Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's Disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and Magnetic Resonance Imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and Mini-Mental State Exam (MMSE). A Residual Network with 50 layers (ResNet-50) predicted CDR presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4,139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine Learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.

Residual gas in exetainer vials leads to nitrous oxide (N2O) gas underestimation. This study compared four evacuation methods such as gas exchange by displacement (GED), evacuation with a vacuum pump (VP), evacuation with a syringe (VS) and pre-evacuated Exetainer (PEE) to assess the residual gas effect on gas sample concentration. PEE and VP methods showed superior performance, displaying lower deviations from actual gas concentrations with RMSE values of 0.239 and 0.179, respectively, at 12.5 ppm standard gas levels. Conversely, GED and VS methods exhibited higher deviations with RMSE values of 0.740 and 0.448, respectively, at 12.5 ppm. Errors were more pronounced at lower gas concentrations. To address residual gas effects in GED and VS methods, standard gases were stored for calibration in evacuated vials (EVM) matching the sample collection method, rather than in syringes (SM). This EVM approach enhanced precision by 14.43% and 6.24% for GED and VS methods, respectively, at 0.781 ppm standard gas levels. The study concludes that PEE and VP methods are more reliable for vial evacuation. Using vials prepared using the same method as sample collection vials can mitigate residual gas effects effectively. This study holds substantial importance in enhancing the accurate measurement of greenhouse gas emissions, particularly focusing on nitrous oxide.

Chromium-nickel austenitic stainless steels are widely used due to their high corrosion resistance, good weldability and deformability. To some extent, their application is limited by their mechanical characteristics. As a result of their austenitic structure, increasing the static and dynamic strength of the components can be achieved by surface cold work. Due to the tendency of these steels to undergo intercrystalline corrosion, another approach to improve their mechanical characteristics is the use of low-temperature thermo-chemical diffusion processes. This article proposes a new combined process based on sequentially applied diamond burnishing (DB) and low-temperature gas nitriding (LTGN) to optimally improve the fatigue strength of 304 steel. The essence of the proposed approach is to combine the advantages of the two processes (DB and LTGN) to create a zone of residual compressive stresses in the surface and subsurface layers–the enormous surface residual stresses (axial and hoop) introduced by LTGN, with the significant depth of the compressive zone characteristic of static surface cold working processes. DB (both smoothing and single-pass hardening), in combination with LTGN, achieves a fatigue limit of 600 MPa, an improvement of 36.4% compared to untreated specimens. Individually, smoothing DB, single-pass DB and LTGN achieve 540 MPa, 580 MPa and 580 MPa, respectively. It was found that as the degree of plastic deformation of the surface layer introduced by DB increases, the content of the S-phase in the nitrogen-rich layer formed by LTGN decreases, with resultant increased content of -phase, and a new (also hard) phase: stabilized nitrogen-bearing martensite.

This study investigates the stress corrosion cracking (SCC) behavior of type 316L stainless steel (SS316L) produced with sinter-based material extrusion additive manufacturing (AM). This technology has been shown to produce SS316L with microstructures and mechanical properties comparable to its wrought counterpart in the annealed condition. However, despite plenty of research on SCC of SS316L, little is known about SCC in sinter-based AM SS316L. This study focuses on the influence of sintered microstructures on susceptibility to SCC initiation and crack-branching. Custom-made C-rings were exposed to different stress levels in acidic chloride solutions at various temperatures. To better understand the SCC behavior of SS316L, solution-annealed (SA) and cold-drawn (CD) wrought SS316L were tested for comparison. Results showed that sinter-based AM SS316L was more susceptible to SCC initiation than SA wrought SS316L but more resistant than CD wrought SS316L. Moreover, sinter-based AM SS316L showed noticeably better resistance to crack-branching than both wrought SS316L counterparts. The investigation was supported by comprehensive pre- and post-test microanalysis using light optical microscopy (LOM), scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and micro-computed tomography (micro-CT)

Laser Powder Directed Energy Deposition (LP-DED) is an Additive Manufacturing process in which a laser beam is used to generate a melt pool onto a substrate. The additional material, in form of powder material, is fed into the generated melt pool and a raised track is obtained. The LP-DED process is very powerful for different applications such as the repair operations and the production of functionally graded material, however the application is still limited and one of the main reasons is related to the lack of knowledge in the physics of the process mechanisms. The process is influenced by a huge number of parameters and three main mechanisms can be identi-fied. These mechanisms are the powder flow, the generation of the molten pool and the solidifica-tion process and for each of these mechanisms the process parameters have a different relevance. In this paper, a review of the main mechanisms and the relationship between the process parame-ters and the outcome of each step of the process is presented.

