Abstract: Reducing odor and particulate matter (PM) in confined swine houses is critical for environmental sustainability and animal welfare. This study evaluated the effects of different housing designs and bedding systems on aerial environmental conditions in swine confinement facilities. Three experimental swine house models (M1, M2, and M3) were constructed with distinct ventilation and bedding configurations. M1 employed circulating airflow with negative pressure and a recirculating ventilation system. M2 featured a plug flow air pattern with negative pressure and a perforated plastic bed, while M3 utilized a sawdust bedding system with recirculating ventilation. Each model housed nine fattening swine in a 12 m² area, and the study was conducted over 110 days (May 6–August 26, 2018). Measurements included odorous compounds, volatile organic acids (VOA), and PM concentrations. Ammonia (NH₃) levels were highest in M1 (9.07 ppm) and lowest in M3 (5.94 ppm). Hydrogen sulfide (H₂S) was undetectable in M3, while M1 and M2 recorded measurable levels. VOA concentrations were significantly lower in M3 (84.86 ppbv) compared to M1 (884.29 ppbv) and M2 (605.31 ppbv). PM10 levels exhibited notable variations, with concentrations of 115.25 μg/m³ in M1, 218.83 μg/m³ in M2, and 312.00 μg/m³ in M3. However, bacterial concentrations were highest in M3. The sawdust bedding system (M3) significantly reduced volatile fatty acids and odorous compounds compared to pit-based systems, while the ventilation system influenced odor control. Although M3 had higher PM levels, the findings emphasize the need for swine farming systems to balance environmental management with animal welfare considerations.

Abstract: Interdisciplinary research has significantly advanced Urban Green Space (UGS) measurement by employing objective methods tailored to diverse research objectives. Despite this progress, the varied development of UGS measurement techniques calls for a comprehensive review to integrate and evaluate these approaches. This narrative review examines constructs underlying UGS measurements, emphasizing objective and GIS-enabled datasets, and highlights recent advancements in measuring greenness visibility. Specifically, it focuses on two methods: the viewshed-based and the image segmentation-based methods, proposing their classification under "Perspective View." Our findings reveal a shift in UGS measurement focus, moving beyond simple quantification to incorporate visibility, accessibility, and availability dimensions. Moreover, this review demonstrates the growing relevance of hybrid techniques integrating quantitative and qualitative approaches, such as combining GPS data with visibility analyses. These techniques bridge the gap between physical and perceptual aspects of greenspaces, enhancing their applicability in urban planning and public health. Furthermore, advancements in computational tools, including AI-driven methods, now enable high-resolution visibility measurements on a city-wide scale, supporting epidemiological research and urban development. These insights aim to guide researchers and practitioners in selecting suitable methodologies and datasets, fostering the collection of diverse UGS data for practical management and policymaking. By providing a structured framework for UGS measurement, this review contributes to the development of more equitable and effective urban greenspace strategies.

Abstract: Green asparagus has the characteristic of growing in clusters, making it inevitable for harvest targets to overlap with weeds and immature asparagus in the field. Extracting stem details in complex spatial positions information presents a significant challenge in identifying suitable harvest targets and high-precision cutting-points. This paper explored YS3AM (Yolo-SAM-3D-Adaptive-Modeling) method for green asparagus detection and 3D adaptive-section modeling using a depth camera, which could furnish harvesting path planning for the selective harvesting robots. Firstly, the model was developed and deployed to extract bounding boxes for individual asparagus stems within clusters. Secondly, the green asparagus stems within these bounding boxes were segment and generate binary mask images. Thirdly, high-quality depth images were obtained using pixel block completion. Finally, based on the cylinder, an adaptive-section 3D reconstruction method fusion with mask and depth was proposed, with a novel evaluation method applied to assess modeling accuracy. The experimental detection results of 1,095 test images demonstrated that the Precision was 98.75%, the Recall was 95.46%, the F1 score was 0.97, and the mAP was 97.16%. The modeling accuracy of 103 asparagus stems under sunny (54) and cloudy (49) conditions was estimated. The average RMSEs of length and bottom depth were 0.74 and 1.105. The detection and modeling for each stem approximately demanded 22 ms. The results of this paper indicated that the 3D model effectively represented the spatial distribution of green asparagus, and further accurately identification of suitable harvest targets and stem cutting-points. This model provided essential spatial pathways for end-effector path planning, thereby fulfilling the operational requirements for efficient green asparagus harvesting robot.

