Minimum Rate Research Articles

Massive MIMO for Serving Federated Learning and Non-Federated Learning Users

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource. While in the downlink of each FL iteration, both groups simultaneously receive data from the base station in the same time-frequency resource, the uplink of each FL iteration requires bidirectional communication to support uplink transmission for FL users and downlink transmission for non-FL users. To overcome this challenge, we present half-duplex (HD) and full-duplex (FD) communication schemes to serve both groups. More specifically, we adopt the massive multiple-input multiple-output technology and aim to maximize the minimum effective rate of non-FL users under a quality of service (QoS) latency constraint for FL users. Since the formulated problem is nonconvex, we propose a power control algorithm based on successive convex approximation to find a stationary solution. Numerical results show that the proposed solutions perform significantly better than the considered baselines schemes. Moreover, the FD-based scheme outperforms the HD-based counterpart in scenarios where the self-interference is small or moderate and/or the size of FL model updates is large.

ОСНОВНІ ІНДИКАТОРИ РИНКУ ПРАЦІ НІМЕЧЧИНИ: КІЛЬКІСНА ОЦІНКА ВПЛИВУ НА МАКРОЕКОНОМІЧНІ ПОКАЗНИКИ

The study of the German labor market makes it possible to assert that Germany is a model of the employment system in the European Union. In the article, the authors studied the main indicators of the country’s labor market: minimum and average wages, nominal and real wages, unemployment rate, immigration to the country. It was revealed that the size of minimum wage rate is influenced not only by the state, but also by employers and trade unions. Thus, in many industries, the minimum wage is higher than the amount established by the state. In addition, the analysis of the size of the average salary demonstrates an important problem of the German labor market - the gender gap in income. In 2021, men earned approximately €700 more than women for equivalent work. The research examines the reasons for this inequality and offers recommendations for overcoming this contradiction. The article calculated the real wage index and found that starting from 2020, prices in Germany grew faster than wages. So, the real wage index for 2022 is -4.0. The main macro indicator of the labor market was analyzed. This is the level of unemployment, which turned out to be lower than the average indicator in the European Union. In addition, the authors draw attention to the problem of the aging of workers and the uneven distribution of the employed across the country’s regions. Thus, 53% of people over the age of 55 are still working in the economy. This is due to the shortage of specialists with higher education among young people. The article describes the immigration process in Germany and reveals an increase in migrants from countries suffering from war and violence. The influence of the number of immigrants on such macroeconomic indicators as GDP, GDP per capita, unemployment rate, and the size of the average wage was analyzed.

Microstructure, mechanical and tribological properties of APC/Al laminated composites prepared by hydrothermal carbon adsorption on plates

Amorphous carbon (APC)/Al laminated composites were prepared by hydrothermal carbon adsorption on plates (HTCAP) and vacuum hot-press sintering process. The effect of the glucose solution concentration on the microstructure, mechanical properties and tribological properties was investigated. The results indicate that APC is uniformly distributed between layers and has good interfacial bonding with the matrix through the diffusion of Al. As the glucose solution concentration increases, the interlayer hardness, ultimate tensile strength and elongation first increase and then decrease, while the wear rate shows the opposite trend and the coefficient of friction (COF) gradually decreases. A maximum elongation of 16.4% and a minimum wear rate of 0.344×10−5 mm3/(N·m) are achieved at a glucose solution concentration of 0.3 mol/L. Adhesive wear is the dominant wear mechanism of APC/Al laminated composites. The improvement in wear resistance is attributed to the formation of uniform APC lubrication film on the wear surface.

Determination of wear rate and coefficient of friction of Al6262 reinforced with different weight percentage of WC/MoS2 under dry sliding condition

A tribological investigation was undertaken on hybrid metal matrix composites based on Aluminum alloy 6262, incorporating varying weight percentages of Tungsten Carbide (WC) and Molybdenum disulfide (MoS2) under dry sliding conditions. Specifically, Tungsten Carbide was incorporated at 3%, 6%, and 9%, while Molybdenum disulphide was introduced at 2%, 4%, and 6%. The fabrication of these hybrid composites was accomplished using the stir casting technique. The experimental design followed an L27 orthogonal array, and Taguchi optimization was employed to identify the optimal combination of input parameters. The orthogonal array, signal-to-noise ratio and analysis of variance were employed to study the optimal testing parameters on developed composites. The optimal formulation, resulting in the minimum wear rate and coefficient of friction, was determined to be 9% WC, 6% MoS2, a load of 10N, a sliding velocity of 1 m/s, and a sliding distance of 400 m. Characterization was carried out for Al6262/WC/MoS2 hybrid composite by using scanning electron microscopy (SEM).

