Search

People may be more likely to exercise if they have self-efficacy for exercise (SEE). We conducted an exploratory analysis of SEE using data from a clinical trial designed to increase physical activity (N = 340). We evaluated correlates of baseline SEE and the relationship between baseline SEE and physical activity. Low SEE at baseline was correlated with lower well-being, physical activity, and higher depression at baseline. Participants with high (vs. low) baseline SEE had higher physical activity (but no differential change in activity) over time. These data highlight the potential role of SEE in psychological health and physical activity.

High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems.

The poultry industry is shifting towards more sustainable and ethical practices, including adopting cage-free (CF) housing to enhance hen behavior and welfare. However, ensuring optimal indoor air quality, particularly concerning particulate matter (PM), remains challenging in CF environments. This study explores the effectiveness of electrostatic particle ionization (EPI) technology in mitigating PM in CF hen houses while considering the height at which the technology is placed and the duration of the electric supply. The primary objectives are to analyze the impact of EPI in reducing PM and investigate its power consumption correlation with electric supply duration. The study was conducted in a laying hen facility with four identical rooms housing 720 laying hens. The study utilized a Latin Square Design method in two experiments to assess the impact of EPI height and electric supply durations on PM levels and electricity consumption. Experiment 1 tested four EPI heights: H1 (1.5 m or 5 ft), H2 (1.8 m or 6 ft), H3 (2.1 m or 7 ft), and H4 (2.4 m or 8 ft). Experiment 2 examined four electric supply durations: D1 (control), D2 (8 h), D3 (16 h), and D4 (24 h), through 32 feet corona pipes. Particulate matter levels were measured at three different locations within the rooms for a month, and statistical analysis was conducted using ANOVA with a significance level of ≤0.05. The study found no significant differences in PM concentrations among different EPI heights (p > 0.05). However, the duration of EPI system operation had significant effects on PM1, PM2.5, and PM4 concentrations (p < 0.05). Longer EPI durations resulted in more substantial reductions: D2—17.8% for PM1, 11.0% for PM2.5, 23.1% for PM4, 23.7% for PM10, and 22.7% for TSP; D3—37.6% for PM1, 30.4% for PM2.5, 39.7% for PM4, 40.2% for PM10, and 41.1% for TSP; D4—36.6% for PM1, 24.9% for PM2.5, 38.6% for PM4, 36.3% for PM10, and 37.9% for TSP compared to the D1. These findings highlight the importance of prolonged EPI system operation for enhancing PM reduction in CF hen houses. However, utilizing 16 h EPI systems during daylight may offer a more energy-efficient approach while maintaining effective PM reduction. Further research is needed to optimize PM reduction strategies, considering factors like animal activities, to improve air quality and environmental protection in CF hen houses.

ABSTRACT: As global demand for poultry products, environmental sustainability, and health consciousness rises with time, the poultry industry faces both substantial challenges and new opportunities. Therefore, this review paper provides a comprehensive overview of sustainable poultry farming, focusing on integrating genetic improvements, alternative feed, precision technologies, waste management, and biotechnological innovations. Together, these strategies aim to minimize ecological footprints, uphold ethical standards, improve economic feasibility, and enhance industry resilience. In addition, this review paper explores various sustainable strategies, including eco-conscious organic farming practices and innovative feed sources like insect-based proteins, single-cell proteins, algal supplements, and food waste utilization. It also addresses barriers to adoption, such as technical challenges, financial constraints, knowledge gaps, and policy frameworks, which are crucial for advancing the poultry industry. This paper examined organic poultry farming in detail, noting several benefits like reduced pesticide use and improved animal welfare. Additionally, it discusses optimizing feed efficiency, an alternate energy source (solar photovoltaic/thermal), effective waste management, and the importance of poultry welfare. Transformative strategies, such as holistic farming systems and integrated approaches, are proposed to improve resource use and nutrient cycling and promote climate-smart agricultural practices. The review underscores the need for a structured roadmap, education, and extension services through digital platforms and participatory learning to promote sustainable poultry farming for future generations. It emphasizes the need for collaboration and knowledge exchange among stakeholders and the crucial role of researchers, policymakers, and industry professionals in shaping a future where sustainable poultry practices lead the industry, committed to ethical and resilient poultry production.

