Load Levels Research Articles

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

A Multi-Agent RL Algorithm for Dynamic Task Offloading in D2D-MEC Network with Energy Harvesting.

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms.

Causal inference did not detect any effect of jump load on knee complaints in elite men's volleyball.

The aim was to determine how jump load affects knee complaints in elite men's volleyball. We collected data from four men's premier league volleyball teams through three seasons in a prospective cohort study (65 players, 102 player-seasons). Vert inertial measurement devices captured the jump load (jump frequency and jump height) from 21 088 daily player sessions, and knee complaints were reported in 3568 weekly OSTRC-O questionnaires. Mixed complementary log-log regression models described the probability of (i) experiencing symptoms if players were currently asymptomatic, (ii) worsening symptoms if players had symptoms, and (iii) recovery from knee complaints. Based on our causal assumptions, weekly jump load was modeled as the independent variable, adjusted for age (years), weight (kg), position on volleyball team, and past jump load. No certain evidence of an association was found between weekly jump load and probability of (i) knee complaints (p from 0.10 to 0.32 for three restricted cubic splines of load), (ii) worsening symptoms if the player already had symptoms (p from 0.11 to 0.97), (iii) recovery (p from 0.36 to 0.63). The probability of knee complaints was highest for above-average weekly jump load (~1.2% for an outside hitter with mean age and height) compared with low loads (~1%) and very high loads (→ ~ 0%). The association between jump load and knee complaints risk remains unclear. Small differences in risk across observed jump load levels were observed. It would likely require substantially increased sample sizes to detect this association with certainty.

Versatile biochar for wood‐plastic composites: Improving mechanical properties, dimensional and thermal stability

AbstractIn this study, three types of biochar including wood biochar (WB), rice husk biochar (RHB), and bamboo biochar (BB) were used as fillers to mix with wood flour (WF), polypropylene (PP), and maleic anhydride polypropylene (MAPP) to prepare wood polymer biochar composites (WPBC). The influences of biochar types and addition amounts (10 wt%, 20 wt%, 30 wt%, and 40 wt%) on the mechanical properties, dimension stability, and thermal stability of WPBC were investigated. The results showed that the water absorption rate and thickness swelling rate of WPBC were lower than control group due to the hydrophobic nature of the biochar. Additionally, the presence of biochar was beneficial to the interfacial bonding and stress transmission in composites, resulting in better mechanical properties. Composites containing 20 wt% RHB showed the highest flexural modulus among all, which increased by 11.27% compared to control group. Adding WB also enhanced the tensile and impact strength of composites at a loading level of 10 wt%, with the value of 39.10 MPa and 15.55 KJ/m2, respectively. The thermal stability and flame retardancy of WPBC was improved due to the presence of SiO2 in biochar, which could hinder heat transmission and thermal degradation during combustion process. Concurrently, the rate of heat release and smoke generation in WPBC also decreased significantly. The oxygen limiting index (LOI) of composites with 20 wt% RHB was as high as 26.64%, increasing by 30.14% compared to control group. The results suggested that biochar was a promising alternative to commercial additives for the cost‐effective production of composites.Highlights Effects of biochar loading level and type were compared. Enhanced the mechanical properties by optimizing interfacial bonding. Biochar improved the dimensional and thermal stability. Biochar is a versatile and cost‐effective additive.

Effects of compressive cyclic loading on the fatigue properties of oil-well cement slurries serving in deep downhole environments

