Developing high-performance and cost-competitive electrocatalysts have great significance for the massive commercial production of water-splitting hydrogen. Ni-based electrocatalysts display tremendous potential for electrocatalytic water splitting. Herein, we synthesize a novel NiFe-layered double hydroxide (LDH) electrocatalyst in nanosheets array on high-purity Ni foam. By adjusting the Ni/Fe ratio, the microstructure, and even the behavior of the electrocatalyst in the oxygen evolution reaction (OER), changes significantly. The as-obtained material shows a small overpotential of 223 mV at 10 mAcm⁻² as well as a low Tafel slope of 48.9 mV·dec⁻¹ in the 1 M KOH electrolyte. In addition, it can deliver good stability for at least 24 h of continuous working at 10 mAcm⁻². This work proposes a strategy for engineering catalysts and provides a method for the development of other Ni-based catalysts with excellent performance. Full article

The role of aromatic amino acids in peripheral protein membrane binding has been reported to involve cation–π interactions with choline lipids. In this study, we have investigated the interactions of the model pentapeptide Ac-WL-X-LL-OH (where X = L, Y, F, or W) with the phospholipid membrane using solid-state NMR. The effect of guest residue X on the peptide-lipid interactome was complementary to the seminal report on the interfacial hydrophobicity scale by Wimley and White. We found that the phospholipids retained a lamellar phase in the presence of each of the peptides with an aromatic X residue, whereas the Leu peptide perturbed the bilayer to an extent where an additional isotropic phase was observed. The solid-state NMR ¹³C and ³¹P data provide additional information on the influence of these short peptides on the membrane that has not been previously reported. The magnitude of membrane perturbation was in the order of guest residue X = L > Y~F > W, which is consistent with the relative amino acid interfacial affinity reported by Wimley and White. Further work is, however, required to uncover the behavior of the peptide and localization in the membrane domain due to ambiguity of the ¹³C NMR data. We have launched efforts in this regard for the objective of better understanding the role of aromatic amino acids in peripheral membrane protein binding. Full article

Maize/peanut intercropping is practiced widely to increase land productivity and considered a sustainable way for using and saving resources through peanut’s complementary N source via biological N₂ fixation. Our study aims to understand how maize/peanut intercropping affects the nodulation of peanuts under water-limiting conditions and different nitrogen inputs. A two-year micro-plot experiment in 2015–2016 and a two-year field experiment in 2017–2018 were conducted to quantify nodulation in maize/peanut intercropping and sole peanut cropping under four N fertilization rates (N-free, low, medium, and high N) in rain-fed water-limited conditions. In the micro-plot experiment, intercropped peanuts increased nodule biomass compared to sole peanuts. The nodule number of intercropped peanuts was 51.6% (p = 0.001) higher than that of sole cropped peanuts, while nodule weights did not differ at high N fertilization rates and were lower in the no-N fertilization control. However, the results were different in the field experiment. Both the nodule number and single weight of the sole cropped peanut were 48.7% (p = 0.020) and 58.9% (p = 0.014) higher than that of the intercropped peanut. The ratio of the nodule weight to aboveground dry matter at the beginning peg in the dry year of 2017 was lower in intercropping than sole cropping, especially at low N fertilization rates. The potential increase in nodulation found in a well-controlled micro-plot environment might be limited by strong water and light competitions in field conditions. The results could contribute to the understanding of interspecific interactions in cereal/legume intercropping. Full article

The field of argument analysis has become a crucial component in the advancement of natural language processing, which holds the potential to reveal unprecedented insights from complex data and enable more efficient, cost-effective solutions for enhancing human initiatives. Despite its importance, current technologies face significant challenges, including (1) low interpretability, (2) lack of precision and robustness, particularly in specialized fields like finance, and (3) the inability to deploy effectively on lightweight devices. To address these challenges, we introduce a framework uniquely designed to process and analyze massive volumes of argument data efficiently and accurately. This framework employs a text-to-text Transformer generation model as its backbone, utilizing multiple prompt engineering methods to fine-tune the model. These methods include Causal Inference from ChatGPT, which addresses the interpretability problem, and Prefix Instruction Fine-tuning as well as in-domain further pre-training, which tackle the issues of low robustness and accuracy. Ultimately, the proposed framework generates conditional outputs for specific tasks using different decoders, enabling deployment on consumer-grade devices. After conducting extensive experiments, our method achieves high accuracy, robustness, and interpretability across various tasks, including the highest F1 scores in the NTCIR-17 FinArg-1 tasks. Full article

