Precision enhancement in variance estimation for complex environmental populations using adaptive cluster sampling.

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

A mechano-regulation model to design and optimize the surface microgeometry of titanium textured devices for biomedical applications.

In a technological context where, thanks to the additive manufacturing techniques, even sophisticated geometries as well as surfaces with specific micrometric features can be realized, we propose a mechano-regulation algorithm to determine the optimal microgeometric parameters of the surface of textured titanium devices for biomedical applications. A poroelastic finite element model was developed including a portion of bone, a portion of a textured titanium device and a layer of granulation tissue separating the bone from the device and occupying the space between them. The algorithm, implemented in the Matlab environment, determines the optimal values of the root mean square and the correlation length that the device surface must possess to maximize bone formation in the gap between the bone and the device. For low levels of compression load acting on the bone, the algorithm predicts low values of root mean square and high values of correlation length. Conversely, high levels of load require high values of root mean square and low values of correlation length. The optimal microgeometrical parameters were determined for various thickness values of the granulation tissue layer. Interestingly, the predictions of the proposed computational model are consistent with the experimental results reported in the literature. The proposed algorithm shows promise as a valuable tool for addressing the demands of precision medicine. In this approach, the device or prosthesis is no longer designed solely based on statistical averages but is tailored to each patient's unique anthropometric characteristics, as well as considerations related to their metabolism, sex, age, and more.

Drought-induced cell wall degradation in the base of pedicel is associated with accelerated cotton square shedding.

Drought significantly impacts cotton square (flower buds with bracts) shedding, directly affecting yield. To address the internal physiological mechanisms of drought affecting cotton square shedding, a polyethylene glycol-simulated drought study was conducted with Dexiamian 1 and Yuzaomian 9110 to investigate cell wall degradation changes in the base of pedicel where the detachment of cotton square takes place, and its relationship with cotton square shedding. Results revealed significant decreases in cellulose, hemicellulose, and pectin contents in the base of square pedicel, leading to cell wall degradation and consequent square shedding. Furthermore, drought stress exacerbated the hydrolysis of cellulose and pectin in the base of pedicel, although not hemicellulose, resulting in more noticeable alterations in the morphology and structure of the base of pedicel, such as more significant degradation in the epidermis, cortex, and phloem. Regarding the cellulose hydrolysis, drought mainly increased the expression of genes ß-glucosidase (GhBG1) and endoglucanase (GhEG1), and the activity of ß-glucosidase and endoglucanase in the base of pedicel, promoting the conversion of cellulose to cellobiose, and eventually glucose. Regarding the pectin hydrolysis, drought significantly enhanced the expression of the gene pectin methylase (GhPE1), thereby accelerating pectin hydrolysis to generate polygalacturonic acid. Additionally, drought increased the expression of genes pectin lyase (GhPL1) and polygalacturonase (GhPG1), as well as the activity of pectin lyase, which further accelerated the hydrolysis of polygalacturonic acid into galacturonic acid. These findings suggest that drought mainly promotes cellulose and pectin hydrolysis in the base of pedicel, hastening cell wall degradation and final cotton square shedding.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

Changes in saccadic intrusions over time as an objective biomarker to follow ALS disease progression.

Objective: Saccadic Intrusions (SIs) are abnormal eye movements during gaze fixation. Studies have indicated the clinical relevance of SIs, especially of square wave jerks (SWJ) in ALS. We used a software-based platform to extract SIs as a part of an interventional drug trial. The objective was to examine SIs' change over time as a potential biomarker of ALS disease progression. Methods: 28 ALS patients (61.95 ± 8.6 years) were assessed with the revised ALS Functional Rating Scale (ALSFRS-R) and with an oculometric test. Changes of SIs over time and correlations with ALSFRS-R and its bulbar subscale were calculated. A power calculation was conducted to understand the practical implications of results. Results: A significant increase of SWJ over trial duration was observed, with an increase in frequency (mean rise of 0.14 ± 0.28, p < 0.01), amplitude (0.001 ± 0.0016 degrees, p < 0.005), overall duration of SWJ (0.13 ± 0.25, in %, p < 0.01), and in their relative part out of all intrusions (0.18 ± 0.32, in %, p < 0.005). Negative correlations were found with the bulbar subscale (R=-0.43, -0.41, -0.39 and -0.47, respectively, p < 0.001). The required sample size for observing a 40% reduction in bulbar aspects when using the oculometric test (α = 0.05 and ß = 0.8), was found to be 150 patients per arm, compared with 200 patients using the bulbar subscale. Conclusions: Evaluation of saccadic intrusions during fixation was able to detect disease progression over time, correlated with ALSFRS-R bulbar subscale. Eye movements can potentially serve as an objective biomarker in ALS clinical trials and reduce the required sample size to show clinical effect of therapies.

