bluuebunny/crossref_metadata_2025 huggingface dataset:
Mitigating the Environmental Impacts from Pig and Broiler Chicken Productions: Case Study on a Citrus Extract Feed Additive
Authors: Bui Hoa , Cisse Sekhou Hedaly , Ceccaldi Mathilde , Perrin Aurélie , Benarbia Mohammed El Amine , Chicoteau Pierre
Published: November 2023

The rapid expansion of the livestock production sector to meet the world population’s demand is posing a big challenge to environmental sustainability. Plant-based feed additives extracted from agro-food byproducts could potentially result in multiple outcomes: reducing food-processing wastes and improving animal growth performances, hence mitigating environmental impacts of meat production chains. This presented study was carried out to assess the environmental impacts of the use of a commercial citrus extract feed additive (CEFA) in swine and broiler chicken farming. Life-cycle assessment (LCA) was applied to assess the impact of manufacturing and distributing one 25 kg bag of CEFA and its use in feed in broiler chicken and swine productions. With regards to CEFA manufacturing and distribution, results showed that most of the impact came from the production of CEFA ingredients, accounting for 70% of the impact generated. The remaining 30% effect was divided between transportation to the customer (25%), CEFA packaging (3%), and CEFA manufacturing and production loss (2%). When enlarging the scope, the use of the CEFA in pigs and broilers’ diets was shown to improve the measured environmental indicators, compared to such standard systems. Indeed, CEFA-added feeds have demonstrated enhanced growth performances, hence reducing the required amount of consumed feed to achieve the same level of growth. Consequently, this helped reduce environmental issues from animal feed ingredients’ agriculture. To be more specific, the use of one 25 kg bag of CEFA in feed at 250 g per ton of feed led to a reduction of 6 tons of CO2 equivalent (CO2 eq) emitted along the life cycle of poultry production and 5 tons in the case of fattening pigs. The inclusion of this CEFA in the diet also led to a reduction in the land use footprint by 0.7 hectares and reductions in water consumption by 201 m3 and 82 m3 for broiler chicken and swine production, respectively. The environmental performance assessment thus showed the interest in using this CEFA in swine and broiler chicken diets to mitigate the environmental impacts.
In Vitro Evaluation of Potentially Edible Brazilian Trees and Shrubs in Ruminant Nutrition
Authors: de Morais Jozivaldo Prudêncio Gomes , Campana Mariana , Gregorini Pablo , Garcia Thainá Moreira , Minussi Júlia Franco de Aguiar , Pereira Stela Naetzold , Pereira Fabiellen Cristina , Del Valle Tiago Antonio
Published: November 2023

The present study aims to evaluate the nutritional value of different tree and shrub leaves in Brazilian ruminant production systems. Eight potentially edible trees and shrubs were identified from interviews with 30 ruminant producers: Aroeira (ARO; Lithraea molleoides), Black Mulberry (BMU; Morus nigra), Candeia (CAN; Eremanthus erythropappus), Jatobá (JAT; Hymenaea courbaril), Gliricídia (GLI; Gliricidia sepium), Santa Bárbara tree (SBT; Mélia azedarach), Tithonia (TIT; Tithonia diversifolia), and White Mulberry (WMU; Morus alba). Four leaf samples of each edible tree were sampled, and chemical analyses and in vitro assays were performed. Edible trees (except CAN and JAT) had lower neutral detergent fiber content than Mombasa grass. In addition, SBT, BMU, WMU, and TIT had lower fiber content than the other evaluated edible trees. Consequently, SBT, TIT, BMU, and WMU had improved dry matter degradation. Among the edible trees and shrubs, SMW and WMU increased the potential for gas production (a parameter). On the other hand, CAN decreased the estimated gas production 48 h after incubation. Furthermore, TIT decreased methane production up to 24 h after in vitro fermentation. Thus, except ARO, CAN, and JAT, the edible trees evaluated in the present study are potential feeds in moderate- to high-producing animals. Additionally, TIT fermentation reduces in vitro methane production.
Effects of Broad Bean Diet on the Growth Performance, Muscle Characteristics, Antioxidant Capacity, and Intestinal Health of Nile Tilapia (Oreochromis niloticus)
Authors: He Xiaogang , Shu Haoming , Xu Tian , Huang Yuting , Mo Jiajun , Ai Chunxiang
Published: November 2023