Escherichia coli (E. coli) is a frequent gram-negative bacterium that causes nosocomial infections, affecting more than 100 million patients annually worldwide. Bacterial lipopolysaccharide (LPS) from E. coli binds to toll-like receptor 4 (TLR4) and its co-receptor’s cluster of differentiation protein 14 (CD14) and myeloid differentiation factor 2 (MD2), collectively known as the LPS receptor complex. LPCAT2 participates in lipid-raft assembly by phospholipid remodelling. Previous research has proven that LPCAT2 co-localises in lipid rafts with TLR4 and regulates macrophage inflammatory response. However, no published evidence exists of the influence of LPCAT2 on the gene expression of the LPS receptor complex induced by smooth or rough bacterial serotypes. We used RAW264.7—a commonly used experimental murine macrophage model—to study the effects of LPCAT2 on the LPS receptor complex by transiently silencing the LPCAT2 gene, infecting the macrophages with either smooth or rough LPS, and quantifying gene expression. LPCAT2 only significantly affected the gene expression of the LPS receptor complex in macrophages infected with smooth LPS. This study provides novel evidence that the influence of LPCAT2 on macrophage inflammatory response to bacterial infection depends on the LPS serotype, and it supports previous evidence that LPCAT2 regulates inflammatory response by modulating protein translocation to lipid rafts. Full article

Multi-channel audio signals provide a better auditory sensation to the audience. However, missing data may occur in the collection, transmission, compression, or other processes of audio signals, resulting in audio quality degradation and affecting the auditory experience. As a result, the completeness of the audio signal has become a popular research topic in the field of signal processing. In this paper, the tensor nuclear norm is introduced into the audio signal completion algorithm, and the multi-channel audio signals with missing data are restored by using the completion algorithm based on the tensor nuclear norm. First of all, the multi-channel audio signals are preprocessed and are then transformed from the time domain to the frequency domain. Afterwards, the multi-channel audio with missing data is modeled to construct a third-order multi-channel audio tensor. In the next part, the tensor completion algorithm is used to complete the third-order tensor. The optimal solution of the convex optimization model of the tensor completion is obtained by using the convex relaxation technique and, ultimately, the data recovery of the multi-channel audio with data loss is accomplished. The experimental results of the tensor completion algorithm and the traditional matrix completion algorithm are compared using both objective and subjective indicators. The final result shows that the high-order tensor completion algorithm has a better completion ability and can restore the audio signal better. Full article

Background: Understanding the concept and dynamic process of the evolution of professional identity and roles of market access (MA) in the pharmaceutical industry (pharma) is critical to personal, interpersonal, and professional levels of development and impact. Objective: The aim was to carry out a scoping review of the conceptualisation of MA within pharma. Data Sources: BioMed Central, WorldCat.org, and Directory of Open Access Journals were searched from 2003 to 2023. Study Selection: All articles on concepts or definitions and other surrogate terms on MA in pharma were selected. Data Extraction: Keywords generated from an initial cursory literature search on MA in pharma were used in conjunction with AND/OR as search terms. Using the data charting method, key findings were mapped and summarised descriptively. inductive analysis was performed, allowing codes/themes that are relevant to the concept to emerge. Data Synthesis: Arskey and O’Malley’s six-stage framework and the PRISMA extension for scoping reviews extension checklist were used as the review and reporting templates. The databases search yielded 222 results. Following title and abstract screening, a total of 146 papers were screened, and 127 of them were excluded. Full-text review was conducted for 19 papers that were deemed by two reviewers to meet the eligibility criteria. One of the authors arbitrated on disputed papers for inclusion. Only 14 of the included papers were found to meet the criteria for the final analysis. Five conceptual dimensions of MA in pharma were identified as “right products”, “right patient”, “right price”, “right point” (time), and “right place” (setting). Conclusions: Market access in pharma is a process that commences with the development and availability of the right products that are proven to be efficacious and disease/condition-specific (including medications, medical devices, and vaccines); specifically produced for the right patients or end users who will maximise best clinical outcomes and economic value; delivered at the right point in a timely, sustained, and efficient manner, given at the right price (commercially viable or reimbursed price that represents good value); and conducted within the economic, policy, societal, and technological contexts, with the overarching goal of achieving the best patient outcomes and ensuring product profitability. Full article

