Muscular temporomandibular joint disorders (M-TMDs) encompass a wide range of painful muscular conditions, which can provoke functional limitation and severely affect quality of life. The aim of the present study was to assess the treatment outcomes in patients affected by M-TMDs in terms of pain scores assessed with pressure pain threshold (PPT). The levels of depression, anxiety, and the Oral Health Impact Profile were also assessed and compared to healthy controls. Patients with a clinical diagnosis of M-TMDs and a control group of healthy subjects were enrolled. At baseline, OHIP-14, PHQ-9, and GAD-7 were administered. PPT was registered at the level of masseter and temporalis muscles. The patients affected by M-TMDs were then treated with oral splints and physio-kinesiotherapy following a standardized treatment protocol. At the 6-month follow-up of M-TMD-affected patients, PPT was registered, and the questionnaires were re-administered to evaluate treatment outcomes. In total, sixty patients and sixty controls were enrolled. The treatment of M-TMDs produced a significant improvement in PPT at the level of the masseter muscle. OHIP-14 at baseline in the M-TMD group was significantly higher compared to the control group (p < 0.05). At the 6-month follow-up, a significant reduction in OHIP-14 scores was registered, although with higher scores compared to the control group (p < 0.05). PHQ-9 was significantly higher at baseline in the M-TMD group (p < 0.05) and decreased to values comparable to the control group after treatment. GAD-7 presented statistically significant differences between the control group and M-TMD patients at baseline (p < 0.05) and between pre- and post-treatment in the M-TMD group. Following treatment, the GAD-7 scores were comparable to the control group. The symptom burden associated with M-TMDs negatively affects quality of life, with higher oral health impairment and a tendency towards depression and anxiety compared to healthy subjects. Following treatment, an improvement in both PPT and quality of life was observed. Full article

A major limitation of most metabolomics datasets is the sparsity of pathway annotations for detected metabolites. It is common for less than half of the identified metabolites in these datasets to have a known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a “pathway category”, as defined by a metabolic knowledge base like KEGG. Past models were implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating the predictions for multiple pathway categories. This past approach multiplied the computational resources necessary for training while diluting the positive entries in the gold standard datasets needed for training. To address these limitations, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts the features both representing a metabolite and representing a pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite–pathway features pair approach not only outperforms the combined performance of training separate binary classifiers but demonstrates an order of magnitude improvement in robustness: a Matthews correlation coefficient of 0.784 ± 0.013 versus 0.768 ± 0.154. Full article

This paper examines the communication difficulties encountered in cross-media wireless optical transmission through simulated research on the utilization of orthogonal time and frequency space (OTFS) modulation technology. Our analysis and comparison demonstrate that OTFS significantly improves the reliability and throughput of data transmission in intricate multipath channel settings. In contrast to conventional orthogonal frequency division multiplexing (OFDM) technology, OTFS displays better resilience and transmission effectiveness. We foresee additional enhancements and progress in OTFS technology to present a sturdier and more efficient resolution for wireless communication, thereby providing valuable perspectives and encouragement for associated research initiatives. Our results underscore the capability of OTFS technology to transform wireless communication systems in demanding multipath channel conditions. Full article

