To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness consistency recovery method is pro-posed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from the images. After image-level processing, a combined underwater acousto-optic feature association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for the visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for performance against the OKVIS framework. The system achieves better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

As the fast growth of information technology (IT), the safety of image transmission and the storing of images are increasingly concerned. Traditional image encryption algorithms have certain limitations in transmission and security, so there is an urgent need for a secure and reliable image encryption algorithm. A universal compression sensing (CS) image encryption (IE) algorithm based on DNA-triploid mutation (DTM) is presented in this paper. Firstly, by using CS algorithm an image is compressed while obtaining a range of chaotic sequences by iteration of chaotic map. Then DNA sequences are generated by encoding the image, and based on the DTM, new mutant DNA sequences are generated according to specific rules. Next, the chaotic sequences are operated at the DNA level to do confusion and diffusion operations on the image to ensure the security of the data. At last, DNA decoding is carried out to obtain the compressed-encrypted image. Both simulation experiments and performance tests fully show that a high level of security and reliability of the proposed algorithm in protecting image privacy is achieved.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.

Despite the strong anticancer activity of SN38 (7-ethyl-10-hydroxy-camptothecin), some defects prevent its clinical application, such as severe side effects and loss of anticancer activity due to lack of selectivity to cancer cells and hydrolysis of ring E, respectively. To address the issue, herein a multifunctional SN38 derivative (compound 9) containing biotin (tumor-targeting group) and valproic acid (histone deacetylase inhibitor, HDACi) was synthesized via click chemistry and evaluated using MTT assay. The in vitro cytotoxicity study showed that compound 9 exhibited superior cytotoxicity as irinotecan against human cervical cancer HeLa cells, albeit inferior to SN38. More significantly, compound 9 significantly reduced toxicity in mouse embryonic fibroblast NIH3T3 cells, indicating that compound 9 had the capacity to enhance tumor targeting due to its cell selectivity. Further studies demonstrated that compared with irinotecan, compound 9 induced similar apoptosis of cancer cells. Consequently, compound 9 can not only improve its tumor-targeting ability mediated by biotin but also exert the potent anticancer activity through the synergism of SN38 and valproic acid, indicating that the design concept is an effective strategy for structural modification of SN38.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

In cancer, the lymphatic system is hijacked by tumor cells to escape from primary tumor and to metastasize to the sentinel lymph nodes. Tumor lymphangiogenesis is stimulated by the vascular endothelial growth factors-C (VEGFC) after binding to its receptor VEGFR-3. However, how VEGFC cooperates with other molecules to promote lymphatic neovessels growth is not fully determined. Here, we found that tumor lymphangiogenesis developed in tumoral lesions and in their surrounding adipose tissue (AT). Interestingly, lymphatic vessel density correlated with an increase of circulating free fatty acids (FFA) in the lymph from tumor-bearing mice. We found that adipocyte-released FFA are uploaded by lymphatic endothelial cells (LEC) to stimulate their sprouting. Lipidomic analysis identified the monounsaturated oleic acid (OA) as the major circulating FFA in the lymph in tumoral context. OA transporters FATP-3, -6 and CD36 were only upregulated on LEC in the presence of VEGFC showing a collaborative effect of these molecules. OA released from adipocytes is taken up by LECs to stimulate the fatty acid β-oxidation, leading to increase adipose tissue lymphangiogenesis. Our results provide new insights on the dialogue between tumors and adipocytes via the lymphatic system and identify a key role for adipocyte-derived FFA in the promotion of lymphangiogenesis, revealing novel therapeutic opportunities for inhibitors of lymphangiogenesis in cancer.

Arylsulfatase and β-glucuronidase are two important enzymes in human, which play important role on dynamic equilibrium of steroidal estrogens. This work probably for the first time reported that hydrogen peroxide (H2O2), hypochlorite and peracetic acid (PAA) could effectively inhibit the activities of arylsulfatase and/or β-glucuronidase. The 50 percent of inhibitions (IC50) of H2O2, and PAA on arylsulfatase were found to be 142.90±9.00, 91.83±10.01, and 43.46±2.92 μM, respectively. The corresponding respective IC50 of hypochlorite and PAA on β-glucuronidase were 704.90±41.40 and 23.26±0.82 μM, while H2O2 showed no inhibition on β-glucuronidase. It was further revealed that the inhibition of hypochlorite on both arylsulfatase and β-glucuronidase was irreversible. On the contrary, the inhibition by H2O2 and PAA was reversible. Moreover, it was found that the inhibitions of arylsulfatase and/or β-glucuronidase by these three chemicals were pH-dependent, among which the inhibition by H2O2 was competitive and non-competitive for PAA. In general, H2O2 and hypochlorite can be endogenously produced in human, which suggested that the two compounds are potential endocrine disruption compounds (EDCs) as they can cause endocrine disruption via inhibition of arylsulfatase and β-glucuronidase. This work further indicated that any agent that can induce production of H2O2 or hypochlorite in human is potential EDC, which explains why some EDCs with very weak or no estrogenic potency can cause endocrine disruption that confirmed in epidemiological studies.

Mammalian species possess sophisticated innate immune mechanisms that collectively combat a wide array of viral pathogens. Among these, the well-characterized interferon (IFN) response has been extensively researched; however, the role of antiviral RNA interference (RNAi) in mammals is emerging as an area of significant interest. Previous research has noted that Dicer, an enzyme crucial for processing double-stranded RNA (dsRNA), exhibits reduced activity in vitro, and that the IFN response may overshadow or inhibit the antiviral functions of RNAi in mammalian cells. Consequently, the functional relevance of RNAi in antiviral defense within mammalian somatic cells remains an open question. The evolution of antiviral systems in human populations reflects their substantial advantages, paralleling the evolutionary pressure on genomes encoding such defense mechanisms. While the well-studied protein-guided immune responses in mammals are essential for survival in viral environments, small RNA-mediated antiviral systems, which utilize complementary base pairing to silence non self-genetic material, also play a crucial role. In mammals, evidence suggests that microRNAs (miRNAs) regulate genes integral to antiviral responses, and emerging data indicate that small interfering RNAs (siRNAs), PIWI-interacting RNAs (piRNAs), and transfer RNAs (tRNAs) can directly target virus-derived nucleic acids. This review aims to highlight some of the recent progress in understanding mammalian antiviral RNAi mechanisms.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

Developing and utilising of underground space is an vital direction for urban growth. Underground commercial streets, as a significant component of underground space accommodating extensive human social activities, and consequently necessitate the creation of human-scale spaces. In the evolution of urban design development towards greater refinement, applying architectural theories and excessively subjective designs has resulted in a deficiency of human-centered design and a disordered spatial environment. This study merges environmental psychology and architectural theory to determine the appropriate length of spatial scale. Two experiments focusing on spatial perception evaluation were conducted using a virtual experimental platform that featured varying dimensions of spatial scale combinations. These quantified combinations were correlated with perception evaluation, and regression analysis was employed to identify appropriate scale ranges, which were superimposed on the range of length selection. Finally, the optimal length and scale combination for underground commercial street spaces were established, providing a reference for the human-centered design of these environments.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

Nucleation is a fundamental and general process at the initial stage of first order phase transition. Although various models based on the classical nucleation theory (CNT) have been proposed to explain the energetics and kinetics of nucleation, detailed understanding on nanoscales are still required. Here we focus on homogeneous bubble nucleation, in which evaluation of the size dependence of bubble free energy is the key issue. We propose application of a formula in stochastic thermodynamics, the Jarzynski’s equality, for data analysis of molecular dynamics (MD) simulation to evaluate the free energy of bubble nucleation. As a test case, we performed a series of MD simulations with Lennard-Jones (LJ) fluid system. By applying an external spherical force field to equilibrated LJ liquid, we evaluated the free energy change during bubble growth as the Jarzynski’s ensemble average of required works. A fairly smooth free energy curve was obtained as a function of bubble radius in metastable liquid of mildly negative pressure conditions.

A dioxomolybdenum catalyst [(MoO2Cl2-O-MoO2Cl2)(L)] (1) (with L (1S,2S)-N,N´-bis(2-pyridinecarboxamide)-1,2-cyclohexane) was prepared and used as catalyst for the desulfurization of a multicomponent model fuel containing the most refractory sulfur compounds in real fuels. This complex showed to have a high efficiency to oxidize the aromatic benzothiophene derivative compounds present in fuels, mainly using a biphasic 1:1 model fuel/MeOH system. This process conciliates catalytic oxidative and extractive desulfurization, occurring the oxidation of the sulfur compounds in the polar organic solvent. The oxidative catalytic performance of (1) showed to be influenced by the presence of water in the system. Using 50% aq. H2O2 it was possible to reuse the catalyst and the extraction solvent MeOH during ten consecutive cycles without loss of desulfurization efficiency.

The interaction of solution of poly (diallyldimethylammonium) chloride, PDADMAc and sodium dodecylsulfate, SDS has been studied using conductometric, densimeter and zeta potential methods. the critical micellization concentration (cmc) was determinated with conductance results and increased with the addition of PDADMAc comparing with solutions containing pure SDS. Further the degree of dissociation,  and thermodynamic parameters viz. free energy of micellization, and entropy of micellization, has been evaluated. there results demonstrated that surfactant/polymer complex is governed by both electrostatic and hydrophobic interactions. The apparent molar volume, Vϕ and adiabatic compressibility, Kϕ;s have been determined for the SDS solution from density and speed sound data. the negative charge of the SDS solutions with polymer shows that these complexes probably do not contain surfactant in the form of micelles.

The ubiquitous nature of polymers have led to a widespread demand for sustainable polymers in numerous industrial applications. However, lack of well laid out guidelines, product development pathways and certifications have resulted in a lot of commotions and confusions within the polymer value chain. Herein, a meticulous review is conducted on the topic of polymer sustainability shedding light on the standards, product declarations, biobased-biomass concepts, product carbon footprint etc. It is critical that companies significantly contribute on such sustainability efforts in lieu of market readiness and competitive advantages. Any discussion within the sustainability horizon references a couple of terms/ abbreviations/concepts. In this article, such key terminologies and concepts related to polymer sustainability are reviewed with a holistic outlook on the widespread approaches within the polymer sustainability horizon.

