Precision Range Research Articles

Machine learning for predicting colon cancer recurrence

Colorectal cancer (CRC) is a global public health concern, ranking among the most commonly diagnosed malignancies worldwide. Despite advancements in treatment modalities, the specter of CRC recurrence remains a significant challenge, demanding innovative solutions for early detection and intervention. The integration of machine learning into oncology offers a promising avenue to address this issue, providing data-driven insights and personalized care. This retrospective study analyzed data from 396 patients who underwent surgical procedures for colon cancer (CC) between 2010 and 2021. Machine learning algorithms were employed to predict CC recurrence, with a focus on demographic, clinicopathological, and laboratory characteristics. A range of evaluation metrics, including AUC (Area Under the Receiver Operating Characteristic), accuracy, recall, precision, and F1 scores, assessed the performance of machine learning algorithms. Significant risk factors for CC recurrence were identified, including sex, carcinoembryonic antigen (CEA) levels, tumor location, depth, lymphatic and venous invasion, and lymph node involvement. The CatBoost Classifier demonstrated exceptional performance, achieving an AUC of 0.92 and an accuracy of 88% on the test dataset. Feature importance analysis highlighted the significance of CEA levels, albumin levels, N stage, weight, platelet count, height, neutrophil count, lymphocyte count, and gender in determining recurrence risk. The integration of machine learning into healthcare, exemplified by this study's findings, offers a pathway to personalized patient risk stratification and enhanced clinical decision-making. Early identification of individuals at risk of CC recurrence holds the potential for more effective therapeutic interventions and improved patient outcomes. Machine learning has the potential to revolutionize our approach to CC recurrence prediction, emphasizing the synergy between medical expertise and cutting-edge technology in the fight against cancer. This study represents a vital step toward precision medicine in CC management, showcasing the transformative power of data-driven insights in oncology.

Targeted Intraoperative Radiotherapy (TARGIT-IORT) in Breast Cancer Surgery

According to the WHO, breast cancer (BC) has become the largest increase cancer in the world. The vast majority of cancers require radiotherapy and it is the mainstream treatment modality. Although there are numerous treatments, it still does not have a suitable one because of the side effects of radiotherapy. To reduce the harm to the body, people are attending to find a fewer side-effect one. Nowadays, the intraoperative radiation therapy（IORT）is starting to come into its own. TARGIT-IORT is a type of accelerated partial breast irradiation (APBI). The differences between TARGIT-IORT and traditional radiation are the localized radiating space and radiotherapy can be administered during surgery. Researchers have now found that it has a precise radiation range and can be performed simultaneously during surgery. Besides, it performs even better than traditional radiation therapy in various aspects. This is certainly a new trend in breast cancer treatment. TARGIT-IORT is expected to be the next trend in the treatment of BC.

Floating-Point Embedding: Enhancing the Mathematical Comprehension of Large Language Models

The processing and comprehension of numerical information in natural language represent pivotal focal points of scholarly inquiry. Across diverse applications spanning text analysis to information retrieval, the adept management and understanding of the numerical content within natural language are indispensable in achieving task success. Specialized encoding and embedding techniques tailored to numerical data offer an avenue toward improved performance in tasks involving masked prediction and numerical reasoning, inherently characterized by numerical values. Consequently, treating numbers in text merely as words is inadequate; their numerical semantics must be underscored. Recent years have witnessed the emergence of a range of specific encoding methodologies designed explicitly for numerical content, demonstrating promising outcomes. We observe similarities between the Transformer architecture and CPU architecture, with symmetry playing a crucial role. In light of this observation and drawing inspiration from computer system theory, we introduce a floating-point representation and devise a corresponding embedding module. The numerical representations correspond one-to-one with their semantic vector values, rendering both symmetric regarding intermediate transformation methods. Our proposed methodology facilitates the more comprehensive encoding and embedding of numerical information within a predefined precision range, thereby ensuring a distinctive encoding representation for each numerical entity. Rigorous testing on multiple encoder-only models and datasets yielded results that stand out in terms of competitiveness. In comparison to the default embedding methods employed by models, our approach achieved an improvement of approximately 3.8% in Top-1 accuracy and a reduction in perplexity of approximately 0.43. These outcomes affirm the efficacy of our proposed method. Furthermore, the enrichment of numerical semantics through a more comprehensive embedding contributes to the augmentation of the model’s capacity for semantic understanding.