ABSTRACT: Chickens’ behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing. Six different CNN models were developed, based on YOLOv5, YOLOv8, ByteTrack, DeepSORT, and StrongSORT. The final results demonstrated that the combination of YOLOv8 and DeepSORT exhibited the highest performance, achieving a multiobject tracking accuracy (MOTA) of 94%. Further application of the optimal model could facilitate the detection of abnormal behaviors such as smothering and piling, and enabled the quantification of flock activity into 3 levels (low, medium, and high) to evaluate footpad health states in the flock. This research underscores the application of deep learning in monitoring poultry activity index for assessing animal health and welfare.

Background: Recently, Otoendoscopy, a novel technique, serves as a pivotal non-invasive diagnostic tool for the initial evaluation of ear disorders. Retraction pockets of the tympanic membrane are clinically significant because they can lead to hearing loss. Therefore, they necessitate careful evaluation and ongoing monitoring. Oto-endoscope because of the wide viewing angle gives a good view of the tympanic membrane status. The objective of this study was to clinically evaluate and grade tympanic membrane retractions and compare otoscopy and otoendoscopy findings. Material and Methods: This was a cross-sectional prospective study. A total 190 ears with tympanic membrane retractions of 111 patients were included in the study. Each of the patients was evaluated for retractions grade according to the Sade and Tos system of classification using pneumatic otoscope and otoendoscope. Results: In 79 (71.2%) patients had bilateral tympanic retraction whereas 32 (28.8%) patients had unilateral. Isolated pars tensa and pars flaccida retraction was found in 109 (57.3%) ears, and 4 (2.1%) ears respectively, while 77 (40.5%) ears having both type of retraction. A cross-tabulation of retraction grades revealed a significant association (p

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models and frameworks, and a thorough assessment of performance metrics employed in the surveyed studies. Additionally, it includes a comparative analysis between imitation learning techniques and conventional control methodologies in the realm of robotics. The findings derived from this survey unveil profound insights into the applications of imitation learning in agricultural robotics. These methods are highlighted for their potential to significantly improve task execution in dynamic and high-dimensional action spaces prevalent in agricultural settings, such as precision farming. Despite promising advancements, the survey discusses considerable challenges in data quality, environmental variability, and computational constraints that IL must overcome. The survey also addresses the ethical and social implications of implementing such technologies, emphasizing the need for robust policy frameworks to manage the societal impacts of automation. These findings hold substantial implications, showcasing the potential of imitation learning to revolutionize processes in agricultural robotics. This research significantly contributes to envisioning innovative applications and tools within the agricultural robotics domain, promising heightened productivity and efficiency in robotic agricultural systems. It underscores the potential for remarkable enhancements in various agricultural processes, signaling a transformative trajectory for the sector, particularly in the realm of robotics and autonomous systems.

Dengue virus (DENV), a common and prevalent mosquito-borne endemic disease, is caused by four serotypes (DENV-1–4) and has spread rapidly on a global scale over the past decade. A crucial step in the development of antiviral therapeutics requires the utilization of in vitro cell-based techniques, such as plaque assays and focus-forming assays (FFA) for virus quantification. Vero cells have been widely used for FFA and plaque assay; however, there are instances when their efficacy and efficiency in the detection of certain clinical DENV isolates are low. Here, we showed that BHK-21 cells are more sensitive than Vero cells in the detection of all DENV-1–4 plaques and foci. In addition, we developed an improved FFA protocol for the quantification of all four DENV serotypes. Using a pan-flavivirus envelope (E) antibody, we reduce the possibility of false positives by defining a focus to consist of a minimum of eight infected cells. We outlined a protocol using the Operetta® high-content imaging system to automate the digital capture of these infected cells. A pipeline was also designed using the CellProfilerTM automated image analysis software to detect these foci. We then compare the results of the improved FFA with plaque assay. Notably, the improved FFA detected clear foci of the DENV-4 strain that does not form distinct plaques. We subsequently demonstrated the potential application of the improved FFA protocol in antiviral testing, utilizing a nucleoside inhibitor of DENV, NITD008 as a control. The protocol is amenable to a diverse array of applications, including high-throughput compound screening (HTS).

