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Background: The subtotal petrosectomy procedure may be useful for cochlear implantation in selected patient groups. Although it is highly effective, complications can arise, which may have economic implications for the patient due to the high cost of the device. Therefore, several authors have attempted to identify the most effective concept for obliteration. Methods: We present a pilot descriptive study of application techniques for obliterating cavities after subtotal petrosectomy using a temporoparietal fascial flap (TPFF) modified with injectable platelet-rich fibrin (IPRF+) for three cochlear implant (CI) patients. Results: Our concept preserves important anatomical structures, such as the temporalis muscle, which covers the CI receiver–stimulator. Injection of IPRF+ also increases the available tissue volume for obliteration and enhances its anti-inflammatory and regenerative potential. Conclusions: To the best of our knowledge, the use of TPFF for filling the cavity has not been adopted for CI with SP and for blind sac closure. Our literature review and our experience with this small group of patients suggest that this procedure, when combined with IPRF+ injections, may reduce the risk of potential infection in the obliterated cavity, particularly when used with CI. This technique is applicable only in cases when the surgeons are convinced that the middle ear cavity is purged of cholesteatoma.

Abstract Nowadays many tools are in use in processes related to handling bug reports, feature requests, supporting questions or similar related issues which should be handled during software development or maintenance. Part of them use machine learning techniques. In introduction is presented a review of fundamental methods used for evaluation of machine learning models. This paper points out weak points of currently used metrics for evaluation in specific context of the cases related to software development especially bug reports. The disadvantages of state of the art are related to disregarding time dependencies which are important to be applied for creating train and test sets as they may have impact on results. Extensive research of the art has been conducted and has not been found any article with the use of time dependencies for evaluation of machine learning models in the context of works related to software development applications like machine learning solutions to supporting bug tracking systems. This paper introduces a novel solution which is devoid of these drawbacks. Experimental research showed the effectiveness of the introduced method and significantly different results obtained compared to the state-of-the-art methods.

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear.

ASPnet’s collaboration within the UNESCO networks. The 70th anniversary of the UNESCO Associated Schools Network The article presents the Polish UNESCO Associated Schools Network (ASPnet). The Polish ASPnet was established in 1956. The activities undertaken in 2023 (for instance, the international Gift a Poem campaign) are presented together with the Polish celebrations of the 70th anniversary of this oldest UNESCO network. The analysis of the reports submitted by the Polish network members is provided with a special focus on networks, cooperation with other UNESCO family members, and UNESCO designations. Keywords: UNESCO networks ASPnet schools strategy Streszczenie: W artykule zaprezentowana została Polska Sieć Szkół Stowarzyszonych UNESCO, która powstała w 1956 r. Przedstawiono działania podejmowane w 2023 r. (m.in. międzynarodową kampanię „Gift a Poem”) oraz polskie obchody 70-lecia tej najstarszej sieci UNESCO. Dokonano analizy raportów nadesłanych przez polskich członków sieci, ze szczególnym uwzględnieniem współpracy z innymi członkami „rodzin” UNESCO i desygnacji UNESCO.

SummaryThe algorithm of follow-up in patients with head and neck cancer (HNC) has been prepared by a board of Polish Head Neck and Oncology Experts. The aim of this research is to focus on the specificity of HNC monitoring, to review the current trends in follow-up, and to adapt the evidence-based medicine international standards to the capabilities of the local healthcare service.Materials and methodsThe first methodological step was to categorize HNCs according to the estimated risk of failure after the adequate first-line treatment and according to the possibility of effective salvage treatment, resulting in improved overall survival. The final method used in this work was to prepare an authors’ original monitoring algorithm for HNC groups with a high, moderate, and low risk of recurrence in combination with a high or low probability of using an effective salvage.ResultsFour categories were established: Ia. low risk of recurrence + effective organ preservation feasible; Ib. low risk of recurrence + effective salvage feasible; II. moderate risk of recurrence + effective salvage feasible; III. high risk of recurrence + effective salvage feasible; and IV. high risk of recurrence + no effective salvage feasible. Follow-up visit consisting of 1. ENT examination + neck ultrasound, 2. imaging HN tests, 3. chest imaging, 4. blood tests, and 5. rehabilitation (speech and swallowing) was scheduled with a very different frequency, at the proposed monthly intervals, tailored to the needs of the group. The number of visits for individual groups varies from 1 to 8 in the first 2 years and from 1 to 17 in the entire 5-year monitoring period. Group IV has not been included in regular follow-up, visits on own initiative of the patient if symptomatic, or supportive care needs, having in mind that third-line therapy and immune checkpoint inhibitors are available.ConclusionUniversal monitoring algorithm for HNC four groups with a high, moderate, and low risk of recurrence after the adequate treatment in combination with a high or low probability of using an effective salvage is an innovative approach to redeploying system resources and ensuring maximum benefit for patients with HNC.

