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Recent studies project that temperature-related mortality will be the largest source of damage from climate change, with particular concern for the elderly whom it is believed bear the largest heat-related mortality risk. We study heat and mortality in Mexico, a country that exhibits a unique combination of universal mortality microdata and among the most extreme levels of humid heat. Combining detailed measurements of wet-bulb temperature with age-specific mortality data, we find that younger people who are particularly vulnerable to heat: People under 35 years old account for 75% of recent heat-related deaths and 87% of heat-related lost life years, while those 50 and older account for 96% of cold-related deaths and 80% of cold-related lost life years. We develop high-resolution projections of humid heat and associated mortality and find that under the end-of-century SSP 3-7.0 emissions scenario, temperature-related deaths shift from older to younger people. Deaths among under-35-year-olds increase 32% while decreasing by 33% among other age groups.

The article presents a clinical case of successful treatment of a patient with a BCLC stage B hepatocellular carcinoma (HCC), with the initial prediction of unfavorable radical treatment results and poor outcomes. The described approach allows the achievement of better results in patients with delayed treatment of HCC diagnosed at the advanced stage of the disease. A 69-year-old patient with pain and severe weakness was admitted to the hospital, where, according to the results of instrumental examination methods, a hepatic tumor was detected, qualified as HCC T3N0M0, stage II. Given the size and location of the tumor, combined treatment was performed, including two sessions of transarterial chemoembolization (TACE) with doxorubicin, targeted therapy with lenvatinib, microwave ablation, and surgical resection of the tumor. As a result, a progressive decrease in the tumor size was achieved with its subsequent radical removal and improved quality of life. The presented clinical case has shown that specialized care of HCC patients with advanced stages of the disease may be successful, and the accumulation of relevant experience may improve the quality and effectiveness of treatment for this group of patients.

The results of modeling and research of casting, rolling, and extrusion processes for producing wire from Al–Zr system alloys and determining its physical and mechanical properties have been presented. When implementing combined processes for processing aluminum alloys, the number of operations is significantly reduced, productivity and yield are increased. As a result of the conducted research, the influence of alloy preparation modes and their chemical composition on the physical, mechanical and electrical properties of longish semifinished products from Al–Zr system alloys obtained by combined rolling-extrusion (CRE) and ingotless rolling-extrusion (IRE) methods were investigated. The simulation of the CRE process for one of these alloys of the system was carried out in the DEFORM 3D software package using data on its rheological properties obtained by the hot torsion method. Based on the modeling results, the optimal modes for conducting experimental studies were selected. The features of metal forming were experimentally studied, the temperature-velocity and technological parameters of combined processing were found, as well as the properties of cast billets, including those obtained using an electromagnetic mold (EMM). The results of experimental studies of the CRE and IRE processes suggest that it is possible to obtain longish deformed semifinished products from alloys Al–Zr system with a level of physical, mechanical and electrical properties that meet international standards. At all technological stages, including drawing, the structure of the metal was studied, and data on the physical and mechanical properties of hot-extruded rods and wire in cold-deformed and annealed states was obtained. The heat resistance of wire made from the investigated alloys was studied and it was found that after testing at a temperature of 280 °C and a holding time of 1 h, it satisfies the requirements of the AT3 type standard with a maximum permissible long-term operating temperature of 210 °C. Recommendations are given for industrial implementation; alloys containing 0.15–0.20% zirconium and 0.10–0.15% iron are recommended for the manufacture of AT1 type wire without heat treatment, as well as 0.25–0.30% Zr and 0.2–0.25% Fe for AT3 wire type with heat treatment.

Primary biliary cholangitis (PBC) is a chronic cholestatic progressive liver disease associated with cholangiopathies. The detection of antimitochondrial autoantibodies (AMAs) plays an important role in the diagnosis of classical PBC. AMAs are formed against the antigenic component associated with the dihydrolipoyl transacetylase of pyruvate dehydrogenase complex (E2 PDC) localized on the inner membrane of mitochondria. The loss of immune tolerance of E2 PDC in PBC is thought to be the cause of the mechanism of AMA formation and immune-mediated destruction of biliary epithelial cells (BECs) of the small- and medium-sized intrahepatic bile ducts. E2 PDC is not only present in BECs, but is also abundant in the mitochondria of all nucleated cells. The question remains as to why E2 PDC of only small BECs is the target of autoimmune attack. There is no evidence that AMAs have a deleterious effect on BECs. New scientific data has emerged that explains the damage to BECs in PBC by the defect of the biliary bicarbonate (HCO3–) “umbrella” that protects BECs from the detergent action of bile acids under physiological conditions. Disruption of HCO3– production by BECs in PBC leads to changes in the pH of hepatic bile, accompanied by accumulation of bile acids in the small BECs. The detergent action of bile acids leads to damage of membrane structures of BECs and their apoptosis, development of ductulopenia, and intrahepatic cholestasis. For the first time, it has been suggested that under the influence of bile acids, the E2 PDC antigen may undergo conformational changes that alter its immunological properties. E2 PDC becomes a neoantigen that is recognized by the normal (“healthy”) immune system as a foreign antigen, leading to the production of AMAs. For the first time, the authors of this review provide an explanation for why only small BECs are damaged in PBC.

