The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

The Covid-19 pandemic spreads in waves for a year and a half, despite significant worldwide efforts, the development of biochemical diagnostic methods and population vaccination. One of the reasons for the infection spread is the impossibility of early disease detection through biochemical diagnostics, since biochemical processes slowly develop in a body. At the same time, well known that behavioral characteristics of a person, measured based on reflex movements, are capable for inertialess assessment of psychophysiological parameters. Vibraimage technology is the method of head micromovements video processing by inter-frame difference accumulation and converting spatial and temporal characteristics of the inter-frame difference into behavioral and psychophysiological parameters. Here we shown that behavioral parameters measured by vibraimage changed during COVID-19 infection. The identification of changes signs in behavioral parameters detected by AI trained on patients and controls. The best diagnostic accuracy (higher 94%) obtained using instantaneous values of behavioral parameters measured with the following vibraimage settings: 10Hz frequency of basic measurements; 25 inter-frame difference accumulations and averaging the diagnostic results over period of at least 5 seconds. COVID-19 diagnoses by behavioral parameters showed earlier (5-7 days) detection of the disease compared to symptoms and positive results of biochemical RT-PCR testing. Proposed method for COVID-19 diagnosis indicates infected persons within 5 seconds video processing using standard television cameras (web, IP) and computers, allows mass testing/selftesting and will stop the pandemic spread. We assume that head micromovements analysis for diagnosis of various diseases is possible not only with the help of vibraimage technology. Further research of human head micromovement analysis will help stop the COVID-19 pandemic and will contribute to the development of new contactless and environmentally friendly methods for early diagnosis of diseases.

Longitudinal dispersion coefficient (LDC) plays an essential role in modeling the transport of pollution and sediment in the natural rivers. As a result of transportation processes, the concentration of pollution changes along the river. Different studies have been conducted to provide simple equations for estimating LDC. In this study, Support Vector Regression (SVR), Gaussian Process Regression (GPR), M5P and Random Forest (RF) examined to predict the LDC in the natural streams. The hydraulic and geometric features of different rivers gathered for developing the mentioned models for LDC estimation and various statistical criteria were utilized to scrutinize of the models. Furthermore, the Taylor chart was used to evaluate the models and achieved results showed that among machine learning models, M5P displayed the superior performance with CC of 0.823, SI of 0.812, NS of 0.577 and WI of 0.879. As well, S-D model with CC of 0.795 SI of 0.827, NS of 0.558 and WI of 0.890 had more precise results than other empirical models. The results indicates that the developed M5P model with simple formulations was superior to other machine learning models and empirical models and therefore, it can be used as a proper tool for estimating LDC in natural rivers.

Anaerobic digestion has emerged as an increasingly popular method for the effective management of organic waste and wastewater with the potential to generate renewable energy. However, efficient digestion depends heavily on the complex and diverse microbial communities mediating this process. This review paper delves into the complex microbiome that exists within anaerobic digestion digesters, playing a critical role in the efficient and sustainable conversion of organic waste into biogas. Its primary focus falls within the scope of how various operational and environmental factors, including temperature, pH, hydraulic retention time, substrate loading rates, and the presence of inhibitory compounds, can substantially impact the microbiome of these systems. Through an extensive analysis of the existing literature, the microbial ecology of anaerobic digestion, including the composition and function of the microbial community, their interactions, and responses to different stressors, are evaluated. By offering critical insights into the distribution patterns of active microbial populations upon alterations in various process parameters, a better understanding of the microbial dynamics within these systems is provided, enhancing their efficiency. Overall, this paper emphasizes the importance of unraveling the microbial evolution within AD reactors to optimize biogas production and sustainable waste management and proposes future perspectives in this direction.

This study investigates the use of programming language Python to analyse and model chemical engineering processes. Using municipal sewage sludge (MSS) drying as an example, the study demonstrates how Python can support analyses and model drying data. The drying process was performed on a flat plate while maintaining a uniform, parallel drying air speed of 1.15 m s-1. The Python program helped to analyse the digitalised weight measurements from each sample. The program enables sorting input data, determining the drying critical point and evaluating the first and second drying periods.

We conducted an experiment on lambs to determine the effect of infection with the gastrointestinal nematode (GIN) Haemonchus contortus on growth performance, nutrient composition, microbiota, and ruminal fermentation in vivo and in vitro. Twelve female lambs were equally divided into two groups: uninfected animals (CON) and animals infected with approximately 5000 third-stage ISE larvae derived from the MHCo1 strain of H. contortus (INF). Two lambs per group were killed on days 48, 49, and 50 after infection and ruminal content was collected separately from each lamb. Batch cultures of ruminal fluid from CON and INF were incubated in vitro with 0.25 g meadow hay, a plant mixture and chicory used as substrates. The ruminal populations of protozoa, bacteria, total Archaea, and the archaean orders Methanobacteriales and Methanomicrobiales were significantly higher in INF than CON. In vivo concentrations of n-butyrate, iso-butyrate, and iso-valerate were significantly lower in INF than CON. In vitro values after 24 h of ruminal fermentation varied within the substrate. GIN infection by H. contortus was associated with increased methanogenesis. In conclusion, chicory was a promising substrate for mitigating methane production and may modulate the ruminal microbiome of GIN-infected lambs due to its chemical and phytochemical composition.

This study assessed the relationship between head posture displacements and biomechanical parameters in three different tasks. One hundred male and female students (20 ± 3yrs) were assessed via the PostureScreen Mobile app to quantify postural displacements of head rotations and translations including: 1) the cranio-vertebral angle (CVA) (°), 2) anterior head translation (AHT) (cm), 3) lateral head translation in the coronal plane (cm), and 4) lateral head side bending (°). Biomechanical parameters during gait and jumping were measured using the G-Walk sensor. The assessed gait spatiotemporal parameters were cadence (steps / min), speed (m / s), symmetry index, % left and right stride length (% height), and right and left propulsion index. The pelvic movement parameters were: 1) tilt symmetry index, 2) tilt left and right range, 3) obliquity symmetry index, 4) obliquity left and right range, 5) rotation symmetry index, and 6) rotation left and right range. The jump parameters measured were: 1) flight height (cm), 2) take off force (kN), 3) impact Force (kN), 4) take off speed (m / s), 5) peak speed (m / s), 6) average speed concentric phase (m / s), 7) maximum concentric power (kW), 8) average concentric power (kW) during the counter movement jump (CMJ), and 9) CMJ with arms thrust (CMJAT). At a significance-level of p ≤ 0.001, moderate to high correlations (0.4 < r < 0.8) were found between CVA, AHT, lateral translation head and all the gait and jump parameters. Weak correlations (0.2 < r <  0.4) were ascertained for lateral head bending and all the gait and jump parameters except for gait symmetry index and pelvic symmetry index, where moderate correlations were identified (0.4 < r < 0.6). The findings indicate moderate to high correlations between specific head posture displacements, such as CVA, lateral head translation and AHT with the various gait and jump parameters. These findings highlight the importance of considering head posture in the assessment and optimization of movement patterns during gait and jumping. Our findings contribute to the existing body of knowledge and may have implications for clinical practice and sports performance training. Further research is warranted to elucidate the underlying mechanisms and establish causality in these relationships, which could potentially lead to the development of targeted interventions for improving movement patterns and preventing injuries.

Accurate and robust methods are needed to monitor crop growth and predict grain yield and quality in breeding programs under variable agrometeorological conditions. Field experiments were conducted during two successive cropping seasons (2021, 2022) at four trial locations (Estonia, Latvia, Lithuania, Norway). The focus was on assessment of grain yield (GY), grain protein content (GPC), and UAV-derived NDVI measured at different plant growth stages. Performance and stability of 16 selected spring wheat genotypes were assessed under two N application rates (75, 150 kg N ha−1) and over contrasted agrometeorological conditions were assessed, and the quantitative relationships between agronomic traits and UAV-derived variables were figured out. None of the traits were subject to a significant (p<0.05) genotype by nitrogen interaction. High-yielding and high-protein genotypes were detected with high WAASB stability, specifically under high and low N rates. This study highlights the significant effect of NDVI analysis on growth stages GS55 and GS75 as key linear predictors, especially in the context of spring wheat GY. However, the effect of these indices depends on the specific growing conditions in the respective locations, thus limiting their universal utility.

Taking a typical forest underlying surface as the research area, this study employed the unmanned aerial vehicle (UAV) photogrammetry to explore more accurate canopy parameters including tree height and canopy radius, which were used to improve the Noah-MP land surface model conducted in Dinghushan Forest Ecosystem Research Station (CN-Din). While the canopy radius was fitted as a Burr distribution, the canopy height of CN-Din forest followed a Weibull distribution. The replacement of the parameters by these observed UAV would result in the Noah-MP model. It was found that the influence on the simulation of the energy fluxes could not be negligible, and the main influence of these canopy parameters was on the latent heat flux which could decrease up to -11% in the midday while increase up to 15% in the nighttime. Additionally, this work indicated that the description of the canopy characteristics for the land surface model should be improved to accurately deliver the heterogeneity for the underlying surface.

The solid-state welding method known as friction stir welding (FSW) bonds two metallic work parts, whether the same or different, by plastically deforming the base metal. The frictional resistance between both metallic work pieces causes them to produce heat, which produces plastic deformation and welds them. However, the weldability and strength of FSW joints mainly depend on the FSW parameters. This review work highlights the previous research work on the FSW parameters and their effects on the weldability and quality of the aluminum alloys joined with similar and dissimilar metals through the FSW method. About 150 research studies were systematically reviewed, and the articles included data from peer-reviewed journals. It has been concluded that the key parameters, including welding speed, “rotational speed”, “plunge depth”, “spindle torque”, “shoulder design”, “base material”, “pin profile” and “tool type", significantly affect the weldability of the aluminum joint through the FSW method. Also, the selection of these parameters is important and fundamental as they directly affect the joint. It is recommended that future work focus on FSW for aluminum. Among these, the most essential is the application of artificial intelligence (AI) techniques to select the optimum FSW parameters for aluminum welding.

Laser Powder Bed Fusion (LPBF), as a transformative technology in the field of additive manufacturing, offers unprecedented design flexibility and material efficiency. This review paper delves into the latest advancements in LPBF technology and the various alloys used in the process, including titanium alloys, aluminum alloys, nickel-based superalloys, steel alloys, copper alloys, and high-entropy alloys. For each alloy category, we discuss their specific applications, performance characteristics, and challenges faced in LPBF usage. The paper discusses important technological breakthroughs, including innovations in laser systems, scanning strategies, and in-situ monitoring technologies, which have enhanced the precision and reliability of LPBF.

The development of Al-Ni alloys with a controlled microstructure has had a great impact on the field of study of aluminum-based alloys. In the present research, the influence of thermal parameters on the grain structures resulting from the directional solidification process of Al-Ni alloys with different alloy content has been studied. It has also been evaluated how different structures and the distribution of second phases influence the corrosion behavior and microhardness of alloys. The columnar-to-equiaxed transition (CET) was observed to occur when the temperature gradient in the melt decreased to values between 1.3 to 2.9 ° C/cm. In addition, a small increase in the microhardness values was observed as a function of the Ni-content. When the Ni-content increase, the resistance to polarization decreases for samples with equiaxed grain structure, throughout the range of compositions studied. Furthermore, the equiaxed grain structure presents higher resistance to polarization values than the columnar grain zone, for alloys with a composition equal to or lower than the eutectic composition.

Resistance training is beneficial to adults for improving muscle strength and reducing the body composition. Nevertheless, little is known regarding online resistance training on anthropometric parameters and muscular strength (MS) in overweight female adolescents. This study aimed to examine the effects of online resistance training on anthropometric parameters and muscular strength (MS) in overweight female adolescents. This prospective, single-center, single-blinded, randomized controlled study included 60 overweight female adolescents who were divided into a control group (n=30) and an intervention group (n=30). The intervention group performed online resistance training, which focused on body weight training of 12 components (4 sets, 12 repetitions) for 45 min, three times per week, for 12 weeks. The intensity of training was 65–75%. Anthropometric parameters, including body weight (BW), body mass index (BMI), body circumference measurements (BCM), and muscle strength (MS), were assessed. All parameters were measured at weeks 0, 8, and 12. Within-group and between-group comparisons were performed. The results revealed improvements in MS, BW, BMI, and BCM in the intervention group. However, chest circumference was not significant. These findings suggest that online resistance training was effective in improving MS and reducing the body composition and circumference.

