Filters
Opracowanie i wdrożenie systemu do oceny jakości tusz wieprzowych z wykorzystaniem technik laserowych
Computer-Aided Structural Diagnosis of Bridges Using Combinations of Static and Dynamic Tests: A Preliminary Investigation
Reinforced concrete bridges deteriorate over time, therefore displaying a regular need for structural assessment and diagnosis. The reasons for their deterioration are often the following: (a) intensive use, (b) very dynamic loads acting for long periods of time, (c) and sometimes chemical processes that damage the concrete or lead to corrosion of the reinforcement. Assuming the hypothesis that both the stiffness of the material and its density change over time, these parameters shall be identified, preferably in a non-destructive way, in different locations of the investigated structure. Such task is expected to be possibly exerted by means of one or more tests, which must not be laborious or cause the bridge to be out of service for a long time. In this paper, an attempt is made to prepare a procedure based on dynamic tests supplemented with several static measurements, in order to identify the largest number of parameters in the shortest possible time, within an inverse analysis methodology. The proposed procedure employs a popular algorithm for minimizing the objective function, i.e., trust region in the least square framework, as part of the inverse analysis, where the difference between measurements made in situ and those calculated numerically is minimized. As a result of the work performed, optimal sets of measurements and test configurations are proposed, allowing the searched parameters to be found in a reliable manner, with the greatest possible precision.
Machine learning approach to inline monitoring of apple puree consistency through process data and fruit characteristics
Review of Seed Hemp (Cannabis sativa L.) Harvesting Techniques and the Challenges of Harvesting Technologies for This Crop
Industrial hemp (Cannabis sativa L.) harvesting for grain represents a critical technological bottleneck in the modern supply chain, driven by a fundamental conflict between the plant’s resilient morphology and standard agricultural machinery. This review provides an analytical synthesis of harvesting methodologies, evaluating their performance against specific biological constraints such as extreme plant height (up to 4.5 m), high tensile fiber strength, and indeterminate ripening. Data synthesis reveals that hemp cutting is approximately 80 times more energy-intensive than for traditional forage crops, requiring an average maximum force of 243 N per stem. Comparative analysis demonstrates that while conventional whole-plant harvesting faces seed losses ranging from 26% to 46%, selective systems like specialized panicle mowers reduce these losses to nearly 2 kg·ha−1 by targeting only the mature inflorescences. To ensure seed integrity and operational stability, the review identifies concrete technological priorities: the use of abrasion-resistant alloys for cutting edges, the implementation of non-binding shaft shielding (e.g., ABS piping), and a 40–50% reduction in threshing cylinder speeds compared to cereal settings. Future advancements must focus on specialized, high-clearance selective machinery and adaptive control systems to reconcile hemp’s unique physiology with industrial-scale efficiency.
Use of Computer Digital Techniques and Modern Materials in Dental Technology in Restoration: A Caries-Damaged Smile in a Teenage Patient
Prosthodontic treatment of developmental age patients presents a significant challenge to the dentist. The growth and development of the stomatognathic system must be considered in treatment planning. Temporary prosthetic restorations must be regularly inspected and recemented, and final prosthetic restoration should not be delivered until the growth of the body is complete. In addition, due to the complex nature of morphological and functional disorders during the developmental period, simultaneous prosthetic and orthodontic treatment may be required. The case presented in this article is a 16-year-old boy with severe tooth destruction caused by untreated caries disease and poor oral hygiene. The patient required conservative, endodontic, and surgical treatment to restore the occlusion and aesthetics to allow the proper development of the masticatory organ. This article also presents the treatment case of a young patient with damaged crowns in the upper arch, which were restored with standard root–crown posts and cores and temporary 3D-printed composite crowns.
Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland
In the presented study, data regarding the size and structure of cattle herds in voivodeships in Poland in 2019 were analysed and modelled using artificial neural networks (ANNs). The neural modelling approach was employed to identify the relationship between herd structure, biogas production from manure and slurry, and the geographical location of herds by voivodeship. The voivodeships were categorised into four groups based on their location within Poland: central, southern, eastern, and western. In each of the analysed groups, a three-layer MLP (multilayer perceptron) with a single hidden layer was found to be the optimal network structure. A sensitivity analysis of the generated models for herd structure and location within the eastern group of voivodeships revealed significant contributions from dairy cows, heifers (both 6–12 and 12–18 months old), calves, and bulls aged 12–24 months. For the western voivodeships, the analysis indicated that only dairy cows and herd location made significant contributions. The optimal models exhibited similar values of RMS errors for the training, testing, and validation datasets. The model characterising biogas production from manure in southern voivodeships demonstrated the smallest RMS error, while the model for biogas from manure in the eastern region, as well as the model for slurry in central parts of Poland, yielded the highest RMS errors. The generated ANN models exhibited a high level of accuracy, with a fitting quality of approximately 99% for correctly predicting values. Comparable results were obtained for both manure and slurry in terms of biogas production across all location groups.
Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp (Cannabis sativa L.) Using Artificial Intelligence Methods
Currently, there is a significant increase in interest in hemp cultivation and hemp products around the world. The hemp industry is a strongly developing branch of the economies of many countries. Short-term forecasting of the hemp seed and grain yield will provide growers and processors with information useful to plan the demand for employees, technical facilities (including appropriately sized drying houses and crop cleaning lines) and means of transport. This will help to optimize inputs and, as a result, increase the income from cultivation. One of the methods of yield prediction is the use of artificial intelligence (AI) methods. Neural modeling proved to be useful in predicting the yield of many plants, which is why work was undertaken to use it also to predict hemp yield. The research was carried out on selected, popular hemp varieties—Białobrzeskie and Henola. Their aim was to identify characteristic factors: climatic, cultivation and agrotechnical, affecting the size and quality of the yield. The collected data allowed the generation of Artificial Neural Network (ANN) models. It has been shown that based on a set of characteristics obtained during the cultivation process, it is possible to create a predictive neural model. Modeling using one output variable, which is seed yield, can be used in short-time prediction of industrial crops, which are gaining more and more importance.
Artificial Intelligence Methods in the Detection of Oral Diseases on Pantomographic Images—A Systematic Narrative Review
Background: Artificial intelligence (AI) is playing an increasingly important role in everyday dental practice and diagnosis, especially in the area of analysing digital pantomographic images. Through the use of innovative and modern algorithms, clinicians can more quickly and accurately identify pathological changes contained in digital pantomographic images, such as caries, periapical lesions, cysts, and tumours. It should be noted that pantomographic images are one of the most commonly used imaging modalities in dentistry, and their digital analysis enables the construction of AI models to support diagnosis. Objectives: This paper presents a systematic narrative review of studies included in scientific articles from 2020 to 2025, focusing on three main diagnostic areas: detection of caries, periapical lesions, and cysts and tumours. The results show that neural network models, such as U-Net, Swin Transformer, and CNN, are most commonly used in caries diagnosis and have achieved high performance in lesion identification. In the case of periapical lesions, AI models such as U-Net and Decision Tree also showed high performance, surpassing the performance of young dentists in assessing radiographs in some cases. Results: The studies cited in this review show that the diagnosis of cysts and tumours, on the other hand, relies on more advanced models such as YOLO v8, DCNN, and EfficientDet, which in many cases achieved more than 95% accuracy in the detection of this pathology. The cited studies were conducted at various universities and institutions around the world, and the databases (case databases) analysed in this work ranged from tens to thousands of images. Conclusions: The main conclusion of the literature analysis is that, thanks to its accessibility, speed, and accuracy, AI can significantly assist the work of physicians by reducing the time needed to analyse images. However, despite the promising results, AI should only be considered as an enabling tool and not as a replacement for the knowledge of doctors and their long experience. There is still a need for further improvement of algorithms and further training of the network, especially in identifying more complex clinical cases.