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.

Although the problem of adenoid hypertrophy (AH) has been diagnosed and treated by doctors and scientists from around the world for the last 150 years, there is still no consensus regarding appropriate diagnosis, conservative treatment options, and qualification for surgery. This manuscript presents current knowledge on these issues and compares diagnostic methods and the effectiveness of treatment options. Factors that may influence the obtained treatment results are also described, and a questionnaire is proposed to compare the results of treatment. The objective of drawing attention to this problem is to obtain better results from conservative treatment in the future and better-qualified patients for surgical treatment.

Abstract The Tenebrio molitor has become the first insect added to the catalogue of novel foods by the European Food Safety Authority due to its rich nutritional value and the low carbon footprint produced during its breeding. The large scale of Tenebrio molitor breeding makes automation of the process, which is supported by a monitoring system, essential. Present research involves the development of a 3-module system for monitoring Tenebrio molitor breeding. The instance segmentation module (ISM) detected individual growth stages (larvae, pupae, beetles) of Tenebrio molitor, and also identified anomalies: dead larvae and pests. The semantic segmentation module (SSM) extracted feed, chitin, and frass from the obtained image. The larvae phenotyping module (LPM) calculated features for both individual larvae (length, curvature, mass, division into segments, and their classification) and the whole population (length distribution). The modules were developed using machine learning models (Mask R-CNN, U-Net, LDA), and were validated on different samples of real data. Synthetic image generation using a collection of labelled objects was used, which significantly reduced the development time of the models and reduced the problems of dense scenes and the imbalance of the considered classes. The obtained results (average $$F1>0.88$$ F 1 > 0.88 for ISM and average $$F1>0.95$$ F 1 > 0.95 for SSM) confirm the great potential of the proposed system.

Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some improvement may be obtained.

The paper deals with a production scheduling process, which is a problematic and it requires considering a lot of various factors while making the decision. Due to the specificity of the production system analysed in the practical example, the production scheduling problem was classified as a Job-shop Scheduling Problem (JSP). The production scheduling process, especially in the case of JSP, involves the analysis of a variety of data simultaneously and is well known as NP-hard problem. The research was performed in partnership with a company from the automotive industry. The production scheduling process is a task that is usually performed by process engineers. Thus, it can often be affected by mistakes of human nature e.g. habits, differences in experience and knowledge of engineers (their know-how), etc. The usage of heuristic algorithms was proposed as the solution. The chosen methods are genetic and greedy algorithms, as both of them are suitable to resolve a problem that requires analysing a lot of data. The paper presents both approaches: practical and theoretical aspects of the usefulness and effectiveness of genetic and greedy algorithms in a production scheduling process.

Fault management is an expensive process and analyzing data manually requires a lot of resources. Modern software bug tracking systems may be armed with automated bug report assignment functionality that facilitates bug classification or bug assignment to proper development group. For supporting decision systems, it would be beneficial to introduce information related to explainability. The purpose of this work is to evaluate the use of explainable artificial intelligence (XAI) in processes related to software development and bug classification based on bug reports created by either software testers or software users. The research was conducted on two different datasets. The first one is related to classification of security vs non-security bug reports. It comes from a telecommunication company which develops software and hardware solutions for mobile operators. The second dataset contains a list of software bugs taken from an opensource project. In this dataset the task is to classify issues with one of following labels crash, memory, performance, and security. Studies on XAI-related algorithms show that there are no major differences in the results of the algorithms used when comparing them with others. Therefore, not only the users can obtain results with possible explanations or experts can verify model or its part before introducing into production, but also it does not provide degradation of accuracy. Studies showed that it could be put into practice, but it has not been done so far.