Night target detection algorithm based on improved YOLOv7.

Aiming at the problems of error detection and missing detection in night target detection, this paper proposes a night target detection algorithm based on YOLOv7(You Only Look Once v7). The algorithm proposed in this paper preprocesses images by means of square equalization and Gamma transform. The GSConv(Group Separable Convolution) module is introduced to reduce the number of parameters and the amount of calculation to improve the detection effect. ShuffleNetv2_×1.5 is introduced as the feature extraction Network to reduce the number of Network parameters while maintaining high tracking accuracy. The hard-swish activation function is adopted to greatly reduce the delay cost. At last, Scylla Intersection over Union function is used instead of Efficient Intersection over Union function to optimize the loss function and improve the robustness. Experimental results demonstrate that the average detection accuracy of the proposed improved YOLOv7 model is 88.1%. It can effectively improve the detection accuracy and accuracy of night target detection.

Associations between co-exposure to per- and polyfluoroalkyl substances and organophosphate esters and erythrogram in Chinese adults.

Erythrogram, despite its prevalent use in assessing red blood cell (RBC) disorders and can be utilized to evaluate various diseases, still lacks evidence supporting the effects of per- and polyfluoroalkyl substances (PFASs) and organophosphate esters (OPEs) on it. A cross-sectional study involving 467 adults from Shijiazhuang, China was conducted to assess the associations between 12 PFASs and 11 OPEs and the erythrogram (8 indicators related to RBC). Three models, including multiple linear regression (MLR), sparse partial least squares regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate both the individual and joint effects of PFASs and OPEs on the erythrogram. Perfluorohexane sulfonic acid (PFHxS) showed the strongest association with HGB (3.68%, 95% CI: 2.29%, 5.10%) when doubling among PFASs in MLR models. BKMR indicated that PFASs were more strongly associated with the erythrogram than OPEs, as evidenced by higher group posterior inclusion probabilities (PIPs) for PFASs. Within hemoglobin and hematocrit, PFHxS emerged as the most significant component (conditional PIP = 1.0 for both). Collectively, our study emphasizes the joint effect of PFASs and OPEs on the erythrogram and identified PFASs, particularly PFHxS, as the pivotal contributors to the erythrogram. Nonetheless, further investigations are warranted to elucidate the underlying mechanisms.

Nearly Ideal Random Tiling with Dodecagonal Symmetry from Pentablock Quarterpolymers of the AB1CB2D Type.

Two-dimensional tiling manners as cross-sectional views of cylindrical domain assembly formed by pentablock quarterpolymers of the AB1CB2D type in bulk were investigated. Several binary and ternary blends from three mother polymers having different ϕB1/ϕB2 ratios (ϕB1 and ϕB2 are the volume fractions of the B1 and B2 blocks, respectively) represent nonperiodic but ordered triangle/square tilings, where the N3/N4 ratios (N3 and N4 are the numbers of triangles and squares in the observed area, respectively) are all close enough to the theoretical value of 4/√3 ≑ 2.31 for the dodecagonal quasicrystalline (DDQC) state, irrespective of the total number of polygons. The TEM images, having almost the same N3/N4 ratios, were proved to show 4- and 6-fold symmetries in terms of the angular appearance of equilateral polygon sides via image analyses. Among them, a ternary blend showed a nearly ideal random tiling pattern that is almost equivalent to the theoretically predicted tiling by SCFT. Moreover, the magnitude of phason strain estimated for a TEM image from the ternary blend was proved to be quite small when the observing area is narrow, while it deviates from the ideal quasicrystalline tiling with an increasing number of vertices in the observing area.

The Behavior of Long Thin Rectangular Plates under Normal Pressure-A Thorough Investigation.

Thin rectangular plates are considered basic structures in various sectors like aerospace, civil, and mechanical engineering. Moreover, isotropic and laminated composite plates subjected to transverse normal loading and undergoing small and large deflections have been extensively studied and published in the literature. Yet, it seems that the particular case of long thin plates having a high aspect ratio appears to be almost ignored by various scholars despite its engineering importance. The present study tries to fill this gap, yielding novel findings regarding the structural behavior of long thin plates in the small- and large-deflection regimes. In contrast to what is normally assumed in the literature, namely that a long plate with a high aspect ratio can be considered an infinitely long plate, the present results clearly show that the structural effects of the ends continue to exist near the remote ends of the long plate. An innovative finding is that long plates would (only on movable boundary conditions for the large-deflection regime) exhibit a larger mid-width displacement in comparison with deflections of infinitely long plates. This innovative higher deflection appears for both small and large-deflection regimes for both all-around simply supported and all-around clamped boundary conditions. This new finding was shown to be valid for both isotropic and orthotropic materials and presents a novel engineering approach for the old assumption well quoted in the literature that a relatively long plate on any boundary condition can be considered an infinite plate. Based on the present research, it is recommended that this assumption should be used carefully as the largest plate mid-deflection might occur at finite aspect ratios.

'Small volume-big problem': culturing Yarrowia lipolytica in high-throughput micro-formats.

With the current progress in the 'design' and 'build' stages of the 'design-build-test-learn' cycle, many synthetic biology projects become 'test-limited'. Advances in the parallelization of microbes cultivations are of great aid, however, for many species down-scaling leaves a metabolic footprint. Yarrowia lipolytica is one such demanding yeast species, for which scaling-down inevitably leads to perturbations in phenotype development. Strictly aerobic metabolism, propensity for filamentation and adhesion to hydrophobic surfaces, spontaneous flocculation, and high acidification of media are just several characteristics that make the transfer of the micro-scale protocols developed for the other microbial species very challenging in this case. It is well recognized that without additional 'personalized' optimization, either MTP-based or single-cell-based protocols are useless for accurate studies of Y. lipolytica phenotypes. This review summarizes the progress in the scaling-down and parallelization of Y. lipolytica cultures, highlighting the challenges that occur most frequently and strategies for their overcoming. The problem of Y. lipolytica cultures down-scaling is illustrated by calculating the costs of micro-cultivations, and determining the unintentionally introduced, thus uncontrolled, variables. The key research into culturing Y. lipolytica in various MTP formats and micro- and pico-bioreactors is discussed. Own recently developed and carefully pre-optimized high-throughput cultivation protocol is presented, alongside the details from the optimization stage. We hope that this work will serve as a practical guide for those working with Y. lipolytica high-throughput screens.

Spatio-temporal variation in water quality due to the anthropogenic impact in Rudrasagar Lake, a Ramsar site in India.

Rudrasagar Lake, a vital habitat for diverse flora and fauna, supports over 2000 households to sustain their daily livelihoods. The current study attempts to examine the impact of human activities on spatio-temporal variation in the water quality of the study area. The study integrates extensive field surveys, sample processing, and statistical analysis to assess the recent status of wetland health. Latin Square Matrix (LSM) was employed to select the sampling sites while the Inverse Distance Weighting (IDW) interpolation technique was used for spatial variation mapping. Modified Weighted Arithmetic Water Quality Index (MWAWQI) and Comprehensive Pollution Index (CPI) were utilized for assessing seasonal variation water quality and pollution loads, respectively. The results showed that dissolved oxygen (DO) was strongly influenced by the tributaries, and recreational activities have substantially influenced the highest concentrations of biochemical oxygen demand (BOD), and total suspended solids (TSS). The central portion of the lake is particularly susceptible to pollution from extensive fishing and recreational activities while peripheral sites are strongly influenced by agricultural run-offs, seepages from brick industries, and municipal wastes characterized by high concentrations of pH, total hardness (TH), oxidation-reduction potential (ORP). The findings reveal remarkable spatio-temporal fluctuations and highlight the areas within the lake susceptible to anthropogenic activities. The study proposed a sustainable management model to ameliorate anthropogenic threats. Moreover, the study contributes to the scientific understanding of the challenges and ensures the long-term viability of wetland health as a vital ecological and socio-economic resource.

