Search

The frequency deviation from the nominal working frequency in power systems is a consequence of the imbalance between total electrical loads and the aggregate power supplied by production units. The sensitivity of energy system frequency to both minor and major load variations underscore the need for effective frequency load control mechanisms. In this paper, frequency load control in single-area power system with multi-source energy is analysed and simulated. Also, the effect of the photovoltaic system on the frequency deviation changes in the energy system is shown. In the single area energy system, the dynamics of thermal turbine with reheat, thermal turbine without reheat and hydro turbine are considered. The simulation results using Simulink/Matlab and model analysis using eigenvalue analysis show the dynamic behaviour of the power system in response to changes in the load.

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.

Slow decomposition rates of cereal crop residues can lead to agronomic challenges, such as nutrient immobilization, delayed soil warming, and increased pest pressures. In this regard, microbial inoculation with efficient strains offers a viable and eco-friendly solution to accelerating the decomposition process of crop residues. However, this solution often focuses mostly on selecting microorganisms based on the appropriate enzymic capabilities and neglects the metabolic versatility required to utilize both structural and non-structural components of residues. Therefore, this study aimed to address these limitations by assessing the metabolic profiles of five previously identified cellulolytic bacterial strains, including Bacillus pumilus 1G17, Micromonospora chalcea 1G49, Bacillus mobilis 5G17, Streptomyces canus 1TG5, and Streptomyces achromogenes 3TG21 using Biolog Phenotype Microarray analysis. Moreover, this study evaluated the impact of wheat straw inoculation with single strains and a bacterial consortium on soil organic carbon and nitrogen content in a pot experiment. Results revealed that, beyond the core subset of 12 carbon sources, the strains exhibited diverse metabolic capacities in utilizing 106 carbon sources. All strains demonstrated effective straw biomass degradation compared to the negative control, with significant differences detected only in oil seed rape straw biodegradation estimations. Furthermore, wheat straw inoculated with a bacterial consortium showed a significant increase in soil organic carbon content after 180 days in the pot experiment. Overall, these findings underscore the critical role of metabolic profiling in gaining a deeper understanding of microbial capabilities and addressing the complexities of residue composition and environmental variability.

Ipomoeassin F (Ipom-F) is a plant-derived macrocyclic resin glycoside that potently inhibits cancer cell growth through blockage of Sec61-mediated protein translocation at the endoplasmic reticulum. Recently, detailed structural information on how Ipom-F binds to Sec61α was obtained using Cryo-EM, which discovered that polar interactions between asparagine-300 (N300) in Sec61α and four oxygens in Ipom-F are crucial. One of the four oxygens is from the carbonyl group at C-4 of the fatty acid chain. In contrast, our previous structure–activity relationship (SAR) studies suggest that the carbonyl group is not essential. To resolve this discrepancy, we designed and synthesized two new open-chain analogues (10 and 11); 10 without the C-4 carbonyl had a dramatic activity loss, whereas 11 with an amide functional group was even more potent than Ipom-F. These new SAR data, in conjunction with some previous SAR information, imply two functional roles of the C-4 carbonyl: (1) to form H-bonds with N300; and (2) to regulate interactions of the fatty acid chain with membrane lipids. Impacts of these dual functions on antiproliferation depend on the overall structure of an Ipom-F derivative. Moreover, 11 can serve as a lead compound for developing future amino acid/peptide-modified analogues of Ipom-F with improved therapeutic properties.

This article presents the results obtained from a high-resolution wide-azimuthal 3D seismic reflection method used for the prediction and detailed exploration of complex ore targets in the Zhezkazgan ore district of Central Kazakhstan. We demonstrate the ability of modern seismic data processing and interpretation systems to identify underground mine objects associated with stratiform copper sandstones and improve geological models. The 3D seismic imaging tools, along with the implementation of a modern seismic processing sequence, allow for the clarification of geological structures in the studied area. The target stratigraphic horizons, large faults, and microtectonic disturbances (small faults and cracks) are clearly delineated in the seismic volumes. The use of seismic attribute analyses on geological data is tested to identify ore horizons and deposits with volumetric predictions of copper mineralization. Recommendations for further exploration drilling were developed, and five new wells were drilled. Copper mineralization was confirmed in all recommended wells. We carried out a marketing review in Kazakhstan and uncovered an increased interest among subsoil use companies in 3D seismic exploration technology to investigate existing mining objects of different genetic types. These results demonstrate the expediency of 3D seismic exploration aimed at identifying ore targets.

Protein identification in complex biological samples using the shotgun mode of LC-MS/MS is typically enhanced by employing longer LC columns and extended gradient times. However, improved identification rates can also be achieved by optimizing MS acquisition frequencies and employing advanced software, without increasing analysis time, thus maintaining the throughput of the method. To date, we found only one study in the literature examining the influence of MS acquisition frequency on protein identification, specifically using two ion trap mass spectrometer models. This study aims to address the gap by analyzing the impact of MS acquisition tuning of the QTOF instrument on the analysis of complex samples. Our findings indicate that increasing acquisition frequency generally improves protein identification, although the extent of improvement depends on the sample type. For CHO cell lysates, protein identifications increased by over 10%, while E. coli and albumin-depleted plasma samples demonstrated gains of 3.6% and 2.6%, respectively. Higher contributions to protein identification were also achieved with extended LC gradients, resulting in improvements of 21.6% for CHO, 18.2% for E. coli, and 10.3% for plasma. Moreover, enabling PEAKS’ deep learning feature significantly boosted identifications, with increases of 22.9% for CHO, 23.2% for E. coli, and 9.2% for plasma.

Background/Objectives: Intranasal vaccination enhances protection against respiratory viruses by providing stimuli to the immune system at the primary site of infection, promoting a balanced and effective response. Influenza vectors with truncated NS1 are a promising vaccine approach that ensures a pronounced local CD8+ T-cellular immune response. Here, we describe the protective and immunomodulating properties of an influenza vector FluVec-N carrying the C-terminal fragment of the SARS-CoV-2 nucleoprotein within a truncated NS1 open reading frame. Methods: We generated several FluVec-N recombinant vectors by reverse genetics and confirmed the vector’s genetic stability, antigen expression in vitro, attenuation, and immunogenicity in a mouse model. We tested the protective potential of FluVec-N intranasal immunization in naïve mice and seropositive Th2-prone mice, primed with aluminium-adjuvanted inactivated SARS-CoV-2. Immune response in immunized and challenged mice was analyzed through serological methods and flow cytometry. Results: Double intranasal immunization of naïve mice with FluVec-N reduced weight loss and viral load in the lungs following infection with the SARS-CoV-2 beta variant. Mice primed with alum-adjuvanted inactivated coronavirus experienced substantial early weight loss and eosinophilia in the lungs during infection, demonstrating signs of enhanced disease. A single intranasal boost immunization with FluVec-N prevented the disease enhancement in primed mice by modulating the local immune response. Protection was associated with the formation of specific IgA and the early activation of virus-specific effector and resident CD8+ lymphocytes in mouse lungs. Conclusions: Our study supports the potential of immunization with influenza vector vaccines to prevent respiratory diseases and associated immunopathology.