Morbidity rate of children's cerebral palsy in Baku city

To study the morbidity rate of children's cerebral palsy (CP) in Baku in children born in 2006-2016. During 2006-2016, 35 891, 37 130, 37 459, 40 050, 34 726, 34 192, 33 258, 30 373, 29 722, 29 654 and 29 564 children were born in Baku. CP was diagnosed in 1162 of these children. Newborns and children with CP were divided into 3 groups by body mass (<1500; 1500- 2500; 2500 and more) and into 4 groups by the gestational age (<28, 28-32; 32-36, 36 and more weeks). The morbidity rates were detected in these groups. The statistical significance of morbidity rates was assessed by χ2 criteria. The morbidity rate of CP changed within the interval 0.27% (95% CI 0.22-0.33%) in 2006 to 0.37% (95% CI 0.30-0.44%) in 2016. The difference between the minimum and maximum morbidity rates was not statistically significant (p>0.05). The morbidity rate of CP in newborns with body mass <1500 grams was 10.35% (95% CI 0-21.65%) and was 6.1 times higher than in the group of newborns weighing 1500-2500 grams at birth (1.72%; 95% CI 0.20-3.25%) and 30.4 times higher than in the group of newborns with body mass ≥2500 grams (0.34%; 95% CI 0.27-0.41%). The difference between morbidity rates of CP in groups depending on birth body mass was significant (p<0.0001). The incidence rate of CP in a cohort of newborns during the first 60 months after birth in 2006-2016 fluctuated in the range of 0.27-0.37% and had a weak growth trend. The morbidity rate of children's CP is significantly higher in children with body mass at birth <1500 (10.34%) and 1500-2500 grams (1.72%) than in children with body mass at birth 2500 and more grams (0.34%). The gestational age of newborns affects the morbidity rate of CP, which is 5.48; 1.78 and 0.34% at gestational age <32, 32-36 and ≥36 weeks. The structure of subtypes of this pathology is close to that in other populations against the background of the comparative morbidity rate of CP in Baku, the proportion of spastic unilateral, spastic bilateral, dyskinetic and atonic CP is 35.2; 58.0; 3.9 and 1.4%, respectively.

Experimental study on the combustion characteristics of carbon fiber composite materials coated in different coatings

The special environment in which the aircraft operates in terms of flight and safety assurance has special requirements for coating performance. Based on the combustion characteristics of four kinds of high-performance aviation coatings coated with carbon fiber composites, for parameters such as average ignition time and heat release rate of the composite, the results show that the carbon fiber composite has the longest average ignition time, Minimum heat release rate, while the 2-ethyl-4-Mylimidazole cured epoxy coated fiber composite had the shortest average ignition time. The maximum heat release rate was observed.

Magnetic nanoparticle interaction with a hydrogel in an oscillating magnetic field

A study was conducted of the effect of superparamagnetic nanoparticles on a hydrogel in the presence of an oscillating magnetic field directed tangent to the hydrogel surface. The oscillating magnetic field causes the particles to oscillate laterally in the hydrogel, with some of the particles adhering to the hydrogel matrix and other particles moving freely through the hydrogel pore spaces. The analysis was performed for a three-phase matrix-water-particles model, in which the solvent (water) and hydrogel matrix are interacting continua and the particles are a discrete phase. The study examined the effect of fluid elasticity on wave propagation due to the no-slip boundary condition acting under the transversely oscillating magnetic field. A memory effect within the fluid results in a deviation of the minimum and maximum shear rates observed in one half of the oscillation period from those observed in the other half of the oscillation period. The behavior of the hydrogel with different values of the governing dimensionless parameters was assessed. The matrix Reynolds number, the Deborah number, and the ratio of matrix relaxation to retardation times were all observed to have significant influence on the hydrogel viscoelastic response and on the wave propagation within the hydrogel. The phase difference between the water and matrix oscillations is strongly influenced by the phase interaction force coefficient, the Deborah number, and the ratio of free to captured particles. The system is found to approach an asymptotic state at a high Deborah number, which is independent of the value of the Deborah number.