The conducted investigation encompassed the comprehensive integration of mechanical, envi-ronmental, chemical, and microstructural evaluations of a composite amalgamating sandy soil and a synthetic polymer at two distinct concentrations (2.5% and %) across multiple curing in-tervals (0, 1, 2, 4, 7, 15, 30, and 45 days). The studied soil originates from an environmentally sig-nificant protected area in Brazil. The implementation of mechanisms for soil improvement in the region must adhere to technical criteria without causing environmental harm. Direct shear testing was conducted, permeability was assessed, and microstructure analysis and XRD and XRF/EDX studies of both the soil and composites were conducted. It was observed that longer curing times yielded improved results in shear stress, friction angle, and cohesive intercept, with SP_5% exhibiting the highest values compared to the soil and SP_2.5%. Adding the polymeric so-lution to the soil contributed to cementation and cohesion gains in the substrate. Through mi-crostructural characterization, the polymer's role as a cementing agent for the grains is evident, forming a film on the grains and binding them together. Based on the analyses and studies con-ducted in the research, it can be concluded that there is technical feasibility for applying the pol-ymeric solution at both dosages in geotechnical projects.

Biosensors based on an oxygen electrode, a mediator electrode and a mediator microbial biofuel cell (MFC) using on the bacteria Gluconobacter oxydans B-1280 have been formed and tested to determine the integral toxicity. G. oxydans bacteria exhibit high sensitivity to the toxic effects of phenol, 2,4-dinitrophenol, salicylic and trichloroacetic acid and a number of heavy metal ions. The system “G. oxydans bacteria – ferrocene – graphite-paste electrode” is superior in sensitivity to biosensors formed using an oxygen electrode and MFC, in particular regarding heavy metal ions (EC50 of chromium (II), manganese (II) and cadmium (II) was 0.8 mg/dm3, 0.3 mg/dm3 and 1.6 mg/dm3, respectively). It has been determined that the period of stable functioning of electrochemical systems during measurements is reduced by half due (from 30 to 15 days) due to changes in the enzyme system of microbial cells when exposed to toxicants. Samples of products made from polymeric materials were analyzed using developed biosensor systems and standard biotesting methods based on inhibiting the growth of duckweed L. minor, reducing the motility of bull sperm and quenching the luminescence of the commercial test system "Ecolum". The developed bioelectrocatalytic systems are comparable in sensitivity to commercial biosensors, which made it possible to correlate the results and identify by all methods a highly toxic sample containing diphenylmethane-4,4'-diisocyanate according to GC-MS data.

Staudinger taught us that macromolecules were made up of the covalently bonded monomer repeat units chaining up as polymer chains. The chemical nature of the monomer directed the type of covalent bonds conferring most of the specific properties of the polymer. The more the number of repeat units the longer the chains and the more the possibility for the chains to assume a variety of shapes, from an extended elongated one to a more compact coiled one. Also, the chemical process that resulted in the synthesis of macromolecules produced many chains, often not with the same shape or size. The properties of the polymers improved when the chains became longer but it was more difficult to process them: their viscosity increased with molecular weight; viscosity was no longer an intensive property like it was for small liquids. The main question raised in polymer physics remains: how do these long chains interact and move as a group when submitted to shear deformation at high temperature when they are viscous liquids? This question is debated in a field of polymer physics called RHEOLOGY, whose purpose is to understand the viscoelastic aspects of polymer melts deformation. The current consensus is that we need to distinguish two cases: the deformation of “un-entangled chains” for macromolecules with molecular weight, M, smaller than Me, “the entanglement molecular weight”, and the deformation of “entangled” chains for M > Me. Several eminent scientists have extensively studied these 2 cases over the last 70 years. Paul J. Flory, in 1974, and Pierre-Gilles de Gennes, in 1991, have been awarded the Nobel Price in Chemistry and Physics, respectively, for their significant theoretical contribution to understand these challenging problems. For both of these authors the properties of polymers derive from the statistical characteristics of the macromolecule itself, the designated statistical system that defines the thermodynamic state of the polymer. Me, the molecular weight between entanglements, is defined from the rubber elasticity theory and is known to be equal to Mc/2 where Mc is the molecular weight for the entanglements when viscosity measurements are made. The current paradigm is that the viscoelasticity of un-entangled melts ( M < Mc) is well described by the Rouse model and that the entanglement issues raised by the impact of the increase of the length of the macromolecules on the melt viscoelasticity, when M > Mc, are well understood by the reptation model introduced by de Gennes and co-workers. Both models can be classified in the category of “chain dynamics statistics”. In this paper we examine in details the failures and the current challenges facing the current paradigm of polymer rheology: the Rouse model for M < Mc, the reptation model for M > Mc, the time-temperature superposition principle, the strain induced time dependence of viscosity, shear-refinement and sustained-orientation. The basic failure of the current paradigm and its inherent inability to fully describe the experimental reality is documented in this paper. In the discussion and conclusion of the paper we suggest that a different solution to explain the viscoelasticity of polymer chains and of their “entanglement” is needed. This requires a change of paradigm to describe the dynamics of the interactions within the chains and across the chains. A brief description of our currently proposed open dissipative statistical approach, “the Grain-Field Statistics”, is presented.

The use of carbon nanotubes to improve the mechanical properties of polymers is one of the promising directions in materials science. The addition of single-walled carbon nanotubes (SWCNT) to a polymer results in significant improvement of mechanical, electrical, optical, and structural properties. However, the addition of SWCNTs does not always improve the polymer properties. Also, when a certain content of SWCNTs is exceeded, the mechanical properties of the nanocomposite are getting worse. This article reports the results of computer simulations for predicting the mechanical properties of polymer/single-walled carbon nanotube nanocomposites. The efficiency of reinforcing polymer composites is considered depending on the concentration of carbon nanotubes in the polymer matrix, their size and structure. The elastic moduli of the nanocomposites were predicted using computer simulations for unit cell tension (0.1%). General trends in the mechanical properties of composites with polypropylene, poly (ethyl methacrylate), polystyrene matrices and SWCNTs are shown.

Deformation behavior of the composite "hen's eggshell - polymer plomb" under bending is examined. Polymer coating does not change the type of behavior of the composite based on eggshell that continues to be brittle despite the mechanical characteristics of the composite could vary in wide limits for brittle structure. Joining "hen's eggshell – polymer plomb" never crack under loading and, hence, it exhibits the high cohesion strength under stresses applied in these experiments. It seems that the composite "hen's eggshell - polymer plomb" could apply as the substitution of the composite "tooth enamel - polymer plomb" under elaboration of novel restorative materials for dentistry, namely, for estimation of their mechanical properties including cohesive strength.

Recent developments in mid-infrared (MIR) spectroscopic ellipsometry enabled by quantum cascade lasers (QCLs) resulted in a drastic improvement in signal-to-noise ratio compared to conventional thermal emitter based instrumentation. Thus, it was possible to reduce the acquisition time for high-resolution broadband ellipsometric spectra from multiple hours to less than 1 second. This opens up new possibilities for real-time in-situ ellipsometry in polymer processing. To highlight these evolving capabilities we demonstrate the benefits of a QCL based MIR ellipsometer by investigating single and multilayered polymer films. The molecular structure and reorientation of a 2.5m thin biaxially oriented polyethylene terephtalate film is monitored during a stretching process lasting 24.5 s to illustrate the perspective of ellipsometric measurements in dynamic processes. In addition, a polyethylene/ethylene vinyl alcohol/polyethylene multilayer film is investigated at continuously varying angle of incidence ( 0∘ – 50∘) in 17.2 s, highlighting an unprecedented sample throughput for the technique of varying angle spectroscopic ellipsometry in the MIR spectral range. The obtained results underline the superior spectral and temporal resolution of QCL ellipsometry and qualify this technique as suitable method for advanced in-situ monitoring in polymer processing.

A new two-dimensional nanomaterial – Titanium Carbide MXene (Ti₃C₂-MXene) – was reported in 2011. In this work, the microscale models of Ti₃C₂-MXene nanomaterial are considered with polymer matrix. The nanocomposites are modeled using nacre-mimetic brick-and-mortar assembly configurations due to enhanced mechanical properties and interlocking mechanism between the Ti₃C₂-MXene (brick) and polymer matrices (mortar). The polymer matrix material (Epoxy-resin) is modeled with elastic and viscoelastic behavior (Kelvin-Voigt Model). The Finite Element Method is used for numerical analysis of the microscale models with the multi-point constraint method to include Ti₃C₂-MXene fillers in the polymer matrix. Ti₃C₂-MXenes are considered as thick plate elements with transverse shear effects. The response of elastic and viscoelastic models of polymer matrix are studied. Finally, a tensile and compressive load is applied at the microscale and the effective load transfer due to nacre-mimetic configuration is discussed. This paper provides nacre-mimetic models to pre-design the nanocomposite for optimal performance with damage resistance and enhanced strength.

The control of the three-dimensional (3D) polymer network structure is important for permselective materials when specific biomolecules detection is needed. Here we investigate conditions to obtain a tailored hydrogel network that combine both molecular filtering and molecular capture capabilities for biosensing applications. Along this line short oligonucleotide detection in a displacement assay is set within PEGDA hydrogels synthetized by UV radical photopolymerization. To provide insights on the molecular filter capability, diffusion studies of several probes (sulforhodamine G and dextrans) with different hydrodynamic radii were carried out using NMR technique. Moreover, fluorometric analyses of hybridization of DNA oligonucleotides inside PEGDA-hydrogels shed light on the mechanisms of recognition in 3D, highlighting that mesh size and crowding effect greatly impact of hybridization mechanism onto polymer network. Finally, we found the best probe density and diffusion transport conditions to allow the specific oligonucleotide capture and detection inside PEGDA-hydrogels for oligonucleotide detection and the filtering out of higher molecular weight molecules.

