Bartosz Wieczorek

Internal combustion engine-powered working machines often operate under rapidly varying loads, while conventional controllers typically infer operator demand from mechanical inputs and machine sensors. This review examines whether electromyography (EMG), which records electrical activity generated by skeletal muscles, could provide earlier indications of operator intention to improve engine operation and reduce fuel consumption and exhaust emissions. A structured narrative review of scientific publications and patent documents was conducted, covering EMG-based machine interfaces, intention recognition, engine–machine power management, and environmental performance. Four potential control architectures were identified: EMG-derived commands, EMG-based power-demand prediction, multimodal adaptive control, and predictive supervisory optimization. Experimental studies of engine-control strategies that did not use EMG reported a 12.5% reduction in the overall fuel consumption rate of a bulldozer and a 33% reduction in fuel consumption for a wood chipper using adaptive engine-speed control. In the latter study, CO, CO₂, and NO_x emissions decreased by 30%, 37%, and 33%, respectively, whereas hydrocarbon emissions increased by 290%, highlighting important environmental trade-offs. Although EMG-based systems have demonstrated the feasibility of recognizing operator actions and controlling machine functions, none of the reviewed studies directly demonstrated reductions in fuel consumption or emissions from EMG-informed internal combustion engine control. Future research should determine whether EMG provides a measurable prediction advantage over conventional machine sensors and improves fuel efficiency and environmental performance without compromising productivity or safety.

The increasing integration of electronic control, powertrain management, and autonomous functions has diversified the methods used to set and determine demanded engine torque in vehicles and machinery. This study systematizes these solutions and identifies their functional differences with respect to signal origin, operator involvement, and control autonomy. A structured narrative review was conducted using scientific and technical literature covering road vehicles, agricultural, construction, forestry, material handling, industrial, and stationary machinery, with sources available up to September 2026. The analyzed solutions were classified into four groups: Direct Operator Input, Indirect Operating State Input, Automatic Determination of Demanded Torque, and Predictive or Autonomous Determination of Demanded Torque. The review shows a progressive transfer of responsibility for torque determination from the operator to increasingly integrated control systems. It also indicates that classification should be based on the function and semantic meaning of the control signal rather than solely on the physical form of the input device. Predictive and autonomous systems extend conventional automatic control by incorporating information on future operating conditions, predicted load, environment, and process state. The proposed framework provides a consistent basis for comparing heterogeneous control architectures and highlights the transition toward integrated, adaptive, and predictive powertrain management.

Manual wheelchair users may have reduced physical activity and total energy expenditure, while wearable devices may not adequately capture external mechanical demand during wheelchair propulsion. This pilot study examined whether wheel-level mechanical power combined with heart rate could estimate mass-specific oxygen uptake (V̇O2/kg) during manual wheelchair propulsion. Ten long-term manual wheelchair users aged 29–38 years completed 3-min trials under three progressively loaded conditions on a wheelchair ergometer. Wheel velocity and braking torque were measured, mechanical power was calculated from trial-mean torque and angular velocity, and metabolic variables were assessed using a wearable respiratory gas analyzer. A two-predictor linear regression model was developed, with participant-clustered standard errors and leave-one-participant-out cross-validation (LOPO-CV) for internal evaluation. V̇O2/kg increased across conditions despite lower velocity under the highest load. The final model was V̇O ₂ /kg = 5.5738 + 0.2444P + 0.03587HR. Mechanical power (95% CI: 0.1330–0.3558) and heart rate (95% CI: 0.01342–0.05833) were significant positive predictors. The model explained 72.2% of the variance ( R 2 = 0.722; adjusted R 2 = 0.701). LOPO-CV yielded an RMSE of 1.128 mL O ₂ ·kg ⁻¹ ·min ⁻¹ , an MAE of 0.917 mL O ₂ ·kg ⁻¹ ·min ⁻¹ , and R 2 = 0.575. Repeated-measures Bland–Altman analysis of LOPO-CV predictions showed a mean bias of −0.156 mL O ₂ ·kg ⁻¹ ·min ⁻¹ (95% CI: −0.6259 to 0.2022), with limits of agreement from −2.389 (95% CI: −3.1667 to −1.7711) to 2.077 mL O ₂ ·kg ⁻¹ ·min ⁻¹ (95% CI: 1.6096 to 2.6636). Mechanical power and heart rate may provide complementary information for estimating metabolic demand during manual wheelchair propulsion. However, the model was developed in a small exploratory sample, was only internally evaluated, and requires external validation before practical application.

The effectiveness of mechanical tactical ventilation during rescue and firefighting operations can be improved by multiplying the number of positive pressure ventilators. Such action can shorten the time of the operation, improve its execution, and reduce the energy consumed by the ventilators used during the operation. In order to determine the proper conditions for the two ventilators to work together, tests were conducted on their positioning relative to the doorway. These values were determined from the flow parameters (flow rate and static overpressure generated). A set of conventional rotor ventilators (each with an output of 4.4 kW) was tested. The article performs an analysis of the magnitude of flow parameters depending on the setting of the positive pressure ventilator. Proper positioning of the set of ventilators can increase the flow rate through the facility by up to 32% and the pressure by 50%. The analysis conducted in the article can be used as recommendations for rescue teams for the proper selection of the setting of the positive pressure ventilator during firefighting operations of internal fires.

This paper investigates the grip strength of individual fingers, utilising a medical dynamometer to measure the force exerted by each finger and both hands across multiple series of tests. The objective of this study is to provide a comprehensive analysis of finger grip strength among different groups, segmented by centile, gender, activity levels, and BMI. The scope includes detailed measurements of each finger's strength, aggregate hand strength, and statistical processing of the data to compute mean values, standard deviations, confidence intervals, and Student's t-distributions for the various centile groups. Results highlight significant variations in grip strength, correlated with physical activity and BMI. This detailed data set is critical for indexing, abstracting, and retrieval purposes, offering valuable insights for fields such as ergonomics, physical therapy, and sports science

The use of plastic waste as a filler in concrete, particularly in paving block production, represents an approach aligned with circular economy principles. While previous studies have focused on mechanical properties, the effect of such materials on slip risk remains insufficiently investigated, especially for pedestrian applications. This study evaluates the influence of the volumetric content of recycled plastic waste from electrical cable insulation on slip resistance of concrete paving blocks. A series of specimens was prepared with 0–45% replacement of natural aggregate by granulated cable insulation (GCI). Slip resistance was measured using the British Pendulum Tester and expressed as Skid Resistance Value (SRV) after statistical processing. Two sliders were used, Mounted Shoe 55 and Mounted Shoe 96, corresponding to road and pedestrian conditions. The results show that increasing GCI content reduces mass by approximately 9.6 g per 1% GCI, reaching a reduction of about 20% at 50% GCI. For polished surfaces, SRV increased up to 77 (MS55) and 75 (MS96) at 40–45% GCI. For ground surfaces, optimal performance was observed at 10% GCI, while higher contents reduced SRV and caused mechanical degradation above 30–35% GCI. The results indicate that optimized GCI content can improve slip resistance while reducing material weight.

This paper proposes an approach to optimize Kazakhstan’s track maintenance machine fleet by incorporating forecasted railway track condition. The study analyzes freight turnover and infrastructure wear trends for 2014-2024 under growing transit/export loads: freight turnover increased from 214 to 327.9 billion t-km, while infrastructure wear rose from 38% to 57%; according to JSC «KTZ», in January 2025 mainline loading grew by 7.3% to 21.7 million tons, and export transportation increased by 13.4%. Maintenance demand is justified using regulatory track-condition thresholds, speed decisions under defects, and repair assignment criteria. The scientific novelty lies in a decision-making algorithm that explicitly links forecasted track condition to regulatory limits and selects the optimal «retain – modernize – renew» scenario using profit maximization (Π = D – C) as the objective. The practical outcome is an applied methodology for defining the rational fleet composition (machine types/ quantity) for planned maintenance volumes in a system operating approximately 543 units of track machinery; it is intended to reduce equipment downtime, improve resource allocation, and enhance maintenance program deliverability under increasing operational loads

The efficiency of manual wheelchair propulsion is largely determined by rolling resistance, which depends on tire pressure, surface type, and wheel construction. A particularly significant source of energy loss arises from anti-rollback modules, where the contact between a rigid roller and an...

The article discusses the issue of improving the management efficiency of spare parts at railway stations to reduce operational costs and enhance the effectiveness of rolling stock maintenance. A comparative analysis was conducted between traditional inventory storage strategies and models involving the redistribution of surplus spare parts between stations. For the quantitative assessment of the proposed approach, a mathematical model based on a multi-criteria decision analysis (MCDA) methodology was developed that includes the calculation of storage, transportation, and redistribution costs. The results show that redistributing spare parts reduces storage costs by 25%, while implementing the proposed strategy shortens the average downtime of railway wagons by 12.5%, confirming its economic feasibility. Moderate savings were achieved at the Zharyk and Zhanaaul stations, where excessive stockpiling of spare parts was reduced, and the main logistics hub—Karaganda Sortirovochnaya—helped minimize the risk of downtime. At the same time, the analysis revealed certain limitations, such as increased transportation costs and reduced network stability when redistribution volumes are high. The practical implications of the study lie in the potential implementation of the developed inventory management improvement model, not only for railway transport but also for other industries such as aviation and automotive logistics. Spare parts redistribution reduces the financial losses associated with frozen capital, increases the capital turnover, and decreases the risk of depreciation of stored components. The proposed approach ensures a more balanced distribution of resources between stations, facilitating an increase in the profitability of railway enterprises.