In this paper, the integrated performance of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and a supplementary supercritical-CO2 (sCO2) Brayton cycle is analyzed. The aim is to leverage the high efficiency supplementary sCO2 cycle to increase net generation and energy efficiency from the existing biomass boiler. Two sCO2 heater configurations situated within the flue gas flow path are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. A quasi-steady-state 1D model was developed to simulate the integrated cycle, including detailed component characteristics for the Rankine and Brayton cycles. The model solves the mass, energy, momentum, and species balance equations. The system is analyzed for three cases: (i) the existing Rankine cycle without the sCO2 integration, (ii) with the single convective-dominant sCO2 heater configuration, and (iii) the dual sCO2 heater configuration. The results show the required rate of overfiring for the sCO2 configurations, with a 15.3% increase in fuel flowrate resulting in an additional 21.2% in net power output. The model quantifies the impact of the sCO2 heaters, with reduced heat uptakes for downstream boiler heat exchangers. Furnace water wall heat uptake increased due to overfiring, offsetting the reduced heat uptakes at downstream evaporative heat exchangers. The dual configuration has more impact on Rankine cycle operation due to the radiative sCO2 heater placement in front of the second superheater, absorbing some of the direct radiation from the furnace.

With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of PV installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.

Recent satellite measurements of glacier mass balance show mountain glaciers all over the world had generally negative mass balances in the first decades of the 21st century. Mean summer temperatures all over the world rose from 1961-90 to 1991-2020, implying increasingly negative mass balances. We study archived annual balances for 38 Northern Hemisphere glaciers to assess changes within in the 19612020 period. We use a modified doublemass curve to visualize mass balance changes occurring around 1990. Mean balances 196190 were already small negative for many of the studied glaciers and became even more negative in 19912020 for glaciers in the Alps, at high latitudes and in western North America. The largest mass balance changes are for some glaciers in the Alps. We are unable to explain the lack of change in mean balance for one glacier in High Mountain Asia. We find complex changes for eight glaciers in Scandinavia, even including one glacier with positive balance. We explain these changes by visualizing deviations of winter and summer balances from their respective 1961-90 mean values. High winter balances in the 1990s for Scandinavia partly obscured the emerging trend of increasingly negative summer balances, which we expect to continue in the future.

Many protein expression systems are primary utilised to produce a single, specific recombinant protein. In contrast, most biological processes such as virus assembly rely upon a complex of several interacting proteins rather than the activity of a sole protein. The high complexity of the baculovirus genome, coupled with a multiphase replication cycle incorporating distinct transcriptional steps, made it the ideal system to manipulate for high-level expression of a single or co-expression of multiple foreign proteins within a single cell. We have developed and utilised a series of recombinant baculovirus systems to unravel the sequential assembly process of a complex non-enveloped model virus, Bluetongue virus (BTV). The high protein yields expressed by the baculovirus system not only facilitated structure-function analysis of each viral proteins but were also advantageous to crystallography studies and supported the first atomic level resolution of a recombinant viral protein, the major BTV capsid protein. Further, the formation of recombinant double-shelled virus-like particles (VLPs) provided insights into the structure-function relationships among the four major structural proteins of the BTV whilst also representing a potential candidate for a viral vaccine. The baculovirus multi-gene expression system facilitated the study of structurally complex viruses (both non-enveloped and enveloped viruses) and heralded in a new generation of viral vaccines.

An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Based on three assumptions of our 4G model of final unification, we have developed simple logics for estimating the unified atomic mass unit, atomic electrochemical equivalents, Faraday constant and Avogadro number. We appeal the science community to review the following points. 1) Unified atomic mass or energy unit can be considered as ‘average rest energy of nucleons minus average binding energy per nucleon of all atomic nuclides plus electron rest energy’. 2) Electrochemical equivalent of any atomic ion is nothing but the ratio of its mass to its charge. 3) Faraday charge is nothing but the ratio of elementary charge to unified atomic mass unit. 4) Faraday mass can be considered as the inverse of Faraday charge. 5) Avogadro number is nothing but the inverse of unified atomic mass unit. 6) Number of atoms per kg can be called as Kg-mole and number of atoms per gram can be called as gram-mole. Following our 4G model, starting form A=5 to 340, for isobars, ‘average binding energy per nucleon’ having maximum binding energy can be estimated with a simple relation of the form, BE/A=A^-1[1-(0.5+0.000935A²)-A^1/3]10.1MeV. It needs a review and fine tuning.

In rock blasting for mining production, stress waves play a major role in rock fracturing along with explosion gases. Better energy distribution improves fragmentation and safety, lowers production costs, increases productivity, and controls ore losses and dilution. Blast outcomes vary significantly with the choice of the explosive and the properties of the rock mass encountered. This study analyzes the effects of rock mass and explosives properties on the blast outcomes through numerical simulation using data from the case study and later validates the simulation results from the field blast fragmentation. The outcomes suggest that, for a given set of rock properties, the choice of explosive has a major influence on the resulting fragmentation. Strong explosives favor large fracture extents in hard rocks, while less strong explosives offer a better distribution of explosive energy and fractures. The presence of rock structures such as rock con-tacts and joints influences the propagation of stress waves and fractures depending on the structures' material properties, intensity and orientations, and the direction and strength of the stress wave. To achieve effective fragmentation, the blast design should mitigate the effect of variability in the rock mass by ensuring adequate energy distribution within the limits of geo-metrical design.

Ionic liquid MIE-NH2 displays a new role of development of modification of glycoproteins of lactoferrin through a reductive amination mechanism to synthesize versatile pharmaceuticals. This work introduces a new strategy of modification of Lactoferrin by using methylimidazolium N-ethylamine, this ionic liquid MIE-NH2 linked to N-glycans in Lactoferrin derivatives. Using UPLC/ESI-QTOF and MALDI-TOF mass spectrometry to perform and detect the modifying of ionic liquid-linked glycoproteins. Relevantly, modifying the lactoferrin by MIE-H2 as a small molecule of ion liquid lactoferrin (IL-Lf), which could be a potential antiviral drug and it is achieved by inhibiting various targets. The probability of the lactoferrin modified as a small IL-Lf-molecule to inhibit any of these targets was investigated to find out its potency as a SARS-CoV-2 inhibitor. Molecular docking disclosed the activity of modifying glycoproteins - small IL-Lf-molecules containing amino groups and interaction with targeted Mpro, RdRp, TMPRSS2, and PLpro. Clinically, this study shows the a volubility to provide small IL-Lf-molecules as significantly important drugs that target main protease (Mpro), RNA dependent RNA polymerase (RdRp), transmembrane protease serine 2 (TMPRSS2), and Papain-like protease (PLpro).

Regeneratively cooled scramjet has already been successfully verified as propulsion device for hypersonic vehicle. As a result, dynamic process of flow and heat transfer in cooling channel is commonly occurred which may bring hazard and control difficulties for scramjet. To study transient flow and heat transfer process in cooling channel, a dynamic numerical model is established. Dynamic characteristics of flow and heat transfer under different conditions are calculated and discussed, which includes change of inlet fuel mass flow, heat flux and pressure working conditions. Results show that stable time of cooling channel outlet fuel temperature is related to the change rate of inlet mass flow and heat flux. The stable time of outlet fuel temperature under heat flux decreasing working conditions are about 12.5 s. The initial response of working pressure fluctuation will lead to fuel temperature fluctuation, which has little effect on wall temperature.

FLH 13-11 is an F1 interspecific hybrid muscadine grape genotype that was developed to produce new anthocyanins for pigment color stability. This hybrid resulted from a cross between ‘Marsh’ (Vitis munsoniana) and ‘Magoon’ (V. rotundifolia). This report characterizes the anthocyanins produced in fully ripe berries, and reveals a significant difference in total anthocyanin contents from two continuous cropping seasons. High-performance liquid chromatography with a diode array detector (HPLC-DAD) and HPLC-quadrupole time-of-flight tandem mass spectrometry (HPLC-qTOF-MS/MS) were used to profile anthocyanins in berries. The resulting data showed that fourteen anthocyanins were detected, six from 2011 and nine from 2012, with only one produced in both seasons. However, the anthocyanidin profiles of berries were the same. Five anthocyanins were annotated as diglucosides of anthocyanidins based on MS/MS features, including delphinidin 3,5-diglucoside produced in both seasons, cyanidin 3,5-diglucoside mainly formed in 2011, petunidin 3,5-diglucoside, malvidin 3,5-diglucoside, and peonidin 3,5-glucoside only detected in 2012. Also, three anthocyanidin-diglucoside like anthocyanins and three monoglucosides including peonidin 3- glucoside, delphinidin 3- glucoside like, and pelargonidin 3- glucoside like anthocyanins, were detected in 2011 and 2012, respectively. These results indicate that FLH 13-11 can produce both anthocyanidin-diglucosides and -monoglucosides, and their biosynthesis is closely dependent on cropping years.

Healthcare visits were reduced during COVID-19 pandemic causing the disturbance of the sustainability in NAFLD monitoring. Telemedicine acts to maintain connectivity between patients and healthcare professionals. This review aimed to assess telemedicine helps to monitoring in NAFLD. The database was searched from The PubMedCentral and ScienceDirect from 2020 to 2023. We assessed the Cochrane Risk of Bias (RoB) for randomized controlled trial (RCT) and the Newcastle-Ottawa scale for non-RCTs systematic reviews. Meta-analyses with random-effects model to determine the pooled mean difference (MD) and p-value. There were three RCT and two non-RCT were included (n=239) with 56.9% males and the mean age was 51.3 years. Median intervention lasted 5.5 months. The parameter were body weight (BW), body mass index (BMI), waist circumference, liver function (AST/ALT), lipid profile and HbA1c. Metaanalysis showed telemedicine had a significant effect on improving outcomes for BW (MD -2.81: 95% CI, -4.11, -1.51, P 0.0001) and BMI (MD -1.01: 95% CI, -1.47, -0.55, P 0.0001) compared to standard care, while the AST/ALT were not significantly reduced. Other laboratory parameter were decreased from systematic reviews. Telemedicine through mobile-based applications could be an option for monitoring body weight and BMI in NAFLD patients facing the pandemic.

In this paper we address the approximation of the coupling problem for the wave equation and Maxwell's equations of electromagnetism in the time domain in terms of the electric field, by means of a nodal linear finite-element discretization in space, combined with a classical'explicit finite-difference scheme for the time discretization. Our study applies to the particular case where the dielectric permittivity has a constant value outside a sub-domain, whose closure does not intersect the boundary of the domain where the problem is defined. Inside this sub-domain Maxwell's equations hold. Outside this sub-domain the wave equation holds, which may correspond to the Maxwell's equations with a constant permittivity under certain conditions. We consider as a model the case of first order absorbing boundary conditions. Optimal error estimates that hold in natural norms under reasonable assumptions are given, among which lies a typical CFL condition for hyperbolic equations.

Brimonidine, a selective alpha-2 adrenergic agonist, used for treatment of open-angle glaucoma, has been shown to cause neurological side effects such as unresponsiveness, lethargy, hypoventilation, and stupor, mimicking opioid toxicity. We report one case of transient encephalopathy in a toddler, in whom brimonidine accidental toxicity was sus-pected and then confirmed by a toxicology study. The 8-month-old healthy girl child was taken to the pediatric ER since she was drowsy and hypotonic, with miosis. Brain CT scan and toxicological work-up on blood and urine were negative. Starting from hour +4, the child progressively improved, and by hour +6, she recovered to a normal state of consciousness. A survey of available drugs at the child’s reach showed the presence of brimonidine. Thus, ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was applied to quantify brimonidine in urine and plasma samples, showing 8.40 ng/mL level and 0.79 ng/mL, respectively. To our knowledge, this is the first report for determining brimonidine levels in urine and plasma by UPLC-MS/MS. Insufficient knowledge of the family members about the potential hazards of an apparently innocuous, topical medication such as eye drops may put children at a greater risk of poisoning. Necessary warnings should be better given to parents when prescribing this medication.