With the increasing demand for energy from deep oil and gas reservoirs, oil-well cement slurries are subjected to complex compressive cyclic loading in high-temperature and high-pressure (HTHP) environments. Currently, no standard to guide the evaluation of the fatigue performance of cement slurries under cyclic loading or a systematic investigation to help understand it exists. To fill this gap, this study used a set of innovative HTHP curing and testing devices to mimic the actual operating conditions of deep wells. Cement slurries were cast, cured, and subjected to quasi-static and cyclic loading tests at four downhole temperatures (25, 90, 115, and 140 °C). In the cyclic-loading tests, three loading levels (30%, 50%, and 70%) under 20 MPa confining pressure were applied to study the fatigue performance, and nuclear magnetic resonance (NMR) was employed to identify internal microcrack damage. When the downhole temperature increased from 25 to 140 °C and the cyclic loading level increased from 30% to 70%, the accumulated plastic strain of samples increased by 13.8 times. A large increase in the plastic strain of oil-well cement slurries can significantly reduce the fatigue resistance and service life of oil and gas wells. Based on this research, the authors propose the addition of compressive cyclic loading tests (particularly under HTHP testing conditions) to the current oil-well cement slurry testing standards, which will be very significant for the fatigue performance evaluation of cement slurries used in HTHP environments in deep wells.

Effects of age and flight experience on prefrontal cortex activity in airline pilots: An fNIRS study

It is essential for airlines to have a deep understanding of the cognitive impact of aging among pilots. The current literature on executive function indicates that compensatory mechanisms in the brain may counteract age-related cognitive decline, at least up to certain task load levels. However, few studies have been administered to evaluate changes in aircrew competence as they age. The present study focuses on dorsolateral prefrontal cortex (DLPFC) activity as it is implicated in cognitive performance and working memory, which are associated with skill proficiency. We measured the DLPFC activity for airline pilots, including trainees, during maneuvering using a flight simulator. Our preliminary results indicated that only expert (aged) pilots demonstrated higher activity of the left DLPFC than the right one. However, for youth trainees, not only was the error rate high while using the flight simulator, but the activity of the DLFPC was also lower than that of the expert pilots, and there was no statistically significant difference between the left and right DLPFC. Although these findings partially differ from those reported in previous studies on age-related changes, it is evident that training as an airline pilot for over 20 years may affect such results. We believe that this noninvasive approach to objective quantification of skill will facilitate the development of effective assessment competence in aging.

Structural behavior of steel tube reinforced resilient RAC shear walls under static cyclic loading

This paper proposed a method to enhance the energy dissipation capacity of recycled aggregate concrete (RAC) shear walls reinforced with ultra-high strength bars (UHSB). The proposed method encases circular steel tubes within the boundary elements of walls. With the presence of steel tubes, steel fibers and axial load level as primary research variables, five RAC shear walls were fabricated and tested under reversed cyclic lateral force and constant axial compression to verify the effectiveness of the proposed method. The test results showed that the presence of steel tubes upgraded lateral resistance and enhanced energy dissipation capacity, while causing some subtle splitting cracks. Due to the yielding of the steel tubes, the encasement of the steel tube increased residual drift ratio at large drift, but the measured residual drift ratios of test walls still were kept less than 0.5 % until 2.5 % drift ratio. The addition of steel fibers delayed cracking, constrained the cover concrete and reduced damage to the specimens. Test results also indicated that increasing concrete strength and axial compression level could enhance the lateral resistance of the proposed wall. In addition, a numerical analytical method is presented to analyze the cyclic behavior of the proposed shear walls. Good agreement was observed between the measured and calculated hysteretic loops.

Nano and micro co‐filled aluminum nitride/magnesium dihydrate—Silicone rubber composites for high voltage insulation

AbstractCo‐filled composites of micro magnesium dihydrate (MDH) and hydrophobically treated nano aluminum nitride (nH‐AlN) fillers in high temperature vulcanizing silicone gum are studied for thermal and dielectric characteristics for use as an insulating material in high voltage applications. The study begins with description of the compounding protocol adopted, followed by spectroscopic and optical characterization. Fourier transform infrared spectroscopy (FTIR) and energy dispersive X‐ray spectroscopy (EDX) analysis are used to verify the presence of nH‐AlN and MDH in the composites. Thermal analysis through thermogravimetric analysis revealed a stark enhancement in the onset of polymer degradation temperature, with an improvement of 208 °C achieved at 30 phr by weight loading level of MDH. Volume resistivity of 9.71 E14 Ω‐cm is obtained at 30 phr weight loading of MDH and 5 phr of nH‐AlN. AC, +DC, and −DC dielectric strength improved by 1.09, 2.06, and 1.08 times for co‐filled composites at 30 phr MDH and 5 phr nH‐AlN loading levels over unitary filled composite. Effect of dielectric polarization on dielectric constant and dissipation factor is studied at various frequencies. Limitations while conducting dry arc resistance test at standard voltage of 12.50 kV is discussed, with modification in dry arc resistance test voltage to 16.95 kV. The results are presented and discussed here.