Background: A careful work-up before Transcatheter aortic valve replacement (TAVR) consists not only of measuring anatomical conditions but also assessment of frailty. Previous studies have evaluated body composition parameters, primarily fat and muscle mass. Few data are available on other frailty parameters, which, however, can be easily determined by pre-TAVR computed tomography (CT). Objectives: The primary objective was to investigate the impact of total muscle area (TMA) and visceral adipose tissue (VAT), as frailty parameters, on 5-year all-cause mortality in patients undergoing TAVR. Methods: Between 01/2017 and 12/2018, consecutive TAVR patients undergoing CT-angiography scan enabling TMA and VAT measurement were included. A single axial image was acquired at the level of the middle third lumbar vertebra to perform TMA and VAT measurements using dedicated slicing software (Sliceomatic, TomoVision). Results: A total of 500 patients qualified for TMA analysis. Age was not associated with a higher risk of 5-year mortality HR 1.02 (95% CI: 0.998 - 1.049; p=0.069). Body surface area normalized TMA (nTMA) was significantly associated with 5-year all-cause mortality (HR 0.927, 95% CI: 0.927-0.997; p= 0.033), while VAT had no effect (HR 1.002, 95% CI: 0.99-1.015; p= 0.7). The effect of nTMA on 5- year all-cause mortality was gender-dependent: the protective effect of higher nTMA was found in male patients (pinteraction: sex * nTMA = 0.007). The combination of normalized TMA and VAT was not associated with higher mortality rates. Conclusion: Normalized TMA derived from routine CT before TAVR complements frailty assessment in patients undergoing TAVR.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electricity power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electricity power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Because of the large number of parameters that interact in their function, determining dynamic regime parameters as well as the mode of function of amplifying stages is an extremely complex problem. This paper describes a LabVIEW application for studying the functioning of an ampli-fier in various connections. The user selects the generator's parameters, the type of connection and its parameters, as well as the electric charge characteristics. The application can determine both the stage characteristics and the Bode characteristics. The amplifier's stability zone, as well as its gain and phase, are determined based on these characteristics. An important advantage of this application is that the design of the amplifier stage can be made starting from some param-eters that the amplifier can establish, from which the values of components can be determined. In order to validate the simulation results using the LabVIEW application, a specialized program Multisim was used, as well as experimental measurements using the Electronics Explorer Board. Both Multisim and Electronics Explorer Board can determine Bode characteristics. In both simu-lations and experimental amplifiers, the same schemes with the same transistor was used. The application can be used for educational purposes as well as to design the amplifier's stage to achieve specific parameters.

Aquaculture is one of the fastest-growing animal food-producing agricultural industries in the world and proper performance of fish in morphological, physiological and immunological aspects is important for fish production and sustainable expansion of aquaculture. But several inhibitors like disease, pathogen, and adverse environment can overpower these performances. At present, antibiotics in preventing these inhibitors have been seen as becoming favorable to those inhibitors. So, Bacillus, an important group of probiotic bacteria can be an alternative to these antibiotics in aquaculture. Bacillus has been seen used in different experiments, mainly as a supplement in feed at various concentrations. Bacillus showed effective results like improved growth with minimum cost, improvement in reproduction, hematology, improved immune response and disease, and stress resistance as well as better proximate composition in different fish species. Application of Bacillus strains has proven efficient in improving water quality by reducing ammonia and nitrite toxicity, harmful algal blooms and utilization of H⁺ ion. Larger application of probiotic Bacillus instead of the hazardous synthetic chemicals would promote eco-friendly low-input sustainable aquaculture for food and nutritional security of the increasing world population. So many more experiments should be conducted in commercially important fishes for better growth and health of fishes which will certainly increase fish

This paper presents the perspective that universe is relative. The universe, which changes according to the traveler’s proper time, is dynamic. While the universe generally expands, it could also contract if traveler accelerates sufficiently. The Hubble parameter merely represents that change. If the Hubble parameter is dependent on the proper time, the measurement can only be uncertain. Identifying the factors that influence the proper time could potentially lead to a more accurate measurement of the universe’s age, and it could also offer insights into the state of the early universe and the causes of the accelerated expansion observed in the current epoch.

Four-polarisation camera was used to map absorbance of olivine micro-grains before and after high temperature annealing (HTA). It is shown that HTA of olivine xenolith at above 1200∘C in O2 flow makes it magnetised. Different mode of operation of polariscope with polarisation control before and after the sample in transmission and reflection modes was used. The reflection type was assembled for non-transparent samples of olivine after HTA. Sample for optical observation in transmission was placed on an achromatic plastic quarter-wavelength waveplate as a sample holder. Inspection for sample’s birefringence (retardance) as well as absorbance can be made. A best fit of transmitted intensity or transmittance T (hence absorbance A=−log10T) becomes accessible by simple best-fit using only three orientations (from measured four orientations by the camera). When intensity of transmitted light at one of the orientations was very low due to cross-polarised condition (polariser-analyser arrangement), the three-points fit can be used. Three-point fit in transmission and reflection modes was validated for T(θ)=Amp×cos(2θ−2θshift)+offset, where amplitude Amp, offset offset and orientation azimuth θshift are extracted for each pixel via the best fit.

Maize, a crop of international relevance, frequently undergoes xylem embolism due to water shortage, negatively impacting growth, yield, and quality. Consequently, a refined comprehension of xylem embolism is vital for enhancing maize cultivation. Notwithstanding extensive research and the generation of analytical models for embolism mechanisms, prevalent models often disregard crop-specific hydraulic processes and the formation of embolisms via air bubbles in the xylem conduit. In this research, we present an inventive model applying pipe cavitation parameters to discern water potential and bubble formation in maize leaf xylem. The model integrates pivotal physiological traits of maize—leaf count, leaf vein count, and average leaf radius—demonstrating robust correlations. Furthermore, we constructed a percentage conductivity loss (PLC) curve based on pre-dawn leaf water potential and compared it with our model, offering interval data to observe embolisation events triggered by air bubbles. Utilizing experimental data, our novel cavitation-parameters based model effectively corresponds with observed bubble phenomena and appropriately characterizes water transport in plant xylem conduits. This method enabled us to observe the transition from bubble occurrence to cavitation embolism microscopically, which aligned with the embolism intervals provided by the model. This procedure reveals potential trends in bubble-induced embolism and deepens our knowledge of microscopic plant hydraulics and crop embolism. This work establishes a basis for understanding the generation of bubble embolisms in maize, assists in evaluating maize plant water status for efficient water supply management throughout the growth cycle, and contributes towards potential water management strategies for maize.

This work presents the electrochemical degradation of the herbicide Diuron by anodic oxidation on a Ti/Ru0.3Ti0.7O2 dimensionally stable anode (DSA) using sulfate as electrolyte. The study includes the influence of the Diuron concentration and the current density on the anodic oxidation. The results evidence a first-order degradation, with the highest degradation capacity achieved at 40 mA cm2 and an initial Diuron concentration of 38 mg L1. Nevertheless, in terms of efficiency and energy demand, the operation at 10 mA cm2 is favored due to the more efficient and less energy-consuming condition. To discern the optimum design and operation conditions, this work presents the results of a preliminary technical-economical analysis, demonstrating that, to minimize the total costs of the system, it is recommended to seek the most efficient conditions, i.e., the conditions demanding the lowest applied charges with the highest Diuron degradation. At the same time, attention must be given to the required cell voltage to not increase excessively the operating costs.

The objective of this study was to evaluate the effect of the addition of atomized black Maca in the cryopreservation medium of epididymal spermatozoa of alpaca species. Fifteen pares of testes and epididymis from alpacas of reproductive age were collected from a local (Huancavelica) slaughterhouse and transported in NaCl 0,9% at 10°C to the laboratory. Each sample of epididymal sperm was divided into six groups: 1) Fresh, 2) Yolk medium (YM), 3) YM+10mg/ml Maca, 4) YM+20mg/ml Maca, 5) YM+30mg/ml Maca, and 6) YM+ Resveratrol (antioxidant positive control). Sperm cryopreservation was performed through the slow-freezing method. Markers associated with functionality such as motility, vitality and plasma membrane integrity. Cellular markers associated with oxidative damage, such as DNA fragmentation, total ROS production and lipid peroxidation, were analyzed before and after freezing. The results showed that the supplementation with black Maca at 20mg/ml improved the sperm motility, viability and integrity of the membrane. Similarly, total ROS production decreased with Maca at 10 mg/ml (25 ± 2.23%) and 20mg/ml (20 ± 2.52%) (P<0.05). However, DNA fragmentation and lipid peroxidation were similar in all groups. These results suggest that Maca at 20mg/ml has a protective effect during the freezing/thawing process of epididymal sperm of alpaca species.

The removal of metal ions by biosorption on inexpensive materials is still a challenge for environmental engineering research. In this study, marine green algae biomass (Ulva lactuca sp.) and the biochars obtained from this biomass, at 320 C (BC-320) and 550 C (BC-550), were used as biosorbents for the removal of Cu(II) ions from aqueous solution. In addition to comparing the biosorption capacities, the determination of the thermodynamic parameters allows the choice of the most suitable material for the biosorption processes. The experimental results, obtained for Cu(II) ions biosorption on each biosorbent (algae biomass (AB), BC-320 and BC-550), at three different temperatures (10, 30 and 50 C) were analyzed using Langmuir and Freundlich isotherm models, while pseudo-first order, pseudo-second order and intra-particle diffusions models were used to model the kinetic data. The biosorption of Cu(II) ions is best described by the Langmuir model and the pseudo-second kinetic model, regardless of the type of biosorbent. The maximum biosorption capacity (qmax, mg/g) depends on the temperature, but also on the type of biosorbent, and follow the order: BC-320 &lt; AB &lt; BC-550. Using the experimental isotherms, the thermodynamic parameters (G0, H0 and S0) for the biosorption of Cu(II) ions on each biosorbent were calculated. The analysis of the obtained values constitutes the main arguments in choosing BC-550 as the most effective biosorbent for the removal of Cu(II) ions from aqueous media.

With the aim to investigate the effect of parameter and quenching process on the joint of hot stamping steel by laser welding, the BR1500HS boron steel was welded by filling-wire laser welding with ER70-G welding wire under different parameters. The welded specimens were heated to 900℃ and held for 5min before water quenching. The universal material test machine, Optical micro-scope, Vickers hardness tester, scanning electron microscope and electron backscatter diffraction (EBSD) were used to characterize. The results showed that the macroscopic morphology of fusion zone (FZ) becomes from funnelform to hyperbolic curve shape when heat input increases and from hyperbolic curve shape to funnelform when wire-feed speed increases. The strength after quenching is more than 1557Mpa at heat input of 1040J/cm, wire feeding speed of 1.6m/min~1.8m/min and welding speed of 1.5m/min. EBSD test showed that the FZ and fine grain zone (FGZ) have more retained austenite (RA) than coarse grain zone (CGZ) before quenching and RA in FZ and heat affect zone (HAZ) decreased and distributed uniformed after quenching. The grain diameter in FZ distribute unevenly, with the maximum grain diameter larger than 40μm before quenching. After quenching, the grain diameter of FZ, HAZ and BM is more even and coarse grains in the FZ was refined. Before quenching, the microhardness of FZ and HAZ is of 450HV~500HV at different heat input and wire-feed speed and all region of joint keeps at 450HV~550HV after quenching. Most dimple and little river pattern in the joint fracture mor-phology before quenching indicates a well plasticity and most cleavage facet is observed after quenching due to the joint combine with martensite.

In this study, we investigate the existence of positive solutions within a system of Riemann-Liouville fractional differential equations that incorporate the (r1,r2,r3)-Laplacian operator while being subject to three-point boundary conditions. These equations incorporate various fractional derivatives and are influenced by parameters represented as (ψ1,ψ2,ψ3). Our approach involves employing techniques such as cone expansion and compression of the functional type, in conjunction with the Leggett-Williams fixed point theorem, to establish the existence of positive solutions. To emphasize the practical significance of our findings in the realm of fractional differential equations, we provide two illustrative examples.

Prionace glauca, blue shark is a cosmopolitan species. Together with Carcharhinus falciformis, it co-dominated the landings of carcharhinidae sharks at the Artisanal Fishing Port of Cotonou. The objective of this study is to determine the demographic parameters of the blue shark in Gulf of Guinea marine waters. The different routines of the FISAT II v 1.2 software associated with the empirical equations of Froese and Nohland, allowed the evaluation of the different demographic parameters of the blue shark in Benin's marine waters. From August to December 2015, 244 individuals shark sizes; ranging from 175cm to 325cm were sampled. The total asymptotic length obtained was 341.25cm with a growth rate of 0.35/year, which indicates that the species is growing rapidly and the optimal total length of the species is 232.01cm. The age of first maturity of the species is 3 years with a longevity of 10 years. The exploitation rate is E= 0.74 with total mortality being 2.4; natural mortality is 0.6 and fishing mortality is 1.8. The sex ratio remains in favour of males throughout the study period. Monitoring programme were suggested with effective management measures established and executed for sustainability of the blue shark’s stock in the Gulf of Guinea.

The present study uses the 3 years of data from a vertically pointed profile Radar (VPPR) at the mountain site (Huancayo at 3.2 km msl; HYO) in Peru to investigate the precipitation characteristics/patterns including diurnal variation, bright band (BB) characteristics and vertical structure of rain (VSR). VPPR provides continuous 1­min profiles of radar reflectivity (Ze), rain rate (RR), liquid water content (LWC) and Doppler velocity. At the same time, field campaign data are used to investigate the role of the surface and atmospheric variables in generating the rainfall and organization of the cloud systems over HYO. The precipitation shows the bimodal distribution; frequent and higher RR during afternoon and nighttime. The BB height also shows a diurnal pattern and BB height is higher during the afternoon and nighttime compared; and follows the diurnal heating of the Andes. More than 70% BB height lies between ~4.3­4.7 km and only 20% BB has altitude higher than 5 km. The austral summer monsoon (December to March months) have higher intense vertical profiles (higher Z e ) compared to pre monsoon seasons (September to November) and shows the nega­tive gradient for most of the altitude. The RR and LWC show the opposite characteristics, and both have a positive gradient below the 6 km altitude and then negative gradient above 6 km altitude. The raindrop size distribution (DSD) parameters show most of the variation below the freezing level, and a higher concentra­tion of large sized of hydrometeors are observed for higher RR, however the dominant modes of Dm are less than 1 mm. The changes in the VSR in DSD parameters are significant for the RR>20 mm/h, whereas for RR<2 mm/h the vertical structure in DSD parameters do not have much differences during monsoon and premonsoon seasons. Satellite and reanalysis data reveal the short periods of convective activity with higher ac­ cumulated rainfall over HYO compared to stratiform precipitation, which is more common in the nighttime and sustain for many hours. Wet spells (higher rainy days) have low pressure circulation, which favours the transport of moisture from the Amazon to the central Sierra of Peru, while the anticyclonic circulation at high levels favours the divergence at higher pressure levels and, enhances the convective in the region. During the dry spells, low level weaker circulation at the west of Brazil, restricts the transport of moisture to the central Sierra, while the circulation at high levels does not favor rain processes. The improved understanding of the tropical Andes precipitation would be very important for assessing climate variability and changes as well as to forecast precipitation using the numerical models.

The current paper aims to study the impact properties of additively manufactured Maraging steel (1.2709) using laser powder bed fusion (PBF-L) processing. The specimens were manufactured using 3Dsystems ProX 300 equipment under constant specific power input, or Andrew Number. The interactions between the build strategy and parameters, such as Hatch spacing and Scan speed was, and the impact strength and fracture were investigated. The Impact energy anisotropy was also investigated parallel and perpendicular to the build direction. Instrumented impact testing was performed, and the fractography supported that the fusion zone geometry dictated the fracture behaviour. The influence from gaseous elements such as Nitrogen, Oxygen and Hydrogen was found insignificant at the levels found in the printed material.

Teledermatology, the dermatological consultation and diagnosis via digital communication technologies, has become increasingly important in modern healthcare. The integration of teledermatology into clinical practice has gained significant interest, especially during the COVID-19 pandemic. Numerous studies have shown the effectiveness and reliability of teledermatology, with high levels of patient satisfaction and diagnostic accuracy. Pathpoint® eDerma is a cloud-based dermatology platform that aims to streamline the documentation and management of dermatological conditions. It offers several clinical benefits, including faster diagnosis, reduced unnecessary procedures, improved access to care, and cost savings. The platform follows a comprehensive risk management plan and complies with safety regulations. It undergoes regular recertification and reviews to ensure its safety and effectiveness. The clinical evaluation of eDerma confirms its ability to deliver significant clinical benefits while effectively mitigating risks. The benefit-risk ratio of eDerma is highly acceptable, supporting its use in clinical settings to enhance dermatological care.

The delivery of therapeutical molecules through the skin, particularly to its deeper layers is impaired due to the stratum corneum layer, which acts as barrier to foreign substances. Thus, for the past years, scientists have focused on the development of more efficient methods to deliver molecules to skin distinct layers. Microneedles, as a new class of biomedical devices, consists on an array of microscale needles. This particular biomedical device had been drawing attention due to their ability to breach the stratum corneum, forming micro-conduits to facilitate the passage of therapeutical molecules. The microneedle device has several advantages over conventional methods, such as better medication adherence, easiness and painless self-administration. Moreover, it is possible to deliver the molecules swiftly or over time. Microneedles can vary in shape, size and composition. The design process of a microneedle device must take in account several factors, like the location delivery, the material and manufacturing process. Microneedles have been used in a large number of fields from drug and vaccine application, cosmetics, therapy, diagnosis, tissue engineering, sample extraction, cancer research, wound healing, among others.

Estimating the vehicle crashworthiness parameters experimentally is expensive and time consuming. For these reasons different modelling approaches are utilized to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognized modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for early analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper a simple piecewise LPM composed of a mass-spring-damper system, is used to estimate the vehicle crashworthiness parameters, focusing on the dynamic crush and the acceleration severity index (ASI). The model is first calibrated against a full-scale crash test and a FEM, post-processed with the LS-DYNA software, at an impact velocity of 56 km/h. The genetic algorithm is used to calibrate the model by estimating the piecewise lumped parameters (stiffness and damping of the front structure of the vehicle). After calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h). The predictions for crashworthiness parameters from the LPM were compared with the predictions from the FEA and the results are much similar. It is shown that the LPM can assist in crash analysis, since LPM has some predictive capabilities and requires less computation time in comparison with the explicit nonlinear FEA.

In this paper, the distributed formation tracking control problem of quadrotor unmanned aerial vehicles (UAVs) is considered. Adaptive backstepping technology is used to design flight control schemes for quadrotor UAVs. For the position subsystem, a distributed robust formation tracking control scheme is developed to achieve formation flight of quadrotor UAVs and track the desired flight trajectory. For the attitude subsystem, an adaptive disturbance rejection control scheme is proposed to achieve attitude stabilization during UAV flight under uncertain disturbances. Compared with the existing results, the novelty of this paper is that the proposed formation control scheme does not require the use of the quadrotor model parameters. Finally, a quadrotor UAV swarm system is used to verify the effectiveness of the proposed control scheme.

The welding and construction processes of H-type thick-plate-bridge steel involve complex multi-pass welding processes, which is difficulty to ensure its welding performance. Accordingly, it is crucial to explore the inherent correlation among the welding processes parameters and welding quality, and applied it into the welding robots, eliminating the instability from manual welding. In order to improve the welding quality, the GMAW (Gas Metal Arc Welding) welding process parameters are simulated using the Q345qD bridge steel flat joint model. Four welds with X-shaped grooves are designed to optimize the parameters of welding current, welding voltage, and welding speed. The optimal welding process parameters are investigated through thermal-elastic-plastic simulation analysis and experimental verification. The results indicate that when the welding current is set to 230A, the welding voltage to 32V, and the welding speed to 0.003m/s, the maximum deformation of the welded plate is 0.52mm, with a maximum welding residual stress of 345MPa. Both the simulation results of multi-pass welding and experimental tests meet the welding requirements, as they show no excessive stress or strain. These parameters can be applied into building large steel frame bridges with welding robots, improving the quality of welded joints.

In this paper the influence of austempering temperature and salt bath agitation on the final microstructure and mechanical properties of the ferritic ductile iron were studied. 17 samples had been subjected to different heat treatment parameters. Different microstructures were recorded upon the completion of the tests. From the obtained micro images, it is obvious that both the austempering temperature and salt bath agitation affect the final microstructure of the austempered ductile iron. Lower austempering temperatures and salt bath agitation produce more ausferrite in the microstructure, hence the harder and tougher phases are present. This was confirmed with hardness and toughness test of the 17 heat-treated samples. Lower austempering temperatures give more ausferrite phase and therefore higher hardness, but hardness decreases with increasing austempering temperatures. Toughness rises with rising austempering temperatures, but drops significantly with temperatures above 395°C because of the final microstructure.

While some research supports utilizing plain radiography for measuring biomechanical alignment of the spine for prognosis and treatment, there are contrasting viewpoints regarding both the value and utilization of these procedures in conservative care. Evaluation of both conservative and non-conservative approaches to spinal care revealed vast differences in radiographic utilization and interpretation between orthopedic surgeons, primary care physicians, chiropractic physicians, and physical therapists, which may account for the different viewpoints and rationale in the literature. A research summary is provided regarding the various methods of spinal analysis utilized by these professions to help determine if there is any unique diagnostic or prognostic value associated with radiographic utilization that could assist in advancing conservative spinal assessment and treatment. Understanding any unique value provided through biomechanical assessment utilizing plain radiography of the spine established in the literature may help chiropractic physicians determine the appropriate use of radiographic procedures in clinical practice and how to coordinate efforts with other conservative and non-conservative professions to improve spinal healthcare.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. Here, we developed a fully computer-supported pipeline to predict the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery through preoperative X-ray images. The automated pipeline included two primary stages. First, a deep learning model was used to extract essential features from X-ray images. Second, five machine learning algorithms were trained to identify the optimal models to predict the interbody cage height and postoperative PI-LL. Lasso regression and support vector regression exhibited superior performance for predicting the interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. In addition, the model demonstrated adequate performance for predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, the interbody cage height and postoperative PI-LL can be reliably predicted using artificial intelligence and ML models.

In this work, we explore new constraints on phantom scalar field cosmologies with a scalar field employing early times catalogues related to CMB measurements, along with the local standard observables, like Supernovae Type Ia (SNIa), H(z) measurements (Cosmic Clocks), and Baryon Acoustic Oscillations (BAO) baselines. In particular, we studied a tracker phantom field with hyperbolic polar coordinates that have been proposed in the literature. The main goal is to obtain precise cosmological constraints for H0 and σ8, in comparison to other constructions that present tension in early cosmological parameters. Our results show that phantom scalar field cosmologies have a reduced statistical tension on H0 that it is less than 3σ using model-independent CMB catalogues as SPT-3G+WMAP9 and ACTPol DR-4+WMAP9 baselines. This suggests these models using a different phantom potential might address the Hubble constant problem and reduce the systematics involved.

The paper highlights the performance of the seismic isolation devices, installed on retrofitted buildings in reducing the seismic response when subjected to earthquakes. Two buildings from the beginning of the XX-th century in Bucharest are chosen from many more, monitored over the city area. We discuss the response of these base-seismic isolated structures, relying on good quality data acquired from the recent strong earthquake (03.11.2022, MW = 5.0). Elastic response spectra computed from recordings at two levels of each structure are used, placed under and right above the isolating layer. At one building the existence of previous recordings and the particularity of the sensors allow a comparison with other two relatively recent medium intensity earthquakes. The assessment is carried out in terms of maximum acceleration amax, measured at certain levels in each structure, spectral acceleration amplitude SAmax and spectral-peak corresponding period. We find that the base-isolation methodology is effective in reducing the response of the building right above the isolating layer, an observation valid for both structures, all components of the recordings, and spectral acceleration values. Moreover, the outcomes from the modal evaluation performed prior to rehabilitation and seismic isolation process are presented, by pointing out a higher newly acquired fundamental period of the isolated structures.