Iron oxide nanozymes are a form of nanomaterial with both superparamagnetic and enzyme-mimicking properties. Given the multifunctional nature of iron oxide nanozymes, it is attractive for creating iron oxide hybrid nanozymes through biomolecular modifications to imbue with auxillary properties. Such iron oxide hybrid nanozymes can be useful for rapid and cost-effective analysis of circulating tumor nucleic acids (ctNAs) in patient liquid biopsies during minimal residual disease (MRD) monitoring of cancer recurrence. Herein, the use of streptavidin-modified iron oxide hybrid nanozymes is reported for magnetic enrichment and bioelectrocatalytic sensing of three prostate cancer (PCa) ctRNA biomarkers with high detection specificity and sensitivity (10 copies) over an ultrabroad dynamic range (five orders of magnitude). Furthermore, the feasibility of ctRNA analysis for pre- and post-cancer treatment MRD monitoring is demonstrated using PCa urinary liquid biopsy samples.

This work aims to investigate the influence of reaction time and catalyst-to-residual fat ratio by catalytic upgrading from pyrolysis vapors of residual fat at 400 °C and 1.0 atmosphere, on the yields of reaction products, physicochemical properties (density, kinematic viscosity, and acid value) and chemical composition of bio-oils, over a catalyst fixed bed reactor of activated carbon pellets impregnated with 10.0 M NaOH, in semi pilot scale. The experiments were carried out at 400 °C and 1.0 atmosphere, using a process schema consisting of a thermal cracking reactor of 2.0 L coupled to a catalyst fixed bed reactor of 53 mL, without catalyst and using 5.0, 7.5, and 10.0% (wt.) activated carbon pellets impregnated with 10.0 M NaOH, in batch mode. Samples of liquid phase products were withdrawn during the course of reaction at 50, 60, 70, 80, 90, 100 and 120 minutes in order to investigate the process kinetics. The physicochemical properties (density, kinematic viscosity, and acid value) of bio-oils were determined by official methods. The chemical composition of bio-oils determined by GC-MS. The thermal catalytic cracking of residual fat show bio-oils yields from 55.55 to 30.22 (wt.%), aqueous phase yields between 2.83 and 3.19 (wt.%), solid phase yields between 13.56 and 9.75 (wt.%), and gas yields from 27.89 to 55.60 (wt.%). The yields of bio-oil decreases from 74.41 to 30.22% (wt.) with increasing catalyst-to-Tallow kernel oil ratio, while that of gaseous phase increases from 12.87 to 55.60% (wt.). For all the thermal and thermal catalytic cracking experiments, the density, kinematic viscosity, and acid value of bio-oils decreases as the reaction time increases varying from 0.9266 to 0.8220 g/cm³, 8.10 to 2.24 mm²/s, and 144.14 to 2.37 mg KOH/g. The GC-MS of liquid reaction products identified the presence of hydrocarbons (alkanes, alkenes, ring-containing alkanes, ring-containing alkenes, and aromatics) and oxygenates (carboxylic acids, ketones, esters, alcohols, and aldehydes). For all the pyrolysis and catalytic cracking experiments, the hydrocarbon selectivity in bio-oil increases with increasing reaction time, while those of oxygenates decrease, reaching concentrations of hydrocarbons up to 95.35% (area.). The best results for the physicochemical properties density, kinematic viscosity, and acid value were 0.8220 g/cm³, 3.03 mm2/s, and 2.37 mg KOH/g, respectively, with a maximum hydrocarbon concentration of 97.194% (area.) and 2.806% ketones (area.) were obtained at 400 °C and 1.0 atmosphere, 80 minutes, without catalyst. For the catalytic cracking experiments, the maximum hydrocarbon content of 75.763% (area.) and 17.041% (area.) carboxylic acids, 4.702% (area.) ketones (area.), and 2.494% (area.) non-identified oxygenates was obtained at 400 °C and 1.0 atmosphere, 90 minutes, using a catalyst fixed bed reactor, with 10.0% (wt.) activated carbon pellets impregnated with 10.0 M NaOH as catalyst.

Recently, many authors have been interested to introduce fuzzy implications over t-norms and t-conorms. In this paper, we introduce (S,N) and residuum fuzzy implication for Dubois t-norm and Hamacher's t-norm. Also, new concepts so-called (T,N) and residual fuzzy co-implication in dual Heyting Algebra are investigated. Some examples as well as application are discussed as well.

