototype accelerator has been implemented using Samsung’s 14 nm CMOS technology. It achieves 1.075 TOPS, a peak performance with a 400 MHz clock frequency. Full article