The use of the Google Scholar produces about 278 hits on the term ‘‘inertial propulsion.’’ If patents are also included, the number of hits increases to 536. This paper discusses, in a critical way, some characteristic aspects of this controversial topic. The review starts with the halteres of athletes in the Olympic games of ancient times and then continues with some typical devices which were developed or/and patented from the second fourth of the twentieth century till today.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

The lactic acid bacteria Streptococcus thermophilus and Lactobacillus helveticus are commonly used as starter cultures in dairy product production. This study aimed to investigate the characteristics of fermented milk using different ratios of these strains and analyze the changes in volatile compounds during fermentation and storage. The 10:1 ratio of Streptococcus thermophilus CICC 6063 to Lactobacillus helveticus CICC 6064 showed optimal fermentation time (4.2 h), viable cell count (9.64 log10 colony-forming units/mL), and sensory evaluation score (79.1 points). In total, 56 volatile compounds were identified and quantified by solid-phase microextraction and gas chromatography–mass spectrometry (SPME-GC-MS), including aldehydes, ketones, acids, alcohols, esters, and others. Among these, acetaldehyde, 2,3-butanedione, 2,3-pentanedione, 2-heptanone, acetoin, and 1-heptanol had the highest odor activity values and contributed to the characteristic flavor of fermented milk. The concentrations and types of key flavor compounds reached their peak at the end of fermentation. Throughout the fermentation and storage process, the levels of 2,3-pentanedione and acetoin exhibited synergistic dynamics. These findings enhance our understanding of the chemical and molecular characteristics of milk fermented with Streptococcus thermophilus and Lactobacillus helveticus, providing a basis for improving the flavor and odor of dairy products during fermentation and storage.

The metrological performance of FBG strain sensing systems is a critical premise for the precision of plastics water support pipeline structural deformation measurement, but the strain transfer loss of FBG attached on the plastics pipe subjected by dynamic force is unclear. This paper presents the first study on the effects of strain transfer between FBG, and acrylate polymer blended with poly resin (ABR)pipe sheets loaded by static and dynamic force. Theoretical methods of dynamic strain transfer theory combine with the exponentially decaying sinusoidal shock force time course is deduced and various dynamic parameters sensitivities studies are carried out, as well as that of the geometric parameters. The result of the dynamic theory is compared with the calibration test outcomes, while the ASTR of FBG attached on ABR sheet is compared with distributed optical fiber sensors (DOFS) and other pipe materials such as PVC, concrete, and steel. Results show that the ASTR of FBG is more accurate than that of the DOFS, and the grows of ASTR depending on the paste length of FBG, the Young's modulus of the middle materials, the thickness of the middle materials and attenuation coefficient. Paste length produced the largest increase, yet the magnitude and loading speed of the external force has unexpectedly little effect on ASTR. FBG seem low error, insensitive with dynamic force and independent with the material of matrix, and this demonstrates benefits that are stronger across the board than DOFS. This is not currently recognized by standards or by most reported studies.

Amidst the burgeoning global concerns surrounding skin cancer, the quest for precise and efficient diagnostic methodologies has intensified. Conventional diagnostic approaches relying on subjective visual inspection by dermatologists often grapple with subjectivity and resource constraints. This predicament underscores the critical role of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms by significantly enhancing accuracy and accessibility. This study embarks on an extensive exploration of skin cancer classification, specifically targeting the intricate task of 8-class skin cancer classification. Employing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis was conducted utilizing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering "Naturalize" augmentation technique was introduced, leading to the creation of indispensable tools—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer becomes evident through this research. AI’s proficiency in analyzing vast datasets and discerning subtle patterns augments the diagnostic prowess of dermatologists significantly. Integration of AI-powered systems in skin cancer diagnosis holds immense promise in facilitating early detection and substantially improving patient outcomes. The meticulous evaluation employed quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations. Astonishingly, the culmination of these endeavors culminated in an unprecedented achievement—100% average accuracy, precision, recall, and F1-Score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. This remarkable feat underscores the transformative potential of AI-driven methodologies, exemplifying their prowess in redefining the landscape of skin cancer diagnosis and management. In summary, this research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care.

Monolayer graphene absorbs 2.3 percent of the incident visible light. This “small” absorption has been used to emphasize the visual transparency of graphene, but it in fact means that multilayer graphene absorbs a sizable fraction of incident light, which causes non-negligible fluorescence. In this paper, we formulate the light emission properties of multilayer graphene composed of tens to hundreds of layers using a transfer matrix method and confirm the method’s validity experimentally. We could quantitatively explain the measured contrasts of multilayer graphene on SiO2/Si substrates and found sizable corrections, which cannot be classified as incoherent light emissions, to the reflectance of visible light. The new component originates from coherent emission caused by absorption at each graphene layer. Multilayer graphene thus functions as a partial coherent light source of various wavelengths, and it may have surface-emitting laser applications.

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are po-tentially applicable in artificial and spiking neural networks, memory arrays, and many other de-vices and circuits for artificial intelligence. In this paper, a complete electronic realization of ana-log circuit model of modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an im-portant step towards the forthcoming design of complex memristor-based neural networks for ar-tificial intelligence, for implementation in very high-density integrated circuits and chips.

The Yang-Mills conjecture addresses the fundamental nature of the strong force, one of the four fundamental forces in nature, responsible for binding quarks together to form protons, neutrons, and other hadrons. It was proposed in the early 1950s, the Yang-Mills suggests a mathematical framework for describing the behaviours of gauge fields, which are the carriers of fundamental forces. In particular, it focuses on non-abelian gauge theories, where the gauge fields themselves interact with each other. The non-abelian aspect refers to the fact that the order of operations matters when combining these gauge transformations, leading to a more intricate structure compared to abelian theories. This conjecture posits the existence and significance of certain mathematical structures, known as Yang-Mills fields, which play a crucial role in the formulation of the Standard Model of particle physics. The Standard Model describes the electromagnetic, weak, and strong nuclear forces, as well as the elementary particles that constitute matter. Despite its foundational importance, proving the Yang-Mills conjecture has proven to be a formidable challenge. The conjecture is intimately connected to the concept of mass gap, which refers to the idea that the particles involved in the strong force interactions exhibit mass, in contrast to the photon in electromagnetism, which is massless. The successful resolution of the Yang-Mills conjecture is expected to deepen our understanding of the nature of particle physics and may provide insights into the unification of fundamental forces. In this regard we hereby provide solutions to solve Yang-Mills conjecture using the method of Self Information Entanglement and Quantum Information Transport Equations to elucidate the origin of Universe from Unperceivable Information called “Avyaktham” through the emergence of perceivable information “Vyaktham”. For the first time in the world we provided an empirically verifiable solution to the origin of emergence and existence of Universe based on the methods to predict the emergence using working model of clinical Ayurveda. It is computed that the value of mass gap mentioned in Yang-Mills conjecture is around mYM = mbit ≈ 1.06×10⁻⁴¹kg.

Pool boiling heat transfer is recognized as one of the most effective heat transfer methods and has extensive applications in industry. The application of non-azeotropic binary mixtures can not only meet the environmental requirements of modern industrial development, but also improve the coefficient of performance (COP) of many systems. Therefore, studying the boiling heat transfer characteristics of non-azeotropic mixtures is important on improving their potential industrial application. This article selected ethylene glycol/deionized water (EG/DW) at varies concentration as the research working fluids, and conducted experimental research on the pool boiling performance. At the same time, the thermophysical parameters of the mixtures were studied and analyzed. The results showed that the concentration of EG in the mixture had a significant impact on the thermal properties and temperature glide of the mixture, where both affected the heat transfer deterioration. Based on experimental data, six widely used heat transfer coefficient prediction correlations were used for calculation, and two of them were modified. The deviation between the modified correlations and the experimental results remained within 20%.

Taraxacum kok-saghyz, a rubber-producing plant with excellent potential, emerges as a viable substitute for rubber tree (Hevea brasiliensis). While natural rubber is a desirable material, conventional techniques for assessing rubber content have faced challenges in meeting practical production requirements. To address this issue, we have developed a pyrolysis-mass spectrometry (PY-MS) instrument for the quantitative evaluation of natural rubber (NR) content in rubber-producing plants. The derived standard curve equation, established through external standard calibration, demonstrates a correlation coefficient (R2) surpassing 0.99. The method exhibits commendable recovery rates (93.27%-107.83%), relative standard deviations (RSD≤ 3.39%), and a swift analysis time of merely 10 minutes per sample, thereby enabling accurate and efficient quantification of NR. This approach possesses significant potential for extensive utilization in the assessment of rubber content in rubber-producing plants other than TKS. The integration of pyrolysis-mass spectrometry for the identification of polymers with high molecular weight offers a valuable pathway for the examination of diverse polymers.

In this paper, we consider particles together with the surrounding space as an indivisible system, and then investigate the maximum symmetry of the system at the quantum level. We introduce a new quantity to describe the evolution of this system before and after measurement. The effective mass of gravity is estimated. We also show that a free quantum particle as the source of energy and momentum do not produce gravity.

Contactless charging is evolving fast in many domains and applications such as the electrical vehicles (EV). The principle consists of transferring energy between two systems without having any electrical connection between them. This power transfer can be realized by emitting a magnetic field for Inductive Power Transfer (IPT). In the automotive domain, the application of contactless charging to supply power to the vehicle while driving is one of the solutions envisaged to solve the limited autonomy range of EV and reduced its cost but the system must comply with existing standards (e.g., allocated frequency range and radiated electromagnetic emissions), as summarized in this paper.

The Self-Forming Dynamic Membrane BioReactor (SFD MBR) is a cost-effective alternative to conventional MBR, where the synthetic membrane is replaced by the “cake layer”, an accumulation of the biological suspension over a surface of inert low-cost support, caused by a suction force. Under optimized conditions, the cake layer reveals easy to remove and quick to form again, resulting a “dynamic membrane”. The permeate of SFD MBR has chemo-physical characteristics comparable to those of conventional MBR. In this paper, two nylon meshes with a pore-size of 20 and 50 µm respectively were tested in a bench scale SFD MBR where a periodic air mass load (AML) was supplied tangential to the filtration surface to maintain filtration effectiveness. The SFD MBR equipped with the 20 µm nylon mesh coupled to 5 min of AML every 4 hours showed the best performance both ensuring a permeate with turbidity values always below 3 NTU and revealing no critical fouling. A benchmark test, conducted in the same experimental time, with identical operating conditions, and the only difference of a suction break (relaxation) instead of the AML. This latter test produced a permeate of very good quality, but it needed frequent mesh cleanings, showing that a periodic AML coupled with the use of a 20 µm mesh can be a good strategy for long term operation of SFD MBR.