Four crisping diets were designed to conduct a feeding experiment to investigate the use of broad bean in the formulated feed of Nile tilapia and optimize its use. The growth performance, muscle characteristics, antioxidant capacity, and intestinal health of Nile tilapia with an initial body weight of 617.32 ± 1.64 g were evaluated after being fed with different diets for 90 days. The results showed no adverse effect on the growth performance of Nile tilapia fed with broad bean diets. Contrastingly, some improvements were found in WGR and SGR, but a lower FCR was obtained. The supplementation of broad beans weakened the antioxidant capacity of fish but did not influence liver health and the immune system. Increasing the amount of broad bean addition can increase muscle quality values, and an embrittling functional package being added to the diet can also improve muscle hardness, adhesiveness, and chewiness of Nile tilapia muscle. In addition, the crisping functional package can be applied to tilapia crisping formulated feed, which helps to improve the integrity of the intestinal tissue structure and optimize the intestinal microbiota of Nile tilapia. The final achievement of this study is to provide a theoretical reference for optimizing the breeding technology of crispy Nile tilapia and developing a specialized crisping diet for the species.
Effect of Puncture Vine on Growth Performance, Carcass and Meat Traits, Metabolic and Immunological Blood Indicators, and Selected Cecal Microbiota in Broiler Chickens
Authors: Al-Baadani Hani H. , Alharthi Abdulrahman S. , Al-Ghamdi Saleh , Alhidary Ibrahim A.
Published: November 2023

This study investigated the effects of puncture vine (Tribulus Terrestris) addition on the performance, carcass and meat characteristics, selected metabolic and immunological blood indicators, some microbiota, and short-chain fatty acids. A total of 252 1-day-old broilers were distributed to three treatments with 12 cages as replicates per treatment (T1 = 0.0%, T2 = 0.08%, and T3 = 0.16% puncture vine). Performance parameters and metabolic and immunological serum indicators were measured in each feeding phase, while carcass characteristics, meat quality, cecal microflora, and short-chain fatty acids were measured at 35 days. Results showed that live weight, weight gain, production efficiency, and meat component color were lower in initial and ultimate at T3, while the percentages of the legs and gizzard were higher at T2 than T1. The relative weight of cooking loss was higher in T2 and T3, but the myofibril fragmentation index was lower than T1. Total protein and globulin were higher in T2 and T3 (14 days old), and the glucose level was lower in T2 (35 days old) than at T1. Interleukins (IL-4, IL-6, and IL-10) and tumor necrosis factor-α (TNF-α) were higher in T2 than T1 and T3 (35 days old). Puncture vine has antimicrobial activity against Escherichia coli and Salmonella spp., while Lactobacillus spp. was higher in T2. The total short-chain fatty acid content was higher in chickens fed puncture vine. These results indicate that the use of puncture vine powder as a natural alternative at a dosage of 0.08% has a safe effect on the performance, carcass and meat characteristics, and health of broilers.
Antimicrobial Resistance in Papua New Guinea: A Narrative Scoping Review
Authors: Page Brady , Adiunegiya Simeon
Published: November 2023

Antimicrobial-resistant bacterial infections are a known threat to the public health of low-income countries and are undercharacterized in Papua New Guinea. A scoping literature review of scientific peer-reviewed publications on antimicrobial resistance in Papua New Guinea was conducted, and their results were summarized. Many of the available data on resistant bacteria in Papua New Guinea have come from Port Moresby and Goroka and have been focused on Staphylococcus aureus, as well as important pediatric pathogens such as Streptococcus pneumoniae and Haemophilus influenzae. Progressive resistance to the commonly used antibiotics penicillin and chloramphenicol among most clinically important bacterial pathogens has prompted healthcare workers to adopt expensive broad-spectrum antibiotics. There is already evidence of resistance to newly adopted antibiotics among several Gram-negative organisms. Drivers of antimicrobial resistance in Papua New Guinea include a high burden of infectious diseases, inappropriate antibiotic prescription practices, poor regulation of antibiotics, incomplete adherence, substandard drug quality, and overcrowding of healthcare facilities. There is a lack of information on antimicrobial resistance among priority pathogens and from several important regions of Papua New Guinea.
Antibiotic Guideline Adherence at the Emergency Department: A Descriptive Study from a Country with a Restrictive Antibiotic Policy
Authors: Cartuliares Mariana B. , Søgaard Sara N. , Rosenvinge Flemming S. , Mogensen Christian B. , Hertz Mathias Amdi , Skjøt-Arkil Helene
Published: November 2023