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

Empirical equations representing, interpolating and smoothing groups of measurement results by regression methods are in widespread use in metrology and other fields of science and engineering. Standard equations for the propagation of measurement uncertainties to values computed from such equations are available but suffer from a lack of general acceptance and are only infrequently applied in practice. One reason for the slow uptake is the lack of clear methods to account for systematic error. In this paper, uncertainty propagation equations in terms of covariance matrices are generalized to allow for systematic errors in least-squares regression, and effects of the resulting uncertainties are investigated analytically. A stochastic ensemble model of systematic error is proposed for the computation of the non-diagonal elements of the weight matrix of the Generalized Least-Squares method (GLS) from the measurement uncertainty. A GLS projector formalism is described which separates the effects of measurement scatter on values calculated by the equation from those on the related “residuals”, i.e., the residual errors in the fitted data. The same projectors act similarly on the associated uncertainties and covariance matrices and permit the effects of systematic errors on the simulation covariance to be quantified. It is demonstrated that, in order to include systematic errors in the uncertainty estimates of GLS, covariance matrices may be substituted by novel, specifically defined dispersion matrices that are not specified yet by the GUM[1]. Systematic measurement errors may be estimated from various sources; a particularly easy way suggested here involves analyzing the structure in the regression residuals. It is demonstrated that GLS projectors exhibit inherent features of “error blindness” also with respect to systematic errors. [1] GUM: Guide to the Expression of Uncertainty in Measurement, http://www.bipm.org/en/publications/guides

With the increasing availability of large digitized fine art collections, automated analysis and classification of paintings is becoming an interesting area of research. However, due to domain specificity, implicit subjectivity, and pervasive nuances that vaguely separate art movements, analyzing art using machine learning techniques poses significant challenges. Residual networks, or variants thereof, are one the most popular tools for image classification tasks, which can extract relevant features for well-defined classes. In this case study, we focus on the classification of a selected painting 'Portrait of the Painter Charles Bruni' by Johann Kupetzky and the analysis of the performance of the proposed classifier. We show that the features extracted during residual network training can be useful for image retrieval within search systems in online art collections.

Residual stresses in fiber metal laminates (FML) inevitably develop during the manufacturing process. The main contributor to these stresses is the difference in the coefficients of thermal expansion (CTE) between fibers and metal in combination with high process temperatures. To quantify these stresses, the use of specimens with an asymmetric layup is an easily adaptable method. The curvature that develops after the manufacturing of flat laminates with an asymmetrical layer stack is a measure of the level of residual stresses evolving during cure. However, the accuracy of the curvature evaluation is highly dependent on specimen design and other influencing parameters. In this work a large set of FML specimens is investigated to identify relevant influencing parameters and derive conclusions about specimen design and evaluation techniques. For certain layups and process parameters, there is a good correlation between the curvature and the stress-free temperature, which is further covered by analytical solutions for bimetals. This correlation is the basis to transfer curvature into a stress-free temperature that can consequently be used for the quantification of residual stress levels in more complex FMLs. The transfer is validated by in-situ strain measurements during cure using a strain gage technique. Based on the results, the application of asymmetric specimens for residual stress characterization in more complex laminates is presented in the form of a workflow.

Residual dizziness after a liberating maneuver is often referred to by patients as a more disabling set of symptoms than the positional vertigo itself. This situation seems to involve more than half of subjects with labyrintholithiasis. The authors examine the hypothesis according to which residual dizziness involves subjects with labyrintholithiasis on the basis of otoconia.

Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacinii GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable waste management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37°C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacinii GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing waste valorization efforts through the use of eco-friendly materials.

The article is devoted to the analytical and numerical study of the pattern of propagation and attenuation, due to Coulomb friction, of shear waves in an infinite thin elastic plate with a circular orifice of radius lying on a rough base. In the field of motion, an exact analytical solution of a nonlinear boundary value problem for tangential stresses and transversal velocities is obtained in quadratures by the method of Laplace transformations. It turned out that the complete exhaustion of the wave front of a strong rupture occurs at a finite distance from the center of the hole and an elementary formula is given for this distance (the case of tangential forces instantly applied to the orifice boundary, and then constant in time, is considered). For various ratios of the magnitude of the limiting friction force to the amplitude of the applied load, the trailing wave fronts are obtained, after which a state of static equilibrium between the elastic and friction forces with a nonlinear distribution of residual deformations is established in the region .