Joint Beamforming Design in Reconfigurable Intelligent Surface-Assisted Rate Splitting Networks

Reconfigurable Intelligent Surface (RIS) is an emerging technology that can improve the spectrum and energy efficiency of next-generation wireless networks. However, attaining accurate channel state information (CSI) for the cascaded RIS channel is particularly challenging. Imperfect CSI is a major bottleneck to achieving the spectral efficiency benefit of RIS-assisted networks. Rate splitting (RS), a promising multiple access technology, has been shown to be able to achieve an improved spectrum efficiency and be robust to channel uncertainties. This paper investigates the interplay between RIS and RS by considering a RIS-assisted RS beamforming problem. Active beamforming at the base station (BS) and passive beamforming at the RIS are jointly considered to maximize the minimum user rate under both the perfect CSI case and the imperfect CSI case. A block coordinate descent (BCD) algorithm is developed to solve this non-convex problem. Compared with the conventional semi-definite relaxation (SDR) approach, the proposed method does not require that the covariance matrix of the common beamforming vector be rank-one. Moreover, we present theoretical results to help reveal the impact of the system parameters and explain the performance gain resulting from the integration of RIS and RS. Extensive simulation results are also provided to show that RIS-assisted RS can improve the max-min rate performance significantly compared with conventional multiple access technologies, such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) with/without RIS, especially in overloaded systems. With the proposed method, RS shows great potential in combating realistic CSI errors in RIS-assisted networks.

Harnessing deep transient testing for reservoir characterization and CO2 emission reduction in challenging geological settings

This paper provides a comprehensive overview of Deep Transient Testing (DTT), a cutting-edge technique for reservoir characterization that has revolutionized the oil and gas industry. The main aim of DTT is to characterize the reservoir with a deeper radius of investigation. The optimization of the radius of investigation with the DTT approach is studied in detail. Reveal is a commercial numerical simulation application used to simulate the DTT process and evaluate the pressure wave analysis in the porous media. The main aim of the simulation is to understand the impact of the reservoir quality on the pressure response and use it to address the noise-to-pule ratio, which is a determinantal parameter in testing duration. The tested wells with the DTT tool show that measured well productivity can deliver the minimum commercial rate. The has been delivered within 2 days compared to the potential test time of 21 days which saved the 19 rig days and contributed to CO2 emission reduction of (gas flaring 1340 + rig emission 600) 1940 Metric tons equivalent to 421 cars emission in a year. However, DTT also presents certain limitations, such as the requirement for specialized equipment and expertise, as well as the potential for formation damage during testing. This study provides a detailed description of the DTT technique, encompassing its history, theory, and practical applications. Furthermore, it discusses the benefits and limitations of DTT and presents case studies to illustrate its effectiveness across various reservoir types. Overall, this study serves as a valuable resource for reservoir engineers, geologists, and other professionals involved in the exploration and production of oil and gas.

Machine Learning Algorithms for Predicting Factitious Disorder Using the Learning Vector Quantization Method

Factitious disorder is classified as a mental problem because it is related to severe emotional disorders. A person who has factitious disorder intentionally produces symptoms of the disease for the purpose of receiving care and attention in a medical setting. People with Factitious disorder act with the aim of attracting the sympathy and attention of others. Diagnosing factitious disorder is very difficult. The reason is, the sufferer looks fine. The doctor must eliminate any physical and mental illness before confirming the diagnosis of Factitious disorder. Along with the development of machine learning technology. Incorporation of patient data and the use of machine learning technology can help detect the disease. The purpose of this study was to build a system to predict the likelihood of a person being exposed to factual or unrelated disorders with the inputs that the patient entered. The method for diagnosing factitious disorder uses the Learning Vector Quantization method whether a person is a sufferer or not. The data was obtained from the questionnaire using 14 parameters and managed to get data as much as 30 training data. This research resulted in a maximum epoch value of 1000, a learning rate value of 0.1, a learning rate multiplier of 0.1, a minimum learning rate of 0.0001 and a training data of 5. The accuracy result obtained is 70%.

Impact of periodic vaccination in SEIRS seasonal model.