Optimised graphite/carbon black loading of recycled PLA for the production of low-cost conductive filament and its application to the detection of ß-estradiol in environmental samples.

The production, optimisation, physicochemical, and electroanalytical characterisation of a low-cost electrically conductive additive manufacturing filament made with recycled poly(lactic acid) (rPLA), castor oil, carbon black, and graphite (CB-G/PLA) is reported. Through optimising the carbon black and graphite loading, the best ratio for conductivity, low material cost, and printability was found to be 60% carbon black to 40% graphite. The maximum composition within the rPLA with 10 wt% castor oil was found to be an overall nanocarbon loading of 35 wt% which produced a price of less than £0.01 per electrode whilst still offering excellent low-temperature flexibility and reproducible printing. The additive manufactured electrodes produced from this filament offered excellent electrochemical performance, with a heterogeneous electron (charge) transfer rate constant, k0 calculated to be (2.6 ± 0.1) × 10-3 cm s-1 compared to (0.46 ± 0.03) × 10-3 cm s-1 for the commercial PLA benchmark. The additive manufactured electrodes were applied to the determination of ß-estradiol, achieving a sensitivity of 400 nA µM-1, a limit of quantification of 70 nM, and a limit of detection of 21 nM, which compared excellently to other reports in the literature. The system was then applied to the detection of ß-estradiol within four real water samples, including tap, bottled, river, and lake water, where recoveries between 95 and 109% were obtained. Due to the ability to create high-performance filament at a low material cost (£0.06 per gram) and through the use of more sustainable materials such as recycled polymers, bio-based plasticisers, and naturally occurring graphite, additive manufacturing will have a permanent place within the electroanalysis arsenal in the future.

Synthesis of ultrasmall cerium oxide nanoparticles in deep eutectic solvent and their application in an electrochemical sensor to detect dopamine in biological fluid.

A solvothermal synthesis of ultrasmall cerium oxide nanoparticles (USCeOxNPs) with an average size of 0.73 ± 0.07 nm using deep eutectic solvent (DES) as a stabilizing medium at a temperature of 90 ºC is reported. Transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS) were used to morphologically characterize the USCeOxNPs. These revealed approximately spherical shapes with emission lines characteristic of cerium. Selected area electron diffraction (SAED) was used to determine the crystalline structure of the cerium oxide nanoparticles (CeO2NPs), revealing the presence of crystalline cubic structures. The USCeOxNPs-DES/CB film was characterized by scanning electron microscopy (SEM), which demonstrated the spherical characteristic of CB with layers slightly covered by DES residues. DES was characterized by Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR), indicating its formation through hydrogen bonds between the precursors. An electrochemical sensor for dopamine (DA) determination in biological fluids was developed using the USCeOxNPs together with carbon black (CB). An enhanced current response was observed on DA voltammetric determination, and this can be attributed to the USCeOxNPs. This sensor displayed linear responses for DA in the range 5.0 × 10-7 mol L-1 to 3.2 × 10-4 mol L-1, with a limit of detection of 80 nmol L-1. Besides detectability, excellent performances were verified for repeatability and anti-interference. The sensor based on USCeOxNPs synthesized in DES in a simpler and environmentally friendly way was successfully applied to determine DA in biological matrix.

Portable electrochemical sensing platform based on amidated GO-MOF and PEDOT:PSS for high-efficient detection of ponceau 4R.