Continuous agricultural activities lead to soil organic carbon (SOC) depletion, and agroecological intensification practices (i.e., reduced soil disturbance and crop diversification) have been suggested as strategies to increase SOC storage. The study aims to assess the effect of agroecological intensification levels (lower (T1) and highest (T2)) on the soil C pool and aggregate stability and validate the correlation between different variables compared to the control (lowest/none (T3), where agroecological intensification was not applied. The C-stock, soil microbial biomass carbon (SMB-C), SOC, water extractable organic carbon (WEOC) in bulk soil, fine and coarse soil aggregates, and water-stable aggregates (WSA) were measured during maximum nutrient uptake in plants under diversified agroecological practices across different environmental conditions (core sites: Italy (CS1), France (CS2), Denmark (CS4), Spain (CS5), Netherlands (CS6), Lithuania (CS7), Turkey (CS8), and Belgium (CS9)). The soil aggregate stability varied among the CSs and treatments. At sites CS7 and CS9, WSA was higher in T1 and T2 compared to the control; a similar trend was observed at other sites, except CS1. SMB-C differed among the core sites, with the lowest value obtained in CS5 (52.3 μg g−1) and the highest in CS6 (455.1 μg g−1). The highest average contents of SOC and WEOC were obtained in bulk soil at CS2 (3.1 % and 0.3 g kg−1 respectively). Positive and statistically significant (p < 0.001) correlations were detected among all variables tested with SOC in bulk soil and WSA. This study demonstrates the significance of agroecological practices in improving soil carbon stock and optimizing plant–soil–microbe interactions.

This article is devoted to the study of geoheritage objects and the scientific justification for the creation of a geopark in the Ulytau region of Central Kazakhstan. This region is the largest copper-bearing province in the world and has a unique natural and cultural heritage. The purpose of this article is to show the scientific and tourist significance of geoheritage objects of the potential Ulytau Geopark. The geological history of this area tells about no less than 500 million years of the planet’s development. Geological, historical and sacred objects make this area extremely interesting for the development of geotourism and the creation of geoparks as a basis for the sustainable development of the area. The research methods included a bibliographic method, which made it possible to collect information on the geoheritage of the territory; field survey techniques; and methods based on the evaluation of the criteria for eligibility for UNESCO Geoparks. The methodology included five main blocks of assessment (geology and landscape, structure and management model, interpretation and environmental education, geotourism and sustainable development at the regional level) and represents an integrated interdisciplinary approach to present regional features in the context of the geological heritage of the world. Despite the length of time geology has been studied and the resources actively used, geoheritage sites have not been previously studied and characterised. Geoparks in Kazakhstan are at an early stage of development and this article aims to show the potential for establishing geoparks in the Ulytau region.

Background: This paper introduces an enhanced multi-period p-mobile hub location model that accounts for critical factors such as service time, flow processing delays, and congestion impacts at capacity-constrained hubs. As (urban) transportation networks evolve, mobile hubs play an increasingly vital role in promoting sustainable logistics solutions and addressing complex operational challenges. By enabling the repositioning of hubs across periods, this model seeks to minimize overall costs, particularly in response to dynamic demand fluctuations. Method: To solve this problem, we propose a bi-objective optimization model and introduce a hybrid meta-heuristic algorithm tailored to this application. The algorithm involves a clustering-based technique for evaluating solutions and a refined genetic approach for producing new sets of solutions. Results: Various experiments have been conducted on the Australian Post dataset to evaluate the proposed method. The results have been compared with Multiple-Objecti-ve Particle Swarm Optimization (MOPSO) and Non-Domi-nated Sorting Genetic Algorithm (NSGA-II) using several performance evaluation metrics. Conclusions: The results indicate that the proposed algorithm can provide remarkably better Pareto sets than the other competitive algorithms.

One of the most challenging aspects of designing a humanoid robot is ensuring stable walking. To achieve this, the kinematic architecture must support 3D motion and maintain equilibrium, particularly during single-foot support. Without proper configuration, the robot may experience unbalanced weight distribution, significantly increasing the risk of falling while walking. While adding redundant degrees of freedom (DOFs) can enhance adaptability, it also raises the system’s complexity and cost and the need for more sophisticated control strategies and higher energy consumption. This paper explores a reduced-DOF bipedal robot, which, despite its limited number of DOFs, is capable of performing 3D motion. It features an inverted pendulum and elastic ankles made of thermoplastic polyurethane (TPU), enabling effective balance control and attenuation of disturbances. The robot’s electromechanical design is introduced alongside the kinematic model. Momentum equilibrium in a pseudo-static mode is considered in both the frontal and sagittal planes, taking into account the pendulum and the swinging leg during the single support phase. The TPU ankle’s performance is assessed based on its ability to resist external bending forces, highlighting challenges related to the robot’s weight equilibrium stability and ankle inversion. Experimental results from both Finite Element Analysis (FEA) and real-world tests are compared. Lastly, the joint movements of the inverted pendulum-based biped robot are evaluated in both a virtual environment and a physical prototype while performing lateral tilting and various gait sequences.

With growing concerns over energy and heat-related mortality/morbidity rates, enhancing building performances is key to improving the health and well-being of building occupants while reducing CO2 emissions, in line with the UK Government’s Net-Zero targets. This study investigates the impacts of different retrofitting scenarios on overheating risk and energy performance in social housing for current and future climate conditions. Dynamic thermal simulations were carried out using Design Summer Year (DSY) weather files in DesignBuilder software for selected case study buildings. Winter performance was analysed using the Predicted Mean Vote (PMV) index, while summer results were assessed according to the Chartered Institution of Building Services Engineers Technical Memorandum 59 (CIBSE TM59) guidelines. The findings revealed that bedrooms, especially those facing south, were at high risk of overheating. Factors such as building construction, the number of exposed surfaces, and window area influenced the risks. External wall insulation outperformed internal wall insulation in improving summer comfort. In the winter, Passivhaus standards with natural ventilation ensured thermal comfort across all zones, with a 41–53% reduction in heating energy consumption under current weather conditions. The risk of overheating and associated health issues significantly increased for the future weather scenarios. Further investigation into ventilation strategies, occupant behaviour, and passive design is required to mitigate overheating risks while reducing energy consumption in buildings.

On the one side, the operational expenses of mining enterprises are showing an upward trend; and on the other side, conventional mining fleet management systems (FMSs) are falling short in addressing the high-dimensionality, stochasticity, and autonomy needed in increasingly complex operations. These major drivers for change have convinced researchers to search for alternatives including artificial-intelligence-enabled algorithms recommended by Mining 4.0. The present study endeavors to scrutinize this transition from a business management point of view. In other words, a literature review is carried out to gain insight into the evolutionary trajectory of mining FMSs and the need for intelligent algorithms. Afterward, a holistic supply chain layout and then a detailed value chain diagram are depicted to meticulously inspect the effect of technological advancements on FMSs and subsequently the profit margin. The proposed value-chain diagram is advantageous in explaining the economic justification of such intelligent systems, illustratively, for shareholders in the industry. Moreover, it will show new research directions for mining scholars.