Antimicrobial resistance (AMR) is one of the biggest threats in modern times. It was estimated that in 2019, 1.27 million deaths occurred around the globe due to AMR. Methicillin-resistant Staphylococcus aureus (MRSA) strains, a pathogen considered of high priority by the World Health Organization, have proven to be resistant to most of the actual antimicrobial treatments. Therefore, new treatments are required to be able to manage this increasing threat. Under this perspective, an important metabolic pathway for MRSA survival, and absent in mammals, is the shikimate pathway, which is involved in the biosynthesis of chorismate, an intermediate for the synthesis of aromatic amino acids, folates, and ubiquinone. Therefore, the enzymes of this route have been considered good targets to design novel antibiotics. The fifth step of the route is performed by shikimate kinase (SK). In this study, an in-house chemical library of 170 benzimidazole derivatives was screened against MRSA shikimate kinase (SaSK). This effort led to the identification of the first SaSK inhibitors, and the two inhibitors with the greatest inhibition activity (C1 and C2) were characterized. Kinetic studies showed that both compounds were competitive inhibitors with respect to ATP and non-competitive for shikimate. Structural analysis through molecular docking and molecular dynamics simulations indicated that both inhibitors interacted with ARG113, an important residue involved in ATP binding, and formed stable complexes during the simulation period. Biological activity evaluation showed that both compounds were able to inhibit the growth of a MRSA strain. Mitochondrial assays showed that both compounds modify the activity of electron transport chain complexes. Finally, ADMETox predictions suggested that, in general, C1 and C2 can be considered as potential drug candidates. Therefore, the benzimidazole derivatives reported here are the first SaSK inhibitors, representing a promising scaffold and a guide to design new drugs against MRSA. Full article

We present a classical treatment of the ionization and electron-capture processes in the interaction of protons with neutral noble-gas atoms, namely, Ne, Ar, Kr, and Xe. We used a three-body classical-trajectory Monte Carlo (CTMC) method to calculate the total (TCS) and differential (DCS) cross sections of single-electron processes. The Garvey-type model potential was employed in the CTMC model to describe the collision between the projectile and the target, accounting for the screening effect of the inactive electrons. The TCSs are evaluated for impact energies in the energy range between 0.2 keV and 50 MeV for a number of sub-shells of the targets. The ionization DCS are evaluated for an impact energy of 35 keV, focusing on the outer sub-shells only. We found that our ionization and electron-capture TCSs are in very good agreement with the previous theoretical and experimental data for all targets. Moreover, we presented single (SDCS)- and double (DDCS)-differential cross sections as a function of the energy and ejection angle of the ionized electron for all collision systems. Full article

This study evaluated the differences in the metabolite profile of three n-3 FA fish oil formulations in 12 healthy participants: (1) standard softgels (STD) providing 600 mg n-3 FA; (2) enteric-coated softgels (ENT) providing 600 mg n-3 FA; (3) a new micellar formulation (LMF) providing 374 mg n-3 FA. The pharmacokinetics (PKs), such as the area under the plot of plasma concentration (AUC), and the peak blood concentration (C_max) of the different FA metabolites including HDHAs, HETEs, HEPEs, RvD1, RvD5, RvE1, and RvE2, were determined over a total period of 24 h. Blood concentrations of EPA (26,920.0 ± 10,021.0 ng/mL·h) were significantly higher with respect to AUC_0-24 following LMF treatment vs STD and ENT; when measured incrementally, blood concentrations of total n-3 FAs (EPA/DHA/DPA3) up to 11 times higher were observed for LMF vs STD (iAUC _0-24: 16,150.0 ± 5454.0 vs 1498.9 ± 443.0; p ≤ 0.0001). Significant differences in n-3 metabolites including oxylipins were found between STD and LMF with respect to 12-HEPE, 9-HEPE, 12-HETE, and RvD1; 9-HEPE levels were significantly higher following the STD vs. ENT treatment. Furthermore, within the scope of this study, changes in blood lipid levels (i.e., cholesterol, triglycerides, LDL, and HDL) were monitored in participants for up to 120 h post-treatment; a significant decrease in serum triglycerides was detected in participants (~20%) following the LMF treatment; no significant deviations from the baseline were detected for all the other lipid biomarkers in any of the treatment groups. Despite a lower administered dose, LMF provided higher blood concentrations of n-3 FAs and certain anti-inflammatory n-3 metabolites in human participants—potentially leading to better health outcomes. Full article