We report on the effects of the film morphology on the fluorescence spectra for a thin film including a quinoxaline-based co-polymer (TQ1) and a fullerene derivative (PC₇₀BM). The ratio between the polymer and the fullerene derivative, as well as the processing solvent were varied. Beside the main emission peak at 700 nm in the fluorescence spectra of thin films of this phase-separated blend, a broad emission band is observed with a maximum at 520 - 550 nm. The intensity of this emission band decreases with an increasing degree of mixing in the film and becomes most prominent in thicker films, films with high PC₇₀BM content, and films that were spin-coated from solvents with lower PC₇₀BM solubility. We assign this emission band to aggregated PC₇₀BM.

Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited by these implants in service. This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smart polymer sensor array using conductive polyaniline (PANI) structures embedded within a polymeric parent phase. The piezoresistive characteristics of PANI were studied to characterize the sensing behaviours inherent to these embedded pressure sensor arrays. PANI's stable response to a continuous load, its stability throughout loading and unloading cycles, and its repeatable and linear response to incremental loading cycles together with the accuracy of these measurements were investigated. It is demonstrated that this specially developed multi-material additive manufacturing process for polyaniline is an attractive approach for the fabrication of implant components having embedded smart-polymer sensors for the measurement and analysis of joint loads in orthopaedic implants.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

Steel surface protection with hard coatings is essential in the metalworking industry, yet developing high-performance hard coatings still remains a challenge. Quaternary nitride coatings, particularly TiAlSiN, have been extensively studied, but commercial TiAlSiN coatings vary widely, and there is a lack of consistent data. This study presents findings on the development and evaluation of TiAlSiN and TiAlSiN/TiN thin films, with an 800 nm thick TiAlSiN top coating and a 100 nm thick TiN mid-coating. The films were deposited on C120 tool steel discs via reactive DC magnetron sputtering. Ti and TiAlSi 75–20–5 (at.%) disc-shaped targets (50.8 mm diameter × 6.3 mm thickness), fabricated by our team through spark plasma sintering, were used for the sputtering process. Coatings were grown on smooth steel substrates heated to 300 °C, resulting in a uniform micro-structure with nanoparticles of globular (TiN) or pyramidal shape (TiAlSiN), as evidenced by SEM analysis. Nanoindentation tests showed superior mechanical properties for all coatings compared to the steel substrate. Micro-scratch tests indicated better scratch resistance for TiAlSiN/TiN bilayer (BL) coatings than for TiAlSiN single-layer (SL) coatings. Electrochemical impedance spectroscopy tests in a 3.5 wt.% NaCl solution revealed that the BL coatings demonstrated superior corrosion resistance compared to the SL coatings, even after thermal aging at 800 °C for 1 hour. The superior properties of the synthesized TiAlSiN/TiN films recommend them as protective coatings for tool steel surfaces.

Glaucoma, a global leading cause of blindness, necessitates accurate segmentation of the optic disc (OD) and optic cup (OC) for effective screening. However, existing segmentation methods based on convolutional neural networks (CNNs) suffer from high computational complexity and long inference times. In this paper, we propose an end-to-end segmentation method for the OD and OC using a lightweight MobileNetv3 as the feature extraction network. Our approach incorporates boundary branches and an adversarial learning network to achieve multi-label segmentation of the OD and OC. We evaluate the proposed method on three publicly available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The experimental results demonstrate segmentation accuracies of 0.974/0.900, 0.966/0.875, and 0.962/0.880 for the OD and OC on the respective datasets, while significantly reducing inference time.

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as Smart Dust. Several efforts focused primarily on computing and networking capabilities but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data was processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

The monocular structured light measurement system has low cost, simple algorithms, and a wide measurement range, making it widely applicable in multiple fields. However, in practical applications, the system may be affected by various factors including noise, non-linear intensity, changes in the reflectivity of the object being measured, and the calibration method and accuracy of the system, thereby reducing the measurement accuracy. In order to solve these issues, this paper proposes a new monocular structured light measurement method, mainly comprises the following two points: (a) A denoising algorithm for phase images based on DAE is proposed, which calculates the wrapped phase of the phase-shifted fringes after initial denoising by DAE. Then, a new phase-shifted fringe pattern is generated and input into DAE for iterative denoising, achieving high-performance image denoising and high-precision wrapped phase solution. (b) A new absolute phase height calibration algorithm is proposed, which introduces camera internal and external parameters and uses a two-layer feedforward network to directly establish the relationship between phase and the 3D coordinate system. Without the need for high-precision motion platforms, high-precision phase height calibration is attainable. In general, compared to conventional methods, the experimental results indicate the effectiveness of the proposed method for low-quality phase-shifted fringe 3D reconstruction. In addition, the measurement method described in this article also demonstrates effectiveness in high-dynamic scenes.

This letter presents a dual-frequency terahertz amplitude modulator based on a cross-shaped structure and U-shaped metal patches. Its structural unit is a typical metal-substrate structure. The top metal consists of a cross-shaped structure and U-shaped metal patches, and the bottom substrate layer is made of quartz. Amplitude modulation is achieved by adjusting the temperature of the vanadium dioxide (VO2) patches. The modulation depth can be approximately 75%. The mechanism of the resonance frequency is explained by the equivalent circuit model. The proposed structure shows good amplitude modulation capability at different incidence and azimuth angles by changing the location of the VO2 patches. The dual-frequency terahertz amplitude modulator can be used in terahertz communication, imaging, etc.

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

Fabrication of durable materials with excellent oil adsorption capacity and separation performance for the treatment of oily wastewater is meaningful based on smart responsiveness. Herein, a solvent-responsive melamine sponge (MS) was developed via silanization followed by the in-situ growth of zeolitic imidazolate framework-8 (ZIF-8). Detailed characterization of the resultant composite MS was conducted using scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The multiscale hierarchical MS substrate exhibited highly hydrophobic property in the pH range of 1-11, along with a satisfactory adsorption capacity in the range of 65.4-134.2 g/g for different oils. The modified surface transformed from superhydrophobicity/superlipophilicity to superhydrophilicity/underwater superoleophobicity upon ethanol wetting, reverting to its original superhydrophobic state upon drying. The separation flux of the MS substrate was above 1.5×104L/m2h for both oil and water removal, along with the separation efficiency more than 98.7%. No obvious changes of the separation performance after 50 successive immiscible oil/water separation indicated the excellent durability and robustness of the anchored ZIF-8 nanoparticles on the surface of modified MS substrate. More importantly, oil-in-water emulsion separation was successfully carried out via the ZIF-8 MS composite with a high separation efficiency over 99.1%. The developed smart sponge with high oil absorption capacity, excellent chemical stability and fire resistance exhibited a wide range of potential practical applications in the convenient treatment of oily wastewater.

Background: Diets based on meat products are not recommended in the case of ulcerative colitis (UC). However, some foods, as those containing high oleic acid and a low omega-6/omega-3 ratio show anti-inflammatory properties. The objective here is to test if some traditional cured meat products, as acorn-fed ham (high levels of oleic acid), may be useful for controlling inflammatory diseases as UC in animal models. Methods: 3 rat cohorts have been used: vegetable rat feed, control ham and acorn-fed ham (a traditional ham where high oleic acid concentration from acorns is storage in the muscle fat). UC was induced with DSS in drinking water ad libitum for one week. Short-chain fatty acids (SCFAs) and 16S rRNA from bacterial populations were analyzed in cecum samples. Colon samples were analyzed for histological parameters (inflammatory cell density, mucosa damages, myeloperoxidase). Results: In the acorn-fed ham cohort, a protective effect was observed with respect to UC disease activity index, inflammatory cells density, colon mucosa alterations, myeloperoxidase levels, blood total antioxidant capacity and lower levels of pro-inflammatory cytokines, in comparison with feed cohort. Both ham diets caused a reduction in Firmicutes and an increase in Actinobacteria, Bacteroidetes and Proteobacteria in comparison with rat feed diet. Also, acorn-fed ham diet induced changes in gut microbiota composition, with pronounced enrichments in anti-inflammatory bacterial genera such as Alistipes, Bacteroides, Blautia, Butyricimonas and Parabacteroides. Conclusions: In the acorn-fed ham cohort, as a result of the dietary intake of oleic acid and low intake of omega-6 fatty acids, a strong preventive effect against UC symptoms was observed, indicating a valuable effect of this traditional Mediterranean cured meat product.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

The Bacillus megaterium LVN01 species native to Colombia has demonstrated the ability to metabolize different co-products or industrial waste (such as fique juice, cane molasses, residual glycerol) and accumulate polyhydroxybutyrate (PHB), giving it potential in the bioplastics industry. In this research, the potential of liquid digestate as a carbon source for the production of PHA in fermentation processes with this bacterial strain was evaluated. Favorably, it was found that B. megaterium utilizes the nutrients from this residual substrate to multiply appropriately and efficiently synthesize poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV). Bench scale aerobic batch fermentation, under the operational conditions of this research [Volume: 3L; Temperature: 30.8°C; Agitation: 400 rpm; pH: 7.0 ± 0.2; Disolved Oxygen: 100% saturation; Antifoam 10% (v/v)], generated maximum values of DCW (0.56 g cell L-1) at 60 h, while the maximum PHB yield (360 mg PHB L-1) occurred at 16 h, which is very favorable for sustainable degradable bioplastics production. Additionally, GC-MS and NMR analyses confirmed that the PHBV copolymer synthesized by B. megaterium is made up of the monomers 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). Furthermore, the thermal properties determined by TGA (Tonset=283.1 ºC, Tendset= 296.98 ºC. Td =290.114 °C) and DSC (Tm= ºC155.7 °C, ΔHf= 19.80 J g-1, Xcr = 18.17%), indicate that it is a thermally stable biopolymer with low percentages of crystallinity, providing flexibility that facilitates molding, adaptation, and application in various industrial sectors.

