Processing Methods Research Articles

Carbon Dots: A Review with Focus on Sustainability.

Carbon dots (CDs) are an emerging class of nanomaterials with attractive optical properties, which promise to enable a variety of applications. An important and timely question is whether CDs can become a functional and sustainable alternative to incumbent optical nanomaterials, notably inorganic quantum dots. Herein, the current CD literature is comprehensively reviewed as regards to their synthesis and function, with a focus on sustainability aspects. The study quantifies why it is attractive that CDs can be synthesized with biomass as the sole starting material and be free from toxic and precious metals and critical raw materials. It further describes and analyzes employed pretreatment, chemical-conversion, purification, and processing procedures, and highlights current issues with the usage of solvents, the energy and material efficiency, and the safety and waste management. It is specially shown that many reported synthesis and processing methods are concerningly wasteful with the utilization of non-sustainable solvents and energy. It is finally recommended that future studies should explicitly consider and discuss the environmental influence of the selected starting material, solvents, and generated byproducts, and that quantitative information on the required amounts of solvents, consumables, and energy should be provided to enable an evaluation of the presented methods in an upscaled sustainability context.

Development of ANP-based greenness evaluation model of construction technologies in China

Purpose Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study aims to establish a Greenness of Construction Technologies (GCT) evaluation model using the method of analytic network process (ANP). Design/methodology/approach Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study establishes a GCT evaluation model using the method of ANP. Findings Among the four formwork technologies of plastic, steel, plywood and wooden formwork, the plastic formwork exhibits the best performance in terms of environmental friendliness, economic and social effects, while facing issues like material wastage and low static strength. The results align with practical observations which validates the model’s applicability. Originality/value This research contributes to the field by introducing the concept of greenness into construction technology evaluation for the first time. The establishment of the GCT evaluation model promotes the adoption of green construction technologies and advances sustainable practices in the construction industry.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Application of Feature, Event, and Process Methods to Leakage Scenario Development for Offshore CO2 Geological Storage

Application of Feature, Event, and Process Methods to Leakage Scenario Development for Offshore CO2 Geological Storage

METHODS OF MATHEMATICAL ANALYSIS AND PROCESSING OF EEG SIGNALS: OVERVIEW OF THE CURRENT STATE AND LATEST TRENDS

Aim. With the help of retrospective analysis, establish the current level of achievements in the field of applying methods of mathematical analysis and processing of biomedical signals, in particular EEG, and characterize the main trends. Materials and methods. The research was conducted using theoretical methods, by studying and analyzing freely available literature, publications, materials of working groups of domestic and foreign scientists over the past few years. Results. The analysis of scientific works devoted to the development and development of EEG showed that over the past five years there has been a deepening of the tendency to separate the methods of improving the hardware and software parts of electroencephalographic complexes. The last of them, in turn, can be divided into several main directions. The first direction is the improvement of real-time EEG analysis methods, including automatic processing included in the software of EEG complexes. The second direction combines mathematical methods of signal analysis to detect existing pathologies and predict the onset of pathological conditions, such as statistical, spectral-correlation analysis, building mathematical models. The next direction brings together the application of mathematical methods for EEG signal processing and further use in deep learning, the use of convolutional neural networks to detect epileptic seizures, sleep disorders, differentiation of neuropsychiatric disorders, etc. The direction of using EEG in prosthetics deserves special attention. Methods of mathematical processing of the EEG signal are widely implemented in the development of bionic prostheses. The main achievements in these directions are considered in detail in the work. Conclusions. While the improvement of the material and technical part of the EEG is focused on the issues of improving the methods of recording biological signals, finding optimal methods of suppressing noise without losing the useful signal and does not currently have breakthrough results, the methods of analysis and processing of the already received signal have developed rapidly. The use of EEG to detect pathological conditions, the study of the neurophysiological basis of cognitive processes, etc., has always attracted the interest of scientists, but the use of neural networks for EEG processing and analysis and their implementation in robotics developments has given this direction a new impetus.

Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data

Abstract. Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing approaches. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty to enhance inter-comparison and empower physical process insights across glacier elevation-change studies.

Evaluating the transferability of airborne laser scanning derived stem size prediction models for Pinus taeda L. stem size estimation to two different locations and acquisition specifications

ABSTRACT Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS’s ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted.

Concentrated Deoiled Fat: A Novel Method of Fat Processing to Improve Fat Graft Survival-A Basic Research.