By extending the dipole Pomeron (DP) model, successful in describing elastic nucleon–nucleon scattering, to proton single diffractive dissociation (SD), we predict a dip-bump structure in the squared four-momentum transfer (t) distribution of proton’s SD. Structures in the t distribution of single diffractive dissociation are predicted around $t=-4$ GeV${}^2$ at LHC energies in the range of 3 GeV${}^2$$\lesssim \left|t\right|\lesssim$ 7 GeV${}^2$. Apart from the dependence on s (total energy squared) and t (squared momentum transfer), we predict also a dependence on missing masses. We include the minimum set of Regge trajectories, namely the Pomeron and the Odderon, indispensable at the LHC. Further generalization, e.g., by the inclusion of non-leading Regge trajectories, is straightforward. The present model contains two types of Regge trajectories: those connected with $t-$channel exchanges (the Pomeron, the Odderon, and non-leading (secondary) reggeons) appearing at small and moderate $-t$, where they are real and nearly linear, as well as direct-channel trajectories $\alpha \left(M^2\right)$ related to missing masses. In this paper, we concentrate on structures in t neglecting (for the time being) resonances in $M^2$. Full article

Introduction: The prognostic factors for extremity soft-tissue sarcomas (ESTSs) treated with multimodal surgery and radiotherapy (RT) remain a subject of debate across diverse and heterogeneous studies. Methods: We retrospectively analyzed nonmetastatic ESTS patients treated with RT between 2007 and 2020 in Strasbourg, France. We assessed local control (LC), distant control (DC), overall survival (OS), and complications. Results: A total of 169 patients diagnosed with localized ESTS were included. The median age was 64 years (range 21–94 years). ESTS primarily occurred proximally (74.6%) and in the lower limbs (71%). Most tumors were grade 2–3 (71.1%), deep-seated (86.4%), and had R0 margins (63.9%). Most patients were treated with helical tomotherapy (79.3%). The median biologically effective dose (BED) prescribed was 75 BEDGy₄ (range 45.0–109.9). The median follow-up was 5.5 years. The 5- and 10-year LC, DC, and OS rates were 91.7%, 76.8%, and 83.8% and 84.2%, 74.1%, and 77.6%, respectively. According to the univariate analysis, LC was worse for patients who received less than 75 BEDGy₄ (p = 0.015). Deep tumors were associated with worse OS (p < 0.05), and grade 2–3 and undifferentiated pleomorphic sarcoma (UPS) were linked to both shorter DC and shorter OS (p < 0.05). IMRT was associated with longer LC than 3DRT (p = 0.018). Multivariate analysis revealed that patients with liposarcoma had better OS (p < 0.05) and that patients with distant relapse had shorter OS (p < 0.0001). Conclusion: RT associated with surgical resection was well tolerated and was associated with excellent long-term rates of LC, DC, and OS. Compared with 3DRT, IMRT improved local control. Liposarcoma was a favorable prognostic factor for OS. Intermediate- and high-grade tumors and deep tumors were associated with lower DC and OS. Full article

Proteins are essential molecules that play crucial roles in maintaining cellular homeostasis and carrying out biological functions such as catalyzing biochemical reactions, structural proteins, immune response, etc. However, proteins also are highly susceptible to damage by reactive oxygen species (ROS) and reactive nitrogen species (RNS). In this review, we summarize the role of protein oxidation in normal aging and Alzheimer’s disease (AD). The major emphasis of this review article is on the carbonylation and nitration of proteins in AD and mild cognitive impairment (MCI). The oxidatively modified proteins showed a strong correlation with the reported changes in brain structure, carbohydrate metabolism, synaptic transmission, cellular energetics, etc., of both MCI and AD brains compared to the controls. Some proteins were found to be common targets of oxidation and were observed during the early stages of AD, suggesting that those changes might be critical in the onset of symptoms and/or formation of the pathological hallmarks of AD. Further studies are required to fully elucidate the role of protein oxidation and nitration in the progression and pathogenesis of AD. Full article