Abstract: In this research, we investigate an advanced methodology for process monitoring in manufacturing that employs synthetic data with deep learning to enhance production and process quality monitoring. Traditionally, process supervision in the manufacturing industry has depended on manual checks and physical sensors, which are costly, time-consuming, and prone to errors. The study introduces a new approach that utilizes synthetic data to train deep learning models for precise defect detection in 3D-printed objects. We further recommend diverse data augmentation and fine-tuning techniques to improve model efficacy. Our evaluation demonstrates an impressive 90% overall accuracy in real-object defect detection, with a 93% precision rate in identifying defects. These results suggest that models trained on synthetic data are effectively equivalent to those trained on real-world data, indicating that the technique used to generate synthetic data is pivotal to model performance. This innovative strategy mitigates the challenges associated with conventional process monitoring methods, and addresses the practical challenges of applying such technological advancements in actual manufacturing environments. By enabling the creation of extensive training datasets and applying deep learning algorithms for object and pattern recognition, this approach boosts the precision and efficiency of operational supervision within the manufacturing process. The approach and findings of this study significantly enhances efficiency, accuracy, and the overall effectiveness of quality assurance in manufacturing.

Abstract: Although significant progress has been made in recent years regarding jumping biomechanics of agility dogs, there is still a lack of data on the specific biomechanical pattern of the jump. The main objective of the present study was to describe the biomechanic variables involved in the jumping pattern of agility dogs, analyzing both intra- and inter-individual variability. Eleven agility dogs were analyzed while jumping over a 60-cm hurdle. Results of low or acceptable and similar intra and inter-individual coefficients of variation obtained in variables such as percentage of take-off and landing distances, percentages of take-off and landing durations, maximum jump height, jump height at the hurdle and percentage duration to maximum jump height, may indicate these variables as components of a general biomechanical pattern of jumping in agility dogs. Low or acceptable and consistent intra- and inter-individual variation observed in variables such as percentage of take-off and landing distances, percentages of take-off and landing durations, maximum jump height, jump height at the hurdle, and percentage duration to maximum jump height suggests that these variables could represent key elements of a general biomechanical jumping pattern in agility dogs. This stability, maintained across multiple jumps by the same dog and across different dogs, highlights their potential as reliable indicators of a shared biomechanical framework for jumping. Lower intra-individual than interindividual coefficients of variation obtained in most of the angular variables, jump distance, jump duration and speed, take-off, and landing distances, and jump heights at take-off and at landing indicate these variables as related with the individual technique of each animal. All of this data can be used in the design of training plans as well as when monitoring the progress of canine athletes.

Abstract: Type 2 diabetes mellitus (T2DM) is a common, lifelong metabolic disorder. Adults with T2DM bear a greater burden of cardiometabolic risk factors than the general popula-tion. T2DM presents as a spectrum of clinical manifestations, where uncontrolled dia-betes leads to progressive or irreparable damage to various organs. Neurologic end-organ damage due to uncontrolled diabetes may include neuropathy, nephropa-thy, and retinopathy. T2DM can be categorized into three levels: (1) prediabetes, with a blood sugar level between 110 and 125 mg/dL, (2) T2DM, with a blood sugar level higher than 126 mg/dL, and (3) uncontrolled T2DM, with a blood sugar level exceeding 180 mg/dL despite multiple medications. If blood sugar remains consistently high in level 1, it may cause pathological and functional changes in healthy vascular tissues and various systems, often without noticeable clinical symptoms. However, the latter two levels are important for identifying individuals at high risk of nerve damage, kid-ney damage, and cardiovascular (CV) events. Research shows that reducing modifia-ble risk factors can help prevent the progression from prediabetes to T2DM, while an-tidiabetic drugs can help prevent long-term complications of hyperglycemia in indi-viduals with T2DM. Considerable effort is being made to increase diabetes awareness and develop new pharmacological interventions to better treat the underlying causes of T2DM. This review provides a comprehensive overview of current knowledge in common genetic variants and novel targets for potential therapeutic use in T2DM and discusses recent advances in the pharmaceutical management of uncontrolled T2DM, including those currently in phase II and III development.

Abstract: The amount of non-revenue water is around 126 billion cubic meters annually worldwide mostly due to leakage. A more efficient wastewater management strategy would use a parametric design for on-demand customized pipe fittings, following the principles of distributed manufacturing. To fulfill this need, this study introduces an open-source parametric design of a 3D printable easy connect pipe fitting that offers compatibility with different dimensions and materials of the pipes available in the market. Custom pipe fittings were 3D printed using a RepRap-class fused filament 3D printer, with polylactic acid (PLA), polyethylene terephthalate glycol (PETG), acrylonitrile styrene acrylate (ASA), and thermoplastic elastomer (TPE) as filament feedstocks for the validation. The 3D-printed connectors underwent hydrostatic water pressure tests to meet standards for residential, agricultural, and renewable energy production applications. All the printed parts passed numerous hydrostatic pressure tests. PETG couplings can tolerate up to 660±20 psi of hydrostatic pressure, which is eight times larger than the highest standard water pressure for the residential sector. Based on the economic analysis, the cost of 3D printing a pipe coupling is 3 to 17 times lower cost than purchasing a commercially available pipe fitting of a similar size. The new open-source couplings demonstrate particular potential for use in developing countries and remote areas.