Harmonic FMCW Radar System: Passive Tag Detection and Precise Ranging Estimation.

This paper details the design and implementation of a harmonic frequency-modulated continuous-wave (FMCW) radar system, specialized in detecting harmonic tags and achieving precise range estimation. Operating within the 2.4-2.5 GHz frequency range for the forward channel and 4.8-5.0 GHz for the backward channel, this study delves into the various challenges faced during the system's realization. These challenges include selecting appropriate components, calibrating the system, processing signals, and integrating the system components. In addition, we introduce a single-layer passive harmonic tag, developed specifically for assessing the system, and provide an in-depth theoretical analysis and simulation results. Notably, the system is characterized by its low power consumption, making it particularly suitable for short-range applications. The system's efficacy is further validated through experimental evaluations in a real-world indoor environment across multiple tag positions. Our measurements underscore the system's robust ranging accuracy and its ability to mitigate self-interference, showcasing its significant potential for applications in harmonic tag detection and ranging.

Enhancing Coronary Artery Diseases Screening: A Comprehensive Assessment of Machine Learning Approaches Using Routine Clinical and Laboratory Data.

Introduction: Coronary artery disease (CAD) stands among the leading global causes of mortality, underscoring the critical necessity for early detection to facilitate effective treatment. Although Coronary Angiography (CA) serves as the gold standard for diagnosis, its limitations for screening, including side effects and cost, necessitate alternative approaches. This study focuses on the development and comparison of machine learning techniques as substitutes for CA in CAD screening, leveraging routine clinical and laboratory data.Material and Methods: Various machine learning classification algorithms—decision tree, k-nearest neighbor, artificial neural network, support vector machine, logistic regression, and stacked ensemble learning—were employed to differentiate CAD and healthy subjects. Feature selection algorithms, namely LASSO and ReliefF, were utilized to prioritize relevant features. A range of evaluation metrics, including accuracy, precision, sensitivity, specificity, AUC, F1 score, ROC curve, and NPV, were applied. The SHAP technique was employed to elucidate and interpret the artificial neural network model.Results: The artificial neural network, support vector machine, and stacked ensemble learning models demonstrated excellent results in a 10-fold cross-validation evaluation using features selected by LASSO and ReliefF. With the LASSO feature selection algorithm, these models achieved accuracies of 90.38%, 90.07%, and 90.39%, sensitivities of 94.43%, 93.03%, and 93.96%, and specificities of 80.27%, 82.77%, and 81.52%, respectively. Using ReliefF, the accuracies were 88.79%, 88.77%, and 90.06%, sensitivities were 92.12%, 91.66%, and 93.98%, and specificities were 80.13%, 81.38%, and 80.13%, respectively. The SHAP technique revealed that typical and atypical chest pain, hypertension, diabetes mellitus, T inversion, and age were the most influential features in the neural network model.Conclusion: The machine learning models developed in this study exhibit high potential for non-invasive screening and diagnosis of CAD in the Z-Alizadeh Sani dataset. However, further studies are essential to validate and apply these models in real-world and clinical settings.

Optimizing Electric Vehicle Performance, Range and Parameter Estimation through NEDC Urban and Suburban Analysis using MATLAB

This research paper focuses on electric vehicle (EV) driving range and parameter estimation, with an emphasis on analyzing the New European Driving Cycle (NEDC) urban and suburban drive cycles. By examining these drive cycles, we obtain critical data on the moments when an electric vehicle transitions between constant velocity, acceleration, and deceleration phases, which significantly affect power consumption and driving range. We investigate various techniques for parameter estimation, including battery capacity, energy consumption rates, and powertrain efficiency, essential for improving EV performance and providing realistic range expectations. Empirical experiments in diverse driving conditions contribute valuable data to refine our understanding of EV driving range and performance in different scenarios. The research underscores the role of predictive modeling, data analytics, and advanced technologies in real-time parameter estimation, offering precise and convenient range predictions to enhance user confidence.