We investigate the statistics of the available Pantheon+ dataset. Noticing that the χ2 value for the best-fit ΛCDM model to the real data is small, we quantify how significant its smallness is by calculating the distribution of χ2 values for the best-fit ΛCDM model fit to mock Pantheon+-like datasets, using the provided covariance matrix. We further investigate the distribution of the residuals of the Pantheon+ dataset with respect to the best-fit ΛCDM model, and notice that they scatter less than would be expected from the covariance matrix but find no significant kurtosis. These results point to the conclusion that the Pantheon+ covariance matrix is over-estimated. One simple interpretation of these results is a ∼7% overestimation of errors on SN distance moduli in Pantheon+ data. When the covariance matrix is reduced by subtracting an intrinsic scatter term from the diagonal terms of the covariance matrix, the best-fit χ2 for the ΛCDM model achieves a normal value of 1580 and no deviation from ΛCDM is detected. We further quantify how consistent the ΛCDM model is with respect to the modified data with the subtracted covariance matrix using model-independent reconstruction techniques such as the iterative smoothing method. We find that the standard model is consistent with the data. There are a number of potential explanations for this smallness of the χ2, such as a Malmquist bias at high redshift, or accounting for systematic uncertainties by adding them to the covariance matrix, thus approximating systematic uncertainties as statistical ones.

Background: The differential diagnosis of tuberculosis (TB) and lung cancer (LC) is often challenging due to similar clinicopathological presentations when bacterial shedding is negative, which can lead to delays in treatment. In this study, we tested the potential of plasma-circulating microRNAs (miRNAs) for the early and differential diagnosis of TB and LC. Methods: We conducted a two-phase study: profiling 188 miRNAs in pooled plasma samples and validating 14 selected miRNAs in individual plasma samples from 68 LC patients, 38 pulmonary TB patients, and 41 healthy controls. Results: Twelve miRNAs were significantly elevated in LC patients compared to controls and TB patients, while two miRNAs were significantly elevated in TB patients compared to controls. ROC analysis demonstrated that miR-130b-3p, miR-1-3p, miR-423-5p, and miR-200a-3p had good discriminatory ability to distinguish LC patients (including those with stage I tumours) from healthy individuals and miR-130b-3p, miR-423-5p, miR-15b-5p, and miR-18b-5p effectively distinguished LC patients (including those with stage I tumours) from TB patients. Additionally, miR-18b-5p showed good discriminatory ability between SCLC and NSCLC patients. Conclusions: Circulating miRNAs hold strong potential for the early detection of LC and for distinguishing LC from TB.

This study adapted the mean age of air, a time scale widely utilized in evaluating indoor ventilation, to assess the impact of building layouts on urban ventilation capacity. To distinguish it from its applications in enclosed indoor environments, the adapted index was termed the effective mean age of air (τ¯E). Based on an experimentally validated method, computational fluid dynamic (CFD) simulations were performed for parametric studies on four generic parameters that describe urban morphologies, including building height, building density, and variations in the heights or frontal areas of adjacent buildings. At the breathing level (z = 1.7 m), the results indicated three distinct distribution patterns of insufficiently ventilated areas: within recirculation zones behind buildings, in the downstream sections of the main road, or within recirculation zones near lateral facades. The spatial heterogeneity of ventilation capacity was emphasized through the statistical distributions of τ¯E. In most cases, convective transport dominates the purging process for the whole canopy zone, while turbulent transport prevails for the pedestrian zone. Additionally, comparisons with a reference case simulating an open area highlighted the dual effects of buildings on urban ventilation, notably through the enhanced dilution promoted by the helical flows between buildings. This study also serves as a preliminary CFD practice utilizing τ¯E with the homogenous emission method, and demonstrates its capability for assessing urban ventilation potential in urban planning.

The present article is devoted to the issue of studying the patterns of displacement of superincumbent rock over panels of a mine obtained using advanced seismic technologies, allowing for the study of the boundaries of caving zones in the depths of rock mass. A seismic exploration has been performed in local areas of Zhomart mine responsible for the development of Zhaman-Aybat cuprous sandstone deposits in Central Kazakhstan at the stage of repeated mining with pulling of previously non-mined ore pillars and superincumbent rock caving. A 2D field seismic exploration has been accomplished, totaling to 8000-line m of seismic lines using seismic shot point. The survey depth varied from 455 m to 625 m. The state-of-the-art technologies of kinematic and dynamic analysis of wavefield have been widely used during data processing and interpretation targeted at identifying anomalies associated with the structural heterogeneity of the pays and rock mass, engaging modern algorithms and mathematical apparatuses of specialized geodata processing systems. The above effort resulted in new data regarding the location and morphology of the reflectors, characterizing geological heterogeneity of the section, zones of smooth rock displacement, and displacement of strata with significant disturbance of the rocks overlying mined-out productive pay. The potential of the application of modern 2D seismic exploration to studying an underworked zone with altered physical and mechanical properties located over an ore deposit has been assessed. The novelty and practical significance of the research lies in the determination of the boundaries of zones of displacement and superincumbent rock caving over the panels obtained using state-of-the-art technologies of seismic exploration. The deliverables may be used to improve the process of recognizing specific types of technogenic heterogeneities in the rock mass, impacting the efficiency and safety of subsurface ore mining, both for localization and mining monitoring.

This paper aims to develop a meshless radial point interpolation (RPI) method for obtaining the numerical solution of fractional Navier–Stokes equations. The proposed RPI method discretizes differential equations into highly nonlinear algebraic equations, which are subsequently solved using a fixed-point method. Furthermore, a comprehensive analysis regarding the effects of spatial and temporal discretization, polynomial order, and fractional order is conducted. These factors’ impacts on the accuracy and efficiency of the solutions are discussed in detail. It can be shown that the meshless RPI method works quite well for solving some benchmark problems accurately.

This paper introduces a novel numerical technique for solving fractional stochastic differential equations with neutral delays. The method employs a stepwise collocation scheme with Jacobi poly-fractonomials to consider unknown stochastic processes. For this purpose, the delay differential equations are transformed into augmented ones without delays. This transformation makes it possible to use a collocation scheme improved with Jacobi poly-fractonomials to solve the changed equations repeatedly. At each iteration, a system of nonlinear equations is generated. Next, the convergence properties of the proposed method are rigorously analyzed. Afterward, the practical utility of the proposed numerical technique is validated through a series of test examples. These examples illustrate the method’s capability to produce accurate and efficient solutions.

Venous thromboembolism (VTE) is a serious health condition and represents an important cause of morbidity and, in some cases, mortality due to the lack of effective treatment options. According to the Centers for Disease Control and Prevention, 3 out of 10 people with VTE will have recurrence of a clotting event within ten years, presenting a significant unmet medical need. For some VTE patients, symptoms can last longer and have a higher than average risk of serious complications; in contrast, others may experience complications arising from insufficient therapies. People with VTE are initially treated with anticoagulants to prevent conditions such as stroke and to reduce the recurrence of VTE. However, thrombolytic therapy is used for people with pulmonary embolism (PE) experiencing low blood pressure or in severe cases of DVT. New drugs are under development, with the aim to ensure they are safe and effective, and may provide an additional option for the treatment of VTE. In this review, we summarize all ongoing trials evaluating anticoagulant interventions in VTE listed in clinicaltrials.gov, clarifying their underlying mechanisms and evaluating whether they prevent the progression of DVT to PE and recurrence of thrombosis. Moreover, this review summarizes the available evidence that supports the use of antiplatelet therapy for VTE. Since thrombolytic agents would cause off-target effects, targeted drug delivery platforms are used to develop various therapeutics for thrombotic diseases. We discuss the recent advances achieved with thrombus-targeting nanocarriers as well as the major challenges associated with the use of nanoparticle-based therapeutics.