The present study describes the preparation of a composite material consisting of thermoplastic polyurethane-poly (n-isopropylacrylamide) (TPU-PNIPAM), which was employed for the se-lective adsorption separation of alcohol/water mixed solutions for the first time. The findings revealed that TPU and PNIPAM were interconnected through interchain hydrogen bonding, resulting in exposed hydrophobic isopropyl groups while covering the hydrophilic hydrogen bond sites. Selective separation of alcohols from alcohol/water mixed solutions occurred via London dispersion forces between alkyl groups. Increasing the PNIPAM load on the TPU surface led to a decrease in available hydrogen bond sites capable of bonding with water molecules, an increase in dispersible isopropyl groups interacting with alcohol alkyl groups, and enhanced selective adsorption capacity of TPU-PNIPAM towards alcohol/water mixed solutions. Moreover, TPU-PNIPAM exhibited a characteristic fast-slow-fast-equilibrium ad-sorption curve for different initial concentrations of alcohol/water mixtures, and its selective adsorption capacity increased when temperature reached 55℃. In addition to these general trends, under identical conditions, the selective adsorption capacity of TPU-PNIPAM gradually increased by methanol < ethanol < isopropyl alcohol < propyl alcohol, thus indicating a positive correlation between selectivity performance and hydrophobicity level.

In contrast to the traditional fillers, clay, in particular, natural smectite clay represents an environmentally significant alternative to improve the properties of polymers. Compared to con-ventional nanofillers, smectite clay can effectively enhance the physical and mechanical properties of polymer nanocomposites with a relatively small amount of addition (< 5 wt%). The present study focuses on investigating the reinforcing efficiency of different amounts (up to 5 wt%) of a natural Brazilian smectite clay on the mechanical and thermal properties of poly(butylene terephthalate) (PBT) nanocomposites. Natural Brazilian clay modified by addition of quaternary salt and sodium carbonate (MBClay) was infused into the PBT polymer by melt extrusion, using a twin-screw extruder. It was found that the best properties for PBT were obtained at 3.7 wt% of modified BClay. Tensile strength at break exhibited an increase of about 60 %, flexural strength increased by 24 % and flexural modulus increased by 17 %. In addition, an increase in the crystallinity percentage of PBT/BClay nanocomposite was confirmed by DSC and XRD analysis, and a gain of about 45 % in HDT was successfully achieved due to incorporation of 3.7 wt% of MBClay.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

In recent years, the rapid development of computer-aided planning and design technology has provided a new perspective for the study of complex problems such as the generation of architectural complex forms. This study focuses on how to apply parameterization technology to analyze and articulate traditional spatial form composition rules, aiming to minimize reliance on subjective human judgment in the protection and renewal design of the historical style of traditional villages. It aims to establish digital generative design tools to address the challenges of accurately inheriting and innovatively utilizing historical and cultural information in traditional settlements. It introduces how to rely on parameterization technology to analyze the spatial form composition rules, parameter extraction rules, and spatial reconstruction rules of traditional villages, facilitating the complete process from spatial features to parameterization rules, and then to the application of computational methods to deduce spatial features. It also includes case studies demonstrating the application of parameterization technology tools for village protection and explores the role of generative design tools in preserving the spatial style of these settlements.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

Abstract The processing of anatase ores by sulfuric acid digestion is well known for its low titanium dissolution yields, which makes the process economically and technically unfeasible. Generally, to enhance the dissolution of titanium from anatase, thermal processes along with acid and/or alkaline leaching are necessary. Few existing works report yields of direct digestion of anatase with sulfuric acid <48%. This study investigated the sulfuric digestion of anatase and yields around 80% were obtained at relatively mild conditions such as temperature at 220°C, sulfuric acid/concentrate ratio of 1:2 and 3 hours of reaction. A comprehensive characterization of anatase ore was also presented.

The endothelium is a monolayer of cells lining the inner blood vessels. Endothelial cells (ECs) play indispensable roles in angiogenesis, homeostasis, and immune response under normal physiological conditions, and their dysfunction is closely associated with pathologies such as cardiovascular diseases. Abnormal EC metabolism, especially fatty acid (FA) dysfunctional metabolism, contributes to the development of many diseases including pulmonary hypertension (PH). In this review, we focus on discussing the latest advances in FA metabolism in ECs under normal and pathological conditions with an emphasis on PH. We also highlight areas of research that warrant further investigation.

Since its first isolation in 1934, hyaluronic acid (HA) has been studied across a variety of research areas. This unbranched glycosaminoglycan consisting of repeating disaccharide units of N-acetyl-D-glucosamine and D-glucuronic acid is almost ubiquitary in humans and in other vertebrates. HA is involved in many key processes -including cell signaling, wound reparation, tissue regeneration, morphogenesis, matrix organization and pathobiology-, and has unique physico-chemical properties -such as biocompatibility, biodegradability, mucoadhesivity, hygroscopicity and viscoelasticity. For these reasons, exogenous HA has been investigated as drug delivery system and treatment in cancer, ophthalmology, arthrology, pneumology, rhinology, urology, aesthetic medicine and cosmetic. To improve and customize its properties and applications, HA can be subjected to chemical modifications: conjugation and crosslinking. The present review gives an overview regarding HA, describing its history, physico-chemical, structural and hydrodynamic properties, and biology –occurrence, biosynthesis (by hyaluronan synthases), degradation (by hyaluronidases and oxidative stress), roles, mechanisms of action and receptors. Furthermore, both conventional and recently emerging methods developed for the industrial production of HA and its chemical derivatization are presented. Finally, the medical, pharmaceutical and cosmetic applications of HA and its derivatives are reviewed, reporting examples of HA-based products which currently are on the market or are undergoing further investigations.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. We developed a robot controller using Reynolds’ flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we used a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds’ rules in a swarm of e-puck robots. Our algorithms outperformed this method in terms of cohesion performance and aggregation completion time.

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backstepping sliding mode control (BSMC) approach to achieve high-accuracy tracking control. In particular, the model uncertainties, including nonlinearities, parameter perturbations and external disturbances are analyzed and treated together as a lumped disturbance. Then, a fourth-order compensation function observer (CFO) is constructed, which fully utilizes the system state information to accurately estimate the lumped disturbance. On this basis, the estimate of the lumped disturbance is incorporated into the design of an backstepping sliding mode controller, allowing the control system to compensate for the disturbance effect. The stability of the closed-loop control system under the CFO and BSMC is rigorously proven through the use of Lyapunov theory, which guarantees that all the tracking error signals converge exponentially to the origin. Comparative simulations are carried out to show the effectiveness and efficiency of the proposed approach, i.e., compared with PID and ESO-based BSMC methods, the tracking accuracy is improved by 99.93% and 99.76%, respectively.

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.

This paper presents a study on the development of the cross-curricular learning outcome (CCLO) "Ethical, environmental and professional responsibility" by the students of different Bache-lor’sDegrees taught at Universitat Politècnica de València. The work and development of this learning outcome entails great complexity, given the double dimension of responsibility that it involves. At the end of their training at the university, students are expected to show ethical, en-vironmental, and professional responsibility towards themselves and others. Interviews have been conducted with lecturers who work and assess this outcome in their subjects, most/all of them related to science and engineering. The objective was to identify the learning approach used at the different subjects to guarantee the acquisition of this CCLO by the students. A focus group has also been carried out with students to determine the importance they give to this learning outcome, and to know their degree of satisfaction with the training received. The methodology used to obtain the data from lecturers and students and to process the information to get a precise diagnosis is fully described in the paper. Results are satisfactory to some extent: most of the lecturers carry out appropriate activities and most of students achieve the expected proficiency level. Finally, recommendations are given to improve the development of this cross-curricular learning outcome.

Phage therapy, the use of bacteriophages (phages) to treat bacterial infections, is regaining momentum as a promising weapon against the rising threat of multidrug-resistant (MDR) bacteria. This comprehensive review explores the historical context, the modern resurgence of phage therapy, and phage-facilitated advancements in medical and technological fields. It details the mechanisms of action and applications of phages in treating MDR bacterial infections, particularly those associated with biofilms and intracellular pathogens. The review further highlights innovative uses of phages in vaccine development, cancer therapy, and as gene delivery vectors. Despite its targeted and efficient approach, phage therapy faces challenges related to phage stability, immune response, and regulatory approval. By examining these areas in detail, this review underscores the immense potential and remaining hurdles in integrating phage-based therapies into modern medical practices.

During the last few decades, the main focus of numerus studies has been on the human breast milk microbiota and its influence on the infant intestinal microbiota and overall health. The presence of lactic acid bacteria in breast milk affects both the quantitative and qualitative composition of the infant gut microbiota. The aim of this study was to assess the dominant cultivable rod-shaped lactic acid bacteria, specific for breast milk of healthy Bulgarian women and fecal samples of their infants over the first month of life, in 14 mother-infant tandem pairs. Additionally, we evaluated strain diversity among the dominant newly isolated species. 68 gram-positive and catalase negative strains were subjected to identification by using MALDI-TOF technique. Predominant culturable populations belonging to the rod-shaped lactic acid bacteria have been identified as Lacticaseibacillus rhamnosus, Limosilactobacillus fermentum, Lacticaseibacillus paracasei and Limosilactobacillus reuteri. Also, we confirmed the presence of Lactiplantibacillus plantarum and Lactobacillus gasseri. 27 isolates were selected as representatives and analyzed by 16S rDNA sequencing for strain identity confirmation and a phylogenetic tree based on 16S rDNA gene sequence was constructed. Comparative analysis by four RAPD primers revealed genetic differences between newly isolated L. rhamnosus strains. This pilot study provides data for the current first report concerning investigation on characteristic microbiota of human breast milk and infant feces in Bulgaria.