Diabetic kidney disease (DKD) is a major cause of morbidity and mortality of type 2 diabetes mellitus (T2DM). Aim of this study was to investigate whether albumin structural alterations correlate with DKD severity and evaluate whether native and reduced albumin concentrations could complement the diagnosis of DKD. To this aim, one hundred and seventeen T2DM patients without (n=42) and with (n=75) DKD (DKD I-III upon KDIGO classification) were evaluated; total albumin concentration (tHA) was quantified by bromocresol green assay, while structural alterations were profiled assessed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). The concentrations of albumin in native (eHA, effective albumin) and reduced (rHA) forms were then assessed. HRMS analyses showed a reduced relative amount of native albumin in DKD patients along with an increased abundance of altered forms, especially those bearing oxidative modifications. Accordingly, both eHA and rHA values varied along the stages of progressive renal failure, and alterations dose-dependently correlated with renal dysfunction. ROC curves analysis showed a significantly higher sensitivity and specificity of eHA and rHA with respect to tHA for the diagnosis of DKD. Importantly, at multivariable logistic regression analysis, eHA was identified as independent predictor of DKD.

The ability to control the temperature distribution T(t,r) and the rate of temperature change Rt,r inside glasses is important for their microstructuring. The lattice temperature is considered at time t, exceeding the electron-phonon thermalization time, and at a distance r from the center of the model spherical heating zone. In order to describe thermal excitations, the heat capacity of glasses must be considered as a function of time due to its long-term relaxation. A method for analytical calculation of T(t,r) and R(t,r) for glasses with dynamic heat capacity cdyn(t) is proposed. It is shown that during laser microstructuring the local cooling rate -R(t,r) significantly depends on the time dispersion of cdyn(t). It has been established that at the periphery of the model heating zone of the laser beam focus, the local cooling rate can reach more than 1011 K/s. Strong cooling rate gradients were found at the periphery of the heating zone, affecting the microstructure of the material. This effect is significantly enhanced by the time dispersion of cdyn(t). The effect associated with this time dispersion is significant even well above the glass transition temperature Tg, since even short relaxation times of the dynamic heat capacity cdyn(t) are significant.

This research paper examines the effects, advantages, challenges, and economic aspects of a research town. The study concludes that inclusion and diversity not only constitute fundamental values but also serve as crucial drivers of business success and sustained competitiveness. Universities play a central role in shaping the prosperity of the research town by functioning as knowledge hubs, fostering collaboration, nurturing talent, and contributing to the broader innovation ecosystem. The symbiotic relationship between universities and industries within the cluster establishes a dynamic and resilient environment for research and development. The presence of startups within the research cluster introduces novel ideas, enhancing the overall vibrancy and competitiveness of the local economy. In an era where social media algorithms increasingly influence people's choices, the potential for foreign interference emerges as a significant threat.

Adults with obesity experience high rates of disability and rapid functional decline. Identifying movement dysfunction early can direct intervention and disrupt disability development, however subtle changes in movement are difficult to detect with the naked eye. This study evaluated how a portable, inertial measurement unit (IMU)-based motion capture system compares to a laboratory-based optokinetic motion capture (OMC) system for evaluating gait kinematics in adults with obesity. Ten adults with obesity performed overground walking while equipped with the OMC and IMU systems. Fifteen gait cycles for each participant were extracted for 150 total cycles analyzed. Kinematics were compared between OMC and IMU across the gait cycles (coefficient of multiple correlations), at clinically significant time points (interclass correlations), and over clinically relevant ranges (Bland Altman plots). Sagittal plane kinematics were most similar between systems, especially at the knee. Sagittal plane joint angles at clinically meaningful timepoints were poorly associated except for ankle dorsiflexion at heel strike (ρ=0.38) and minimum angle (ρ=0.83). Most motions had more than 5° difference between systems across the range of angles measured. IMU-based motion capture shows promise for detecting subtle gait changes in adults with obesity. However, more work is needed before this method can replace traditional OMC.

The increasing complexity of the space flight program and the increase in the duration of missions require improvement of psychological monitoring tools for astronauts in orbit. The article summarizes the experience of using quantitative content analysis of communication between the crews and the Mission control center (MCC). This method allows to assess dynamics of astronaut’s psycho-emotional state, to identify the communicative style, and to detect the communication phenomena of board-MCC communication. The method is based on a combination of coping strategies approach by Lazarus and Folkman as well as B.F. Lomov’s concept about the three functions of communication. We found influence of workload on the structure and volume of communication, defined the main stable communication styles of crewmembers, and confirmed presence of the emotional transfer phenomenon. We detected that astronauts successfully solve problems that arise in orbit using the capabilities of their communication style. An ineffective MCC communication style the leads to the psycho-emotional ill-being manifesting in emotional transfer phenomenon. The presence of “third quarter” phenomenon was not confirmed by materials from six-month space flights.

Traditional image steganography conceals secret messages into unprocessed natural images by modifying the pixel value, causing the obtained stego different from the original image in terms of statistical distribution, thereby could be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial Intelligence Generated Content (AIGC) becoming popular in social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image style transformation neural network with steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder-decoder model, and finally generates a styled image containing the secret messages at the same time. An adversarial training technique is applied to enhance the imperceptibility of the generated art-styled stego image with from plain style-transferred images. The lack of the original cover image makes it difficult for the opponent learning a steganalyzer to identify the stego. The recommended approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of 3 bits per pixel for a color image, according to experimental results.

To investigate whether high body mass index (BMI) increases the risk of recurrence and corre-lates with higher glucose uptake in recurrent lesions in post-menopausal female patients with breast cancer. The hospital database was searched to retrieve breast cancer patients, who had undergone an [18F]FDG PET/CT scan before neoadjuvant chemotherapy and curative-intent sur-gery. BMI was calculated at the baseline [18F]FDG PET/CT scan. There was a median follow-up of 5 years after the baseline PET/CT scan to identify: recurrence in the breast (T_rec); lymph nodes (N_rec); distant locations (M_rec). Furthermore, SUVmax was measured in the sites of recur-rence. A chi-square test was used to investigate any difference in the frequency of any recur-rence, T_rec, N_rec, and M_rec between overweight women (BMI≥25 kg/m2) and women with a BMI<25 kg/m2 (p<0.05). SUVmax, was compared using a t-test (p<0.05) between the two groups. A total of post-menopausal 142 women (BMI: 26.84±5.59; 84 overweight and 58 with normal weight) were retrieved. There were 48 recurrences at the follow-up. The chi-square test demon-strated in overweight women an increased frequency of any recurrence (35 vs. 13; p=0.025) and T_rec (15 vs. 2; p=0.018) and a higher T_rec SUVmax (4.74±2.90 vs. 1.85 ± 0.63; p=0.09) compared to women with a BMI<25 kg/m2. BMI seems to correlate with an increased rate of recurrence, espe-cially in the breast, and a higher glucose uptake in post-menopausal patients with recurrent breast cancer.

Heat dissipation from Li-ion batteries is a potential safety issue for large-scale energy storage applications. Maintaining low and uniform temperature distribution, and low energy consumption of the battery storage is very important. We studied the fluid dynamics and heat transfer phenomena of a single cell, 16-cell modules, battery packs, and cabinet through computer simulations and experimental measurements. The results show that the temperature distribution of single cell is relatively uniform when the cell is facing the air flow. In a module, very high air flow rate and large gap between the cells can reduce the cell temperature. A pack with a good thermally conductive case is very important to ensure good heat dissipation of the battery module. Due to limited space in the cabinet, additional air inlets and outlets can be opened outside of the cabinet to slightly lower the pack temperature. Installing fins outside the cabinet can also slightly reduce the temperature inside the cabinet. Liquid cooling medium, such as water, is much better than the air-cooling medium.

In this work, we develop a software suite for studies of atmosphere – underlying SNOW- spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for local optical properties of atmosphere and snow. The model developed is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 linked to warming event, and a 4 times larger grain size at Aviator Glacier compared to Dome C.

Many challenges in proteomics result from the high-throughput nature of the experiments. The paper first presents pre-analytical problems, which still occur, although the call for standardisation in omics has been ongoing for many years. This article also discusses aspects, which affect bioinformatic analysis, based on three sets of reference data measured with different orbitrap instruments. Despite continuous advances in mass spectrometer technology as well as analysis software, data set-wise quality control is still necessary and decoy-based estimation, although challenged by modern instruments, should be utilized. We draw attention to the fact that numerous young researchers perceive proteomics as a mature, readily applicable technology. However, it is important to emphasize that the maximum potential of the technology can only be realized by an educated handling of its limitations.

Objectives of this study were to investigate effects of repeated superstimulation on the success of ovum-pick up and in vitro production of embryos (OPU-IVP) and donor health and evaluate the efficiency of a modified SOF sequential medium in producing viable embryos. Over 1 y, six cross-bred beef heifers and three beef cows were subjected to superstimulations and OPU-IVP. Oocytes recovered were used for in vitro embryo production and the resulting embryos were transferred as fresh or frozen-thawed. Number of follicles, oocytes recovered, cleavage and blastocyst rates substantially varied among individuals. However, repeated trials did not significantly affect the efficiency of OPU-IVP. The pregnancy rates (PR) 22 to 30 d after embryo transfer for in vitro-derived fresh embryos was similar to that of fresh in vivo embryos, whereas transfer of in vitro-derived frozen embryos reduced PR compared to frozen-thawed in vivo embryos. All recipient heifers diagnosed pregnant delivered a viable calf, suggesting developmental competence of the embryos. Repeated OPU-IVP had no apparent deleterious effects, as all donors had viable calves after ET. We concluded that multiple OPU-IVP cycles can be effectively performed for a prolonged interval to maximize embryo production from elite donors without compromising their reproductive health.

In response to the growing need for efficient energy conversion technologies, this pa-per introduces an innovative Tubular Thermoelectric Generator (TTEG), specifically de-signed for high-efficiency conversion of waste heat into electrical energy. Unlike conven-tional flat plate thermoelectric generators (FTEGs), the TTEG incorporates full-ring ther-moelectric elements within a tubular layout, offering a novel approach to thermal energy recovery from fluid-based systems. This design significantly enhances the heat transfer capabilities, thereby improving the efficiency of energy conversion. Comprehensive simulations and analyses form the core of this study, where the per-formance of the TTEG is rigorously compared with traditional FTEGs. These comparisons highlight the advantages of the tubular design in terms of energy conversion efficiency and practical applicability in various scenarios. The findings reveal that the TTEG not only outperforms its flat plate counterparts but also presents a scalable and adaptable so-lution for waste heat recovery in a wide range of industrial and automotive applications. This research contributes to the field of energy science and technology by presenting a detailed experimental setup, complete with reproducible procedures and results. It opens new pathways for the development of more sustainable and efficient energy conversion systems, aligning with the goal of harnessing renewable and sustainable energy sources. The potential applications of this technology in enhancing energy efficiency and reducing environmental impact are vast, making it a significant step forward in the quest for clean and sustainable energy solutions.

Candidiasis is one of the global public health problems. In fluconazole resistant Candida spp. (FRCS), mutations in ergosterol biosynthesis gene 11 (ERG 11) have been commonly found. This study aimed to determine the profile of FRCS isolates and to detect mutations in part of ERG 11 gene in fluconazole resistant Candida albicans (FRCA) isolates. Study was conducted on 150 isolates of FRCS obtained from patients admitted in a tertiary care hospital, South India from September 2019 to December 2021. Candida spp. was identified by MALDI-TOF MS and fluconazole resistance was detected by VITEK 2 system. Following which a part of the ERG 11 gene of the FRCA isolates were amplified, purified, sequenced and a phylogenetic tree was constructed. Out of the 150 FRCS, the most common FRCS was C. auris followed by C. glabrata, C. krusei, C. parapsilosis, C. albicans, C. tropicalis and C. guilliermondii. Sequencing of amplicons detected a single missense mutation and 44 silent mutations. We opined that fluconazole resistance in FRCA isolates may be due to mecha-nisms other than the studied one in our region. Phylogenetic analysis of amplified sequences of 12 FRCA isolates observed three clusters and all 12 sequences showed relatedness to Malaysian 2019 isolates.

