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Industry 4.0 is fast becoming a mainstream goal, and many companies are lining up to join the Fourth Industrial Revolution. Small and medium-sized enterprises, especially in the manufacturing industry, are the most heavily challenged in adopting new technology. One of the reasons why these enterprises are lagging behind is the motivation of the key personnel, the decision-makers. The factories in question often do not have a pressing need for advancing to Industry 4.0 and are wary of the risk in doing so. The authors present a rapid, low-cost prototyping solution for the manufacturing companies with legacy machinery intending to adopt the Industry 4.0 paradigm with a low-risk initial step. The legacy machines are retrofitted through the Industrial Internet of Things, making these machines both connectable and capable of providing data, thus enabling process monitoring. The machine chosen as the digitization target was not connectable, and the retrofit was extensive. The choice was made to present the benefits of digitization to the stakeholders quickly and effectively. Indeed, the solution provides immediate results within manufacturing industrial settings, with the ultimate goal being the digital transformation of the entire factory. This work presents an implementation cycle for digitizing an industrial broaching machine, supported by state-of-the-art literature analysis. The methodology utilized in this work is based on the well-known DMAIC strategy customized for the specifics of this case study.

To date, many machine learning models have been used for peach maturity prediction using non-destructive data, but no performance comparison of the models on these datasets has been conducted. In this study, eight machine learning models were trained on a dataset containing data from 180 ‘Suncrest’ peaches. Before the models were trained, the dataset was subjected to dimensionality reduction using the least absolute shrinkage and selection operator (LASSO) regularization, and 8 input variables (out of 29) were chosen. At the same time, a subgroup consisting of the peach ground color measurements was singled out by dividing the set of variables into three subgroups and by using group LASSO regularization. This type of variable subgroup selection provided valuable information on the contribution of specific groups of peach traits to the maturity prediction. The area under the receiver operating characteristic curve (AUC) values of the selected models were compared, and the artificial neural network (ANN) model achieved the best performance, with an average AUC of 0.782. The second-best machine learning model was linear discriminant analysis with an AUC of 0.766, followed by logistic regression, gradient boosting machine, random forest, support vector machines, a classification and regression trees model, and k-nearest neighbors. Although the primary parameter used to determine the performance of the model was AUC, accuracy, F1 score, and kappa served as control parameters and ultimately confirmed the obtained results. By outperforming other models, ANN proved to be the most accurate model for peach maturity prediction on the given dataset.

The main goal of this study was to investigate and better understand the evolution of the main non-destructive and destructive quality parameters of peach ‘Redhaven’ during ripening process. This study was conducted from 8 to 21 July 2023, during which peaches ‘Redhaven’ were harvested each second day from a commercial orchard located in Novaki Bistranjski. Maturity categories were defined according to different firmness thresholds: maturity for long-distance chain stores (H1), maturity for medium-distance chain stores (H2), maturity below the defined maximum firmness in order to preserve optimal quality traits (H3), ready to buy (H4), ready to eat (H5), and overripe (H6). The chlorophyll absorbance index was the non-destructive parameter that was mostly distinguished between maturity categories (r = 0.78 with firmness), followed by a* and h° ground colour parameters. During the first three maturity categories (H1–H3), firmness had a notably smaller correlation with titratable acidity and the ratio of total soluble solids and titratable acidity, which is not the case for a* and h° ground colour parameters, chlorophyll absorbance index, and the share of additional colour. During the last three maturity categories (H4–H6), non-destructive parameters are not reliable for maturity prediction. When ground colour parameters are measured near petiole insertion, mostly smaller segregation between maturity categories is obtained compared to when measured at the rest of the fruit. Total polyphenol and flavonoid content in peach juice notably corelated only in the last two maturity categories with L* ground colour parameter.

