Thigmomorphogenesis (or mechanical stimulation-MS) is a term created by Jaffe and means plant response to natural stimuli such as the blow of the wind, strong rain, or touch, resulting in a decrease in length and an increase of branching as well as an increase in the activity of axillary buds. MS is very well known in plant morphology, but physiological processes controlling plant growth are not well discovered yet. In the current study, we tried to find an answer to the question if MS truly may affect auxin synthesis or transport in the early stage of plant growth, and which physiological factors may be responsible for growth arrest in petunia. According to the results of current research, we noticed that MS affects plant growth but does not block auxin transport from the apical bud. MS arrests IAA and GA₃ synthesis in MS-treated plants over the longer term. The main factor responsible for the thickening of cell walls and the same strengthening of vascular tissues and growth arrestment, in this case, is peroxidase (POX) activity, but special attention should be also paid to AGPs as signaling molecules which also are directly involved in growth regulation as well as in cell wall modifications.

Water management in agriculture and horticulture has a strong ecological importance related to the necessity of optimizing the use of water resources. To achieve sustainable water use, it is necessary to optimize the time, frequency and the methods of water application. In this study, we hypothesized that the right moment for watering may be established on the grounds of the observation of the physiological state of the plant (if the plant is still in turgor) and the biomass of plant and the substrate. The proper irrigation scheduling, that is, just before the plant loses turgor, which appears at ca. 73% of LRWC in dahlias, determined with the use of the proposed measurement and computing system, makes it possible to save ca. 30% of irrigation water, in comparison to standard watering. Controlled watering also affected plant growth parameters, such as the content of chlorophyll a and b and carotenoid, as well as total and reducing sugar content (ca. 7%, 9% and 23% more than in plants watered in a standard way, respectively). Plants watered in a controlled way were 12% more compact when compared with the ones watered in a standard way. The results clearly proved that the computing system connected to scales made it possible to save water used for irrigation without a negative impact on the parameters of plant growth.

The main objective of this study was to conduct initial evaluations of the performance of a classifier utilizing the EVA-02 model, which was designed to identify tool wear states by analyzing scanned images of machined, melamine-faced MDF workpieces produced under varying tool degradation levels. Tool wear was quantified using the flank wear indicator VB and grouped into four wear classes (Class I: VB = 0–0.1 mm, Class II: VB = 0.1–0.2 mm, Class III: VB = 0.2–0.3 mm, Class IV: VB = 0.3–0.4 mm). The classifier automatically analyzed the delamination level observed on the edges of the workpieces to predict the corresponding tool wear class. To increase the number of training samples and capture local manifestations of edge damage, each edge image was divided into 28 overlapping image windows of size 250 × 250 pixels. A few different data aggregation strategies were investigated. The most accurately recognized tool wear class was Class IV (Precision 89%; Recall 93% and F1-score 91%—rounded to whole numbers). However, the classification metrics for the remaining classes were considerably lower. For instance, the F1-score for each of them fell well below 80%. Most notably, the recall for class III was particularly poor, reaching a mere 65%. The overall accuracy, averaged across all four classes, was 79%. This result is comparable to previous research reports and can be considered promising, but it does not represent a major breakthrough.

Appliance recognition from aggregate household measurements is challenging under real deployment conditions, where multiple devices operate concurrently and sensor data are affected by imperfections such as noise, missing samples, and nonlinear meter response. In contrast to many studies that rely on curated or idealized datasets, this work investigates appliance recognition using real multimodal utility data (electricity, water, gas) collected at the building entry point, in the presence of substantial uninstrumented background activity. We present a case study evaluating transparent, rule-based detectors designed to exploit characteristic temporal dependencies between modalities while remaining interpretable and robust to sensing imperfections. Four household appliances—washing machine, dishwasher, tumble dryer, and kettle—are analyzed over six weeks of data. The proposed approach achieves reliable detection for structured, water-related appliances (22/30 washing cycles, 19/21 dishwashing cycles, and 23/27 drying cycles), while highlighting the limitations encountered for short, high-power events such as kettle usage. The results illustrate both the potential and the limitations of conservative rule-based detection under realistic conditions and provide a well-documented baseline for future hybrid systems combining interpretable rules with data-driven adaptation.