Physical and Chemical Characteristics of Droppings as Sensitive Markers of Chicken Health Status.

The aim of this study was to analyze the physical and chemical characteristics of chicken droppings (n = 73), which were collected during different age periods and classified by visual inspection into normal (N) and abnormal (A). Significant differences were found in the texture, pH, dry matter (DM), fatty acids (FAs), short-chain fatty acids (SCFAs), and volatile compounds (VCs) between the tested dropping groups ( ≤ 0.05).

Towards Early Poultry Health Prediction through Non-Invasive and Computer Vision-Based Dropping Classification.

The use of artificial intelligence techniques with advanced computer vision techniques offers great potential for non-invasive health assessments in the poultry industry. Evaluating the condition of poultry by monitoring their droppings can be highly valuable as significant changes in consistency and color can be indicators of serious and infectious diseases. While most studies have prioritized the classification of droppings into two categories (normal and abnormal), with some relevant studies dealing with up to five categories, this investigation goes a step further by employing image processing algorithms to categorize droppings into six classes, based on visual information indicating some level of abnormality.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques.

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems.

Investigation of the Robotized Incremental Metal-Sheet Forming Process with Ultrasonic Excitation.

During the single-point incremental forming (SPIF) process, a sheet is formed by a locally acting stress field on the surface consisting of a normal and shear component that is strongly affected by friction of the dragging forming tool. SPIF is usually performed under well-lubricated conditions in order to reduce friction. Instead of lubricating the contact surface of the sheet metal, we propose an innovative, environmentally friendly method to reduce the coefficient of friction by ultrasonic excitation of the metal sheet.

Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors.

Forecasting municipal solid waste (MSW) generation and composition plays an essential role in effective waste management, policy decision-making and the MSW treatment process. An intelligent forecasting system could be used for short-term and long-term waste handling, ensuring a circular economy and a sustainable use of resources. This study contributes to the field by proposing a hybrid k-nearest neighbours (H-kNN) approach to forecasting municipal solid waste and its composition in the regions that experience data incompleteness and inaccessibility, as is the case for Lithuania and many other countries.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force.

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces.

A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes.

There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn.

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement.

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time.

Towards the automation of early-stage human embryo development detection.

Background: Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer.