The utilization of electroporation for delivering the CRISPR/Cas9 system components has enabled efficient gene editing in mammalian zygotes, facilitating the development of genome-edited animals. In our study，This research focused on targeting the ACTG1 and MSTN genes in sheep, revealing a threshold phenomenon in electroporation with a voltage tolerance in sheep in vitro fertilization (IVF) zygotes. Various poring voltages near 40V and pulse durations were examined for electroporating sheep zygotes. The study concluded that stronger electric fields required shorter pulse durations to achieve the optimal conditions for high gene mutation rates and reasonable blastocyst development. The investigation also assessed the quality of Cas9/sgRNA ribonucleoprotein complexes (Cas9 RNPs) and their influence on genome editing efficiency in sheep early embryos. It was highlighted that pre-complexation of Cas9 proteins with single-guide RNA (sgRNA) before electroporation was essential for achieving a high mutation rate. The use of suitable electroporation parameters for sheep IVF zygotes led to significantly high mutation rates and heterozygote ratios. By delivering Cas9 RNPs and single-stranded oligodeoxynucleotides (ssODNs) to zygotes through electroporation, targeting the MSTN (Myostatin) gene, a knock-in efficiency of 26% was achieved. The successful generation of MSTN-modified lambs was demonstrated by delivering Cas9 RNPs into IVF zygotes via electroporation.

The lithium-ion batteries are widely used as a power source for portable devices, including cell phones. The useful life is about 2 years or 500 cycles, contributing to the generation of waste from electrical and electronic equipment (WEEE). Mining of lithium and cobalt damages the environment and is onerous; therefore, sustainable alternatives, such as obtaining these elements from secondary sources as recycling of lithium-ion batteries, are essential to provide the inputs used in the sector. However, the metallurgical route which will used to recovering them must be considered, due to this work aims for a more environmentally favorable process using DL-malic acid 1.5 M and instead of compared with sulfuric acid 2 M, heat pretreatment of 1 h and 3 h, and for all conditions, experiments were carried out with and without adding the oxidizing agent hydrogen peroxide. The best yields occurred in presence of H2O2 10 % v/v, and heat pretreatment of 1 h: 33.49 % Co and 4.63 % Li, and 29.78 % Co e 3.44 % Li were recovered by sulfuric acid and DL-malic acid, respectively.

Epithelial–mesenchymal transition (EMT) is defined as a cellular process during which epithelial cells acquire mesenchymal phenotypes and behavior following the downregulation of epithelial features. EMT and its reversed process, the mesenchymal-epithelial transition (MET), and the special form of EMT, the endothelial-mesenchymal transition (EndMT), have been thought as a universal dogma controlling developmental and pathological processes. More than half a century of EMT study has made it a large research field and a mainstream concept. However, discrepancies and disputes over EMT and EMT research have also grown over time. Particularly, neither the epithelial and mesenchymal states/properties nor their regulatory networks nor specific markers have been defined, rendering the EMT, MET and EndMT concepts groundless. Moreover, EMT and MET effects should not be a cause, but rather a consequence of or an accompanying effect during developmental and pathological processes. EMT and MET represented by the change in cell shapes or adhesiveness or symbolized by EMT factors are biased interpretation of the overall change in cellular property and regulatory networks during development and cancer progression. The true meaning of EMT effects in some developmental and pathological processes, such as fibrosis, needs re-evaluation. But the core EMT factors are actually a few components of the regulatory networks of neural stemness, which determines tumorigenicity and pluripotency. The EMT effects in cancer progression and neural crest formation are wrong attribution of the role of neural stemness during cancer progression and the cell-intrinsic property of neural crest cells to the unknown mesenchymal state. It is time to reassess the significance of EMT and its related concepts in scientific research.

Serum biochemical indicators serve as vital proxies that reflect the physiological state and functions of different organs. The genetic parameters and molecular mechanisms underlying serum biochemical indicators of sheep (Ovis aries) have not been well understood. Therefore, the aim of the present study was to identify genetic architecture and genomic loci underlying ten serum biochemical indicators in sheep, including alanine transaminase, aspartate transferase, lactate dehydrogenase, cholesterol, glucose, phosphorus, calcium, creatinine, urea concentrations and total protein levels. We implemented genetic parameter estimations and GWAS for each trait in 422 Akkaraman lambs. Overall, low to moderate heritability estimates were found, in the range 0.14–0.55. Additionally, low to high genetic correlations were observed among traits. In total, 23 SNP loci were associated with serum biochemical indicators leading to 19 genes . These are SPTA1, MGST2, CACUL1, IGFBP7, PARD3, PHB1, SLC15A5, TRIM35, RGS6, NUP93, CNTNAP2, SLC7A11, B3GALT5, DPP10, HST2ST1, NRP1, LRP1B, MAP3K9, ENSOARG00020040484.1 as well as LOC101103187, LOC101117162, LOC105611309 and LOC101118029. To our knowledge, these data provide the first association between SPTA1 and serum cholesterol and between ENSOARG00020040484.1 and serum glucose. The current findings provide a comprehensive inventory of the relationships between serum biochemical parameters, genetic variants, and disease-relevant characteristics. This information may facilitate the identification of therapeutic targets and fluid biomarkers and establish a strong framework for comprehending the pathobiology of complex diseases as well as providing targets for sheep genetic improvement programmes.

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. It is pertinent to continue the refinement and discussion of nutrient utilization and requirements for the purposes of enhancing the efficiency of production systems, maximizing resource economy, and minimizing the carbon footprint, among others. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter technique and minimum endogenous loss method. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies had been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size; with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that the guidelines of the major feeding systems for the entire growth phase may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that that metabolizable protein synthesis in the rumen can be predicted from energy intake using combined equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. Species, level of Intake, genotype, mature size, age, gender, body composition, use of tissue energy, activity, season, and acclimatization continue to be important considerations for the estimation of nutrient requirements in sheep and goats, with emerging factors such as climate change, heat stress, parasitism and secondary plant compounds. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential and limitation of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement.

Thousands of people worldwide suffer from injuries in the peripheral nerve and deal daily with the resulting physiological and functional deficits. Recent advances in this field are still insufficient to guarantee effective outcomes, and the development of new and effective therapeutic options requires the use of valid preclinical models that effectively replicate the characteristics and challenges associated with these injuries in humans. In this study, we established a sheep model for common peroneal nerve injuries that can be applied in preclinical research with the advantages associated with the use of large animal models. In an integrative way, this article includes a detailed description of the anatomy and functionality of the peripheral nerves of sheep’s hind limb, the surgical protocol for accessing the common peroneal nerve, the induction of different types of nerve damage and the application of possible therapeutic options. A neurological exam protocol directed to the common peroneal nerve was also established, allowing to identify the changes and deficits related with the nerve injury and to evaluate the functional progression over time. Finally, a preliminary stereological study was carried out to establish control values for the healthy peroneal common nerves of this model and to identify preliminary differences between therapeutic methods. The ultimate goal is to demonstrate that sheep is a valid model of peripheral nerve injury to be used in pre-clinical and translational works and to evaluate the efficacy and safety of nerve injury therapeutic options before its clinical application in human and veterinary patients.

The objective of this study was to determine how application with dolomitic limestone and grazing type (continuous or deferred) affect sheep grazing location and feed preference when the pasture growth rate is maximum. A 4ha field was divided into 4 plots: P1 and P2- without application of dolomitic limestone, continuous (CG) and deferred grazing (DG), respectively; P3 and P4- with application of dolomitic limestone, DG (1AUE), and CG (2.4 AUE), respectively. In DG, animals were placed and removed from the plots depending on the height of the pasture. In each plot, 12 georeferenced sampling points were identified. Throughout the pasture's vegetative cycle (autumn, winter, spring, and early summer) several measurements of pasture height and cut were carried out. From the beginning of March to the beginning of June, animal behavior was observed, by trained observers, through binoculars on six dates. Animals' activity grazing and location was recorded. The results show that: (i) the application of dolomitic limestone combined with CG provided higher values of pasture height; (ii) there were no significant differences in pasture quality between treatments; (iii) Deferred grazing led to 50% more sheep grazing days than continuous grazing; (iv) there were no significant differences in soil compaction between CG and DG; (v) the type of grazing and the treatment with dolomitic limestone did not seem to change the grazing pattern between treatments. This work contributes to understanding the relationships between different types of grazing in dryland pastures, with and without application of dolomitic limestone, and preferred grazing locations for sheep. This work could help agricultural managers make more informed decisions with the aim of promoting the sustainable intensification of livestock production in the Montado ecosystem.

Wool quality and yield are important economic traits of livestock. In this study, the skin tissues of the upper scapula of Super Merino sheep (SM) and Small-Tailed Han sheep (STH) during the growing period were studied. Differentially expressed (DE) long non-coding RNAs (lncRNAs) and messenger RNAs (mRNA) were identified via histopathological observation and high-throughput RNA sequencing. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze differentially expressed genes. Reverse transcription quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify nine selected DE lncRNAs and five DE mRNAs. Significant differences were found in the expression levels of LOC114113396 (KRTAP15-1), CRABP1, KRT10, KRTAP1-3, KRTAP4.3, KRT2, FGF1, IGF-1, and KIT in SM and STH skin tissues. These results indicate that these mRNAs may play an important role in the growth and development of hair follicles and the regulation of hair fiber traits. Differentially expressed lncRNA target genes and mRNAs were mainly enriched in biological processes and pathways related to skin development and fiber traits, which is consistent with previous studies. LncRNA MSTRG.9225.1, MSTRG.124320.1, MSTRG.96951.1, MSTRG.91685.1, MSTRG.122229.1, MSTRG.89255.1, MSTRG.98769.1, and MSTRG.84658.1 may indirectly participate in the growth and development of sheep hair follicles and the regulation of hair fiber diameter by regulating the expression of their respective target genes. These candidate lncRNAs and mRNAs may provide a basis for the study of sheep hair follicle growth and development and hair fiber traits.

This study finds that in northern Jordan, breeder indicators present challenges to the sustainability and growth of sheep and goat farming, while economic indicators suggest a combination of financial stability alongside hurdles related to profitability and resource ownership. Breeder age plays a role in the management of livestock herds, due to factors such as experience, resources, or adherence to traditional practices. Experience significantly affects breeders' decisions on male insemination replacements, likely due to skill. Crop cultivation influences breeders' returns by reducing feeding costs, while strategic lamb sales management boosts individual animal profitability. Winter raises animal mortality due to cold stress. Other animals in herds increase kid mortality by spreading diseases. Breeder experience significantly affects lamb survival, stressing its role in care. Increasing sheep proportion enhances lamb production and reduces lamb mortality while managing rams and selling lambs positively affects herd health and productivity. A larger goat herd proportion positively correlates with kids sold but inversely with breeding males and overall kids, implying trade-offs affecting herd productivity and mortality rates. Sheep herds (92.5%) show slightly better reproductive success than goats (90.6%), implying superior long-term viability. Flock excels in birth, survival, and sales, but low sheep and lamb%. Improvement is needed for sustainability, and the potential is high. Goat breeding shines in birth, sales, and overall success, but needs attention to kid survival and other sustainability issues. In conclusion, despite challenges, there are promising opportunities to enhance sheep and goat farming in Jordan. Effective management, informed by breeders' experience and economics, is crucial. Empowering small-scale herders and implementing targeted interventions like winter mortality solutions and selective breeding is vital for sustainable growth.

Kynurenic acid (KYNA), a tryptophan metabolite, is believed to exert neuromodulatory and neuroprotective effects in the brain. The study aimed to examine the KYNA’s capacity to modify gene expression and the activity of cellular antioxidant enzymes in specific structures of the sheep brain. Anestrous sheep were infused intracerebroventricularly with two KYNA doses: lower (4×5 μg/60 μL/30 min, KYNA20) and higher (4×25 μg/60 μL/30 min, KYNA100) at 30min intervals. The abundance of superoxide dismutase (SOD2), catalase (CAT) and glutathione peroxidase (GPx1) mRNA, as well as enzyme activities (µM/µg of protein), were determined in the hypothalamic medial-basal (MBH) and preoptic (POA) areas, and in the hippocampal CA1 field. Both doses of KYNA caused a decrease (P<0.01) in the expression of SOD2 and CAT mRNA in all structures examined compared to the control group (except for CAT in the POA at the KYNA100 dose). Furthermore, lower levels of SOD2 mRNA (P<0.05) and CAT mRNA (P<0.01) were found in the MBH and POA and in the POA and CA, respectively, in sheep administered with the KYNA20 dose. Different stimulatory effects on GPx1 mRNA expression were observed for both doses (P<0.05-P<0.01). KYNA exerted stimulatory, but dose-dependent effects on SOD2, CAT and GPx1 activities (P<0.05-P<0.01) in all brain tissues examined. The results indicate that KYNA may influence the level of oxidative stress in individual brain structures in sheep by modulating the expression of genes and activity of at least SOD2, CAT and GPx1. The present findings also expand the general knowledge about the potential neuroprotective properties of KYNA in the central nervous system.

“Pecorino” is a typical semi-hard cheese obtained with raw or heat-treated sheep milk using procedures to valorize the raw material's chemical and microbiological properties. In the present study, using a high throughput method of 16S rRNA gene sequencing, we assessed the evolution of the microbiome composition from milk to Pecorino cheese in artisanal processes using milk from Comisana and Lacaune sheep breeds. The comparative analysis of the bacterial com-munity composition revealed significant differences in the presence and abundance of specific taxa in the milk microbiome of the Comisana and Lacaune breeds. NGS analysis also revealed differences in the curd microbiome related to dairy farming practices, which have a relevant effect on the final structure of the Pecorino-cheese microbiome.

The advancement of sequencing technology and molecular breeding methods have provided technical support and assurance for accurate breeding. Genomic Selection (GS) utilizes genomic information to improve livestock breeding and, it is more accurate and more efficient than traditional selection methods. GS has been widely applied in domestic animals breeding, especially in cattle. However, there is still limited study on the application and research of GS in sheep and goat. In this paper, we outlined the principle, analysis methods, the influential factors of GS, and elaborated on the research progress, challenges, and prospects of applying GS in sheep and goat breeding. Through the review of these aspects this paper is expected to provide valuable references for the implementation of GS in the field of sheep and goat breeding.

Peripheral nerve injury remains a medical challenge with no easy resolution. Over the last few decades, significant advances have been achieved in promoting peripheral nerve regeneration, and new assessment tools have been developed, both functional and imaging, to quantify the proportion and quality of nerve recovery. The exploration of new animal models, larger, more complex and with more similarities to humans, has made it possible to reduce the gap between the results obtained in classic animal models such as rodents and the application of new therapies in humans and animals of clinical interest. Ultrasonography is an imaging technique recurrently used in clinical practice to assess the peripheral nerves, allowing its anatomical and topographic characterization, and aiding in the administration of anesthesia and in the performance of nerve blocks. The use of this technique in animal models is scarce, but it could be a useful tool in monitoring the progression of nerve regeneration after the induction of controlled experimental lesions. In this work, sheep, a promising animal model in the area of peripheral nerve regeneration, were subjected to an ultrasonographic study of three peripheral nerves of the hind limb, the common peroneal, tibial and sciatic nerves, in order to establish values of dimensions and ultrasound appearance in healthy nerves and also landmarks for their identification. At the same time, an echographic evaluation of the cranial tibial muscle, an effector muscle of the common peroneal nerve, was also carried out, establishing normal values for the echographic aspect and dimensions. The results obtained will allow the use of these data as control values in future work exploring new therapeutic options for nerve regeneration after induction of common peroneal nerve injuries in sheep.

The aim of the study was to assess the effect of the addition of inulin and the replacement of part of inulin with apple fiber on the physicochemical and organoleptic properties. Moreover, the survival of Bifidobacterium animalis ssp. Lactis Bb-12 and Lactobacillus rhamnosus was studied in sheep milk ice cream. There was no effect of apple fiber and the type of bacteria on the number of bacteria of the probiotics after fermentation. As a result of freezing, mixture containing Bifidobacterium animalis ssp. Lactis Bb-12, there was a significant reduction in the bacterial from 0.39 log cfu g −1 to 0.46 log cfu g −1. In all ice cream on the 21st day of storage, it exceeded 10 log cfu g –1, which means that the ice cream retained the status of probiotic products. The Lactobacillus rhamnosus ice cream showed a lower yellow colour compared to the Bifidobacterium Bb-12 ice cream. The overrun of sheep's milk ice cream was within a range from 78.50% to 80.41%. The appearance of sheep's milk ice cream is influenced considerably by the addition of fiber and the type of bacteria and the interaction between the type of bacteria and the addition of fiber and storage time and fiber.

This study aimed to investigate the appearance and frequencies of the booroola polymorphism of the bone morphogenetic protein receptor 1b (BMPR1B) gene (FecB) and the embrapa polymorphism of the growth differentiation factor 9 (GDF9) gene (FecGE) in sheep in Thailand. A total of 454 cross-bred sheep blood samples were collected from four provinces in Thailand during August 2022 to July 2023. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to identify the FecB and FecGE genotype. The history of ewe birth types was collected from the owners to analyze the association between Fecundity (Fec) genotypes and the history of birth types. The genotypic frequencies of FecB for homozygous genotype (B/B), heterozygous genotype (+/B) and wild-type (+/+) were 0.22%, 1.54%, and 98.24%, respectively. Meanwhile, the genotypic frequencies of FecGE for homozygous genotype (E/E), heterozygous genotype (+/E), and wildtype (+/+) were 0.00%, 2.42%, and 97.58%, respectively. Furthermore, three ewes exhibited both FecB and FecGE genotypes. Fisher’s exact test revealed that the possessor of FecB genotype was associated with multiple births (p<0.01). Both FecB and FecGE mutations were identified in crossbred sheep in Thailand. Sheep containing FecB allele is a better candidate to be selected to improve the prolificacy of crossbred sheep in Thailand.

Several studies suggests that ghrelin (GHRL) has neurobiological effects that extend beyond control of food intake. Our previous results confirmed that GHRL modulates the secretory activity of the pineal gland (PG) through nocturnal melatonin (MEL) secretion in sheep, the seasonally reproductive animals. Here we investigated the effects of GHRL (10 ng/ml) on the expression of enzymes limiting synthesis of MEL, including tryptophan 5-hydroxylase 1 (TPH1), serotonin N-acetyltransferase (AA-NAT) and its phosphorylated form p31T-AA-NAT in sheep PG explants (n = 72) during the 4-hour incubation in a gas-liquid interface, at a short (SD) and long (LD) photoperiods. After each hour of incubation selected explants were frozen in liquid nitrogen and stored at -80°C for subsequent analysis (real-time PCR, western-blotting, ELISA). Results show that GHRL regulates nightly MEL secretion in a TPH1-independent manner. The factor modulating GHRL activity was photoperiod. During SD photoperiod GHRL significantly reduced the expression of p31T-AA-NAT, AA-NAT and inhibited MEL secretion from PG explants. Whereas, during LD photoperiod no effect of GHRL on MEL secretion and expression of examined enzymes was noted. Studies indicate that GHRL directly affects PG under in vitro conditions and causes MEL secretion in animals which exhibit seasonality in reproductive and metabolic processes.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

Prenatal maternal feeding plays an important role in fetal development and has potential to cause long-lasting epigenetic modifications. MicroRNAs (miRNAs) are non-coding, single-stranded RNAs that serve as one epigenetic mechanism. Though miRNAs have crucial roles in fetal programming, growth, and development, there are limited data regarding maternal diet and sheep miRNA expression. Therefore, weanalyzed high and low maternal dietary protein for miRNA expression in sheep fetal longissimus dorsi. Pregnant ewes were fed an isoenergetic high-protein (HP, 160–270 g/day), low-protein (LP, 73–112 g/day), or standard protein diet (SP, 119–198 g/day) during pregnancy. miRNA expression profiles were evaluated using the Affymetrix GeneChip miRNA 4.0 Array. Twelve up-regulated, differentially expressed miRNAs (DE miRNAs) were identified targeting 65 genes. The oar-3957-5p miRNA was highly up-regulated in LP and SP compared to HP. Previous transcriptome analysis identified integrin and non-receptor protein tyrosine phosphatase genes targeted by miRNAs detected in the current experiment. A total of 28 GO terms and 10 pathway-based gene sets were significantly (padj < 0.05) enriched in target genes. Most genes targeted by identified miRNAs are involved in immune and muscle disease pathways. Our study shows dietary protein intake during pregnancy affects fetal skeletal muscle epigenetics via miRNA expression.