New Stability Indicating HPLC Method for Related Substances in Dapagliflozin

The requirements established by the International Council for Harmonization (ICH) for the measurement of dapagliflozin from tablets were satisfied by the development and successful implementation of an reversed-phase high performance liquid chromatography (RP-HPLC) technique that is characterized by its speed, precision, and accuracy. In order to separate the samples, an isocratic mode was used on a Princeton C18 column that was operating at a flow rate of 1-mL per minute. The phase was composed of acetonitrile and 0.1% triethylamine in a volume-to-volume ratio of 50:50, with a pH of 5.0 and a detection wavelength of 224 nm. 5.163 minutes was the retention time that was determined to be best under the circumstances of the chromatographic analysis. Based on the correlation value of 0.999, it can be concluded that the procedure adhered to Beer-Lambert’s law within the measurement range of 10 to 70 μg/mL. The findings not only verified the claim that was made on the label, but they also validated the mean percent drug concentration that was specified for the commercial tablet formulation, which was 100.57%. Following the completion of an investigation, it was discovered that the approaches that were proposed have sufficient proportions of precision measurements, robustness, accuracy, range, and linearity. As part of the technique for stress testing, a number of criteria were evaluated and evaluated overall. The elements that were considered to be among these were light, humidity, temperature, oxidation, and pH (acid/base).

Identification of soil properties influencing primary productivity of fish ponds under red and lateritic soil zones

Aim: The present study aimed to identify the key soil factors influencing the primary productivity of water in the fish ponds situated under low productive red and lateritic soil zones. Methodology: General properties of fish pond soils (n=42) from different red and lateritic soil zones of West Bengal, India were determined along with the gross primary productivity (GPP) values of corresponding pond waters. All the soil properties were correlated with GPP values and the soil factors which were significantly correlated with GPP of pond water were further used for step down regression analysis with regard to GPP.This helped to develop a minimum data set of the key soil factors which showed maximum influence on GPP. The efficiency of the regression equation, generated with the critical soil parameters, in predicting the GPP values of other ponds was also assessed through a study on the variations in calculated and on-farm GPP values of eight fish ponds. Results: Among all the studied soil properties, pH and availability of three major nutrients viz. N, P and K were observed to contribute 84.15% variations in gross production of primary fish food organisms in these pond environment. Of these 4 soil properties, again, pH and available P values of the pond soils appeared to be more important. A regression equation was also developed to predict the GPP of such pond water using soil pH and available P values only. This equation showed a precision range of 83.0 to 99.7% and was found to be statistically at par with the GPP values observed under actual pond condition. Interpretation: High efficiency in predictability of pond GPP values using the minimum data set of two key soil factors viz. soil pH and available P indicates that proper management of these productivity attributing soil properties will be particularly helpful to improve the primary productivity levels of the predominantly low yielding fish ponds under the red and lateritic soil zones. Key words: Fish ponds, Minimum data set, Productivity attributes, Red and lateritic soil zones, Soil factors

Abstract 5117: CK2 inhibiton exhibits synergistic effect in acute lymphoblastic leukemia