Background: Denmark has a low level of antimicrobial resistance (AMR). Patients hospitalized with suspected infection often present with unspecific symptoms. This challenges the physician between using narrow-spectrum antibiotics in accordance with guidelines or broad-spectrum antibiotics to compensate for diagnostic uncertainty. The aim of this study was to investigate adherence to a restrictive antibiotic guideline for the most common infection in emergency departments (EDs), namely community-acquired pneumonia (CAP). Method: This multicenter descriptive cross-sectional study included adults admitted to Danish EDs with a suspected infection. Data were collected prospectively from medical records. Results: We included 954 patients in the analysis. The most prescribed antibiotics were penicillin with beta-lactamase inhibitor at 4 h (307 (32.2%)), 48 h (289 (30.3%)), and day 5 after admission (218 (22.9%)). The empirical antibiotic treatment guidelines for CAP were followed for 126 (31.3%) of the CAP patients. At 4 h, antibiotics were administered intravenously to 244 (60.7%) of the CAP patients. At day 5, 218 (54.4%) received oral antibiotics. Conclusion: Adherence to CAP guidelines was poor. In a country with a restrictive antibiotic policy, infections are commonly treated with broad-spectrum antibiotics against recommendations.
BET Protein Inhibitor JQ1 Ameliorates Experimental Peritoneal Damage by Inhibition of Inflammation and Oxidative Stress
Authors: Marchant Vanessa , Trionfetti Flavia , Tejedor-Santamaria Lucia , Rayego-Mateos Sandra , Rotili Dante , Bontempi Giulio , Domenici Alessandro , Menè Paolo , Mai Antonello , Martín-Cleary Catalina , Ortiz Alberto , Ramos Adrian M. , Strippoli Raffaele , Ruiz-Ortega Marta
Published: November 2023

Peritoneal dialysis (PD) is a current replacement therapy for end-stage kidney diseases (ESKDs). However, long-term exposure to PD fluids may lead to damage of the peritoneal membrane (PM) through mechanisms involving the activation of the inflammatory response and mesothelial-to-mesenchymal transition (MMT), leading to filtration failure. Peritoneal damage depends on a complex interaction among external stimuli, intrinsic properties of the PM, and subsequent activities of the local innate–adaptive immune system. Epigenetic drugs targeting bromodomain and extra-terminal domain (BET) proteins have shown beneficial effects on different experimental preclinical diseases, mainly by inhibiting proliferative and inflammatory responses. However the effect of BET inhibition on peritoneal damage has not been studied. To this aim, we have evaluated the effects of treatment with the BET inhibitor JQ1 in a mouse model of peritoneal damage induced by chlorhexidine gluconate (CHX). We found that JQ1 ameliorated the CHX-induced PM thickness and inflammatory cell infiltration. Moreover, JQ1 decreased gene overexpression of proinflammatory and profibrotic markers, together with an inhibition of the nuclear factor-κB (NF-κB) pathway. Additionally, JQ1 blocked the activation of nuclear factor erythroid 2-related factor 2 (NRF2) and restored changes in the mRNA expression levels of NADPH oxidases (NOX1 and NOX4) and NRF2/target antioxidant response genes. To corroborate the in vivo findings, we evaluated the effects of the BET inhibitor JQ1 on PD patients’ effluent-derived primary mesothelial cells and on the MeT-5A cell line. JQ1 inhibited tumor necrosis factor-α (TNF-α)-induced proinflammatory gene upregulation and restored MMT phenotype changes, together with the downmodulation of oxidative stress. Taken together, these results suggest that BET inhibitors may be a potential therapeutic option to ameliorate peritoneal damage.
Extracts and Scirpusin B from Recycled Seeds and Rinds of Passion Fruits (Passiflora edulis var. Tainung No. 1) Exhibit Improved Functions in Scopolamine-Induced Impaired-Memory ICR Mice
Authors: Sie Yi-Yan , Chen Liang-Chieh , Li Cai-Wei , Wang Ching-Chiung , Li Cai-Jhen , Liu Der-Zen , Lee Mei-Hsien , Chen Lih-Geeng , Hou Wen-Chi
Published: November 2023

In this paper, the seeds and rinds of passion fruit, which are the agricultural waste of juice processing, were recycled to investigate their biological activities for sustainable use. De-oiled seed powders (S) were successively extracted by refluxing 95% ethanol (95E), 50E, and hot water (HW), respectively, to obtain S-95EE, S-50EE, and S-HWE. Dried rind powders were successively extracted by refluxing HW and 95E to obtain rind-HWE and rind-95EE, respectively. S-50EE and S-95EE showed the most potent extracts, such as anti-amyloid-β1-42 aggregations and anti-acetylcholinesterase inhibitors, and they exhibited neuroprotective activities against amyloid-β25-35-treated or H2O2-treated SH-SY5Y cells. Scirpusin B and piceatannol were identified in S-95EE, S-50EE, and rind-HWE, and they showed anti-acetylcholinesterase activity at 50% inhibitory concentrations of 62.9 and 258.9 μM, respectively. Daily pretreatments of de-oiled seed powders and rind-HWE (600 mg/kg), S-95EE, and S-50EE (250 mg/kg) or scirpusin B (40 mg/kg) for 7 days resulted in improved learning behavior in passive avoidance tests and had significant differences (p < 0.05) compared with those of the control in scopolamine-induced ICR mice. The seeds and rinds of passion fruit will be recycled as materials for the development of functional foods, promoting neuroprotection and delaying the onset of cognitive dysfunctions.
Protective Role of Taurine on Rat Offspring Hypertension in the Setting of Maternal Chronic Kidney Disease
Authors: Tain You-Lin , Hou Chih-Yao , Chang-Chien Guo-Ping , Lin Sufan , Hsu Chien-Ning
Published: November 2023