Sheep (Ovis aries) provide a vital source of protein and fibre to human populations. In coming decades, as the pressures associated with rapidly changing climates increase, breeding sheep sustainably as well as producing enough protein to feed a growing human population will pose a considerable challenge for sheep production across the globe. High quality reference genomes and other genomic resources can help to meet these challenges by: 1) informing breeding programmes by adding a priori information about the genome, 2) providing tools such as pangenomes for characterising and conserving genetic diversity, and 3) improving our understanding of fundamental biology using the power of genomic information to link cell, tissue and whole animal scale knowledge. In this review we describe recent advances in the genomic resources available for sheep, discuss how these might help to meet future challenges for sheep production, and provide some insight into what the future might hold.

Subacute rumen acidosis (SARA) is a prevalent issue in intensive livestock farming, particularly associated with high-concentrate diets. This study investigates the prevalence and impact of SARA in Greek dairy sheep farming and explores the use of computerized rumen colorimetry as a management and diagnostic tool for SARA in dairy sheep farms. Forty-two dairy sheep flocks were monitored regularly for rumen pH, milk composition, and milk yield over an approximately 9-month milking period. Computerized Rumen Mucosal Colorimetry was applied to 280 culled, aged ewes from these farms, with rumen wall samples taken for histological examination. SARA cases were identified in 19 farms (45%, n=48). Farms with SARA cases had lower milk fat levels, although milk yield was higher in these farms, while milk protein levels did not differ based on the SARA status of the farms. In culled ewes, rumen color was significantly associated with the farm's SARA status. Additionally, ewes from farms with more SARA cases exhibited increased thickness in the ruminal epithelium layers, particularly in the non-keratinized and total epithelium layers. In conclusion, SARA is a common disorder in the Greek dairy sheep industry, lowering the fat content of milk. The study suggests that computerized rumen epithelium colorimetry in aged culled ewes shows promise as a diagnostic tool for SARA in dairy sheep farms.

The mutton sheep breeding industry has transformed significantly in recent years, from traditional grassland free-range farming to a more intelligent approach. As a result, automated sheep face recognition systems have become vital to modern breeding practices and have gradually replaced ear tagging and other manual tracking techniques. Although sheep face datasets have been introduced in previous studies, they have often involved pose or background restrictions (e.g., fixing of the subject’s head, cleaning of the face), which restrict data collection and have limited the size of available sample sets. As a result, a comprehensive benchmark designed exclusively for the evaluation of individual sheep recognition algorithms is lacking. To address this issue, this study develops a large-scale benchmark dataset, Sheepface-107, comprised of 5,350 images acquired from 107 different subjects. Images were collected from each sheep at multiple angles, including front and back views, in a diverse collection that provides a more comprehensive representation of facial features. In addition to the dataset, an assessment protocol is developed by applying multiple evaluation metrics to the results produced by three different deep learning models: VGG16, GoogLeNet, and ResNet50, which achieved F1-scores of 83.79%, 89.11%, and 93.44%, respectively. A statistical analysis of each algorithm suggested that accuracy and the number of parameters were the most informative metrics for use in evaluating recognition performance.

Enterobacteriaceae can contaminate meat during various stages of processing, including slaughter, evisceration, and butchering, causing foodborne illnesses. The purpose of the study was to investigate the prevalence of Enterobacteriaceae associated with carcasses obtained from slaughterhouses and raw cut meat collected from butcher shops. A total of 120 samples of camels and cattle were identified using biochemistry and PCR testing. Total viable count (TVC) was range from 4.91 to 5.37 Log10 CFU/g in slaughterhouses and butcher shops. E. coli was predominating with 84 (70%) among all samples, where the camels had the highest with 100% and sheep the lowest with 30%. Salmonella spp. was confirmed in 40% of camel samples, 47.5% of cattle samples, and 32.5% in sheep samples. Twenty-five Enterobacteriaceae genera were confirmed using PCR. Where sheep’s samples had the highest occurrence of Enterobacteriaceae with 15 different genera followed by camels and cattle samples with 14 different genera. The prevalence of Enterobacteriaceae among camel, cattle, and sheep carcasses raises significant concerns regarding food safety. Adherence to good hygiene practices throughout animal slaughtering is crucial to minimize the risk of infection and transmission and ensure food safety.

Mannheimia haemolytica is the main pathogen contributing to pneumonic pasteurellosis in sheep. The aim of this study was to investigate the antimicrobial resistance levels in M. haemolytica from the lungs of slaughtered sheep and the genetic resistance mechanisms involved. A total of 256 M. haemolytica isolates, 169 from lungs with pneumonic lesions and 87 without, were analyzed by the disk diffusion method for 12 antimicrobials, and the whole genome of 14 isolates sequenced to identify resistance determinants. Levels of phenotypic resistance ranged from <2 % for 10 antimicrobials (amoxicillin, amoxicillin-clavulanic, ceftiofur, cefquinome, lincomycin/spectinomycin, gentamicin, erythromycin, florfenicol, enrofloxacin, and doxycycline) to 4.3% for tetracycline and 89.1% for tylosin. Six isolates carried tetH genes and four carried, in addition, strA and sul2 genes in putative plasmid sequences. No mutations associated with macrolide resistance were identified in 23 rDNA sequences suggesting that M. haemolytica phenotypic results for tylosin should be interpreted with care in the absence of well-established epidemiological and clinical breakpoints. The identification of strains phenotypically resistant to tetracycline and of several resistance genes, some of which were present in plasmids, highlight the need for continuous monitoring of susceptibility patterns in Pasteurellaceae isolates from livestock

Intrammary infections are a major problem for dairy sheep farms, and Streptococcus uberis is one of the main etiological agents of ovine mastitis. Surveys on antimicrobial resistance are still limited in sheep and characterization of isolates is important for acquiring information on resistance and for optimizing therapy. In this study, a sampling of 124 S. uberis isolates collected in Sardinia (Italy) from sheep milk was analysed by multilocus-sequence typing (MLST) and pulsed field gel electrophoresis (PFGE) for genetic relatedness. All isolates were also subjected to antimicrobial susceptibility analysis by the disk diffusion test using a panel of 14 antimicrobials. Resistance genes were detected by PCR assays. MLST analysis revealed that the isolates were grouped into 86 sequence types (STs), of which 73 were new genotypes, indicating a highly diverse population of S. uberis. The most frequently detected lineage was the clonal complex (CC)143, although representing only 13.7% of all characterized isolates. A high level of heterogeneity was also observed among the SmaI PFGE profiles, with 121 unique patterns. Almost all (96.8%) isolates were resistant to at least one antimicrobial, while all exhibited phenotypic susceptibility to oxacillin, amoxicillin-clavulanic acid and ceftiofur. Of the antimicrobials tested, the highest resistance rate was found against streptomycin (93.5%), kanamycin (79.8%) and gentamicin (64.5%), followed by novobiocin (25%) and tetracycline-TE (19.3%). Seventy-four (59.7%) isolates were simultaneously resistant to all aminoglycosides tested. Seventeen isolates (13.7%) exhibited multidrug resistance. All aminoglycosides-resistant isolates were PCR negative for aad-6 and aphA-3’ genes. Among the TE-resistant isolates, the tetM gene was predominant, indicating that the resistance mechanism is mainly mediated by the protection of ribosomes and not through the efflux pump. Three isolates were resistant to erythromycin, and two of them harboured the ermB gene. This is the first study reporting a detailed characterization of the S. uberis strains circulating in Sardinian sheep. Further investigations will be needed to understand the relationships between S. uberis genotypes, mastitis severity, and intra-mammary infection dynamics in the flock, as well as to monitor the evolution of antimicrobial resistance.

Forty-five of intact three sheep breeds (Awassi, Harri, and Najdi) were used in this study (15 animals of each breed), with weight ranged from 23.40 to 25.87 kg. Five animals from each group at the end of growth trial were used for digestibility and nitrogen balance trials. The three groups of sheep had effect (p > 0.05) in the final live weight (FLW). Average daily feed intake (ADI), average daily gain (ADG) and feed conversion ratio (FCR) were significantly (p < 0.05) different among the three groups. For digestibility coefficients, the three breeds of sheep had no effect (p > 0.05) in dry matter (DM), organic matter (OM), crude protein (CP), crude fiber (CF), nitrogen free extract (NFE), neutral detergent fiber (NDF) and acid detergent fiber (ADF). In the similar approach, no significant differences were founds among three breeds of sheep in digestible organic matter, digestible of crude protein, total digestible nutrients and nitrogen retention. The current study concluded that the sheep breeds affected average daily feed intake, average daily gain and feed conversion ratio (FCR). Awassi breed showed the best of average daily gain and feed conversion ratio followed by Najdi then Harri breeds. The breed of sheep had no effect on digestibility coefficients and nitrogen retention.

The in vitro production and cryopreservation of mammalian embryos generate reactive oxygen species (ROS) due to conditions of the system that can overcome their antioxidant protection. Resveratrol is an antioxidant used in in vitro systems improving blastocyst rates, but its effect on antioxidant enzymes such as glutathione (GSH) in embryos produced by in vitro fertilization (IVF) after vitrification has not been reported. The objective of this study was to evaluate the effect of resveratrol in the in vitro maturation medium (IVM) of sheep oocytes (Ovis aries) on the levels of ROS and GSH in embryos produced by IVF subjected to vitrification. Resveratrol was added at 0µM, 0.25µM, 0.5µM and 1µM during oocyte in vitro maturation (IVM). Matured oocytes were fertilized with thawed ram sperm. Embryos were cultured in sequential media until blastocysts, then were vitrified for 24 h and after heating they were stained with DCFH-DA (2',7'-dichlorodihydrofluorescein diacetate) to determine the presence of ROS and with Cell Tracker Blue® for the presence of GSH. The quantitative values of ROS and GSH were obtained through the Image J image processor. The results showed that resveratrol increases GSH and decreases ROS production (p<0.05). It is concluded that its use in sheep oocytes during IVM has a beneficial effect on embryos produced by IVF subjected to vitrification, protecting them from oxidative damage by promoting the synthesis of antioxidants.