Abstract Survival for pre-B cell Acute lymphoblastic leukemia (B ALL) has improved greatly, however. High risk (HR) subgroups continue to result in significant mortality and morbidity of pediatric oncology patients. Pediatric T-cell acute lymphoblastic leukemia (T-ALL) is a lethal disease, often associated with high-risk clinical features. T-ALL therapy has made less improvement than B ALL and continues to represent a significant number of high-risk outcomes. Novel treatment strategies are required to overcome chemotherapy resistance and improve morbidity for HR B ALL. The development of treatments that can overcome chemotherapy resistance is essential for improving survival in T-ALL. Deletions of IKZF1, resulting in decreased IKAROS function, are present in High-risk ALL patients. Casein kinase 2 (CK2), a protein kinase upregulated in ALL, phosphorylates IKAROS preventing DNA binding reducing transcriptional regulation in ALL patients. Past studies have shown that pharmacologic inhibition of CK2 with CX-4945 rescues IKAROS function as a transcriptional regulator. Receptor tyrosine kinase c-KIT is transcriptionally regulated by IKAROS and is often upregulated in cancer. The purpose of this study was to determine whether restoration of IKAROS via CX-4945 would act in a synergistic manner with c-KIT inhibitor imatinib in ALL.Methods/results B-ALL Nalm6 cells were treated with imatinib over 72-hours to establish the half-maximal inhibitory concentration (IC50). The IC50 was used to determine the concentration range of imatinib to be used in combination with CX-4945 in a 72-hour synergy analysis of cytotoxicity.. Cell viability was measured using WST-1 cell proliferation assay. The data were analyzed using Calcusyn and synergy was defined as a combination index below 0.85. The study was repeated to determine a more precise range of synergy. The cytotoxicity of single drug c-KIT inhibitor, Imatinib, in Nalm6 B-ALL yielded an IC50 of 25uM while CK2 inhibitor CX-4945 resulted in 4uM. When a combination was administered with a constant dose of CX-4945 at 4uM and varying concentrations of imatinib, synergy was observed at multiple dose combinations. Conclusion: The data shows that CK2 inhibitor, CX-4945, and c-KIT inhibitor, imatinib, exhibit a synergistic therapeutic effect. Imatinib is indicated for use in Philadelphia positive (Ph+) acute lymphoblastic leukemia so future studies will include testing CX-4945/imatinib synergy in Ph+ BALL where we expect to see more potent synergistic effect. Citation Format: Daniel Gary Bogush, Joseph Schramm, Katarina Dovat, Yali Ding, Chingakham Singh, Sinisa Dovat. CK2 inhibiton exhibits synergistic effect in acute lymphoblastic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5117.

Geotechnical Model Testing with High-Strength Gypsum Similar Materials: A Case Study on Quartzite-Like Formulations

This paper explores the formulation and optimization of high-strength gypsum-based similar materials for geomechanical model tests, with a specific focus on their application in simulating conditions analogous to quartzite. Employing an orthogonal experimental design, key factors such as quartz sand-to-gypsum ratio, cement-to-gypsum ratio, and barite content were systematically varied. Through a series of indoor experiments, comprehensive assessments of physical and mechanical properties, including density and compressive strength, were conducted for diverse material ratios. The results reveal a broad spectrum of physical and mechanical indicators, aligning with the requisites for model tests across varying similarity ratios. Notably, a high-strength gypsum formulation yielding a uniaxial compressive strength within the precise range of 10~20 MPa was identified, providing valuable insights for researchers engaged in hard rock model tests using similar materials. The study underscores the influential roles of barite content, quartz sand-to-gypsum ratio, and cement-to-gypsum ratio on the density and compressive strength of similar materials. This research contributes essential data for the application of high-strength gypsum in geotechnical model testing, particularly in replicating the mechanical behavior of quartzite.

CBCT and modern intraoral scanners as tools for developing comprehensive, interdisciplinary treatment plans.

The aim of this narrative literature review is to present the possibilities of using cone beam computed tomography (CBCT) and 3D dental scanners to prepare comprehensive, interdisciplinary treatment plans. Scanners are instruments whose usage seems to be a key element of modern digital dentistry. Their importance in orthodontic treatment with overlay appliances, planning modern prosthetic treatments (CAD/CAM) and implantology cannot be overestimated. These scanners allow for accurate imaging of the tooth structures and their positioning independently in the maxilla and mandible as well as in the occlusion. As a result, dentists can plan treatment, e.g., in the case of the need to implant dental implants, prosthetic crowns or orthodontic braces. Dentistry was revolutionized to a similar extent by the introduction of CBCT to everyday diagnostics, which is the most advanced imaging technology that provides even more detailed images in 3 dimensions. Its use has enabled a wider and more precise range of diagnostics, which in turn has improved the quality of multidisciplinary treatment planning. This paper explains how scanners and CBCT can be used in orthodontics and prosthetics based on the articles found in 3 databases: PubMed, Scopus and Embase. The review included 28 articles on the aforementioned topics and was presented with a brief description of the content of each article.

Selective Detection of Creatinine Using Enzymatic Route for Diagnosis of Chronic Kidney Diseases

Chronic kidney diseases are becoming most common health issues due to growing age, hypertension and high cholesterol that causes poor kidney functioning and eventually death also. Creatinine is a serum present in blood and urine that measures the kidney functioning. We have designed a highly sensitive and precise sensor for the creatinine sensing. Eu—doping in tin oxide (SnO2) is used to enhance the sensitivity and limit of detection. A precise working range of this sensor is also found by the calibration curve between FWHM and figure of Merit with respect to the concentration of creatinine. A selectivity experiment with enzymatic route is followed to make this sensor selective for creatinine only. This sensor may find plentiful deliberation in the field of sensing and mainly in medical applications.

Comparison between model-based RSA and an AI-based CT-RSA: an accuracy study of 30 patients.

Radiostereometry (RSA) is the current gold standard for evaluating early implant migration. CT-based migration analysis is a promising method, with fewer handling requirements compared with RSA and no need for implanted bone-markers. We aimed to evaluate agreement between a new artificial intelligence (AI)-based CT-RSA and model-based RSA (MBRSA) in measuring migration of cup and stem in total hip arthroplasty (THA). 30 patients with THA for primary osteoarthritis (OA) were included. RSA examinations were performed on the first postoperative day, and at 2 weeks, 3 months, 1, 2, and 5 years after surgery. A low-dose CT scan was done at 2 weeks and 5 years. The agreement between the migration results obtained from MBRSA and AI-based CT-RSA was assessed using Bland-Altman plots. Stem migration (y-translation) between 2 weeks and 5 years, for the primary outcome measure, was -0.18 (95% confidence interval [CI] -0.31 to -0.05) mm with MBRSA and -0.36 (CI -0.53 to -0.19) mm with AI-based CT-RSA. Corresponding proximal migration of the cup (y-translation) was 0.06 (CI 0.02-0.09) mm and 0.02 (CI -0.01 to 0.05) mm, respectively. The mean difference for all stem and cup comparisons was within the range of MBRSA precision. The AI-based CT-RSA showed no intra- or interobserver variability. We found good agreement between the AI-based CT-RSA and MBRSA in measuring postoperative implant migration. AI-based CT-RSA ensures user independence and delivers consistent results.

Theoretical Investigation of Spherical Bubble Dynamics in High Mach Number Regimes

The compressibility of fluids has a profound influence on oscillating bubble dynamics, as characterized by the Mach number. However, current theoretical frameworks for bubbles, whether at the first or second order of the Mach number, are primarily confined to scenarios characterized by weak compressibility. Thus, a critical need to elucidate the precise range of applicability for both first- and second-order bubble theories arises. Herein, we investigate the suitability and constraints of bubble theories with different orders through a comparative analysis involving experimental data and numerical simulations. The focal point of our investigation encompasses theories such as the Rayleigh–Plesset, Keller, Herring, and second-order bubble equations. Furthermore, the impact of parameters inherent in the second-order equations is examined. For spherical oscillating bubble dynamics in a free field, our findings reveal that the first- and second-order bubble theories are applicable when Ma⩽0.3 and 0.4, respectively. For a single sonoluminescence bubble, we define an instantaneous Mach number, Mai. The second-order theory shows abnormal sensibility when Mai is high, which is negligible when Mai⩽0.4. The results of this study can serve as a valuable reference for studying compressible bubble dynamics.