Laboratory and Field Performance Evaluation of Cracking of Airfield Warm Mix Asphalt with Reclaimed Asphalt Pavement at the National Airport Pavement and Materials Research Center

The U.S. Federal Aviation Administration is exploring initiatives to decrease transportation greenhouse gas emissions by developing carbon reduction strategies, including using low-embodied carbon materials, such as reclaimed asphalt pavement (RAP), in airport pavement construction. However, verifying RAP’s viability for airfield pavements with laboratory and field performance measures is essential. This study compared the cracking susceptibility of a P-401 hot mix asphalt and warm mix asphalt (WMA)+RAP airfield mixes using laboratory performance tests and conducted a preliminary evaluation of pavement responses from accelerated pavement testing (APT) fatigue tests. The observations and outcomes presented in this paper found that adding 20% of recycled materials with WMA additives had a decreasing impact in mix performance but did not overly affect the asphalt mix properties, as cracking properties from the different test procedures were found with comparable outcomes. Additional observations from strain gauges and temperature probes in the National Airport Pavement and Materials Research Center test cycle 2 APT fatigue test sections showed that the maximum tensile strains in the WMA surface lane 5 south section were consistently higher than those in the WMA+RAP lane 6 south section, which may be because of the higher load level that it was exposed. Additionally, the hardened or aged RAP binder in the entire lane 6 south section may have increased its stiffness, resulting in lower strain levels than in the lane 5 south section. Notably, both sections showed an increase in tensile strains with an increase in asphalt concrete temperature, which confirmed the temperature dependency behavior of asphalt concrete.

Experimental investigation of fatigue for one way power transmission clutches using accelerated life tests

One way clutch (OWC) has emerged as one of the most vital devices used in heavy industries to safeguard the machines running with prime movers. It acts as a safety device to restrict the motion in the reverse direction. OWC also transmits the torque in one direction using wedge contact developed at the highly localized stress regions. The work presented consists of the development of a test bench specifically designed to simulate one way torque transmission of OWC for estimating the fatigue life based on the accelerated load tests. Accelerated load tests are performed to increase the severity within a short testing period. The microstructure disorder of the material is one the prime reasons for scattering in fatigue lives of identical batches of specimens. Therefore, several experimental tests are performed at distinct elevated load levels for different sizes of OWC. The effect of contact pressure and contact angle on fatigue lives are investigated. The fatigue life results obtained using accelerated testing correlate well with the Lundberg-Palmgren model. The load-life relationship follows the Weibull distribution. The failed ramp geometries are studied using a scanning electron microscope (SEM) to understand the governing failure mechanism for OWC.

Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method

This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.

Boosting the Sodium-Ion Transport and Surface Pseudocapacitance of a SnO2 Nanoflower at a High Mass Loading Level for High Areal Capacity and Fast Sodium-Ion Storage

Boosting the Sodium-Ion Transport and Surface Pseudocapacitance of a SnO₂ Nanoflower at a High Mass Loading Level for High Areal Capacity and Fast Sodium-Ion Storage

Improving Cancer Therapy: Design, Synthesis, and Evaluation of Carboplatin-Based Nanoliposomes against Breast Cancer Cell Lines

