Inventions

Individuals’ lifestyles are affected by valgus and varus deformities in the rearfoot, causing pain in the joints and plantar surface due to the misalignment between the tibial and calcaneus. In orthopedics, medical professionals measure this misalignment by using X-ray systems and goniometers. The X-ray emits ionizing radiation that can cause damage through cumulative exposure over a lifetime, whereas the goniometer will produce measurement errors. This patent review conducted a technological search of systems and methods across various databases using inclusion and exclusion criteria. These thirty-five obtained patents provide valuable information about mechanical, electronic, and mechatronic technologies and non-ionizing radiation to evaluate valgus and varus deformities. The patents are classified into stationary mechanisms, stationary electronic devices, dynamic mechanisms, dynamic electronic devices, stationary mechatronic devices, and dynamic mechatronic devices. They are further categorized based on their measurement methods as either visual or automatic. Additionally, the patents are grouped by usage mode into sitting, standing, and walking. This patent review aims to provide medical professionals with little-known techniques for measuring and evaluating the rearfoot alignment. Full article

Date palm tree (DPT) and pine tree (PT) needles in forests form a combustible mat, posing fire risks during summer in Pakistan that damage vegetation, wildlife habitats, and biodiversity and impact local livelihoods. In this article, sintered ceramic specimens were prepared at different weight concentrations (DPT5, DPT10, DPT20, and DPT 30 and PT5, PT10, PT20, and PT30) of date palm tree leaf ash and pine tree needle ash as secondary additives in ceramic manufacturing along with primary material kaolinite (China clay). Raw materials composition was analyzed using X-ray diffraction (XRD), taking loss on ignition, water absorption, bulk density, saturated surface dry density (SSD), weight per unit area, and thermal cycling as measurement indexes. The result indicates that loss on ignition increases while increasing the quantity of secondary additives and the maximum increase for DPT30 was 19.6% and for PT30, it was 22.1%. As the secondary additives increase, the water absorption rate also increases and the maximum increase for DPT30 and PT30 is 4.5%. Meanwhile, with the increase in secondary additives, the density decreased; for DPT 30, it was 1457.7 kg/m³ and for PT30, it was 1829.8 kg/m³. Thermal performance was investigated by heating and cooling cycles. It was observed that thermal performances increase with the increase in secondary additives. The results reveal this novel approach has the potential to form a ceramic and good properties can be achieved. The prepared specimens have the potential to be used in the fields of electronics, aerospace, construction, and building engineering, alleviating environmental strain, curbing the exhaustion of China clay reserves, and most importantly, lowering the risk of forest fires in Pakistan. Full article

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO₂ levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO₂ and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines. Full article

The virtual power plant (VPP) is an excellent approach for mitigating the intermittency and fluctuation of renewable energy sources. The present work proposes an optimal scheduling model for VPPs to leverage the benefits of joint electricity and carbon trading from the perspective of multiple energy-sharing mechanisms. First, the optimal sharing scheduling model of the electric, thermal, and hydrogen energy was established. The model integrates various components, including wind turbines, photovoltaic units, electrolytic cells, combined heat and power units, hydrogen-doped gas boilers, electric energy storage, thermal storage tanks, and hydrogen storage tanks. Then, the model incorporates a tiered carbon trading mechanism to minimize operating and trading costs. Finally, numerical results indicate that, compared with the independent operation of virtual power plants and the lack of joint electricity and carbon trading, the optimal scheduling scheme proposed in this paper reduces the total cost and carbon emissions of the three VPPs by 3.3% and 49.7%, respectively. This demonstrates that the proposed model can effectively reduce the total operating expenses of VPPs by facilitating the allocation of electric, thermal, and hydrogen energy and achieving low-carbon emission operations. Full article

As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions. In Toyama City, Toyama Prefecture, where the next-generation light rail transit (LRT) system has been introduced, the number of users has significantly increased compared to before its introduction, with some users riding the LRT for the sake of the experience itself. On the other hand, there is a demand for a more micro-level and quantitative evaluation of the impact that the LRT has on the liveliness of areas along its route. Therefore, this study uses inverse reinforcement learning (IRL), a type of machine learning, to build a model that estimates route-choice behavior along the LRT lines based on behavioral trajectories generated from smartphone location data. The model is capable of evaluating the characteristics of location data with high accuracy. The findings indicate that routes along the LRT lines tend to be selected, suggesting that both the appeal of the LRT itself and the attractiveness of the spaces along its route contribute to this tendency. Full article