In this study, we successfully obtained crystalline silicon from silica powder using a metal-induced crystallization method. For this purpose, powders were first prepared from organosilicon compounds and finely dispersed aluminum particles, then their metal-induced crystallization was carried out by annealing at the temperature of 570 °C. Powders of organosilicon compounds (tetraethoxysilane and polyethylhydrosiloxane) were prepared by different technological operations in order to determine precisely the presence of carbon in the product. The results showed that the presence of carbon in silica powder affects the production of crystalline silicon. In silica powders containing no carbon, the formation of crystalline substances does not occur at the annealing temperature of 570 °C. The results of this study are of great importance for the production of polycrystalline silicon powders obtained at reduced temperatures.

The study develops a method for monitoring and management of the quality of educational programs using software instruments. The method describes each of the monitoring implementation stages in detail specifying the input and output data, as well as the controls that affect the time and quality of the implementation of the corresponding stage. Algorithm programs in the form of flowcharts reflecting all the content aspects are developed and formalized for the automatization of monitoring. The presented algorithm models allow implementing the program complex of the system for monitoring and management of the quality of educational programs using the modern object-oriented design and programming environments. The goal of the study is to develop a model of algorithms for the organization of monitoring and managing the quality of educational programs, which allow reducing the time costs of these processes while increasing the objectivity of quality assessment. The obtained results are intended for the audit of educational services, public control over the implementation of educational activities, accreditation of directions of training, or licensing of educational organizations. The developed algorithms provide for evaluating the content of each stage of the audit of an educational program, generate a list of notes ranked by importance, and ensure the rational allocation of resources for the implementation of each stage of the educational process.

Veterinary medicine for small domestic animals (SDA) in Russia began its development in the 1990s and is currently undergoing professional formation, engaging in a dialogue with traditional agricultural veterinary medicine. Over the last decade, the evidence-based approach has been actively developing as a foundation for making informed decisions in various aspects of professional practice. SDA veterinary medicine, as a relatively young and emerging professional field within the conservative profession of veterinary medicine, is facing a legitimacy crisis. This crisis is related to the lack of specialized education in the pathology of dogs and cats, as well as the absence of treatment protocols for SDA approved by the professional community and the state. The evidence-based approach is the foundation of modern medicine and serves as a method of legitimizing SDA veterinary practice. It addresses the needs of the primary stakeholders: colleagues, government regulators in case of disputes, and animal owners as service consumers.

The article analyzes the problems associated with recognizing a cyberattack on a NATO member state as a reason to declare war (casus belli). The article reveals the evolution of the Alliance's approaches to the issues of cyber threats and cyber defense from the Prague Summit in 2002 to the Brussels Summit in 2021. The article discusses the criteria proposed in the Tallinn Manual on the International Law Applicable to Cyber Warfare that make it possible to classify a cyberattack as an act of use of force. Statistics on cyberattacks are presented and their relevance to international law and cybersecurity is noted. Purpose: to analyze the conditions and circumstances under which a cyberattack may be considered by NATO countries as a reason to declare war. Methods: formallegal, historical-systemic, retrospective. Results: the study has led to the following conclusions. First, despite existing international legal norms, the criteria for recognizing a cyberattack as a casus belli remain unclear, which allows NATO countries to retain flexibility in responding to cyber threats. Second, the application of Article 5 of the North Atlantic Treaty by NATO countries does not oblige them to unconditionally and without alternative to launch armed actions against the source state, but only requires other NATO members to provide any possible and appropriate assistance to the affected party.