In recent years, there has been a growing interest in the prediction of facial age due to its diverse applications in fields such as security, entertainment, and healthcare. The current scourge of underaged voting in Nigeria is a problem and this research delves into the realm of facial age prediction by employing four well-known convolutional neural network (CNN) architectures, namely VGG16, ResNet50, Mobile Net, and VGG19 to predict chronological ages from facial pictures of person. The objective is to achieve precise age estimation from facial images, utilizing two datasets: UTKFace and CASIA African Facial Datasets. The results of this investigation are noteworthy. Specifically, the VGG16 model which demonstrated remarkable performance, yielding a Mean Absolute Error (MAE) of 1.76 when applied to the UTK-Face dataset. Additionally, when utilizing Mobile-Net, an unprecedented MAE of 1.10 was achieved for the Casia Africa Face Dataset. Notably, this marks the first instance of employing the dataset for facial age detection with CNNs, and this approach outperformed previous works, yielding the lowest MAE among all the studies reviewed.

Background & aims: Malnutrition in lung transplant (LT) candidates increases postoperative morbidity and mortality. Early diagnosis of malnutrition could attenuate adverse prognostic factors. This study aimed to assess the prevalence of nutritional risk and malnutrition using GLIM criteria in LT candidates and clinically characterize those with malnutrition. Methods: A prospective longitudinal study was conducted from 2000 to 2020 of LT candidates who underwent complete nutritional assessment (nutritional screening, anthropometry, bioelectrical impedance, blood laboratory tests and malnutrition diagnosis using GLIM criteria). Results: Obstructive diseases (45.6%), interstitial diseases (36.6%) and cystic fibrosis/non-cystic fibrosis bronchiectasis (15.4%) were the main conditions assessed for LT. Of the 1060 candidates evaluated, 10.6% were underweight according to BMI, 29% were at risk of malnutrition and 47% were diagnosed with malnutrition using GLIM criteria. Reduced muscle mass was the most frequent GLIM phenotypic criterion. Malnutrition was more prevalent in patients with cystic fibrosis/non-cystic fibrosis bronchiectasis (84.5%), obstructive (45.4%) and interstitial (31.3%) diseases. GLIM criteria detected some degree of malnutrition in all diseases requiring LT and identified patients with higher CRP levels and worse respiratory function, anthropometric measurements and visceral protein and lipid profiles. Conclusions: LT candidates present a high prevalence of malnutrition using the GLIM algorithm. GLIM criteria detected malnutrition in all diseases requiring LT and defined patients with worse clinical-analytical profiles.

Gut luminal dysbiosis and pathobiosis result in compositional and biodiversify alterations in the microbial and host co-metabolites. The main mechanism of bacterial evolution is horizontal gene transfer (HGT) and acquisition of new traits can be achieved through mobile genetic element (MGEs) exchange. Introducing genetically engineered microbes (GEMs) might break the harmonized balance in the intestinal compartment. The present objectives are: 1. To reveal the role played by GEMs' horizontal gene transfers in changing the landscape of the enteric microbiome eubiosis 2. To expand on the potential detrimental effects of those changes on human genome and health. A search of articles published in PubMed/MEDLINE, EMBASE, and Scielo from 2000 to august 2023, using appropriate MeSH entry terms were performed. GEMs' horizontal gene exchanges might induce multiple human diseases. The new GEMs can change the long-term natural evolution of the enteric pro- or eukaryotic cells inhabitants. The worldwide regulatory authority's safety control of GEMs is not enough to protect public health. Viability and biocontainment and many other aspects are only partially controlled and harmful consequences for public health should be avoided. Always remember that prevention is the most cost- effective strategy and "primum non nocere" is the name of the game.

This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise. Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

The agricultural sector faces challenges due to climate change and the growing global population. Alkaline stress could adversely impact plant growth and crop production. This stressor diminishes water quality essential for crop cultivation, consequently impairing plant growth and overall productivity. Gamma-aminobutyric acid (GABA), a non-protein amino acid, may exhibit multifaceted roles in plant responses to adverse environmental conditions. Optimization of crop production in controlled environmental agriculture under artificial light attracted much attention. In the present study, we investigated the effects of different light spectra and GABA concentrations on Lollo Rosso lettuce plants under alkaline stress. Seedlings were placed under different light spectra [red (R), blue (B), white (W), 3R:1B, and 2R:1B:1far-red (FR)] and treated by GABA (0 and 50 µmol). Alkaline conditions were instituted by applying NaHCO3 at a concentration of 40 mM. The experiment incorporated a control treatment without NaHCO3. In general, R light exposure caused the highest growth performance of lettuce plants. GABA augmented biomass production in control and alkaline-treated plants across all light spectra. Additionally, it increased the concentrations of photosynthetic and protective pigments under alkaline conditions. GABA also improved the photosynthetic performance under all light spectra. Non-photochemical quenching and pigmentation were enhanced by exposure to B light, especially in plants treated with GABA. Alkaline stress induced an increase in soluble carbohydrate content in the lettuce leaves. These findings highlight the support of GABA application in facilitating coping plants to alkaline stress.

In this paper, we consider the formation of quantum black holes (QBHs) through a genuine quantum process. We treat particles at the Planck energy as QBHs. The Hawking temperature and entropy of QBHs are estimated. We also show that a QBH has a discrete energy spectrum. By analogy with supersymmetric theory, the mass of the superpartner of a QBH is estimated.

A new biodegradable paper-based film is a hot research topic in the development of green agriculture. In this paper, a black paper base film coated with cooked tung oil with excellent mechanical properties, hydrophobic surface, high heat transfer and strong weather resistance was prepared by spraying high pigment carbon black solution on the surface of base paper. The results showed that the surface solidified oil film with rough structure was made by the brush coating process of cooked tung oil, and the base film of black paper was given hydrophobic structure, and the contact angle reached 98.9 °. Cooked tung oil permeates into the inside of the paper base, and after curing, it forms a multi-dimensional network film structure. The maximum tensile stress of the black paper base film is about 123% higher than that of the original paper base film. The coloring of carbon black makes the black paper base film have heat conduction effect, and the average heat transfer rate reaches 15.12 °C/s. Cooked tung oil is combined with the paper-based fiber high-toughness layer to form a stable system. The existence of the cured film improves the basic mechanics and hydrophobicity, and the resistance to ultraviolet radiation and hot air is greatly improved. This study provides a feasible scheme for the application of a black paper base film coated with cooked tung oil.

The design of randomized controlled trials for low-resource settings has unique challenges. One challenge is ensuring recruitment success in the face of a lack of information about prevalence of disease in the population. What disease burden data may exist is old and from distant regions, adding guesswork into site design. This leads to a high chance that the sample size will not be recruited in any reasonable timeframe—a challenge even in high-resource settings. While cross-sectional prevalence studies could be used as a type of feasibility study informing trials by generating near real-time prevalence estimates for the future trial site, such studies take years to complete. This duration makes insertion of such a study in trial design impossible. An empirically supported combination of new approaches that sidesteps unnecessary hurdles is presented, the Pop-Up Prevalence with Private Ethics (PUPPE) design. PUPPE includes using a COVID-validated, more targeted method of data collection, use of accepted ethics review from universities or hospitals to avoid lengthy public ethics review, and—if needed—post-study in silico participant randomization. Only this design would enable clinical trial designers to insert fit-for-purpose prevalence studies within the design phase of their trials, leading to informative outcomes.

Ultra-low radioactive titanium alloy is a promising material for the manufacture of low-background detectors, which are being developed for experiments in astroparticle physics and neutrino astrophysics. Structural titanium is manufactured on an industrial scale from titanium-sponge. The ultra-low-background titanium-sponge can be produced on an industrial scale with a contamination level of less than 1 mBq/kg of uranium and thorium isotopes. The pathways of contaminants during the industrial production of structural titanium have been analyzed. The measurements have been carried out using two methods: inductively coupled plasma spectroscopy (ICP-MS) and gamma-spectroscopy using high-purity germanium detectors (HPGe). It has been shown that the level of contamination with radioactive impurities does not increase during remelting of titanium-sponge and mechanical processing. We examined titanium-alloys samples obtained at different stages of titanium production, namely, an electrode compaction, a vacuum arc remelting with a consumable electrode (VAR), and a cold rolling of titanium-sheets. It has been found out that all studied samples will be a source of uranium and thorium contamination of the final titanium-alloys. It has been established that the only product allowed obtaining ultra-low-background titanium was the VT1-00 which is manufactured without master alloys addition..

This paper deals with a method of calculating the self and mutual inductances of various air coils located arbitrarily in space. Known elementary solutions (the Biot-Savart formulas) were used to determine the magnetic field of infinitely thin current loops and infinitely thin wires of finite length linking magnetically other coils. Unlike commonly used algorithms, these elementary solutions were not extensively transformed analytically but were used to perform calculations by direct numerical integration. This enabled very quick and accurate obtaining of the self-inductance values as well as determining the dependence of mutual inductances on the position of both coils. This method allows analysis of different coil configurations (misaligned coils, inclined to each other, etc.) that other methods do not cover. It also enables the determination of the forces acting on the coils, as well as the calculation of the magnetic field distribution from any coil configuration. The obtained results were compared with those presented by other authors.

Individuals with Parkinson’s Disease (PD) are at an increased risk of falls due to associated motor symptoms and impaired postural control. Gait dysfunction, observed soon after diagnosis, leads to disability with disease progression. The purpose of this study was to determine if there were differences in dynamic postural control during self-paced walking and obstacle-crossing between persons with mild to moderate PD and healthy controls. Participants included nine individuals with mild to moderate PD (PD cohort) and nine age- and gender-matched controls (CON cohort). Anatomical marker trajectories and ground reaction forces were collected as participants walked along the pathway. Three successful representative right and left gait cycles were analyzed using Visual3D biomechanical software. Analyses, involving a mixed model ANOVA using R Statistical Software, examined spatiotemporal (ST) gait parameters for strides involving the leading and trailing limbs and dynamic balance variables, including COM - COP inclination angle, and the COP - COM M/L, COP - XCOM M/L, UMAX - XCOM M/L distances, at distinct points from the stance limb related to the leading and trailing legs. There were no differences between CON and PD for all ST gait parameters during obstacle crossing, except for a significantly reduced step length (p = 0.00489) for the PD group related to the trailing limb. However, there were significant main effects of Step and Condition interacting with Step for swing time, stance time, and step length. For both CON and PD groups the COP - COM , COP - XCOM , UMAX - XCOM metrics demonstrated that the COM was medial to the BOS from first double support, generally moving more medially through second double support for the stance limb during motion of the leading and trailing limbs. Event and Event interacting with Condition were significant for the COP - COM and COP - XCOM variables, while UMAX – XCOM was significant only for Event (p < 0.001). The PD group demonstrated significantly larger mean values for COP – COM M/L at first double support (p = 0.00435) and COP - XCOM M/L at second double support (p = 0.0158) for the second step. Our results suggest that the margin of stability may be useful in discerning dynamic postural control adaptations during walking and obstacle negotiation in persons with mild PD.