Initially, fuzzy sets and intuitionistic fuzzy sets were used to address real-world problems with imprecise data. Eventually, the notion of the hesitant fuzzy set was formulated to handle decision makers’ reluctance to accept asymmetric information. However, in certain scenarios, asymmetric information is gathered in terms of a possible range of acceptance and nonacceptance by players rather than specific values. Furthermore, decision makers exhibit some hesitancy regarding this information. In such a situation, all the aforementioned expansions of fuzzy sets are unable to accurately represent the scenario. The purpose of this article is to present asymmetric information situations in which the range of choices takes into account the hesitancy of players in accepting or not accepting information. To illustrate these problems, we develop matrix games that consider the payoffs of interval-valued intuitionistic hesitant fuzzy elements (IIHFEs). Dealing with these types of fuzzy programming problems requires a significant amount of effort. To solve these matrix games, we formulate two interval-valued intuitionistic hesitant fuzzy programming problems. Preserving the hesitant nature of the payoffs to determine the optimal strategies, these two problems are transformed into two nonlinear programming problems. This transformation involves using mathematical operations for IIHFEs. Here, we construct a novel aggregation operator of IIHFEs, viz., min-max operators of IIHFEs. This operator is suitable for applying the developed methodology. The cogency and applicability of the proposed methodology are verified through a numerical example based on the situation of conflict between hackers and defenders to prevent damage to cybersecurity. To validate the superiority of the proposed model along with the computed results, we provide comparisons with the existing models. Full article

With the development of information technology, tremendous vulnerabilities and backdoors have evolved, causing inevitable and severe security problems in cyberspace. To fix them, the endogenous safety and security (ESS) theory and one of its practices, the Dynamic Heterogeneous Redundant (DHR) architecture, are proposed. In the DHR architecture, as an instance of the multi-heterogeneous system, a decision module is designed to obtain intermediate results from heterogeneous equivalent functional executors. However, privacy-preserving is not paid attention to in the architecture, which may cause privacy breaches without compromising the ESS theory. In this paper, based on differential privacy (DP), a theoretically rigorous privacy tool, we propose a privacy-preserving DHR framework called DP-DHR. Gaussian random noise is injected into each (online) executor output in DP-DHR to guarantee DP, but it also makes the decision module unable to choose the final result because each executor output is potentially correct even if it is compromised by adversaries. To weaken this disadvantage, we propose the advanced decision strategy and the hypersphere clustering algorithm to classify the perturbed intermediate results into two categories, candidates and outliers, where the former is closer to the correct value than the latter. Finally, the DP-DHR is proven to guarantee DP, and the experimental results also show that the utility is not sacrificed for the enhancement of privacy by much (a ratio of 4–7% on average), even in the condition of some executors (less than one-half) being controlled by adversaries. Full article

Peripheral blood is the most practical tissue for human immune system gene expression profiling because it is easily accessible, whereas the site of primary infection in certain diseases may not be easily accessible. Due to the ex vivo instability of RNA transcripts, a key challenge in the gene expression analysis of blood samples is the rapid sample handling and stabilization of the mRNA by adding an RNA preservative (PAXgene^TM Blood RNA Tubes, Tempus^TM Blood RNA tubes, RNAlater Stabilization Reagent, RNAgard^® Blood Tubes). BioMole (Turin, Italy) has developed a novel blood stabilizer, called RNApro, in which RNA is stabilized during phlebotomy and sample storage. In this study, RNApro performance intended as RNA yield, integrity, and stability was evaluated. Our results show that blood samples stored at −80 °C and re-extracted after 7 years show no differences in terms of quantity, quality, and amplificability. The samples in the RNAlater stabilization solution can be stored at room temperature for up to one week or at 4 °C for up to one month. Similar results can also be observed for PAXgene tubes, Tempus tubes, and RNAgard tubes. In agreement with these data, the RNApro stabilization solution preserves the RNA from degradation for up to 1 month at 4 °C and 1 week at room temperature. RNApro can be stored indifferently at −80, −20, 4 °C, or room temperature for up to 2 months after, and then could be stored at −80 °C for up to seven years. In summary, our study is the first to analyze the performance of an RNA stabilizer called RNApro. We can conclude that several studies have shown significant differences in gene expression analysis when the sample was preserved in different RNA stabilizers. Therefore, it is desirable to standardize the method of nucleic acid conservation when comparing data from transcriptomic analyses. Full article