The anterior cruciate ligament (ACL) is crucial for knee stability and is often injured in sports, leading to significant issues like degenerative changes and meniscal tears. ACL tears are prevalent in high-school sports injuries, accounting for 50% of knee injuries in the U.S. Surgical reconstruction, often involving bone-patellar tendon-bone (BPTB) or hamstring autografts, is common, with varying success rates and complications. Emerging alternatives like the peroneus longus tendon show promise but require further comparative studies. This prospective and multicentric study included 110 patients who underwent ACL reconstruction from 2020 to 2022. Fifty-five patients received hamstring tendon autografts (Group H) and fifty-five received peroneus longus tendon autografts (Group P). Surgeries were performed by experienced surgeons using standardized techniques. Patients were evaluated using clinical tests and functional scores including the Lysholm Knee Questionnaire and IKDC-2000 at various postoperative intervals up to 24 months. Data were analyzed using SPSS with a significance level set at p < 0.05. Group H showed superior knee function preoperatively and at 24 months postoperatively compared to Group P. Group H had higher Lysholm and IKDC scores consistently throughout the study period. The anterior drawer and Lachman’s tests indicated better knee stability for Group H. Complications were comparable between groups, with specific issues related to donor site morbidity and muscle weakness observed in each. The six-strand hamstring tendon autograft (Group H) demonstrated superior functional outcomes and knee stability compared to the peroneus longus tendon autograft (Group P) for ACL reconstruction. Despite some donor site morbidity, the hamstring tendon showed better long-term recovery and fewer complications. Future studies should explore larger, multicentric cohorts and integrate regenerative medicine techniques to further enhance ACL reconstruction outcomes.

Introduction. The work is aimed at revealing the dialectical relationship between the sense of conscience and historical memory, the conditionality of both by the dominant ideals of culture. Theoretical analysis. Using a psychoanalytic approach and the method of critical philosophical reflection, the author focuses on the conditionality of the cognition of history, and at the same time historical memory, by the restrictive framework of conscience, set by the specific orientation of an individual to the ideals of culture. Empirical analysis. The consequences of the predominant emphasis in the media and educational environment on the negative aspects of the Soviet stage of history, which was one of the main reasons for the moral and spiritual disorientation of the inhabitants of the post-Soviet space, are analyzed. This disorientation, right up to moral degradation to Nazi ideals, with the corresponding framework of shame and conscience, manifested itself most clearly in modern Ukraine. Conclusion. The selectivity of this knowledge of history, and, consequently, this memory of the past, depends on the characteristics of the manifestations of a sense of conscience, coupled with the requirements of moral ideals that guide a person in his actions and thoughts. For the successful development of Russia, it is necessary to enrich the historical memory with positive, significant events of the past, building a line of continuity of ideals that orient towards social justice, past and present.

A possible way to increase the operating speed and field capacity of a cultivator-fertiliser is to modify its configuration scheme. According to our hypothesis, placing the fertiliser hopper directly on the tractor frame could reduce the traction resistance of the cultivator-fertiliser and consequently increase its operating speed and field capacity. Validating this hypothesis requires a comparative evaluation of the existing configuration schemes for cultivator-fertilisers. However, there are currently no such comprehensive studies available. This research aims to develop a mathematical model for conducting a comparative theoretical evaluation of various configuration schemes for cultivator-fertilisers. The development of this model incorporated principles from classical and agricultural mechanics. We developed a mathematical model and conducted a theoretical evaluation of existing configuration schemes for cultivator-fertilisers, such as mounting the hopper on a separate trailed chassis, mounting the hopper and tillage working bodies on one frame, and mounting the hopper directly on the tractor frame. The working width of the cultivator-fertiliser was constant and amounted to 6.2 m. The effective engine power of the tractor was taken to be 220 × 103 W. It was found that placing the fertiliser hopper on the tractor frame maximises the field capacity to 3.9 ha∙h−1. The increase in field capacity was 8.9–16.0%. In this case, the fertiliser hopper volume should be 1.89 m3 and travel speed 8.45 km∙h−1.

Kesenjangan dan ketidakmerataan pembangunan menjadi sebuah tantangan bagi daerah-daerah yang sedang berkembang sehingga menentukan pusat-pusat pelayaan menjadi hal yang sangat perlu dilakukan. Penelitian ini bertujuan untuk mengetahui tingkat status perkembangan desa di Kabupaten Karawang, Provinsi Jawa Barat. Metode yang digunakan untuk menganalisis tingkat status perkembangan kawasan perdesaan adalah Skalogram dengan variabel aksesbilitas dan ketersediaan sarana dan prasaran pendidikan, kesehatan, ekonomi, dan lingkungan. Hasil penelitian menunjukkan bahwa pada hirarki I yang menyebar di 25 perdesaan yang menjadi pusat pertumbuhan dengan kelengkapan aksesbilitas dan jumlah jenis fasilitas memadai. Hirarki II yang menyebar di 83 perdesaan di berbagai kecamatan memiliki jumlah jenis fasilitas dan aksesbilitas yang cukup memadai, dan hirarki III dengan penyebaran di 201 perdesaan yang menjadi daerah terbelakang, dapat di artikan bahwa aksesbilitas yang sulit dan didukung oleh jumlah jenis fasilitas yang masih kurang memadai.