Since tool wear accumulates in the cutting process, the condition of the cutting tool shows a degradation trend, which ultimately affects the surface quality. Tool wear monitoring and prediction are of significant importance in intelligent manufacturing. The cutting signal shows short-term randomness due to non-uniform materials in the workpiece, making it difficult to accurately monitor tool condition by relying on instantaneous signals. To reduce the impact of transient fluctuations, this paper proposes a novel network based on deep learning to monitor and predict tool wear. Firstly, a CNN model based on residual connection was designed to extract deep features from multi-sensor signals. After that, a temporal model based on an encoder and decoder was built for short-term monitoring and long-term prediction. It captured the instantaneous features and long-term trend features by mining the temporal dependence of the signals. In addition, an encoder and decoder-based temporal model is proposed for smoothing correction to improve the estimation accuracy of the temporal model. To validate the performance of the proposed model, the PHM dataset was used for wear monitoring and prediction and compared with other deep learning models. In addition, CFRP milling experiments were conducted to verify the stability and generalization of the model under different machining conditions. The experimental results show that the model outperformed other deep learning models in terms of MAE, MAPE, and RMSE. Full article

The purpose of this article is to review the effects of four commonly consumed beverage types—sugar-sweetened beverages (SSBs), caffeinated beverages, green tea, and alcohol—on five common benign gynecological conditions: uterine fibroids, endometriosis, polycystic ovary syndrome (PCOS), anovulatory infertility, and primary dysmenorrhea (PD). Here we outline a plethora of research, highlighting studies that demonstrate possible associations between beverage intake and increased risk of certain gynecological conditions—such as SSBs and dysmenorrhea—as well as studies that demonstrate a possible protective effect of beverage against risk of gynecological condition—such as green tea and uterine fibroids. This review aims to help inform the diet choices of those with the aforementioned conditions and give those with uteruses autonomy over their lifestyle decisions. Full article

Water availability is a limiting factor for plant production, especially in Brazilian semi-arid regions. The main aim of the study was to investigate the physiological effects of drought during the fruiting stage of watermelon cultivation. A completely randomized block design with four replications and six treatments varied by the number of lateral drip tapes (1 or 2) and the duration of drought stress (0, 4, and 8 days) was used. The following parameters were evaluated: relative chlorophyll content, relative leaf water content, electrolyte leakage, CO₂ assimilation (A), stomatal conductance (g_s), internal CO₂ concentration, leaf temperature, transpiration (E), water use efficiency (WUE), carboxylation efficiency (CE), yield, thickness, diameter, length, and fruit °brix, at 4 and 8 days of drought. Drought negatively affected photosynthesis, particularly in treatments with a single dripper and 4 days of drought, resulting in reductions of up to 60% in A, 68% in g_s, 44% in E, 58% in WUE, and 59% in CE, but did not have a significant effect on watermelon yield after 4 or 8 days of irrigation. It was concluded that drought influences the physiological responses of watermelon plants, mainly in reducing photosynthesis, but does not drastically affect fruit productivity in short periods of stress. Full article

Xanthorrhizol, an important marker of Curcuma xanthorrhiza, has been recognized for its different pharmacological activities. A green strategy for selective xanthorrhizol extraction is required. Herein, natural deep eutectic solvents (NADESs) based on glucose and organic acids (lactic acid, malic acid, and citric acid) were screened for the extraction of xanthorrhizol from Curcuma xanthorrhiza. Ultrasound-assisted extraction using glucose/lactic acid (1:3) (GluLA) gave the best yield of xanthorrhizol. The response surface methodology with a Box–Behnken Design was used to optimize the interacting variables of water content, solid-to-liquid (S/L) ratio, and extraction to optimize the extraction. The optimum conditions of 30% water content in GluLA, 1/15 g/mL (S/L), and a 20 min extraction time yielded selective xanthorrhizol extraction (17.62 mg/g) over curcuminoids (6.64 mg/g). This study indicates the protective effect of GluLA and GluLA extracts against oxidation-induced DNA damage, which was comparable with those obtained for ethanol extract. In addition, the stability of the xanthorrhizol extract over 90 days was revealed when stored at −20 and 4 °C. The FTIR and NMR spectra confirmed the hydrogen bond formation in GluLA. Our study reported, for the first time, the feasibility of using glucose/lactic acid (1:3, 30% water v/v) for the sustainable extraction of xanthorrhizol. Full article

In this article, the main problem under investigation is the detection and diagnosis of short-circuit faults in power transmission lines. The proposed fault detection (FDD) approach is mainly based on principal component analysis (PCA). The proposed fault diagnosis/identification (FAI) approach is mainly based on sliding-window versions of the discrete Fourier transform (DFT) and discrete Hilbert transform (DHT). The main contributions of this article are (a) a fault detection approach based on principal component analysis in the two-dimensional scores space; and (b) a rule-based fault identification approach based on human expert knowledge, combined with a probabilistic decision system, which detects variations in the amplitudes and frequencies of current and voltage signals, using DFT and DHT, respectively. Simulation results of power transmission lines in Portugal are presented in order to show the robust and high performance of the proposed FDD approach for different signal-to-noise ratios. The proposed FDD approach, implemented in Python, that can be executed online or offline, can be used to evaluate the stress to which circuit breakers (CBs) are subjected, providing information to supervision- and condition-based monitoring systems in order to improve predictive and preventive maintenance strategies, and it can be applied to high-/medium-voltage power transmission lines as well as to low-voltage electronic transmission systems. Full article

Nodular or keloidal scleroderma is a rare condition with unclear cause and sporadic mentions in the medical literature. It was first recognized in the 19th century, yet its classification is still debated due to the limited number of reported cases. This rare variant of scleroderma is associated with either progressive systemic sclerosis or localized morphea. Clinically, it presents with asymptomatic nodules or plaques, resembling spontaneous keloid formation, often found on the trunk and proximal extremities. Recent literature reviews show a predominance of women with a mean age of 44 years. Diagnosis relies on clinical and histopathological findings, which usually show overlapping features of both scleroderma and true keloids, secondarily to an excessive fibrosing reaction attributed to collagen formation. We present an unusual case of a 70-year-old female patient who displayed the coexistence of two distinct subtypes of morphea (nodular/keloidal and linear), and exclusive skin involvement, which contrasts with the typical presentation of nodular/keloidal scleroderma, often associated with organ-specific disease. However, recent publications have diverged from previous ones regarding systemic sclerosis, with no systemic involvement reported between 2018 and 2024, which we evaluated in our descriptive literature review. With less than 50 cases reported in total, our case underlines the importance of recognizing this rare disease, ensuring appropriate evaluation, treatment, and follow-up. Full article

The protozoan parasite, Toxoplasma gondii, has been linked to several psychiatric disorders, including schizophrenia. The aim of this study was to assess the prevalence of T. gondii IgG antibodies and risk factors associated with seroprevalence in patients diagnosed with schizophrenia. This seroepidemiological study assessed 196 participants, divided into two groups. The study group consisted of 98 schizophrenic patients and was matched with 98 healthy blood donors. A questionnaire was used to gather information regarding potential risk factors associated with T. gondii seroprevalence. Results revealed a higher seroprevalence of T. gondii IgG antibodies in schizophrenic patients (69.39%, 68/98) when compared to healthy controls (51.02%, 50/98) (OR: 2.18; 95% CI: 1.21–3.9; p = 0.01). Patients with schizophrenia who consumed raw or undercooked meat (80.65%, 25/31) (OR: 3.75; 95% CI: 1.25–11.21, p = 0.02) and those with a lower educational level (77.59%, 45/58) (OR: 3.5; 95% CI: 1.59–7.54, p = 0.002) presented increased T. gondii seropositivity rates versus their control counterparts. Our findings indicate a high T. gondii IgG seroprevalence in patients diagnosed with schizophrenia compared to healthy blood donors. Factors associated with T. gondii seroprevalence were consumption of raw or uncooked meat and a lower educational attainment. This study provided the first data regarding the potential risk factors for toxoplasmosis in Romanian patients diagnosed with schizophrenia and may serve as a foundation for future research and the development of preventive strategies. Full article

One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs. Full article

Soft robots claim the architecture of actuators, sensors, and computation demands with their soft bodies by obtaining fast responses and adapting to the environment. Sensory-motor coordination is one of the main design principles utilized for soft robots because it allows the capability to sense and actuate mutually in the environment, thereby achieving rapid response performance. This work intends to study the response for a system that presents coupled actuation and sensing functions simultaneously and is integrated in an arbitrary elastic structure with ionic conduction elements, called as soft sensory-motor system based on ionic solution (SSMS-IS). This study provides a comparative analysis of the performance of SSMS-IS prototypes with three diverse designs: toroidal, semi-toroidal, and rectangular geometries, based on a series of performance experiments, such as sensitivity, drift, and durability. The design with the best performance was the rectangular SSMS-IS using silicon rubber RPRO20 for both internal and external pressures applied in the system. Moreover, this work explores the performance of a bioinspired soft robot using rectangular SSMS-IS elements integrated in its body. Further, it investigated the feasibility of the robot to adapt its morphology online for environment variability, responding to external stimuli from the environment with different levels of stiffness and damping. Full article