Recent advances in embedded antenna and sensor technologies for 5G communications have galvanized a response toward the investigation of their electromagnetic (EM) performance for urban context and civil engineering applications. This article quantitatively investigates the effects of material loading on an evolved antecedent-based design of a hexagonal-stubbed Complementary Split-Ring Resonator (CSRR)-loaded antenna through simulation and experimentation. The optimized antenna design is first conceptualized within a simulation environment to achieve EM resonance at 3.50 GHz before delving into the analysis of operational performance characteristics. As a proof-of-concept, a physical antenna prototype is fabricated on a printed circuit board for the in-situ evaluation of S11 parameter plot. Subsequently, a simulation-based parametric study is conducted on antenna prototypes embedded into Ordinary Portland Cement pastes with varying weight percentages of iron(III) oxide. Simulation-derived and experimental results are mutually verified, achieving a systemic downward shift in resonant frequency and corresponding variations in impedance matching induced by changes in the loading reactance. Finally, an inversion modeling procedure is employed using perturbation theory to extrapolate the relative permittivity of the dielectric embedding materials. Our proposed analysis contributes to optimizing concrete-embedded 5G antenna sensor designs and establishes a foundational framework for estimating unknown EM parameters of cement-based composites.

This paper presents the super-twisting algorithm (STA) direct power control (DPC) scheme for the control of active and reactive powers of grid-connected DFIG. Simulations of 5 KW DFIG has been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC). The proposed controller schemes with fixed gains are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effe This paper presents a comparative study of two approaches for the direct power control (DPC) of doubly-fed induction generator (DFIG) based on wind energy conversion system (WECS). Vector Control (VC) and Sliding Mode Control (SMC). The simulation results of the DFIG of 5 KW in the presence of various uncertainties were carried out to evaluate the capability and robustness of the proposed control scheme. The (SMC) strategy is the most appropriate scheme with the best combination such as reducing high powers ripple, diminishing steady-state error in addition to the fact that the impact of machine parameter variations does not change the system performance. cts of parametric uncertainties not affecting system performance.

In wideband radar systems, the performance of motion parameters estimation can significantly affect the performance of object detection and the quality of inverse synthetic aperture radar (ISAR) imaging. Although the traditional motion parameters estimation methods can reduce the range migration (RM) and Doppler frequency migration (DFM) effects in ISAR imaging, the computational complexity is high. In this paper, we propose a new fast non-searching motion parameters estimation method based on cross-correlation of adjacent echoes (CCAE) for wideband LFM signals. A cross-correlation operation is carried out for two adjacent echo signals, then the motion parameters can be calculated by estimating the frequency of the correlation result. The proposed CCAE method can be applied directly to the stretching system, which is commonly adopted in wideband radar systems. Simulational results demonstrate that the new method can achieve better estimation performances, with much lower computational cost, compared with existing methods. The experimental results on real radar data sets are also evaluated to verify the effectiveness and superiority of the proposed method compared to the state-of-the-art existing methods.

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable.

Nickel-based Superalloy Inconel 718 is widely used in the aerospace industry for its excellent high-temperature strength and thermal stability. However, milling Inconel 718 presents challenges due to significantly increased cutting force and vibration, which is a typical difficult-to-machine material. This paper focuses on the milling process of Inconel 718, establishing a milling force model to analyze the force trends under various processing parameters. Finite element analysis is employed to study the stress and temperature fields during milling. Dynamic equations for milling Inconel 718 are developed, and stability lobe diagrams are generated based on modal experiments. Milling experiments on Inconel 718 validate the milling force model and finite element analysis results. The fmincon optimization algorithm is utilized to identify the optimal machining parameters for Inconel 718. Through this research, valuable insights into enhancing the efficiency and quality of Inconel 718 machining are provided. This study contributes to a deeper understanding of Inconel 718 milling behavior, offering crucial guidance for more efficient machining processes.

We have shown that the use of evolutionary gravitational constants G, the speed of light c, and cosmological constant Λ in the Einstein’s field equations leads to a very simple model that fits the supernovae Ia data with a single parameter as well as the standard ΛCDM model with two parameters, and has the predictive capability superior to the latter. We have shown that at the current time G and c both increase as dG/dt = 5.4GH₀ and dc/dt = 1.8cH₀ with H₀ as the Hubble constant, and Λ decreases as dΛ/dt = −1.2ΛH₀. Negative finding on the variation of G with time derived from the analysis of lunar laser ranging data in several studies has been attributed to the fact that these studies did not consider the variation of c simultaneously with G.

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for the automation system. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review study, bone drilling vibration data collected in the University laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone which are processed and classified using several machine learning methods. LSTM is, used in the bone drilling classification study to prove that the layers of the bone drilling are associated with the vibration signal and can be classified and predicted using the machine learning method.

Per- and polyfluoroalkyl substances (PFASs) are a group of fluorinated, organic, man-made chemicals; they do not occur naturally in the environment. This study aimed to determine the profile and content of PFASs in volunteers’ blood plasma and urine after fermented red beetroot juice consumption and correlated it with the blood parameters. During 42 days, 24 healthy volunteers intake 200 mL/60 kg of body weight fermented red beetroot juice. PFASs were analyzed using micro-HPLC-MS/MS method. Five perfluoroalkyl substances were found in human body fluids. After juice consumption, PFASs, due to the consumption of food contaminated with them, was discovered that for perfluorocarboxylic acids, a downward trend was observed, while for perfluoroalkane sulfonates, their plasma content showed a statistically significant upward trend. Analysis of the hematology parameters indicated that the intake the fermented red beetroot juice showed a significant decrease in mean corpuscular volume (MCV), platelets concentration, mean platelet volume (MPV), platelet large cell ratio (P-LCR) at the significance level p < 0.01, and he-matocrit (p < 0.05). On the other hand, the dietary intervention also indicated a significant (p < 0.01) increase corpuscular/ cellular hemoglobin concentration (MCHC). In the case of blood biochemi-stry, no significant change in blood samples after intake the fermented beetroot juice was observed. However, a decreasing tendency of total cholesterol and low-density lipoprotein concentration (LDL-C) were observed. Based on the presented results, there is a need to analyze and monitor health-promoting food regarding undesirable substances and their impact on consumer health.

Determining genetic and non-genetic sources of variation in a breed is vital for the formulation of strategies for its conservation and improvement. The present study was aimed at estimating the (co)variance components and genetic parameters of Mecheri sheep by fitting six different animal models in the restricted maximum likelihood method, with a preliminary investigation on the performance of animals for non-genetic sources of variation. A total of 2616 lambs were studied, and varying levels of significance were found for the effect of period, season, parity of dam and birth type on different body weight traits. Direct heritability estimates derived from the best animal model for body weight at birth, 3 months, 6 months, 9 months and 12 months were 0.21, 0.24, 0.10, 0.15 and 0.09, respectively, and maternal heritabilities of the corresponding traits were 0.12, 0.05, 0.04, 0.04 and 0.04, respectively. The genetic correlations between body weight traits were all positive and moderate to strong except for birth weight with the other body weight traits. The significance of non-genetic factors studied in this work demanded a correction to improve the accuracy of the direct selection of lambs for body weight traits. The estimated genetic parameters identified the weaning weight as a selection criterion for the improvement in body weight of Mecheri lambs at different ages. Inbred individuals accounted for approximately 13% of the total population in the Mecheri sheep population studied. There were 877 founders in the population, and the actual effective size of the population was 128.48. The population's mean generation interval was 3.26. The mean inbreeding values ranged from 0.005 to 0.010 across generations. The population's average relatedness ranged from 0.001 to 0.014 across generations. Individual inbreeding was found to be 0.45 per cent for the entire population and 3.4 per cent for the inbred population.

Keywords: Deep mining; Drill bit structural parameters; Fidelity coring; Abaqus simulation and analysis.

This study presents the manufacturing process driven development of an interlocking metasurface mechanism (ILM) for Fused Filament Fabrication (FFF) with a focus on open-source accessibility. The presented ILM is designed to enable strong contact between to planar surfaces. The mechanism consists of spring elements and locking pins which snap together when forced into contact. The mechanism is designed to optimize mechanical properties, functionality and printability with common FFF printers. The mechanisms is printed from thermoplastic polyurethane (TPU) filament which was selected for its flexibility, crucial for spring element performance and tolerances of the fabrication method. To characterize the designed mechanism a tensile test is carried out to assess the holding force of the ILM. The force-displacement profiles are analyzed and categorized into distinct phases, highlighting the interplay between spring deformation, sliding, and disengagement. The results show variations in holding forces attributed to geometric and material-related factors. The testing results are compared and discussed to a numerical simulation carried out with a frictionless approach with a nonlinear Neo–Hookean material law. The study underscores the importance of meticulous parameter control in 3D printing for consistent and reliable performance of interlocking metasurface mechanisms. The investigation leads to a scalable model of a ILM element pair with a distinct three-phase snapping characteristics ensuring reliable holding capabilities.

The Yamuna River in Delhi is a prevalent epitome of the depleted ecosystem that has transformed into a sewage drain due to intense pollution and blooming anthropogenic pressure. As a leading polluter, Delhi contributes to more than 70% of Yamuna's pollution load. Its drains discharge a massive B.O.D. load daily into the river, making it severely polluted, as reflected by the water quality index. The paper uses secondary data to analyze the parameters to determine the water quality of Yamuna at different monitoring sites. It performs correlation analysis to determine the relationship among factors contributing to river water pollution. The results of correlation analysis suggest a highly significant association between parameters COD-BOD, but the association is mild to a minimum among other parameters such as BOD-DO, BOD-pH, COD-DO, COD-pH and DO-pH. Most parameters were observed to be above the hazardous level acceptable for using river water. The analysis of S.T.P.s indicates the need to increase the capacity in terms of treatment, storage, reopening of closed plants and efficient operation to meet the increasing demand for fresh Water. Also, there is a need to create demand for wastewater in different sectors of urban areas like construction, horticulture, industrial coolants, etc.

The article presents the results of the technical-economical study regarding the optimization of the FDM parameters (Lh – the height of the layer deposited in one pass and Id – the filling percentage) for the manufacture of PETG and ASA parts. To carry out the technical-economical study, we used the fundamental principle of value analysis, which consists in maximizing the ratio between Vi/Cp, where Vi - represents the mechanical characteristic, and Cp - represents the production cost. The results of the study show that for the tensile specimens made of PETG, the parameter that significantly influences the results of the Vi/Cp ratios is Lh, and in the case of the compression specimens made of PETG, the parameter that significantly influences the results of the Vi/Cp ratios is Id. In the case of specimens manufactured by FDM from ASA, the parameter that decisively influences the results of the Vi/Cp ratios of the tensile and compression specimens is Id – the filling percentage. By performing optimization of the process parameters with multiple responses, we identified the optimal parameters for FDM manufacturing of parts from PETG and ASA: Lh = 0.20 mm and Id = 100%.

This work presents the mathematical framework of the Second-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint Linear Systems (abbreviated as “2nd-CASAM-L”), which enables the most efficient computation of exactly obtained mathematical expressions of first- and second-order sensitivities of a generic system response with respect to functions (“features”) of model parameters. Subsequently, the first- and second-order sensitivities with respect to the model’s uncertain parameters, boundaries, and internal interfaces are obtained analytically and exactly, without needing large-scale computations. Within the 2nd-FASAM-L methodology, the number of large-scale computations is proportional to the number of model features (defined as functions of model parameters), as opposed to being proportional to the number of model parameters, which are considerably more than the number of features, being incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities (of any order) with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces. The application of the 2nd-CASAM-L methodology is illustrated using a simplified energy-dependent neutron transport model of fundamental significance in nuclear reactor physics.