Oil compromises graft outcomes via inflammation, which accounts for the unpredictability of volume retention rates as low as 20%. Existing techniques for oil removal are relatively inefficient. In this study, a novel approach was taken to prepare concentrated deoiled fat (CDF) by utilizing flocculation and centrifugation to remove the oil. The hypothesis put forward in this study was that CDF would exhibit improved volume retention and quality by enhancing purification efficiency and reducing inflammation. This basic research involved both in vitro and in vivo experiments using samples obtained from women who underwent abdominal liposuction. The CDF was prepared by flocculation and centrifugation. In the vitro experiments, the microstructure of fat was assessed using Calcein acetoxymethyl ester (AM) staining for living cells and propidium iodide (PI) staining for dead nuclei in two groups: Coleman fat group and CDF group. Additionally, the glucose uptake capacity of these two groups was evaluated using the glucose transport test (GTT). In the vivo experiments, the study included three groups: two experimental groups (low-volume concentrated deoiled fat, LCDF; high-volume concentrated deoiled fat, HCDF) and one control group (Coleman fat), with 10 healthy female BALB/c nude mice in each group, 1ml of the graft was injected subcutaneously to each mouse. After 8weeks, the fat grafts were harvested and subjected to volume evaluation, HE staining and immunostaining for perilipin to assess graft outcomes. In the vitro experiments, the concentration rate of the CDF was found to be 79.6% that of Coleman fat, with 15.1% more oil separated. Cell viability, as assessed by AM/PI staining, did not show a significant difference between the two grafts, but the results of the GTT showed that the tissue viability of the CDF was higher than that of Coleman fat. In the vivo experiments, the CDF had higher volume retention than Coleman fat, as measured by water displacement. Histopathologic scoring indicated that HCDF group and LCDF group had a more intact fat structure with fewer vacuoles, inflammation, and fibrosis compared to Coleman fat. Additionally, the percentages of perilipin-positive area in the LCDF group and HCDF group were higher than in the Coleman group, indicating improved graft quality and outcome with the use of concentrated deoiled fat. "Concentrated deoiled fat" refers to an autologous fat graft from which oil has been removed by flocculation and centrifugation. This process increases volume retention and viable cells and decreases infiltrated inflammatory cells. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Triple-0: Zero-shot denoising and dereverberation on an end-to-end frozen anechoic speech separation network.

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset. Transfer learning, i.e. using a pretrained network for a new task, comes to the rescue by reducing the amount of training time, computational resources, and the required dataset, but the network still needs to be fine-tuned for the new task. This paper presents a novel method of speech denoising and dereverberation (SD&D) on an end-to-end frozen binaural anechoic speech separation network. The frozen network requires neither any architectural change nor any fine-tuning for the new task, as is usually required for transfer learning. The interaural cues of a source placed inside noisy and echoic surroundings are given as input to this pretrained network to extract the target speech from noise and reverberation. Although the pretrained model used in this paper has never seen noisy reverberant conditions during its training, it performs satisfactorily for zero-shot testing (ZST) under these conditions. It is because the pretrained model used here has been trained on the direct-path interaural cues of an active source and so it can recognize them even in the presence of echoes and noise. ZST on the same dataset on which the pretrained network was trained (homo-corpus) for the unseen class of interference, has shown considerable improvement over the weighted prediction error (WPE) algorithm in terms of four objective speech quality and intelligibility metrics. Also, the proposed model offers similar performance provided by a deep learning SD&D algorithm for this dataset under varying conditions of noise and reverberations. Similarly, ZST on a different dataset has provided an improvement in intelligibility and almost equivalent quality as provided by the WPE algorithm.

Obtention of Suitable Pregnant Leach Solution (PLS) for Copper Solvent Extraction Plants from Copper Concentrate Using Hydrogen Peroxide and Iodine in a Sulfuric Acid–Chloride Medium

Copper leaching presents an environmentally friendly alternative to traditional sulfide ore processing methods. This study investigates an efficient leaching process for copper concentrate, utilizing a solution of sulfuric acid (H2SO4) and potassium iodide (KI) in a chloride medium (NaCl), enhanced by hydrogen peroxide (H2O2) at room temperature. A significant aspect of this research was optimizing the KI concentration to minimize iodide sublimation into iodine gas (I2). Through the experimental design, the optimal dosages of reagents were determined, leading to maximized copper extraction of approximately 27% in 45 min of testing at room temperature. The results showed that it is possible to obtain a suitable pregnant leach solution (PLS) (i.e., in the range of 3 to 8 g/L of Cu) for treatment in available copper solvent extraction (SX) plants with a cost of less than 4.5 USD/t Cu, according to the economic analysis carried out. The results of this study determine the most effective operational conditions for leaching and ensure a suitable PLS for SX plants in a cost-effective and environmentally friendly manner. This approach could significantly contribute to more sustainable practices in the mining and processing of copper ores.