The frictional properties of lithium compound grease (LCG) with different percentage compositions of few-layer graphene (FLG) were investigated, and the mechanisms of temperature and loading effects on LCG containing FLG are also considered. The concluding effect shows that 1 wt% FLG is more appropriate for friction and wear modifiers for lithium compound grease at elevated temperatures and less suitable at ordinary temperatures. Thickener chemisorption film, FLG layering film, and tribo-reaction film consisting of FeO(OH), Fe₂O₃, Fe₃O₄, Li₂O, and other oxides assist in the establishment of a lubricating boundary film on the friction interfaces lubricated with LCG containing FLG. The poor fluidity of lithium compound grease at low temperatures leads to poor dispersion of FLG, decreasing friction reduction capability. Under elevated temperature and low load condition, adding 1wt% FLG to LCG can only improve its wear-resistant property, the abrasion volume of steel plate reduced by 24.49%. Under elevated temperature and high load condition, adding 1wt% FLG to LCG can only enhance its anti-friction characteristics.. Conversely, FLG is unsuitable as an anti-friction and wear-resistant additive for LCG at low-temperature conditions. Full article

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection. Full article

In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions. Full article

Clinical and basic science applications using adipose-derived stem cells (ADSCs) are gaining popularity. The current adipose tissue harvesting procedures introduce nonphysiological conditions, which may affect the overall performance of the isolated ADSCs. In this study, we elucidate the differences between ADSCs isolated from adipose tissues harvested within the first 5 min of the initial surgical incision (well-vascularized, nonpremedicated condition) versus those isolated from adipose tissues subjected to medications and deprived of blood supply during elective free flap procedures (ischemic condition). ADSCs isolated from well-vascularized and ischemic tissues positively immunostained for several standard stem cell markers. Interestingly, the percent change in the CD36 expression for ADSCs isolated from ischemic versus well-vascularized tissue was significantly lower in males than females (p < 0.05). Upon differentiation and maturation to adipocytes, spheroids formed using ADSCs isolated from ischemic adipose tissue had lower triglyceride content compared to those formed using ADSCs isolated from the well-vascularized tissue (p < 0.05). These results indicate that ADSCs isolated from ischemic tissue either fail to uptake fatty acids or fail to efficiently convert those fatty acids into triglycerides. Therefore, more robust ADSCs suitable to establish in vitro adipose tissue models can be obtained by harvesting well-vascularized and nonpremedicated adipose tissues. Full article

This article explores the intertwining connections among artificial intelligence, machine learning, digital transformation, and computational sustainability, detailing how these elements jointly empower citizens within a smart city framework. As technological advancement accelerates, smart cities harness these innovations to improve residents’ quality of life. Artificial intelligence and machine learning act as data analysis powerhouses, making urban living more personalized, efficient, and automated, and are pivotal in managing complex urban infrastructures, anticipating societal requirements, and averting potential crises. Digital transformation transforms city operations by weaving digital technology into every facet of urban life, enhancing value delivery to citizens. Computational sustainability, a fundamental goal for smart cities, harnesses artificial intelligence, machine learning, and digital resources to forge more environmentally responsible cities, minimize ecological impact, and nurture sustainable development. The synergy of these technologies empowers residents to make well-informed choices, actively engage in their communities, and adopt sustainable lifestyles. This discussion illuminates the mechanisms and implications of these interconnections for future urban existence, ultimately focusing on empowering citizens in smart cities. Full article

Electrochemical measurements are vital to a wide range of applications such as air quality monitoring, biological testing, food industry, and more. Integrated circuits have been used to implement miniaturized and low-power electrochemical potentiostats that are suitable for wearable devices. However, employing modern integrated circuit technologies with low supply voltage precludes the utilization of electrochemical reactions that require a higher potential window. In this paper, we present a novel circuit architecture that utilizes dynamic voltage at the working electrode of an electrochemical cell to effectively enhance the supported voltage range compared to traditional designs, increasing the cell voltage range by 46% and 88% for positive and negative cell voltages, respectively. In return, this facilitates a wider range of bias voltages in an electrochemical cell, and, therefore, opens integrated microsystems to a broader class of electrochemical reactions. The circuit was implemented in 180 nm technology and consumes 2.047 mW of power. It supports a bias potential range of 1.1 V to −2.12 V and cell potential range of 2.41 V to −3.11 V that is nearly double the range in conventional designs. Full article