Taurine is a natural antioxidant with antihypertensive properties. Maternal chronic kidney disease (CKD) has an impact on renal programming and increases the risk of offspring hypertension in later life. The underlying mechanisms cover oxidative stress, a dysregulated hydrogen sulfide (H2S) system, dysbiotic gut microbiota, and inappropriate activation of the renin–angiotensin–aldosterone system (RAAS). We investigated whether perinatal taurine administration enables us to prevent high blood pressure (BP) in offspring complicated by maternal CKD. Before mating, CKD was induced through feeding chow containing 0.5% adenine for 3 weeks. Taurine was administered (3% in drinking water) during gestation and lactation. Four groups of male offspring were used (n = 8/group): controls, CKD, taurine-treated control rats, and taurine-treated rats with CKD. Taurine treatment significantly reduced BP in male offspring born to mothers with CKD. The beneficial effects of perinatal taurine treatment were attributed to an augmented H2S pathway, rebalance of aberrant RAAS activation, and gut microbiota alterations. In summary, our results not only deepen our knowledge of the mechanisms underlying maternal CKD-induced offspring hypertension but also afford us the impetus to consider taurine-based intervention as a promising preventive approach for future clinical translation.
Federated Learning: Centralized and P2P for a Siamese Deep Learning Model for Diabetes Foot Ulcer Classification
Authors: Toofanee Mohammud Shaad Ally , Hamroun Mohamed , Dowlut Sabeena , Tamine Karim , Petit Vincent , Duong Anh Kiet , Sauveron Damien
Published: November 2023

It is a known fact that AI models need massive amounts of data for training. In the medical field, the data are not necessarily available at a single site but are distributed over several sites. In the field of medical data sharing, particularly among healthcare institutions, the need to maintain the confidentiality of sensitive information often restricts the comprehensive utilization of real-world data in machine learning. To address this challenge, our study experiments with an innovative approach using federated learning to enable collaborative model training without compromising data confidentiality and privacy. We present an adaptation of the federated averaging algorithm, a predominant centralized learning algorithm, to a peer-to-peer federated learning environment. This adaptation led to the development of two extended algorithms: Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer. These algorithms were applied to train deep neural network models for the detection and monitoring of diabetic foot ulcers, a critical health condition among diabetic patients. This study compares the performance of Federated Averaging Peer-to-Peer and Federated Stochastic Gradient Descent Peer-to-Peer with their centralized counterparts in terms of model convergence and communication costs. Additionally, we explore enhancements to these algorithms using targeted heuristics based on client identities and f1-scores for each class. The results indicate that models utilizing peer-to-peer federated averaging achieve a level of convergence that is comparable to that of models trained via conventional centralized federated learning approaches. This represents a notable progression in the field of ensuring the confidentiality and privacy of medical data for training machine learning models.
On the Chemical Composition and Hygroscopicity of Aerosols Deposited on the Insulators of Italian Power Lines
Authors: Gini Irene , Balzarini Alessandra , Pirovano Guido , Toppetti Anna Maria , Fialdini Lucio , Omodeo Paolo , Pirovano Giovanni , Marzinotto Massimo , Mancini Alessandro , Losi Niccolò , Cefalì Amedeo Manuel , Doldi Andrea , Bolzacchini Ezio , Ferrero Luca
Published: November 2023

The reliability of the national power grid is a key issue in modern society. Atmospheric aerosols are the main cause of the reduction in the performance of insulators and the increase in the possibility of flashovers, resulting in power line failures. Under high ambient humidity, the water-soluble compounds of atmospheric aerosols collected on the insulators’ surface can dissociate in ions and form a conductive layer, which may lead to flashover events. With a view to investigating the processes that drive these phenomena, the chemical composition of aerosol deposits on insulators in Italy was determined by ion chromatography analysis and thermos-optical and X-ray techniques. In addition, a synthetic aerosol with the same analyzed chemical composition was generated in a laboratory and deposited on PTFE filters and glass specimens allowing us to determine the deliquescence and crystallization relative humidity and the conductive effect in an aerosol exposure chamber. The results evidenced the presence of a hazardous inorganic ion layer, which generates a sharp phase transition of the aerosol deposit as a function of the ambient relative humidity; this layer poses a dangerous threat to the reliability of the power grid, increasing the probability of flashover events where the conductive layer facilitates the flow of electrical current across the insulator surface, potentially causing power outages or damage to the power lines.
Applying Design Thinking to Enhance Programming Education in Vocational and Compulsory Secondary Schools
Authors: Díaz-Lauzurica Belkis , Moreno-Salinas David
Published: November 2023