Abstract The inevitable drawbacks of petrochemical polymer‐based packaging (e.g., extreme loss of fossil resources, excessive products’ carbon footprint, and inordinate environmental pollution resulting from the accumulation of disposed nonbiodegradable plastic‐based packages) have urged scientists to develop novel packaging materials from nature‐inspired biopolymers. Due to their biodegradability, non‐toxicity, film‐forming ability, and barrier properties versus gasses/aroma, polysaccharides have been increasingly valued in developing food packaging materials at the lab or industrial scale. Nonetheless, these valuable biopolymers also suffer from some inherent deficiencies, that is, low resistance to water and poor mechanical attributes. Hitherto, tackling such bottlenecks via the modification of biopolymers through chemical/physical approaches and applying a combination of several biopolymers has been the main focus of numerous recent studies. In this context, the present article, for the first time, provides a comprehensive update on the most recent utilization of common polysaccharides (e.g., starch, chitosan, xanthan gum, gum Arabic, alginate, gellan, pectin, and carrageenan) for food packaging applications.

During the life of a well, treatments are carried out to boost productivity by stimulating initially unproduced zones. These treatments include hydraulic fracturing, matrix acidization, and acid fracturing, among others. Hydraulic fracturing treatment is generally applied to deeper reservoirs of oil or natural gas for enhanced recovery. By infusing proppant, water, and chemicals under extreme pressure during the fracturing procedure, fissures in and beneath the reservoir layer can be accessed and expanded. Another stimulating procedure, matrix acidization, involves injecting acid down the drilling hole to permeate the rock fissures at stresses lower than the fracture stress. In addition, carbonate reservoir acid fracturing stimulation is commonly used as an acid treatment technique whereby a pressure greater than the formation disintegration pressure or spontaneous fracture closure pressure is used to compress acid into the reservoir. These treatments allow existing wells to sustain hydrocarbon production without new wells being drilled. Diverters, when employed efficiently, can prevent the need to use a rig to provide momentary physical barriers, thus lowering the cost of the workover. Recent improvements in diversion technology make use of a variety of degradable particles that act as momentary bridges, either at the perforation entries or inside the existing fractures. The aim of this study is to introduce different types of mechanical and chemical diverters used to enhance the productivity of wells. This study explains the concepts of different types of diverters and their applications in several formations, it will also help readers to understand the selection procedures based on the suitability and requirements of diverter use by case studies from around the world.

Hydrogen is an industrially significant gas and is considered a potential clean fuel. In the time of climate change; when the global impetus is towards reducing the reliance on fossil fuel, there is a need to go for other alternatives to secure the supply of this strategic gas as almost 95 % of Hydrogen is generated from fossil fuels. Geothermal energy is a reliable green energy source that harnesses crust thermal heat to generate power and is independent of physical weather processes. In this study, we outlined the different production techniques of hydrogen and evaluated the utilization of geothermal resources in the generation of green hydrogen. This study evaluated the different electrolyzers and geothermal energy-based green hydrogen production models and we concluded that cogeneration of hydrogen with the geothermal plant is strongly dependent on the temperature of the geothermal fluid, its flow rate, the cycle of the plant, and the ORC working fluid. Green hydrogen production utilizes the waste thermal energy of the plant thus increasing the plant efficiency and plant output diversification adds up to its economy. In this study, we also outlined the feasibility prospects of geothermal-based green hydrogen and concluded that geothermal energy-based green hydrogen can be a promising way to decarbonization.