A promising electrochemical sensing platform for the detection of ponceau 4R in food has been fabricated based on the carboxylated graphene oxide (GO-COOH), metal-organic framework (MOF) UIO-66-NH2, and poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). To this end GO-COOH was covalently coupled with UIO-66-NH2 through amide reaction, endowing the material (GO-CONH-UIO-66) unique hierarchical pores and high chemical stability and as a result improving the conductivity of MOF and the dispersion of GO. After the addition of PEDOT:PSS into GO-CONH-UIO-66, the continuity and conductivity of the composite (PEDOT:PSS/GO-CONH-UIO-66) have been further enhanced, due to the high conductivity, favorable film-forming, and hydrophilic properties of PEDOT:PSS. Systematic electrochemical experiments confirm that the PEDOT:PSS/GO-CONH-UIO-66/GCE shows satisfactory electrochemical sensing properties towards the detection of ponceau 4R, with a wide linear detection range of 0.01-30 µM, a low limit of detection of 3.33 nM, and a high sensitivity of 0.606 µA µM-1 cm-2. The PEDOT:PSS/GO-CONH-UIO-66 sensing platform was successfully used to detect ponceau 4R in beverage, and the detection results were compared with  high-performance liquid chromatography. As a result, the PEDOT:PSS/GO-CONH-UIO-66 composite shows a promising application prospect for rapid detection of ponceau 4R in food and will play significant role in food safety detection and supervision.

Changes in phenolic compounds of Phyllanthus emblica juice during different storage temperature and pH conditions.

The aim of this experiment was to investigate the effect of storage temperature and pH on phenolic compounds of Phyllanthus emblica juice. Juice was stored at different temperatures and pH for 15 days and sampled on 2-day intervals. The browning index (BI, ABS420 nm), pH, centrifugal precipitation rate (CPR), and phenolic compounds were evaluated. The results showed 4°C and pH 2.5 could effectively inhibit browning and slow down pH drop of P. emblica juice. The result of orthogonal partial least square-discriminant analysis showed P. emblica juice stored at 4°C and pH 2.5 still had a similar phenolic composition, but at 20°C, 37°C, and pH 3.5, the score plots were concentrated only in the first 3 days. Additionally, gallic acid (GA) and ellagic acid (EA) were screened out to be the differential compounds for browning of P. emblica juice. The contents of GA, epigallocatechin (EGC), corilagin (CL), gallocatechin gallate (GCG), chebulagic acid (CA), 1,2,3,4,6-O-galloyl-d-glucose (PGG), and EA were more stable at 4°C and pH 2.5. Overall, during storage at 4°C and pH 2.5, it could inhibit the increase of GA and EA and decrease of CL, GCG, CA, and PGG, whereas EGC did not show significant difference between storage conditions. The CPR was higher at 4°C, while pH 2.5 could reduce the CPR. In conclusion, in order to maintain stability of phenolic compounds and extended storage period, the P. emblica juice could be stored at low temperature and adjust the pH to increase the stability of juice system.

Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

Cell-SELEX for aptamer discovery and its utilization in constructing electrochemical biosensor for rapid and highly sensitive detection of Legionella pneumophila serogroup 1.

This study introduces an innovative electrochemical aptasensor designed for the highly sensitive and rapid detection of Legionella pneumophila serogroup 1 (L. pneumophila SG1), a particularly virulent strain associated with Legionellosis. Employing a rigorous selection process utilizing cell-based systematic evolution of ligands by exponential enrichment (cell-SELEX), we identified new high-affinity aptamers specifically tailored for L. pneumophila SG1. The selection process encompassed ten rounds of cell-SELEX cycles with live L. pneumophila, including multiple counter-selection steps against the closely related Legionella sub-species. The dissociation constant (Kd) of the highest affinity sequence to L. pneumophila SG1 was measured at 14.2 nM, representing a ten-fold increase in affinity in comparison with the previously reported aptamers. For the development of electrochemical aptasensor, a gold electrode was modified with the selected aptamer through the formation of self-assembled monolayers (SAMs). The newly developed aptasensor exhibited exceptional sensitivity, and specificity in detecting and differentiating various Legionella sp., with a detection limit of 5 colony forming units (CFU)/mL and an insignificant/negligible cross-reactivity with closely related sub-species. Furthermore, the aptasensor effectively detected L. pneumophila SG1 in spiked water samples, demonstrating an appreciable recovery percentage. This study shows the potential of our aptamer-based electrochemical biosensor as a promising approach for detecting L. pneumophila SG1 in diverse environments.