To enhance the participation enthusiasm of distributed energy resources (DERs) and DER aggregators in their demand response, this paper develops a three-level distributed scheduling model for the distribution network operators (DNO), DER aggregators, and DERs based on the conditional value-at-risk (CVaR) theory. First, a demand response model is established for the DNO, DER aggregators, and DERs. Next, we employ the analytical target cascading (ATC) method to construct a three-level distributed scheduling model, where incentive and compensation prices are shared as consensus variables across the model levels to amplify the influence of DER aggregators on incentive prices and DERs on compensation prices. Then, the photovoltaic output model is restructured using the CVaR theory to effectively measure the risk associated with photovoltaic output uncertainty. Finally, an analysis is conducted using the IEEE 33-node distribution network to validate the effectiveness of the proposed model. Full article

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts. Full article

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system. Full article

In this paper, a multi-objective grey wolf optimization (GWO) algorithm based Bidirectional Long Short Term Memory (BiLSTM) network machine learning (ML) model is proposed for finding the optimum sizing of distributed generators (DGs) and shunt capacitors (SHCs) to enhance the performance of distribution systems at any desired load factor. The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. In order to save utility engineers time and effort, this study introduces a BiLSTM network-based machine learning model to directly estimate the optimal values of DGs and SHCs, rather than relying on load flow estimates. At first, a multi-objective grey wolf optimizer determines the most suitable locations and capacities of DGs and SHCs at the unity load factor and the same locations are used to obtain optimum sizing of DGs and SHCs at other load factors also. The base case data sets consisting of substation apparent power, real power load, reactive power load, real power loss, reactive power loss and minimum node voltage at various load factors in per unit values are taken as input training data for the machine learning model. The optimal sizes of the DGs and SHCs for the corresponding load factors obtained using GWO algorithm are taken as target data sets in per unit values for the machine learning model. An adaptive moment estimation (adam) optimization approach is employed to train the BiLSTM ML model for identifying the ideal values of distributed generations and shunt capacitors at different load factors. The efficacy of the proposed ML-based sizing algorithm is demonstrated via simulation studies. Full article

In contemporary academic settings, end-of-semester student feedback on a lecturer’s teaching abilities often fails to provide a comprehensive, real-time evaluation of their proficiency, and becomes less relevant with each new cohort of students. To address these limitations, an innovative feedback method has been proposed, utilizing image processing algorithms to dynamically assess the emotional states of students during lectures by analyzing their facial expressions. This real-time approach enables lecturers to promptly adapt and enhance their teaching techniques. Recognizing and engaging with emotionally positive students has been shown to foster better learning outcomes, as their enthusiasm actively stimulates cognitive engagement and information analysis. The purpose of this work is to identify emotions based on facial expressions using a deep learning model based on a convolutional neural network (CNN), where facial recognition is performed using the Viola–Jones algorithm on a group of students in a learning environment. The algorithm encompasses four key steps: image acquisition, preprocessing, emotion detection, and emotion recognition. The technological advancement of this research lies in the proposal to implement photonic hardware and create an optical neural network which offers unparalleled speed and efficiency in data processing. This approach demonstrates significant advancements over traditional electronic systems in handling computational tasks. An experimental validation was conducted in a classroom with 45 students, demonstrating that the level of understanding in the class as predicted was 43–62.94%, and the proposed CNN algorithm (facial expressions detection) achieved an impressive 83% accuracy in understanding students’ emotional states. The correlation between the CNN deep learning model and the students’ feedback was 91.7%. This novel approach opens avenues for the real-time assessment of students’ engagement levels and the effectiveness of the learning environment, providing valuable insights for ongoing improvements in teaching practices. Full article