Industrial pure zirconium plays an essential role as a structural material in the nuclear energy sector. Understanding the deformation mechanisms is crucial for effectively managing the plasticity and texture evolution of industrial pure zirconium. In the present study, the texture and microstructure evolution of industrial pure zirconium during the cold-rolling process have been characterized by XRD, EBSD, and TEM. The influences of various twins on texture evolution have also been simulated by the reaction stress model. The effects of slip and twinning on the deformation behavior and texture evolution have been discussed based on crystallographic and experimental considerations. Cold rolling yields a typical bimodal texture, resulting in the preferential <2110>//RD orientation. The activation of the deformation mechanisms during cold rolling follows the sequential trend of slip, twinning, local slip. Experimental characterization and reaction stress simulation illustrate that T1 twins dominate in the early stage, whereas C2 twins develop at the later stage of the cold-rolling process. Twinning, especially the T1 twin, contributes to the formation of the {0001}<1010> orientation. Full article

Disinformation is a phenomenon of concern to all political systems, as it poses a threat to freedom and democracy through the manipulation of public opinion aimed at eroding institutions. This paper presents a bibliometric and systematized study which allows the establishment of a comprehensive view of the research and current state of academic investigations on disinformation. To this end, a content analysis of the scientific articles indexed in Scopus up to 31 December 2023 has been carried out based on three categories of analysis: journals, authors and investigations. Similarly, a systematic study of the 50 most cited articles in this sample was performed in order to gain a deeper understanding of the nature, motivations and methodological approaches of these investigations. The results indicate that disinformation is a research topic which has gained great interest in the academic community since 2018, with special mention to the impact of COVID-19 and the vaccines against this disease. Thus, it can be concluded that disinformation is an object of study which attracts significant attention and which must be approached from transdisciplinarity to respond to a phenomenon of great complexity. Full article

Studies on the distribution of lymphatic filariasis (LF) have mostly focused on reporting prevalence at the community level and distribution at the district levels. Understanding the distribution patterns at community levels may help in designing surveillance strategies. This study aimed to characterize the spatial distribution of LF infections in four hotspot communities in Ghana. The research, involving 252 participants, collected demographic data, mass drug administration (MDA) information, household GPS coordinates, and antigen detection test results. The LF prevalence varied significantly among the communities, with Asemda having the highest (33.33%) and Mempeasem having the lowest (4.44%). Females had lower odds of infection than males (OR = 2.67, p = 0.003 CI: 1.39–5.13). Spatial analysis using kernel density, Anselin Local Moran’s, Getis-Ord Gi models, Ordinary Least Squares, and Geographic Weighted Regression revealed mixed patterns of spatial autocorrelation. This study identified LF hotspots, indicating clusters of high or low prevalence with some areas showing disparities between MDA coverage and LF positivity rates. Despite these hotspots, the overall distribution of LF appeared random, suggesting the importance of purposeful sampling in surveillance activities. These findings contribute valuable insights into the micro-epidemiology of LF, emphasizing the need for community-specific investigations to understand the factors influencing the effectiveness of MDA programs in controlling filarial infections. The study highlights the importance of refining surveillance strategies based on community-level distribution patterns. Full article