The ocean, with its intricate processes, plays a pivotal role in shaping marine life, habitats, and the Earth’s climate. This study addresses issues such as beach erosion, the survival of propagules from species like Posidonia oceanica, and nutrient distribution. To tackle these challenges, we propose an innovative sensor that quantifies hydrodynamic velocity by measuring the output voltage derived from detecting changes in light absorption and scattering using LEDs and LDRs. Our results not only demonstrate the effectiveness of the sensor but also the accuracy of the processing algorithm. Notably, the blue LED exhibited the lowest mean relative error of 7.59% in freshwater, while the yellow LED was most precise in chlorophyll-containing water, with a mean relative error of 6.80%. In a runoff simulation, we observed similar velocities with the blue, green, and white LEDs, 6.89 cm/s, 6.99 cm/s, and 7.05 cm/s, respectively, for nearly identical time intervals. It is important to highlight that our proposed sensor is not only effective but also highly cost-efficient, representing less than 0.43% of the cost of a Nortek Vector 6 MHz and 0.18% of the Teledyne Workhorse II 300 kHz Marine. This makes it a key tool for managing marine ecosystems sustainably.

Chronic wounds complicated by infection pose significant clinical challenges, necessitating comprehensive treatment approaches. The widespread use of antibiotics has led to resistant microorganisms, complicating traditional therapies. This study aims to develop and evaluate modified alginate wound dressings with enhanced antimicrobial and regenerative properties. Alginate dressings were synthesized with silver nanoparticles, cefepime, and fibroblast growth factor-2 (FGF-2). The two-stage therapy involved an initial antibacterial dressing followed by a regenerative dressing. In vitro tests demonstrated high antibacterial activity, with maximum inhibition zones for P. aeruginosa (41.3 ± 0.4 mm) and S. aureus (36.6 ± 1.8 mm). In vivo studies on rats with purulent wounds showed significant healing progression in the experimental group. Histological analysis revealed complete re-epithelialization, thicker neoepithelium, dense collagen deposition, and minimal inflammation in treated wounds. These findings suggest that the modified alginate dressings significantly enhance the reparative process and are promising for treating chronic infected wounds in both veterinary and medical practices.

This study evaluates the utilization of in vitro embryo production (IVEP) technology for the conservation and breeding of the Auliekol cattle breed, a primary beef breed in Kazakhstan facing population decline due to the cessation of breeding programs and the incursion of transboundary diseases. We assessed the effect of consecutive ovum pick-up (OPU) procedures on oocyte yield and embryo production in Auliekol and Aberdeen Angus cows. A total of 2232 and 3659 oocytes were aspirated from Auliekol and Aberdeen Angus donors, respectively, with significantly higher yields and embryo production observed in Aberdeen Angus cows. The application of a meiotic block using Butyrolactone I (BLI) and subsequent in vitro fertilization (IVF) protocols was employed, with embryo development monitored up to the morula/blastocyst stage. Results indicated that Auliekol cows exhibited lower oocyte recovery, cleavage, and blastocyst rates compared to Aberdeen Angus cows, likely due to genetic characteristics. Despite the challenges, IVEP presents a valuable tool for the preservation and future propagation of the Auliekol breed, highlighting the need for further research to enhance reproductive outcomes and conservation strategies.

This paper presents a study on developing a new exoskeleton for ankle joint rehabilitation with three degrees of freedom (3 DOFs). The primary attention is paid to the process of designing and modelling the device aimed at restoring the lost functions of joint mobility. The authors conducted a complex analysis of the functional requirements of the exoskeleton based on research into the potential user’s needs, which allowed for the development of a conceptual model of the proposed device. In this study, a prototype of the exoskeleton is designed using modern additive technologies. The prototype underwent virtual testing in conditions maximally close to reality, which confirmed its effectiveness and comfort of use. The main results of this study indicate the promising potential of the proposed solution for application in rehabilitation practices, especially for patients with ankle joint injuries and diseases.

In this paper, a new design strategy is developed for the Wireless Charging Electric Transit Bus (WCETB). The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. In particular, an improved version of the Whale Optimization Algorithm (IWOA) is adopted for the WCETB system in the road map of Wakefield City, located in the United Kingdom. The main challenge in the WCETB is to select the power transmitter and battery size efficiently from an economical point of view. For this purpose, both factors are considered in the objective function to achieve the benefits of WCETBs from an energy perspective. Two analytical economic design optimization models are developed in this work. The first model is the real- environment model, which considers a WCETB system operating under typical traffic conditions characterized by vehicle interactions and inherent uncertainties. In this scenario, vehicle speeds vary with time, and specific traffic routes may encounter congestion. The second model concentrates on a WCETB system operating in a traffic-free environment with minimal vehicle interactions and uncertainties. The IWOA is implemented for the WCETB to operate in the real environment. Under traffic-free environment conditions, we utilize mathematical procedures and General Algebraic Modeling System (GAMS) software to solve the optimization problem. This approach not only allows us to comprehensively analyze the WCETB system’s behavior but also examine the interactions among different components of the objective function and constraints. Finally, a comprehensive numerical analysis under various conditions, including changes in the number of buses and increases in the length of routes, is conducted.

Total kidney volume measured on MRI is an important biomarker for assessing the progression of autosomal dominant polycystic kidney disease and response to treatment. However, we have noticed that there can be substantial differences in the kidney volume measurements obtained from the various pulse sequences commonly included in an MRI exam. Here we examine kidney volume measurement variability among five commonly acquired MRI pulse sequences in abdominal MRI exams in 105 patients with ADPKD. Right and left kidney volumes were independently measured by three expert observers using model-assisted segmentation for axial T2, coronal T2, axial single-shot fast spin echo (SSFP), coronal SSFP, and axial 3D T1 images obtained on a single MRI from ADPKD patients. Outlier measurements were analyzed for data acquisition errors. Most of the outlier values (88%) were due to breathing during scanning causing slice misregistration with gaps or duplication of imaging slices (n = 35), slice misregistration from using multiple breath holds during acquisition (n = 25), composing of two overlapping acquisitions (n = 17), or kidneys not entirely within the field of view (n = 4). After excluding outlier measurements, the coefficient of variation among the five measurements decreased from 4.6% pre to 3.2%. Compared to the average of all sequences without errors, TKV measured on axial and coronal T2 weighted imaging were 1.2% and 1.8% greater, axial SSFP was 0.4% greater, coronal SSFP was 1.7% lower and axial T1 was 1.5% lower than the mean, indicating intrinsic measurement biases related to the different MRI contrast mechanisms. In conclusion, MRI data acquisition errors are common but can be identified using outlier analysis and excluded to improve organ volume measurement consistency. Bias toward larger volume measurements on T2 sequences and smaller volumes on axial T1 sequences can also be mitigated by averaging data from all error-free sequences acquired.