Development of focusing multi-knife-edge camera for proton beam range verification: A Monte Carlo study

Proton range uncertainties limit the ability to make full clinical use of the sharp distal falloff of the proton beam. To overcome this limitation, prompt gamma (PG) imaging system with mechanical collimation can be used in non-invasive proton beam range verification. In this study, we developed a focusing multi-knife-edge collimator to improve the counting efficiency and accuracy in detecting proton beam range. Unlike the common design of a single-knife-edge slit (SKE) collimator, the multi-knife-edge slit (MKE) collimator consists of three slits and focuses on the target detection area. To compare the performances of these two collimator designs, Monte Carlo simulation was unitized to model the cameras for measuring the PGs during proton irradiation. The simulation was performed using the GATE v9.1. The setup included a pencil beam of 100 MeV, 160 MeV, and 230 MV protons incident upon a cylindrical PMMA phantom with three different proton numbers of 107, 108, and 109. The detection PG profiles were fitted with a 3-line-segment fitting method to estimate the range of proton beams. Both cameras were aimed at the expected range depth for the corresponding proton beam energies. Experiments for range shifts were conducted by shifting the phantom between −20 mm and 20 mm along the beam axis. Results demonstrate that the MKE camera exhibits approximately twice the efficiency of the SKE camera in detecting PGs. In range shift retrieval, with the delivery of 108 protons, the MKE camera achieves submillimeter accuracy (except for incident protons at 230 MeV), while the SKE camera achieves accuracy within 2 mm. In conclusion, this study assesses the feasibility of the MKE camera and illustrates its potential application for range shift detection, contributing to precise range monitoring in proton therapy.

High-precision two-dimensional displacement metrology based on matrix metasurface.

A long-range, high-precision, and compact transverse displacement metrology is of crucial importance in both industries and scientific researches. However, it is a great challenge to measure arbitrary two-dimensional (2D) displacement with angstrom-level precision and hundred-micrometer range. Here, we demonstrated a prototype of high-precision 2D-displacement metrology with matrix metasurface. Light passing through the metasurface is diffracted into three beams in horizontal (H), vertical (V), and diagonal (D) linear polarization. 2D transverse displacement of the metasurface relative to the incident light beam is retrieved from the interferential optical powers arisen from coherent superposition between H-polarized and D-polarized beams or V-polarized and D-polarized beams. We experimentally demonstrate that arbitrary displacement in 2D plane can be determined with high precision down to 0.3 nm in a large range of 200 micrometers. Our work broadens the application scope of metasurface and paves the way for development of ultrasensitive optical 2D displacement metrology.

Enhancing IoT Security Development and Evaluation of a Predictive Machine Learning Model for Attack Detection

The research aims to develop and evaluate a model for Internet of Things (IoT) attack identification utilizing IoT data from the Global Cyber Alliance’s (GCA) Automated IoT Defense Ecosystem (AIDE). In the growing landscape of IoT security, the need for enhanced predictive solutions is vital. Our research leverages an enormous dataset, overall historical data from various IoT devices and network interactions, to develop a model to identify potential security threats. The key to our methodology concerns exploratory data analysis, which is focused on understanding complex patterns and anomalies in IoT data. This step is vital for feature engineering, where we meticulously select and transform data attributes to advance the model’s predictive strength. The data pre-processing stage further improves the dataset, ensuring the model training and testing on high-quality, relevant data. Model development is a composite process in this research. We tried out a few machine-learning algorithms, finally selecting the one that exhibited outstanding performance in preliminary tests. The chosen model endured strict training, with a basis on balancing accuracy and validity to effectively predict IoT attacks in various scenarios. The evaluation of our model is as durable as its development. We utilized a range of metrics, including accuracy, precision, recall, and F1 score, to evaluate the model’s behavior overall. The results show that our model not only attains high accuracy but also maintains a notable level of precision in predicting IoT attacks, which is crucial in minimizing false positives. In conclusion, our research contributes to the enhancement of IoT security by providing a very effective predictive model for IoT attack detection.

Efficacy of Citicoline Delivered via Brain Extracellular Space against Experimental Acute Ischemic Stroke in Rats.