The micronutrient Zinc (Zn) is essential for the proper growth and development of crops. Zinc oxide nanoparticles (NPs) and fine particles are presented as an emerging alternative to more traditional fertilizers. In this study, the possible toxic effect of four laboratory-synthesized ZnO NPs and fine particles with different characteristics on tomato seed germination and vigor parameters was examined in comparison to bulk ZnO. Different metal precursors were used for the chemical synthesis of the particles: Zn(NO₃)₂ and ZnSO₄, for particles named NIT-. and SUL-., respectively. In addition, the synthesis process was modified to obtain coated particles (denoted as UW-, NIT-UW and SUL-UW) and washed particles (denoted as W-, NIT-W and SUL-W). These particles were applied at different toxic doses (0, 1.4, 2.8, 5.6 and 11.2 g L⁻¹). The results indicated that although the constant contact time between the ZnO particles did not affect the germination capacity of the seeds, it affected the growth of roots and hypocotyls, with a higher impact on the roots’ development. This toxicity was more evident from the lowest particle dose used, although it did not prevent radicle and hypocotyl elongation during the development period studied (14 days). The synthesized coated particles (NIT-UW, SUL-UW) generated high toxicity on radicle and hypocotyl development, and this effect was observed from the first days of contact with the particles. The observed toxic effects on radicle length were minimized by the application of bulk ZnO particles. In the case of hypocotyl growth, these minor toxic effects were observed by using NIT-W particles and bulk ZnO. The possibility of positive effects on seed germination and development (radicle and hypocotyl length) when in continuous contact with ZnO, whether in fine particles, nanoparticles, or bulk sizes, was excluded. Furthermore, no benefits on germination parameters were observed by suppressing the final washing step in the particle’s synthesis process, suggesting that particle coating did not provide any advantage for seed germination under these continuous contact conditions. Full article

Since the onset of the COVID-19 pandemic in 2019, the role of weather conditions in influencing transmission has been unclear, with results varying across different studies. Given the changes in border policies and the higher vaccination rates compared to earlier conditions, this study aimed to reassess the impact of weather on COVID-19, focusing on local climate effects. We analyzed daily COVID-19 case data and weather factors such as temperature, humidity, wind speed, and a diurnal temperature range from 1 March to 15 August 2022 across six regions in Taiwan. This study found a positive correlation between maximum daily temperature and relative humidity with new COVID-19 cases, whereas wind speed and diurnal temperature range were negatively correlated. Additionally, a significant positive correlation was identified between the unease environmental condition factor (UECF, calculated as RH*Tmax/WS), the kind of Climate Factor Complex (CFC), and confirmed cases. The findings highlight the influence of local weather conditions on COVID-19 transmission, suggesting that such factors can alter environmental comfort and human behavior, thereby affecting disease spread. We also introduced the Fire-Qi Period concept to explain the cyclic climatic variations influencing infectious disease outbreaks globally. This study emphasizes the necessity of considering both local and global climatic effects on infectious diseases. Full article

Ulcerative colitis (UC) is characterized by continuous mucosal ulceration of the colon, starting in the rectum. 5-Aminosalicylic acid (5-ASA) is the main therapy for ulcerative colitis; however, it has side effects. Physical exercise effectively increases the number of anti-inflammatory and anti-immune cells in the body. In the current study, the effects of simultaneous treatment of treadmill exercise and 5-ASA were compared with monotherapy with physical exercise or 5-ASA in UC mice. To induce the UC animal model, the mice consumed 2% dextran sulfate sodium dissolved in drinking water for 7 days. The mice in the exercise groups exercised on a treadmill for 1 h once a day for 14 days after UC induction. The 5-ASA-treated groups received 5-ASA by enema injection using a 200 μL polyethylene catheter once a day for 14 days. Simultaneous treatment improved histological damage and increased body weight, colon weight, and colon length, whereas the disease activity index score and collagen deposition were decreased. Simultaneous treatment with treadmill exercise and 5-ASA suppressed pro-inflammatory cytokines and apoptosis following UC. The benefits of this simultaneous treatment may be due to inhibition on nuclear factor-κB/mitogen-activated protein kinase signaling activation. Based on this study, simultaneous treatment of treadmill exercise and 5-ASA can be considered as a new therapy of UC. Full article