Conservation agriculture (CONS A) is a sustainable agriculture system which is based on rota-tion crops with no tillage. It has various environmental advantages compared to conventional agriculture (CONV A), namely decrease water evaporation, erosion and CO2 emission. We report herein the first study aiming evaluation of the impact of this type of sustainable agri-culture on sheep gastrointestinal parasites. Two lamb groups aged between 6 and ten months were randomly included to graze separately on CONS A and CONV A pastures. Each group was constituted of two batches of three lambs which were followed up for two rearing months during which liveweight, haematological parameters variation and digestive parasites were studied. At the end of the study period, lambs were slaughtered and the carcass yield was determined and a helminthological autopsy was performed on digestive tracts to estimate different parasitological indicators. There was no difference between lambs rearing on CONS A and those rearing on CONV A for all parasite indicators (infestation intensity, abundance and prevalence), the same trend was also obtained for haematological parameters, liveweight evolution and carcass yield. These results prove that there is no impact of the CONS A on the sheep digestive parasitism. Further studies are needed to support these findings on a bigger animal samples and to investigate the impact of this agriculture on other parasites and for other animal species.

A better understanding of diseases progress in tissues vest on the accurate understanding of tissues under mechanical loading. Also, development of therapies for injuries may depend on the available mechanical data for soft tissues. In this study, the raw data of biaxial tensile testing of sclera soft tissue is presented in this paper. Biaxial mechanical testing of soft tissues presents details understanding of how soft tissues behave when compared to uniaxial testing. Biomechanical properties of soft tissues are vital in the development of accurate computational models. Reliable computational models of studying mechanisms of diseases depends mainly on the accurate and more details mechanical behavior of soft tissues. These accurate and detailed computational models may be utilized to further develop the understanding and therapies of various diseases. The mechanical tensile testing was conducted on the passive sheep sclera. Engineering stress vs strain of several samples of the sheep sclera are further presented determined from force and displacement experimental data. The goal of this paper is to make available biaxial data of sheep sclera soft tissue that can be further utilized.

Lessons learned and risk-based outcomes from modernising the Australian Standard for post-mortem inspection and disposition judgement of beef, sheep, goat and pig carcases are described. Initial Risk Profiles identified priorities for quantitative assessments. The main difficulty encountered was the paucity of quantified performance of current inspection. Resolving this involved acquiring gross abnormality data representing regional production/proportional abattoir volumes, the range of gross abnormalities appearing nationally, proportional occurrence at carcase sites, and seasonality to enable comparisons. Evidence and discussion are provided for associated challenges experienced including preventing contamination, use of food chain information to support amendment, inspection as a part of industry Quality Assurance and opportunities to improve inspector training. Criteria considered by the Competent Authority for determination of equivalence of alternative post-mortem inspection techniques included comparisons of public health risk, non-detection rates for gross abnormalities, and microbial contamination resulting from inspection activities, as appropriate. Most gross abnormalities detected arose from animal health and welfare conditions affecting wholesomeness and did not present as food safety hazards. Non-detection rates between current and alternative inspection (observation) were negligible. A Quantitative Risk Assessment for Cysticercus bovis was conducted. Carcases with multiple gross abnormalities predominantly reflected historic infections (prior septicaemia) where trimming achieved wholesomeness unless cachexic.

Small ruminant lentivirus (SRLV) are infected and transmitted among ovine and caprine species. This disease is a severe problem for small ruminant production, not only for animals’ wellbeing but also for the herd’s efficiency. The main aim of this research was to quantify the seroprevalence and associated risk factors for SRLV infection in the north region of Portugal. Collected samples from a total of 150 herds, of which 129 (86.0%; 95% CI: 80.67% - 91.33%) had at least one seropositive animal. Out of 2607 individual blood samples, 1074 (41.2%) were positive for SRLV. The risk factors associated with SRLV infection were: specie (caprine), age (&gt; 2 years old), herd size (&gt; 100 animals), production system (intensive), production aptitude (milk), type of activity (professional), participation in livestock competitions (yes), buy replacement young ewe (yes) and rearing (natural). This knowledge empowers the implementation of effective preventive measures. Overall, biosecurity measures should be promoted and implemented to aim reducing viral transmission, with the main goal of reducing the prevalence of this disease. Completely, we understand that government authorities should promote and audit voluntary control and eradication programs in small ruminant herds in the region studied.

The Lin28B gene is involved in the initiation of puberty, but its regulatory mechanisms remain unclear. Therefore, in this study, we aimed to study the regulatory mechanism of the Lin28B promoter by cloning the Lin28B proximal promoter for bioinformatics analysis. Next, a series of deletion vectors were constructed based on the bioinformatics analysis results for dual-fluorescein activity detection. The transcriptional regulation mechanism of the Lin28B promoter region was analyzed by detecting mutations in transcription factor-binding sites and overexpression of transcription factors. The dual-luciferase assay showed that the Lin28B promoter region -837 to -338 bp had the highest transcriptional activity, and the transcriptional activity of the Lin28B transcriptional regulatory region decreased significantly after Egr1 and SP1 mutations. Overexpression of the Egr1 transcription factor significantly enhanced the transcription of Lin28B, and the results indicated that Egr1 and SP1 play an important role in regulating Lin28B. These results provide a theoretical basis for further research on the transcriptional regulation of sheep Lin28B during puberty initiation.

The type of tail of sheep is an important economic trait. However, the candidate genes associated with the tail type are uncertain. The objective of this study was to identify the genetic region and genotype responsible for the tail type phenotype. Here we perform a genome-wide association study (GWAS) in 40 large tailed Han sheep and 40 Altay sheep as case and 40 Tibetan sheep as control. The results indicated that a total 31 genome-wide significant SNPs associated with type of tail traits were detected. For significant SNPS loci, determine its physical location, and screening of candidate genes within section. By combining information of previously reported and annotated biological functional genes, we identified SPAG17, Tbx15, VRTN, NPC2, BMP2 and PDGFD as the most promising candidate genes for type of tail traits. Based on the above identified candidate genes on type of tail traits, we selected BMP2 and PDGFD to conduct the genetic effect analysis in a large Altay sheep and Tibetan sheep population. Rs119 T>C in the exon1 of BMP2 gene and 1 SNPs in the exon4 (rs69 C>A) of PDGFD gene were detected, rs119 that located on exon1 of BMP2 gene was TT genotype in Altay sheep, while with CC genotype in Tibetan sheep. On rs69 of PDGFD gene, Altay sheep with CC genotype, however, Tibetan sheep with AA genotype. These results indicated that the significant associations of SNPs detected in GWAS were indirectly caused by the genetic effects of BMP2 and PDGFD on sheep tail fat deposition.

The pharmacokinetics of salicylic acid (SA) in sheep was evaluated following intravenous (IV) and oral administration of sodium salicylate (sodium salt of salicylic acid) at different doses. Six healthy sheep were administered sodium salicylate (SS) IV at doses of 10, 50, 100 and 200 mg/kg body weight and another six sheep were drenched with 100 and 200 mg/kg of SS orally. Both studies were randomised crossover trials. A one-week washout period between each treatment was allowed in both studies. Blood samples were collected at 0, 15, 30 minutes and 1, 2, 4 and 6 hours after IV and oral SS administrations. Plasma SA concentrations were determined using high performance liquid chromatography with diode array detection method. Pharmacokinetic variables were calculated in a non-compartmental model. The elimination half-life (T_{1/2 el}) of SA after IV administration of 200 mg/kg SS was 1.16 ± 0.32 hours. Mean bioavailability of SA was 64%, and mean T_{1/2 el} was 1.90 ± 0.35 hours, after 200 mg/kg of oral SS. The minimum plasma SA concentration (16.8 µg/mL) required to produce analgesia in humans was achieved after IV administration of 100 and 200 mg/kg SS in sheep for about 0.17 hour in this study. Experiments on pharmacokinetic-pharmacodynamics modelling are required to determine the actual effective plasma concentration range of SA in sheep.

Among the alternatives pointed out as able to make livestock a more efficient activity and reduce its environmental impact, the use of feeds with high digestibility and the use of non-conventional fodder species can be highlighted. Tithonia diversifolia (Hemsl.) A. Gray (TD) has already gained attention as a feed for ruminants. Nonetheless, there has been little discussion about the impacts of this plant on the rumen dynamics that lead to better animal performance. The aim of this study was to assess how increasing levels of TD could affect ruminal microbial biomass synthesis, degradability, and in vitro gas production (IVGP) of diets. Four diets were evaluated: a control diet (TD0: 400 g kg-1 soybean meal and corn grain, and 600 g kg-1 Tifton 85 hay (Cynodon spp)) and three increasing levels of TD (TD9: 90, TD27: 270, and TD45: 450 g kg-1 – dry matter basis) as a replacement for dietary roughage. A 96-hour IVGP assay was carried out and five incubation times (2, 4, 10, 24, and 96 hours) were used for degradability determination. Gas produced, methane (CH4), degradability of organic matter (IVDOM), short-chained fatty acid (SCFA) production, partitioning factor (PF), and microbial biomass (MB) were evaluated among treatments. There was no significant effect (P&gt;0.05) between TD inclusion and IVGP at most incubation times; only at 6 hours of incubation did gas production increase linearly with TD inclusion (R2=0.19; P&lt;0.05). TD inclusion had no effects on CH4. IVDOM increased linearly with TD inclusion at 6 and 10 hours, with TD45 being more degradable than the control diet at 6 hours only. There was a positive linear relationship (R2=0.20; P&lt;0.05) between TD inclusion and PF. TD45 had significantly higher PF values than the control. MB also increased linearly (R2=0.30; P&lt;0.05) with TD inclusion, and all diets with TD had significantly more MB than the control diet. The inclusion of TD at the levels used in this work was revealed to have a positive impact on microbial protein synthesis, which could be related to the higher microbial efficiency of increased substrate quality.