The earliest archaeological evidence of wine came from ceramic vessels of the Transcaucasian ‘Shulaveri-Shomutepe’ or ‘Aratashen-Shulaveri-Shomutepe culture’ (SSC/AShSh: c. 6000–5200 BC). Western European ‘Bell Beaker culture’ (BB: c. 2500–2000 BC) is characterized by bell-shaped pottery vessels but has so far not been found with residues consistent with wine. Knowing that wild grapes populated both habitats, the absence of wine during the Bell Beaker period remains to be explained. The main goal of this work was to investigate whether the shape of the vessels could influence the performance of spontaneous fermentation, specifically regarding the production of volatile acidity. Crushed grapes or juices from various grape cultivars were fermented in two types of vessels: (i) borosilicate glass beakers (4–5 L) to imitate bell beakers and (ii) Erlenmeyer flasks (5 L) to imitate SSC/AShSh vessels. Fermentations occurred spontaneously, and the wines were analyzed for their conventional physical–chemical parameters (e.g., ethanol content, total acidity, volatile acidity, pH), chromatic characteristics (e.g., wine color intensity, wine hue), and volatile composition by gas-chromatography-flame ionization detection (GC-FID). At the end of fermentation, the yeast species were identified by molecular methods. In addition, wine yields and phenolic composition (e.g., total phenols, anthocyanins, total pigments) were determined for wild grapes in comparison with six red varieties Vitis vinifera L. subsp. sativa (Vinhão, Marufo, Branjo, Melhorio, Castelão and Tempranillo Tinto), chosen as a function of their genetic relatedness with the wild counterpart. Wines produced from V. sylvestris grapes showed higher total acidity and color intensity when compared to the cultivated varieties. Saccharomyces cerevisiae dominated at the end of all spontaneous fermentations in all types of vessels and conditions. Wines fermented in Erlenmeyers showed ethanol concentrations as high as 14.30% (v/v), while the highest ethanol level was 12.30% (v/v) in beakers. Volatile acidity increased to a maximum of 4.33 g/L (acetic acid) in Erlenmeyers and 8.89 g/L in beakers. Therefore, the shape of the vessels influenced the performance of fermentation, probably due to the different exposures to air, leading to vinegary ferments more frequently in open mouths than in conical-shaped flasks. These results provide a hypothesis based on fermentation performance for the absence of wine produced in the Iberian Peninsula until the arrival of Phoenician settlers.

Integrating renewable energy sources (RESs), such as offshore wind turbines (OWTs), into the power grid demands advanced control strategies to enhance efficiency and stability. Consequently, a Deep Fractional-order Wind turbine eXpert control system (DeepFWX) model is developed, representing a hybrid proportional/integral (PI) fractional-order (FO) model predictive random forest alternating current (AC) bus voltage controller designed explicitly for OWTs. DeepFWX aims to address the challenges associated with offshore wind energy systems, focusing on achieving the smooth tracking and state estimation of the AC bus voltage. Extensive comparative analyses were performed against other state-of-the-art intelligent models to assess the effectiveness of DeepFWX. Key performance indicators (KPIs) such as MAE, MAPE, RMSE, RMSPE, and R2 were considered. Superior performance across all the evaluated metrics was demonstrated by DeepFWX, as it achieved MAE of [15.03, 0.58], MAPE of [0.09, 0.14], RMSE of [70.39, 5.64], RMSPE of [0.34, 0.85], as well as the R2 of [0.99, 0.99] for the systems states [X1, X2]. The proposed hybrid approach anticipates the capabilities of FO modeling, predictive control, and random forest intelligent algorithms to achieve the precise control of AC bus voltage, thereby enhancing the overall stability and performance of OWTs in the evolving sector of renewable energy integration.

The effective operation of model-based control strategies in modern energy systems, characterized by significant complexity, is contingent upon highly accurate large-scale models. However, achieving such precision becomes challenging in complex energy systems rife with uncertainties and disturbances. Controlling different parts of the energy system poses a challenge to achieving optimal power system efficiency, particularly when employing model-based control strategies, thereby adding complexity to current systems. This paper proposes a novel model-independent control approach aimed at augmenting transient stability and voltage regulation performance in multi machine energy systems. The approach involves the introduction of an optimized model-free fractional-order-based excitation system stabilizer for synchronous generators in a multi machine energy system. To overcome the limitations associated with complex system model identification, which add degrees of simplification at defined operating conditions and assume the system model remains fixed despite high uncertainty and numerous disturbances, an optimal model-independent fractional-order-based excitation control strategy is introduced. The efficacy of the proposed approach is validated through comparative numerical analyses using the MATLAB/Simulink environment. These simulations were conducted on a two-area, 12-bus multi-machine power system. Simulation results demonstrate that the presented excitation system stabilizer outperforms conventional controllers in terms of transient and small-signal stability. It also suppresses the low-frequency electromechanical oscillations within the multimachine energy system.

Biological nitrogen fixation in legume plants depends on the diversity of rhizobia present in the soil. Rhizobial strains exhibit specificity towards host plants and vary in their capacity to fix nitrogen. The increasing interest in rhizobia diversity has prompted studies of their phylogenetic relations. Molecular identification of Rhizobium is quite complex, requiring multiple gene markers to be analysed to distinguish strains at the species level or to predict their host plant. In this research, 50 rhizobia isolates were obtained from the root nodules of five different Pisum sativum L. genotypes (“Bagoo”, “Respect”, “Astronaute”, “Lina DS”, and “Egle DS”). All genotypes were growing in the same field, where ecological farming practices were applied, and no commercial rhizobia inoculants were used. The influence of rhizobial isolates on pea root nodulation and dry biomass accumulation was determined. 16S rRNA gene, two housekeeping genes recA and atpD, and symbiotic gene nodC were analysed to characterize rhizobia population. The phylogenetic analysis of 16S rRNA gene sequences showed that 46 isolates were linked to Rhizobium leguminosarum; species complex 1 isolate was identified as Rhizobium nepotum, and the remaining 3 isolates belonged to Rahnella spp., Paenarthrobacter spp., and Peribacillus spp. genera. RecA and atpD gene analysis showed that the 46 isolates identified as R. leguminosarum clustered into three genospecies groups (B), (E) and (K). Isolates that had the highest influence on plant dry biomass accumulation clustered into the (B) group. NodC gene phylogenetic analysis clustered 46 R. leguminosarum isolates into 10 groups, and all isolates were assigned to the R. leguminosarum sv. viciae.