Deconvolution-based correction of pre-strike arc voltage measurement in medium voltage switches

Abstract Switching arc characterization, e.g. measuring the arc voltage and current, is crucial for assessing the performance of power switchgears under different working conditions. This is especially challenging for short circuit current making as one of the most severe operations for medium voltage load break switches. By closing the switch, a short circuit current flows through the arc which is ignited by the electrical breakdown in the gap between contacts before the contacts touch. This results in an abrupt change from the rated voltage of the power network, i.e. tens of thousands of volts, to the switching arc voltage, i.e. a few tens of volts and ultimately zero when the contacts touch. The inherent response of the measurement system to this very large step preceding the arc formation overlaps with the switching arc voltage and results in unrealistic arc voltages which makes correction of the distorted voltage waveform necessary. In this paper, a post processing method based on deconvolution correction technique, i.e. deconvolution of the measured voltage in time and frequency domains, is examined using experimentally determined impulse responses of the measurement system. To demonstrate its effectiveness, this method is applied to three different measurement system configurations with various step responses. The results show practically identical deconvoluted (corrected) voltage waveforms in all three measurement systems, although the directly measured waveforms have significant differences. The corrected arc voltage waveforms are in agreement with the indirect measurements proposed in previous studies.

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer

Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep learning-based automated cell segmentation with a 2D U-Net convolutional neural network. The model was trained and tested using images of pathologically diverse human thyroid cells and evaluated by comparing the number of cells selected, segmented areas, and Fpol values obtained using automated (AU) and manual (MA) data processing methods. Overall, the model segmented 15.8% more cells than the human operator. Differences in AU and MA segmented cell areas varied between − 55.2 and + 31.0%, whereas differences in Fpol values varied from − 20.7 and + 10.7%. No statistically significant differences between AU and MA derived Fpol data were observed. The largest differences in Fpol values correlated with greatest discrepancies in AU versus MA segmented cell areas. Time required for auto-processing was reduced to 10 s versus one hour required for MA data processing. Implementation of the automated cell analysis makes quantitative fluorescence polarization-based diagnosis clinically feasible.

FİZİKİ MƏHDUDLUĞU OLAN ATICILARIN MƏŞQ PROSESİ ÜÇÜN SƏMƏRƏLİ VASİTƏ VƏ METODLARIN TƏYİNİ

Determination of Effective Tools and Methods for the Training Process of Physically Disabled Shooters Summary The topic we are addressing has been relevant for almost all time. We are talking about people with physical disabilities. It is a fact that this issue will always be on the agenda of the society as long as humanity exists. Disability is not only the result of certain unpleasant events, but also a consequence of birth. That is, this urgent issue is approached from a medical point of view. A more important issue is the rehabilitation of disabled people, their integration into society. As one of the main means of effectively organizing the leisure time of the disabled, sports has attracted great interest among the disabled. Key words: shooting, air pistol, training process, physical limitation, instability, efficient tool, method

Possibilities of decontaminating organic waste from swine-farming complexes using anaerobic digestion

AbstractAnaerobic digestion with simultaneous production of biogas is one of the most common methods of recycling, processing and decontamination of organic waste to produce alternative fuels. A commonly used indicator of the sanitary safety of the substrate is eggs of Ascaris suum nematodes of pigs, which are characterised by extremely high resistance to environmental factors. The aim is to investigate the effect of the mesophilic mode of anaerobic digestion on the activity of pathogens, particularly Ascaris suum eggs. The eggs of the studied nematode species were placed in a biogas installation at a temperature of 37 °C. The digestate samples with eggs were then taken every 4 days for 28 days, followed by larvae culture to determine the viability of the eggs. The results of our research have shown that the mesophilic regime of anaerobic digestion is an effective method of controlling parasites, but at the same time, it needs to be improved, since only 7.6% of A. suum eggs remained alive after a 1-day stay, about 50% after the week stay and about 9% in the third week stay in the biogas reactor. Thus, further optimisation of anaerobic fermentation in the mesophilic mode can be aimed at improving the suppression of pathogenic activity.