A proper and complete formation in technology (science, communications, programming, robotics, Computational Thinking, etc.) must be imparted at all educational levels for a lifelong education. However, students may lose motivation or interest due to the complexity and abstraction of some of the concepts imparted. In line with this, the work at hand looks to improve the interest and commitment of students by presenting the programming concepts and contents in a practical way. The teaching–learning process is based on the development of projects about robotics, which are adapted for courses and groups of different educational levels. The Design Thinking methodology is used to impart the content. This methodology allows the students to experiment, design and test different solutions for a given problem, increasing their motivation and interest, promoting creativity, and making the students conscious of their learning process. Two different projects are considered, a simulated one based on a sensor network to localise and track a robot in a closed area for vocational education students, and an experimental one about constructing a robot with several capabilities using Lego Mindstorms for compulsory secondary education students. The results obtained over three different groups of students are analysed and compared, and show that the methodology and projects selected can be adopted and adapted for different educational levels, increasing the proficiency of the students, their development, motivation and self-learning despite the difficulty and complexity of some concepts related to computer science.
Application of 4 × 44 Oligo Microarray to Transcriptomic Analysis of Immune Response in Rainbow Trout Infected with Aeromonas salmonicida
Authors: Małachowicz Magdalena , Siwicki Andrzej K. , Dobosz Stefan , Wenne Roman
Published: November 2023

Rainbow trout, one of the most economically important aquaculture fish species worldwide, is affected by the pathogenic bacteria A. salmonicida, which causes furunculosis outbreaks, leading to huge economic losses. In this study, an oligonucleotide microarray was applied to identify transcriptional changes in the skin of rainbow trout individuals in response to a bacterial infection. Overall, 656 and 434 differentially expressed genes (DEGs) were identified at 2 and 6 days after a bacterial challenge (dpi), respectively. A comparison of moribund (2 dpi) and survivor fish (6 dpi) revealed 169 DEGs. Between these were many genes involved in immune response, including lysozymes, pattern recognition receptors (c-type lectins), antimicrobial peptides (cathelicidin and hepcidin), acute-phase proteins (serum amyloids and haptoglobin), complement cascade proteins (c3, c4, c6 and c7), interleukins (il11 and il1b) and chemokines (ccl19 and cxcl8). Alterations of leptin, eicosanoids and prostaglandins have been found, which suggest metabolic remodeling in conjunction with immune response. Further, the regulation of programmed cell death genes (caspase 8, bcl2 apoptosis regulator, nfkb inhibitor alpha and heme oxygenase) and structural proteins (collagens, myosins, keratins and metalloproteinases) was observed. This study provides, for the first time, a gene expression analysis of rainbow trout skin in response to A. salmonicida infection, revealing the complexity of defense strategies in response to furunculosis.
Multi-Scale CNN-Transformer Dual Network for Hyperspectral Compressive Snapshot Reconstruction
Authors: Huang Kaixuan , Sun Yubao , Gu Quan
Published: November 2023

Coded aperture snapshot spectral imaging (CASSI) is a new imaging mode that captures the spectral characteristics of materials in real scenes. It encodes three-dimensional spatial–spectral data into two-dimensional snapshot measurements, and then recovers the original hyperspectral image (HSI) through a reconstruction algorithm. Hyperspectral data have multi-scale coupling correlations in both spatial and spectral dimensions. Designing a network architecture that effectively represents this coupling correlation is crucial for enhancing reconstruction quality. Although the convolutional neural network (CNN) can effectively represent local details, it cannot capture long-range correlation well. The Transformer excels at representing long-range correlation within the local window, but there are also issues of over-smoothing and loss of details. In order to cope with these problems, this paper proposes a dual-branch CNN-Transformer complementary module (DualCT). Its CNN branch mainly focuses on learning the spatial details of hyperspectral images, and the Transformer branch captures the global correlation between spectral bands. These two branches are linked through bidirectional interactions to promote the effective fusion of spatial–spectral features of the two branches. By utilizing characteristics of CASSI imaging, the residual mask attention is also designed and encapsulated in the DualCT module to refine the fused features. Furthermore, by using the DualCT module as a basic component, a multi-scale encoding and decoding model is designed to capture multi-scale spatial–spectral features of hyperspectral images and achieve end-to-end reconstruction. Experiments show that the proposed network can effectively improve reconstruction quality, and ablation experiments also verify the effectiveness of our network design.
Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey
Authors: Bonci Andrea , Gaudeni Francesco , Giannini Maria Cristina , Longhi Sauro
Published: November 2023