The individual and combined effects of extracellular products (ECP) from Arthrobotrys oligospora (Ao) and A. musiformis (Am) growth in liquid media against Haemonchus contortus L3 (HcL3) were assessed. The following methodological steps were established: isolation, morphological (MI) and molecular identification (Mol-I), assessment of nematocidal activity (NA) of fungal liq-uid culture filtrates (LCF) produced in two liquid media alone and in combination and finally, the myco-compound profile identification (MCP). The MI of both isolates suggested that the fungi corresponded to the species Ao and Am. This result was confirmed by PCR analysis followed by sequencing, alignment and obtaining coverage and similarity with respect to isolates reported in the NCBI database. Likewise, the highest Hc mortalities were 91.4% with individual LCF of Am and 86.2% with those of Ao at the highest concentration (100 mg/mL) in Czapek-Dox Broth. The combination of both LCF resulted in a similarly high larval mortality with no statistical differ-ences in relation to individual activity (P>0.05). The MCP showed the presence of alkaloids in both fungi. Coumarins, triterpenes and saponins were found only in Ao. Both fungi produced ECP with a high NA that could be identified and assessed in future studies as potential natural an-thelmintic compounds.

The objective of this study was to identify genomic regions and genes associated with resistance to gastrointestinal nematodes in Australian Merino sheep in Uruguay, using the single-step GWAS methodology (ssGWAS), which is based on genomic estimated breeding values (GEBVs) obtained from a combination of pedigree, genomic and phenotypic data. This methodology converts GEBVs into SNP effects. Analysis included 26,638 animals with fecal egg count (FEC) records obtained in two independent parasitic cycles (FEC1 and FEC2), and 1,700 50K SNP genotypes. Comparison of genomic regions was based on genetic variances (gVar(%)) explained by non-overlapping regions of 20 SNPs. For FEC1 and FEC2, 18 and 22 genomic windows exceeded the significance threshold (gVar(%) ≥ 0.22%), respectively. The genomic regions with strong associations with FEC1 were located on chromosomes OAR 2, 6, 11, 21 and 25, and for FEC2 on OAR 5, 6 and 11. The proportion of genetic variance attributed to the top windows was 0.83% and 1.9% for FEC1 and FEC2, respectively. The 33 candidate genes shared between the two traits were subjected to enrichment analysis, revealing a marked enrichment in biological processes related to immune system functions. These results contribute to the understanding of the genetics underlying gastrointestinal parasite resistance and its implications for other productive and welfare traits in animal breeding programs.

Background: The UK dairy sheep industry is relatively small but growing, particularly for cheese and yogurt products. Anecdotally, sheep milk (SM) is better tolerated by humans than cows’ milk and could have environmental as well as health benefits. All milk contains sub-micron particles called extracellular vesicles (EV) which are mainly derived from the mammary epithelium. Physiologically, milk derived EV are thought to aid development of infant immunity and the microbiome but may also have health benefits to adult humans. Methods: Using sequential ultracentrifugation vesicles of &lt;1µm (LEV) followed by &lt;200nm (sEV) were isolated from six individual sheep during mid lactation. RNA was extracted and microRNA analysed by RTqPCR for sequences previously identified in cows’ milk. Human THP-1 monocytes were differentiated into macrophages and incubated with SM-derived LEV and sEV in the presence of pro-inflammatory LPS to measure effects on secretion of the chemokine CCL-2 or in the presence of DMNQ and fluorescent dihydrorhodamine-1,2,3 to measure reactive oxygen species. Results: LEV induced an increase in ROS in both monocytes and macrophages, whilst sEV decreased DMNQ-mediated ROS in macrophages but not monocytes. Interestingly, the LEV did not induce CCL2 release, however they increased LPS-induced CCL2 secretion in monocytes but not macrophages. miR26a, miR92a, miR125b, miR155, miR223, were identified in both sEV and LEV by RT-qPCR and could be responsible for modulation of ROS and CCL2 expression. Conclusions: These findings suggest that sheep milk can influence human monocyte/macrophage responses and so is worthy of further investigation for its potential human and non-human animal health benefits.

Somatic cell nuclear transfer (SCNT) is a reproductive biotechnology with great potential in the reproduction of different species of zootechnical interest, including sheep. This study aimed to carry out a bibliometric analysis of scientific papers published on the application of SCNT in sheep reproduction in the period 1997-2023. The search involved the Science Citation Index Expanded and Social Sciences Citation Index databases of the Main Collection of Web of Sciences with different descriptors. A total of 124 scientific papers were analyzed for different bibliometric indicators using VOSviewer software. Since 2001, the number of SCNT-related documents published concerning sheep reproduction increased and has fluctuated in ensuing years. The main authors, research groups, institutions, countries, documents and journals with the highest number of publications related to SCNT in sheep reproduction were identified, as well as the topics that address the research papers according to the terms: somatic cell, embryo, oocyte, gene expression, SCNT, and sheep.

This work examines dairy and sheep farmer attitudes toward antimicrobial use (AMU) in New Zealand. There is increasing public demand on livestock producers to reduce AMU in livestock. The demand stems from concerns about potential antimicrobial resistance (AMR) that could originate from food animals. There is limited practical data on farmer knowledge of AMU. An electronic survey was sent to dairy (n= 378) and sheep farmers (n= 551). Seventy-six dairy farmers (20%, n=76/378) returned the survey. Dairy farmers (69%) showed low levels of concern about antimicrobial resistance and awareness of the need to reduce AMU. Additionally, 76% of dairy farmers didn’t think it was possible to reduce AMU. Thirty-nine sheep farmers (7%, 39/551) returned the survey. 76% of sheep farmers were supportive of restricted use of AMU. The dairy and sheep farmers sourced most of the advice from veterinarians (&gt;90%), the livestock industry (&gt;80%) and their colleagues (&gt;70%). This study shows that farmers showed varied concerns about AMR and AMU. Moreover, sheep farmers were more amenable to increased restriction on AMU than dairy farmers. This study suggests that knowledge gaps in farmers may best be filled by veterinarian input.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

This paper deals with the formulation of a Cobot kinematic model by developing both the direct and inverse kinematics and choosing the noa transformation matrix for defining the end-effector position and orientation with respect to the base frame. These algorithms have been validated via software in Matlab environment and by means of suitable experimental tests, which have been carried out by using a UR5e Cobot. Moreover, a specific industrial application regarding the robotized assembling of a multi-cylinder IC engine, has been designed in terms of layout and corresponding automatic cycle that is intended as a sequence of the robot elementary actions. In particular, the proposed robotized cell is composed by the UR5e Cobot along with its controller and a suitable electropneumatic circuit that includes a PLC with its user interface, which are both governed by means of a specific electronic board and a switch control box. The IC engine in scale of 1:5 and specific parts of the end-effector, which takes the role of vacuum gripper and torque limiting screwdriver, have been designed and manufactured by using a 3D printer.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Somatic cell nuclear transfer (SCNT) has great potential in the replication of high commercial value animals, threatened wild species for conservation purposes, and transgenic animals for biomedical purposes. However, SCNT has a low success rate, due to intrinsic factors of the technique itself, which will lead to low rates of embryonic development and epigenetic alterations in the cloned embryos. The objective of the present study was to evaluate the effect of OF-UF on the intracellular concentration of ROS, GSH, and the development of Ovis aries cloned and parthenogenetic embryos. The results did not show a beneficial effect on the development of parthenogenetic and cloned embryos at concentrations of 0.5% OF-UF. Furthermore, at 1% OF-UF, an adverse effect was observed in cloned embryos at the blastocyst stage and 2% OF and UF in parthenogenetic embryos in the first divisions. A decrease in ROS and GSH levels was observed in the parthenogenetic blastocysts treated with 1% OF-UF, but not in the clones, in which a higher concentration of GSH and a similar concentration of ROS was observed. No effect was observed of OF-UF on embryonic development and redox balance on sheep clone embryos by Handmade Cloning.

Refractoriness to antiseizure medications is still a major concern in the pharmacotherapy of epilepsy. For this reason, we decided to evaluate the combination of levetiracetam and cannabidiol, administered at a subthreshold dose, to limit the possible adverse effects of this phytocannabinoid. We administered levetiracetam (300 mg/kg/day, via osmotic minipumps), cannabidiol (120 mg/kg/day, injected once a day subcutaneously), or their combination for one week in epileptic rats. Saline-treated epileptic rats were the control group. Animals were monitored with video electroencephalography the week before and after the treatment. No changes were found in the controls. Levetiracetam did not significantly reduce the total seizure number or the overall seizure duration. Still, the overall number of seizures (p&lt;0.001, Duncan’s new multiple range test) and their total duration (p&lt;0.01) increased in the week following treatment withdrawal. Cannabidiol did not change seizures when administered as a single drug. Instead, levetiracetam combined with cannabidiol resulted in a significant reduction in the overall number and duration of seizures (p&lt;0.05), when comparing values measured during treatment with both pre- and post-treatment values. These findings depended on changes in convulsive seizures, while non-convulsive seizures were stable. These results suggest cannabidiol determined a remarkable potentiation of levetiracetam antiseizure effects at a subthreshold dose.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

Fisetin has been shown to be beneficial for brain injury and age-related brain disease via different mechanisms. The purpose of this study was to determine the presence of senescent cells and the effects of fisetin on cellular senescence in the brain and other organs in old sheep, a more translational model. Approximately 6-7 years old female sheep (N=6) were treated with 100mg/kg fisetin or vehicle two consecutive days a week for 8 weeks. All organs were harvested at the time of sacrifice. Histology, immunofluorescent staining, as well as Q-PCR was performed on different regions of brain tissues and organs. Our results indicated that Fisetin treatment at the current regimen did not affect general morphology of the brain. The presence of senescent cells in both cerebral brain cortex and cerebellum was detected by SA-β-Gal staining. More senescent cells were observed in the gray matter when compared to the white matter of the cerebral brain cortex. These senescent cells are mainly neurons in both gray and white matter of either cerebral brain cortex or cerebellum. Fisetin treatment showed a trend of decrease SA-β-Gal cells in gray matter of both cerebral brain cortex and cerebellum and significantly decreased in brain cortex white matter. Furthermore, fisetin treatment significantly decreased P16+ cells in brain cortex NEUN+ neurons, GFAP+ astrocytes, IBA+ microglia cells in both gray and white matter of cerebral brain cortex. Fisetin treatment also significantly decreased P16+ cells in microglia cells and a trend of decrease of P16+ cells in astrocytes in the non- (Cornu Ammonis) CA area of hippocampus. However, fisetin treatment did not change P16+ cells in the NEUN+ neurons in the CA1-4 area of the hippocampus. At the mRNA level, fisetin showed a decreased in GLB1 in heart ventricle muscle tissue and spleen tissues but not in other organs tested. Fisetin treatment also showed a decreased antioxidant gene SOD1 and increased CAT in spleen and bone marrow. Fisetin treatment showed variable effects in SASP and inflammasome genes in different organs. In conclusion, we found senescent cells are widely present in the cerebral brain cortex and cerebellum of old sheep. In addition, fisetin treatment decreased senescent cells as well as P16+ cells in the neurons in both gray and white matter of cerebral brain cortex and in astrocytes and microglia cells of cerebral brain cortex and non-CA area of hippocampus. Fisetin treatment represents a promising therapeutic strategy for age-related brain disease.