The paper deals with the issue of production scheduling for various types of employees in a large manufacturing company where the decision-making process was based on a human factor and the foreman’s know-how, which was error-prone. Modern production processes are getting more and more complex. A company that wants to be competitive on the market must consider many factors. Relying only on human factors is not efficient at all. The presented work has the objective of developing a new employee scheduling system that might be considered a particular case of the job shop problem from the set of the employee scheduling problems. The Neuro-Tabu Search algorithm and the data gathered by manufacturing sensors and process controls are used to remotely inspect machine condition and sustainability as well as for preventive maintenance. They were used to build production schedules. The construction of the Neuro-Tabu Search algorithm combines the Tabu Search algorithm, one of the most effective methods of constructing heuristic algorithms for scheduling problems, and a self-organizing neural network that further improves the prohibition mechanism of the Tabu Search algorithm. Additionally, in the paper, sustainability with the use of Industry 4.0 is considered. That would make it possible to minimize the costs of employees’ work and the cost of the overall production process. Solving the optimization problem offered by Neuro-Tabu Search algorithm and real-time data shows a new way of production management.

This paper deals with an assessment of the effectiveness of resource management in production processes. A comprehensive and standardized approach to the issue of effectiveness contributes to the maximization of the potential of production resources. In this paper, the concept of effectiveness was used in the sense of the praxeological approach, and its components were distinguished by availability, performance and quality, which were assessed through the prism of losses occurring in production processes. The main purpose of this work was to develop a method for assessing the effectiveness of the use of available resources when implementing production processes. The method is based on a set of quantitative indicators and takes into account the categorization of production losses. Losses in availability, performance and quality in terms of areas of production, logistics, and maintenance, as well as organization and management, were identified. It was developed in response to the research gap identified in the literature review, which showed that there is a lack of a quantitative method for assessing the effectiveness of production processes. The method also takes into account the categorization of production losses, and at the same time allows the contribution of the main areas (production and production-related) with regard to the generation of these losses. The method is primarily intended to identify the causes of problems occurring in any production process, to determine their importance in a measurable way, and to monitor the course of processes after introducing changes of a different nature. The method was verified on a real example in a branch of an automotive industry company located in Lower Silesia.

Standardization is a key element in the effective use of lean manufacturing methodologies and tools for achieving process sustainability. Their combination is conducive to eliminating waste and improving the efficiency of production processes and guarantees the company that employees use the most efficient tools and do not waste time on unnecessary activities. These activities can be further improved by using digital solutions, in accordance with the concept of Industry 4.0. Therefore, the authors have developed the e-Lean system, whose task is to digitize selected lean manufacturing tools. The subject of this work is analysis of the functionality and effectiveness of the essential part of the e-Lean system in the form of specialized TPM (Total Productive Maintenance) software as an application. During implementation in a construction production company, the TPM application was tested by lean manufacturing and maintenance specialists. The research consisted of assessing the functionality and efficiency of processes in relation to conventional TPM solutions. Additional functionalities of the e-Lean system have been confirmed, such as systemic approval of machinery inspection, which requires passing all necessary steps at individual inspection points, direct access for supervisors to the results of inspection activities and their status, direct and easy access to photographic documentation of machines added during inspection both in optimization of working time and its course (e.g., the optimal number of steps taken by the employee during the inspection), as well as an efficient system of motivating employees (collecting points). The improvement in the effectiveness of processes was determined by measuring the control times for three control points (polymerization furnace, packing area, and defibering machines). The average control time was reduced from 16,200 to 13,923 s. Thus, thanks to the use of the application, it was found that the efficiency of using the TPM tool was increased by approx. 15% compared to previously used non-digital solutions.

(1) Objective: We aimed to evaluate the effect of treatment with prednisone on nasal and systemic periostin and eotaxin expression, IgE in plasma and eosinophils in tissue. (2) Methods: We compared the values of nasal and systemic periostin, eotaxin, IgE and eosinophils in tissue in patients treated with only nasal steroids before FESS, group 1, with those treated with an oral steroid–prednisone, group 2. (3) Results: A statistically significant decrease in the level of periostin, eotaxin and IgE in plasma was achieved in patients treated with prednisone one week before and after surgery (in sequence: p < 0.0476, p < 0.0006, p < 0.0031). In patients treated with steroids, we also observed a lower level of periostin in the epithelium (p < 0.044), eotaxin in the stroma (p limit value < 0.075) and eosinophils (p < 0.031) in the tissues collected during the operation. (4) Conclusions: Systemic steroid treatment with prednisone distinctly decreases periostin, eotaxin and IgE expression in plasma. We also observed a lower level of periostin in the epithelium, eotaxin in the stroma and eosinophils in the tissues. We need more attempts to find inflammatory markers associated with chronic rhinosinusitis. Identifying drugs that decrease inflammatory parameters would allow for more targeted therapy.