Hypertension (HTN) is the foremost contributor to cardiovascular diseases (CVD), which remain a leading cause of death. However, adherence to the properly prescribed dosage form is critical and challenging for patients with chronic diseases such as HTN to prevent illness progression and death. The study’s objective was to develop AMB-loaded subcutaneous implants to reduce the frequency of administration, thus improving patient compliance during HTN management. AMB subcutaneous implants were prepared using continuous hot melt extrusion technology using PCL and PLGA with dimensions of 7.5 cm (length) by 2 mm (diameter). The implants were characterized for thermal characteristics, drug-excipient incompatibilities, surface morphology, fracturability, in vitro release, and stability studies. DSC showed that the drug was in the crystalline state in the fabricated implants. Scanning Electron Microscopy (SEM) revealed a smooth surface. The lead formulation presented good drug content, showed an extended drug release profile over 30 days, and followed zero-order release kinetics with a mean dissolution time of 15 days. The formulation was stable over 30 days in accelerated stability conditions (the last time point was tested, 40℃/75% RH), and the release profile was similar to the initial drug release profile. The lead formulation showed good fracturability during textural analysis. Therefore, the hot melt extruded implants could be an alternative management strategy for conventional amlodipine besylate (AMB) formulation.

Abstract Background: Cluster randomized controlled trials are increasingly becoming popular in public health research interventions and health services research in primary health care design and use in low and middle-income countries. However, the majority of researchers in these trials did not account correctly for the effect of confounders and clustering in the design and analysis stages. Objectives: This article aimed to review the main implications of implementing a cluster randomized controlled trials design in public health research and emphasize the practical application of proper analytical methods and the performance of different statistical models. Methods: The practical application of various analytical methods is validated via the utilization of experimental data from a public health research case study. This article also demonstrated the performance of hierarchical models and made comparisons in terms of empirically precise effect size estimates. Results: Three hierarchical statistical models were provided with highly varied effect sizes during the analysis of the intervention effect on outcome, such as model 1 (AOR: 14.32; 95% CI: 3.75–54.68), model 2 (ARR: 3.52; 95% CI: 2.16–5.77), and model 3 (ARR: 4.79; 95% CI: 2.69–5.52).Conclusion: Inaccurate results and potentially misleading conclusions can arise from improper cluster randomized controlled trial designs and analyses. This article has shown that real-world data can be adjusted for the effects of confounders and clustering, and we advocate for the routine use of suitable design and analytical methods. This article concludes that researchers should be careful when selecting hierarchical models to analyze cluster randomized controlled trials.

In areas where conventional fundus cameras are logistically unaffordable, the smartphone-based approach is considered as a promising future of glaucoma screening due to their affordability and ease-of-use. Optic disc localization is a critical stage during glaucoma screenings. For fundus images captured with standard fundus cameras, the majority of the models available out there can locate the optic disc with satisfactory performance. However, for images that are captured with smartphone-based fundus cameras, the inherent noise and lower quality of the fundus image makes it difficult for models to detect the optic disc region. In this study, we have proposed to utilize YOLOv8n for optic disc localization due to the model’s cutting-edge performance in diverse tasks and its lightweight nature. We have used a public dataset which has 2000 low quality fundus images that are captured using a smartphone-based fundoscopy device. From these, 60% of the data was used for fine-tuning the model, and 25% for testing. By using a confidence of 50% set as threshold, the model was able to detect the optic disc successfully on over 97% of test images with intersection over union of above 0.85. These results highlight the potential of the lightweight YOLOv8n model for deployment on resource-constrained environments, offering a promising performance on accurately localizing the OD and enhancing the feasibility of affordable glaucoma screening on smartphones.

In recent years, wireless power transfer (WPT) has progressed rapidly in both theory and commercialization abroad. However, existing research into WPT coil design for low-power devices is limited. This paper investigates coil structure using printed circuit board (PCB) for magnetic resonant coupling WPT to address this gap. Specifically, the impacts of key geometric parameters on the coil quality factor and coupling coefficient are analysed through models and simulations. Equivalent PCB coils were formed for mutual inductance models, and four basic compensation circuits were analysed. The impacts of changes in coil thickness, line width, turn spacing, and number of turns on the quality factor of PCB coils were analysed with a fixed outer diameter of the coil. In this paper, to mitigate the coil offset while retaining PCB manufacturing simplicity for practical uses, a dual layer PCB coil structure is proposed. The structure optimizated PCB coil could offer remarkable improvements in the WPT system for low power electronic devices.

A sintered wick was formed in a heat pipe through the process of sintering a mixture of copper powder with particle sizes of 100㎛ and 200㎛, mixed with a pore-forming agent. On application to the heat pipes. The thermal resistance of heat pipes containing a sintered wick was calculated for different tilting angles (0°, 45°, and 90°) according to the type of powder, thickness of the sintered wick, and the filling ratio of the working fluid. The heat transfer efficiencies were evaluated through temperature tests. The capillary force, which is closely related to heat transfer efficiency, is determined by the effective pore radius resulting from the sintering of the copper powder. Furthermore, it was observed that the heat transfer performance of the heat pipe was at its best when the working fluid filling ratio was 30%. Additionally, the width of the wick was found to have a direct correlation with the heat transfer performance.

The increasing importance of batteries in the 21st century and the challenges posed by the anticipated growth in end-of-life batteries, particularly in Europe, are addressed. Projections indicate a significant increase in battery waste, with a specific focus on the recycling of black mass (BM)—a complex and hazardous byproduct of the battery recycling process. The research employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to systematically analyze the hazardousness of BM derived from various types of batteries. The findings reveal the inherently hazardous nature of BM, classifying it under multiple hazardous properties (HPs) according to European regulations. The urgent need for a definitive legislative classification of the hazardousness of BM is emphasized, and the potential of innovative technologies and strategies to improve recycling performance while mitigating risks is highlighted. The research significantly contributes to understanding the complexities of BM recycling, providing a foundation for future advancements in this area and emphasizing the need for further exploration and economic feasibility analysis of recycling technologies.

The application of dry lubrication in rolling contacts is challenging due to wear. In previous studies graphene platelets applied as dry lubricants on the surfaces of angular contact ball bearings demonstrated superior properties. These specific bearings, experiencing both rolling and spinning motion, create more severe conditions for dry lubricants. The applied graphene platelets have a thickness of few graphene layers in the nm range and a diameter in the µm range. Such platelets can be produced by mechanical exfoliation from graphite. To delve deeper into the lubrication effects, investigations were carried out using a milli tribometer. In the tests, a fixed steel ball is sliding against an oscillating counterpart under defined load. During the measurements, the applied load and tangential forces on the ball are recorded to calculate the friction. In our case, a steel ball with a diameter of 6 mm was used against graphene-based films on flat bearing surfaces (axial bearing washer). Comparative investigations included nano graphite particles and fullerene as dry lubricants, in addition to graphene platelets of various staple thicknesses. To increase the adhesion of the films to the surfaces, a pre-rolling process was implemented. Afterwards, the friction on the compressed films was measured. The results indicate that the pre-rolling process effectively reduces the friction of the system. After testing, the surfaces underwent analysis using laser scanning microscopy to assess the formed films, wear, and material transfer. It has been demonstrated that the pre-rolling process leads to the formation of a very thin compacted film with surface protective properties. On the ball as counterpart, the graphene platelets generate a transfer film on the contacting surface.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

Combustion parameters are the important aspects in the development and research of an internal combustion engines. Hence present work is an attempt to develop an artificial neural network (ANN) model for anticipating the combustion characteristics of computerized CI DI VCR, four strokes, single cylinder, and water cooled engine using mixture of two biodiesel and diesel blends as a fuel. The two biodiesel selected are Jatropha and Simarouba and mixed in the ratio of 75:25. Two biodiesel mixtures is used for the preparation of various blends with diesel. Experiments are carried out at different compression ratio, at different % load and blends B20, B40, B60and neat diesel (B00) fuel to accumulate the assorted combustion data. The input variables are % load, compression ratio, blends and crank angle. The outputs well thought-out are cylinder pressure (CP), net heat release rate (NHRR), cumulative heat release (CHR), rate of pressure rise (RPR), mass fraction burned (MFB) and maximum cylinder pressure. The assessment is focused on developing artificial neural network for the prediction and verifying the combustion characteristics of engine. MATLAB software is used for analysis of different combustion aspects of the engine. Multilayer neural network perceptions are used with Feed-Forward Back Propagation algorithm with performance function for mapping the input and output parameters. The experimental outcomes are compared with ANN estimated results for the analysis. The outcome from the study authenticate that experimental results are in match with ANN results. The accuracy of the ANN model shows the developed model can effectively predict the engine combustion characteristics with excellent regression coefficients ‘R’ and its values are close to unity and the mean square errors are extremely less. ANN model approach is competent towards predicting the combustion aspect of diesel engine with good accuracy

A series of polyacrylonitrile (PAN)-based block copolymers with poly(methyl methacrylate) (PMMA) as sacrificial bock were synthesized by atom transfer radical polymerization and used as precursors for the synthesis of porous carbons. The carbons enriched with O- and S-containing groups, introduced by controlled oxidation and sulphuration, respectively, were characterized by Raman spectroscopy, scanning electron microscopy, and X-ray photoelectron spectrometry, and their surface textural properties were measured by a volumetric analyzer. We observed that the presence of sulphur tends to modify the structure of the carbons, from microporous to mesoporous, while the use of copolymers with a range of molar composition PAN/PMMA between 10/90 to 47/53 allows the obtainment of carbons with different degrees of porosity. The amount of sacrificial block only affects the morphology of carbons stabilized in oxygen, inducing their nanostructuration, but has not effect on their chemical composition. We also demonstrated their suitability for separating a typical N2/CO2 post-combustion stream.

Significant advances in Penning trap measurements of atomic masses and mass ratios of the proton, deuteron, triton, helion, and alpha-particle have occurred in the last five years. These include a measurement of the mass of the deuteron against 12C with 8.5 x 10-12 fractional uncertainty; resolution of vibrational levels of H2+ as mass and the application of a simultaneous measurement technique to the H2+/D+ cyclotron frequency ratio, yielding a deuteron-proton mass ratio at 5 x 10-12; new measurements of HD+/3He+, HD+/T+ and T+/3He+ leading to a tritium beta-decay Q-value with an uncertainty of 22 meV, and atomic masses of the helion and triton at 13 x 10-12; and a new measurement of the mass of the alpha-particle against 12C at 12 x 10-12. Some of these results are in strong disagreement with previous literature values. Their impact in determining a precise proton/electron mass ratio and electron atomic mass from spectroscopy of the HD+ molecular ion is also discussed.

In this study, we report a novel synthesis of hydrophobic silica aerogel powder composites, functionalized and binary-doped with [Tb(phen)₂](NO₃)₃ and [Eu(phen)₂](NO₃)₃ nanocrystals, employing a two-step sol-gel methodology. The investigation delves into the structural elucidation, optical properties and thermal conductivity of these functionalized Tb(III)-Eu(III) composites. Our analysis encompasses diffuse reflectance spectra, along with excitation and luminescence spectra, highlighting the quantum yields of composites with varying chemical composition. Remarkably, these samples exhibit a strong luminescence, with distinct hues of red or green color upon the specific doping type and level. The detailed examination of excitation spectra and quantum yields unequivocally establishes robust energy transfer mechanisms from the 1,10-phenanthroline molecule to the lanthanide ions. Notably, our study uncovers a Tb³⁺→Eu³⁺ energy transfer phenomenon within the binary functionalized samples, providing compelling evidence for a structural formation process occurring within the mesoporous framework of the aerogel powders.