Addressing the significant challenge in high-speed cotton dibbling operations in Xinjiang—specifically, the duck bill’s ("Duck bill" is a type of duck-billed bunch planting mechanism or component). shortened opening duration leading to seed throwing and floating—a new crawler-type delayed hole-forming mechanism has been designed. This mechanism extends the duck bill horizontally, thereby prolonging its opening time, enabling cotton sowing speeds of up to 6 km/h. Following agronomic principles for cotton planting, the overall layout of the crawler-type hole-forming mechanism, the structural parameters of the key components and the trajectory of the hole-forming are determined. We use the Discrete Element Method (DEM) and multi-body dynamics coupling method to simulate and analyze the motion process of the device. The fixed the tilt angle of the duck bill α(A), the depth of the duck bill hole-forming into the soil h(B), and the angle of rotation of the moving duck bill β(C) were taken as the experimental factors, with the longitudinal length of cotton seed hole (Y1) and the hole-forming depth of cotton seed hole (Y2) as the response values. The parameter range of each factor and the preliminary influence on the response value were determined through pre-experiments and single-factor simulation test. Based on RecurDyn-EDEM coupling, a quadratic regression orthogonal rotation combination simulation test was carried out. The results show that the order of the primary and secondary factors affecting the Y1 of the time-lapse hole-forming device was: A > C > B. The order of primary and secondary factors affecting the Y2 of the delayed hole forming device was: B > C > A. When the A was 2.4 °, the B was 42.4 mm, and the C was 30.5 °, the performance of the hole-forming was the best, and the Y1 was 26.4 mm, and the Y2 was 25.0 mm. The bench verification test results show that under the optimized combination of operating parameters, the average Y1 was 29.1 mm, and the average Y2 was 23.2 mm. Compared with the results of the previous coupled simulation model, the average relative errors of the two are 7.23 % and 4.36 %, respectively, indicating the accuracy and effectiveness of the established DEM simulation model and virtual test analysis. The hole-forming area and the opening time of the duck bill after the stable operation of the machine were recorded and measured. The calculated hole dislocation rate Hc was 4.33 %, which did not exceed the national standard requirement of 6 %. The qualified rate of hole distance Hx was 94.33 %, which was much higher than the national standard requirement of 80 %. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill 0.11 s. It meets the requirements of high-speed cotton planting in China and facilitating advancements in future high-speed planter technology.

Water scarcity, particularly in arid and semi-arid regions, poses a significant challenge to crop production, with drought being a prevalent factor limiting grain production globally. Enhancing agricultural water use efficiency is crucial for managing water resources effectively in these areas. Water resource management involves planning, developing, distributing, and optimizing the use of water resources. Water use efficiency management is also essential for crop growth in arid regions. Various agronomic and irrigation strategies can improve crop water use efficiency and drought tolerance. The primary goal of these strategies is to maximize irrigation efficiencies by applying the precise amount of water needed to restore soil moisture to the desired level. These sustainable practices not only reduce the cost of water and labor for farmers through reduced irrigation but also optimize the use of soil moisture storage. This review paper explores practical techniques to improve water use efficiency, highlighting the role of nutrients in enhancing water use efficiency in crop plants.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.

In this paper we report the possibility of using the X-ray powder diffraction (XRPD) technique to detect the gamma-hydroxybutyric acid (GHB) as its sodium salt in different beverages but because it is not possible to freely buy them, beta-hydroxybutyric acid (BHB) and its sodium salt (NaBHB) were used as a model to fine tune an X-ray diffraction method for the qualitative analysis of the sodium salt of GHB. The method requires a small quantity of beverage, and an easy sample preparation, that consists only in the NaOH addition to the drink and a subsequent drying step. The dry residue obtained can be easily analyzed with XRPD using a single crystal X-ray diffractometer to exploit its high sensitivity that allows very fast patterns collection. Several beverages with different NaBHB:NaOH molar ratios were tested and the results show that NaBHB is detected in all drinks analyzed when NaBHB:NaOH molar ratio is 1:50 using characteristic peak at very low 2θ values that permits to detect its presence also in complex beverage matrices. Moreover, depending on the NaOH amount added, shifting and/or splitting of the characteristic peak of the NaBHB salt was observed, and the origin of this behavior was investigated.

Kojic acid (KA) has emerged as a prominent tyrosinase inhibitor with considerable potential in cosmetic applications; however, its susceptibility to instability during storage poses a challenge to its widespread use. This review explores the advancements in addressing this limitation through the development of various KA derivatives, focusing on the modification of the C-7 hydroxyl group. Strategies such as esterification, hydroxy-phenyl ether formation, glycosylation, and incorporation into amino acid or tripeptide derivatives have been employed to enhance stability and efficacy. Among these derivatives, Kojic Acid Dipalmitate (KDP), a palmitic ester derivative of KA, stands out for its notable improvements in stability, permeability, and low toxicity. Recent developments indicate a growing utilization of KDP in cosmetic formulations, with over 132 available products on the market, encompassing various formulations. Additionally, three patents and seven advanced system deliveries of KDP further underscore its significance. Despite its increasing prevalence, the literature on KDP remains limited. This comprehensive review aims to bridge this gap by providing insights into the synthesis process, physicochemical properties, pharmaceutical preparation, diverse applications of KDP in cosmetic products, and recent nanotechnology formulations of KDP. This review paper seeks to explore the recent developments in the use of KDP in cosmetics. The goal is to enhance stability, permeability, and reduce the toxicity of KA, with the intention of promoting future research in this promising sector.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

In the text, the synthesis and characteristics of the novel ONS-type vanadium(V) complexes with thioanilide derivatives of amino acids are described. They have shown inhibition of human protein tyrosine phosphatases (PTP1B, LAR, SHP1 and SHP2) in the submicromolar range, as well as inhibition of non-tyrosine phosphatases (CDC25A and PPA2) similar to bis(maltolato)oxovanadium(IV)(BMOV). The ONS complexes increased [14C]-deoxy-D-glucose transport into C2C12 myocytes, and one of them, VC070, also enhanced this transport in 3T3-L1 adipocytes. These complexes inhibited gluconeogenesis in hepatocytes HepG2, but none of them decreased lipid accumulation in the non-alcoholic fatty liver disease model using the same cells. Compared to the tested ONO-type vanadium complexes with 5-bromosalicylaldehyde and substituted benzhydrazides as Schiff base ligand components, the ONS complexes revealed stronger inhibition of protein tyrosine phosphatases, but the ONO complexes showed greater activity in the cell models in general. Moreover, the majority of the active complexes from both groups showed better effects than VOSO4 and BMOV. Complexes from both groups activated AKT and ERK signaling pathways in hepatocytes to a comparable extent. One of the ONO complexes, VC068, showed activity in all of the above models, also including glucose utilization in the myocytes and glucose transport in insulin-resistant hepatocytes. The discussion section explicates the results within the wider scope of the knowledge about vanadium complexes.

Microbial competition within plant tissues affects invading pathogens' fitness. Metabolomics is a great tool for studying their biochemical interactions by identifying accumulated metabolites. Xylella fastidiosa, a Gram-negative bacterium causing Pierce's disease (PD) in grapevines, secretes various virulence factors including cell wall degrading enzymes, adhesion proteins, and quorum sensing molecules. These factors, along with outer membrane vesicles, contribute to its pathogenicity. Previous studies demonstrated that co-inoculating X. fastidiosa with Paraburkholderia phytofirmans strain PsJN suppressed PD symptoms. Here, we further investigated the interaction between the phytopathogen and the endophyte by analyzing the exometabolome of wild-type X. fastidiosa and a diffusible signaling factor (DSF) mutant lacking quorum sensing, cultivated with 20% P. phytofirmans spent media. LC-MS and MAGI were used to detect and map metabolites to genomes revealing a total of 121 metabolites, of which 25 were further investigated. These metabolites potentially relate to host adaptation, virulence, and pathogenicity. Notably, this study presents the first comprehensive profile of X. fastidiosa in the presence of P. phytofirmans spent media. The results highlight that P. phytofirmans and the absence of a functional quorum sensing affect the ratios of glutamine to glutamate (Gln:Glu) in X. fastidiosa. Additionally, two compounds with plant metabolism and growth properties, 2-Aminoisobutyric acid and Gibberellic Acid, were downregulated when X. fastidiosa interacted with P. phytofirmans. These findings suggest that P. phytofirmans-mediated disease suppression involves modulation of the exometabolome of X. fastidiosa, impacting plant immunity.

Physical frailty and sarcopenia (PF&S) are hallmarks of aging that share a common pathogenic background. Perturbations in protein/amino acid metabolism may play a role in the development of PF&S. In this preliminary study, 68 community-dwellers aged 70 years and older, 38 with PF&S and 30 non-sarcopenic, non-frail controls (nonPF&S), were enrolled. A panel of 37 serum amino acids and derivatives was assayed by UPLC-MS. Partial Least Squares Discriminant Analysis (PLS-DA) was used to characterize the amino acid profile of PF&S. The optimal complexity of the PLS-DA model was found to be three latent variables. The proportion of correct classification was 76.6 ± 3.9% (75.1 ± 4.6% for enrollees with PF&S; 78.5 ± 6.0% for controls). Older adults with PF&S were characterized by higher levels of asparagine, aspartic acid, citrulline, ethanolamine, glutamic acid, sarcosine, and taurine. The profile of nonPF&S individuals was defined by higher levels of α-aminobutyric acid and methionine. Distinct profiles of circulating amino acids and derivatives characterize older individuals with PF&S. The dissection of these patterns may provide novel insights into the role played by protein/amino acid perturbations in the disabling cascade and possible new targets for interventions.