Introduction: Endoscopic sinus surgery can lead to crusting or synechiae formation, which can affect the healing process. Objective: The aim of our study was to compare the influence of steroid versus antibiotic versus saline solution impregnated absorbable nasal spacers on postoperative wound healing and patient satisfaction. Methods: Eighty patients, 33 women and 47 men, were enrolled in this study. At the end of the surgery, two pieces of 4 cm biodegradable material were applied in each ethmoid cavity. One of them was impregnated with saline solution, while the second one with steroid, or with antibiotic. Results: We observed statistically significant differences in the Lund–Kennedy score between the control and both treatment groups: for the Antibiotic-group on days 10 and 30 (p = 0.009; p = 0.009) and for the Steroid-group on day 90 (p = 0.008). The extended endoscopic appearance of nasal mucosa indicated statistically significant differences in crust formation on day 10 comparing the steroid and control dressing (p = 0.025), in secretion type on days 10 and 30 comparing the antibiotic and control dressing (p = 0.003; p = 0.016) and additionally for steroid and control on day 90 (p = 0.046). On Day 90 we observed statistically significant differences in the absence of mucosal edema in the S-group compared to controls (p = 0.007). Conclusions: The results of this study reveal the significant positive influence of steroid- and antibiotic-impregnated biodegradable nasal packing on the postoperative healing process and patient satisfaction compared to the saline soaked dressing. Resumo: Introdução: A cirurgia endoscópica nasossinusal pode levar à formação de crostas e sinéquias, o que pode afetar o processo de cicatrização. Objetivo: O objetivo do nosso estudo foi comparar a influência do espaçador nasal absorvível embebido em esteroide versus antibiótico versus solução salina na cicatrização de ferida pós-operatória e na satisfação do paciente. Método: Oitenta pacientes, 33 mulheres e 47 homens, foram incluídos neste estudo. Ao final da cirurgia, dois tampões de material biodegradável de 4 cm foram aplicados em cada cavidade etmoidal. Um deles foi embebido em solução salina, enquanto no segundo foi utilizado esteroide, ou antibiótico. Resultados: Observamos diferenças estatisticamente significantes no escore de Lund-Kennedy entre os grupos controle e ambos os grupos tratamentos: para o grupo antibiótico nos dias 10 e 30 (p = 0,009; p = 0,009) e para o grupo esteroide no dia 90 (p = 0,008). O aspecto endoscópico da mucosa nasal indicou diferenças estatisticamente significantes na formação de crostas no dia 10, na comparação do esteroide com o curativo controle (p = 0,025), no tipo de secreção nos dias 10 e 30, na comparação do antibiótico com o curativo controle (p = 0,003; p = 0,016) e adicionalmente para esteroide e controle no dia 90 (p = 0,046). No dia 90, observamos diferenças estatisticamente significantes na ausência de edema da mucosa no grupo E (esteroide) em relação aos controles (p = 0,007). Conclusões: Os resultados deste estudo revelam uma influência positiva significante no uso de tampão nasal biodegradável embebido em esteroides e antibióticos no processo de cicatrização pós-operatória e satisfação do paciente em comparação com o curativo embebido em solução salina. Keywords: Anti-bacterial agents, Steroids, Wound healing, Endoscopic sinus surgery, Nasopore/biodegradable packing, Palavras-chave: Agentes antibacterianos, Esteroides, Cicatrização de feridas, Cirurgia endoscópica nasal, Nasopore/tampão biodegradável

Plethora of ensemble techniques have been implemented and studied in order to achieve better classification results than base classifiers. In this paper an algorithm for integration of decision trees is proposed, which means that homogeneous base classifiers will be used. The novelty of the presented approach is the usage of the simultaneous distance of the object from the decision boundary and the center of mass of objects belonging to one class label in order to determine the score functions of base classifiers. This means that the score function assigned to the class label by each classifier depends on the distance of the classified object from the decision boundary and from the centroid. The algorithm was evaluated using an open-source benchmarking dataset. The results indicate an improvement in the classification quality in comparison to the referential method - majority voting method.

In order to improve the classification performance of a single classification model, Multiple Classifier Systems (MCS) are used. One of the most common techniques utilizing multiple decision trees is the random forest, where diversity between base classifiers is obtained by bagging the training dataset. In this paper, we propose the algorithm that uses horizontal partitioning the learning set and uses decision trees as base models to obtain decision regions. In the proposed approach feature space is divided into disjoint subspace. Additionally, the location of the subspace centroids, as well as the size and location of decision regions, are used in order to determine the weights needed in the last process of creating MCS, i.e. in the integration phase. The proposed algorithm was evaluated employing multiple open-source benchmarking datasets, compared using accuracy and Matthews correlation coefficient performance measures with two existing MCS methods - random forest and majority voting. The statistical analysis confirms an improvement in recognition compared to the random forest. In addition, we proved that for infinitely dense space division proposed algorithm is equivalent to majority voting.