Nepal's burgeoning poultry industry which is a key sector in its economy leads to a considerable generation of slaughterhouse waste (SHW), necessitating effective and sustainable disposal methods. This study explores anaerobic digestion as an optimal solution for poultry SHW management, aiming to produce energy-rich biogas while efficiently mitigating pollution in these facilities. Particularly, the feasibility and effectiveness of co-digestion with chicken litter (CL) were investigated to enhance biogas production and waste utilization. Four distinct runs of anaerobic digestion were performed, each utilizing varying substrate compositions with SHW to CL ratios of 1:0, 4:1, 1:1, and 1:4. Throughout the process, essential parameters, including total solids (TS), volatile solids (VS), biological oxygen demand (BOD5), pH, temperature, biogas generation, were meticulously measured. The cumulative biogas production for each run was as follows: 14.87 liters and a biogas yield of 88.73 ml/gVS with a 17.39 % VS reduction for Run 1, 25.98 liters and a biogas yield of 147.21 ml/gVS with a 28.64 % VS reduction for Run 2, 78.32 liters and a biogas yield of 314.69 ml/gVS with a 54.32 % VS reduction for Run 3, and 89.195 liters and a biogas yield of 344.36 ml/gVS with a 63.05 % VS reduction for Run 4. Notably, as the proportion of CL increased in the mixture from 4:1 to 1:1, a considerable VS reduction was observed. Furthermore, when the ratio of SHW to CL reached 1:1 and 1:4, a significant BOD5 reduction of 50 % and 63.13 % was achieved, respectively, surpassing previous runs. The results reveal that the addition of CL in an appropriate ratio effectively manages poultry SHW, with the optimum SHW:CL ratio for significant biogas yield lying between 1:1 and 1:4.

ABSTRACT: Dustbathing (DB) is a functionally important maintenance behavior in birds that clean plumage, realigns feather structures, removes feather lipids, which helps to remove parasites and prevents feathers from becoming too oily. Among different natural behaviors birds perform in cage-free (CF) housing, DB is one of the important behavior related to bird welfare. Earlier studies have identified DB behavior using manual method such as counting number of DB bouts, and duration of DB bouts from video recordings. The manual detection of DB behavior is time-consuming, sometimes prone to errors, and have limitations. Therefore, an automated precision monitoring method is needed to detect DB behavior in laying hens from an early age in CF housing environment. The objectives of this study were to (1) develop and test a deep learning model for detecting DB behavior and find out the optimal model; and (2) assess the performance of the optimal model in detecting DB behavior at different growing phases. In this study, deep learning models, i.e., YOLOv7-DB, YOLOv7x-DB, YOLOv8s-DB and YOLOv8x-DB, networks, were developed, trained, and compared in tracking DB behavior in 4 CF rooms each with 200 hens (W-36 Hy-Line). Results indicate that the YOLOv8x-DB model outperform all other models on detecting DB behavior with a precision of 93.4%, recall of 91.20%, and mean average precision (mAP@0.50) of 93.70%. All models performed with over 90% detection precision; however, model performance was affected by equipment like drinking lines, perches, and feeders. Based on the optimal model (YOLOv8x-DB), DB detection precision was highest during grower phase (precision of 96.80%, recall of 97.10%, mAP@0.50 of 98.60%, and mAP@0.50-0.95 of 79.10% followed by prelay, layers, developer, and peaking phases. This study provides a reference for poultry and egg producers that DB behavior can be detected automatically with precision of at least 89% or more using optimal model at any growing phase of laying hens.

ABSTRACT: Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure that hens engage in perching behavior from an early age, as manual observation is often labor-intensive and sometimes inaccurate. The objectives of this study were to (1) develop and test a deep learning model for detecting perching behavior; and (2) evaluate the optimal model's performance on detecting perching behavior of laying hens of different ages. In this study, recent deep learning models, that is, YOLOv8s-PB, YOLOv8x-PB, YOLOv7-PB, and YOLOv7x-PB, were developed, trained and compared in detecting perching behavior in 4 CF rooms (200 hens/room). Perch height was up to 1.8 m from the litter floor and situated 1.5 m below the cameras. A total of 3,000 images were used, with each image featuring at least 1 hen perching. The models' detection accuracies and their performance across different age groups of hens were compared using 1-way ANOVA at a 5% significance level. The results showed that the YOLOv8x-PB model outperform all other models used, achieving the precision of 94.80%, recall of 95.10%, and mean average precision (mAP@0.50) of 97.60%. While all models proved over 94% detection precision. With optimal model, PB detection precision was highest (97.40%) for peaking phase followed by prelay (95.20%), grower (94.80%), developer (94.70%) and layers (92.70%) phases while the lowest detection precision (88.80%) was for starter phase. Detection performance was somewhat reduced by the overlapping of birds during perching and occlusion. Overall, the YOLOv8x-PB model was the most optimal in detecting perching behavior, proposing a valuable tool for CF producers to monitor the perching activities of laying hens automatically.