We study three different strategies of vaccination in an SEIRS (Susceptible-Exposed-Infected-Recovered-Susceptible) seasonal forced model, which are (i) continuous vaccination; (ii) periodic short-time localized vaccination, and (iii) periodic pulsed width campaign. Considering the first strategy, we obtain an expression for the basic reproduction number and infer a minimum vaccination rate necessary to ensure the stability of the disease-free equilibrium (DFE) solution. In the second strategy, short duration pulses are added to a constant baseline vaccination rate. The pulse is applied according to the seasonal forcing phases. The best outcome is obtained by locating intensive immunization at inflection of the transmissivity curve. Therefore, a vaccination rate of 44.4% of susceptible individuals is enough to ensure DFE. For the third vaccination proposal, additionally to the amplitude, the pulses have a prolonged time width. We obtain a non-linear relationship between vaccination rates and the duration of the campaign. Our simulations show that the baseline rates, as well as the pulse duration, can substantially improve the vaccination campaign effectiveness. These findings are in agreement with our analytical expression. We show a relationship between the vaccination parameters and the accumulated number of infected individuals, over the years, and show the relevance of the immunization campaign annual reaching for controlling the infection spreading. Regarding the dynamical behavior of the model, our simulations show that chaotic and periodic solutions as well as bi-stable regions depend on the vaccination parameters range.

Service Time Maximization for Data Collection in Multi-UAV-Aided Networks

Unmanned aerial vehicles (UAVs) have been enormously gaining attention to offload traffic or collect data in wireless networks due to their key attributes, such as mobility, flexibility, and cost-effective deployment. However, the limited onboard energy inhibits the UAV from serving for a longer duration. Therefore, this paper studies a UAV-aided network where multiple UAVs are launched to collect data from the mobile nodes. In particular, we aim to maximize the service time of the UAVs by jointly optimizing the three-dimensional (3D) trajectory of the UAVs and resources allocated to each node by the UAVs such that each mobile node receives a minimum specified data rate. To facilitate a solution, we construct an equivalent problem that considers the UAV's energy consumption. In particular, we minimize the maximum energy consumed by the UAVs in each time slot. To solve the problem, an iterative approach is presented that decouples the problem into two sub-problems. The optimal location of the UAVs is computed in the first sub-problem, while resource allocation is carried out in the second sub-problem. These two sub-problems are solved in an iterative manner using the alternating optimization approach. We show that the proposed approach improves the service time of the network by 20% on average compared to the existing approaches.

Power Allocation for NOMA With Cache-Aided D2D Communication

Communication networks are becoming increasingly content-centric, and reusable content like viral information and video on demand are often requested by multiple users. Caching at the user equipment enables device-to-device (D2D) communications to be used to deliver requested contents which will help reduce data traffic at base stations and also enhance the achievable data rates. In this paper, we propose a system whereby users are able to exchange valuable cached content with each other via a D2D link which underlays the reception of a downlink non-orthogonal multiple access (NOMA) signal. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived optimal solutions depending on which user is the D2D transmitter. Additionally, sub-optimal solutions based on a negligible self-interference assumption are also proposed. Simulation results demonstrate the significant performance gains of underlaid D2D communications as compared with optimal downlink cellular NOMA. Furthermore, results on the self-interference (SI) cancellation factor highlight that the sub-optimal power allocation solution offers sum rate performance close to the optimal case. The performance gains are further enhanced when SI cancellation is high.

Pay practices and safety organizing: Evidence from hospital nursing units.

Our understanding of how highly reliable care delivery is brought about remains elusive, in part, because there is limited evidence regarding the organizational practices that enable safety organizing-the behaviors and processes underlying high reliability. Because safety organizing relies on discretionary effort and lowering barriers to sharing expertise and discussing threats to safety and errors, we investigate three pay practices and their effects on information sharing and, in turn, safety organizing. Specifically, we examine average pay level, minimum pay rates, and pay dispersion on nursing units and their relationship with information sharing and safety organizing. Cross-sectional analyses of survey data from 1,461 registered nurses in 45 nursing units in three Midwestern hospitals on safety organizing linked to administrative data on pay practices from the organization's human resource systems. Pay data and survey responses were aggregated to the nursing unit level. PROCESS and structural equation modeling were used to simultaneously test for direct and indirect effects of pay variables on information sharing and safety organizing. PROCESS and Mplus path analysis indicated that paying a higher minimum rate in the unit and having lower pay dispersion have indirect, desirable associations with safety organizing through information sharing. Pay practices can help organizations enhance safety organizing. In particular, higher pay rates for the lowest level nurses and lower pay dispersion among nurses are associated with unit-level information sharing and safety organizing. Having pay practices associated with lower within-unit variation and higher pay for the lowest paid members of a unit may be viable strategies for greater information sharing and safety organizing.