Abstract: Because of their superior environmental performance, mycelium-based composites (MBCs) offer a promising sustainable lightweight alternative material. MBCs are frequently prepared using agricultural by-products as substrates. However, because agricultural by-products are seasonal, their output is limited by a constant need for substrates. In order to examine the feasibility of creating MBCs using sewage sludge (SS) in place of bagasse, this study used SS and bagasse as nutrient substrates and cultivated MBCs on ready-made mycelium (Pleurotus ostreatus). The physico-mechanical properties, morphological properties, and thermal stability analysis were tested and analyzed. The results showed that the bagasse MBCs’ colonization appearance took precedernce over that of the SS MBCs at early stagy. However, both the bagasse and SS promoted fungal growth to create a dense mycelial network on day 10. The bagasse-based MBCs were the lightest and adding SS increased the density and compressive strength of the MBCs. The volume shrinkage first decreased and then increased. The optimal ratio of mycelium to sewage sludge was 2:1, which produced an outstanding compressive strength of 690.20 KPa. The thermal conductivity of bagasse-based and SS-based MBCs were 0.12 Wm-1K-1 and 0.13 Wm-1K-1. The physico-mechanical characteristics satisfiy the requirements of lightweight backfill materials in highway. Additionally, SS improved the hyphae of MBCs, giving them a denser and stronger structure. In comparison to bagasse, it also showed better thermal stability and a higher residual mass. It is feasible to produce MBCs with SS in place of bagasse, and the biocomposite proposed in this study could be used as lightweight backfill materials that are widely needed in highway.

Abstract: The proliferation of Internet of Things (IoT) devices has introduced new challenges for digital forensic investigators due to their diverse architectures, communication protocols, and security vulnerabilities. This research paper presents a case study focusing on the forensic investigation of an IoT device, specifically a Raspberry Pi configured with Kali Linux as a hacker machine. The study aims to highlight differences and challenges in investigating Weaponized IoT as well as establish a comprehensive methodology to analyse IoT devices involved in cyber incidents. The investigation begins with the acquisition of digital evidence from the Raspberry Pi device, including volatile memory, and disk images. Various forensic tools and utilities are utilized to extract and analyse data, tools such as Exterro FTK, Magnet AXIOM and open-source tools such as and Volatility, Wireshark, Autopsy. The analysis encompasses examining system artefacts, log files, installed applications, and network connections to reconstruct the device's activities and identify potential evidence proving that the user perpetuated security breaches or malicious activities. The findings of this research contribute to the advancement of IoT forensic capabilities by providing insights into the methodologies and best practices for investigating IoT devices, particularly those configured as hacker machines. The case study serves as a practical demonstration of the forensic techniques applicable to IoT environments, facilitating the development of protocols, standards, and training programs for IoT forensic investigators. Ultimately, enhancing forensic readiness in IoT deployments is essential for mitigating cyber threats, preserving digital evidence, and ensuring the integrity of IoT ecosystems.

Abstract:

Benzotriazole and its derivatives show good tribological properties, anti-oxidation, anti-corrosion, rust prevention and dispersion capabilities as lubricating additives, becoming the common multifunctional additives. And how to avoid sulfur- or phosphorus-introduction to improve their functionality and the compatibility with hydrocarbons is the forefront research. In this study, methylbenzotriazole and oleic acid were applied to synthesize a new methylbenzotriazole-amide that with long alkyl-chain (E)-N-(2-(5-methyl-2H-benzodiazole-2-yl)ethyl)octadec-9-enamide (MeBz-2-C18), which was characterized by nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), FT-IR and thermogravimetric analysis (TGA). The thermal stability and tribological properties of MeBz-2-C18 were compared with the commercially available benzotriazole oleamide (T406). The results show that MeBz-2-C18 has better thermal stability and base oil compatibility than that of T406, and 0.5 wt.% addition of MeBz-2-C18 could decrease the average wear scar diameter (ave. WSD) by 21.6%. The wear surface analysis and DFT calculation show that the amide group in MeBz-2-C18 is preferentially broken during friction, which would reduce the interfacial shear force and easily react with the metal surface to form iron oxide film, thus demonstrating a better anti-wear and friction reducing performance, indicating its potential application as an environmental friendly multifunctional additive.