Future challenges in manufacturing will require automation systems with robots that are increasingly autonomous, flexible, and hopefully equipped with learning capabilities. The flexibility of production processes can be increased by using a combination of a flexible human worker and intelligent automation systems. The adoption of middleware software such as ROS2, the second generation of the Robot Operating System, can enable robots, automation systems, and humans to work together on tasks that require greater autonomy and flexibility. This paper has a twofold objective. Firstly, it provides an extensive review of existing literature on the features and tools currently provided by ROS2 and its main fields of application, in order to highlight the enabling aspects for the implementation of modular architectures to increase autonomy in industrial operations. Secondly, it shows how this is currently potentially feasible in ROS2 by proposing a possible high-level and modular architecture to increase autonomy in industrial operations. A proof of concept is also provided, where the ROS2-based framework is used to enable a cobot equipped with an external depth camera to perform a flexible pick-and-place task.
Exploring the Mechanisms of Humidity Responsiveness in Plants and Their Potential Applications
Authors: Bae Haejin , Kim Jinhee
Published: November 2023

Plant structures exhibit complex behaviors through unique shape changes and movements closely related to moisture factors. When the plants absorb moisture, their inside has a higher tension than their outside, so the entire structure is folded to closure or opened. The principle and property could be applied to bio-inspired technology, which is the process of fusion mimicking the structure, function, metabolism, mechanism, and ecological system of those creatures adapted to their environments. In this study, we analyzed the functions and physical characteristics of environment-sensing plants to demonstrate the principles of plants with opening-and-closing and curling-and-uncurling mechanisms and to better understand these behavior principles. From a biological and ecological viewpoint, the target sensory and cognitive plants that respond to external humidity and vibration were found to undergo structural changes in the size of the xylem and the degree of adhesion of the leaf and stem, as well as the opening, closing, and curling of the external shapes of the plants. The phenomenon of external form changes based on the microstructural characteristics of plants showed a promising direction for addressing issues in existing technology, such as non-powered operation. Therefore, in this study, we presented a biomimetic humidification model that was biocompatible and reversible. Pinecone samples with the applied opening-and-closing mechanism were to apply these biological properties to biomimetics. The results provide biomimetic knowledge for understanding the functions of biological and ecological features underlying the morphological changes in humidity-sensing plants and plant bioacoustics. These bio-inspired plant resources could provide sustainable new-growth power and valuable scientific information for advancing the research and technological development of biomimetics.
TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
Authors: Alvi Ahmad Naseem , Ali Bakhtiar , Saleh Mohamed Saad , Alkhathami Mohammed , Alsadie Deafallah , Alghamdi Bushra
Published: November 2023

Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on IoTs that are composed of sensor networks with limited processing and computing capabilities and are connected with different types of networks. Due to limited computational capability, IoT sensor nodes require more time to compute different tasks and are required to offload some tasks to remotely placed cloud servers for task execution. Fog nodes are preferred over the cloud as they are placed in close access to IoT nodes distributed in different networks. Different types of networks make it more vulnerable to malicious attacks. Malicious nodes offload complex and high computing tasks to fog nodes to compromise their performance and create delays in the computing tasks of legitimate nodes. In addition, fog nodes even after removing the malicious nodes are unable to process all the legitimate tasks within a specific time frame. In this work, a Trust-based Efficient Task Execution Scheme (TETES) is proposed for fog node that scrutinizes the offloaded tasks sent by the malicious nodes and efficiently execute most of the trusted tasks within a stipulated time cycle. The simulated results show that TETES execute more offloaded tasks as compared to well-known First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms.
Multivariate and Dimensionality-Reduction-Based Machine Learning Techniques for Tumor Classification of RNA-Seq Data
Authors: Al-khassaweneh Mahmood , Bronakowski Mark , Al-Sharoa Esraa
Published: November 2023

Cancer, a genetic disease, is considered one of the leading causes of death globally and affects people of all ages. Ribonucleic acid sequencing (RNA-Seq) is a technique used to quantify the expression of genes of interest and can be used to classify cancer tumor types. This paper describes a machine learning technique to classify cancer tissue samples by tumor type, such as breast cancer, lung cancer, colon cancer, and others. More than 60,000 RNA-Seq features were analyzed using six different machine learning classification algorithms, both individually and as an ensemble. Numerous dimensionality reduction techniques addressed the challenges of working with enormous amounts of genetic data. In particular, we were able to reduce the number of features from over 60,000 to 660 in the random forest feature selection and to 68 factor features using factor analysis with an accuracy of 99% in classifying tumor types.
Change of Direction Performance in Soccer Players: Comparison Based on Horizontal Force–Velocity Profile
Authors: Sánchez-López Sergio , López-Sagarra Andrés , Ortega-Becerra Manuel , Jiménez-Reyes Pedro , Rodríguez-Pérez Manuel A.
Published: November 2023