Cities are important sources of anthropogenic methane emissions. Municipal governments can play a role in reducing those emissions to support climate change mitigation, but they need information on the rate of emissions to context mitigation actions and track progress. Here, we examine the application of satellite data from the TROPOspheric Monitoring Instrument (TROPOMI) to estimate city-level methane emissions rates in a case study of the City of Calgary, Alberta, Canada. While satellites are touted as a global methane monitoring system, we found limited observational coverage over Calgary that precluded the reliability of measurement-based models to track the annual emissions. In this work, we integrated valid observations over three years (2020-22) and used mass balance modeling to derive a long-term averaged emission estimate. The column-averaged dry-air mole fraction (XCH4) enhancement over Calgary was small, 4.7 ppb, but within the city boundary, we identified local hot spots in the vicinity of known sources (wastewater treatment facility and landfills). The city-scale emissions estimate from mass balance was 215.4 ± 132.8 t/d CH4. This estimate is approximately four- and six-times larger than estimates from Canada's gridded national inventory report of anthropogenic CH4 emissions and the Emissions Database for Global Atmospheric Research (EDGAR v8.0), respectively. Valid observations are more common in warmer months and occur during a narrow daily overpass timeslot over Calgary – these effects may bias the emissions estimate. Overall, the findings from this case study highlight the challenge of deriving routine (sub-annual to annual) satellite-based estimates of city-scale methane emissions in similar geographic settings and suggest that more granular emissions inventories are needed to improve methane reporting.

Sphingosine 1-phosphate (S1P) is a signaling lipid molecule involved in various cellular processes. It is important to develop a quantitative method for S1P to determine endogenous levels and to investigate its functions. Especially since S1P is a tiny lipid component of most biological samples, highly sensitive analysis by LC-MS/MS is required. The main challenge in S1P analysis by chromatography is peak broadening due to the presence of a polar phosphate and zwitterion formation in S1P. In this study, we used hydrogen fluoride (HF) to efficiently remove a phosphate and then analyzed the surrogate, sphingosine, as a sharp peak by LC-ESI-MS/MS. We optimized the dephosphorylation reaction in terms of reaction time, temperature, and HF concentration. Multiple reaction monitoring (MRM) for dephosphorylated form of S1P and C17-S1P as internal standard at m/z transition 300.4&gt;282.4 (quantification ion), 300.4&gt;262.4 (qualification ion), 286.3&gt;268.2 (internal standard). This method was validated by essential parameters such as specificity, linearity, range, LOQ, LOD, accuracy, precision, and repeatability. To confirm this new method, we quantified S1P levels in various serum products (100.02 ~ 284.38 nM). To apply the method in vivo, we analyzed S1P in plasma and kidney tissues obtained from a chronic kidney disease (CKD) mouse model. S1P levels were increased only in CKD kidney tissue but not in plasma. In conclusion, by applying the dephosphorylation step with HF, we established a new sensitive LC-MS/MS quantitative method for S1P that can be applied to biological samples.

Machine learning is a fundamental aspect of artificial intelligence that involves the development of algorithms and models that allow computers to learn and make predictions or decisions without explicit programming. With the development of neural networks, back-propagation algorithms and deep learning, machine learning has made breakthroughs in the fields of image recognition, natural language processing and handwriting recognition using machine learning techniques. The advent of deep learning has revolutionized the field of handwriting recognition, using convolutional neural networks, recurrent neural networks, and sequence-to-sequence models to provide solutions that go beyond machine learning methods and significantly improve the accuracy and robustness of handwriting recognition systems. But challenges remain, including the need for large labelled datasets, computational resources and addressing potential biases. As research in deep learning techniques continues to drive handwriting recognition closer towards realisability, machine learning approaches remain at the forefront.

Photophysics properties such as fluorescence quenching, and photoexcitation dynamics of bimolecular non-covalent systems consisting of cationic poly[(9,9-di(3,3’-N,N’-trimethyl-ammonium) propyl fluorenyl-2,7-diyl)-alt-co-(9,9-dioctyl-fluorenyl-2,7-diyl)] diiodide salt (PFN) and anionic graphene carboxylate (GC) have been discovered for the first time by the steady state and time-resolved femtosecond transient absorption (TA) spectroscopy with broadband capabilities. The steady-state fluorescence of PFN is quenched with high efficiency by GC acceptor. Fluorescence lifetime measurements revealed that the quenching mechanism of PFN by GC is static. Here, the quenching mechanisms are well proven by TA spectra of PFN/GC systems. For PFN/GC systems, the photo electron transfer (PET) and charge recombination (CR) processes are ultrafast (within few tens of ps) related to static interactions whereas for PFN/DCB systems the PET takes place in few hundreds ps (217.50 ps)1 suggesting diffusion-controlled PET process. In the latter case, the PFN+•-DCB-• radical ion pairs as the result of PET from the PFN to DCB are clearly resolved and they are long-lived. The slow CR process (in 30 ns time scales)1 suggest PFN+• and DCB-• may already form separated radical ion pairs through charge separation (CS) process, which recombined back to the initial state with a characteristic time constant of 30 ns. The advantage of the present positively charged polyfluorene used in this work is the control over the electrostatic interactions and electron transfers in non-covalent polyfluorene/quencher systems in DMSO solution.

AbstractIntroduction: Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has not been comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.Methods: An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multi-level mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects. Results: One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39). Conclusions: A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations. Trial registration: NCT05865873

Dirac's Large Number Hypothesis (LNH), proposed in 1937, has captivated the scientific community with its exploration of profound correlations between cosmic and atomic scales. This hypothesis delves into the intricate interplay between the infinitesimally small and the vastly large, offering potential insights into the fundamental nature of the universe. As we continue to uncover the mysteries of the cosmos, the LNH oscillates on the precipice of scientific acceptance—neither fully validated nor entirely dismissed. In this review paper, we embark on a comprehensive journey through Dirac's LNH, shedding light on its theoretical underpinnings, its implications for universe models, and its resonance with the Anthropic Principle. We delve into the possibilities of variable gravitational constants and continuous mass creation, inviting further exploration into the intricacies of our cosmic symphony. By embracing the ongoing quest for understanding, we endeavor to unravel the profound harmonies that connect the infinitesimal with the infinite, contributing to the symphony of knowledge in theoretical physics and cosmology.

The present study investigates the role of a simple inerter in supplemental devices for possible implementation in the matured seismic base isolation technique. Firstly, the response of the base-isolated structure with an optimally tuned mass damper-inerter (TMDI) is investigated to see the tuning effects. The time required to tune the TMDI was found to be significantly longer than the duration of a strong-motion earthquake. There was still a reduction in the response of the isolated structure, which is primarily due to the added damping and stiffness (ADAS) of TMDI and not because of the tuning effects. Hence, it is proposed that the corresponding ADAS of the TMDI be directly added to the isolation device. Secondly, the response of the base-isolated structures with the fluid inerter damper (FID) is studied. It was observed that the inerter of the FID does not influence the displacement variance of an isolated structure under broadband earthquake excitation. It implies that the response of the isolated structure with FID is primarily controlled by its counterpart fluid damper (FD). The performance of optimal TMDI, ADAS, FID, and FD to mitigate the seismic response of the flexible multi-story base-isolated structure under real earthquake excitations is also investigated. In terms of suppressing the displacement and acceleration responses of the isolated structure, it has been found that TMDI and ADAS perform similarly. Comparing the response of the isolated structure with FID and FD demonstrated that the inerter in the FID has detrimental effects on the isolated structures, in which the top floor's acceleration and base shear are substantially increased.

Osteoarthritis (OA) is a pathology of great impact worldwide which its physiopathology is not completely known and usually diagnosed by imaging techniques performed at advanced stages of the disease. The aim of this study was to evaluate early serum metabolome changes and identify the main metabolites involved in an inflammatory OA animal model. The study was performed with thirty rats, OA in all animals was induced by intra-articular injection of monoiodoacetate into the knee joint. Blood samples were taken from all animals and analyzed by mass spectrometry before OA induction, and 28, 56 and 84 days following induction. Histological study confirmed OA in all samples. The results of this study allow the identification of several changes in the serum metabolome over time, including organic acids, benzenoids, heterocyclic compounds and lipids at T28, organic acids at T56 and lipid classes at T84. We conclude that OA induces serological changes in the metabolome and, more specifically, 18 metabolites which could serve as potential biomarkers. However, it was not possible to establish a relationship between the identified metabolites and the time at which the samples were taken. Therefore, these findings should be confirmed in future OA studies.

There are two major proposed forms of dark energy: the cosmological constant and the quintessence scalar field. However, the former has the cosmological constant problem and the latter does not have observational evidence to support the theory. In this paper, we propose a dark energy paradigm to solve the cosmological constant problem and to predict lepton masses as evidence. The dark energy paradigm is a unified dark sector that is based on Planck's dimensional analysis, Lambda CDM, energy conservation, and force equilibrium. We find that dark energy can vary from the cosmological constant to the Planck scale. We find that the predicted values of lepton masses based on the dark energy paradigm agree with the observed values to 1% (or within error ranges).

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

This paper investigates the adaptability of four state-of-the-art Artificial Intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n=856 cancer cases and n=856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish image subsets. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&amp;H and End2End AI models, both in the pre-trained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by GLAM model (0.909), I&amp;H (0.893) and End2End (0.875). There were significant differences (P&lt;0.05) in the performances of the four AI models between high-concordance subset and entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with strong concordance of radiologists’ annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

We consider boundary value problems for a nonlinear mass transfer model, which generalizes the classical Boussinesq approximation, under inhomogeneous Dirichlet boundary conditions for the velocity and the substance’s concentration. It is assumed that the leading coefficients of viscosity and diffusion and the buoyancy force in the model equations depend on concentration. We develop mathematical apparatus of studying inhomogeneous boundary value problems under consideration. It is based on using of a weak solution of the boundary value problem and construction of liftings of the inhomogeneous boundary data. They remove the inhomogeneity of the data and reduce original problems to equivalent homogeneous boundary value problems. Based on this apparatus we will prove the theorem of the global existence of a weak solution to boundary value problem under study and establish important properties of the solution. In particular, we will prove the validity of the maximum principle for the substance’s concentration. We will also establish sufficient conditions for the problem data, ensuring the local uniqueness of weak solutions.

To follow the growth and development of each tree (N = 1200) Pinus sylvestris L., var. Negorelskaya starting from the seed and continuing at the juvenile (or even generative) stage is the goal of an ambitious project to create a plant passport if possible. Five datasets of replenished empirical (tab-ular and file) and systematic data reflect the early results of a unique ongoing experiment conducted with families moved along a climatic gradient from the collection area (1731 degree days, 722 mm) to the experimental area (2326 degree days; 786 mm). Datasets of morphometric, VIS-spectrometric, and germination seed data, combined with the first results of biometric data (including the Dickson quality index) of seedlings produced from these seeds, will allow scientists in the future to conduct correlation and other statistical analysis, or train a neural network to determine the presence of connections between a seed and a plant. These data contribute to the early non-destructive diag-nosis and grading of seeds according to VIS-spectrometric properties. This dataset structure can complement the FLR-Library. In the future, by tracing seedlings in their early growth (for example, over three growing seasons at the site being restored), it will be possible to determine the degree of intensification of FRM production depending on the initial properties of the seed.

Gangliosides are highly abundant in the human brain where they are involved in major biological events. In brain cancers, alterations of ganglioside pattern occur, some of which being correlated with the neoplastic transformation, while others with the tumor proliferation. Of all the techniques, mass spectrometry (MS) has proven to be one of the most effective in gangliosidomics, due to its ability to characterize heterogeneous mixtures and discover species with biomarker value. This review highlights the most significant achievements of MS in the analysis of gangliosides in human brain cancers. The first part presents the state-of-the-art in the MS development for discovery of ganglioside markers in primary brain tumors, with a particular emphasis on the ion mobility separation (IMS) MS and its contribution to the elucidation of the gangliosidome associated with aggressive tumors. The second part is focused on the MS of gangliosides in brain metastases, highlighting the ability of matrix-assisted laser desorption/ionization (MALDI)-MS, microfluidics-MS and tandem MS to decipher and structurally characterize species involved in the metastatic process. In the end, several conclusions and perspectives are presented, among which the need for development of a reliable software and a user-friendly structural database as a search platform in brain tumor diagnostics.