Decision-making based on complex human-machine algorithms can lead to discrimination of citizens based on gender, race and other grounds. However, in world science there is no idea of algorithmically conditioned discrimination of citizens by their place of residence. This also applies to the adoption of algorithmic decisions on the socio-economic development of regions. Therefore, the purpose of our study was to detect algorithmic bias in the results of socio-economic clustering of Russian regions. To achieve this goal, it was necessary to identify sensitive operations in cluster analysis that could lead to spatial injustice, form an array of articles on socio-economic clustering of subjects (regions) of the Russian Federation, analyze all articles for the possibility of algorithmic bias and identify Russian regions with potentially biased attitudes towards them as a result of clustering. The term ‘spatial algorithmic bias’ is proposed. Using the author’s semantic search algorithm in bibliographic databases, six hundred articles with empirical results of cluster analysis of Russian regions by socio-economic indicators were identified. The characteristics of the identified articles are given. The analysis of all the articles showed that algorithmic bias is most evident in the four operations of the clustering algorithm – deploying a conceptual model into an optimal set of indicators, selecting regions, choosing a way to combine regions into clusters and determining the number of clusters. Examples of discriminated Russian regions are presented for each operation. Three directions of further research are indicated. Practical significance may be associated with the adoption of unbiased decisions on regional socio-economic development based on fair clustering of the Russian Federation’s subjects

In the domain of scene interpretation for autonomous vehicles, it is very crucial to identify and classify the objects in an environment. Deep learning techniques like semantic segmentation, depth estimation, instance segmentation, object detection, etc., enable us to build efficient models with sufficient accuracy in classifying and identifying the objects in the scene. In this work, an innovative architectural framework called the Unified Scene Perception Network (USPNet) is designed to simultaneously handle semantic segmentation and depth estimation tasks. USPNet consists of a joint encoder that implements vision transformers (ViT) and is responsible for capturing and extracting high-level features from input data, along with two dedicated decoders specifically crafted for semantic segmentation and depth estimation, respectively. Within these decoder components, residual attention blocks such as the Convolutional Block Attention Module (CBAM) are implemented. Extensive experimental validation shows USPNet’s performance in segmentation accuracy, a high mean Intersection over Union (mIoU) of 71.26%. Its segmentation accuracy of 86.47% outperforms other models, highlighting its efficiency in pixel-wise classification. Thus, the proposed USPNet is effective and offering promising prospects for advancing the capabilities of autonomous driving systems in their ability to thoroughly analyze and navigate complex real-world scenarios.

Fungus Mycelium mediated Calcite Precipitation (FMCP) is a novel bio-inspired method for enhancing the mechanical and microstructural properties of sand. This study investigates the potential of Aspergillus Niger fungus mycelium to induce calcite precipitation in sand, thereby improving its unconfined compressive strength (UCS) and permeability. The experimental approach involves mixing sand with Aspergillus Niger and subsequently injecting a cementation solution containing urea and [Formula: see text] in an equimolar 1:1 ratio at 24-hour intervals. The effect of different treatment durations (7, 14, 21, and 28 days) on the UCS of the samples is examined. Our findings demonstrate that the highest average UCS of 3.93 MPa was achieved after 28 days using FMCP, corresponding to an average calcium carbonate content of 15.19%. Additionally, permeability of FMCP treated sand decreased to 99.54% compared to untreated sand. The Scanning electron microscopy identified the presence of fungus mycelia and calcium carbonate crystals that bind the sand grains together resembling fibrous structure. The Energy-Dispersive X-ray Spectroscopy analysis confirmed the presence of calcium carbonate crystals associated with the sand grains and fungal mycelium. These findings contribute valuable insights into the feasibility of FMCP as a sustainable approach to soil improvement, with implications for geotechnical and construction applications.

Cardiovascular diseases (CD), or heart diseases (HD), lead to approximately 17.9 million deaths each year, constituting 32% of global fatalities. Early detection and appropriate treatment of HDs can significantly reduce mortality rates, with timely intervention before disease progression enhancing treatment efficacy. Early detection is achievable through routine medical examinations and monitoring key symptoms, such as cholesterol levels, blood pressure variations, diabetes and obesity. This manuscript introduces a heart disease prediction (HDP) model designed to identify the presence of HDs at an initial stage. The study explores three methodologies: (a) traditional convolutional neural network (CNN), (b) CNN augmented with flower pollination optimization (FPO) algorithm and (c) CNN combining Teaching Learning-Based Optimization (TLBO) coupled with genetic algorithm (GA) for refined HDP. The model progresses through stages of data preparation, model construction, training and evaluation. The traditional CNN model resulted an accuracy of 81.97%, precision of 84%, recall of 81% and F1-score of 83%. Incorporating the FPO algorithm, model’s performance is enhanced with accuracy, precision, recall, F1-score of 85.25%, 90%, 81% and 85%, respectively. Further, optimized with TLBO and GA hybrid approach lead to superior performance with an accuracy, precision, recall, F1-score of 86.9%, 87.5%, 87.5% and 87.5%, respectively. The area under curve (AUC) for the receiver operating characteristics (ROC) and precision–recall curve (PRC) highlights the performance of the proposed hybrid methodology. These outcomes underscore the effectiveness of merging bio-inspired algorithms with CNN for early stage HD prediction (HDP), offering significant advancements in healthcare diagnostics.

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCM). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete that was developed for ground-granulated blast furnace slag (GGBS) along with Alccofine 1203 (AF). Boosting regressions of Gradient Boosting Regression (GBR), Extreme Gradient Boosting Regression (XGBR) and Light Gradient Boosting Regression (LGBR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 120 experimental conditions. Moreover, a comparative study was performed among GBR, XGBR and LGBR to benchmark the proposed model against a given combination of features (parameters/factors) and compressive strength. By using CPC-X Neural Power software, the experimental conditions were optimized using the Particle Swam Optimization (PSO) algorithm after the model was created by the Artificial Neural Network (ANN). Of the three boosting algorithms, the smallest errors along with the largest coefficient of determination were observed in the GBR algorithm and the optimized values of CS is 77.4 MPa and its optimized process factors (Alccofine, GGBS, Cement, C.A, F.A, SP dosage and days) were 24.77 kg/m3, 24.63 kg/m3, 534.92 kg/m3, 1100.08 kg/m3, 628.64 kg/m3, 4.20 lit/m3 and 29.03 days respectively.

Before using concrete for a particular purpose, its strength must be determined because its physical properties vary depending on the type of supplementary cementitious material (SCMs). Initially, researchers anticipated it with statistical processes; more recently, they started implementing deep learning (DL) and machine learning (ML) models. This research employed ensemble machine learning (EML) approaches to predict the compressive strength (CS) of concrete developed for ground-granulated blast furnace slag (GGBFS) along with Alccofine 1203 (AF). Ensemble regressions of Random Forest Regression (RFR) and AdaBoost Regression (ABR) were considered for model prediction using the Jupyter Notebook. The models were created based on the outcome of the compressive strength under the 90 experimental conditions. Moreover, a comparative study was performed between the RFR and ABR to benchmark the proposed model against a given combination of features and CS. The experimental conditions were optimized using a genetic algorithm (GA) after the model was created by ANN. With the two EML algorithms, the smallest errors, along with the largest coefficient of determination, were observed in the RFR algorithm. It was recognized that the optimized value of CS is 48.606 MPa, and their optimized process elements such as cement, alccofine, GGBS, water, and days were 261.506 kg/m3, 0.055 kg/m3, 52.468 kg/m3, 141.007 L, and 27.999 days, respectively.

The compressive strength of concrete is a critical parameter that affects the safety, durability, and performance of structures. This is a key factor in the design, construction quality control, and long-term functionality of concrete elements in various engineering applications. This is a fundamental indicator of the ability of the concrete to withstand axial loads or pressures. Understanding the compressive strength is essential for ensuring the safety of structures. This helps engineers design concrete elements with sufficient load-bearing capacity to withstand the expected loads and maintain structural stability. The objective of this study is to assess the effectiveness of linear and non-linear machine learning algorithms in accurately predicting the compressive strength of concrete based on varying quantities of constituent components. To address outliers in the data, we utilized an iterative implicit technique during the data preprocessing stage. Our implementation included Linear models such as Linear regression and polynomial regression, Non-linear models such as Decision Tree and Random Forest, and Boosting models such as Gradient Boosting and Extreme Gradient Boosting. The accuracy scores and overfitting of the models were compared. The models were evaluated using the R2 scores for both the training and test sets. Among the three categories of models tested, boosting models demonstrated higher accuracy than the other models, while also exhibiting limited overfitting.

The challenge of swift and reliable earthquake location prediction within earthquake early warning (EEW) systems underscore the need for innovative solutions. Existing methods provide predictions, yet there is a clear demand for enhanced accuracy and expediency in the process of resolving. The article intends in prediction of both magnitude of earthquakes and the affected areas in a specific region, focusing on California, United States, by leveraging historical data using neural networks (NNs). Neural networks outperform other algorithms such as XGBoost, linear regression, random forest, gradient boosting and support vector machine (SVM), in terms of high R2 score and low score of MAE and also mean squared error (MSE). The derived results of R2 score, MSE and MAE are 0.1607, 0.1615 and 0.2951, respectively. It seamlessly integrates geospatial visualization through Folium and GeoPandas. These tools enhance the predictive model’s capabilities by generating dynamic maps enriched with markers, each representing the anticipated impact of earthquakes. The user interface facilitates interactive input, enabling users to input earthquake parameters for real-time predictions. The resultant map not only showcases the predicted impact zone but also provides valuable insights into the severity of seismic events. It stands as a testament to the synergy between machine learning and geospatial visualization, offering a holistic solution for earthquake prediction and geographical representation.

Electromagnetic pollution, or electromagnetic interference (EMI), is a phenomenon that has arisen due to the fast spread of electronic gadgets. To overcome EMI problem, polymer-based composites have sparked considerable attention among researchers owing to their superior qualities. Hence, this work utilizes magnetite-modified graphene (MMG) filler with polyvinylidene fluoride (PVDF) polymer to form polymer composites in various proportions ranging from 2 to 10 wt% to study the EM properties in the X-band. It was observed that the sample composite having a MMG filler content of 10 wt% possesses a relatively higher electrical conductivity of 0.65 S/cm as compared to the other prepared composites in this research work. The same sample composite also attained a total shielding efficacy of 53.04 dB at a thickness of 3 mm. Moreover, it was observed that the filler has improved the material's thermal stability and microwave absorption capacity, making it a high-efficiency EMI shielding material appropriate for usage in the electronic and aviation industries.

This article aims to investigate the mechanical properties and substrate adhesion of the pull-off method in polymer mortars modified with styrene-butadiene resin polymer (SBR) containing glass powder and composite fiber-reinforced slag. Different mix designs were investigated with and without SBR, taking into account different amounts of glass powder and slag separately and in combination, along with the effect of glass, polypropylene, and steel fibers alone and in combination. The flexural performance and energy absorption of beams retrieved with these layers were also assessed. The results revealed significant differences and increases in the substrate adhesion of the restored modified polymer layers containing SBR compared to the polymer-free repair overlays. Furthermore, an improvement was observed in the adhesion performance of the repair overlay using a combination of slag and glass powder and the glass and polypropylene fiber composite. The highest adhesion was related to the modified polymer mortar design containing composite fibers of glass, polypropylene, and steel with 25% replacement of SBR polymer for 10% glass powder, 10% slag, and 5% slag with 5% glass powder. The adhesion was increased by about 3.74, 3.72, and 3.78 times compared to the repair overlay of the control design. Modified polymer mortars had a higher T150D toughness. Moreover, the energy absorption was significantly improved by the presence of SBR polymer. The highest toughness values were found in the beams restored with modified polymer mortars containing polypropylene, glass, and steel composite fibers with an increase of 48.51%–66.42% compared to the samples without polymer as a result of the pozzolans used in this mix.

South Africa currently has the highest carbon emission intensity per kilowatt of electricity generation globally, and its government intends to reduce it. Some of the measures taken by the government include a reduction of emissions in the building sector using solar water heating (SWH) systems. However, there is currently no study in the country that comprehensively assesses the technical, economic, and environmental impact of SWH systems across the country. This study therefore used the System Advisor Model (SAM) to model two different technologies of SWH systems (i.e., flat plate (FPC) and evacuated tube (EPC) SWH) at five different locations (i.e., Pretoria, Upington, Kimberley, Durban, and Cape Town) strategically selected across the country. According to the study, the optimum azimuth for both the evacuated tube and flat plate SWH system in South Africa is 0°. Installing FPC and EPC at the different locations would yield payback periods of 3.2 to 4.4 years and 3.5 to 4.3 years, respectively. Comparably, levelized cost of energy for the FPC and EPC will range from 7.47 to 9.62 cents/kWh and 7.66 to 9.24 cents/kWh, respectively, based on where the SWH system is located. Depending on where the facility is located, the annual cost savings for the FPC system would be between $486 and $625, while the EPC system would save between $529 and $638. Using SWHs can reduce CO2 emissions by 75–77% for the evacuated tube system and 69–76% for the flat plate system annually, depending on the location.

We aim to evaluate the incidence of incontinence following laser endoscopic enucleation of the prostate (EEP) comparing en-bloc (Group 1) versus 2-lobe/3-lobe techniques (Group 2). We performed a retrospective review of patients undergoing EEP for benign prostatic enlargement in 12 centers between January 2020 and January 2022. Data were presented as median and interquartile range (IQR). Univariable and multivariable logistic regression analysis was performed to evaluate factors associated with stress urinary incontinence (SUI) and mixed urinary incontinence (MUI). There were 1711 patients in Group 1 and 3357 patients in Group 2. Patients in Group 2 were significantly younger (68 [62–73] years vs 69 [63–74] years, P = 0.002). Median (interquartile range) prostate volume (PV) was similar between the groups (70 [52–92] ml in Group 1 vs 70 [54–90] ml in Group 2, P = 0.774). There was no difference in preoperative International Prostate Symptom Score, quality of life, or maximum flow rate. Enucleation, morcellation, and total surgical time were significantly shorter in Group 1. Within 1 month, overall incontinence rate was 6.3% in Group 1 versus 5.3% in Group 2 (P = 0.12), and urge incontinence was significantly higher in Group 1 (55.1% vs 37.3% in Group 2, P < 0.001). After 3 months, the overall rate of incontinence was 1.7% in Group 1 versus 2.3% in Group 2 (P = 0.06), and SUI was significantly higher in Group 2 (55.6% vs 24.1% in Group 1, P = 0.002). At multivariable analysis, PV and IPSS were factors significantly associated with higher odds of transient SUI/MUI. PV, surgical time, and no early apical release technique were factors associated with higher odds of persistent SUI/MUI.

The results of studies technological processes for obtaining welding wire from alloys of the Al–Mg system with a scandium content of 0.12 (alloy 1580) and 0.25 wt % (alloy 01570) presented. A feature of the technology is the production of a billet for further cold deformation by an energy-efficient method of ingotless rolling-extrusion (IRE). It was found that hot-extruded rods from the investigated alloys obtained by the IRE method have the required level of plastic and strength properties necessary for further cold deformation up to 80%. Thermomechanical processing of hot-extruded rods manufactured by IRE have been developed and experimentally tested, which made it possible to obtain a billet with a square side of 5 × 5 mm by sectional rolling and wire with a diameter of 3 mm by drawing. Studies of the structure and mechanical properties of deformed semi-finished products have shown that the developed modes make it possible to obtain a welding wire from the investigated alloys that meets the requirements of current standards. Studies of the operational properties of the obtained welding wire were carried out. It was found that alloy 1580 with a minimum content of scandium (0.12 wt %) is not inferior to alloy 01570 in terms of corrosion resistance and weld quality. Developed technology for the production of welding wire from alloys of the Al–Mg system with different scandium content can be recommended for industrial use. It is advisable to implement alloy 1580, which has a lower cost compared to alloy 01570.

The objective of this research is 1) to explore time and space characteristics, as well as the origin of labor shortage and 2) to find out whether there exists a long-run equilibrium relationship between labor shortage and its determinants using panel data for 82 subjects of the Russian Federation over 2000–2021 and some tools of panel data econometrics. Since 2012 the number of job vacancies has exceeded the number of unemployed persons, and this excess has become a permanent feature of the labor market. The job vacancy rate grew by 0,12% annually and doubled over the observation period, which means ‘labor hunger’ did not fall as snow from the sky. In 78% of panel observations the job vacancy rate does not exceed the market benchmark of 3,5% , and the number of regions with a very low job vacancy rate greatly exceeds the number with a high rate, which does not support the idea of ‘labor hunger’ countrywide. The demand for blue-color workers is well above the average in industrial Russia, and the demand for white-color workers is above average in in less industrialized regions. There is a shortage of blue-color workers in regions with developed manufacturing, transportation, and service sector. Hence a high job vacancy rate for blue-color labor. Yes, there is another Russia, where these sectors are underdeveloped generating weak demand for blue-color labor. Labor shortage is multiregional shortage of predominantly blue-color workers. The results of econometric exercises suggest there is a long-run equilibrium relationship between labor shortage and its determinants such as real wage, the level of unemployment and business cycle

The goal of the research is to study the risks and innovative benefits of distance learning. The main research method is an anonymous survey of academic staff (university teachers) and students (future philologists, foreign language teachers), based on the need to clarify the situation associated with the use of distance learning technologies in the conditions of the quarantine. The following data have been obtained in the course of the study: the most significant opportunities for distance learning, the level of satisfaction with distance learning technologies, the compliance with the previously drawn up training schedule in the distance learning conditions, the satisfaction with the promptness of informing about the training schedule, the use of distance learning technologies to provide feedback, the factors that affect the quality of the implementation of distance learning technologies, the teachers' positive experience of using distance learning technologies, the use of distance learning technologies when providing students with lecture materials, as well as the use of information resources of the institution by the students. The results of the study have revealed that both students and teachers are aware of the need to work in a distance learning environment, but the coronavirus pandemic has created new risks for the higher education system, which require an innovative approach to overcoming barriers in the distance learning system. The results of the article can be used in the organization of distance learning at a humanitarian university.

В статье рассказывается о том, как на кафедре «Организация строительства и управления недвижимостью» (ОСУН) МГСУ было положено начало еще одной, новой уникальной традиции — организации зарубежной производственной практики студентов. В соответствии с достигнутыми договоренностями прошла зарубежная стажировка студентов кафедры ОСУН на АЭС «Аккую» в Турецкой Республике. Государственная корпорация по атомной энергии «Росатом» — многопрофильный холдинг, объединяющий активы в энергетике, машиностроении, строительстве. АЭС «Аккую» — первая атомная электростанция в Турецкой Республике. Проект первой турецкой АЭС включает в себя четыре энергоблока с реакторами российского дизайна ВВЭР-1200. Сооружение АЭС «Аккую» — первый проект в мировой атомной отрасли, реализуемый по модели Build-Own-Operate. Кафедра ОСУН осуществляет свою деятельность на протяжении более 90 лет, имеет свою историю и устоявшиеся традиции. Идя в ногу со временем, принимая во внимание нынешние инновационные тренды и вызовы в сфере строительства, экономики, образования, на кафедре была организована зарубежная стажировка студентов. С какими же трудностями они столкнулись и почему этот «пилотный проект» является очень ценным, знаковым опытом не только для кафедры, но и для НИУ МГСУ в целом. По итогам совместного проекта можно отметить, что поставленные цели и задачи решены. Стороны остались довольны результатами, отметили важность такого формата сотрудничества и запланировали дальнейшие взаимоотношения.

This study evaluates the performance of a geosynthetic reinforced soil integrated bridge system (GRS-IBS) in terms of total displacement by varying different design parameters simultaneously and also suggests optimum values of them. These parameters include, i. backfill internal friction angle (∅b) and reinforcement spacing (Sv), ii. Backfill internal friction angle (∅b) and geogrid axial stiffness (EA) at varying reinforcement spacing (Sv), iii. Backfill internal friction angle (∅b) and number of bearing bed layers, and the effect of retained backfill slope (mb). Simulations were conducted using PLAXIS 2D software. Analysis showed that the cumulative effect of these parameters had a significant effect on total displacement but after a certain point increase or decrease in their values showed no effect on the results while some parameters showed negligible effect on the deformation of the wall. Furthermore, due to the notable effect of ∅b, Sv and EA on the total displacement of the wall, the impact of these parameters was also investigated on the development of tensile force in the topmost layer of geogrid in GRS IBS. It was noted that the shape of the tensile force distribution graph was the same for all the cases and the order of the parameters in terms of their effect on tensile force was Sv > ∅b > EA. Also, a detailed analysis of tensile force development in all the layers of geogrids showed that if Sv ≤ 0.2 m, the spacing between reinforcement in the lower portion of GRS IBS can be increased as these layers showed approximately zero tensile load.

Africa faces a significant energy crisis, with over 600 million people lacking access to affordable and reliable power to meet their energy demands, despite the continent's vast solar energy potential. Several studies have indicated the high potential of solar water heaters (SWH) in various African countries in Africa due to the enormous solar energy resource. However, the diffusion and use of the same in Africa, especially in the West African sub-region, have been very slow compared to other sub-regions. This study thus assesses the barriers that affect the adoption and use of SWH systems using the Delphi method and the Fuzzy Analytical Hierarchy Process to prioritize and rank them for decision-making. The study identified five main barriers and prioritized them in the order of financing, gender, and market-related barriers (51 %), political, policy, and regulatory barriers (19 %), technical barriers (14 %), socio-cultural and public awareness barriers (11 %), as well as lack of standardization and quality assurance (6 %). The high cost of finance as well as difficulties in accessing finance prevent private sector investment, which ultimately affects the large-scale use of the SWH system in the sub-region. Also, the uneducated and a section of the educated do not fully appreciate the merits of the SWH system. This paper is expected to inform academics, practitioners, and regulators on future efforts to adopt for the SWH technologies in Africa, and to improve policies and strategies for their adoption across the continent.