The complement cascade is a vital system in the human body’s defense against pathogens. During the natural aging process, it has been observed that this system is imperative for ensuring the integrity and homeostasis of the retina. While this system is critical for proper host defense and retinal integrity, it has also been found that dysregulation of this system may lead to certain retinal pathologies, including geographic atrophy and diabetic retinopathy. Targeting components of the complement system for retinal diseases has been an area of interest, and in vivo, ex vivo, and clinical trials have been conducted in this area. Following clinical trials, medications targeting the complement system for retinal disease have also become available. In this manuscript, we discuss the pathophysiology of complement dysfunction in the retina and specific pathologies. We then describe the results of cellular, animal, and clinical studies targeting the complement system for retinal diseases. We then provide an overview of complement inhibitors that have been approved by the Food and Drug Administration (FDA) for geographic atrophy. The complement system in retinal diseases continues to serve as an emerging therapeutic target, and further research in this field will provide additional insights into the mechanisms and considerations for treatment of retinal pathologies.

This research focuses on developing an artificial vision system for a flexible delta robot manipulator and integrating it with machine-to-machine (M2M) communication to optimize real-time device interaction. This integration aims to increase the speed of the robotic system and improve its overall performance. The proposed combination of an artificial vision system with M2M communication can detect and recognize targets with high accuracy in real time within the limited space considered for positioning, further localization, and carrying out manufacturing processes such as assembly or sorting of parts. In this study, RGB images are used as input data for the MASK-R-CNN algorithm, and the results are processed according to the features of the delta robot arm prototype. The data obtained from MASK-R-CNN are adapted for use in the delta robot control system, considering its unique characteristics and positioning requirements. M2M technology enables the robot arm to react quickly to changes, such as moving objects or changes in their position, which is crucial for sorting and packing tasks. The system was tested under near real-world conditions to evaluate its performance and reliability.

In a recent study devoted to the influence of electromagnetic radiation reaction on the motion of radiating charged particles in magnetized black hole spacetimes the authors claim that the tail term cannot be neglected in the complete DeWitt–Brehme equation, putting into doubt the previous papers where such an approximation was used. Here, we demonstrate by using simple dimensional arguments that such a statement is misleading in many astrophysically relevant situations. In the case of relativistic electrons moving around a stellar-mass black hole, the tail term is ignorable if a magnetic field of at least a few Gauss is present.On the other hand, in different situations, the tail term can be relevant, as demonstrated in the case of orbital widening, where it can even amplify the effect.

Intelligent control methodologies and artificial intelligence (AI) are essential components for the efficient management of energy storage modern systems, specifically those utilizing superconducting magnetic energy storage (SMES). Through the implementation of AI algorithms, SMES units are able to optimize their operations in real time, thereby maximizing energy efficiency. To have a more advanced understanding of this issue, DynamoMan is presented in this paper. For use with SMES systems, DynamoMan, an Artificial Neural Network (ANN)-tuned Fractional Order PID Brain Emotional Learning-Based Intelligent Controller (ANN-FOPID-BELBIC), has been developed. ANN tuning is employed to optimize the key settings of the reward/penalty generator of a BELBIC, which are important for its overall efficacy. Following this, DynamoMan is integrated into the SMES control system and compared to scenarios in which a BELBIC, PID, PI, and P are utilized. The findings indicate that DynamoMan performs considerably better than other models, demonstrating robust and control attributes alongside a considerably reduced period of settling time, especially when incorporated with the power grid.

In this work, the surfaces of poly (ethylene terephthalate) track-etched membranes (PET TeMs) with pore sizes of 670–1310 nm were hydrophobized with 2,2,3,3,4,4,5,5,6,6,7,7-dodecafluoroheptyl acrylate (DFHA) by photoinitiated graft polymerization. Attenuated total reflection FTIR spectroscopy (ATR-FTIR), scanning electron microscopy (SEM) coupled to an energy-dispersive X-ray spectrometer (EDX), and contact angle measurements were used to identify and characterize the TeMs. The optimal parameters for graft polymerization were determined as follows: polymerization time of 60 min, monomer concentration of 30%, and distance from the UV source of 7 cm. The water contact angle of the modified membranes reached 97°, which is 51° for pristine membranes. The modified membranes were tested for water desalination using direct contact membrane distillation (DCMD) method. The effects of membrane pore size, the degree of grafting, and salt concentration on the performance of membrane distillation process were investigated. According to the results obtained, it has been concluded that large pore size hydrophobic TeMs modified by using DFHA could be used for desalinating water.

Sustained cognitive deficits are a common and debilitating feature of “long COVID”, but currently there are no FDA-approved treatments. The cognitive functions of the dorsolateral prefrontal cortex (dlPFC) are the most consistently afflicted by long COVID, including deficits in working memory, motivation, and executive functioning. COVID-19 infection greatly increases kynurenic acid (KYNA) and glutamate carboxypeptidase II (GCPII) in brain, both of which can be particularly deleterious to PFC function. KYNA blocks both NMDA and nicotinic-alpha-7 receptors, the two receptors required for dlPFC neurotransmission, and GCPII reduces mGluR3 regulation of cAMP-calcium-potassium channel signaling, which weakens dlPFC network connectivity and reduces dlPFC neuronal firing. Two agents approved for other indications may be helpful in restoring dlPFC physiology: the antioxidant N-acetyl cysteine inhibits the production of KYNA, and the α2A-adrenoceptor agonist guanfacine regulates cAMP-calcium-potassium channel signaling in dlPFC and is also anti-inflammatory. Thus, these agents may be helpful in treating the cognitive symptoms of long COVID.

Magnesium (Mg) and its alloys are promising materials for temporary bone implants due to their mechanical properties and biocompatibility. The most challenging aspect of Mg-based implants involves adapting the degradation rate to the human body, which requires extensive in vitro and in vivo testing. Given that in vivo tests are significantly more labour-intensive than in vitro and ethics prohibit direct experiments on animals or humans, attempts are commonly undertaken to infer conclusions on in vivo degradation behavior from in vitro experiments. However, there is a wide gap between these tests, and in vitro testing is often a poor predictor of in vivo outcomes. In the development of biodegradable Mg-based implants, considerable efforts are being made to reduce the overall time and cost of in vitro and in vivo testing. Finding a suitable alternative to predict the degradation of Mg alloys, however, remains challenging. We present computational modelling as a possible alternative to bridge the gap between in vitro and in vivo testing, thus reducing overall cost, duration and number of experiments. However, traditional modelling approaches for complex biodegradable systems are still rather time-consuming and require a clear definition of the relations between input parameters and the model result. In this study, Kriging surrogate models based on the peridynamic in vitro degradation model were developed to simulate the degradation behavior for two main alloys, Mg-5Gd and Mg-10Gd, for both in vitro and in vivo cases. Using Kriging surrogate models, the simulation parameters were calibrated to the volume loss data from in vitro and in vivo experiments. In vivo degradation of magnesium has one order of magnitude higher apparent diffusion coefficients than in vitro degradation, thus yielding the higher volume loss observed in vivo than in vitro. On the basis of the diffusivity of the Mg2+ ions modeled under in vitro degradation, Kriging surrogate models were able to simulate the in vivo degradation behavior of Mg-xGd with a ratio between 0.46 and 0.5, indicating that the surrogate-modelling approach is able to bridge the gap between in vitro and in vivo degradation rates for Mg-xGd implants.

Abdominal imaging of autosomal dominant polycystic kidney disease (ADPKD) has historically focused on detecting complications such as cyst rupture, cyst infection, obstructing renal calculi, and pyelonephritis; discriminating complex cysts from renal cell carcinoma; and identifying sources of abdominal pain. Many imaging features of ADPKD are incompletely evaluated or not deemed to be clinically significant, and because of this, treatment options are limited. However, total kidney volume (TKV) measurement has become important for assessing the risk of disease progression (i.e., Mayo Imaging Classification) and predicting tolvaptan treatment’s efficacy. Deep learning for segmenting the kidneys has improved these measurements’ speed, accuracy, and reproducibility. Deep learning models can also segment other organs and tissues, extracting additional biomarkers to characterize the extent to which extrarenal manifestations complicate ADPKD. In this concept paper, we demonstrate how deep learning may be applied to measure the TKV and how it can be extended to measure additional features of this disease.

Kazakhstan’s government has launched many policy reforms to enhance the well-being of its underrepresented citizens and develop human capital by providing education to all. Therefore, the government has adopted a policy on inclusive education and recognised the rights to education of all learners, including those with disabilities, based on equal opportunity, without any discrimination. This aims to facilitate equal access to lifelong education opportunities, including in Technical Vocational Education Training Institutes (TVETIs). Thus, inclusive education in TVET settings and lifelong learning, especially for adults with special needs, has become an educational priority in Kazakhstan. Therefore, this collaborative action research study intended to explore classroom practices promoting the inclusion of adult students with disabilities at Kazakhstani Technical Vocational Education and Training Institutions (TVETIs). Qualitative data were collected through reflective journals, student focus-group interviews, documentary analysis, and reflective meetings of the professional learning group of selected teachers and students during the four stages of collaborative action research (CAR). Data were analysed through group interpretative meetings by the research team and inductive thematic content analysis by the researcher. Among the findings is that the educative process in TVETIs does not sufficiently provide reasonable inclusive support for adult students with disabilities. As a result, a comprehensive review of inclusive practices in these institutions was critical. This paper discusses some practices that could promote inclusion in TVET classrooms.

Lid-driven cavity (LDC) flow is a significant area of study in fluid mechanics due to its common occurrence in engineering challenges. However, using numerical simulations (ANSYS Fluent) to accurately predict fluid flow and mixed convective heat transfer features, incorporating both a moving top wall and a heated hemispherical obstruction at the bottom, has not yet been attempted. This study aims to numerically demonstrate forced convection in a lid-driven square cavity (LDSC) with a moving top wall and a heated hemispherical obstacle at the bottom. The cavity is filled with a Newtonian fluid and subjected to a specific set of velocities (5, 10, 15, and 20 m/s) at the moving wall. The finite volume method is used to solve the governing equations using the Boussinesq approximation and the parallel flow assumption. The impact of various cavity geometries, as well as the influence of the moving top wall on fluid flow and heat transfer within the cavity, are evaluated. The results of this study indicate that the movement of the wall significantly disrupts the flow field inside the cavity, promoting excellent mixing between the flow field below the moving wall and within the cavity. The static pressure exhibits fluctuations, with the highest value observed at the top of the cavity of 1 m width (adjacent to the moving wall) and the lowest at 0.6 m. Furthermore, dynamic pressure experiences a linear increase until reaching its peak at 0.7 m, followed by a steady decrease toward the moving wall. The velocity of the internal surface fluctuates unpredictably along its length while other parameters remain relatively stable.

This paper presents an approach to community detection in complex networks by simultaneously incorporating a connectivity-based metric and Max-Min Modularity. By leveraging the connectivity-based metric and employing a heuristic algorithm, we develop a novel complementary graph for the Max-Min Modularity that enhances its effectiveness. We formulate community detection as an integer programming problem of an equivalent yet more compact counterpart model of the revised Max-Min Modularity maximization problem. Using a row generation technique alongside the heuristic approach, we then provide a hybrid procedure for near-optimally solving the model and discovering high-quality communities. Through a series of experiments, we demonstrate the success of our algorithm, showcasing its efficiency in detecting communities, particularly in extensive networks.

The use of phase change materials (PCMs) has become an increasingly common way to reduce a building’s energy usage when added to the building envelope. This developing technology has demonstrated improvements in thermal comfort and energy efficiency, making it a viable building energy solution. The current study intends to provide a comprehensive review of the published studies on the utilization of PCMs in various constructions of energy-efficient roofs, walls, and ceilings. The research question holds massive potential to unlock pioneering solutions for maximizing the usefulness of PCMs in reducing cooling demands, especially in challenging high-temperature environments. Several issues with PCMs have been revealed, the most significant of which is their reduced effectiveness during the day due to high summer temperatures, preventing them from crystallizing at night. However, this review investigates how PCMs can delay the peak temperature time, reducing the number of hours during which the indoor temperature exceeds the thermal comfort range. Additionally, the utilization of PCMs can improve the building’s energy efficiency by mitigating the need for cooling systems during peak hours. Thus, selecting the right PCM for high temperatures is both critical and challenging. Insulation density, specific heat, and thermal conductivity all play a role in heat transfer under extreme conditions. This study introduces several quantification techniques and paves the way for future advancements to accommodate practical and technical solutions related to PCM usage in building materials.

The increasing scarcity of virgin natural resources and the need for sustainable waste management in densely populated urban areas have heightened the importance of developing new recycling technologies. One promising approach involves recycling agricultural waste in construction applications and transforming it into secondary products. This is anticipated to reduce the demand for new resources and lower the environmental impact, aligning with industrial ecology principles. Combined with a low carbon emission binder (i.e., alkali-activated), utilizing agro-waste to produce zero-cement particle boards is a promising method for green construction. Traditionally, particle boards are engineered from wood or agricultural waste products that are pressed and bonded with a binder, such as cement or synthetic resins. However, alternative binders replace cement in zero-cement particle boards to address environmental concerns, such as the carbon dioxide emissions associated with cement production. This study investigated the effects of accelerated aging on the performance of alkali-activated agro-waste particle boards. Accelerated aging conditions simulate natural aging phenomena. Repeated wetting–drying and freezing–thawing cycles increased water absorption and thickness swelling and reduced flexural strength. The thermal performance of the alkali-activated particle boards did not exhibit significant changes. Hence, it was confirmed that agro-waste has a high potential for utilization in producing particle boards provided that the working environment is carefully selected to optimize performance.

The present work offers a thorough analysis of the impact of water velocity on phase change material (PCM) melting in a vertical cylindrical container. A detailed quantitative analysis uses sophisticated numerical techniques, namely the ANSYS/FLUENT 16 program, to clarify the complex relationship between enthalpy and porosity during the melting process. The experimental focus is on phase transition materials based on paraffin wax, particularly Rubitherm RT42. This study’s primary goal is to evaluate the effects of different water velocities (that is, at velocities of 0.01 m/s, 0.1 m/s, and 1 m/s) on the PCM’s melting behavior at a constant temperature of 333 K. This work intends to make a substantial contribution to the development of thermal energy storage systems by investigating new perspectives on PCM behavior under various flow circumstances. The study’s key findings highlight the possible ramifications for improving PCM-based thermal energy storage devices by revealing significant differences in melting rates and behavior that correlate to changes in water velocities. Future research is recommended to explore the impact of temperature variations, container geometries, and experimental validation to improve the accuracy and practicality of the results and to advance the creation of sustainable and effective energy storage solutions.