Inadequate protein intake can impair protein balance and lead to skeletal muscle atrophy, impaired body growth, and functional decline. Foods provide both non-essential (NEAAs) and essential amino acids (EAAs) that may convey different metabolic stimuli to specific organs and tissues. In this study, we sought to evaluate the impact of six diets with various EAA/NEAA blends on body composition and the risk of developing tissue wasting in late middle-aged male mice. Mice consuming NEAA-based diets, although showing increased food and calorie intake, suffered the most severe weight loss. Interestingly, even moderate NEAAs prevalence was able to induce inflammatory catabolic stimuli, generalized body wasting and systemic metabolic alterations. Complete depletion of retroperitoneal white adipose tissue and a severe loss (>75%) of brown adipose tissue were observed together with muscle wasting. Conversely, EAA-based diets induced significant decreases in weight by reducing primarily fat reserves, but improved clinical parameters. Tissue wasting was caused by altered AA quality, independent of reduced nitrogen or caloric intake. Our results indicate that an optimized balance of AA composition is necessary for preserving overall bodily energy status. These findings are particularly relevant in the context of aging and may be exploited for contrasting its negative correlates including body wasting.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

In the present study, the ability of novel carbon microspheres (CMs) derived from a date palm (Phoenix dactylifera) biomass using a hydrothermal carbonization (HTC) process and activated using phosphoric acid in removing methylene blue dye was investigated. Three types of palm-based wastes (seeds, leaflet and inedible crystallized date palm molasses) were used and converted to CMs via HTC process. The prepared samples were then activated using phosphoric acid via incipient wetness impregnation method. The CMs samples before and after activation were analyzed using scanning electron microscopy (SEM), elemental analysis and scanning (CHNS), Fourier Transform-Infrared (FTIR) and Brunauer–Emmet–Teller (BET) method. The samples exhibited high BET surface areas after activation (1,584 m2/g). The methylene blue adsorption results showed good fittings to the Langmuir and Redlich-Peterson isotherm models for all activated samples. The maximum adsorption capacity achieved was 409.84 mg/g for activated CM obtained from the palm date molasses, indicating high potential for application as dye-based adsorption materials.

Waste biogenic materials from seafood exploitation are valuable resources of new compounds exploitation within the blue bioeconomy concept. Here, we describe the effectiveness of Raman technology implementation as an in-line tool for demineralization process control of crustaceans or gastropods. We produced transparent chitin polymer foils from three waste crustacean shells (C. sapidus, S. mantis and M. squinado) using a slow, green chemical approach employing acetic acid, and showed the progressive process of biogenic carbonate dissolution and increasing the Raman characteristic signal of chitin, in a time dependence manner. It turned that chitin foils obtaining is species-specific, and the demineralization bath of waste shell mixture can be effectively tracked by Raman spectroscopy, for solvent control and the recovery of the calcium acetate as valuable by-product. Comparatively, calcium acetate drug, a compound widely used in kidney failure diseases, or as additive in nutraceuticals and food industry, has been obtained here, following a green demineralization path of the sea snail Rapana venosa intact shell, assisted by Raman tech-nology, using 9% acetic acid (vinegar) solution for 14 days. To validate the final products identity and quality, The process of waste shells demineralization and the final products of the treatment were investigated using various Raman techniques and technologies, cross-validating the results with FT-IR, XRD and SEM-EDX techniques. NIR Raman spectroscopy revealed chitin bands in a time-dependent manner, while Resonance Raman spectroscopy showed the preservation of ca-rotenoids after two weeks of acetic acid treatment. A hand-held flexible TacticID Raman system with a 1064 nm excitation demonstrated its effectiveness as a rapid, in-line decision making tool during process control and revealed excellent reproducibility of the lab-based instrument signal, suitable for in situ evaluation of the demineralization status and solvent saturation control. The calcium acetate recovered from residual treatment solutions was assessed regarding its hydration status, purity, and suitability as recrystallized material for further use as pharmaceutical product or ingredient in complex formulations.

Extracellular vesicles (EVs) hold enormous potential in the diagnosis and treatment of diseases, benefit from their roles in intercellular signaling, as well as their plentiful presence and stability in body fluids. However, conventional EV isolation methods are labor-intensive, and harvest EVs with low purity and compromised recovery. In addition, the drawbacks, such as limited sensitivity and specificity of traditional EV analysis methods hinder the application of EVs in clinical use. Therefore, it is urgent to develop effective and standardized methods for isolating and detecting EVs. Microfluidics technology is a powerful and rapidly developing technology that has been introduced as a potential solution for the above bottlenecks. It holds the advantages of high integration, short analysis time, low consumption of samples and reagents. In this review, we summarize the traditional techniques alongside microfluidic-based methodologies for the isolation and detection of EVs. We emphasize the separation advantages of microfluidic technology in enhancing EV capture efficiency and precise targeting, while summarizing its analytical role in targeted detection and discussing the significance of EV detection in the tumor microenvironment, providing a direction for achieving automated and high-throughput EV detection in clinical samples.

(1) Background: Loneliness, little studied in Nursing Homes (NHs), can affect physical and mental health. We aimed to verify the prevalence of loneliness in 5 NHs and its associated factors. (2) Methods: Cross-sectional study. Older adults aged 65 or over with preserved cognitive status were included. The De Jong Gierveld Loneliness Scale was used to assess overall, social, and emotional loneliness; sociodemographic and health-related variables were collected. The chi-square (or Fisher’s) test and logistic regression were used for bivariate and multivariate analysis respectively. (3) Results: The final sample consisted of 65 participants (81.5% female) with a mean age of 84±7.13. Prevalence of overall loneliness was 70.7% (95%CI:58.2-81.4), social loneliness 44.6% (95% CI: 33.1-56.6) and emotional loneliness 46.2% (95% CI: 34.5–58.1). Overall loneliness was associated with lower perceived quality of life (Odds Ratio-OR= 5.52, 95% CI:1.25-24.38) and NH with state subsidized places (OR=0.19, 95% CI: 0.05-0.74); social loneliness with having 0-1 children (OR=0.25, 95% CI: 0.08-0.77), and emotional loneliness with depression (OR=4.54, 95% CI: 1.28-16.08) and urinary incontinence (UI) (OR=4.65, 95% CI: 1.23-17.52). (4) Conclusions: Loneliness was present in almost 71% of residents and was associated with the type of NH and poorer quality of life, emotional loneliness with depression and UI and social loneliness with having less than 2 children.

Diamidophosphate has been identified as a possible prebiotic compound used in the precursor membranes of the first ‘life’. Compounds such as these will be helpful in developing novel biomimetic approaches in synthetic chemistry. Thermochemical data for this type of compounds are not available. Hess’ law and estimates from calorimetric measurements used by Wakefield in the 1970s for other amido phosphates have been used to estimate the thermochemical values for the diamido and monoamido-phosphoric acid. Enthalpy of formation at 298.15 ^oC is calculated as – 821.9 kJ/mol and the free energy of formation calculated as -813.5 kJ/mol for the diamidophosphoric acid. The calculated enthalpy of formation of monoamidic phosphoric acid is -1117.1 kJ/mol and its free energy of formation is - -1105 kJ/mol.

Continuous-flow microwave heating stands out for its ability to rapidly and uniformly heat substances, making it widely applicable in chemical production. However, in practical applications, the permittivity of the heated liquid changes dramatically as the reaction progresses, affecting the efficiency and uniformity of continuous-flow heating. Herein, this work presents a novel microwave heating device based on coaxial structure for high-performance heating. Our approach commenced with the development of a multi-physical field model, incorporating a spiral polytetrafluoroethylene (PTFE) as a water channel and the coaxial waveguide as a container was established. The analysis shows that the uniform distribution of the sectional electric field of electromagnetic waves in the TEM mode within the coaxial structure can enhance heating uniformity. Then, a continuous-flow microwave heating system for different liquid loads was established, and experimental measurements were conducted. The heating efficiency for all loads exceeded 90%, which basely matched the simulation results, validating the accuracy of the model. Finally, the heating efficiency and uniformity under different permittivity loads were analyzed, as well as the impact of channel radius on heating efficiency. The device demonstrated high heating efficiency and stable heating uniformity across different loads. This continuous-flow microwave device is suitable for chemical research and production, contributing to the promotion of microwave energy applications in the chemical industry.

This work presents the results of optimizing the physicochemical properties of composite briquettes based on microsilica for metallurgical-grade silicon production. Microsilica, a waste product of silicon production, contains a sufficiently high amount of silicon dioxide (96-97%). This fact makes microsilica a promising composite material for metallurgical processing. However, its granulometric composition limits its direct use. The method of briquetting on an industrial briquetting press was used. The influence of the granulometric composition of the composite mixture on the heat tolerance and strength of the finished briquettes was studied. The methods of obtaining and testing the briquettes, their composition, and the briquetting and drying process are described. The obtained briquettes were tested for the smelting of metallurgical silicon. The silicon recovery rate in the metal with the standard charge was 71%, while the recovery rate with the briquetted monocharge was 85%. The results of silicon smelting from the briquettes demonstrated the high quality of the obtained metal, corresponding to the standards of metallurgical-grade silicon. The implementation of these technologies not only improves the quality of the final product but also addresses environmental issues related to the disposal of microsilica, reducing its accumulation and minimizing its impact on the environment.

This study introduces an advanced dual-mode composite attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers' weighting coefficients, culminating in an optimized dual-mode composite attitude control system. A series of comparative simulation analyses are meticulously executed to appraise the system's responsiveness, stability, precision, and resilience to interference. The findings unequivocally substantiate the superior performance of the proposed control system, affirming its substantial contribution to the advancement of RLV attitude control.

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

Predicting the outcome of a future game between two National Basketball Association (NBA) teams poses a challenging problem of interest to statistical scientists as well as the general public. In this article, we formalize the problem of predicting the game results as a classification problem and apply the principle of maximum entropy to construct NBA maximum entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs by the NBAME model. The best NBAME model is able to correctly predict the winning team 74.4 percent of the time as compared to some other machine learning algorithms which is correct 69.3 percent of the time.