The Fourth Industrial Revolution has ushered in several changes in the Oil and Gas (O&G) sector—all with a unified goal of improving, optimizing, and digitalizing existing processes and techniques. Digitalization in the O&G sector provides superior solutions for the efficiency of operations. In digitalization, the machines are connected to digital systems such as sensors; they generate accurate insights, respond in real-time and are efficient. Current digital narratives in the O&G sector are limited in space and use bottom-up technologies. However, a top-down strategy that not only overcomes the digital deluge but also assists O&G executives in following a digital revolution might be beneficial to the business. The advantages of prominent technologies such as artificial intelligence, the Internet of Things, and big data analytics are the same, yet they are interconnected. While conventional approaches like SCADA and wireless sensor networks (WSN) have served their purpose, the advent of edge computing and IoT heralds a new era of data-driven decision-making and operational efficiency. Digital transformation can deliver increased safety while minimising the environmental impact of the industry. The technologies that are used in the past and the development of such technologies are discussed in detail in this study.

ABSTRACT: Cage-free (CF) housing systems are expected to be the dominant egg production system in North America and European Union countries by 2030. Within these systems, bumblefoot (a common bacterial infection and chronic inflammatory reaction) is mostly observed in hens reared on litter floors. It causes pain and stress in hens and is detrimental to their welfare. For instance, hens with bumblefoot have difficulty moving freely, thus hindering access to feeders and drinkers. However, it is technically challenging to detect hens with bumblefoot, and no automatic methods have been applied for hens' bumblefoot detection (BFD), especially in its early stages. This study aimed to develop and test artificial intelligence methods (i.e., deep learning models) to detect hens' bumblefoot condition in a CF environment under various settings such as epochs (number of times the entire dataset passes through the network during training), batch size (number of data samples processed per iteration during training), and camera height. The performance of 3 newly developed deep learning models (i.e., YOLOv5s-BFD, YOLOv5m-BFD, & YOLOv5x-BFD) were compared in detecting hens with bumblefoot of hens in CF environments. The result shows that the YOLOv5m-BFD model had the highest precision (93.7%), recall (84.6%), mAP@0.50 (90.9%), mAP@0.50:0.95 (51.8%), and F1-score (89.0%) compared with other models. The observed YOLOv5m-BFD model trained at 400 epochs and batch size 16 is recommended for bumblefoot detection in laying hens. This study provides a basis for developing an automatic bumblefoot detection system in commercial CF houses. This model will be modified and trained to detect the occurrence of broilers with bumblefoot in the future.

The present study focuses on the utilization of non-conventional starch (NS) derived from guinea grass seed to produce porous starch (PS) and octenyl succinic anhydride-modified PS (OSAPS). The study also introduces a novel approach to produce PS, aiming to overcome the high cost associated with glucoamylase. The formation of porous granules was successfully achieved through the combination of alpha-amylase and ultrasonication, as confirmed by electron micrographs and analysis of pore size and specific surface area. The results demonstrate that OSAPS exhibits superior emulsion stability, well-defined pore structures, and desirable amphiphilic characteristics. All three starches were loaded with lycopene, flaxseed oil, and a combination of both to prepare microcapsules. The presence of flaxseed oil within the starch matrix was confirmed through micrographs and functional group analysis. Notably, OSAPS exhibited the highest encapsulation efficiency at 74.80 %, along with a lycopene retention rate of up to 27.32 mg/100 g. Moreover, OSAPS showcased favorable antioxidant activity (89.29 %), phenolic content (0.45 mg GAE/g), and flavonoid content (97.61 mg QE/100 g). These findings underscore the remarkable potential of OSAPS as a delivery system for bioactive compounds, particularly for the co-encapsulation of oils and pigments.