Barrier tissue dysfunction is a fundamental feature of chronic human inflammatory diseases 1 . Specialized subsets of epithelial cells—including secretory and ciliated cells—differentiate from basal stem cells to collectively protect the upper airway 2 – 4 . Allergic inflammation can develop from persistent activation 5 of type 2 immunity 6 in the upper airway, resulting in chronic rhinosinusitis, which ranges in severity from rhinitis to severe nasal polyps 7 . Basal cell hyperplasia is a hallmark of severe disease 7 – 9 , but it is not known how these progenitor cells 2 , 10 , 11 contribute to clinical presentation and barrier tissue dysfunction in humans. Here we profile primary human surgical chronic rhinosinusitis samples (18,036 cells, n  = 12) that span the disease spectrum using Seq-Well for massively parallel single-cell RNA sequencing 12 , report transcriptomes for human respiratory epithelial, immune and stromal cell types and subsets from a type 2 inflammatory disease, and map key mediators. By comparison with nasal scrapings (18,704 cells, n  = 9), we define signatures of core, healthy, inflamed and polyp secretory cells. We reveal marked differences between the epithelial compartments of the non-polyp and polyp cellular ecosystems, identifying and validating a global reduction in cellular diversity of polyps characterized by basal cell hyperplasia, concomitant decreases in glandular cells, and phenotypic shifts in secretory cell antimicrobial expression. We detect an aberrant basal progenitor differentiation trajectory in polyps, and propose cell-intrinsic 13 , epigenetic 14 , 15 and extrinsic factors 11 , 16 , 17 that lock polyp basal cells into this uncommitted state. Finally, we functionally demonstrate that ex vivo cultured basal cells retain intrinsic memory of IL-4/IL-13 exposure, and test the potential for clinical blockade of the IL-4 receptor α-subunit to modify basal and secretory cell states in vivo. Overall, we find that reduced epithelial diversity stemming from functional shifts in basal cells is a key characteristic of type 2 immune-mediated barrier tissue dysfunction. Our results demonstrate that epithelial stem cells may contribute to the persistence of human disease by serving as repositories for allergic memories. Single-cell RNA sequencing is used to characterize cell types in nasal tissues from human patients with chronic rhinosinusitis, revealing a role for tissue stem cells in allergic inflammatory memory.

Net application in agriculture has a long history. Nets were usually used for the protection of plants against different hazards (hail, wind, birds, pests, excessive sun radiation) and, lately, from insects (nets with smaller mesh size). In recent years, photoselective netting technology has emerged, which adds desired plant responses caused by light quality changes to their basic protective properties. A combination of anti-insect and photoselective net technology (anti-insect photoselective nets) may present a notable contribution to the sustainable food production concept. Notable positive effects of this eco-friendly approach on agroecosystems are mainly achievable due to its non-pesticide pest protection of cultivated plants and, at the same time, promotion of special beneficial morphological and physiological plant responses. Although netting has been extensively studied over the last decade, there is a pronounced lack of publications and analyses that deal with their mode of action on fruit trees, which is especially true for new netting concepts. A better understanding of such mechanisms can lead to improved development and/or utilization of this technology and enhanced generation of value-added products. This review was based on a revision of the literature regarding netting in agriculture, with emphasis on fruit cultivation, and the following databases were used: Web of Science, ScienceDirect, Scopus, and Google Scholar. Although this study aims to comprehend a majority of fruit species, it narrows down to those usually net-protected and, hence, studied, such as apple, peach or nectarine, kiwifruit, blueberry, etc. Nets mainly differ in their mesh size and color, which are the parameters that mostly determine their capacity for light quantity and quality modification. Such light modifications, directly or indirectly (e.g., change in microclimate), initiate different fruit tree responses (in some cases, mechanisms) through which the final effect is realized on their vegetative and generative traits. For instance, some of them include a shade avoidance mechanism (initiated by changes in red to a far-red ratio, blue light levels, etc.), source–sink relationship, and carbohydrate availability (actualized by changes in photosynthesis efficiency, vegetative and generative growth, etc.), plant stress response (actualized by microclimate changes), etc. In most cases, these responses are interconnected, which contributes to the complexity of this topic and emphasizes the importance of a better understanding of it.

Background Our study aimed to assess the efficacy of posterior conduit fixation with retroperitoneal ureteroileal anastomosis (UIA) in reducing perioperative complications after radical cystectomy (RC) with ileal conduit (IC) urinary diversion. Methods We conducted a retrospective case-control study, including 150 patients who underwent either modified IC technique (extraperitonealized anastomosis with posterior conduit fixation; n  = 79) or the conventional IC technique ( n  = 71). The primary endpoints were the incidence of clinical parastomal hernia (PSH) and ileus. Secondary endpoints included operative time, postoperative complication rates, and length of hospital stay (LOS). Multivariate logistic regression was performed to identify predictors of early and late stoma- related complications. Results The modified group showed significantly lower incidence of both early and late postoperative complications, including ileus and PSH, compared to the conventional group (8.86% vs. 28.1%, p  = 0.01 and 7.6% vs. 17%, p  = 0.03, respectively) after a median follow-up of 34 months. Corresponding hazard ratios were 0.312 (95% CI: 0.047–0.798, p  = 0.01) for early complications and 0.267 (95% CI: 0.105–0.611, p  = 0.03) for late complications. Conclusion The results support our hypothesis that extraperitoneal ureteroileal anastomosis combined with posterior conduit fixation effectively reduces the risk of both early and late postoperative complications, including parastomal hernia and ileus.