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC starts to convert once an optimal temperature, usually known as the light-off temperature, is reached. There are many methods to reduce the warm-up period of the TWC, among which is using a burner. The initial question underlying this study was to see if the use of a relatively straightforward extra-combustion device mounted upstream the TWC, without complex elements, was able to serve the purpose of reducing the light-off time. Consequently, an original burner was designed and investigated numerically via the CFD method and experimentally via measurements of the temperature evolution within a TWC, along with the emissions specific to the burner’s operation. The main findings of this study are: (1) the CFD-based examination is a good way to decide on how to achieve the so-called fit-for-purpose internal aerodynamics of the burner (i.e., to obtain a homogeneous mixture) and (2) to reach the light-off temperature, conventionally taken as 500 K, the burner was operated for 5.2 s, i.e., 3.6 g of gasoline injected, 2.7 g of CO₂ and 1.351 g of CO, respectively, emitted. Moreover, this study identified measures for improving the burner’s design as well as an enhanced procedure for the burner’s operating control both aiming to produce a cleaner combustion during the TWC pre-heating. Full article

The heat transfer surfaces of heat exchangers are usually made of metals which may suffer from severe corrosion. When corrosive fluids are present, highly corrosion-resistant metals, graphite or ceramics are used, resulting in high costs. This study presents measured data on the thermophysical and mechanical properties of recently developed corrosion-resistant polymer composite tubes for use in heat exchangers. Extruded polymer composite tubes based on polypropylene or polyphenylene sulfide filled with graphite flakes were investigated. The anisotropic thermal conductivities of the polymer composite tubes were measured at various temperatures. The through-wall thermal conductivity of the tubes made of polypropylene filled with 50 vol.% graphite is increased by a factor of 30 compared to pure polypropylene, resulting in a thermal conductivity of 6.5 W/(m K) at 25 °C. The tubes composed of polyphenylene sulfide filled with 50 vol.% graphite have a through-wall thermal conductivity of 4.5 W/(m K) at 25 °C. The mechanical properties of the polymer composites were measured using tensile and flexural tests at different temperatures. The composite materials are more rigid and keep their mechanical properties up to a higher temperature level compared to the unfilled polymers. Surface roughness measurements show the very smooth and sealed surface of the composite tubes. The results contribute to establishing the viability of using polymer composites for heat exchanger applications with corrosive fluids. Full article

To alleviate the shock boundary layer interaction adverse effects, various active or passive flow control strategies have been investigated in the literature. This research sheds light on the behavior of perforated plates as passive flow control techniques applied to NACA0012 airfoils in cascade configurations. Two identical perforated plates with shallow cavities underneath are accommodated on the upper and lower surfaces of each airfoil in the cascade arrangement. Six different cascade arrangements, including a baseline configuration with no control applied, are additively manufactured, with different perforated plate orifice sizes in the range of 0.5–1.2 mm. A high-speed wind tunnel with Schlieren optical diagnosis and wall static pressure taps is used to investigate the changes in the shock waves pattern triggered by the perforated plates. Steady 3D density-based numerical simulations in Ansys FLUENT are conducted for further analysis and validation. In the cascade configuration, the perforated plates alter the shock structure, and the strong normal shock wave is replaced by a weaker X-type shock structure. Eventually, a 1% penalty in overall total pressure loss is induced by the perforated plates because of the negative loss balance between the reduced shock losses and the enhanced viscous losses. Further studies on perforated plate geometrical features are needed to improve this outcome in a cascade arrangement. Full article

This paper explores the application of conceptual hydrological models in optimizing the operation of hydroelectric power plants (HPPs) in steppe regions, a crucial aspect of promoting low-carbon energy solutions. The study aims to identify the most suitable conceptual hydrological model for predicting reservoir inflows from multiple catchments in a steppe region, where spring runoff dominates the annual water volume and requires careful consideration of snowfall. Two well-known conceptual models, HBV and GR6J-CemaNeige, which incorporate snow-melting processes, were evaluated. The research also investigated the best approach to preprocessing historical data to enhance model accuracy. Furthermore, the study emphasizes the importance of accurately defining low-water periods to ensure reliable HPP operation through more accurate inflow forecasting. A hypothesis was proposed to explore the relationship between atmospheric circulation and the definition of low-water periods; however, the findings did not support this hypothesis. Overall, the results suggest that combining the conceptual models under consideration can lead to more accurate forecasts, underscoring the need for integrated approaches in managing HPP reservoirs and promoting sustainable energy production. Full article

This study emphasizes the urgent need for systems that monitor the operational states of primary electrical equipment, particularly power transformers. The rapid digitalization of and increasing data volumes from substations, coupled with the inability to retrofit outdated equipment with modern sensors, underscore the necessity for algorithms that analyze the operational parameters of digital substations based on key power system metrics such as current and voltage. This research focuses on digital substations with Architecture III and aims to develop an algorithm for processing digital substation data through an appropriate mathematical tool for time-series analysis. For this purpose, the fast discrete wavelet transform was chosen as the most suitable method. Within the framework of the research, possible transformer faults were divided into two categories by the nature of their manifestation. A mathematical model for two internal transformer fault categories was built. The most effective parameters from the point of view of the possibility of identifying an internal fault were selected. The proposed algorithm shows its effectiveness in the compact representation of the signal and compression of the time series of the parameter to be monitored. Full article

This paper outlines the development phases of a wave-driven Helicon Plasma Thruster for cutting-edge Low Earth Orbit (LEO) constellations. The two-stage ambipolar electric propulsion (EP) system combines the efficient ionization of an ultra-compact helicon reactor with plasma acceleration based on an ambipolar electric field provided by a magnetic nozzle. This paper reveals maturation challenges associated with an emerging EP system in the hundreds-watt class, followed by outlook strategies. A 3 cm diameter helicon reactor was operated using argon gas under a time-modulated RF power envelope ranging from 250 W to 500 W with a fixed magnetic field strength of 400 G. Magnetically enhanced inductively coupled plasma reactor characteristics based on half-wavelength right helical and Nagoya Type III antennas under capacitive (E-mode), inductive (W-mode), and wave coupling (W-mode) were systematically investigated based on Optical Emission Spectroscopy. The operation characteristics of a wave-heated reactor based on helicon configuration were investigated as a function of different operating parameters. This work demonstrates the ability of two-stage HPT using a compact helicon reactor and a cusped magnetic field to outperform today’s LEO spacecraft propulsion. Full article

Shipping containers that remain in ports after exporting or importing products cause an environmental and logistical problem. Transporting them to the port of origin is costly; therefore, some of them are stored in the regions of destination. Recycling or reusing them in an efficient and sustainable way represents a clean alternative. The purpose of this article is to analyze the feasibility and impact of implementing different insulating configurations on the energy demands required by a house based on a construction with standardized shipping containers. More specifically, it assesses the impact of the different orientations in which the dwelling can be arranged, depending on the location and its meteorological data. To this aim, a construction model will be developed in which first, the geometrical parameters are defined, and second, the energy characteristics are identified. The results show that, in Southwest Europe, the western orientation generates a saving of 10% of the energy demand compared to the less favourable orientation, which is the southern one. Full article

Ultrasonic pretreatment is a crucial step in the bioconversion of lignocellulosic biomass, such as peapods, into valuable products. Ultrasonic pretreatment is a highly effective physical method that utilizes ultrasonic waves to enhance various processes. Biomass pretreatment is achieved through physical effects such as acoustic cavitation, which disrupts the biomass structure, and chemical effects like radical formation, which breaks down complex molecules. This article focuses on the characteristics, types, and applications of ultrasonic pretreatment in peapods, with a particular emphasis on its role in lignin removal and ultrasound design. An innovative mechanical design in a CAD application of a continuous ultrasound treatment with a capacity of 5 L and an FEA analysis of the equipment are presented as results, providing insights for the design and optimization of ultrasonic pretreatment processes. Full article

As space missions proliferate and the payload requirements increase, the environmental impact of thrusters can no longer be considered negligible. Therefore, less impactful fuels such as methane are starting to be considered for launchers. In this paper we present a design case study for such a turbopump. Using both analytical models and Computational Fluid Dynamics techniques, we were able to reduce the size and weight of the turbopump assembly. Also, due to the elimination of some auxiliary systems, the overall efficiency was enhanced. This paper’s findings and methods can be transferred not only to launchers in its own class, but also to larger scale engines with a similar construction. Full article