This paper proposes a simulation technique for investigating the battery thermal management system based direct refrigerant cooling (BTMS-DRC). It employs finite element method for a combined-conduction-convection heat transfer to predict the module temperature and to prove the temperature uniformity. The refrigerant side cooling is based on the two-phase flow evaporation which is represented by the convection heat transfer (flow evaporation) under a certain refrigerant saturation temperature. The battery heat generation is modeled as the constant heat flux. The main module is modeled as the conduction heat transfer. The real BTMS-DRC is constructed for experimentation with the test bench which is based on the dual-evaporators vapour compression refrigeration system. The simulated result is validated with the experimental results to ensure the correction of the modelling. The simulation also investigates the impact of the heat generation, convection heat transfer coefficient, refrigerant saturation temperature, and thermal conductivity on the module temperature and temperature uniformity. It is found that the simulated results agree well with the experimental results. The errors are around 2.9 - 7.2% throughout the specified working conditions. Overall, the proposed technique can be used as a design tool for further developing the BTMS-DRC.

Surface processes such as coatings, corrosion, photocatalysis, and tribology are greatly diversified by acid-base interaction at the surface overlayer. This study focuses on the action of metallic copper surface as an electron donor/acceptor related to inactivation of viruses; it was found that regarding Cu2O or Cu materials electrostatic interaction plays a major role in virus inactivation. We applied TPPE method to clarify the mechanism of electron transfer (ET) occurring at light irradiated copper surfaces. The TPPE characteristics was strongly influenced by the environments, which correspond to the temperature and environment dependence of the total count of emitted electrons in the incident light wavelength scan (PE total count, NT), the photothreshold, and further the activation energy (E) analyzed from Arrhenius plot of NT values obtained in the temperature-increase and subsequent temperature-decrease processes. In this study we reexamined the dependence of the TPPE data from two types of Cu metal surfaces: Sample A, which was mechanically abraded in alcohols, water, and air, and Sample C, which was only ultrasonically cleaned in these liquids. The NT for both Samples slowly increased with increasing temperature, reached a maximum (NTmax) at 250C (maximum temperature, Tmax), and after that decreased. For Sample A the NTmax value decreased in the order H2O > CH3OH > C2H5OH > (CH3)2CHOH > C3H7OH, although the last alcohol gave Tmax = 100C, while with Sample C the NTmax value decreased in the order: C3H7OH > (CH3)2CHOH > C2H5OH > CH3OH > H2O. Interestingly, both orders of the liquids were completely opposite: this means that Cu surface can possess two-way character. The NT intensity was found to be strongly associated with the change of the hydroxyl group (Cu-OH) to the oxide oxygen (O2) in the O1s spectra in the XPS measurement. The difference between the above orders was explained by the acid-base interaction mode of the Cu-OH group with the adsorbed molecule on the surfaces. The H2O adsorbed on Sample A produces the electric dipole Cu-OH+---:OH2 (--- hydrogen bond), while the C3H7OH and (CH3)2CHOH adsorbed on Sample C produce ROH+---:O(H)-Cu (R = alkyl groups). Gutmann’s acceptor number (AN) representing the basicity of the liquid molecules was found to be related to the TPPE characteristics: (CH3)2CHOH (33.5), C2H5OH (37.1), CH3OH (41.3), H2O (54.8) (the AN of C3H7OH could not be confirmed). With Sample A the values of NTmaxa and EaUp1 both increased with increasing AN (Up1 means the 1st temperature-increase process). On the other hand, with Sample C the values of NTmaxc and EcUp1 both decreased with increasing AN. These findings suggest that Sample A acts as an acid, while Sample C functions as a base. However, in the case of both types of Samples A and C the NTmax values were found to increase with increasing EUp1. It was explained that the EUp1 values depending on the liquids originate from the difference in the energy level of the hydroxyl group radical at the surface denoted. This is able to attract electrons in the neighborhood of the Fermi level of the base metal by tunnelling. After that due to Auger emission electrons are released, leading to the ET in the overlayer. These electrons are considered to have a strong ability of reducibility.

‘Inner mitochondrial membrane peptidase 2 like’ (IMMP2L) is a nuclear encoded mitochondrial peptidase that has been conserved through evolutionary history as has its target enzyme ‘mitochondrial glycerol phosphate dehydrogenase 2’ (GPD2). IMMP2L is known to cleave the mitochondrial transit peptide from GPD2 and another nuclear encoded mitochondrial respiratory related protein cytochrome C1 (CYC1). However, it is not known whether IMMP2L peptidase activates or alters the activity or respiratory related functions of GPD2 or CYC1. Previous investigations found compelling evidence of behavioural change in the Immp2lKD-/- KO mouse and in this study EchoMRI analysis found that many organs of the Immp2lKD-/- KO mouse were smaller and that the KO mouse had significantly less lean mass and overall body weight compared with wildtype littermates (p&lt;0.05). Moreover, all of the organs analysed from the Immp2lKD-/- KO had lower relative levels of mitochondrial reactive oxygen species (mitoROS). The kidneys of the Immp2lKD-/- KO mouse displayed the greatest decrease in mitoROS levels that were over 50% less compared with wildtype litter mates. Mitochondrial respiration was also lower in the kidney of the Immp2lKD-/- KO mouse compared with other tissues when using succinate as the respiratory substrate, whereas respiration was similar to wildtype when glutamate was used as substrate. When glycerol-3-phosphate (G3P) was used as the substrate for Gpd2 we observed ~20% and ~7% respective decreases in respiration in female and male Immp2lKD-/- KO mice over time. Together these findings suggest that the respiratory related functions of mGpd2 and Cyc1 have been compromised to different degrees in different tissues in the Immp2lKD-/- KO mouse. Structural analyses using AlphaFold2-Multimer further predicted that the interaction between Cyc1 and mitochondrial encoded cytochrome b (Cyb) in Complex III had been altered as had the homodimeric structure of the mGpd2 enzyme within the inner mitochondrial membrane of the Immp2lKD-/- KO mouse. mGpd2 functions as an integral component of the glycerol phosphate shuttle (GPS) which positively regulates both mitochondrial respiration and glycolysis. Interestingly, we found that nonmitochondrial respiration (NMR) was also dramatically lowered in the Immp2lKD-/- KO mouse. Primary mouse embryonic fibroblast (MEF) cell lines derived from the Immp2lKD-/- KO mouse displayed a ~27% decrease in total respiration comprised of a ~50% decrease in NMR and a ~12% decrease in total mitochondrial respiration where the latter was consistent with the decreases in substrate specific mitochondrial respiration reported here. This study is the first to report the role of Immp2l in enhancing Gpd2 structure and function, mitochondrial respiration, nonmitochondrial respiration, organ size and homeostasis.

Sodium-phosphate-based glass-ceramics (GCs) are promising materials for a wide range of applications, including solid-state sodium-ion batteries, microelectronic packaging substrates, and humidity sensors. This study investigates the impact of 24-hour heat-treatments at varying temperatures on Na-Ge-P glass, with a focus on (micro)structural, electrical, and dielectric properties of prepared GCs. Various techniques such as powder X-ray diffraction (PXRD), infrared spectroscopy-attenuated total reflection (IR-ATR), and scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) are employed. With the elevation of heat-treatment temperature, crystallinity progressively rises; at 450 °C, the microstructure retains amorphous traits featuring nanometric grains, whereas at 550 °C, heat-treatment results in fully crystallized structures characterized by square-shaped micron-scale grains of NaPO3. The insight into the evaluation of electrical and dielectric properties is provided by Solid-State Impedance Spectroscopy (SS-IS), revealing a strong correlation with the conditions of controlled crystallization and observed (micro)structure. The glass-ceramic heat-treated at 450 °C achieves the highest DC conductivity of 2.30×10–8 Ω–1 cm–1 at 393 K. As the heat-treatment temperature rises, the crystallization of the NaPO3 phase depletes the glass matrix of mobile Na+ ions, resulting in a reduction in DC conductivity. Dielectric parameters also decrease with rising heat-treatment temperature, with the glass-ceramic heat-treated at 550 °C displaying the lowest permittivity of 19.37 and the dielectric loss of 0.008. This research uncovers the intricate relationship between heat-treatment conditions and material properties, emphasizing that controlled crystallization allows for precise modifications to microstructure and phase composition within remaining glassy phase, ultimately facilitating fine-tuning of material properties.

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

Thermostatically controlled loads have great potential to make a significant contribution to improving energy efficiency in the building sector, which is responsible for 40% of greenhouse gas emissions in the EU. This, in addition to the environmental damage, represents a huge expense in the electricity bill. Therefore, it is very important to train engineers on how to design energy management systems for TCLs. With this goal in mind, it would be very useful to have a simulation-based educational tool (SBET) to understand thermostatically controlled loads, their characteristics and possibilities in energy efficiency. In addition, it would be very useful if this tool could be introduced in engineering curricula to help students to be better trained and enter the labor market with more opportunities. Based on the shortcomings detected, this work develops a SBET specifically designed to teach about TCLs (SBET-TCLs), both about their intrinsic characteristics and their better management. To verify the developed SBET-TCL, it has been tested in a real scenario: A survey was carried out among the students of the subject 'Alternative Energy Sources' of the degrees of Industrial Engineering. The results show that the use of SBET-TCL has very positive effects on the learning process.

Transdermal drug delivery systems have become an intriguing research topic in healthcare technology and one of the most frequently developed pharmaceutical products in the global market. In recent years, researchers and pharmaceutical companies have made significant progress in developing new solutions in the field. This study sheds light on current trends, collaboration patterns, research hotspots, and emerging frontiers of transdermal drug delivery. Herein, a bib-liometric and patent analysis of data recovered from Scopus and The Lens databases, respectively, is reported over the last 20 years. From 2000 to 2022, the annual global publications increased from 131 in 2000 to 659 in 2022. Researchers in the United States, China, and India produced the highest number of publications. Likewise, most patent applications have been filed in the USA, China, and Europe. The recovered patents are 7,275, grouped into 2,997 patent families, of which 314 were granted. This study could support the work of decision-makers, scientific managers, or scientists to create new business opportunities or save money, time, and intellectual capital, thus, defining when a research or technology project should be a priority or not.

Infertility affects millions worldwide, posing a significant global health challenge. The proteomic analysis of follicular fluid provides a comprehensive view of the complex molecular landscape within ovarian follicles, offering valuable information on the factors influencing oocyte development and on the overall reproductive health. The follicular fluid is derived from the plasma and contains various proteins that can have different roles in oocyte health and infertility and this fluid is a critical microenvironment for the developing oocytes as well. Using HPLC/MS method we investigated the protein composition of the follicular fluid and after classification we did relative quantification of the identified proteins in the pregnant (P) and non-pregnant (NP) groups. Based on the protein-protein interaction analysis ALB and ApoA1 was found to be hub proteins and the quantitative comparison of the P and NP groups resulted significantly lower concentration of ApoA1 and HDL-cholesterol in the P group. As both molecules are involved in the cholesterol transport, we also investigated their role in the development of oocytes and in the prediction of fertility.