Glaciation and mountain orogeny have generated new ecologic opportunities for plants, favoring an increase in the speciation rate. Moreover, they also act as corridors or barriers for plant lineages and populations. High genetic diversity ensures that species are able to survive and adapt. Gene flow is one of the most important determinants of the genetic diversity and structure of out-crossed species, and it is easily affected by biotic and abiotic factors. The aim of this study was to characterize the genetic diversity and structure of an alpine species, Festuca ovina L., in Xingjiang, China. A total of 100 individuals from 10 populations were analyzed using six amplified fragment length polymorphism (AFLP) primer pairs. A total of 583 clear bands were generated, of which 392 were polymorphic; thus, the percentage of polymorphic bands (PPB) was 67.24%. The total and average genetic diversities were 0.2722 and 0.2006 (0.1686-0.2225), respectively. The unweighted group method with arithmetic mean (UPGMA) tree, principal coordinates analysis (PCoA) and STRUCTURE analyses revealed that these populations or individuals could be clustered into two groups. The analysis of molecular variance analysis (AMOVA) suggested that most of the genetic variance existed within a population, and the genetic differentiation (Fst) among populations was 20.71%. The Shannon differentiation coefficient (G’st) among populations was 0.2350. Limited gene flow (Nm = 0.9571) was detected across all sampling sites. The Fst and Nm presented at different levels under the genetic barriers due to fragmentation. The population genetic diversity was significant relative to environmental factors such as temperature, altitude and precipitation.

Geothermal energy, being a clean energy source, has immense potential, and accurate wellbore modeling is crucial for optimizing the drilling process and ensuring safety. This paper presents a novel geothermal wellbore model based on the drift-flux approach, tested under three different temperature and pressure well conditions. The proposed model integrates the conservation equations of mass, momentum, and energy, incorporating the gas-liquid two-phase flow drift-flux model and heat transfer model. Key features include handling heat transfer between the for-mation and the wellbore, addressing the slip relationship between gas and liquid phases, and accounting for wellbore friction. The nonlinear equations are discretized using the finite differ-ence method, and the highly nonlinear system is solved using the Newton-Raphson method. Numerical simulation, validation, and comparison with existing models demonstrate the en-hanced accuracy of this model. In our tests, the model achieved high accuracy in calculating bottom hole pressure and temperature, with Mean Relative Errors (MRE) significantly lower than those of other models. These results offer valuable insights for optimizing drilling parameters and ensuring drilling safety. Comparisons indicate that this approach significantly outperforms others in capturing the complex dynamics of geothermal wellbores, making it a superior tool for geo-thermal energy development.

Determining genetic and non-genetic sources of variation in a breed is vital for the formulation of strategies for its conservation and improvement. The present study was aimed at estimating the (co)variance components and genetic parameters of Mecheri sheep by fitting six different animal models in the restricted maximum likelihood method, with a preliminary investigation on the performance of animals for non-genetic sources of variation. A total of 2616 lambs were studied, and varying levels of significance were found for the effect of period, season, parity of dam and birth type on different body weight traits. Direct heritability estimates derived from the best animal model for body weight at birth, 3 months, 6 months, 9 months and 12 months were 0.21, 0.24, 0.10, 0.15 and 0.09, respectively, and maternal heritabilities of the corresponding traits were 0.12, 0.05, 0.04, 0.04 and 0.04, respectively. The genetic correlations between body weight traits were all positive and moderate to strong except for birth weight with the other body weight traits. The significance of non-genetic factors studied in this work demanded a correction to improve the accuracy of the direct selection of lambs for body weight traits. The estimated genetic parameters identified the weaning weight as a selection criterion for the improvement in body weight of Mecheri lambs at different ages. Inbred individuals accounted for approximately 13% of the total population in the Mecheri sheep population studied. There were 877 founders in the population, and the actual effective size of the population was 128.48. The population's mean generation interval was 3.26. The mean inbreeding values ranged from 0.005 to 0.010 across generations. The population's average relatedness ranged from 0.001 to 0.014 across generations. Individual inbreeding was found to be 0.45 per cent for the entire population and 3.4 per cent for the inbred population.

Background: Fermented milks are a source of bioactive peptides with different potential benefits on human health (mainly antioxidant and antihypertensive effects) and may be considered as functional foods. Furthermore, those made with sheep milk have higher nutritional value and higher concentrations of proteins, fats, minerals, and vitamins, if compared to milk of other species. Objectives: Aim of this study was to perform a biochemical characterization and a comparison between sheep fermented milk (SFM) made with commercial starter cultures and sheep milk YoAlp®, a fermented milk obtained using local strains of lactic acid bacteria (LABs) (Streptococcus thermophilus and Lactobacillus delbrueckii) which have been isolated during years in Aosta Valley mountain dairy cattle farm. Methods: Sheep milk and fermented milk (Sarda and Lacaune breeds) have been collected and immediately analyzed. Biochemical characterization, by a proteomic approach, GC/MS and microtiter plate assay methods, have been conducted to evaluate their peptide, fatty acid and aromatic profile, and to assess potential health promoting effects. Results: The comparison between SFM and YoAlp® peptide profile shows a higher number of peptides using autochthonous starter cultures (52) than commercial starter cultures (35). Among these peptides 20,78% and 29,87%, respectively, are supposed to be potentially bioactive (DPP - dipeptidyl peptidase-IV - inhibitor, ACE - angiotensin converting enzyme – inhibitor and antioxidant). In both products, the fatty acids profile was similar to that of origin sheep milk; concerning aromatic profile, YoAlp® shows yogurt typical aromatic assets, similar to the commercial one. As far as bioactivity is concerned, ACE inhibitor activity is high for both samples. Similar values, as expected by peptide profile analysis, have been obtained using commercial and autochthonous starters. Even in the case of antioxidant capacity, peptide profile bioactivity prediction has been confirmed by the assay showing a DPPH inhibition higher for SFM than for YoAlp®, but this difference is not statistically significative. Conclusions: Local strains of lactic acid bacteria from Aosta Valley seems to work as well as the commercial, preserving biodiversity and typicality. However, further analyses are needed to identify peptides generated by autochthonous LABs in order to understand microbial proteolytic activities and to investigate gastric digestion resistance of bioactive peptides which can have positive effects on human health.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman &amp; Williams, 2015; Gebhardt &amp; Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

This study aimed at evaluating changes in the clinical and echocardiographic parameters of pregnant sheep, as well as the HRV indexes due to the physiological alterations that happen at this stage implicating in high maternal metabolic demands. For this purpose, the study evaluated 10 Dorper sheep through their pregnancy, starting from the second month until the day before birth, conducting clinical, echocardiographic and electrocardiographic examinations focused on the HRV. The echocardiogram was conducted in a doppler ultrasound device with a multifrequency sectorial transducer in bidimensional mode. The HRV indexes were obtained through the Televet 100® system. There were increases in the thickness of the interventricular septum during diastole starting from the third month, and in the internal diameter of the left ventricle during systole and diastole at the second and third months, while the ejection fraction increased as the pregnancy progressed. The size of the left atrium increased starting at the second month. The SDNN, RMSSD and PNN50 HRV indexes were higher at the fifth month of pregnancy and after delivery. There were no significant differences in the frequency-domain HRV indexes during pregnancy. The gestation leads to alterations in the clinical parameters and the activity of the autonomic nervous system.

Assessing the quality of corn seeds necessitates evaluating their content of water, fat, protein, and starch. This study integrates hyperspectral imaging technology with chemometric analysis techniques to achieve non-invasive and rapid detection of multiple key components in corn seeds. Hyperspectral images of the embryo surface of maize seeds were collected within the wavelength range of 1100~2498nm. Subsequently, image segmentation techniques were applied to extract the germ structure of the corn seeds as the region of interest. Seven spectral data preprocessing algorithms were employed, and the Detrending Transformation (DT) algorithm was identified as the optimal preprocessing method through comparative analysis using the Partial Least Squares Regression (PLSR) model. To reduce spectral redundancy and streamline the prediction model, three algorithms were employed for characteristic wavelength extraction: Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Uninformative Variable Elimination (UVE). Using both the original spectra and the extracted characteristic wavelengths, four content detection models were constructed: PLSR, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Least Squares Support Vector Machine (LSSVM). The experimental results demonstrate that the DT-CARS-LSSVM model exhibits exceptional accuracy and stability in predicting these four key components, with determination coefficients of 0.9877, 0.9344, 0.9827, and 0.9592, respectively. This study not only provides a scientific basis for evaluating the quality of corn seeds but also opens up new avenues for the development of non-invasive detection technology in related fields.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

The assortment of low-cost 3D printers for “in-practice” use, e.g., for clear aligner therapies, is ever increasing. To address concerns about the accuracy of orthodontic models produced on such printers when cost efficient modes of 3D printing are employed, this study examined the effect of print model body type and print angulation on accuracy. Six printing-configuration groups were included: 2 model types (solid or hollow shell) combined with 3 print angles (0°, 70°, or 90°) with 10 models/group; all models were printed with 100-µm layer thickness using a digital light processing-based 3-dimensional printer. Eleven selected structures and distances were measured on the printed models with a digital microscope and compared to the same measures on a digitized master model. The clinically-acceptability range was set at ±0.25 mm difference from the master model for single tooth measurements (intra-tooth) and ±0.5 mm for cross-arch measurements (inter-tooth). For individual measurements across all models, 98% fell within clinical acceptability. For mean measurements within each model group, only canine height for the shell-0° model had a mean difference (0.26 mm ± 0.03) outside the clinically acceptable range for intra-tooth measurements. Standard deviations for all intra-tooth measurements were within 0.07 mm. While none of the mean inter-tooth measurements exceeded the acceptability range, the standard deviations were larger (0.04 to 0.30 mm). Accuracy of orthodontic models for clear aligner therapies was not impacted beyond the clinically acceptable range when altering model body type and print angulation to improve efficiency of 3D printing. These findings suggest greater flexibility of the practitioner to alter print settings to address time and cost efficiency in various clinical scenarios and still maintain clinically acceptable model accuracy.