This study employs COMSOL software v 5.6 to investigate a novel approach to heat transfer via mixed convection in an open hollow structure with an unheated 90° baffle elbow. Two 20 W heat sources are strategically positioned on the cavity’s bottom and right-angled wall for this research. Notably, the orientation of the baffle perpendicular to the airflow is used to direct external, unrestricted flow into the square cavity. The research investigates a range of air velocities (0.1, 0.5, 1.0, and 1.5 m/s) and the intricate interaction between input air velocity, dual heated sources, and the presence of a right-angle baffle on critical thermodynamic variables, such as temperature distribution, isotherms, pressure variation, velocity profile, air density, and both local and mean Nusselt numbers. Validation of the applicable computational method is achieved by comparing it to two previous studies. Significant findings from numerical simulations indicate that the highest velocity profile is in the centre of the channel (2.3–2.68 m/s at an inflow velocity of 1.5 m/s), while the lowest profile is observed along the channel wall, with a notable disruption near the inlet caused by increased shear forces. The cavity neck temperature ranges from 380 to 640 K, with inflow air velocities varying from 0.1 to 1.5 m/s (Re is 812 to 12,182), respectively. In addition, the pressure fluctuates at the channel-cavity junction, decreasing steadily along the channel length and reaching a maximum at the intake, where the cavity neck pressure varies from 0.01 to 2.5 Pa with inflow air velocities changing from 0.1 to 1.5 m/s, respectively. The mean Nusselt number exhibits an upward trend as air velocity upon entry increases. The mean Nusselt number reaches up to 1500 when the entry air velocity reaches 1.5 m/s. Due to recirculation patterns, the presence of the 90° unheated baffle produces a remarkable cooling effect. The study establishes a direct correlation between input air velocity and internal temperature distribution, indicating that as air velocity increases, heat dissipation improves. This research advances our understanding of convective heat transfer phenomena in complex geometries and provides insights for optimising thermal management strategies for a variety of engineering applications.

Stroke is the leading cause of adult disability worldwide. The majority of stroke survivors are left with devastating functional impairments for which few treatment options exist. Recently, a number of studies have used ectopic expression of transcription factors that direct neuronal cell fate with the intention of converting astrocytes to neurons in various models of brain injury and disease. While there have been reports that question whether astrocyte-to-neuron conversion occurs in vivo, here, we have asked if ectopic expression of the transcription factor Neurod1 is sufficient to promote improved functional outcomes when delivered in the subacute phase following endothelin-1-induced sensory–motor cortex stroke. We used an adeno-associated virus to deliver Neurod1 from the short GFAP promoter and demonstrated improved functional outcomes as early as 28 days post-stroke and persisting to at least 63 days post-stroke. Using Cre-based cell fate tracking, we showed that functional recovery correlated with the expression of neuronal markers in transduced cells by 28 days post-stroke. By 63 days post-stroke, the reporter-expressing cells comprised ~20% of all the neurons in the perilesional cortex and expressed markers of cortical neuron subtypes. Overall, our findings indicate that ectopic expression of Neurod1 in the stroke-injured brain is sufficient to enhance neural repair.

The global escalation in cereal production, essential to meet growing population demands, simultaneously augments the generation of cereal crop residues, estimated annually at approximately 3107 × 106 Mg/year. Among different crop residue management approaches, returning them to the soil can be essential for various ecological benefits, including nutrient recycling and soil carbon sequestration. However, the recalcitrant characteristics of cereal crop residues pose significant challenges in their management, particularly in the decomposition rate. Therefore, in this review, we aim to summarize the influence of different agricultural practices on enhancing soil microbial decomposer communities, thereby effectively managing cereal crop residues. Moreover, this manuscript provides indirect estimates of cereal crop residue production in Northern Europe and Lithuania, and highlights the diverse roles of lignocellulolytic microorganisms in the decomposition process, with a particular focus on enzymatic activities. This review bridges the knowledge gap and indicates future research directions concerning the influence of agricultural practices on cereal crop residue-associated microbial consortia.

The fatigue phenomenon has a significant effect on the joints of steel truss bridges, causing the formation of plastic hinges leading to potential collapse configurations. For this reason, in the last few years, the development of new methods to effectively strengthen such joints has gained attention. In this article, a mechanism for the improvement of the resistance to fatigue of these types of connections is proposed, by employing composite joints and post-tensioned metal straps (PTMSs). Following this approach, first the overstressed connections of the structure have been identified during the inspection phase, and subsequently, they have been encased in a concrete casting with a proper level of strength. To analyze the behavior of the steel–concrete mixed solution, a finite element model has been realized in ABAQUS (rel. 2022.0) and it has been validated based on comparison between the numerical results and the experimental ones obtained by laboratory tests. Then, a parametric analysis was performed to investigate the effect of the concrete type employed in the connection behavior. In the second stage of the research, based on the first cracks configuration obtained numerically and observed experimentally, PTMSs have been used to control the cracks and strengthen the joint. The strains and stresses on the concrete and steel parts of the connection were measured before and after the insertion of the PTMSs and compared with the outputs of the model. Additionally, a parametric analysis was carried out to assess the optimal number of straps. The effect of this solution on the resistance of the joint and on the behavior of concrete was analyzed and the effectiveness of the strengthening technique was assessed.

This study examines air quality conditions in and around a classroom located in the Sarıçam/Adana region of Türkiye, near the campus of Adana Alparslan Türkeş Science and Technology University and the Sofulu municipal solid waste (MSW) facility. This academic setting was strategically chosen due to its proximity to the waste facility. The study aims to provide a comprehensive view of the environmental and social impacts of solid waste management through a methodological approach that combines quantitative on-site measurements and qualitative survey studies. Findings from measurements and surveys underline the significant and measurable impacts of MSW facilities on the ambient air quality of university residents. The analysis revealed a marked increase in concentrations of key pollutants, including carbon monoxide (CO), hydrogen sulfide (H2S), dust, and methane (CH4). At sampling point N1, H2S levels rose from 0 ppm in July to 13 ppm in November. Methane increased from 0.2% to 2.5% of the Lower Explosive Limit (LEL) at the same point, although it remained within safety limits. Additionally, CO levels showed a 40% increase, and dust concentration levels rose from 0.21 mg/m3 to 2.36 mg/m3 from summer to winter, indicating a seasonal variation likely influenced by the landfill’s operational dynamics, as well as changes in temperature and relative humidity. In particular, the results indicate high concentrations of CO, H2S and dust, which are directly related to air quality degradation. The study also sheds light on the impacts of these waste disposal facilities on the general well-being and health of the university community, particularly on students and staff. In addition to these findings, the study highlights a general lack of awareness in the university community about the impacts of MSW facilities on air quality. This highlights the need for increased education and information dissemination. The results support the development of comprehensive and effective strategies, including technical solutions and public awareness initiatives, to mitigate the impacts of these facilities on residential areas. In conclusion, the impacts of MSW facilities on air quality should be seen as a multidimensional issue that requires a holistic approach addressing environmental, health, social, and educational dimensions.

Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.