Objective: Cancer poses a significant challenge to modern medicine, with breast carcinoma being one of the most prevalent forms of the disease. Carboplatin is a commonly used chemotherapy drug for treating breast cancer, but its efficacy can be limited due to its poor water solubility and associated side effects. This study intended to enhance the therapeutic potency of carboplatin by encapsulating it within liposomal nanocarriers. Methods: We fabricated nanoscale liposomes loaded with carboplatin via a technique called reverse phase evaporation, and examined their physical attributes. We also studied the toxicity of these nanoliposomes on MDA-MB 231 breast cancer cells. Results: Our findings indicated that the liposomal nanoparticles possessed a negative zeta potential of -18.2 mV and an average size of 278 nm. The drug loading level was 2.2%, while the efficiency of drug encapsulation reached 58.5%. Of note, the cytotoxicity of carboplatin in its nanoliposomal form was markedly more potent against the MDA-MB 231 breast cancer cell line than the free drug (p-value less than 0.05). Conclusion: Our findings suggest that carboplatin-loaded liposomal nanocarriers could potentially serve as an advanced chemotherapeutic approach for the treatment of breast cancer, offering enhanced efficacy and reduced side effects compared to conventional carboplatin therapy. Further research is warranted to explore this novel delivery system’s benefits fully.

An effective multi-time series model of RC column backbone curve identification

Accurate identification of the backbone curves of reinforced concrete (RC) columns is key to engineering design and strengthening renovation. In view of the problems of high cost, long time, low accuracy, large dispersion of calculation results and discontinuous stiffness changes of the existing backbone curve identification methods, such as experimental method, finite element simulation method and semi-theoretical and semi-empirical method, it is proposed to transform the backbone curve identification problem into a multi-time series prediction problem. By introducing the attention mechanism and combining it with the bidirectional long short-term memory (BiLSTM), the backbone curve identification model (BC-ABiLSTM) is established considering the relationship between the front and back points of the backbone curves. Compared with the models for backbone curves with BiLSTM (BC-BiLSTM), long short-term memory (BC-LSTM), multilayer perceptron (BC-MLP), and the existing identification methods, the performance of the BC-ABiLSTM model is better, and the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 of the BC-ABiLSTM model on the testing set are 12.492 kN, 10.595 %, 20.838 kN and 0.9924, respectively, which provides a new method for accurate, efficient and cost-effective prediction of RC column backbone curve points under various cyclic loading levels.

Approaching high rate All-Solid-State Lithium-Sulfur batteries via promoted sulfur conversion with nickel oxide nanoparticle electrocatalyst

All-solid-state lithium-sulfur battery (ASLSB) is deemed a promising next-generation energy storage device owing to its combination of high theoretical specific energy (2600 Wh kg−1) derived from the sulfur active material, and exceptional safety characteristics and the ability to suppress the polysulfide shuttle effect through the use of solid electrolyte (SE). However, the low electronic and ionic conductivity of sulfur, coupled with the poor solid–solid interfacial contact inherent in all-solid-state configuration, has resulted in poor electrochemical performance. Herein, NiO electrocatalyst supported on N-doped sites of super-p was synthesized and incorporated as a carbon additive in the composite cathode of the ASLSB. The direct growth of nanosized NiO electrocatalyst on the N-doped sites of super-p ensures facile electron transfer to the electrocatalyst surface, while maximizing the electrocatalyst’s surface area. As a result, the overpotential during charging and discharging of the ASLSB was significantly reduced, as assessed through galvanostatic intermittent titration technique and cyclic voltammetry, indicating enhanced redox conversion of sulfur. Furthermore, the Li6PS5Cl sulfide SE induces solid–solid conversion of sulfur, eliminating the dissolution issue of polysulfide intermediates and ensuring good cycle performance. The assembled ASLSB exhibits a high reversible capacity of 1845 mAh g−1 at 0.05C at room temperature and enhanced rate capability at high C-rates, operated at 60 °C. Even at a substantially high sulfur loading level of 6.65 mg cm−2, a maximum areal capacity of 9.48 mAh cm−2 was achieved at room temperature, highlighting the important role of electrocatalyst in realizing high performance ASLSBs.

Detection of Destructive Processes and Assessment of Deformations in PP-Modified Concrete in an Air-Dry State and Exposed to Fire Temperatures Using the Acoustic Emission Method, Numerical Analysis and Digital Image Correlation.

This article presents the results of tests carried out to assess the condition of PP-modified concrete. The tests were carried out on samples previously stored at ambient temperature and exposed to temperatures corresponding to fire conditions-300 °C, 450 °C, and 600 °C. Axial compression tests of cube-shaped samples and three-point bending of beams were carried out. During strength tests, acoustic emission (AE) signals were recorded and the force and deformation were measured. Recorded AE events were clustered using the k-means algorithm. The analysis of the test results allowed for the identification of signals characteristic of the individual stages of the material destruction process. Differences in the methods of destruction of samples stored in ambient conditions and those exposed to fire temperatures were also indicated. While loading the samples, measurements were carried out using the digital image correlation (DIC) method, which enabled the determination of displacements. Based on the results of the laboratory tests, a numerical model was developed. The results obtained using different research methods (DIC and FEM) were compared. Tomographic examinations and observations of the microstructure of the tested materials were also carried out. The analyses carried out allowed for a reliable assessment of the possibility of using the acoustic emission method to detect destructive processes and assess the technical condition of PP-modified concrete. It was confirmed that the acoustic emission method, due to differences at low load levels, can be a useful technique for assessing the condition of PP-modified concrete after exposure to fire temperatures. So far, no research directions in a similar field have been identified.

Time-dependent soil–structure interaction analysis using a macro-element foundation model

The delayed settlement of foundations due to soil consolidation, creep or particle breakage can alter the internal load distribution and differential settlements in a superstructure through soil–structure interaction (SSI). The study introduces a novel methodology to simulate time-dependent SSI that overcomes the complexity of incorporating the time-dependent behaviour of foundations into routine SSI analysis. The proposed approach represents the superstructure as a condensed stiffness matrix, and replaces the foundations and underlying soils with macro-element foundation models that encapsulate the foundation–soil interaction into load–displacement relationships derived from constitutive models. To examine the performance of the proposed method, a macro-element model for time-dependent analysis of shallow foundations on sand was integrated with structural analysis to simulate two tests performed in a geotechnical centrifuge on a 3D-printed aluminium framed structure supported by footings on sand. The simulated responses of the superstructure and foundations were found to agree well with those observed in the centrifuge tests. Parametric analyses were conducted to investigate the effect of loading history, load level on the superstructure, and creep tendency of soil on post-construction load redistribution and differential settlements. The findings suggest that creep of foundations on sand facilitates load redistribution in the structure from heavily loaded sections to lightly loaded sections. Moreover, post-construction load redistribution depends on the differential creep between footings and should be considered for structures that are quickly constructed or at high levels of strength mobilisation (low factor of safety). Overall, the study highlights the potential of the proposed methodology in analysing the time-dependent SSI and its applicability in practical SSI analysis.

Creating an AI-powered platform for neurosurgery alongside a usability examination: Progressing towards minimally invasive robotics

Recent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.

Creating an AI-powered platform for neurosurgery alongside a usability examination: Progressing towards minimally invasive robotics

Recent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.

Analysis of central pontine myelinolysis in HIV-infected patients

Aim: to conduct a clinical and epidemiological analysis of cases of central pontine myelinolysis (CPM) in HIV-infected patients. Materials and methods. Four patients hospitalized in the infectious diseases department of No. 1 Clinic of the South Ural State Medical University were under dynamic observation. Results and its discussion. Four patients hospitalized in the infectious diseases department of Clinic No. 1 of the South Ural State Medical University were under dynamic observation. Patient management was carried out in accordance with current clinical recommendations for the provision of medical care to patients with HIV infection with a set of clinical and laboratory methods, including examination of lumbar puncture, its cytological and biochemical composition, determination of DNA of cytomegalovirus (CMV) and Epstein-Barr viruses (EBV) herpes simplex types 1 and 2, T. gondii, Cr. Neoformans, M. Tuberculosis, Cryptococcus neoformans, the presence of pathogens of bacterial infections. All patients underwent magnetic resonance imaging (MRI) of the brain. Conclusion. The process of demyelination of the central nervous system develops regardless of the level of CD4 lymphocytes and HIV viral load. The main method for verifying the diagnosis is MRI of the brain