The impact of using educational videos for the subject of management and economics on first-year female students Leadership and Business, in the North Hebron District

Technology has now become an urgent necessity in various fields, and it performs many functions that save people time and effort, and among these fields are: the educational process, where the demand for it has increased; Because of its ability to increase students’ effectiveness and academic achievement, especially educational videos. This study aimed to demonstrate the impact of using educational videos on raising the achievement of first year secondary school students - the Entrepreneurship and Business Branch in the subject of Management and Economics - at Bani Naim Secondary School for Girls. The experimental method was used to achieve the aim of the study. Due to its accuracy in observing the study population and its suitability to the research tool. The study demonstrated the positive impact of educational videos on the study population (students of entrepreneurship and business at Bani Naim Secondary School for Girls), as the students’ academic achievement increased and their grades in that subject rose. The researcher recommends that the teacher introduce educational videos into the teaching process, and consider them one of the modern educational methods in the teaching process

Study of the toxicity of a corrosion inhibitor for macrophytes

This article presents the results of studies of the toxicity of a corrosion inhibitor used to treat oilfield equipment in relation to a representative of submerged aquatic plants – Elodea canadensis. The characteristics of the toxicant and research methods are given in detail. The method of mathematical processing is indicated. The work presents the results of studying the toxicity of the substance in a wide range of concentrations: from 100 to 5000 mg/dm³ (4 days – acute experience) and 0,05–500,0 mg/dm³ (30 days – chronic experience). During the experiment, changes in the condition and survival of plants, morphological indicators (growth of the main shoot, number of lateral shoots and their length, number of roots and their length, total growth of elodea) were observed. It has been established that the maximum amounts of the substance (5000 and 500 mg/dm³ – acute and chronic experiments, respectively) cause 100% plant death. Lower concentration ranges stimulate morphological parameters. According to the research results, 0,5 mg/l can be taken as a threshold concentration for an inhibitor, and 0,05 mg/l is acceptable.

Hazardous High-Energy Seismic Event Discrimination Method Based on Region Division and Identification of Main Impact Factors: A Case Study

An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications.

Digital Transformation Techniques for Manuscripts

The world is witnessing continuous technological developments that have an impact on societies and institutions. Therefore, all countries seek to keep pace with these developments and introduce them into various fields. Digital transformation has become one of the most important necessities that all institutions must adopt today, as digital transformation is capable of creating a competitive technological environment to achieve the highest levels of quality at the lowest costs. Manuscripts are of interest to many scholars and researchers due to their scientific and artistic value. Preserving them means preserving national identity, especially with the emergence of technology. It has become imperative to preserve manuscripts from damage and loss and make them available to the largest number of beneficiaries. In the past, Arabs were among the most important peoples who contributed to the preservation of manuscripts. One of the most important challenges facing modern society is managing, preserving, and retrieving information from the vast amount of paper documents and files that are increasing daily. By using technology, the time taken to search for the required document or distribute documents to the relevant parties leads to a significant increase in the productivity of the institution. Information technology and electronic communication systems have revolutionized traditional methods of processing, preserving, and exchanging information. The presence of advanced technologies provides us with ease and accuracy in accessing information. Archiving as a general concept relies on storing and classifying papers in a way that makes it easy to refer back to them when needed. With the advent of computers, the process has become easier, providing more flexibility and ease in retrieval, as well as reducing the costs of preservation and storage. This technology has a direct impact on the archival profession, as archivists have turned to electronic means and imaging storage systems, as well as audiovisual methods, in an attempt to solve the storage and preservation problem. Modern digital technology affects all areas of life due to the integrated scientific environment it provides. All societies have begun to transition towards the digital age because of its remarkable development in information and communication technology. Keywords: Information, Manuscript, Technology, Scanning, Digitization

Swelling of Sealing Assembles Parts Manufactured from Composite Materials

One of the advantages of plastics over metals and other materials is that there are very simple and inexpensive ways to make products from them. It is known that there are more than 100 methods for manufacturing plastic products. The use of these methods depends on the configuration of the product being manufactured, the technical conditions required of it, its purpose and economic efficiency. Although there are more than 100 processing methods, the most common of them are mainly extrusion in the production of large-sized products, including pipes, sheet materials, webs, various profiles, rods, as well as in the production of medium-sized products. and small parts in terms of quantity, casting pressure and pressing methods. Based on economic indicators, the optimal method for preparing any composite materials should be taken into account and grouped in advance. The development of new technological methods for manufacturing parts and products from polymers and their compositions is of great importance.

A Positioning Alarm System for Explosive Impact Debris Protective Suit Based on an Accelerometer Array

Protection suits are vital for firefighters’ safety. Traditional protection suits physically protect firemen from burns, but cannot locate the position of bodily injuries caused by impact debris. Herein, we present a wearable impact debris positioning system for firefighter protection suits based on an accelerometer array. Wearable piezoelectric accelerometers are distributed regularly on the suit to detect the vibration on different body parts, which is conducive to determining the position of injured body parts. In addition, the injured parts can be displayed on a dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11 ms, attributed to the smart signal processing method. This work provides a reliable and smart method for locating and assessing the position of bodily injuries caused by impact debris, which is significant because it enables fire commanders to rescue injured firefighters in time.