The score function can be used as a measure for evaluating predicted probabilities of the classification models. In multiple classifiers systems, one of the problems is the diversity of the way of determining the scoring function of individual base classifiers. To alleviate this limitation, in this article, we propose a novel concept of calculating a scoring function defined by the probability-based potential function. The proposed potential functions take into account the distance of the recognized object from the decision boundary as well as a prior probability of the class labels. The proposed score function has the same nature for all linear base classifiers, which defined the multiple classifiers model. Additionally, the proposed method is compared with other ensemble algorithms based on homogeneous linear base classifiers. The experiments on seventy databases demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures dedicated to standard and imbalanced datasets. The statistical analysis of the experiments is also performed.

The commercially available battery management and mission scheduling systems for fleets of autonomous mobile robots use different algorithms to calculate the current state of charge of the robot’s battery. This information alone cannot be used to predict whether it will be possible for a single robot in the fleet to execute all of the scheduled missions. This paper provides insight into how to develop a universal battery discharge model based on key mission parameters, which allows for predicting the battery usage over the course of the scheduled missions and can, in turn, be used to determine which missions to delegate to other robots in the fleet, or if more robots are needed in the fleet to accomplish the production plan. The resulting model is, therefore, necessary for mission scheduling in a flexible production system, including autonomous mobile robot transportation networks.

Despite scientific and clinical advances, stroke is still considered one of the main causes of disability, including gait disorders. The search for more effective methods of gait re-education in post-stroke patients is one of the most important issues in contemporary neurorehabilitation. In this paper, we propose a transformation of the feature space and definition of class labels in the post-stroke gait problem to more efficiently study related phenomena and assess gait faster. Clustering is used to define two class labels (improvement and recurrence) in the data labeling process. The proposed approach was tested on a real-world dataset consisting of 50 patients (male and female, aged 49–82 years) after ischemic stroke who participated in a gait rehabilitation program. Gait in the study was described using speed, cadence, and stride length and their normalized values. Ten treatment sessions (10 therapy days) were conducted over two weeks (10 working days). The same specialist took measurements, and hence inter-rater reliability can be neglected. Machine learning methods, support vector machine and quadratic discriminant analysis were used to classify post-stroke gait for three cases with different class labels. The proposed novel approach, characterized by its speed of execution and accuracy of classification, may be helpful for screening, better targeting, and rehabilitation monitoring. The proposed approach minimizes clinical testing and supports the work of physicians, physiotherapists, and diagnosticians.

Studies have been performed to improve the process of waste management. They were fulfilled by changing the base of waste logistics management using a combination of intelligent algorithms and the IMPACT IoT platform instead of a human factor. The research was carried out on the example of real data with respect to waste management in a given area. The proposed solution includes a program that simulates the filling of specific waste containers located in various areas. The determined aspects are inconveniences on the routes, affecting the time of moving between the receiving points and the distances between the containers. The variability of the speed and intensity of the containers filling up over time is an additional factor taken into account. The proposed methods yielded the performance of the control of the containers’ filling status in real time, which apparently results in the possibility of a reaction to the current demand just in time. The proposed solution enables the improvement of the waste logistics management process, including avoiding the too-frequent emptying of containers or overfilling them. The combination of the device prototype, the simulation program, and the developed algorithms opens the possibility for further research in the smart city and optimization areas.

Integration or fusion of the base classifiers is the final stage of creating multiple classifiers system. Known methods in this step use base classifier outputs, which are class labels or values of the confidence (predicted probabilities) for each class label. In this paper we propose an integration process which takes place in the geometric space. It means that the fusion of base classifiers is done using their decision boundaries. In order to obtain one decision boundary from boundaries defined by base classifiers the median or weighted average method will be used. In addition, the proposed algorithm uses the division of the entire feature space into disjoint regions of competence as well as the process of selection of base classifiers is carried out. The aim of the experiments was to compare the proposed algorithms with the majority voting method and assessment which of the analyzed approaches to integration of the base classifiers creates a more effective ensemble.