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

The article presents investigations of rail vehicle bogie of Y25 type. The Y25 bogie family is one of the most commonly used freight car bogie designs. In addition to several significant advantages characterising this design, it also has several disadvantages observed since the beginning of more than fifty years of operation in several types of cargo vehicles. One of these defects observed in real systems is "unsatisfactory running stability", particularly at long straight tracks. The article used commercial engineering software VI-Rail to create a model of the gondola car (type 412W Eaos) with two Y25 bogies. The car model was tested empty and loaded (maximum permissible load). Motion along straight and curved tracks with different radii values was analysed. The vehicle velocity was changed from a few m/s to the maximum values for which stable solutions of the model existed. For each route, a non-linear critical velocity was determined, defining the maximum operating velocity of the modelled car. The model solutions were recorded, while just one was selected to present the results - the first wheelset lateral displacement ylw. The opinion about the "imperfect running quality" in curved tracks was confirmed. The possible appearance of self-exciting wheelset vibrations in the modelled car's operating velocity range in the laden state was also observed. The research results on the impact of the bogie suspension parameters change on the vehicle model stability are presented. The crucial parameter in the bogie suspension was indicated. Reducing its value by several percent about the nominal value increases the critical velocity of the car to values higher than the maximum operating velocity of the modelled vehicle.

Abstract: Background: Exposure to ETS (Environmental Tobacco Smoke) is one of the most toxic environmental exposures. Objective: To investigate the impact of ETS on physiological, biochemical, psychological indicators, on the urine antioxidant capacity (AC) and oxidative damage to lipids in a pilot sample of healthy pregnant women. Methods: The exposure to ETS was investigated by a validated questionnaire, urine cotinine and the marker of oxidative damage to lipids - 8-isoprostane concentrations using an ELISA kit. Urine AC was determined by the spectrophotometric TEAC method. From the sample of pregnant women (n=319, average age 30.84 ± 5.09 years) in 80 the levels of cotinine and oxidative stress markers were analyzed. Results: From our sample, 5 % individuals (7.4 % objectified by cotinine) were current smokers and 25 % reported passive smoking in the household (18.8 % objectified by cotinine). The Kappa was 0.78 for smokers and 0.22 for ETS exposed non-smokers. Smokers as well as non-smokers had significantly higher (p<0.05) urine AC than ETS exposed non-smokers. Non-smokers had significantly lower levels of 8-isoprostane than smokers (p<0.01) and ETS-exposed non-smokers (p<0.05). Correlations between urine levels of cotinine and AC were positive in ETS exposed non-smokers. Conclusion: The harmful effect of active and passive smoking on oxidative stress parameters has been indicated.

In this paper, four lithological parameters, i.e. thermal conductivity, particle size, porosity and saturation, are investigated by combining experimental observations with numerical simulations to study the influence laws and mechanisms of action on the combustion process of methane hydrate sediments. The variation of combustion characteristics parameters such as flame height, effective combustion ratio and dimensionless discharge water mass with the lithological parameters are studied. In addition, the combustion mechanism of lithologic parameters on methane hydrate deposits is revealed. Combining the experimental results and simulation calculations, to optimize the combustion of methane hydrate sediments, it is recommended to use methane hydrate sediment samples with high saturation and low thermal conductivity, while the oxidant concentration and porosity of methane hydrate sediment samples should be increased.

Electroencephalography (EEG) signals provide valuable insights into various activities of the human brain, including the detection of mental stress, which is a complex physiological and psychological response. However, the challenge lies in identifying mental stress accurately while mitigating the limitations associated with a large number of EEG channels. This includes issues such as computational complexity, the risk of overfitting, and the increased setup time for electrode placement, which can be cumbersome for real-life applications. Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. Our approach, known as the Correlation Coefficient of Hjorth Parameters (CCHP), assesses the correlation between activity, mobility, and complexity in the time domain to nominate the most relevant channels. By selecting channels that exhibit high correlation with the stress task while being uncorrelated with each other, CCHP significantly reduces the number of EEG channels required, without compromising accuracy or performance. To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. Furthermore, CCHP outperformed existing channel selection methods by an impressive 8%. These findings strongly indicate that the CCHP algorithm shows great promise in the design of a wearable application for mental stress detection, utilizing a minimal number of EEG channels.

Buckwheat is returning to the countries of Central Europe. There are several reasons for this: firstly, because of its interesting chemical composition, which is reflected in its nutritional value and potential health benefits. And secondly because buckwheat, and especially buckwheat flour, are suitable raw materials for the production of gluten-free foods. Buckwheat flours are classified similarly to wheat flours, but the different anatomy of wheat grains and buckwheat seeds makes this classification partly misleading. While wheat flours are largely produced by one standard process, the production process for buckwheat flours is more varied. For wheat and wheat flours, the basic quality parameters and their required ranges for different types of primary and secondary processing are clearly defined. This is not the case for buckwheat and buckwheat flours, and the definition of the parameters and their ranges that characterize its technological quality remain unclear. The standardization of quality parameters and production processes is likely to be necessary for the potential expansion of the use of buckwheat for food production and in particular for bakery products.

The process of Thermal spray is effective for creating a metal matrix composite (MMC) by embedding zirconium diboride reinforcement into a molybdenum matrix. This allows for the combination of beneficial properties in a new composite, as both materials have a high The materials Mo and ZrB2 possess different characteristics. For example, Mo has a melting point of 2622 ◦C while ZrB2 has a higher melting point of 3246 ◦C. Additionally, Mo has a high thermal conductivity of 139 W/m◦C compared to ZrB2's thermal conductivity of 24 W/m◦C. However, both materials have good thermal shock resistance, and Mo has a low coefficient of thermal expansion of 5.35 µm/m◦C while ZrB2 has a coefficient of thermal expansion of 5.8 × 10−6 K−1., as well as the ability to maintain strength at elevated temperatures and stability in extreme environments. The study focused on creating Mo/Zrb2 composites using a thermal spray coating technique, varying the parameters of spraying distance, number of passes, and gas pressure, and testing the resulting castings to evaluate their hardness and Young's Modulus. The primary objective of the research was to use the Taguchi technique for identifying the optimal parameters for generating the highest Young's Modulus and hardness for the castings. The Taguchi method integrates experimental and analytical principles to identify the most significant parameter affecting the response, which can significantly enhance overall performance. The study found that the optimal parameters were a spraying distance of 20 cm, gas pressure of 6 bar, and the number of passes of 18. The Taguchi method accurately predicted the parameters that produced the highest properties for the composite coatings, which demonstrated good surface formation without hot cracks and fewer pores, with well-formed metallurgical bonding between the coating and the substrate.

Electrode degradation in continuous resistance spot welding process of BH 220 steel was evaluated by electrode life test, and weldability tests were conducted by geometry features measurement, mechanical property analysis, and electrode diameter measurement with 88 or 176 weld intervals. The analysis of weld geometry shows that the HAZ width, nugget diameter, and nugget area tend to decrease rapidly, while the nugget height tends to firstly increase with the weld repetitions till the welding heat input becomes too small to form the valid nugget. The peak load shows a rapid downward trend, while failure energy and maximum displacement slightly increase till the 176th weld and then decrease. The cavities and pores in the nugget mainly appear after the 176th spot weld. The electrode diameter increases during welding. The reason for the increase in electrode diameter may be that the contact area between the electrode and BH 220 steel sheets becomes smaller in the welding process, which causes the continuous sticking phenomenon between the electrode and the BH 220 steel sheets. In the absence of alloying, the edge of the electrode is geometrically deformed, while Cu-Zn-Fe alloying occurs in the area in contact with the BH 220 steel sheet.

Natural systems of units $\{U_i\}$ need to be overhauled to include the dimensionless coupling constants $\{\upalpha_{U_i}\}$ of the natural forces. Otherwise, they cannot quantify all forces in a unified manner. Thus, each force must furnish a system with at least one dimensional and one dimensionless constant. We revisit three natural systems of units (atomic, cosmological, and Planck). The Planck system is easier to rectify, and we do so in this work. The atomic system discounts $\{ G, \upalpha_G \}$, thus it cannot account for gravitation. The cosmological system discounts $\{ \slashed{h}, \upalpha_\slashed{h} \}$, thus it cannot account for quantum physics. Here, the symbols have their usual meanings; in particular, $\upalpha_G$ is the gravitational coupling constant and $\upalpha_\slashed{h}$ is the fine-structure constant. The speed of light $c$ and the impedance of free space $Z_0$ are resistive properties of the vacuum, thus they must be present in all systems of units. The upgraded Planck system $${\rm UPS} \,:=\,\{ c, Z_0, G, \upalpha_G, \slashed{h}, \upalpha_\slashed{h}, \,\dots\,\!\},$$ describes all physical scales in the universe---it is nature's system of units. As such, it reveals a number of properties, most of which have been encountered previously in seemingly disjoint parts of physics, and some of which have been designated as mere coincidences. Based on the UPS results, that relate (sub)atomic scales to the Planck scale and the fine-structure constant to the Higgs field, we can state with confidence that no observed/measured physical properties are coincidental in this universe.

Iyer Markoulakis Helmholtz Hamiltonian mechanics formalisms mathematically modeled physics with vortex rotational fields acting with gradient fields, typically in zero-point microblackhole general fields. Here, Helmholtz metrics have been gaged to Coulombic Hilbert metrics, representing Gilbertian and Amperian natures of electromagnetic fields out of the mechanical fields from Helmholtz Hamiltonian mechanics. This ansatz general gaging helps to properly isolate field effects – mechanical, electric, magnetic components within the analytical processes. Vacuum gravitational fields and the flavor Higgs-Boson matter inertial gravitational fields have been thus quantified extending to stringmetrics constructs matrix showing charge asymmetry gage metrics, having the power to analyze dark energy superluminal phase, dark matter luminal phase, and light matter subluminal phase. Interpreting particle physics gage matrix pointing to Dirac seas electrons, monopoles with positrons, electron-positron annihilation leading to energy production, relativistic energy generating matter, and both monopoles – vacuum and compressed out of vortex Helmholtz decomposition fields have been interpolated. Quantum ASTROPHYSICS gage metrix posits superluminal profile of signals velocity generating electron-positron chain like “curdling” action that is consistent with physics literature reporting nature electron photon observed oscillatory fields configurations. This helps proposing creation of neutrino antineutrino pair orthogonal to electron positron “curdling” planes, that may lead to formation of protonic hydrogen of stars or orthogonally muon. These aspects will explain receding or fast expanding universe with dark matter in terms of flavor metrics versus gage associating metrics flavor. Additional interpretations of virtual gravitational dipoles that have gravitational charge of opposite signs, reported per physics literature at Planck dimensions, that may originate at the center of supermassive blackhole. On the other hands, vacuum monopoles occur probably at infinity with cosmos extent.

This paper examines the influence of physical parameters on the collapse dynamics of a spherical bubble filled with diatomic gas ($\kappa=7/5$). The present numerical investigation shows that each physical parameter affects the bubble collapse dynamics differently. After comparing the contribution of each physical parameter, it appears that, of all the parameters, the surrounding liquid environment affects the bubble collapse dynamics the most. Meanwhile, surface tension has the weakest influence and can be ignored in the bubble collapse dynamics. However, surface tension must be retained in the initial analysis since this, as well as the pressure difference jointly control initial bubble formation. As an essential part of this study, a general Maple code is provided.

Abstract: This study was aimed to determine the influence of the volume fraction of steel fibers and on fracture parameters of concrete. Fifty notched steel fiber reinforced concrete (SFRC) beams and ordinary concrete beams with dimensions of 100mm×100mm×515mm were cast and tested via three-point bending test. Among them, the type of steel fiber is milling type (MF), and the volume fraction of steel fiber added is 0%, 0.5%, 0.5%, 1.5%, 1.5%, 2%, respectively. The effects of the steel fiber volume fraction (VF) on the critical stress intensity factor (KIC), fracture energy (GF), the deflection at failure(δ0), the critical crack mouth opening displacement (CMODC) and the critical crack tip opening displacement (CTODC)were studied. Through the analysis of test phenomena and test data such as load-deflection (P-δ) curve, load-crack mouth opening displacement (P-CMOD) curve and load-crack tip opening displacement (P-CTOD) curve following conclusions are drawn: With the increase of steel fiber volume fraction, some fracture parameters increase gradually and maintain a certain linear growth. The gain ratio of fracture parameters increases significantly, and the gain effect is obvious. Through this law of growth, the experimental statistical formulas of fracture energy and critical stress intensity factor are summarized.

We report on the successful preparation of wet dressings hydrogels based on Chitosan-Poly(N-Vinyl-Pyrrolidone)-Poly(ethylene glycol)-Poly(acrylic acid) and Poly(ethylene oxide) by e-beam cross-linking in weakly acidic media, to be used for rapid healing and pain release of infected skin wounds. The structure and compositions of hydrogels investigated according to sol-gel and swelling studies, network parameters, as well as FTIR and XPS analyses showed the efficient interaction of the hydrogel components upon irradiation, maintaining the bonding environment while the cross-linking degree increasing with the irradiation dose and the formation of a structure with the mesh size in the range 11-67 nm. Hydrogels with gel fraction above 85% and the best-swelling properties in different pH solutions were obtained for hydrogels produced with 15 kGy. The hydrogels are stable in the simulated physiological condition of an infected wound and show appropriate moisture retention capability and the water vapor transmission rate up to 272.67 g m-2 day-1, to ensure fast healing. The hydrogels proved to have a significant loading capacity of ibuprofen (IBU), being able to incorporate a therapeutic dose for the treatment of severe pains. Simultaneously, IBU was released up to 25% in the first 2h, having a release maximum after 8h.

We studied the effect of the structure parameters of engine annular cooling fan with outer ring on the aerodynamic performance by means of experiments and model simulation in fluent^®. Firstly, based on the experiment, a computational model is developed to calculate and analyze the aerodynamic performance of the tested annular fan. The model is validated by comparing the test results with the calculated data. Besides, the aerodynamic performance differences between two types of fans (common fan without outer ring and annular fan with outer ring) are discussed. Based on the computational model, the relation between aerodynamic performance and the outer ring structure parameters are investigated. The results show that the relative parameter on the axial direction has great influence on the aerodynamic performance; while the effect of radial relative parameter is minor. In addition, the outer ring with arc chamfer structure in the downstream side can improve its static pressure efficiency effectively.

This article presents the results of studies of the structure and conductive properties of Na3Fe2(PO4)3 polycrystals synthesized by solid-phase and melt (using concentrated optical radiation) methods. It has been established that with the melt method, the synthesis of the material occurs under significantly nonequilibrium temperature-gradient conditions, which leads to the creation of deformations in the sample that contribute to an increase in ionic conductivity. The ability of the Na3Fe2(PO4)3 structure to adapt to anomalous deformations that occur under temperature-gradient conditions indicates the elasticity of the crystalline framework {[Fe2(PO4)]3-}3∞. Through a comparative assessment of the structural parameters and conductive properties of these materials, it is shown that polycrystals obtained by the melt method have a better texture and greater ionic conductivity. The advantage of this method is that the duration of the synthesis of α-Na3Fe2(PO4)3 is halved. It is concluded that this method is applicable to obtain effective polycrystals of other compositions.

Numerous investigations have suggested that seismic source parameters and properties of the surrounding medium harbor valuable information regarding alterations in stress fields and medium properties at the focal depth. Monitoring the spatial and temporal evolution of these parameters can yield insights into variations in stress fields or medium properties within the seismogenic zone, offering a critical avenue to overcome the Earth's inaccessible nature. In recent years, modern seismic parameters such as seismic moment, focal mechanism solution, source stress drop, corner frequency, radiated seismic energy, rupture radius, and apparent stress have been increasingly used to characterize source characteristics. The Sichuan region is situated on the southeastern edge of the Qinghai-Tibet Plateau and is known for its strong eastward extrusion and structural deformation. The main fault zones in the area include the Xianshuihe Fault, Anninghe Zemuhe Fault, and Longmenshan Fault. This paper estimates source parameters of small to medium-sized earthquakes (ML 1.5-5.2) in the main fault zones and adjacent areas in Sichuan. A total of 4,310 earthquake stress drop measurements from 2019 to 2023 were analyzed to create a stress distribution image along the fault. This earthquake parameters training catalog provides insight into how coseismic stresses change and their relationship with other geophysical factors, as well as their spatial and temporal evolution.

High molecular weight glutenin subunits (HMW-GS) and Low molecular weight glutenin subunits (LMW-GS) in mature grains play important roles in the formation of glutenin macropolymer and gluten quality. To characterize the expressed glutenin genes of the bread wheat variety Xinmai 26 during seed development, in this study, we measured the dough rheological properties of mature grains through farinograph and gluten testing system, and revealed its strong gluten quality. The compositions of HMW-GS and LMW-GS were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Further, a total of 18 full-length transcripts were obtained by third-generation RNA sequencing, including 5 transcripts of HMW-GS genes and 13 transcripts of LMW-GS genes (8 intact genes and 5 pseudogenes). The deduced protein structures of transcript sequences exhibit the typical HMW-GS and LMW-GS structural characteristics. Moreover, a specific functional marker was developed to make better use of the extra cysteine residue of 1Dx5 subunit. This study provides an efficient method to accurately identify glutenin genes in bread wheat through matching full-length transcripts to their spectrum of glutenin, which will be helpful to understand the contributions of glutenin genes to wheat quality.

In this article, the sol-gel method was used as a synthesis method, which shows the physico –chemical nature of the synthesis of a new complex material ferrit Li0.5MnFe1.5O4. The synthesized structure and structure were determined by the method of X-ray phase analysis. According to the analysis indicators, it was found that our compound is single-phase, spinel-structured, and syngony - cubic type. The microstructure of the compound and the quantitative composition of the elements contained were analyzed under a scanning electron microscope (SEM). Through a scanning electron microscope, Microsystems were taken from different parts of l Li0.5MnFe1.5O4-type crystallite, the elemental composition of crystals was analyzed, and the general type of surface layer of complex ferrite was shown. As a result, the fact that the compound consists of a single phase, the clarity of its construction was determined by the topography and chemical composition of the compound. As a result, it was found that the newly synthesized complex ferrites correspond to the Formula Li0.5MnFe1.5O4. The particles of the formed compounds have a large size (between 50.0 μm, 20.0 μm and 10.0 μm). Electro physical measurements were carried out on the LCR - 800 unit at intervals of 293-483 K and at frequencies of 1.5 and 10 kHz. The increase in frequency to 10 kHz led to a decrease in the value ε in the range of the studied temperature (293-483 K).

In recent years, the discharge of various emerging pollutants, chemicals and dyes in water and wastewater has represented one of the prominent human problems. Since water pollution is directly related to human health, highly resistant and emerging compounds in aquatic environments will pose many potential risks to the health of all living. Therefore, water pollution is a very acute problem that has constantly increased in recent years with the expansion of various industries. Consequently, choosing efficient and innovative wastewater treatment methods to remove contaminants is crucial. Among advanced oxidation processes, electrochemical oxidation (EO) is the most common and effective method for removing persistent pollutants from municipal and industrial wastewater. However, there are still many gaps despite the great progress in using EO to treat real wastewater. This is due to the lack of comprehensive information on the operating parameters which affect the process and its operating costs. In this paper, among various scientific articles, the impact of operational parameters on the EO performances, a comparison between different electrochemical reactor configurations, and a report on general mechanisms of electrochemical oxidation of organic pollutants have been reported. Moreover, an evaluation of cost analysis and energy consumption requirements have also been discussed. Finally, the combination process between EO and another important advanced oxidation technology, PEC, called photoelectrocatalysis (PEC), has shortly been discussed and reviewed. This article showed that there is a direct relationship between important operating parameters with the amount of costs and the final removal efficiency of emerging pollutants. Optimal operating conditions can be achieved by paying special attention to reactor design, which can lead to higher efficiency and more efficient treatment. The rapid development of EO for removing emerging pollutants from impacted water and its combination with other green methods can result in more efficient approaches to face the pressing water pollution challenge.

Humming is probably more effective than speech improving mothers' cardiorespiratory function and infants' self-regulation. We intend to understand the effects of: 1) maternal humming vs. speech on preterm infants’ physiological parameters; 2) maternal humming vs. speech on mothers’ physiological parameters; 3) humming melodic contours and of the lengthening of the final note on preterm infants’ physiological parameters. This study was designed as a single group repeated measure, using microanalytical methodology (ELAN software), with a protocol (silent baseline / speech or humming / silence / humming or speech / silence) applied to preterm dyads (N = 36). Audio and video observations were recorded. Infants’ and mothers’ heart rate (HR) and O2 saturation were observed once a minute. Proportion of O2 saturation relative to HR (Prop. O2 saturation/HR) was estimated for both partners during protocol. We found an infants’ HR mean significantly lower during humming (p = .028), a significantly higher Prop. O2 saturation/HR during humming for infants (p = .027) and for mothers (p = .029). The duration of sinusoidal contours together with the lengthening of the final note predict infants’ Prop. O2 saturation/HR. Musical features of humming seem to improve the physiological stability of preterm infants during kangaroo care.

The catalytic parameters describing enzyme catalysis are discussed in the context of the design of de novo enzymes. It is shown that high values of the catalytic efficiency may in some cases reflect tight substrate binding and lead to an immediate levelling-off of rates to reach a low plateau, an undesirable scenario for many industrial applications. It is argued that increasing the catalytic rate constant should be a main target in de novo enzyme engineering.

In order to study the influence of falling rock shape on its rolling characteristics and realize the optimization of falling rock protection design, the following research was carried out. Design model experiments to explore the rolling characteristics of different shapes of rockfalls. Based on the experimental results, it was found that the slenderness ratio, center of gravity, and rotational inertia of the rockfalls can affect their rolling characteristics, leading to swaying and changing the rolling axis during the rolling process, thereby affecting their rolling speed. On this basis, the formation mechanism of rolling resistance is analyzed, and the main cause of energy loss is the rolling resistance arm formed with the rolling surface during rockfall rolling, and a shape parameter to quantify the rolling resistance is proposed. The parameters are substituted into the kinematic formula, in which the influence of the rockfall shape, slope and slope roughness on the rolling speed is considered. Combined with the offset and initial position of the rockfall, the formula can be used to calculate the rolling speed and impact energy in the rolling region and any position in the region. The calculation formula was validated using model experimental data, and the results showed that the error between the experimental and calculated values was small. The error was corrected based on the experimental data. After on-site testing and verification, it can provide reference for the management of rockfall disasters.

This study introduces a multilayer perceptron (MLP) error compensation method for real-time camera orientation estimation, leveraging a single vanishing point and road lane lines within a steady-state framework. The research emphasizes cameras with a roll angle of 0°, predominant in autonomous vehicle contexts. The methodology estimates pitch and yaw angles using a single image and integrates two Kalman filter models with inputs from image points (u, v) and derived angles (pitch, yaw). Performance metrics, including AE, MINE, MAXE, SSE, and STDEV, were utilized, testing the system in both simulator and real-vehicle environments. The outcomes indicate that our method notably enhances the accuracy of camera orientation estimations, consistently outpacing competing techniques across varied scenarios. This method’s potency is evident in its adaptability and precision, holding promise for advanced vehicle systems and real-world applications.

Condition monitoring of rotating shafts is essential for ensuring the reliability and optimal performance of machinery in diverse industries. In this context, as industrial systems become increasingly complex, the need for efficient data processing techniques is paramount. Deep learning has emerged as a dominant approach due to its capacity to capture intricate data patterns and relationships. However, a prevalent challenge lies in the black-box nature of many deep learning algorithms, which often operate without adhering to the underlying physical characteristics intrinsic to the studied phenomena. To address this limitation and enhance the fusion of data-driven methodologies with the fundamental physics of the system under study, this paper leverages physics-informed neural networks (PINNs). Specifically, a simple but realistic numerical case study of an extended Jeffcott rotor model, encompassing damping effects and anisotropic supports for a more comprehensive modelling, is considered. PINNs are used for the estimation of five parameters that characterize the health state of the system. These parameters encompass the radial and angular position of the static unbalance due to the disk installed on the shaft, the stiffness along the principal axes of elasticity, and the non-rotating damping coefficient. The estimation is conducted solely by exploiting the displacement signals from the centre of the disk and, to showcase the efficacy and precision provided by this novel methodology, various scenarios involving different constant rotational speeds are examined. Additionally, the impact of noisy input data is also taken into account within the analysis.

Pool boiling is extensively utilized in various fields, such as electronics cooling, battery cooling, nuclear power plants, and chemical processing. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat transfer capacity of the system. Therefore, it holds immense significance to gain a profound understanding of the mechanisms underlying bubble formation, growth, and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate various parameters of bubbles during the boiling process. These findings were considered valuable references in selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer during the boiling process, the forces exerted on the bubbles was highlighted in this article. And a meticulous review on bubble force analysis and correlation formulas pertaining to various relevant parameters (e.g. nucleation sites density, bubble growth rate, detachment diameter, detachment frequency) were conducted. This review article also were expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

The article presents the influence of ecological agents, i.e. the effective microorganisms and silver compounds addition to lubricating oil on operating parameters of an internal combustion engine. The basic diagnostic parameters of a piston engine determining its technical condition were described. In the further part, the research stand and materials were presented. In the main part of this article for pure oil compared to oil with the effective microorganisms and silver compounds addition were shown. It was found that the addition of effective microorganisms and silver compounds to the oil reduces the emission of toxic components into the atmosphere along with the exhaust gases, while the remaining engine operating parameters for each load value show that these additives do not cause deterioration of the technical condition of the tested engine. Of all the agents used, the best results are obtained with the addition of ceramic tubes, because it is an additive that does not affect the properties of the oil in its composition. The advantage of ceramic tubes is the slow release of effective microorganisms, which has an effect on the parameters of the oil and thus on the operation of the engine. In further research, it will be checked how these additives affect the anti-seize and anti-wear properties of the lubricating oil used, which should give a broader view of the impact of these additives on the technical condition of the operated piston engine.

The challenge of achieving ultrafast switching of electric polarization in ferroelectric materials remains unsolved, as there is no experimental evidence of such switching to date. In this study, we have developed an enhanced model that describes switching within a two-dimensional space of generalized coordinates. Our findings indicate that stable switching in barium titanate cannot be achieved through a single linearly polarized pulse. When the intensity of the linearly polarized pulse reaches a certain threshold, the sample experiences depolarization, but not stable switching. Our study also reveals that phonon friction plays a minor role in the switching dynamics and provides an estimate of the optimal parameters of the perturbing pulse with the lowest intensity that results in depolarization of an initially depolarized sample.

Plant stem cell cultures have so far been established in only a few plant species using cambial meristematic cells. The presence of stem cells or stem cell-like cells in other organs and tissues of the plant body, as well as the possibility of de novo generation of meristematic cells from differentiated cells, allows to consider the establishment of stem cell cultures in a broader range of species. Therefore, this study aimed to establish a stem cell culture of the medicinal plant Calendula officinalis L. Callus tissues were induced from explants taken from leaves and roots. Appropriate combinations of plant growth regulators and light parameters for in vitro cultivation were selected. Already at this stage, stem and dedifferentiated cells could be identified, and cell suspension cultures were established from specific parts of the formed callus. Cell suspension cultures with a high proportion of stem cells originating from roots (92–93%) or leaves (72–73%) were developed. The amount of stem cells derived from the roots in the cell suspension reached a plateau at a level of 5.60–5.72105.

Superconducting YBa2Cu3Oy (YBCO) foams were prepared using commercial open-cell, polyurethane foams as starting material to form ceramic Y2BaCuO5 (Y-211) foams which are then converted into superconducting YBCO by using the infiltration growth process. For modelling the superconducting and mechanical properties of the foam samples, a Kelvin-type cell may be employed as a first approach like done in the literature for pure polyurethane foams. However, for a refined model of a superconducting foam sample, the real sample structure must be considered. Thus, a proper description of the specific microstructure of the superconducting YBCO foams is required. A variety of parameters including the cell size and shape, the window size and shape, the length and shape of the foam struts or ligaments and the respective angles of intersection are used to describe the real foam structure. To obtain a set of reliable data, YBCO foam samples were investigated using optical microscopy, SEM and electron backscatter diffraction (EBSD). The detailed investigation of the foam microstructure reveals not only the differences to the polymeric foams used as base material, but also gives insight to details of the infiltration growth process via the increased surface amount in a foam sample.

Acid fracturing simulation is used widely to optimize carbonate reservoirs and improve acid fracturing treatment performance. In this study, a method was used to minimize the risk of the acid fracturing treatment. First, optimal fracture geometry parameters with UFD methods are calculated. After that, design components change as long as fracture geometry parameters reach their optimal values. The results showed a high flow rate needed to achieve optimal fracture geometry parameters with increasing acid volume. Sensitivity analysis was performed on controllable and reservoir parameters. It observed that a high flow rate should be applied for a low fluid viscosity to achieve the optimization goals. Straight acid reaches optimal conditions at a high flow rate and low volume. These conditions for retarded acids appear only at a low flow rate and high volume. The study of the acid concentration for gelled acid showed that as it increased, the flow rate and volume increased. Besides, for low permeability formation, a large fracture half-length and small fracture width are desirable. In this case, a higher flow rate will be required. The sensitivity analysis showed that the optimum flow rate and acid volume increase and decrease for the high Young's modulus. The effect of closure stress was also investigated and observed for a sample with high closure stress, low flow rate, and high acid volume are required.

Keenjhar Lake is the main source of drinking water for the metropolitan city of Karachi. The release of untreated wastewater from Kotri industrial area and other sources have made the lake water polluted. This study was subjected to determine the impacts of such pollutant sources on the water quality of Keenjhar Lake. The study involves the analysis of water quality parameters of Keenjhar Lake and its Feeding source (KB Feeder). The sampling sites were selected based on the sources of contamination. The water samples are tested for physical, chemical and microbiological parameters. The result of water analysis indicates the contamination level of the lake is quite alarming for the sites of Kotri effluent and WAPDA colony where Total Dissolved Solids (TDS), Chloride and other ionic metals were quite higher in concentration than other sites. These sites are also contaminated with Fluoride and Arsenic which are carcinogenic elements. The study reveals that the contamination level of feeding source is causing big non-reversible damage to the lake if continued to be uncontrolled. This contamination is mainly due to the release of toxic metals and ions in the KB feeder caused by human carelessness.

In the work, the nuclei structure was modeled by a system of shells consisting of combinations of spherical and annular vortices. Having connected the spin and the magnetic moments of the nuclei with the dynamics of the outer shell of the nucleus, we determined its type and carried out a semiempirical calculation of the parameters of its structure for stable and unstable nuclear isotopes in the sequence of the order number Z from 1 to 37. Comparison of the obtained nuclear sizes with the radii of the electron shells of atoms made it possible to reveal a definite correlation. On the basis of it, it was concluded that the periodic law of Mendeleyev can determine the patterns in the change in the structure of the atomic nucleus.

Accurate evaluation of health status of lithium-ion batteries must be deemed as of great significance, insofar as utility and safety of batteries are of concern. Lithium plating, in particular, is notoriously known to be a chemical reaction that can cause deterioration in, or even fatal hazards to, the health of lithium-ion batteries. Electrochemical impedance spectroscopy (EIS), which has distinct advantages such as fast or non-destructive over its competitors, suffices in detecting lithium plating and thus has been attracting increasing attention in the field of battery management, but its ability of assessing quantitatively the degree of lithium plating remains largely unexplored hitherto. On this point, this work seeks to narrow that gap, by proposing an EIS-based method that can quantify the degree of lithium plating. The core conception is to eventually circumvent the reliance on state-of-health measurement, and use instead the impedance spectrum, to acquire an estimate on battery capacity loss. To do so, the effects of solid electrolyte interphase formation and lithium plating on battery capacity must be first decoupled, so that the mass of lithium plating can be quantified. Then, based on an impedance spectrum measurement, the parameters of the fractional equivalent circuit model (ECM) of the battery can be identified. These fractional ECM parameters are received as inputs by an artificial neural network, which is tasked to establish a correspondence between the model parameters and the mass of lithium plating. The empirical part of the work revolves around the data collected from an aging experiment, and the validity of the proposed method is truthfully attested by dismantling the batteries, which is otherwise not needed during the actual uptake of the method.

The vehicles number continuously growing lead to increasingly intense and congested traffic and it will additionally demand the braking system, and drivers behave more aggressively and as result is required that the braking system to be durable and efficient. For this is necessary the study the braking system behavior in conditions of intense and moderate traffic to increase the safety of traffic participants, respectively to demonstrate the need for more frequent replacement of some braking system elements. Thus, on a vehicle were performed a series of successive tests, through which the degree of wear of the brake pads and discs was monitored periodically and as a result the efficiency evolution of the braking system. The tests were carried out both in laboratory (on dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and disks are inconsistent with their actual wear. Therefore, the aim of this paper is the establishment of the braking system efficiency of an auto-vehicle, subject to testing depending on the auto-vehicle mass, travel speed, distance driven, and braking time, based on experimental on stand and in-traffic tests, according the road safety regulations.

For many decades, X-ray diffraction (XRD) has been used for material characterization. With the recent development in material science understanding and technology, various new materials are being developed, which requires upgrading the existing analytical techniques such that intricate problems can be solved. Although, XRD is a well-established non-destructive technique, it still requires further improvements in its characterization capabilities, especially when dealing with complex mineral structures. The present review conducts comprehensive discussions on atomic crystal structure, XRD principle, its applications, uncertainty during XRD analysis, and required safety precautions, all in one place. It further discusses the future research directions, especially the use of artificial intelligence and machine learning tools for improving the effectiveness and accuracy of XRD technique for mineral characterization. It has been focused that how XRD patterns can be utilized for a thorough understanding of the crystalline structure, size, and orientation, dislocation density, phase identification, quantification, and transformation, information about lattice parameters, residual stress, and strain, and thermal expansion coefficient of materials. All these important discussions on XRD for mineral characterization are compiled in this short yet comprehensive review that would benefit specialists and engineers in the chemical, mining, iron, metallurgy, and steel industries.

Background: Pre-pregnancy overweight and obesity of women of childbearing ages, has alarmingly a growing trend in developed countries. This study aimed to evaluate potential associations between maternal pre-pregnancy excess body weight and childhood demographic and anthropometric characteristics, as well as perinatal and postnatal outcomes. Methods: This is a cross-sectional study conducted on 5198 pre-school children aged 2-5 years old and their matched mothers that were enrolled from nine different Greek regions. Maternal and childhood anthropometric and demographic data, perinatal and postnatal outcomes were collected from medical history records or validated questionnaires. Results: A prevalence of 24.4% and 30.6% of overweight/obesity was recorded for the enrolled children and their mothers 2-5 years postpartum. In multivariate analysis, childhood overweight and obesity was significantly more frequently observed when maternal pre-pregnancy BMI status was overweight or obese [OR: 2.11 (1.80-2.42) p=0.0001]. A high prevalence of caesarean section delivery and childhood diabetes type I was also significantly associated with maternal pre-pregnancy overweight/obesity [OR:1.71 (1.33-2.19) p=0.0175) and OR: 1.27 (1.04-1.53) p=0.0014, respectively]. Conclusions: Maternal pre-pregnancy overweight and obesity rates were related with increased childhood weight status at birth and 2-5 years postpartum, highlighting the necessity of encouraging healthy lifestyle promotion, including healthier nutritional habits, and focusing on obesity population policies and nutritional interventions among women of reproducible age.

Electric streamer discharges (streamers) in air are a very important stage of lightning, taking place before formation of the leader discharge, and with which an electric discharge starts from conducting objects which enhance the background elecric field, such as airplanes. Despite years of research, it is still not well understood what mechanism determines the values of streamer parameters, such as its radius and propagation velocity. The Streamer Parameter Model (SPM) is aimed to understand this mechanism, as well as to provide a way to efficiently calculate streamer parameters. Previously, we demonstrated that SPM results compared well with a limited set of experimental data. In this Brief Report, we compare SPM predictions to the published hydrodynamic simulation (HDS) results.

In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R²_adj=0.820), followed by basal area (BA) (R²_adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.

Reading is one of the essential processes during the maturation of an individual. It is estimated that 5-10% of school-age children are affected by dyslexia, the reading disorder characterised by difficulties in the accuracy or fluency of word recognition. There are many studies which have reported that colour overlays and background could improve the reading process, especially in children with reading disorders. As dyslexia has neurobiological origins, the aim of the present research was to understand the relationship between physiological parameters and colour modifications in the text and background during reading in children with and without dyslexia. We have measured differences in electroencephalography (EEG), heart rate variability (HRV), electrodermal activities (EDA), and eye movement of the 36 school-age children (18 with dyslexia and 18 of control group) during the reading performance in 13 combinations of background and overlay colours during the reading task. Our findings showed that the dyslexic children have longer reading duration, fixation count, fixation duration average, fixation duration total, and longer saccade count, saccade duration total, and saccade duration average while reading on white and coloured background/overlay. It was found that the turquoise, turquoise O, and yellow colours are beneficial for dyslexic readers, as they achieved the shortest time duration during the reading tasks when these colours were used. Also, dyslexic children have higher values of beta and the whole range of EEG while reading in particular colour (purple), as well as increasing theta range while reading on the purple overlay colour. We have observed no significant differences between HRV parameters on white colour, except for single colours (purple, turquoise overlay and yellow overlay) where the control group showed higher values for Mean HR, while dyslexic children scored higher with Mean RR. Regarding EDA measure we have found systematically lower values in children with dyslexia in comparison to the control group. Based on present results we can conclude that both colours (warm and cold background/overlays) are beneficial for both groups of readers and all sensor modalities could be used to better understand the neurophysiological origins in dyslexic children.

Background and objectives: The impact of prenatal exposure to polycyclicaromatic 17 hydrocarbons (PAHs) on birth outcomes as weight, length, head circumference, placenta 18 weight, and Apgar. Materials and Methods: Two cohorts of children born in the years 2013 and 19 2014 in Karvina (Northern Moravia, N=144) and Ceske Budejovice (Southern Bohemia, 20 N=198), were studied for the relationship between the prenatal exposure to PAHs and growth 21 parameters up to two years of age. PAHs exposure was evaluated according to the concentration 22 of benzo[a]pyrene (B[a]P) in polluted air and monohydroxylated PAH metabolites (OH-PAHs) 23 in urine of newborns as well as their mothers. Data of growth parameters were obtained from 24 pediatric questionnaires up to 24 months. 25 Results: Concentrations of B[a]P were significantly higher in Karvina (p<0.001). OH-26 PAH metabolites were significantly higher in the mothers´ as well as in the newborns´ urine in 27 Karvina. The length was shorter in newborns in Karvina at birth (p<0.001), but this difference 28 was straightened out during next 3 to 24 months. Birth weight at the delivery did not differ 29 between newborns in Karvina and Ceske Budejovice. Newborns in both locations significantly 30 decreased their weight gain between birth and 3 months after delivery. OH-PAHs metabolites 31 in mother’s or newborn’s urine did not affect birth weight. Top 25% values of concentrations 32 of 2-OH-FLUO, 1-OH-NAP, 2-OH-NAP, 1-OH-PHEN, 2-OH-PHEN, 3-OH-PHEN, 4-OH-33 PHEN, and the sum of all-OH-PAHs higher than median in the newborns´ urine decreased their 34 length. 2-OH-PHEN top 25% of concentrations in the newborns´ urine decreased their head 35 circumference, 2-OH-FLUO, 1-OH-NAP, 2-OH-NAP, 1-OH-PHEN, 2-OH-PHEN, 3-OH-36 PHEN, 4-OH-PHEN, 9-OH-PHEN, 1-OH-PYR, and all-OH-PAHs decreased placenta weight; 37 2-OH-FLUO, 1-OH-NAP, 2-OH-NAP, 1-OH-PHEN, 2-OH-PHEN, 3-OH-PHEN, 4-OH-38 PHEN, and all-OH-PAHs decreased Apgar 5´. Conclusions: We observed that higher 39 concentration of PAHs determined as OH-PAHs metabolites in newborns´ urine decreased their 40 length, head circumference, placenta weight, and Apgar 5´, but did not affect birth weight.

Modeling the reaction time required for the removal of chemical oxygen demand (COD) from wastewater is important because can be an indicator of harmful pollutants that may pose risks to human health.. Here, we evaluate the use of an inverse artificial neural network (ANNi) to optimize the removal of COD from aqueous herbicide solutions using sonophotocatalysis as the treatment process.. This algorithm takes the operating conditions as input and estimates the optimal reaction time to reach the required effluent COD. This model was validated by comparison with both experimental measurements and simulated analysis, resulting in low error (0.5%), high Pearson correlation (R2 = 0.9956), and short computing time (less than one minute). The results for the proposed methodology (ANNi-GAs) were efficient and attractive in a reasonable way providing a satisfactory picture of the relationships between the optimal input parameters. Such parameters control the degradation process of herbicides online and can be used to predict the behavior of COD removal, thus taking actions to reduce the impact of aqueous treatment with alazine and gesaprim herbicides on aquatic, soil biota, and human health. Therefore, this technique constitutes a promising framework for predicting the environmental impact of herbicides within a tolerable degree of error.

Tight gas, a category of Deep Green Energy Resources, rely on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually added rules. Therefore, it is possible to make reservoir evaluation via using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical-tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7,000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR logging based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR logging based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Laser marking on polymer composite surfaces can be difficult to contrast and cause readability problems for electronic decoding equipment on production lines due to poor interaction between the laser and the fibres used to reinforce these materials. This problem can be solved with the right choice of marking parameters, resulting in savings for companies by avoiding production problems such as rejection, scrap and customer complaints. The present work uses the PBT/glass fibre composite used in the manufacture of instrument panels for motorcycles. The tests were carried out with different laser marking parameters using a Nd: YAG laser. Subsequently, the laser-marked DMC codes were analysed using a Verifier to evaluate the quality according to the ISO/IEC 29158:2020 standard. A detailed analysis of these surfaces was also carried out to observe some physical and chemical changes using SEM microscopy and EDS spectroscopy. The optical analysis showed that the lower the radiation power and pulse frequency and the higher the marking speed, the weaker the laser marking and therefore the poorer the DMC code quality, which was confirmed by the SEM. EDS spectroscopy showed that the laser marking process did not affect the chemical changes on the sample surface.

The whitefly leads extensive damage to plants through direct feeding, honeydew secretion, plant physiological disorders, and vectoring plant viruses. This study aimed to evaluate the physiological characteristics of susceptible and resistant soybean cultivars to B. tabaci. The experiments were conducted in a greenhouse. Eleven soybean cultivars were selected and infested with 100 adults of B. tabaci at the V3 stage. The evaluation of photosynthetic parameters, such as photosynthetic rate, leaf transpiration, stomatal conductance, and internal CO2 concentration, revealed that B. tabaci infestation influenced gas exchange in soybean plants. The photosynthetic rate was higher in cultivars AS3810 and M8349 during the V6 stage. Infestations led to alterations in photosynthetic parameters, suggesting increased energy demand to maintain photosynthetic activity. However, the response to infestation varied among different cultivars, indicating varying levels of resistance and tolerance to the whitefly's damage. Additionally, the impact of infestation was more significant during the vegetative phenological. In conclusion, B. tabaci infestation affects soybean plants' physiology, leading to changes in gas exchange parameters and water use efficiency. The response to infestation varied among soybean cultivars, suggesting potential differences in resistance to the pest. The study highlights the importance of evaluating the physiological impact of whitefly infestations on soybean.

The aim of the study was to assess the adaptability of Polish Red cows to extensive housing and feeding conditions. The study was carried out using 22 cows together with their offspring, kept on a certified organic family farm, from 15 October 2017 – the start of the winter feeding season – to 30 October 2018 – the end of the pasture season. The animals were kept as beef suckler cows, which stayed in the pasture during the summer and were kept in buildings with permanent access to a large cattle run in winter. The following were determined in the study: changes in the cows’ weight and body condition, daily gains in calves, 210-day standardized body weight, and estimated milk yield of suckler cows. To evaluate the cows’ health, blood was collected from them twice and analysed for haematological and biochemical parameters. The results indicated that during the study period the Polish Red cows had adequate body condition, normal vital parameters, and a normal course and development of pregnancy despite the sparse winter diet. This definitively confirms the breed’s high capacity to adapt to an extensive system.

A very strong earthquake of magnitude Mw8.2 struck the coastal zone of Alaska (USA), on July 29, 2021. This earthquake was felt around the Gulf of Alaska, on a wide offshore area belonging to USA and Canada. In order to identify an anomalous geomagnetic signal before the onset of this earthquake, we retrospectively analyzed the data collected on the interval June 17 - July 31, 2021, via internet (www.intermagnet.org), at the two geomagnetic observatories, College (CMO) - Alaska and Newport (NEW)-USA, by using the polarization parameter (BPOL) and the strain effect–related to geomagnetic signal identification. Thus, for the both observation sites (CMO and NEW), the daily mean distribution of the BPOL and its standard deviation (STDEV) are carried out using an FFT band-pass filtering in the ULF range (0.001-0.0083Hz). Further on, a statistical analysis based on a standardized random variable equation was applied to emphasize the following: a) the anomalous signature related to Mw8.2 earthquake on the both time series BPOL*(CMO) and BPOL*(NEW); b) the differentiation of the transient local anomalies associated with Mw8.2 earthquake from the internal and external parts of the geomagnetic field, taking the NEW observatory as reference. Finally, on the BPOL*(NEW-CMO) time series, carried out on the interval 07-31 July, 2021, a very clear anomaly of maximum, greater than 1.2 STDEV, was detected on July 22, with 7 days before the onset of Mw8.2 earthquake

The impact of nutrients on the environment, particularly on water bodies, has led to extensive studies for nutrient control. Within this context, studies have been focused on source separation of human urine from domestic wastewater to recover nutrients. Potassium is one of the most important components of human urine. However, data on potassium removal or recovery are quite limited except for some indirect information through use of zeolites for mostly ammonia removal. Potassium struvite or K-struvite (MgKPO₄·6H₂O) is a sparingly soluble salt belonging to struvite and has the potential of being used as a means of potassium and phosphate recovery from segregated human urine. This study aimed to assess the potential of K-struvite precipitation for control and recovery of nutrients. Within this context, K-struvite precipitation experiments were performed on both synthetically prepared samples and synthetic human urine solution to determine effect of operation parameters i.e. pH, stoichiometry, and temperature on potassium recovery performance. Results indicated that process performance as well as type of solid phases co-precipitated with K-struvite were closely related to initial potassium concentration, pH and reaction stoichiometry. At pH 10, the potassium recovery efficiency was maximized up to 87% by application of 100% excess dose of Mg and P for both synthetic samples and synthetic human urine solution. On the other hand, application of excess dose of K did not provide any improvement in K recovery efficiency. The effect of temperature on solubility of K-struvite was insignificant at the temperature of 24-90°C. Solid phase analyses confirmed that K-struvite was co-precipitated with either Mg₃(PO₄)₂, MgNaPO₄·7H₂O, or MgHPO₄·7H₂O depending on pH and stoichiometry instead of a pure compound.

The data from the extended system was recorded on-going basis in the archives of the monitoring system, and the multipoint system was directly stored in the methane-anemometers, which allows one to perform a comparative analysis of the obtained records from many methane detectors. An important element of the analyses was the simultaneous registration of the methane concentration in the longwall, together with information on operating time, direction of shearer operation and type of work: mining/cleaning. Mining practice shows that in methane coal mines, methane is often the basic limitation for modern and high-efficiency longwall complexes. The paper reports on selected observations and tests carried out in the Cw-4 longwall in seam 364/2 of the Budryk mine belonging to JSW SA. In observation of the methane level in the Cw-4 longwall, additional sensors installed in the end part of the longwall from the side of the gobs and registration of air velocity and methane concentration in the multipoint system built in the selected longwall cross-section were used, regardless of the air parameters recorded in the monitoring system.