Augmented reality (AR) provides an opportunity for mobile fitness applications to show users real-time feedback on their current fitness activity. For such applications, it is essential to accurately track the user’s current fitness activity using available mobile sensors, such as inertial measurement units (IMUs). Convolutional neural networks (CNNs) have been shown to produce strong results in different time series classification tasks, including the recognition of activities of daily living. However, fitness activities can present unique challenges to the human activity recognition task (HAR), including greater similarity between individual activities and fewer available data for model training. In this paper, we evaluate the applicability of CNNs to the fitness activity recognition task (FAR) using IMU data and determine the impact of input data size and sensor count on performance. For this purpose, we adapted three existing CNN architectures to the FAR task and designed a fourth CNN variant, which we call the scaling fully convolutional network (Scaling-FCN). We designed a preprocessing pipeline and recorded a running exercise data set with 20 participants, in which we evaluated the respective recognition performances of the four networks, comparing them with three traditional machine learning methods commonly used in HAR. On our data set, all CNN architectures significantly outperformed traditional machine learning methods, reaching up to 97.14±1.36% classification accuracy on the test set for ResNet. Whereas a reduced input data size invariably led to a performance loss, removing specific sensors improved the performance of all CNN architectures, with our Scaling-FCN reaching the highest accuracy of 99.86±0.11%. Our results suggest that CNNs are generally well suited for fitness activity recognition, and noticeable performance improvements can be achieved if sensors are dropped selectively.

Multiple myeloma (MM) is an incurable, diverse cancer in which abnormal plasma cells produce and release monoclonal immunoglobulin (Ig), also known as monoclonal protein or M protein. MM is always preceded by monoclonal gammaglobulinemia of unknown significance (MGUS) and smoldering multiple myeloma (SMM). Diagnosis of MM is made from a bone marrow sample, which involves analysis of the appearance of plasma cells. Quantification of CD138+ plasma cells in core biopsies is performed using techniques such as immunohistochemistry, flow cytometry, fluorescence in situ hybridization (FISH) and conventional cytogenetics. Urinalysis includes total protein by immunofixation and 24-hour urine by serum protein electrophoresis. FISH probes should detect the presence of at least del 13, del 17p, t(4;14), t(11;14), t(14;16), and 1q21 amplifications. Indeed, next-generation sequencing (NGS) has become an increasingly important tool in the diagnosis and treatment of MM. It can be used to identify genetic mutations, copy number changes, and clonal structures to help determine disease prognosis and treatment strategies. NGS can identify subclonal populations of cancer cells that are resistant to therapy and may lead to disease relapse, and can be used to identify potential targets for personalized therapy. For example, NGS can identify mutations in genes involved in drug resistance, enabling the selection of drugs that may be more effective in treating a patient's cancer. This systematic review analyzes 182 papers to determine the position and role of NGS in the diagnosis of MM. We first analyze the platforms and sample types used in the selected studies, and then start discussing the results.

Shot peening has become a well-established technique for the introduction of beneficial compressive Residual Stresses (RS) into the near-surface layers of metallic materials for the purpose of preventing or suppressing fatigue crack nucleation and growth. Ti-alloys are widely used to fabricate compressor blades of aeroengines. Titanium blades that may be susceptible to different modes of damage under cyclic loading (high cycle fatigue, fretting fatigue) are often subjected to shot peening to extend their safe operating time. The control and monitoring of residual stresses in titanium blades is of great importance to assure the safety of aeroengines. The techniques for residual stress evaluation include mechanical methods (material removal) as well as physical methods such as X-ray diffraction. Perhaps the most common and practically accessible form of the latter is the method. Conventional laboratory X-ray diffractometers typically probe samples to the depths up to ~20 µm that correspond to 1x...5x the average grain size for typical metals and alloys. The limitations of the method have been identified and reviewed in the literature, motivating the use of other semi-destructive approaches to evaluate the residual stresses in deeper layers and at more sharply defined locations. In the present report we present a case study of the comparison between non-destructive and semi-destructive evaluation techniques, namely the X-ray diffraction method against the mechanical and FIB-DIC ring-core drilling applied to the samples of VT6 (Russian designation of Ti-6Al-4V) titanium alloy after shot peening with 1 mm steel balls. Mechanical drilling of circular holes of ~2 mm diameter with laser speckle interferometry monitoring of strains gives a rough spatial resolution of a few millimeters, while the Korsunsky FIB-DIC method of Ga-ion beam micro-ring core milling within FIB-SEM with Digital Image Correlation (DIC) deformation analysis delivers spatial resolution down to a few micrometers. Good agreement has been found between the X-ray and FIB-DIC estimates of RS variation profiles as a function of depth in shot peened titanium alloy samples. Some advances in the FIB-DIC method are presented and discussed in terms of the acceleration of data acquisition and interpretation.

SLM Additive Manufacturing has demonstrated great potential for aerospace applications when structural elements of individual design and/or complex shape need to be promptly supplied. 3D-printable AlSi10Mg (RS-300) alloy is widely used for the fabrication of different structures in aerospace industry. The importance of the evaluation of residual stresses that arise as a result of complex 3D-printing process thermal history is widely discussed in literature, but systematic assessment remains lacking for their magnitude, spatial distribution, and comparative analysis of different evaluation techniques. In this study we report the results of a systematic study of residual stresses in a 3D-printed double tower shaped samples using several approaches: the contour method, blind hole drilling laser speckle interferometry, X-ray diffraction, and Xe pFIB-DIC micro-ring-core milling analysis. We show that a high level of tensile and compressive residual stresses is inherited from SLM 3D-printing and retained for longer than 6 months. The stresses vary over a significant proportion of the material yield stress. All residual stress evaluation techniques considered returned comparable values of residual stresses even regardless of dramatically different dimensional scales from millimeters for the Contour Method down, laser speckle interferometry and XRD and down to small fractions of a mm (70 μm) for Xe pFIB-DIC ring-core drilling. The use of residual stress evaluation is discussed in the context of optimizing the printing strategy to enhance the mechanical performance and long-term durability.

Background: The present retrospective study was designed to evaluate the relative diagnostic utility of breast-specific gamma imaging (BSGI) and breast magnetic resonance imaging (MRI) as means of evaluating female breast cancer patients in China. Methods: A total of 229 malignant breast cancer patients underwent ultrasound, mammography, BSGI, and MRI between January 2015 and December 2018 for initial tumor staging. Of these patients, 73 were subsequently treated via definitive breast surgery following neoadjuvant chemotherapy (NAC), of whom 17 exhibited a complete pathologic response (pCR) to NAC. Results: BSGI and MRI were associated with respective 76.8% and 69.6% sensitivity values as a means of detecting residual tumors following NAC, while both of these approaches exhibited comparable specificity in this diagnostic context (58.8% vs 70.6%, P=0.473). Conclusion: These results demonstrate that BSGI is a useful auxiliary approach to evaluating pCR to NAC treatment.

Запропонована методика спрямована на визначення та прогнозування технічного стану елементів мосту, що може слугувати прогресивним інженерним інструментом для оцінки надійності та довговічності. Розроблено на основі фундаментальних досліджень, які узагальнюють досвід вивчення фізико-механічних і фізико-хімічних властивостей матеріалів мостових конструкцій, що експлуатуються в реальних умовах. Теоретичною основою методики є модель надійності та прогнозування залишкового ресурсу елементів мосту на основі теорії Маркова. Розроблена методика призначена для оцінки технічного стану окремих елементів мосту з подальшою комплексною оцінкою всієї конструкції. За показник технічного стану прийнята надійність при експлуатації. Цей кількісний показник надійності в моделі є критерієм для: оцінки рівня безпеки елементів мосту; ранжування елементів мосту за необхідністю для конкретних видів ремонту, реконструкції або заміни; стратегічне планування витрат на ремонт або реконструкцію в умовах обмеженого фінансування; прогнозування залишкового ресурсу елементів. Для застосування розробленої методики запропоновано алгоритм оцінки та прогнозування технічного стану мостів. Було проведено математичний експеримент розробленої методики, який підтвердив адекватність висунутої гіпотези, тобто використання моделі надійності та прогнозування залишкового ресурсу елементів мосту. По-перше, запропоновано триступеневий механізм уточнення технічного стану мосту, суттєво підвищення точності розрахунків. Тому розроблена методика має практичне значення і може слугувати основою для інформаційно-аналітичних систем управління станом мостів.