Experimental and Numerical Simulation of Reinforced Concrete-Filled Square GFRP Tubular Columns under Axial Compression.

To investigate the axial compressive behavior of reinforced concrete-filled square glass-fiber-reinforced polymer(GFRP) tubular (RCFSGT) columns, 17 specimens were designed with variations in GFRP tube wall thickness, spiral reinforcement yield strength, and spiral reinforcement ratio. A detailed model was developed using the finite element software ABAQUS, enabling in-depth mechanistic analysis and expanded parameter studies. The results indicate that the failure types of the specimens are all manifested as GFRP square tube cracking, and the core concrete is subjected to crushing or shear failure. The inclusion of a reinforcement cage can significantly enhance the load-bearing capacity and ductility of the specimen. Furthermore, as the yield strength and reinforcement ratio of the spiral reinforcement increase, so does the load-bearing capacity of the specimen. The finite element simulation results align well with the experimental findings. As the wall thickness of the GFRP square tube increases from 2 mm to 6 mm, the load-bearing capacity improves by approximately 19.69%. With the yield strength of the spiral reinforcement rising from 200 MPa to 400 MPa, the specimen's load-bearing capacity shows an increase of approximately 7.55%. However, as its yield strength continues to increase, there is minimal change in the load-bearing capacity. When the stirrup ratio of spiral reinforcement rises from 0.33% to 2.26%, the specimen's load-bearing capacity experiences an increase of approximately 56.90%.

Novel Response Surface Technique for Composite Structure Localization Using Variable Acoustic Emission Velocity.

The time difference of arrival (TDOA) method has traditionally proven effective for locating acoustic emission (AE) sources and detecting structural defects. Nevertheless, its applicability is constrained when applied to anisotropic materials, particularly in the context of fiber-reinforced composite structures. In response, this paper introduces a novel COmposite LOcalization using Response Surface (COLORS) algorithm based on a two-step approach for precise AE source localization suitable for laminated composite structures. Leveraging a response surface developed from critical parameters, including AE velocity profiles, attenuation rates, distances, and orientations, the proposed method offers precise AE source predictions. The incorporation of updated velocity data into the algorithm yields superior localization accuracy compared to the conventional TDOA approach relying on the theoretical AE propagation velocity. The mean absolute error (MAE) for COLORS and TDOA were found to be 6.97 mm and 8.69 mm, respectively. Similarly, the root mean square error (RMSE) for COLORS and TODA methods were found to be 9.24 mm and 12.06 mm, respectively, indicating better performance of the COLORS algorithm in the context of source location accuracy. The finding underscores the significance of AE signal attenuation in minimizing AE wave velocity discrepancies and enhancing AE localization precision. The outcome of this investigation represents a substantial advancement in AE localization within laminated composite structures, holding potential implications for improved damage detection and structural health monitoring of composite structures.

Employing advanced computational drug discovery techniques to identify novel inhibitors against ML2640c protein: a potential therapeutic approach for combatting leprosy.

Leprosy, caused by Mycobacterium leprae, remains a significant global health challenge, necessitating innovative approaches to therapeutic intervention. This study employs advanced computational drug discovery techniques to identify potential inhibitors against the ML2640c protein, a key factor in the bacterium's ability to infect and persist within host cells. Utilizing a comprehensive methodology, including virtual screening, re-docking, molecular dynamics simulations, and free energy calculations, we screened a library of compounds for their interaction with ML2640c. Four compounds (24349836, 26616083, 26648979, and 26651264) demonstrated promising inhibitory potential, each exhibiting unique binding energies and interaction patterns that suggest a strong likelihood of disrupting the protein function. The study highlights the efficacy of computational methods in identifying potential therapeutic candidates, presenting compound 26616083 as a notably potent inhibitor due to its excellent binding affinity and stability. Our findings offer a foundation for future experimental validation and optimization, marking a significant step forward in the development of new treatments for leprosy. This research not only advances the fight against leprosy but also showcases the broader applicability of computational drug discovery in tackling infectious diseases.