This study provides an innovative and attractive analytical strategy to examine the numerical solution for the time-fractional Schrödinger equation (SE) in the sense of Caputo fractional operator. In this research, we present the Elzaki transform residual power series method (ET-RPSM), which combines the Elzaki transform (ET) with the residual power series method (RPSM). This strategy has the advantage of requiring only the premise of limiting at zero for determining the coefficients of the series, and it uses symbolic computation software to perform the least number of calculations. The results obtained through the considered method are in the form of a series solution and converge rapidly. These outcomes closely match the precise results and are discussed through graphical structures to express the physical representation of the considered equation. The results showed that the suggested strategy is a straightforward, suitable, and practical tool for solving and comprehending a wide range of nonlinear physical models. Full article

Assessing building energy consumption is of paramount significance in sustainability and energy efficiency (EE) studies. The development of an accurate EE prediction model is pivotal for optimizing energy resources and facilitating effective building planning. Traditional physical modeling approaches are encumbered by high complexity and protracted modeling cycles. In this paper, we introduce a novel evolutionary dendritic neural regression (EDNR) model tailored to forecasting residential building EE. Acknowledging the vast landscape and complexity of the EDNR weight space, coupled with the inherent susceptibility of traditional optimization algorithms to local optima, we propose a complex network-guided strategy-based differential evolution algorithm for training the EDNR model. This strategy adeptly strikes a balance between exploration and exploitation during the search process, significantly enhancing the predictive and generalization capacities of EDNR. To our knowledge, this study represents the inaugural application of dendritic neural regression in real-world prediction scenarios. Extensive experimental findings demonstrate the efficacy of EDNR in accurately predicting building EE with commendable performance. Furthermore, the results of two nonparametric statistical tests affirm the validity and stability of EDNR. Consequently, our proposed methodology exhibits high potential and competitiveness in machine learning applications within the energy domain. Full article

Quenched Co-based ribbon strips are widely used in the fields of magnetic amplifier, magnetic head material, magnetic shield, electric reactor, inductance core, sensor core, anti-theft system label, and so on. In this study, Co-based composite CoFeNiSiB ribbon strips with a micron width were fabricated by micro-electro-mechanical systems (MEMS) technology. The carbon and FeCoGa nanofilms were deposited for surface modification. The effect of carbon and FeCoGa nanofilm coatings on the crystal structure, surface morphology, magnetic properties, and magnetoimpedance (MI) effect of composite ribbon strips were systematically investigated. The results show that the surface roughness and coercivity of the composite ribbon strips are minimum at a thickness of the carbon coating of 60 nm. The maximum value of MI effect is 41% at 2 MHz, which is approximately 2.4 times greater than plain ribbon and 1.6 times greater than FeCoGa-coated composite ribbon strip. The addition of a carbon layer provides a conductive path for high frequency currents, which effectively reduces the characteristic frequency of the composite ribbon strip. The FeCoGa coating is able to close the flux path and reduce the coercivity, which, in turn, increases the transverse permeability and improves the MI effect. The findings indicate that a successful combination of carbon layer and magnetostrictive FeCoGa nanofilm layer can improve the MI effect and magnetic field sensitivity of the ribbon strips, demonstrating the potential of the composite strips for local and micro area field sensing applications. Full article

In this paper, we propose a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (DRL) to complete the multi-agent path-finding simulation. In the training of physical robots, collisions and other unintended accidents are very likely to occur in multi-agent cases, so it is required to train the networks within a deep deterministic policy gradient for the virtual environment of the simulator. The simple particle multi-agent simulator designed by OpenAI (Sacramento, CA, USA) for training platforms can easily obtain the state information of the environment. The overall system of the training cycle is designed with a self-designed reward function and is completed through a progressive learning approach from a simple to a complex environment. Finally, we carried out and presented the experiments of multi-agent path-finding simulations. The proposed methodology is better than the multi-agent model-based policy optimization (MAMBPO) and model-free multi-agent soft actor–critic models. Full article