This paper presents one of the approaches to solution of the problem of the repulsion origin in gravity. The approach is based on the property of compactness characteristic for a self-gravitating object in General Relativity. Here we understand compactness as estimation of the upper boundary for mass such an object in a static two--dimensional sphere. Repulsion originates when this boundary is violated. The main hypothesis is formulated in the form of the principle of maximal mass within a two-dimensional static sphere. It is demonstrated that the principle is true for Schwarzschild black holes on absorption of the matter in the process of accretion, both in the classical case and with due regard for quantum-gravitational corrections. The results have been extended to black holes with the Schwarzschild-de Sitter metric in the early Universe. The applicability of the principle suggested is analyzed for the early and for the present Universe.

Hypoxic-ischemic encephalopathy (HIE) is one of the most common causes of childhood disability. Hypothermic therapy is currently the only approved neuroprotective approach. However, early diagnosis of HIE can be challenging, especially in the first hours after birth when the decision to treat with hypothermic therapy is critical. Differentiating HIE from other neonatal conditions, such as sepsis, further complicates the diagnosis. This study investigated the utility of a metabolomic-based approach using the NeoBase 2 MSMS kit to diagnose HIE using dry blood stains in a Rice-Vannucci model of HIE in rats. We evaluated the diagnostic accuracy of this method between 3 and 6 hours after the onset of HIE, including in the context of systemic inflammation and concomitant hypothermic therapy. Discriminant analysis revealed several metabolite patterns associated with HIE. A logistic regression model using glycine levels achieved high diagnostic accuracy with areas under the curve (AUC) of 0.94 at 3 hours and 0.96 at 6 hours after the onset of HIE. In addition, orthogonal partial least squares discriminant analysis, which included five metabolites, achieved 100% sensitivity and 80% specificity within 3 hours of HIE. These results highlight the significant potential of the NeoBase 2 MSMS kit for the early diagnosis of HIE and could improve patient management and outcomes in this serious illness.

The effects of anisotropic properties (wettability and roughness) on microgroove surfaces in pool boiling heat transfer using Novec-7100 as a working fluid were experimentally investigated. The idea of introducing the concept of anisotropic wettability in boiling experiments draws inspiration from biphilic surfaces. The investigation is also motivated by two-phase immersion cooling, which involves phase-change heat transfer by using dielectric liquid as a working fluid. Very few studies have focused on the effects of surfaces with anisotropic properties on boiling performance. Thus, this study aims to examine the pool boiling heat transfer performance on surfaces with microgroove-induced anisotropic properties under the saturation condition. A femtosecond laser–texturing method was employed to create microgroove surfaces with different groove spacing. The results indicated that anisotropic properties affected the heat transfer coefficient and critical heat flux. Relative to the plain surface, microgroove surfaces enhanced the heat transfer performance due to the increased number of bubble nucleation sites and higher bubble detachment frequency. An analysis of bubble dynamics under different surface conditions was conducted with the assistance of high-speed images. The microgroove surface with groove spacing of 100 μm maximally increased the BHTC by 37% compared to the plain surface. Finally, the CHF results derived from experiments were compared with related empirical correlations. Good agreement was achieved between the results and the prediction correlation.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.

In this paper, a special recurrent neural network (RNN) called \emph{Long Short-Term Memory (LSTM)} is used to design a virtual load sensor that estimates the mass of heavy vehicles. The estimation algorithm consists of a two-layer LSTM network, with the two layers based on sequence-to-sequence and sequence-to-one logic, respectively. The network estimates the vehicle mass based on the vehicle speed, longitudinal acceleration, engine speed, engine torque, and the accelerator pedal position. The network is trained and tested with a data set collected in a high-fidelity simulation environment called Truckmaker. Training data is generated in acceleration maneuvers in a range of speeds whereas test data is obtained by simulating the vehicle in a Worldwide harmonized light vehicles test cycle (WLTC). Preliminary results show that, with the proposed approach, the heavy-vehicle mass can be estimated as accurately as commercial load sensors in a range of load mass as wide as four tons.

Our daily activities hinge on the flexibility of our fingers, and a fractured finger can significantly disrupt these routines. The finger bones enable us to bend and fold the fingers and thumb to pick up or grasp objects and do all of our daily activities. A broken finger can cause adverse effects on our daily life activities. It is important to treat broken finger as soon as possible. Swift and precise treatment begins with capturing multiple X-rays, followed by the critical step of fracture detection in these images. Relying on the naked eye for this task carries the risk of overlooking small fractures. To address this issue an automated diagnoses of fractured fingers from images is required for which the current research employs advanced deep learning models—ResNet34, ResNet50, ResNet101, ResNet152, VGG-16, and VGG-19—to classify finger images from the Musculoskeletal Radiographs (MURA) dataset as either fractured or non-fractured. The results emphasize the consistently strong performance of ResNet models, attaining an impressive accuracy of 81.9%. This surpasses VGG models by 3.4% and establishes ResNet as the new benchmark for state-of-the-art accuracy.

Recently considered modifications to public policy and trends in technology transfer practices have underscored the need for validated instruments to assess the maturity of technologies. This paper details a study that aimed to develop and present a generalized technology readiness level (GTRL) scale that a variety of professionals and researchers could use to assess the maturity of technologies in a range of contexts. Of particular interest is developing and validating an instrument that technology transfer professionals in federal laboratories, universities, and the private sector in the United States of America can use to assess the maturity of technologies to facilitate their technology transfer efforts. The primary goal of the research study was to highlight the need for validated measurement instruments for assessing the maturity of technologies in these contexts, test methods for validating the GTRL scale and other readiness level scales, and fill the knowledge gap regarding methods for properly validating such instruments. This included producing insights to inform future efforts to validate the GTRL scale as well as efforts to develop and validate other instruments to measure technology maturity. The study provides specific recommendations relevant to determining the face validity, content validity, inter-rater reliability, and intra-rater reliability of the GTRL scale and other such instruments for assessing technology maturity.

Abstract: Important variables that affect the quality and functionality of sperm and may affect male reproductive health and fertility include telomere length, mitochondrial content, and the ratio of mitochondrial DNA (mtDNA) to nuclear DNA (nDNA). The purpose of this study was to look at the connections between these three sperm characteristics and how they might affect a woman's ability to conceive. Methods: Data were collected from 82 men who underwent IVF/ICSI at the University Hospital of Ioannina's IVF Unit in the Obstetrics and Gynecology Department. Evaluations included sperm morphology, sperm count, sperm motility, and participant history. To address this, males of reproductive age who were categorized into three body mass index (ΒΜΙ) groups—normal, overweight, and obese—had their sperm samples tested. Results: Our findings indicate a negative correlation between relative telomere length and ΒΜΙ for both the normal and overweight groups. Conversely, a positive correlation was observed for the obese group, indicating a possible relationship between mitochondrial health and telomere maintenance. The results were statistically significant only for the obese group. Furthermore, a negative correlation was observed between telomere length and mitochondrial content in both the normal and overweight groups, indicating that a lower mitochondrial content may be linked to longer telomeres. Nonetheless, there was a positive correlation for the group that was obese. For all three groups, the data did not reach statistical significance. Given that mitochondrial con-centration and telomere length are both markers of cellular integrity and health, these correla-tions may have an impact on the quality of sperm. Furthermore, the relative telomere length of the normal and overweight groups had a negative correlation with the ratio of mtDNA to nDNA, though the obese group showed a positive correlation. The findings were not statistical-ly significant in any of the groups under investigation. According to this, male fertility may be negatively impacted by an imbalance in the copy number of the mitochondrial genome com-pared to the nuclear DNA in sperm. Conclusions: Our research, in essence, emphasizes the interaction among human sperm telo-mere length, mitochondrial concentration, and the mtDNA to nDNA ratio. Gaining knowledge of these correlations could help explain the reasons behind male infertility and open up new treatment options for reproductive health problems. The functional importance of these correla-tions and possible uses in therapeutic contexts require more investigation.

In this paper, corticosteroid–β-cyclodextrin (β-CD) inclusion complexes have been prepared by Supercritical AntiSolvent (SAS) precipitation to enhance the dissolution rate of dexamethasone (DEX) and prednisolone (PRED), which are poorly water-soluble drugs. The processing of the active principles in the absence of a carrier led to the almost complete extraction of the active principle (the small amount of obtained material precipitates in the form of crystals). The coprecipitation of the ingredients in the presence of β-CD was investigated at different concentrations, pressures, and molar ratios. For both the corticosteroids, the optimized operating conditions were 40 °C, 120 bar, an equimolar ratio, and a concentration in DMSO of 20 mg/mL; these conditions led to the attainment of microparticles with a mean diameter equal to 0.197±0.180 μm and 0.131±0.070 μm in the case of DEX and PRED, respectively. The Job’s method confirmed the formation of inclusion complexes in correspondence with the 1/1 mol/mol ratio. Compared to the pure ingredients, the obtained powders have an improved release rate, which is about 3 times faster in both cases.

Background - Technological developments induced lifestyle changes and increase population physical inactivity. This change in lifestyle can affect the male-female morphological and physical fitness differences. The aim of this study is to determine and to compare the effect of sex differences in percentage of body fat on speed and endurance running performance. Methods - 72 male and 64 female students aged 20 to 23 years participated in this study. After assessing their morphological characteristics, running performances during 30 m and 400 m, and maximum aerobic velocity (MAV) and maximal oxygen consumption (VO2max) during a 20 m shuttle running test have been estimated for each subject. Male students were re-tested after being overweighed down with a weight equivalent to the mean differences in body fat recorded between the two sexes in the form of a loaded worn vest. Results – Male presented a percentage of fat mass (17.2 ±2%) significantly (p <0.001) lower than that of women subject (25.0 ±2.5%). Running performances during 30 m and 400 m, MAV and VO2max were significantly (p<0.001) better in males. During the weighted condition, male performances during 30 m and 400 m, MAV and VO2max were attenuated by 6.89%, 14.16%, 6.48% and 10.07%, respectively but remain significantly better than those of female students. Conclusion - The persistence of sex differences after overweighting male students indicates that body fat could be responsible for ≈ 50 to 70% of the observed differences between sexes performances during short and long sprints, and 20 m shuttle running test.

Keywords: friction stir processing; additive manufacturing; wire; titanium alloy; material transfer; microstructure; microhardness

This paper proposes a circuit model of Cross-Coupled CMOS AC – DC Charge Pump (XC – CP) operating in subthreshold region. The aim is improving the efficiency to design XC – CPs with variety of specifications, e.g., input and output voltages and AC input frequency. First, it is shown that the output resistance (Ro) of XC – CP is much higher than those of CPs with single diodes (SD – CP) and ultra-low-power diodes (ULPD – CP) as charge transfer switches (CTSs). Second, the reason behind the above feature of XC – CP is identified by a simple model that the gate-to-source voltages of CTS MOSFETs are independent of the output voltage of the CP. Third, high but finite Ro of XC – CP is explainable with a more accurate model that includes the dependence of the saturation current of MOSFETs operating in subthreshold region on the drain-to-source voltage which is a function of the output voltage of CP. The model was in good agreement with measured and simulated results of XC – , SD – and ULPD – CPs fabricated in 250 nm CMOS.

Ransomware attacks have rapidly proliferated, inflicting severe financial damages on businesses and individuals. Machine learning approaches to automate ransomware detection have shown promise but grapple with challenges like limited training data. This study introduces a novel deep learning model for few-shot ransomware classification. The model employs entropy features derived directly from malware binaries coupled with a twin neural network architecture utilizing transfer learning. Tests on over 1000 samples across 11 families demonstrate a weighted F1-score of 85.8\%, surpassing existing methods. The approach mitigates biases in limited training data and preserves intricacies lost in image-based features. It exhibits precise classification capabilities even with sparse samples of new ransomware variants. The research highlights the potential of entropy-driven deep learning to equip defenses against emerging zero-day ransomware strains.