Evaluation of the Second Premolar’s Bud Position Using Computer Image Analysis and Neural Modelling Methods
Panoramic radiograph is a universally used diagnostic method in dentistry for identifying various dental anomalies and assessing developmental stages of the dentition. The second premolar is the tooth with the highest number of developmental abnormalities. The purpose of this study was to generate neural models for assessing the position of the bud of the second premolar tooth based on analysis of tooth–bone indicators of other teeth. The study material consisted of 300 digital pantomographic radiographs of children in their developmental period. The study group consisted of 165 boys and 135 girls. The study included radiographs of patients of Polish nationality, aged 6–10 years, without diagnosed systemic diseases and local disorders. The study resulted in a set of original indicators to accurately assess the development of the second premolar tooth using computer image analysis and neural modelling. Five neural networks were generated, whose test quality was between 68–91%. The network dedicated to all quadrants of the dentition showed the highest test quality at 91%. The training, validation and test subsets were divided in a standard 2:1;1 ratio into 150 training cases, 75 test cases and 75 validation cases.
Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP
Determining the chronological age of children or adolescents is becoming an extremely necessary and important issue. Correct age-assessment methods are especially important in the process of international adoption and in the case of immigrants without valid documents confirming their identity. It is well known that traditional, analog methods widely used in clinical evaluation are burdened with a high error rate and are characterized by low accuracy. On the other hand, new digital approaches appear in medicine more and more often, which allow the increase of the accuracy of these estimates, and thus equip doctors with a tool for reliable estimation of the chronological age of children and adolescents. In this study, the work on a fast and effective metamodel is continued. Metamodels have one great advantage over all other analog and quasidigital methods—if they are well trained, a priori, on a representative set of samples, then in the age-assessment phase, results are obtained in a fraction of a second and with little error (reduced to ±7.5 months). In the here-proposed method, the standard deviation for each estimate is additionally obtained, which allows the assessment of the certainty of each result. In this study, 619 pantomographic photos of 619 patients (296 girls and 323 boys) of different ages were used. In the numerical procedure, on the other hand, a metamodel based on the Proper Orthogonal Decomposition (POD) and Gaussian processes (GP) were utilized. The accuracy of the trained model was up to 95%.
Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters
Dental age is one of the most reliable methods for determining a patient’s age. The timing of teething, the period of tooth replacement, or the degree of tooth attrition is an important diagnostic factor in the assessment of an individual’s developmental age. It is used in orthodontics, pediatric dentistry, endocrinology, forensic medicine, and pathomorphology, but also in scenarios regarding international adoptions and illegal immigrants. The methods used to date are time-consuming and not very precise. For this reason, artificial intelligence methods are increasingly used to estimate the age of a patient. The present work is a continuation of the work of Zaborowicz et al. In the presented research, a set of 21 original indicators was used to create deep neural network models. The aim of this study was to verify the ability to generate a more accurate deep neural network model compared to models produced previously. The quality parameters of the produced models were as follows. The MAE error of the produced models, depending on the learning set used, was between 2.34 and 4.61 months, while the RMSE error was between 5.58 and 7.49 months. The correlation coefficient R2 ranged from 0.92 to 0.96.
Mechanical Properties of 3D Printed Orthodontic Retainers
Orthodontic retention is the final important stage of orthodontic treatment, the aim of which is to consolidate the functional and aesthetic position of teeth. Among adults, fixed retainers made of different types of wires are the most common. The aim of this study was to analyse the mechanical properties of a new generation of fixed orthodontic retainers—printed by 3D printers. Materials and Methods: The study was conducted using samples made of Nextdent MFH C&B N1 resin in the form of cuboid bars with nominal dimensions of width b = 3 mm, thickness d = 0.8 mm; 1 mm; 1.2 mm, length l = 30 mm for each type. The influence of the thickness of the retainers on their strength under loaded conditions was evaluated. Flexural strength, elastic properties, deflection, and creep were compared. The samples were aged in an artificial saliva bath at 37 ± 1 °C during the strength tests. Results: It was shown that differences in the thickness of the samples affected their elastic and strength properties. The highest average flexural modulus, the highest deflection, creep, and strength was characteristic of the samples with the highest thickness (1.2 mm). Samples with an average thickness of 1 mm had the lowest modulus of elasticity. Conclusions: The mechanical properties of 3D printed retainers show that they can be an alternative to metal retainers and the procedure of making new retainers, especially when patients have aesthetic requirements or allergies to metals.