Introduction: Metalloproteinases and their tissue inhibitors play an important role in the metastases formation. A multistage process of carcinogenesis requires the involvement of numerous enzymes and compounds that facilitate the expansion of tumor cells. The formation of metastases depends on both metalloproteinases and tissue inhibitors activation leading to the activation of neoangiogenesis. The changes of the expression in stromal and tumor proteins could be prognostic factors in patients with oropharyngeal squamous cell carcinoma. Materials and Methods: This study was conducted on a total of 34 patients with squamous cell carcinoma of the oropharynx divided into 2 groups, including 20 patients with neck metastasis and 14 patients without lymph node metastasis. Immunohistochemistry was performed with a standard protocol. Results: The results of the present analysis indicated a higher expression of metalloproteinases 2 in the stroma than in tumor with increasing tumor grade. The dynamics of changes in the expression of metalloproteinases showed the increase in metalloproteinases 2 and the decrease in metalloproteinases 9 depending on the tumor size. Dynamics of changes in the expression of tissue inhibitor 1 in the tumor stroma significantly increased with the tumor stage. In the assessment of nodal staging from N0 to N3, the expression of tissue inhibitor 1 and 2 were higher in the tumor tissues. The increase of metalloproteinases 2, tissue inhibitor 1 in the tumor, and metalloproteinases 9 in the stroma were characterized by a reduction in the odds ratio of patient’s survival. Conclusion: The complex evaluation of the expression of metalloproteinases and tissue inhibitors may be used for the prognosis of the patient’s survival.

Background: The reverse superior labial artery (rSLA) island flap can be used to reconstruct the cheek, ala, columella and vestibule of the nose when other techniques appear insufficient or impractical. The aim of this case series was to present applications of rSLA pedicle flaps in the post-ablative oncologic reconstruction of the face. Patients and Methods: Using a retrospective case-series study design, the investigators enrolled a cohort of patients undergoing procedures involving rSLA flaps treated at a Polish Otolaryngology Department for facial reconstruction after tumour excision. The main outcomes were functional and aesthetic aspects. Descriptive statistics were computed as appropriate. Results and Conclusions: The use of rSLA flaps allows surgeons to obtain a large skin island with only minimal cosmetic and functional alterations. In all of the cases in this series, the use of this pedicle flap resulted in both optimal healing and satisfactory cosmetic and functional outcomes.

Objectives: To demonstrate the effectiveness of endoscopic assessment of the pharyngeal tonsil in defining the size of the adenoid hypertrophy in endoscopic examination that would be equivalent to intraoperative assessment as a large adenoid, and to determine the lowest necessary frequency of tests to assess the variability of its size. Methods: The study is based on an analysis of two groups of children diagnosed and treated in a children’s ENT outpatient clinic and ENT department. In the first group, adenoid size was assessed based on flexible endoscopy, and then with a consequent adenoid surgery with assessment of the intraoperative size of the adenoid, we compared the size of the adenoid. The second part of the study included a group of 81 children. We analyzed performed flexible nasopharyngoscopy examinations (FNE) of each child, and compared the change of adenoid size in a minimum of two subsequent examinations over a period of 1 year or more. Results: The sensitivity of flexible endoscopic examination in the assessment of the pharyngeal tonsil was determined at 97.3%, and specificity at 72.7%. The ROC curve shows the value of adenoid-to-choana (A/C) ratio as 75% or more in the preoperative FNE, indicating that the tonsil during surgery is assessed as large. Among the children, 26.3% had a change in adenoid size of more than 15% in the A/C ratio in 1 year of observation, and 45% of the children had A/C ratio changed above 15% in a period of 3 years of observation. Conclusions: FNE examination is highly effective in assessing the size of the pharyngeal tonsil. We proposed a schema for frequencies of FNE examinations and treatment dependent on A/C ratio and worsening of ailments.

The binary classifiers are appropriate for classification problems with two class labels. For multi-class problems, decomposition techniques, like one-vs-one strategy, are used because they allow the use of binary classifiers. The ensemble selection, on the other hand, is one of the most studied topics in multiple classifier systems because a selected subset of base classifiers may perform better than the whole set of base classifiers. Thus, we propose a novel concept of the dynamic ensemble selection based on values of the score function used in the one-vs-one decomposition scheme. The proposed algorithm has been verified on a real dataset regarding the classification of cutting tools. The proposed approach is compared with the static ensemble selection method based on the integration of base classifiers in geometric space, which also uses the one-vs-one decomposition scheme. In addition, other base classification algorithms are used to compare results in the conducted experiments. The obtained results demonstrate the effectiveness of our approach.