We propose a pricing formula for a defaultable zero-coupon bond with imperfect information under a regime-switching model using a structural form of credit risk modelling. While the value of the firm’s equity is observed continuously, we assume that the total value of the firm is only observed at discrete times, such as the dates of the release of the firm’s annual reports, or quarterly reports. This uncertainty about the true value of the firm results in credit spreads that do not approach zero as the debt approaches maturity, which is a problem with many structural models. The firm’s value is typically defined as its equity and debt; however, we consider the asset-to-equity ratio, an accounting ratio used to examine a firm’s financial well-being. A dependent regime-switching process models the components of this ratio, and the Markov chain represents the states of the economy. The main contribution is to study this problem when the dynamics of a firm’s assets have different parameters with regime changes.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

In the intersection of the Internet of Things (IoT) and Machine Learning (ML), the choice between high-level and low-level programming libraries presents a significant dilemma for developers, impacting not only the efficiency and effectiveness of ML models but also their environmental footprint. We have proposed a comprehensive framework to aid in this decision-making process, underpinned by a detailed comparative analysis of both types of libraries on one of the key IoT ML task; image classification. We have introduced a novel algorithm designed to calculate the green footprint of ML model training, factoring in execution time, memory utilization, power consumption, and CPU temperature, addressing the urgent need for sustainable ML practices. Through an empirical evaluation of popular libraries such as PyTorch for high-level and Libtorch for low-level development, we have assessed their performance, development efficiency, and hardware compatibility. The culmination of our research is a decision support system that synthesizes the experimental findings to guide developers toward choices that harmonize model performance with environmental sustainability.

The traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the network incorporates the Squeezed-and-Excitation (SE) attention mechanism to improve. Moreover, the YOLOv5 algorithm’s loss function has been optimized by substituting it with EIoU_loss, which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model , trained with this enhanced network, achieves a 99.4% mAP on a custom dataset, which is 6.3% higher than the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements.

CAMx-LPiG (Comprehensive Air Quality Model with Extensions – Linear Plume in Grid) is an online hybrid model, based on the Chemistry and Transport Model (CTM) CAMx, that includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions, called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model is able to introduce road traffic induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG is shown to reduce the bias compared to CAMx stand-alone simulations.

To address the problems of insufficient semantic fusion between text and questions and the lack of consideration of global semantic information encountered in machine reading comprehension models, we proposed a machine reading comprehension model called BERT_hybrid based on BERT and hybrid attention mechanism. In this model, BERT is utilized to separately map the text and questions into the feature space. Through the integration of Bi-LSTM, attention mechanism, and self-attention mechanism, the proposed model achieves comprehensive semantic fusion between text and questions. The probabilities distribution of answers is computed using Softmax. Experimental results on the public dataset DuReader demonstrate that the proposed model achieves improvements in BLEU-4 and ROUGE-L scores compared to existing models. Furthermore, to validate the effectiveness of the proposed model design, we analyze the factors influencing the model’s performance.

Segment Anything Model 2 (SAM 2) is a state-of-the-art development by Meta AI Research, designed to address the limitations of its predecessor, SAM, particularly in the realm of video segmentation. SAM 2 employs a transformer-based architecture enhanced with streaming memory, enabling real-time processing for both images and videos. This advancement is important given the exponential growth of multimedia content and the subsequent demand for efficient video analysis. Utilizing the SA-V dataset, SAM 2 excels in handling the intricate spatio-temporal dynamics inherent in video data, ensuring accurate and efficient segmentation. Key features of SAM 2 include its ability to provide real-time segmentation with minimal user interaction, maintaining robust performance even in dynamic and cluttered visual environments. This study provides a comprehensive overview of SAM 2, detailing its architecture, functionality, and diverse applications. It further explores the model's potential in improving practical implementations across various domains, emphasizing its significance in advancing real-time video analysis.

The Asiatic apple leafminer, Phyllonorycter ringoniella (Matsumura), is a significant secondary pest of apple trees in Northeast Asia. To better understand its population dynamics, a population model based on temperature-developmental relationships was constructed. This model includes three sub-models: spring emergence, immature stage transition, and adult oviposition. Field data were collected from sex-pheromone baited traps in apple orchards in Andong, Korea, dur-ing 2015 and 2016 to validate the model. Simulations under six pesticide-natural enemy scenari-os showed that the population size of each generation was best simulated when weighted mor-tality factors for pesticides and natural enemies were applied. Using daily temperature inputs, the model demonstrated that P. ringoniella typically undergoes five generations per year, with peak times predicted within a seven-day margin of field data. Sensitivity analyses revealed that population size was influenced by total fecundity and the larval stage model, but peak times remained consistent despite parameter changes. Higher temperatures led to earlier adult peak dates, especially in summer generations. This model serves as a fundamental tool for estimating population dynamics and abundance changes of P. ringoniella and can guide the timings of pesti-cide application. Further validation is necessary to test the model's efficacy in controlling pests in apple orchards.

Dicrocoeliosis is a common parasitic disease in European sheep farming. The prevalence of infec-tion by this parasite can reach almost 70% in areas where the environment is favourable to inter-mediate hosts. In France, only one drug is currently available for the treatment of dicrocoeliosis, albendazole at a dose of 15 mg/kg in a single administration. However, a control coproscopy fol-lowing a routine treatment led us to suspect that the efficacy of albendazole against Dicrocoelium dendriticum had diminished. We therefore carried out an efficacy test on 15 animals by treating them with albendazole at a dose of 15 mg/kg and performing a coproscopy on D0 and a control coproscopy 14 days later. We obtained a 38% reduction in the excretion of Dicrocoelium dendriticum eggs. This shows a re-duction in the expected efficacy of albendazole, which is normally more than 90% in other studies involving this molecule at a dosage of 15 mg/kg.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

In this work model of mirror elements equilibrium of the micromechanical components is developed, the behavior analysis of the mirror element of micromechanical mirrors in case changing of control voltages of electrostatic actuators is carried out, an expression for determining the maximum value of deflection voltage at which the snap-down effect will take the following form is obtained in case of the influence of the coefficient of the electrostatic rigidity of electrostatic actuators. The developed equilibrium model of mirror elements and the obtained results of modeling can be used at design of micromechanical mirrors with internal suspensions.

The introduction of three single nucleotide mutations into the genome of the virulent RVFV ZH548 strain allows the rescue of a fully attenuated virus in mice (ZH548-rA2). These mutations are located in the viral genes encoding the RdRp and the non-structural protein NSs. This paper shows the results obtained after subcutaneous inoculation of ZH548-rA2 in adult sheep and subsequent challenge with the parental virus (ZH548-rC1). Inoculation with ZH548-rA2 virus caused no detectable clinical or pathological effect in sheep, whereas inoculation of the parental rC1 virus caused lesions compatible with viral infection characterized by the presence of scattered hepatic necrosis. Viral infection was confirmed by immunohistochemistry, with hepatocytes within the necrotic foci appearing as the main cells immunolabelled against viral antigen. Furthermore, inoculation of sheep with rA2 virus prevented the liver damage expected after rC1 virus inoculation, suggesting a protective efficacy in sheep which correlated with the induction of both humoral and cell mediated immune responses.

This study presents a unique model for assessing the dependability of continuous parts of combined systems in open-pit mining through the application of fuzzy logic. Continuous sub-systems as part of the combined system of coal exploitation in surface mines have the basic function of ensuring safe operation, high capacity with high reliability and low costs. These subsystems are usually part of the thermal power plant's coal supply system and ensure stable fuel supply. The model integrates various independent partial indicators of dependability into an expert system specifically designed for evaluating these systems. It deconstructs the complex parameter of system dependability into distinct partial indicators: reliability, maintainability, and logistical support. These indicators are then integrated using fuzzy composition (max-min composition). Historical data from 2018 to 2023 is utilized alongside the fuzzy model to provide a retrospective analysis of system dependability, serving to validate the model's effectiveness. What sets this model apart from conventional approaches is its consideration of practical dependability indicators, thereby obviating the need for extensive long-term monitoring and data collection to portray the system's status accurately over time. This model serves as a valuable tool for assisting decision-makers in open-pit mining operations, facilitating planning, exploitation control, and the selection of maintenance strategies to ensure consistent production and cost reduction. Designed for quick assessment, the model relies on expert judgments and assessments to determine system dependability efficiently.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

Background: In response to inflammation, the absorption of nutritional iron is restricted. Multiple inflammation-related signaling pathways can lead to ferroptosis, an iron-dependent mechanism of programmed cell death. Since the pathophysiological significance of the presence and uptake of iron in chronic inflammation is still unknown, we tested the effect of a low iron diet on the clinical course of arthritis in the mouse model of collagen-induced arthritis (CIA). Methods: Six- to eight-week-old male DBA/1 mice were fed either a normal or a low iron diet starting three weeks before arthritis induction. From day four after the collagen-booster made on day 25, the development of arthritis was regularly monitored until the end of the experiment (day 34), using a standard clinical arthritis score. The complete blood count was determined as well as Fe2+, Fe3+, oxidized and reduced glutathione (GSH and GSSG) and malondialdehyde (MDA) in whole paw tissue by ELISA. Quantitative PCR was performed from whole paw tissue for glutathione peroxidase 4 and other key regulator genes of iron metabolism and ferroptosis. We used nonparametric tests to com-pare markers of iron metabolism and ferroptosis as well as nonlinear regression models for longitu-dinal data for signs of arthritis. Results: Mice fed a low iron diet showed a significantly less severe course of arthritis compared to mice fed a normal iron diet (p

During the last decade, Bangladesh has been experienced a significant climatic anomalies which lead to an increase in magnitude and frequency of various climate relate extreme events. Climate scientists substantiate that global temperature and precipitation pattern is expected to change, which may result in consequential impact on agriculture, livelihood, and ecosystem. Bangladesh is therefore likely to face substantial challenges in the coming decades. In order to adequately understand this complex, dynamic phenomena, Analyzing historical Climate change scenarios as well as projecting its future trends is a great concern for researcher. This study aims to analyzes/ historical climatic data from (1901-2020), and predict future temperature and precipitation patterns in Bangladesh using CMIP6 data. Data used in this study (Observed data is from CRU TS 4.05 and future data is from CMIP6) have been acquired from WorldClim v2.1. Various methods including correlation, regression, standard deviation, correlation matrix, percentiles, cell statistics, and IDW interpolation were performed to analyze the trends, variability and spatial patterns of temperature and rainfall. This study revealed that Over the historical study period (1901-2020) Bangladesh has been experienced a significant warming trend with an average increase in temperature 2°C and with annual decline of the total precipitation 607.26 mm along with a shift towards drier conditions despite weak correlation with hotter years. Projected climate models represents that Bangladesh minimum temperature is expected to increase from 1°C to 4.4°C as well as maximum temperatures from 1°C to 4.1°C by 2100. Moreover, projected precipitation is expected to increase by 480.38 mm, with the biggest rises during monsoon months. Regional variations in temperature and precipitation are again expected, with the Southeast (SE) likely experiencing the most significant warming and the Northeast (NE) seeing the most substantial increase in precipitation.

COVID-19 infection is extremely dangerous for persons with comorbidities such as kidney disease and diabetes. Although many people of all ages and socioeconomic backgrounds have suffered due to this pandemic, those with underlying medical conditions like kidney disease and diabetes are more vulnerable to becoming infected with COVID-19 related to weak immunity. However, the role of vaccination in the co-infection of COVID-19 with diabetics is not available in the literature. Therefore, we examine the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on its disease transmission dynamics. A mathematical modelling technique is to be employed in this work to analyse the dynamics of COVID-19 outbreaks with seven compartments, including vaccination with comorbidities such as diabetes. We also investigated analytically by displaying the solutions' existence, boundedness, positivity and sensitivity. After calculating the basic reproduction numbers, the stability analysis of model equilibrium points is also covered. If the reproduction numbers are less than unity, the disease-free equilibrium points of the model appear to be both locally and globally stable. The findings of this study are that as the vaccination rate rises, the incidence of COVID-19 and its co-infections with diabetes decreases. Effective disease treatment strategies may be based partly on the possible impact of vaccination on the co-infection of COVID-19 related to diabetic disease insights.

By repeated unloading and loading of the excavation dynamic and static loads, repeated train dynamic and static loads of multi-factors, the underpass tunnel, cross tunnel engineering influence area rock layer, the existing tunnel structure of the three constitutes a multifaceted structure, multi-factor interaction system. Existing tunnels into the prediction theory model of the new tunnel excavation process of dynamic and static loads, dynamic and static loads of existing tunnels are mostly analyzed individually, the results of synergistic analysis needs to be improved. Based on the theory of the current layer method, the paper comprehensively considers the stiffness of the existing tunnel, the stiffness of the new tunnel, the excavation method load and the existing tunnel load, coupled with Peck's empirical formula, the theoretical model of deformation prediction for excavation of a two-lane highway tunnel under the tunnel through the existing railroad is proposed, and verified and optimized by numerical calculations and monitoring data, which proves the applicability and reliability of the theoretical model in the similar strata and the project promotion.

Currently, in some domestic and foreign mines, the backfill drilling pipeline experience a rupture phenomenon even when the wear degree is low. This results in a delay in production due to the filling becoming 'sick'. This paper presents, for the first time, the damage mechanism from a mechanical perspective and re-derives the anti-extrusion strength model of the backfill drilling pipeline. We investigate the influence of law on the anti-extrusion strength of pipelines from the perspective of stratum and cement ring. We then verify the theoretical and simulation results through engineering examples. The results demonstrate that the Mises stress criterion is a suitable modification principle for the anti-extrusion strength model of the backfill drilling pipeline. The anti-extrusion strength of the pipeline is related to the elastic modulus and Poisson's ratio of the stratum, and the thickness of the cement ring. It is negatively affected by the depth of the stratum. For hard strata, a cement ring with a smaller elastic modulus is suitable, while for soft stratum, a cement ring with a larger elastic modulus is recommended. When the missing angle of the cement ring is less than 60°, the stress concentration factor increases up to 2.2. The stress unloading capacity of the cement ring ranges from 32.7% to 37.8%, and optimal performance of the cement ring is achieved when it has high strength and low rigidity. The backfill filling pipeline of a copper mine abroad was destroyed due to external extrusion force exceeding its anti-extrusion strength value. The modified pipeline anti-extrusion strength model is 18.2% higher than the casing API strength value. This finding can inform the design of the backfill filling pipeline for China's kilometer-deep wells in the future.

This study aims to evaluate the effectiveness of inactivating Salmonella enteritidis in fresh chicken breast by irradiation using a combination of short-wave UV (0, 3, 6, 9, 12 and 15 J / cm2) and a natural antimicrobial such as caffeine (0, 5, 10, 15 and 20 nM/g) as alternative proposals to conventional techniques to reduce pathogens in food. The temperatures used were from 2 to 22°C. The most suitable models were double Weibull in 60% of cases with an adjustment of R2 0.9903-0.9553 and Weibull + cola, in 46.67%, with an adjustment of R2 of 0.9998-0.9981. Noting that the most effective combination in the reduction of Salmonella was 12 J/cm2 of UV light and 15 nM/g of caffeine, with a reduction of 6 CFU/g and an inactivation rate of 0.72. The synergistic effect was observed by increasing caffeine and UV light. Furthermore, the physic-chemical characteristics of the food matrix were not affected by the combination of both technologies. Therefore, these results suggest that this combination can be used in the food industry to effectively inactivate Salmonella enteritidis, without deteriorating product quality.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

Periodontitis is a chronic inflammatory disease which results in the destruction of the tooth’ s supporting tissues and the alveolar bone resorption. The complement becomes a major link between infection and inflammatory pathology including periodontitis. Gingipians as important virulence factors of P. gingivalis have the activity of C5 convertase, could cleave C5 into fully functional C5a to activate C5aR. The above process could be blocked by the C5aR antagonist (PMX-53) to suppress local periodontal inflammation, and then achieves the purpose of treatment of periodontitis. Nanoparticles incorporated within gelatin are promising carrier system for drug delivery in recent years. This study aimed to investigate whether gelatin nanoparticles loaded with PMX-53 prevents alveolar bone resorption in miniature swines model of periodontitis. Four miniature swines were placed ligatures around the maxillary and mandibular fourth premolar and first molar on both sides for seven weeks to induce periodontitis. Then, animals were assigned randomly to four groups: minocycline-treated group, gelatin with PMX53-treated group, gelatin-treated group and a sham control group. They were treated with 1ml related drugs respectively, into gingival sulcusl for 4 times at one-week intervals. We showed that local treatments with gelatin nanoparticles loaded with PMX-53 could inhibit alveolar bone loss of periodontitis. Our study revealed that gelatin nanoparticles loaded with PMX-53 prevented alveolar bone resorption miniature swines model of periodontitis. In addition, provided proof-of-concept for local targeting of gelatin nanoparticles loaded with PMX-53 as a powerful candidate for the treatment of periodontitis.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

Concrete is more stable than filler as a false roof in a downward approach type infill quarry, but the mechanical model of a concrete false roof is not clear. As a result, there has been limited application. Therefore, it is necessary to establish the mechanical model of a concrete artificial false roof to enhance the stability of the downward approach infill quarry. This paper combines theoretical analysis, model derivation, finite element analysis and field application methods. Firstly, the basic assumptions of thin plate theory and elasticity mechanics are used as the premise to establish the differential equations of the deflection curve of the concrete thin plate false roof. Then, the three types of bending moment generation of the concrete thin plate false roof are explored. Secondly, the relationship between the location of dangerous surface and dangerous point in hard-supported weak-slab structure and soft-supported weak-slab structure is analyzed, and the type of uniform load and calculation model of concrete thin-slab false roof are improved. Finally, according to the established mechanical model of thin slab false roof, finite element analysis and field application were carried out. The results show that there are three types of deflection curve differential equations in the concrete thin slab false roof perpendicular to the strike, and three extreme points exist in the middle section of the thin slab and on both sides of the surrounding rock. The hazardous surface of the hard-supported weak-slab structure is present at the intersection of the encircling rock and the thin-slab fake ceiling, with the perilous point situated on the top of this surface. The hazardous surface of the soft-supported weak-slab structure can be found in the centre section of the thin-slab fake ceiling, with the perilous point being located at the bottom of this surface. Moreover, the damage mode for both structures is that of maximum tensile stress damage. Upon conducting a finite element analysis, the results indicate that the concrete's maximum tensile stress is significantly high.

Sentiment analysis has become an important tool for understanding public opinion across languages and domains. Recently, there has been an increase in the number of studies on sentiment analysis in low-resource languages such as Kazakh. This is important to ensure that modern text analysis technologies are accessible to all users, regardless of their language background. The aim of the study is to create a sentiment analysis model for analyzing texts in Kazakh. As part of this work, we aim to use fine-tuning techniques on our own dataset for already existing models, thus improving their accuracy and efficiency for analyzing Kazakh language texts. This paper presents a manually collected dataset "KazIntTelCom" from the city information service 2GIS, consisting of user reviews, manually annotated by the authors taking into account the polarity of sentiment (i.e. negative positive or neutral). This dataset was used to fine-tune two pre-trained multi-lingual Transformer-based sentiment analysis models taken from the HuggingFace platform. The distillBERT and XLM-RoBERTa models were used for tuning. Also, the models were tested on the dataset "KazSAnDRA". The results show that accurate tuning even on a relatively small dataset gives a significant increase in performance, which is confirmed by an increase in the accuracy index by 20%-30%. In addition, false misses and false detections are analyzed, which allows us to identify directions for further improvement of the models. The contribution of this work, in addition to the dataset, is the analysis of model errors, which will help future developers to make more accurate settings of hyperparameters of training for sentiment analysis in Kazakh. These results are important for natural language processing and their adaptation to low-resource languages, promoting more inclusive and equitable access to modern analytical tools. Thus, this study demonstrates the effectiveness of the Transformers architecture for sentiment analysis in Kazakh and opens new opportunities for further model improvement.