Artificial Intelligence Methods in Cephalometric Image Analysis—A Systematic Narrative Review
Background: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images constitute a fundamental element in orthodontic treatment planning. They contain encoded information related to the assessment of craniofacial growth and development, which is the focus of algorithms employing machine learning and process automation. Objectives: The aim of this paper is to present the current state of knowledge regarding the application of artificial intelligence methods in cephalometric image analysis, with particular emphasis on studies published between 2020 and 2025 in the Scopus and Web of Science databases. Results: Twenty key studies were included. The most commonly used models were convolutional neural networks (CNN), You Only Look Once (YOLO), Bayesian convolutional neural networks (BCNN), artificial neural networks (ANN), stacked hourglass networks, and Deep Neural Patchworks (DNP). In landmark detection tasks, the average location errors ranged from 1 to 2 mm compared to expert annotations, remaining within clinically acceptable limits. YOLO- and CNN-based systems achieved accuracy comparable to that of experienced orthodontists, while BCNN models additionally provided uncertainty estimates that improved clinical interpretability. In classification tasks, artificial neural network (ANN) models assessing cervical vertebral maturity (CVM) achieved an accuracy of up to 95%. In screening studies prior to orthognathic surgery, a multilayer perceptron combined with a regional convolutional neural network achieved 96.3% agreement with expert decisions. Conclusions: AI-based tools provide clinically acceptable accuracy in cephalometric analysis, with landmark detection errors typically ranging from 1 to 2 mm compared to expert assessment. These systems improve repeatability and significantly reduce analysis time, especially when used in semi-automated workflows. AI-based assessment of cervical vertebral maturity and surgical eligibility shows high agreement with expert decisions, confirming their role as reliable tools to support clinical decision-making. Nevertheless, broader validation in different patient populations is necessary before routine clinical implementation.
Eco-Friendly and Effective Diatomaceous Earth/Peat (DEP) Microbial Carriers in the Anaerobic Biodegradation of Food Waste Products
This article aims to present the results of research on anaerobic digestion (AD) of waste wafers (WF-control) and co-substrate system—waste wafers and cheese (WFC-control), combined with digested sewage sludge. The aim of this study was to assess the physicochemical parameters of the diatomaceous earth/peat (DEP; 3:1) carrier material and to verify its impact on the enzymatic activity and the process performance. The experiment was conducted in a laboratory, in a periodical mode of operation of bioreactors, under mesophilic conditions. The results of analyses of morphological-dispersive, spectroscopic, adsorption, thermal, and microbiological properties confirmed that the tested carrier material can be an excellent option to implement in biotechnological processes, especially in anaerobic digestion. As part of the experiment, the substrates, feedstock, and fermenting slurry were subjected to the analysis for standard process parameters. Monitoring of the course of AD was performed by measuring the values of key parameters for the recognition of the stability of the process: pH, VFA/TA ratio (volatile fatty acids/total alkalinity), the content of NH4+, and dehydrogenase activity, as an indicator of the intensity of respiratory metabolism of microorganisms. No significant signals of destabilization of the AD process were registered. The highest dehydrogenase activity, in the course of the process, was maintained in the WFC + DEP system. The microbial carrier DEP, used for the first time in the anaerobic digestion, had a positive effect on the yield of methane production. As a result, an increase in the volume of produced biogas was obtained for samples fermented with DEP carrier material for WF + DEP by 13.18% to a cumulative methane yield of 411.04 m3 Mg−1 VS, while for WFC + DEP by 12.85% to 473.91 m3 Mg−1 VS.