This paper investigates an innovative photovoltaic cell configuration, denoted as “Au/CuSCN/RbGeI3/CdTe/ZnO/TiO2/FTO", using SCAPS-1D simulation. The research focuses on optimizing device performance by analyzing key components, including ZnO as the electron transport layer (ETL), RbGeI3 as the perovskite absorber layer, CuSCN as the hole transport layer (HTL), and CdTe as the active buffer layer. A comparison is made with the standard “Au/CuSCN/CH3NH3SnI3/CdTe/ZnO/TiO2/FTO" configuration under dark and illuminated conditions. Through an in-depth investigation involving temperature effects, generation rate analysis, RbGeI3 absorber layer thickness, Nd level, and interface defect levels, an optimized design for a unit area is achieved. The proposed solar cell demonstrates outstanding performance metrics, including an open circuit voltage (Voc) of 0.94V, a short circuit current density (Jsc) of 34.52mA/cm2, a fill factor (FF) of 77.77%, and an energy conversion efficiency (η) of 25.40%. These values significantly outperform the reference models with energy conversion efficiencies of 10.11% and 21.09%. Real-world applicability is validated by implementing these optimized characteristics in a photovoltaic system for Pakistan’s geographical location using PVSyst software. The suggested solar cell achieves a remarkable performance ratio (PR) of 85.6%, surpassing the PR of the reference basic solar cell model at 83%. The findings of this research offer a promising and superior solar cell design, incorporating innovative materials and optimization techniques to achieve exceptional energy conversion efficiency and overall performance. This study’s outcomes have the potential to advance photovoltaic technology and contribute to sustainable energy solutions worldwide.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

For the purpose of improve the sustainability of products sold in the EU, Digital Product Passports is a mandatory solution. These passports would systematically gather information about products, such as detailing the materials involved to aid recycling and remanufacturing efforts, or recording the emissions produced throughout the manufacturing process. The overall goal is the establishment of a circular economy in the EU. Current research on Digital Product Passports is focused on collating content for sector-specific applications. However, achieving sustainability goals through Digital Product Passports will require new methods and technical solutions for digital collaboration between supply chain participants, especially in the manufacturing sector. Collaborative work on Digital Product Passports requires trust and traceability in data and access management to ensure privacy over the data contained. One way to enable this securely and confidentially is through the use of blockchain technology, in which stored data cannot be concealed, which is key to the traceability of digital product data documentation. This paper therefore presents a concept for the implementation of generically applicable cross-industry blockchain-based digital product passports. To this end, generic supply chain processes have been abstracted and condensed into the architecture of smart contracts. Great attention is paid to the establishment of traceability as well as privacy through the selection of the blockchain technology Secret Network in combination with Gaia-X functionalities. An illustrative use case for digital product passports in manufacturing is used to demonstrate the applicability of the presented concept. In this use case information about the digital nameplate and carbon footprint is exchanged securely and confidentially in a multi-tenant supply chain network based on the Industry 4.0 infomation model of the Asset Administration Shell.

The aim of this study was to analyse the biocompatibility of a material based on lactic acid (PLA)/polyhydroxybutyrate(PHB)/thermoplastic starch (TPS) in combination with additives (plasticizers: acetyltributyl citrate (ATBC) and oligomeric lactic acid ( OLA)). Three types of materials were tested: I: PLA/PHB/TPS + 25% OLA, II: PLA/PHB/TPS + 30% ATBC, III: PLA/PHB/TPS + 30% OLA. The 3D printing itself was preceded by the creation of filaments from granulate and the design of samples. A filament production machine – Composer 450 (3devo; The Netherlands) was used to produce the filaments. Experimental samples were produced by FDM technology on a 3D printer DeltiQ2 (Trilab, Czech Republic). The evaluation of the biocompatibility of the materials included cytotoxicity tests such as the monitoring of viability, proliferation, morphology of cells, and their settlement on the surface of the materials. The cell line 7F2 osteoblasts (Mus musculus) was used in the experiments. Based on the tests, results confirmed the significant influence of plasticizers and their proportions in the mixtures. PLA/PHB/ATBC + 25% OLA was evaluated as the optimal material for biocompatibility with 7F2 osteoblasts. The tested biomaterials have the potential for further investigation, with a possible change in the proportion of the plasticizer, which has a fundamental impact on their biological properties.

A new three-dimensional numerical model of a polymer electrolyte fuel cell (PEFC) with a single straight channel has been developed to primarily investigate the important impact of the double-sided microporous layer (MPL) coating on the overall performance of the fuel cell and the distribution of the current and the oxygen concentration within the cathode gas diffusion layers (GDLs). Realistic experimentally estimated interfacial contact resistance values between the gas diffusion layer and each of the bipolar plates and the catalyst layer values were incorporated into the model, and parametric studies were performed. The results showed that the double-sided MPL coating could significantly improve the fuel cell performance by up to 30%. Also, it was shown that the neglect of the contact resistance between the MPL and the catalyst layer could overestimate the fuel cell performance by up to 6%. In addition, the results showed that the fuel cell performance and the distribution of the current and oxygen are more sensitive to the porosity of the MPL facing the bipolar plate than the porosity of the MPL facing the catalyst layer. All the above results are presented and critically discussed in detail.

The inherent value and use of hydrocarbon from waste plastics and solvents can be extended through open-loop chemical recycling as this process converts plastic to range of non-plastic materials. This process is enhanced by first creating plastic-solvent-combinations from multiple sources which are then streamlined through single process stream. We report on the relevant mechanics for streamlining industrially relevant polymers such as polystyrene (PS), polypropylene (PP), high-density polyethylene (HDPE) and acrylonitrile butadiene styrene (ABS) into chemical slurries mixed with various organic solvents such as toluene, xylene and cyclohexane. The miscibility of the polymer feedstock within the solvent was evaluated using the Relative Energy Difference method, and the dissolution process was evaluated using the “Molecular theories in a continuum framework” model. These models were used to design a batch process yielding 1 tonne/h slurry by setting appropriate assumptions including constant viscosity of solvents, disentanglement-controlled dissolution mechanism and linear increase of the dissolved polymer’s mass fraction over time. Solvent selection was found to be the most critical parameter for the dissolution process. The characteristics of the ideal solvent are high affinity to the desired polymer and low viscosity. This work serves as a universal technical guideline for open-loop chemical recycling of plastics avoiding the growth of waste plastic in a circular economy framework.

Traditional classroom teaching model can no longer meet the needs of students' ability development. As a new model actively advocated in the modern education concept, personalized teaching focuses on the development needs of students and emphasizes respect for students' individualized growth. In this work, we utilize deep learning method to recommend teaching strategies for different features of students. Specifically, the proposed method includes the construction of a multi-layer neural network model, which can analyze students' multi-dimensional learning data and behavior patterns, so as to recommend the most suitable teaching strategies for individual students. Through data collection and preprocessing, model architecture design, model training and optimization, the model gradually improves the accuracy of prediction and recommendation. Experimental analysis verifies the effectiveness of the model through field teaching experiments, and the results show that the personalized teaching strategy based on deep learning can significantly improve the learning effect and participation of students.

In recent years, the AHRS has become an important research area. In order to improve the orientation measurement accuracy of AHRS, this paper first expounds the algorithm principle of the AHRS. Then, aiming at the problem of poor stability of the traditional gradient descent method under high dynamics or large magnetic field interference, a gain-scheduled anti-interference AHRS orientation algorithm is designed. By using the switching architecture designed with this algorithm for gain scheduling, it can ensure stable performance while being subjected to apparent acceleration and ferromagnetic interference. Finally, experimental verification is conducted, and the experimental results show that: in the presence of interference, the roll angle error is 0.0350°, the pitch angle error is 0.0277°, and the yaw angle error is 0.9611°, thereby verifying the strong anti-interference performance of the gain-scheduled AHRS orientation algorithm proposed in this paper.

Optical wireless video transmission technology combines the advantages of high data rates, enhanced security, large bandwidth capacity, and strong anti-interference capabilities inherent in optical communication, establishing it as a pivotal technology in contemporary data transmission networks. However, video data comprises a large volume of image information, resulting in substantial data flow with significant redundant bits. To address this, we propose an adaptive block sampling compressive sensing algorithm that overcomes the limitations of sampling inflexibility in traditional compressive sensing, which often leads to either redundant or insufficient local sampling. This method significantly reduces the presence of redundant bits in video images. First, the sampling mechanism of the block-based compressive sensing algorithm was optimized. Subsequently, a wireless optical video transmission experimental system was developed using a Field-Programmable Gate Array chip. Finally, experiments were conducted to evaluate the transmission of video optical signals. The results demonstrate that the proposed algorithm improves the Peak Signal-to-Noise Ratio by over 3 dB compared to other algorithms, with an enhancement exceeding 1.5 dB even in field tests, thereby significantly optimizing video transmission quality. This research contributes essential technical insights for the enhancement of wireless optical video transmission performance.

Background: Endometriosis is a female chronic inflammatory disease in which endometrial tissue develops outside the uterine cavity. It is a complex pathology, that significantly contributes to morbidity in premenopausal women, leading to chronic pain, infertility, and subfertility negatively impacting the physical, and emotional well-being and overall quality of life. The public health burden of endometriosis remains elusive and challenging to determine, and this uncertainty can lead to inadequate healthcare services and treatments. Objective: To estimate the incidence and prevalence of endometriosis in Italy using the hospital discharge records database. Population-based retrospective study. Nationwide between 2011 and 2020. Methods: From the National Hospital Discharge Database were selected all admissions with a diagnosis of endometriosis (ICD-9-CM, codes 617.x), supported by the presence of a procedure code of laparoscopy or any other surgical procedure allowing direct visualisation of the lesions. Main outcomes measures Incidence and prevalence of endometriosis were estimated for the entire 2010-2020 period and by individual year, analysing the time trend and variability in different geographical areas of Italy. Results: A total of 134,667,646 women aged 15–50 years with one or more hospitalisations for endometriosis in all Italian hospitals. The incidence of endometriosis in Italy during this period was 0.839 per 1000 women (CI95% 0.834-0.844), exhibiting a statistically significant decreasing trend over the years. A discernible north-south gradient was observed, with higher rates documented in the northern regions. The prevalence rate stood at 14.0 per 1000 during the same period, and a similar north-south geographical gradient was identifiable in the prevalence rates as well. Conclusions: The utilization of national-level hospital data enables the generation of incidence and prevalence data for endometriosis without variations in methods and definitions, facilitating the evaluation of temporal trends and regional comparisons. Understanding and quantifying this phenomenon is essential for appropriate healthcare planning in various Italian regions.

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

Ti and its alloys are the most commonly used materials for biomedical applications. However, bacterial infection after implant placement is still one of the significant rising complications. Therefore, the application of the antimicrobial agents into implant surfaces to prevent implant-associated infection has attracted lots of attention. Scientific papers have shown that inorganic antibacterial metal element (e.g. Ag, Cu, Zn) can be introduced to implant surfaces with the addition of metal nanoparticles or metallic compounds into electrolyte via micro-arc oxidation (MAO) technology. In this review, the effects of the composition and concentration of electrolyte and process parameters (e.g. voltage, current density, oxidation time) on morphological characteristics (e.g. surface morphology, bonding strength), antibacterial ability and biocompatibility of MAO antimicrobial coating were discussed in detail. Anti-infection and osseo-integration can be simultaneously accomplished with the selection of the proper antibacterial elements and operating parameters. Besides, MAO assisted by magnetron sputtering (MS) to endow Ti-based implant materials with superior antibacterial ability and biocompatibility was also discussed. Finally, the development trend of MAO technology in the future was forecasted.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

Crops obtained by genome editing (GE) technologies represent a notable and disruptive shift from genetically modified or transgenic plants, outpacing public perception and regulatory frame-works. Multiple aspects of GE are contributing to the advancement of modern agriculture, in-fluencing the market structure by introducing specific changes within the existing genetic blue-print of an organism. Generally, the GE toolbox includes distinct technologies able to incorporate site-specific and pre-determined genomic modifications, exploiting well-established genetic modification strategies. For these reasons, GE-derived approaches are considered new plant breeding methods, known as new breeding techniques (NBTs). Particularly, CRISPR/Cas-based technologies represent a considerable improvement over prior techniques, being more precise, accurate and straightforward for targeted gene editing, in a reliable and reproducible way, with numerous applications. CRISPR/Cas system exploitation for non-editing purposes has also been widely documented, ranging from cell imaging to expression regulation and DNA assembly. Recently, CRISPR/Cas systems have been employed for target detection and could be used to ef-ficiently identify multiple organic chemicals. High-sensitivity and high-specificity detection are two main points of the CRISPR/Cas technology, without needing complex expertise or technical pipelines. This manuscript aims to provide an overview of the main advances of CRISPR/Cas-based systems into new frontiers of non-editing. The associated implications (e.g., molecular traceability) and their relative impacts are also presented and discussed.

Aging is a complex process associated with tissue degeneration and an increased risk of age-related diseases. This study aimed to evaluate the impact of Terasen®, a nutraceutical containing standardized extracts of Euterpe oleracea, Myrciaria dubia, and purified oil of Bixa orellana and Astrocaryum aculeatum on the lifespan of Caenorhabditis elegans, a widely used model organism for aging research. The findings demonstrated that Terasen® exhibited significant antioxidant activity and influenced the feeding behavior of C. elegans, leading to a reduced pharyngeal pumping rate and a decreased number of offspring produced by treated individuals. Notably, Terasen® also displayed a remarkable ability to extend the lifespan of C. elegans. These findings suggest that Terasen® may possess promising anti-aging effects in vivo, warranting further investigation.

As the natural resources are getting exhausted, the concept of sustainable development of region has received increasing attentions, especially for resource-dependent cities. In this paper an innovative method that emergy analysis and IPAT (Human Impact Population Affluence Technology) model were combined in order to analyze the quantitative relationship of economic growth and energy consumption and further to evaluate its overall sustainability level. Taiyuan, a traditional, resource-dependent city in China, is selected as the case study region. The main results show that total emergy of Taiyuan increased from 9.023× 1023 sej in 2007 to 9.116× 1023sej in 2014, with 38% reduction on non-renewable emergy and 125% growth on imported emergy. Regional emergy money ratio (RMB) was reduced by 48% from 5.31× 1013sej/$ in 2007 to 2.74× 1013sej/$ in 2014, indicating that the increasing speed of consuming resources and energy was faster than the increase of GDP, and that Taiyuan’s money purchasing power declined. The lower emergy sustainability index (ESI) indicates that Taiyuan was explored and produced large quantities of mineral resources, which puts more stress on the environment as a consequence, and that this is not sustainable in the long run. The IPAT analysis demonstrates that Taiyuan sticks to the efforts of energy conservation and environmental protection, in order to promote regional sustainable development, it is necessary to take an integrated effort. Policy insights suggest that resourceful regions should improve include energy and resource efficiency, optimizing energy and resourceful structure.

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

In this paper, we investigated the formation isolation regulation issue regarding multiple autonomous underwater vehicles (AUVs) characterized by a "leader-follower" framework. Considering the cooperative-competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer was designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme was designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source and the structure converges was achieved, with the absence of zeno behavior also demonstrated.

The augmenting commitment in the entrepreneurial learning process, both at academic and industry level, has led to new mechanisms and methodologies. Entrepreneurship literature has shown evidence of raising interest in the problem-based learning (PBL) methodology with regard to upcoming events from the surrounding learning context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the application of PBL throughout distinct entrepreneurial processes either in higher education, or in industry. It aimed to identify relevant entrepreneurial opportunities for PBL. Bibliographic databases were searched for documents published between 2002 and May 2024 to categorize key topics discussed in the literature on PBL and entrepreneurship. The review process identified 142 empirical and non-empirical papers on entrepreneurship related PBL. Data analysis revealed diverse subthemes for PBL uses in entrepreneurship in both higher education institutions and industry. The research questions “What is the interplay of problem-based learning methodology with entrepreneurship?” guided the study. The central output of the research is the discovery of the varying uses of PBL to cope with diverse events arising within entrepreneurship context. Future research directions are suggested.

Ensuring precise electric locomotive stopping is pivotal for achieving intelligent and unmanned auxiliary transportation systems. Currently, human drivers' expertise plays a central role in the stopping process, posing challenges for automation and cargo location accuracy, particularly in coal mining. This paper focuses on achieving accurate electric locomotive stopping under diverse conditions by utilizing permanent magnet synchronous motor-driven locomotives. This technique introduces a novel stopping control method that combines a fuzzy Proportional-Integral-Derivative (F-PID) controller and a vector control model for permanent magnet synchronous motors (PMSM). After this, we design the F-PID using the PMSM technique based on the new fuzzy rules for each sub-system. In the end, extensive simulations and real-world experiments are conducted on an electric locomotive stopping test bed, which demonstrate the effectiveness of the proposed control method. From the simulation results, it confirming that the ability to achieve precise stopping under various working conditions.

Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyze road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.

Pyrolyzed organic matter – commonly referred to as biochar – generally has positive effects on plant growth and can enhance early seedling development. Biochar-based seed coatings may thus be beneficial, specifically under adverse soil conditions such as high soil salinity. We evaluated germination and early plant development in radish (Raphinus sativa) seeds coated with conifer-feedstock slow-pyrolysis biochar; following the precedent of prior studies, polyvinyl acetate (PVAc) was used as the binding agent. Biochar-based seed coatings reduced germination rates and radicle extension in Petri dish trials; however, leaching of coated seeds largely eliminated this effect. PVAc applied in solution inhibited seed germination to a similar extent, suggesting that toxicity of PVAc or chemical products derived from it is the main mechanism for germination inhibition; a meta-analysis of published literature also indicates negative effects of biochar-based seed coatings using PVAc. In greenhouse trials the same biochar seed coatings improved aspects of plant growth and physiological status relative to controls, specifically enhancing root development. We conclude that biochar-based seed coatings have promise in promoting early seedling growth, and in combating salinity stress in agronomic and restoration applications. However, our findings contraindicate PVAc use as a binding agent. Further research is needed to test non-phytotoxic, readily biodegradable binding agents and to optimize biochars and seed coating processes for specific applications in agronomy and ecological restoration.

In an attempt to reuse fibrous textile waste and, at the same time, to address dye pollution in textile wastewater, waste cotton-based yarn was utilized as both a cheap and sustainable adsorbent and a precursor for carbon adsorbent production. Cotton and cotton/polyester yarn samples underwent thermal modification through carbonization followed by chemical activation with KOH. Various techniques including nitrogen adsorption-desorption isotherms, scanning electron microscopy, Fourier transform infrared spectroscopy, and surface charge determination were employed to analyze the morphological and surface properties of the cotton-based adsorbents. Adsorption properties were evaluated by testing the removal of selected cationic and anionic dyes from water. The impact of temperature, contact time, initial concentration, and pH of the adsorbate solution on adsorption was investigated, with experimental data analyzed using theoretical models. While carbonization alone did not significantly enhance adsorption properties, activated samples exhibited high efficacy in removing both cationic and anionic dyes from water. Despite the negative influence of the polyester component in the carbon precursor on the efficiency of activated samples in removing methyl orange, results indicated that activated cotton and cotton/polyester yarn could be used to prepare highly efficient adsorbents for the rapid removal of methylene blue from real wastewater samples.

The selectivity in the simultaneous detection of ascorbic acid (AA), dopamine (DA), and uric acid (UA) has been an open problem in the biosensing field. Many surface modification methods were carried out for glassy carbon electrodes (GCE), including the use of graphene oxide and amino acids as a selective layer. In this work, molecular dynamics (MD) simulations were performed to investigate the role of serine oligomers on the selectivity of the AA, DA, UA analytes. Our models consisted of a graphene oxide (GO) sheet under a solvent environment. Serine tetramers were added into the simulation box and were adsorbed on the GO surface. Then, the adsorption of each analyte on the mixed surface was monitored from MD trajectories. It was found that the adsorption of AA was preferred by serine oligomers due to the largest number of hydrogen-bond forming functional groups of AA, while UA was the least preferred due to its highest aromaticity. Finally, the role of hydrogen bonds on the electron transfer selectivity of biosensors was discussed with some previous studies.