Insecticides with novel modes of action are required to complement the pyrethroids currently relied upon for controlling malaria vectors. One example of this is the neonicotinoid clothianidin, which is found in SumiShield™ 50WG used in indoor residual spraying (IRS). In a preliminary experiment, mortality in insecticide susceptible and resistant An. gambiae adults exposed to SumiShield™ 50WG-treated filter papers reached 80% by 3-days post-exposure and 100% by 6-days post-exposure. Next, cement, wood, and mud tiles were treated with SumiShield™ 50WG or K-Othrine® WG250 (deltamethrin IRS formulation) and insecticide resistant and susceptible Anopheles and Aedes were exposed to these surfaces periodically for up to 18-months. Pyrethroid resistant Cx. quinquefasciatus were also exposed at 9 months. Between exposures tiles were stored in heat and relative humidity conditions reflecting those found in the field. On these surfaces, SumiShield™ 50WG was effective at killing both susceptible and resistant An. gambiae for 18 months post-treatment, while mortality amongst the resistant strains when exposed to deltamethrin (K-Othrine® WG250) IRS was not above that of the negative control. Greater efficacy of SumiShield™ 50WG was also demonstrated against insecticide resistant strains of An. funestus compared to deltamethrin, though the potency was lower when compared with An. gambiae. In general, a higher efficacy of SumiShield™ 50WG was observed on cement and mud compared to wood. SumiShield™ 50WG demonstrated poor residual activity against Aedes aegypti and Culex quinquefasciatus. Overall, the results suggest SumiShield™ 50WG is well suited for malaria control.

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation, and errors in structural installation. Therefore, coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm to optimize the displacement-strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks, this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing integrated antenna structure verify the efficient of the method on the reconstruction accuracy of the structural body displacement field.

The incidence and mortality of cervical cancer are high in Romania compared to other European countries, particularly for locally advanced cervical cancer cases, which are predominant at the time of diagnosis. Widely accepted therapeutic guidelines indicate that the treatment for locally advanced cervical cancer consists of concurrent chemoradiotherapy (total dose 85-90Gy at point A), with surgery not being necessary as it does not lead to improved survival and results in significant additional morbidity. In Romania, the treatment for locally advanced cervical cancer differs, involving lower-dose chemotherapy (total dose 60-65Gy at point A), followed by surgery which, under these circumstances, ensures better local control. In this regard, we attempted to evaluate the role and necessity of surgery in Romania, considering that in our study, residual lesions were found in 55.84% of cases on resected specimens, especially in cases with unfavorable histology (adenocarcinoma and adenosquamous carcinoma). This type of surgery was associated with significant morbidity (28.22%) in our study. The recurrence rate was 24.21% for operated patients compared to 62% for non-operated patients receiving suboptimal concurrent chemotherapy alone. In conclusion, In Romania, surgery will continue to play a predominant role until radiotherapy achieves the desired effectiveness for local control.

Cladding is typically used to protect components from wear and corrosion while also improving the aesthetic value and reliability of the substrate. The cladding process induces significant residual stresses due to the temperature difference between the substrate and the clad layer. However, these residual stresses could be effectively utilized by modifying processes and geometrical parameters. This paper introduces a novel methodology for using the weld-cladding process as a cost-effective alternative to various existing reinforcement techniques. The numerical analyses are performed to maximize the reinforcement of a cylindrical tool. The investigation of how the weld cladding develops compressive stresses on the specimen in response to a change in the weld beads and the welding sequence is presented. For the benchmark shape, experimental verification of the numerical model is performed. The impact of the distance between the weld beads and the effect of the tool diameter is numerically investigated. Furthermore, the variation in compressive stresses due to temperature fluctuations during the extrusion process has been evaluated. The results showed that adequate compressive stresses are generated on the welded parts through the cladding process after cooling. Hence, the targeted reinforcement of the substrate can be achieved by optimizing the welding sequence and process parameters.

Interference fits are a very common shaft-hub connection due to their low manufacturing cost and excellent technical properties. Plastic Conditioning of this machine element is a new and not very well known method. By exploiting residual stresses and the associated increase in the yield point, these components can absorb operating loads such as rotating bending moments, torsion, temperature changes and centrifugal forces purely elastically and avoid plastic deformation during operation. Compared to conventionally joined interference fits, the load bearing capacity in the elastic range can be increased by almost 200% and a specifically defined additional safety against plastic deformation in the elastic-plastic range can be ensured. This paper examines the effects of Reverse Yielding on the technology of Plastic Conditioning of Interference Fits in Power Transmission Engineering. Based on the Shear Stress Hypothesis (SH), the Plane Stress State (PSS) and the ideal plastic behavior of materials, the stress-mechanical relationships are explained, the influencing parameters are examined and conclusions are drawn for the plastic conditioning process. Taking into account the theoretical knowledge according to the state of the art, modified material behavior assumptions (isotropic hardening) and the Von Mises Yield Criterion (VMYC) are also considered. In addition, the method of plastic conditioning of interference fits is introduced and its basic principles are briefly explained. Along with computational suggestions to avoid Reverse Yielding, open issues requiring further research are identified.