Joint mode selection and resource allocation to flexibly guarantee quality of service for downlink MISO-NOMA systems

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) detection, knownas SIC NOMA, enjoys higher spectral efficiency (SE), but suffers increased processing complexity and additional error propagation compared to Non-SIC NOMA,in whichthe receivers directly detect their own signalsfrom thesuperposed signalwithout SIC detection. We propose a joint Mode Selection and Power Allocation (MSPA) scheme that can flexibly realize different kinds of tradeoffs while guaranteeing minimum rate requirements (MRRs) for two-user MISO-NOMA downlink. Specifically, we first obtain rate region boundaries that guarantee two users’ MRRs for two candidatemodes: maximum-ratio transmission (MRT)/SIC NOMA and minimum mean square error beamforming (MMSE-BF)/Non-SIC NOMA. We then construct an expression of power allocation factors (transmit power factors) that can satisfy the two users’ MRRs for MRT/SIC NOMA (MMSE-BF/Non-SIC NOMA) by introducing a parameter λ. Finally, we achieve outer boundary of the union ofthe two modes' rate regionsby mode selection and derive expected users' rates on it. Numerical results demonstrate that the proposed MSPA can improve SE and flexibly achieve different kinds of tradeoffs by adjusting λ, while guaranteeing the two users’ MRRs simultaneously for high signal to noise ratio (SNR) scenario and alternately guaranteeing the two users’ MRRs for low SNR scenario.

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

Holter ECG Monitoring in Assessing Arrhythmias in Patients with Palpitation

BACKGROUND Palpitation is one of the most common indications for Holter study. 24- hour Holter monitoring helps to detect abnormal cardiac rhythm which can be managed earlier if picked up sooner. Spot ECG has less sensitivity to pick episodic abnormal rhythm so longer monitoring time is essential. Various treatment modalities can thus be planned for non-benign and relatively benign arrhythmias like VT and PSVT respectively.&#x0D; METHODS This was a single center prospective observational cross-sectional study conducted at BPKIHS, Dharan for three months between July 2023 to September 2023. The primary objective was to study abnormal cardiac electrical events in patients having palpation. There were 38 patients of palpitations that underwent 24-hour Holter study after fulfilling inclusion criteria. The data was recorded on an excel sheet and was analyzed using software SPSS 20.&#x0D; RESULTS There were 47.4% (18) males and 52.6% (20) females. The mean age was 49.61± 17.63 years. There were 36.8% (14) patients in age group &lt;45 years while 42.1% (16) were in age group 45 to 65 years and 21.1% (8) were in the age group &gt;65 years. The maximum and minimum Heart rate were 180 and 35 beats per minute respectively. There was abnormal Holter ECG in 57.9% (22) and 42.1% (16) had normal ECG. Most common arrhythmia was VPC 17(77.2%) in our study. The association between the age group and Normal vs Abnormal Holter ECG was statistically significant (P Value=0.023) (Table 8). DM was the most common co-morbid illness in 5(13.2%), one participant had DM and HTN along with Dyslipidemia and other findings were smoking (5.3%), dyslipidemia (5.3%). There was no association between comorbid conditions and normal vs abnormal ECG Holter rhythm (P Value = 0.448).&#x0D; CONCLUSION Out of 38 participants VPC was the most common (77.2%) arrhythmia. The association between Age group and Normal vs Abnormal Holter was statistically significant (P Value=0.023). Most of the arrhythmias were benign. Diabetes was the most common risk factor present while 50% of subjects had no co-morbid illness.

Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems

BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.

Evaluation of the Efficiency of Different Waterproofing Products in Concrete for Sustainability

The efficacy of several waterproofing products in concrete, such as integrated waterproofing compound, water-repellent cement and waterproofing coatings with acrylic and styrene-butadiene latex co-polymer bases was assessed in this experimental study. The mechanical properties, durability and fresh concrete parameters were examined and a comparison was related to control concrete-concrete that had not been treated with any waterproofing compound. Concrete with an integrated waterproofing chemical exhibited the largest slump of 56 mm, whereas the concrete with water-repellent cement displayed the highest density of 2459 kg/m3. Concrete with integral waterproofing chemical showed the best compressive strength of 39.33 N/mm2, flexural strength of 5.85 N/mm2 and split-tensile strength of 3.80 N/mm2, respectively, at 28 days. Cube specimens treated with an acrylic base water-proofing coating showed a minimum average rate of water absorption of 0.36 and a minimum sorptivity of 0.022 mm/min0.5, after 48 hours. It was concluded that the best result for mechanical properties was shown by concrete with an integral waterproofing compound, whereas the concrete with acrylic-based waterproofing coating was the best performer in terms of durability parameters.