Background: Bladder cancer accounts for more than 200,000 deaths annually on a global level, with an age-standardized mortality rate of 2.9 per 100,000 individuals. Despite declining global rates, it remains a substantial public health burden. We aimed to analyze the mortality trend of bladder cancer in Montenegro and identify the measures taken to combat this tumor. Methods: Bladder cancer mortality data in Montenegro from 1990 to 2021 were collected. Mortality rates were age-standardized to the World Standard Population. The joinpoint, linear and Poisson regressions were used to assess bladder cancer mortality trend. Results: There was a consistent increase in mortality rates due to bladder cancer, with statistical significance for both the overall population and specifically for males, with an average annual percent change (AAPC) of 1.5% (95% CI: 1.5 (0.5-2.9)) and 1.6% (AAPC (95% CI): 1.6 (0.4-3.3)) respectively. Additionally, there was a notable annual increase in the number of bladder cancer cases: average annual increase was 3.4% for the overall population, 3.5% in male and 2.9% in female, with statistical parameters (AAPC (95% CI), P-value) for join point regression: 3.4 (2.4-4.8), <0.001; 3.5 (2.3-5.1), 0.003; and 2.9 (1.2-5.1), 0.004, respectively. The majority of bladder cancer deaths occurred in the age groups of 65-74 (35.8%), 75-84 (33.6%), and 55-64 (16.8%). Conclusion: The ongoing increase in bladder cancer mortality in Montenegro, particularly among men and elderly should encourage policymakers to take action to reverse this unfavorable trend.

Background/Objectives: The correlation of handgrip strength (HGS) and morphological characteristics with Big Five personality traits is well documented. However, it is unclear whether these relationships also exist in highly trained and specialized populations, such as tactical athletes, and whether there are specific differences compared to the general population. This study aimed to explore the interplay of handgrip neuromuscular, morphological, and psychological characteristics in tactical athletes and the general population of both genders. Methods: The research was conducted on a sample of 205 participants. A standardized method, procedure, and equipment (Sports Medical solutions) were used to measure the isometric neuromuscular characteristics of the handgrip. Basic morphological characteristics of body height, body mass, and body mass index were measured with a portable stadiometer and the InBody 720 device. Psychological characteristics were assessed with the Mental Toughness Index and Dark Triad Dirty Dozen questionnaires. Results: Numerous significant correlations were obtained, as well as differences between tactical athletes and the general population of both genders. The most prominent correlations were between the excitation index with Psychopathy and the Dark Triad (ρ = −0.41, −0.39) in female tactical athletes, as well as Neuroticism with body height, maximal force, and the maximum rate of force development in the male general population (ρ = 0.49, 0.43, 0.41). The obtained results also revealed gender and occupational specific patterns of researched relationships. Conclusions: Although the results of this study indicated the possibility of the existence of correlations between handgrip neuromuscular, morphological, and psychological characteristics in tactical athletes of both genders, nevertheless, at the moment, there is not enough solid evidence for that. That is why new research is needed. An analysis of muscle contractile and time parameters as neuromuscular indicators in the HGS task proved to be a possible promising method, which brought numerous new insights about the researched relationships. For practical application in the field, we propose including Mental Toughness and the Dark Triad traits in the selection process for future police officers and national security personnel based on the obtained results.

This study attempts to clarify the effect of canopy position on the physico-chemical parameters of apples cv. Braeburn. The experiments were carried out on fruit from the inner and outer part of the canopy in two growing seasons and at two harvest dates. Light measurements revealed that the average value of photo active radiation (PAR) for the inside and outside canopy amounted to 30.3 μmol/m2/s and 133.7 μmol/m2/s, respectively. Production year and canopy position significantly influenced ground color parameters a*, b*, C*, and h°, while the harvest date influenced all color parameters studied. For additional (red blush) coloration, the production year significantly influenced only the L* parameter, harvest date influenced all color parameters, and canopy position influenced L, a*, and C*. Only the fruits of the second harvest date showed more intense additional (red blush) coloration. The production year significantly affected fruit mass, firmness, total soluble solids (SSC), titratable acidity (TA), SSC/TA ratio, DPPH radical scavenging assay (AOP), total phenolic content (TPC), and total flavonoid content (TFC). The harvest date significantly influenced fruit mass, SSC, TA, SSC/TA, AOP, TPC, and TFC. The canopy position significantly influenced SSC, TA, AOP, TPC, and TFC. Regarding mineral content, the production year significantly affected the content of Fe, Ni, Cu, and Ca and the K/Ca ratio. The harvest date significantly affected Fe, Cu, Sr, K and K/Ca. The canopy position affected Fe, Ni, Zn, Sr, Ca, and K/Ca ratio, with a clear significant trend regarding the effect of canopy position only for Ca content (first and second year of the second harvest date) and K/Ca ratio (first year of both harvest dates). PCA analyses identified distinguishing features between apples, with differences defined specifically by AOP, TPC, TFC, Rb, Sr, Ca, and K/Ca on the PC 1 and Mn, Fe, Ni, Cu, and Zn on PC 2.

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.