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential-flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/ approximate the fault and uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

In this study, we introduce a novel framework for the estimation of residual renal function (RRF), based on the population compartmental kinetic behavior of Beta 2 Microglobulin (B2M) and its dialytic removal. Using this model, we simulated a large cohort of patients with various levels of RRF receiving either conventional high-flux hemodialysis or on-line hemodiafiltration. These simulations were used to estimate a novel population kinetic (PK) equation for RRF (PK-RRF) that was validated in an external public dataset of real patients. We assessed the performance of the resulting equation(s) against their ability to estimate urea clearance using cross-validation. Our equations derived entirely from computer simulations and advanced statistical modeling, and had extremely high discrimination (AUC 0.808 – 0.909) when applied to a human dataset of measurements of RRF. A clearance-based equation that utilized pre and post dialysis B2M measurements, patient weight, treatment duration and ultrafiltration had higher discrimination than an equation previously derived in humans. Furthermore, the derived equations appeared to have higher clinical usefulness as assessed by Decision Curve Analysis, potentially supporting decisions that for individualizing dialysis frequency in patients with preserved RRF.

Analysis of Fatigue Strain, Fatigue Modulus and Fatigue Damage for the modeling of concrete plays a vital role in the evolution material behaviour which is heterogeneous and anisotropic in nature. In this paper, the Level-S nonlinear fatigue strain curve, fatigue modulus curve, residual strain curve of concrete in compression, tension, flexure and torsional fatigue loading were proposed using strain life approach. The parameters such as physical meaning, the ranges, and the impact on the shape of the curve were discussed. Then, the evolution model of fatigue modulus was established based on the fatigue strain evolution model, fatigue modulus evaluation model, residual strain evaluation model, secondary strain evaluation model. The hypothesis of fatigue modulus is inversely related with the fatigue strain amplitude. The fatigue evolution of concrete damages the bond between material grains, changed the orientation of structure of molecules and affects the elastic properties resulting in the reduction of material stiffness and modulus by utilizing strain life analysis, regarding stiffness degradation and inelastic deformation by formation of microcracking, macro cracking, cracking which is heterogeneous and anisotropic in nature . This paper presents the Fatigue Strain Life Model and analyses of fatigue strain, fatigue modulus and damage parameters of concrete which is capable of predicting stiffness degradation, inelastic deformation, strength reduction under fatigue loading. Hence, the obtainable results were compared with experimental results for the validation of the proposed model.

Musical instrument recognition is a relatively unexplored area of machine learning due to the need to analyze complex spatial-temporal audio features. Traditional methods using individual spectrograms, like STFT, Log-Mel, and MFCC, often miss the full range of features. We propose a hierarchical residual attention network using a scaled combination of multiple spectrograms, including STFT, Log-Mel, MFCC, and CST features (chroma, spectral contrast, and Tonnetz), to create a comprehensive sound representation. This model enhances focus on relevant spectrogram parts through attention mechanisms. Experimental results with the OpenMIC-2018 dataset show significant improvement in classification accuracy, especially with the "Magnified 1/4 Size" configuration. Future work will optimize CST feature scaling, explore advanced attention mechanisms, and apply the model to other audio tasks to assess its generalizability.

The article presents results of research consisting in an attempt to assess the influence of temperature, heating time and cooling method on microstructure and residual strength properties of steel previously tempered during the production process. Simulated environmental conditions to which high-strength bolts, commonly used in steel construction, were subjected, were intended to reflect conditions of a natural fire that may occur in public facilities where the obligation to ensure safety of users and rescue teams is of key importance, also required by law. Furthermore, the tests carried out also comprised a simulated effect of a rescue and firefighting operation using shock, rapid cooling of some of the heated bolts. Samples cut out from the tested bolts, after they were properly prepared, were subjected to microstructural tests using light microscopy, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), phase analysis with the use of an X-ray diffractometer (XRD) and quantitative analysis of the microstructure involving, inter alia, measuring the surface area of grains, their equivalent diameter and mean diameter. As a result of the tests, considerable microstructure changes were identified occurring in the bolt material as a result of exposing it to fire conditions, leading to a change in key, from the point of view of structural safety, mechanical properties. The results of the microstructure tests were compared with the results of previous strength tests, including hardness of the material after the heat treatment and the residual tensile strength of the material Rm. A conducted comparative analysis showed a significant effect of all such factors as the temperature level of the simulated fire, its duration and the fire-fighting method on the mechanical properties of bolts. Results obtained were provided with required comments and the concept of using the microstructure for the post-fire assessment of steel structures was referred to.