Background: The study aimed to analyze the relationship between the mechanical parameters of the horizontal force–velocity profile (Hzt FV profile) and performance over two different change of direction (COD) protocols (505test and modified 505test [M505test]) to assess the influence of mechanical parameters of Hzt FV profile on COD deficit (CODD) in soccer players among different categories. Methods: Seventy-seven soccer players, divided into playing levels, participated in the following assessments: Hzt FV profile, 505test and M505test. Spearman correlation coefficient (rs [p < 0.05]) and a linear regression model were used to determine the relationship between COD performance and mechanical parameters of sprint (maximum power output [Pmax], maximum horizontal force production [F0] and maximum velocity application [V0]). Results: Results suggest that: (1) V0 is highly correlated with 505test [Professional (rs = −0.682, p < 0.01); Amateurs (rs = −0.721, p < 0.01); U18DH (rs = −0.736, p < 0.01); U18LN (rs = −0.569, p < 0.01)]; (2) F0 is strongly correlated to M505test F0—M505test [Professional (rs = −0.468, p < 0.05); Amateurs (rs = −0.690, p < 0.01); U18DH (rs = −0.642, p < 0.01); U18LN (rs = −0.658, p < 0.01)]; and (3) significant differences were observed in Professionals vs. U18LN comparison [505test (U = −37.7, p = 0.000); M505test (U = −26.9, p = 0.000)]. Conclusions: A significant relationship exists between strength levels and COD performance depending on task demands. Horizontal force (F0) is crucial in statics COD, while high-speed force production (V0) is decisive in dynamics COD. Finally, higher-level players demonstrate greater efficiency in COD.
Investigation of Inner Lining Loss and Correlation with Steel Structure Ovality in Rotary Kilns
Authors: Zahradník David , Vynikal Jakub , Pavelka Karel
Published: November 2023

The article delves into the application of laser scanning within the realm of engineering, concentrating its examination on rotary kilns. Through an in-depth case study, this article meticulously assesses the advantages inherent in utilising laser scanning technology and draws comparisons with conventional measurement methods. The overarching objective of the investigation is to unravel the intricate relationship between the deterioration of the rotary kiln liners and the ovality of the underlying steel structure. By meticulously analysing these aspects, this article seeks to contribute valuable insights into the understanding of this complex interplay in the context of engineering practices. As a measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of the cylindrical shells. The research uncovered significant disparities in inner lining loss between the sections. A correlation was found between the ovality and elimination of the inner lining. Due to the hypothesis of constant inner lining loss in the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the wells. This study offers significant information on the maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.
A Novel Model-Free Adaptive Proportional–Integral–Derivative Control Method for Speed-Tracking Systems of Electric Balanced Forklifts
Authors: Xu Jianliang , Sui Zhen , Xu Feng , Wang Yulong
Published: November 2023

Similar to many complex systems, the operation process of electric balanced forklifts has characteristics such as time-varying model parameters and nonlinearity. Establishing an accurate mathematical model becomes challenging, making it difficult to apply model-based control methods in engineering practice. Aiming at the longitudinal control system of electric forklifts containing external disturbances, this paper proposes an improved full-format dynamic linearization model-free adaptive PID control (iFFDL-MFA-PID) method. Firstly, the full-format dynamic linearization (FFDL) method is employed to transform the operating system of the electric balanced forklift into a virtual equivalent linear data model. Secondly, the nonlinear residual term and pseudo-gradient (PG) of the data model are estimated using the difference estimation algorithm and the optimal criterion function, respectively. Furthermore, in order to enhance the robustness of the system, the idea of intelligent PID (iPID) is introduced and the principle of equivalent feedback is utilized to derive the iFFDL-MFA-PID control scheme. The design process of this scheme only requires the use of the input and output data of the system, without relying on the mathematical model of the system. Finally, the iFFDL-MFA-PID method proposed in this paper is simulated and tested with the EFG-BC/320 counterbalanced forklift equipped in the Special Equipment Testing Center and compared with the model-free adaptive control method (FFDL-MFAC) and the PID control method. Simulation results show that the speed-tracking error of the electric forklift truck under the action of the iFFDL-MFA-PID algorithm is maintained within ±0.132 m/s throughout the process, achieving higher tracking accuracy and better robustness compared to the MFAC and PID methods.
Evaluating Familiarity Ratings of Domain Concepts with Interpretable Machine Learning: A Comparative Study
Authors: Huang Jingxiu , Wu Xiaomin , Wen Jing , Huang Chenhan , Luo Mingrui , Liu Lixiang , Zheng Yunxiang
Published: November 2023