Objective: Citicoline can be used to reduce acute ischemic stroke injury via venous infusion, however, its protective effects in the brain extracellular space remain largely unknown. Herein, we investigated the brain protective effects of citicoline administered via the brain extracellular space and sought precise effective dosage range that can protect against ischemic injury after experimental ischemic stroke in rats. Methods: Fifty-six Sprague-Dawley rats were randomly divided into control, intraperitoneal (IP), caudate-putamen (CPu)-25, CPu-40, CPu-50, CPu-60 and CPu-75 groups based on the infusion site and concentration of citicoline. Two hours after the administration of citicoline, the rats were subjected to a permanent middle cerebral artery occlusion to mimic acute ischemic stroke. Then, the brain infarct volume in rats after stroke was measured and their neurological deficiency was evaluated to explain the protective effects and effective dosage range of citicoline. Results: Compared to the control and IP groups, brain infarct volume of rats in CPu-40, CPu-50, and CPu-60 groups is significant smaller. Furthermore, the brain infarct volume of rats in CPu-50 is the least. Conclusions: Here, we showed that citicoline can decrease the brain infarct volume, thus protecting the brain from acute ischemic stroke injury. We also found that the appropriate effective citicoline dose delivered via the brain extracellular space is 50 mM. Our study provides novel insights into the precise treatment of acute ischemic stroke by citicoline via the brain extracellular space, further guiding the treatment of brain disease.

Some theorems of minimizing makespan and range of lateness of flow shop machines

One of the most challenging and widely debated issues in scheduling theory is the topic of multi-objective job scheduling. The multiobjective function problem including the makespan and the range of lateness of three machines in a flow shop was explored in this study in order to solve it. Some of the theories discussed in this work have to do with the computation of the precise best time and range of lateness. Two types of the three machines have been created, and the theories for each type have been studied. These theories seek to cut down on the amount of time it takes to complete tasks and the range of lateness.

Primena AHP i VIKOR metoda individualnog odlučivanja i Borda metoda grupnog odlučivanja pri izboru najefikasnijeg načina izvršenja pripremnog gađanja na rednom broju 1 dalekometnom puškom M-93 kalibra12,7 mm

Introduction/purpose: The paper presents the method of applying multi-criteria decision-making methods in order to improve shooting and select the most favorable conditions for shooting. Alternatives were ranked by individual multi-criteria decision-making methods AHP and VIKOR by experts, and then the most favorable alternative was selected using the Borda method of group decision making. The aim of the work is to select the most efficient way of shooting with minimal human fatigue, consumption of movable things and in the shortest possible time through a combination of multi-criteria decision-making methods, which will ensure the accuracy and precision of the rifle for subsequent shooting and other operations. Methods: The paper presents one of the ways of using multidisciplinary methods of multi-criteria decision making for a specific problem in the field of tactics with weapon systems. Experts from the field of armaments, using the AHP and VIKOR methods of individual multi-criteria decision making, selected the most favorable alternative in accordance with four given criteria. Then, by applying the Borda method of group decision making, the the most favorable alternative was selected - the most favorable conditions for the execution of preparatory shooting at serial number one with a 12.7 mm long range rifle. The alternatives are four shootings, namely, two ways of performing preparatory shooting with a 12.7 mm long range rifle at serial number one, one from the current Instructions and Program of Shooting with Infantry Weapons and the other newly proposed method, as well as two ways of realizing shooting for testing the accuracy and precision of a long range rifle. Results: The selection of the most effective conditions for the execution of the first initial preparatory shooting at serial number one with a long range rifle, which will ensure maximum accuracy of the weapon and the minimum consumption of resources - time, ammunition, targets, and personnel fatigue. Conclusion: The solution takes into account the simultaneous optimization of several criteria in order to select the most efficient way of performing preparatory shooting at serial number one in order to ensure rested and trained personnel as well as accurate and precise long range rifles for subsequent shootings, which leads to the maximum saving of resources, the satisfaction of personnel, and precise weapons.