The weakness of the dispersion-managed link combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and nonlinear Kerr effect of standard single mode fiber is its limited structural flexibility. We propose dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and rather, the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this paper is that the increase of flexibility can be achieved through random arrangement of RDPS in the former half, and the compensation improvement can be achieved by through inverse arrangement in the latter half which make the distribution profile of each half link roughly symmetric with respect to the midway optical phase conjugator.

The results of numerical experiments with the chemistry-climate model of the Institute of Numerical Mathematics of the Russian Academy of Sciences - Russian State Hydro-Meteorological University (INM-RSHU CCM) in comparison with the MERRA2 reanalysis data are presented. To assess the impact of sea surface temperature (SST) on stratospheric processes and the ozone layer, numerical experiments were carried out for scenarios corresponding to the El Niño and La Niña Southern Oscillation phases. It is shown that the El Niño phase contributes to the enhancement of heat and mass transfer to the polar stratosphere, which strengthen the Brewer-Dobson circulation and the flux of wave activity that affect the zonal wind. As a result, during El Niño, the probability of sudden stratospheric warming increases and the stratospheric polar vortex (SPV) weakens, re-sulting in an increase in total polar stratospheric ozone. The La Niña phase, on the contrary, is accompanied by a weakening of heat and mass transfer from the troposphere to the stratosphere, as a result of which the stratospheric polar vortex strengthens and the total column ozone decreases. It is shown that the effect associated with the Southern Oscillation is more pronounced in the Northern Hemisphere than in the Southern Hemisphere. Numerical experiments were also carried out for scenarios in which the sea surface temperature was set corresponding to the early 1980s, and the results were compared with calculations in which the SST was set corresponding to the late 2010s. This allowed us to assess the influence of the multiyear trend in the SST increase on stra-tospheric processes and the ozone layer. It was shown that the SST increase is most pronounced in the Arctic, which corresponds to the phenomenon of "Arctic amplification", and therefore, the heat and mass transfer to the stratosphere increases in the Northern Hemisphere, which contributes to some weakening of the SPV and increase of the total column ozone. But, compared to the Southern Oscillation, the influence of the SST trend on the SPV and total column ozone is much weaker and is observed mainly in the Northern Hemisphere.

This study investigates the application of deep residual networks for predicting the dynamics of interacting three-dimensional rigid bodies. We present a framework combining a 3D physics simulator implemented in C++ with a deep learning model constructed using PyTorch. The simulator generates training data encompassing linear and angular motion, elastic collisions, fluid friction, gravitational effects, and damping. Our deep residual network, consisting of an input layer, multiple residual blocks, and an output layer, is designed to handle the complexities of 3D dynamics. We evaluate the network's performance using a dataset of 10,000 simulated scenarios, each involving 3-5 interacting rigid bodies. The model achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, representing a 25% improvement over baseline methods. Our results demonstrate the network's ability to capture intricate physical interactions, with particular success in predicting elastic collisions and rotational dynamics. This work significantly contributes to physics-informed machine learning by showcasing the immense potential of deep residual networks in modeling complex 3D physical systems. We discuss our approach's limitations and propose future directions for improving generalization to more diverse object shapes and materials.

A multitude of factors influence our well-being and health in everyday life. Some of which we are totally aware like a delayed train, car exhausts, or continuous work stress. Other factors influence us on a subconscious level, making them, however, not less important to address. One of these factors is the built environment surrounding us almost every second of our lives. The idea of biophilic design states the importance of natural elements implemented in architectural structures to improve the occupants’ health and well-being. Adding to this field of research, this article targets the impact of biophilic design on positive emotions and productivity by using novel approaches like Facial Emotion Recognition (FER) with Residual Masking Networks, and sentiment detection using Large Language Models. Two separate but related studies were conducted and their results reported here. The first study measured emotions of people when confronted with images of different kinds of architecture, via FER (Facial Emotion Recognition) and also via a user survey. We distinguish between self-stated emotions entered through a survey by study participants, and emotions detected from studying the participants’ faces through AI from our FER (Facial Emotion Recognition) system. We found clear trends for most detected emotions, and significant evidence for self-stated emotions, that architecture implementing biophilic design evokes more positive emotions than one that does not. The second study measures the influence of natural elements on productivity and team-engagement. We found that natural elements do influence productivity and sentiment positively; however, only the change in sentiment was shown to be significant. As our sample size, especially for the second study, was relatively small, future research will apply these ideas in a larger setup to acquire further evidence for the importance of biophilic design for human well-being and health.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.