Psycholinguistic properties such as concept familiarity and concreteness have been investigated in relation to technological innovations in teaching and learning. Due to ongoing advances in semantic representation and machine learning technologies, the automatic extrapolation of lexical psycholinguistic properties has received increased attention across a number of disciplines in recent years. However, little attention has been paid to the reliable and interpretable assessment of familiarity ratings for domain concepts. To address this gap, we present a regression model grounded in advanced natural language processing and interpretable machine learning techniques that can predict domain concepts’ familiarity ratings based on their lexical features. Each domain concept is represented at both the orthographic–phonological level and semantic level by means of pretrained word embedding models. Then, we compare the performance of six tree-based regression models (adaptive boosting, gradient boosting, extreme gradient boosting, a light gradient boosting machine, categorical boosting, and a random forest) on domain concepts’ familiarity rating prediction. Experimental results show that categorical boosting with the lowest MAPE (0.09) and the highest R2 value (0.02) is best suited to predicting domain concepts’ familiarity. Experimental results also revealed the prospect of integrating tree-based regression models and interpretable machine learning techniques to expand psycholinguistic resources. Specifically, findings showed that the semantic information of raw words and parts of speech in domain concepts are reliable indicators when predicting familiarity ratings. Our study underlines the importance of leveraging domain concepts’ familiarity ratings; future research should aim to improve familiarity extrapolation methods. Scholars should also investigate the correlation between students’ engagement in online discussions and their familiarity with domain concepts.
Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
Authors: Bezerra Andrea , Fonseca Hélder , Rodrigues Francisca , Delerue-Matos Cristina , Gouvinhas Irene , Garcia Juliana
Published: November 2023

Diabetic foot ulcers (DFUs) are considered a major problem for public health, leading to high rates of lower-limb amputations. Moreover, due to the high prevalence rate of predisposing factors, the incidence rate of DFU is still rising. Although DFUs are complex in nature, foot ulceration usually precedes diabetic foot amputations. These impaired chronic wounds usually promote a microbial biofilm, commonly characterized by the presence of multidrug-resistant microorganisms, hampering the efficacy of conventional antibiotic treatments. Honey has been shown to be an effective antibacterial component, including against multidrug-resistant bacteria. Honey’s physical–chemical characteristics, such as the presence of hydrogen peroxide, its low pH levels, and its high sugar and phenolic contents, promote anti-inflammatory and antioxidative activities, improving wound healing. This review aims to explore honey’s effects in wound healing, especially for DFUs, and to show how the different physical–chemical features among different honey types might influence the treatment’s effectiveness. For this, the mechanisms by which honey can promote wound healing and the potential use of honey dressings in diabetic wounds were investigated in animal models and humans. After revising the diabetic wound impairment mechanisms, we found that most of the clinical studies that treated DFUs with honey in animal models or humans reported accelerated wound healing, greater wound contraction, and lower amputation or hospitalization rates; however, few studies characterized the features of honeys used for wound treatment, hindering the possibility of extensively comparing the different types of honey and identifying characteristics that most successfully promote wound healing. According to this review, honey is a cost-effective and safe option for DFU management.
Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database
Authors: Megalooikonomou Konstantinos G. , Beligiannis Grigorios N.
Published: November 2023

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering.
Research on Quadrant Detector Multi-Spot Position Detection Based on Orthogonal Frequency Division Multiplexing
Authors: Ye Jingmei , Yu Xiaonan , Lin Peng , Chen Shuai , Zhang Ziqi , Tong Shoufeng
Published: November 2023

Free space optical communication is developing towards laser communication networking. A novel method of quadrant detector (QD) multi-spot position detection based on orthogonal frequency division multiplexing (OFDM) is proposed for multi-spot laser communication systems. The mathematical model is constructed, and the Monte Carlo method is used to validate it. The position calculation of three beams incident on QD is simulated. The influence of key parameters on the accuracy of position detection is analyzed when the spots are at the same position and a different position. The results show that when the SNR of the system is 31.74 dB, the radius of the Gauss spot is 2 mm, the number of FFT (fast Fourier transform) points is 1024, and the center of the multi-spot is at the same position of the detector target; the accuracy of signal position detection calculated by the equation is 1.433 μm, and the simulation results are 1.351 μm, 1.354 μm, and 1.389 μm, respectively. When the center position of the multi-spot is at different positions of the detector target, the detection accuracy calculated by the formula is 1.438 μm, 1.433 μm, and 1.434 μm, respectively, and the simulation results are 1.419 μm, 1.387 μm, and 1.346 μm, respectively. The experimental results verify the effectiveness of the proposed multi-target simultaneous detection method. This article proposes a new multi-spot position detection method which can not only achieve one-to-multiple node laser communication but also improve the accuracy of point position detection.
Blocking Linear Cryptanalysis Attacks Found on Cryptographic Algorithms Used on Internet of Thing Based on the Novel Approaches of Using Galois Field (GF (232)) and High Irreducible Polynomials
Authors: Muthavhine Khumbelo Difference , Sumbwanyambe Mbuyu
Published: November 2023

Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms.