bluuebunny/crossref_metadata_2025 huggingface dataset:
Nutrient intake, digestibility, performance, carcass traits and sensory analysis of meat from lambs fed with co-products of Amazon oilseeds
Authors: de Castro Vinicius Costa Gomes , Budel Juliana Cristina de Castro , Rodrigues Thomaz Cyro Guimarães de Carvalho , Silva Bruna Almeida , de Lima Alyne Cristina Sodré , de Souza Shirley Motta , da Silva Jamile Andréa Rodrigues , Joele Maria Regina Sarkis Peixoto , Silva André Guimarães Maciel e , Lourenço-Junior José de Brito
Published: May 2023

Introduction:

The increase in availability and nutritional composition of oilseed co-products has made it essential to study the use of this biomass.

Methods:

The objective of this work was to investigate the effects of including oilseed cakes on intake and digestibility, performance, carcass characteristics and meat sensory in feedlot lambs. Twenty-four crossbred Dorper × Santa Inês lambs, with initial body weight of 30 ± 1.3 kg, male, castrated, aged 4–5 months, were distributed in a completely randomized experimental design with four treatments (diets) and six replications (animals), confined in individual stalls for 70 days.

Results:

The inclusion of tucuma cake (Tuc) reduced dry matter intake (p < 0.01) and diets with cupuassu cake (Cup) and palm kernel cake (Palm) reduced dry matter digestibility (p < 0.05). The Tuc diet also provided the lowest final body weight (p = 0.02); lower average daily gain (p = 0.03); lower feed efficiency (p = 0.03) and lower carcass weight (p < 0.01). However, diets did not influence carcass yield (%), fat thickness (mm) and loin eye area (cm2; p > 0.05). Meat from lambs on the control diet was rated as less fibrous and more tender (p < 0.05).

Conclusion:

The inclusion of tucuma cake does not influence digestibility, but reduces intake, performance and influences carcass characteristics and meat texture. Diets with cupuassu cake or palmiste cake reduced digestibility, however, intake, performance and carcass characteristics were similar to the control diet.
Yeast-Derived Sulfur Host for the Application of Sustainable Li–S Battery Cathode
Authors: He Zhanhui , Dou Xinyi , Liu Weilin , Zhang Luxian , Lv Laixi , Liu Jiehua , Meng Fancheng
Published: May 2023

A porous carbon structure (PCS) is considered as an ideal electrode material for lithium–sulfur (Li–S) batteries, owing to its flexible texture, large surface area, and high electrical conductivity. In this work, we use food-grade yeast as the carbon precursor, which is proliferated in glucose solution, carbonized with a NaCl template to yield a sheet-like carbon structure, and reactivated at different temperatures with KOH. The porous carbon material is then applied as the sulfur host of the Li–S battery cathode, and the electrode is systematically characterized by means of SEM, TEM, XRD, Raman, XPS, thermogravimetric (TG), nitrogen gas adsorption–desorption, and electrochemical measurements. The results show that the PCS obtained at 800 °C has an ultra-high surface area of 2410 m2 g−1 and exhibits excellent performance for a Li–S battery cathode. The initial discharge capacity of the PCS-800/S cathode is 1502 mAh g−1, which accounts for 90% of the theoretical capacity value.
The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis
Authors: Lagopati Nefeli , Valamvanos Theodoros-Filippos , Proutsou Vaia , Karachalios Konstantinos , Pippa Natassa , Gatou Maria-Anna , Vagena Ioanna-Aglaia , Cela Smaragda , Pavlatou Evangelia A. , Gazouli Maria , Efstathopoulos Efstathios
Published: May 2023

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.
Infant Mortality Trends and Determinants in Kazakhstan
Authors: Yerdessov Nurbek , Zhamantayev Olzhas , Bolatova Zhanerke , Nukeshtayeva Karina , Kayupova Gaukhar , Turmukhambetova Anar
Published: May 2023

Infant mortality rate (IMR) is a crucial indicator of healthcare performance and a reflection of a country’s socioeconomic development. We analyzed the trends of IMR in Central Asia (CA) countries and its determinants in Kazakhstan, which is a middle-income country. Linear regression was used for IMR trend analysis in CA countries from 2000 to 2020 and for exploring associations between IMR and socioeconomic factors, health service-related factors, and population health indicators-related factors. A gamma generalized linear model was applied to define associations with various determinants. Our analysis revealed that IMR has decreased in all CA countries, with Kazakhstan having the lowest rate in 2000 and 2020. Our results suggest that socioeconomic indicators, such as total unemployment, Gini index, current health expenditure, gross domestic product (GDP), proportion of people living in poverty, and births by 15–19-year-old mothers, were associated with increased infant mortality rates. Improving socioeconomic conditions, investing in healthcare systems, reducing poverty and income inequality, and improving access to education, are all potential issues for further development. Addressing these factors may be critical for improving maternal and child health outcomes in the region.
A Deep Learning Approach to Optimize Recombinant Protein Production in Escherichia coli Fermentations
Authors: Bonanni Domenico , Litrico Mattia , Ahmed Waqar , Morerio Pietro , Cazzorla Tiziano , Spaccapaniccia Elisa , Cattani Franca , Allegretti Marcello , Beccari Andrea Rosario , Del Bue Alessio , Martin Franck
Published: May 2023

Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned throughout the process; this is usually performed by a biotech production expert relying on empirical rules and personal experience. Although developing a mathematical model to analytically describe how yields depend on CPP values is too challenging because the process involves living organisms, we demonstrate the benefits that can be reaped by using a black-box machine learning (ML) approach based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks to predict real time OD600nm values from fermentation CPP time series. We tested both networks on an E. coli fermentation process (upstream) optimized to obtain inclusion bodies whose purification (downstream) in a later stage will yield a targeted neurotrophin recombinant protein. We achieved root mean squared error (RMSE) and relative error on final yield (REFY) performances which demonstrate that RNN and LSTM are indeed promising approaches for real-time, in-line process yield estimation, paving the way for machine learning-based fermentation process control algorithms.
Development of a Microbial-Assisted Process for Enhanced Astaxanthin Recovery from Crab Exoskeleton Waste
Authors: Abd El-Ghany Mohamed N. , Hamdi Salwa A. , Elbaz Reham M. , Aloufi Abeer S. , El Sayed Rana R. , Ghonaim Ghadeer M. , Farahat Mohamed G.
Published: May 2023

Astaxanthin is a xanthophyll carotenoid possessing impressive nutraceutical, antioxidant, and bioactive merits. Traditionally, astaxanthin is extracted from crustacean wastes via solvent extraction methods. However, the rigid structure of shells that comprise complex proteins and chitin challenges the extraction process. This investigation addressed an efficient microbial-assisted method to facilitate astaxanthin recovery from crab exoskeleton waste utilizing chitinolytic and proteolytic microorganisms. Herein, we evaluated the effect of pretreatment of the exoskeleton waste with a newly isolated probiotic strain, Bacillus amyloliquefaciens CPFD8, showing remarkable protease and chitinase activity and a proteolytic Saccharomyces cerevisiae 006-001 before solvent extraction, using acetone/hexane, on astaxanthin recovery. Furthermore, the antioxidant and anti-inflammatory activities of the recovered astaxanthin were inspected. Results revealed that both strains boosted the astaxanthin yield from the crab (Callinectes sapidus) exoskeleton compared with solvent extraction using acetone/hexane. Under optimum conditions, astaxanthin yield was 217 and 91 µg/g crab exoskeleton in samples treated with B. amyloliquefaciens CPFD8 and S. cerevisiae 006-001, respectively. Interestingly, pretreatment of crab exoskeleton waste with B. amyloliquefaciens CPFD8 yielded more than 6-fold astaxanthin compared with the solvent extraction method that yielded just 35 µg/g. This increase could be attributed to the proteolytic activity of B. amyloliquefaciens CPFD8 that rendered deproteinized shell chitin accessible to chitinase, facilitating the penetration of solvents and the recovery of astaxanthin. The recovered astaxanthin exhibited excellent antioxidant activity in scavenging DPPH or ABTS free radicals with IC50 values of 50.93 and 17.56 µg/mL, respectively. In addition, the recovered astaxanthin showed a remarkable anti-inflammatory impact on LPS-induced murine macrophage RAW264.7 cells and significantly inhibited the production of nitric oxide, TNF-α, and IL-6 compared with the untreated control. These findings suggest the potential use of the developed microbial-assisted method utilizing chitinolytic and proteolytic B. amyloliquefaciens CPFD8 to maximize the recovery of bioactive astaxanthin from crab (C. sapidus) exoskeleton waste.
Lytic Polysaccharide Monooxygenases from Serpula lacrymans as Enzyme Cocktail Additive for Efficient Lignocellulose Degradation
Authors: Li Fei , Liu Yang , Zhao Honglu , Liu Xuan , Yu Hongbo
Published: May 2023

Lytic polysaccharide monooxygenase (LPMO) could oxidize and cleavage the glycosidic bonds of polysaccharides in lignocellulose, thereby promoting the hydrolysis of polysaccharide substrates by glycoside hydrolases and significantly improving the saccharification efficiency of lignocellulose. Brown-rot fungi are typical degraders of lignocellulose and contain multiple LPMO genes of the AA14 family and AA9 family, however, the AA14 LPMO from brown-rot fungi was rarely reported. Herein, the transcriptomic analysis of Serpula lacrymans incubated in the presence of pine exhibited that an AA14 LPMO (SlLPMO14A) was significantly upregulated and there were redox interactions between LPMOs and other enzymes (AA3, AA6, and hemicellulose degrading enzyme), indicating that SlLPMO14A may be involved in the degradation of polysaccharides. Enzymatic profiling of SlLPMO14A showed the optimal pH of 8.0 and temperature of 50 °C and it had higher reaction activity in the presence of 40% glycerol and acetonitrile. SlLPMO14A could significantly improve the saccharification of pine and xylan-coated cellulose substrate to release glucose and xylose by cellulase and xylanase by disturbing the surface structure of lignocellulose based on environmental scanning electron microscope and atomic force microscopy analysis.
Protective Effects of Naringenin and Apigenin in Ameliorating Skin Damage via Mediating the Nrf2 and NF-κB Pathways in Mice
Authors: Li Jie , Mao Bingyong , Tang Xin , Zhang Qiuxiang , Zhao Jianxin , Zhang Hao , Cui Shumao
Published: May 2023

Naringenin and apigenin are common flavonoids derived from edible plants with the potential to alleviate inflammation and improve skin antioxidation. This study aimed to evaluate the effects of naringenin and apigenin on oleic acid-induced skin damage in mice and compare their underlying mechanisms of action. Triglycerides and non-esterified fatty acids were significantly decreased by naringenin and apigenin, while apigenin intervention resulted in a better recovery of skin lesions. Naringenin and apigenin improved the antioxidative abilities of the skin by increasing catalase and total antioxidant capacity levels and decreasing malondialdehyde and lipid peroxide levels. The release of skin proinflammatory cytokines, such as interleukin (IL)-6, IL-1β, and tumor necrosis factor α, was inhibited after naringenin and apigenin pretreatments, but naringenin only promoted the excretion of IL-10. Additionally, naringenin and apigenin regulated antioxidant defense and inflammatory response by activating nuclear factor erythroid-2 related factor 2-dependent mechanisms and suppressing the expression of nuclear factor-kappa B. In summary, naringenin and apigenin are prospective ingredients that contribute to the amelioration of skin damage by activating anti-inflammatory and antioxidative responses.
Diagnostic Errors in Japanese Community Hospitals and Related Factors: A Retrospective Cohort Study
Authors: Fujimori Taichi , Ohta Ryuichi , Sano Chiaki
Published: May 2023

Diagnostic error has recently become a crucial clinical problem and an area of intense research. However, the reality of diagnostic errors in regional hospitals remains unknown. This study aimed to clarify the reality of diagnostic errors in regional hospitals in Japan. A 10-month retrospective cohort study was conducted from January to October 2021 at the emergency room of Oda Municipal Hospital in central Shimane Prefecture, Japan. Participants were divided into groups with or without diagnostic errors, and independent variables of patient, physician, and environmental factors were analyzed using Fisher’s exact test, univariate (Student′s t-test and Welch’s t-test), and logistic regression analyses. Diagnostic errors accounted for 13.1% of all eligible cases. Remarkably, the proportion of patients treated without oxygen support and the proportion of male patients were significantly higher in the group with diagnostic errors. Sex bias was present. Additionally, cognitive bias, a major factor in diagnostic errors, may have occurred in patients who did not require oxygen support. Numerous factors contribute to diagnostic errors; however, it is important to understand the trends in the setting of each healthcare facility and plan and implement individualized countermeasures.
Phytophthora in Horticultural Nursery Green Waste—A Risk to Plant Health
Authors: Schiffer-Forsyth Kadiatou , Frederickson Matika Debra Frederickson , Hedley Pete E. , Cock Peter J. A. , Green Sarah
Published: May 2023

Phytophthora is a genus of destructive plant pathogens. Certain species are damaging to native ecosystems, forestry, and the horticultural sector, and there is evidence of their dissemination in plant imports. Horticultural nurseries are central nodes of the plant trade and previous studies have found a high diversity of Phytophthora associated with plant nursery stock. It was subsequently hypothesized that green waste disposal sites in nurseries could harbour diverse Phytophthora communities and act as a pathogen reservoir and conduit, facilitating further Phytophthora infection of nursery stock and its spread into the wider environment. This project identified Phytophthora species associated with green waste at three Scottish nurseries by sampling material from waste piles, water run-off from piles, and roots from discarded plants. Species were identified using a baiting method and sequencing of environmental DNA. Plant nursery green waste was shown to harbour diverse and varied Phytophthora species assemblages, with differences among nurseries reflecting biosecurity management practices. Eighteen Phytophthora species were detected in the samples, including the highly destructive pathogens P. ramorum and P. austrocedri. Results suggest that the improved management of waste, for example through effective on-site composting, is essential to reduce the risk of Phytophthora pathogens spreading from nurseries into the wider environment.
The MAP Kinase PvMK1 Regulates Hyphal Development, Autophagy, and Pathogenesis in the Bayberry Twig Blight Fungus Pestalotiopsis versicolor
Authors: Yan Xiujuan , Zhang Shuwen , Yu Zheping , Sun Li , Sohail Muhammad Aamir , Ye Zihong , Zhou Lei , Qi Xingjiang
Published: May 2023

Bayberry twig blight caused by the ascomycete fungus Pestalotiopsis versicolor is a devastating disease threatening worldwide bayberry production. However, the molecular basis underlying the pathogenesis of P. versicolor is largely unknown. Here, we identified and functionally characterized the MAP kinase PvMk1 in P. versicolor through genetic and cellular biochemical approaches. Our analysis reveals a central role of PvMk1 in regulating P. versicolor virulence on bayberry. We demonstrate that PvMk1 is involved in hyphal development, conidiation, melanin biosynthesis, and cell wall stress responses. Notably, PvMk1 regulates P. versicolor autophagy and is essential for hyphal growth under nitrogen-depleting conditions. These findings suggest the multifaceted role of PvMk1 in regulating P. versicolor development and virulence. More remarkably, this evidence of virulence-involved cellular processes regulated by PvMk1 has paved a fundamental way for further understanding the impact of P. versicolor pathogenesis on bayberry.
Spectral Analysis of the Infinite-Dimensional Sonic Drillstring Dynamics
Authors: Ammari Kaïs , Beji Lotfi
Published: May 2023

By deploying sonic drilling for soil structure fracturing in the presence of consolidated/ unconsolidated formations, this technique greatly reduces the friction on the drillstring and bit by using energetic resonance, a bit-bouncing high-frequency axial vibration. While resonance must be avoided, to our knowledge, drilling is the only application area where resonance is necessary to break up the rocks. The problem is that the machine’s tool can encounter several different geological layers with many varieties of density. Hence, keeping the resonance of the tool plays an important role in drill processes, especially in tunnel or infrastructure shoring. In this paper, we analyze the sonic drillstring dynamics as an infinite-dimensional system from another viewpoint using the frequency domain approach. From the operator theory in defining the adequate function spaces, we show the system well-posedness. The hydraulic produced axial force that should preserve the resonant drillstring mode is defined from the spectrum study of the constructed linear operator guided by the ratio control from the top to tip boundary magnitudes.
Prescribed Performance Fault-Tolerant Tracking Control of Uncertain Robot Manipulators with Integral Sliding Mode
Authors: Zhang Liyin , Hou Yinlong , Liu Hui , Tang Dafeng , Li Long
Published: May 2023

This paper develops a fault-tolerant tracking control (FTC) for robot manipulators with prescribed performance subject to uncertainties and partial loss in effectiveness of actuators (UPEAs). First, an integral sliding manifold without reaching phase is constructed for guaranteeing the prescribed performance in both the transient and steady states. With this integral sliding manifold, an FTC is proposed for uncertain robot manipulators to obtain advanced tracking performance with prescribed performance constraints under the effects of UPEAs. The stability analysis is guaranteed by the Lyapunov theory and a homogeneous technique. The primary contributions of our design are as follows: (i) the proposed approach removes the reaching phase completely for the sake of the prescribed performance and better chattering-restraining capability; (ii) the nominal control part is also removed in the formulation of the conventional integral sliding mode, and then the proposed approach eliminates the algebraic loop problem; (iii) a simple control structure is accomplished to eliminate the effects of time delay and computational burden. A simulation, along with experiments, is completed for verifying the effectiveness of the proposed approach.
Consistency of Decision in Finite and Numerable Multinomial Models
Authors: Akoto Isaac , Mexia João T.
Published: May 2023

The multinomial distribution is often used in modeling categorical data because it describes the probability of a random observation being assigned to one of several mutually exclusive categories. Given a finite or numerable multinomial model M|n,p whose decision is indexed by a parameter θ and having a cost cθ,p depending on θ and on p, we show that, under general conditions, the probability of taking the least cost decision tends to 1 when n tends to ∞, i.e., we showed that the cost decision is consistent, representing a Statistical Decision Theory approach to the concept of consistency, which is not much considered in the literature. Thus, under these conditions, we have consistency in the decision making. The key result is that the estimator p˜n with components p˜n,i=nin,i=1,⋯, where ni is the number of times we obtain the ith result when we have a sample of size n, is a consistent estimator of p. This result holds both for finite and numerable models. By this result, we were able to incorporate a more general form for consistency for the cost function of a multinomial model.
Apple Root Microbiome as Indicator of Plant Adaptation to Apple Replant Diseased Soils
Authors: Ajeethan Nivethika , Ali Shawkat , Fuller Keith D. , Abbey Lord , Yurgel Svetlana N.
Published: May 2023

The tree fruit industry in Nova Scotia, Canada, is dominated by the apple (Malus domestica) sector. However, the sector is faced with numerous challenges, including apple replant disease (ARD), which is a well-known problem in areas with intensive apple cultivation. A study was performed using 16S rRNA/18S rRNA and 16S rRNA/ITS2 amplicon sequencing to assess soil- and root-associated microbiomes, respectively, from mature apple orchards and soil microbiomes alone from uncultivated soil. The results indicated significant (p < 0.05) differences in soil microbial community structure and composition between uncultivated soil and cultivated apple orchard soil. We identified an increase in the number of potential pathogens in the orchard soil compared to uncultivated soil. At the same time, we detected a significant (p < 0.05) increase in relative abundances of several potential plant-growth-promoting or biocontrol microorganisms and non-fungal eukaryotes capable of promoting the proliferation of bacterial biocontrol agents in orchard soils. Additionally, the apple roots accumulated several potential PGP bacteria from Proteobacteria and Actinobacteria phyla, while the relative abundances of fungal taxa with the potential to contribute to ARD, such as Nectriaceae and plant pathogenic Fusarium spp., were decreased in the apple root microbiome compared to the soil microbiome. The results suggest that the health of a mature apple tree can be ascribed to a complex interaction between potential pathogenic and plant growth-promoting microorganisms in the soil and on apple roots.
Antimicrobial Efficiency of Pistacia lentiscus L. Derivates against Oral Biofilm-Associated Diseases—A Narrative Review
Authors: Milia Egle Patrizia , Sardellitti Luigi , Eick Sigrun
Published: May 2023

Pistacia lentiscus L. (PlL) has been used for centuries in traditional medicine. The richness in antimicrobial biomolecules of Pll derivates can represent an alternative to chemically formulated agents used against oral infections. This review summarizes the knowledge on the antimicrobial activity of PlL essential oil (EO), extracts, and mastic resin against microorganisms being of relevance in oral biofilm-associated diseases. Results demonstrated that the potential of PlL polyphenol extracts has led to increasing scientific interest. In fact, the extracts are a significantly more effective agent than the other PlL derivates. The positive findings regarding the inhibition of periodontal pathogens and C. albicans, together with the antioxidant activity and the reduction of the inflammatory responses, suggest the use of the extracts in the prevention and/or reversal of intraoral dysbiosis. Toothpaste, mouthwashes, and local delivery devices could be effective in the clinical management of these oral diseases.
Safety and Potential Role of Lactobacillus rhamnosus GG Administration as Monotherapy in Ulcerative Colitis Patients with Mild–Moderate Clinical Activity
Authors: Pagnini Cristiano , Di Paolo Maria Carla , Urgesi Riccardo , Pallotta Lorella , Fanello Gianfranco , Graziani Maria Giovanna , Delle Fave Gianfranco
Published: May 2023

Probiotics are microorganisms that confer benefits to the host, and, for this reason, they have been proposed in several pathologic states. Specifically, probiotic bacteria have been investigated as a therapeutic option in ulcerative colitis (UC) patients, but clinical results are dishomogeneous. In particular, many probiotic species with different therapeutic schemes have been proposed, but no study has investigated probiotics in monotherapy in adequate trials for the induction of remission. Lactobacillus rhamnosus GG (LGG) is the more intensively studied probiotic and it has ideal characteristics for utilization in UC patients. The aim of the present study is to investigate the clinical efficacy and safety of LGG administration in an open trial, delivered in monotherapy at two different doses, in UC patients with mild–moderate disease. The UC patients with mild–moderate disease activity (Partial Mayo score ≥ 2) despite treatment with oral mesalamine were included. The patients stopped oral mesalamine and were followed up for one month, then were randomized to receive LGG supplement at dose of 1.2 or 2.4 × 1010 CFU/day for one month. At the end of the study, the clinical activity was evaluated and compared to that at the study entrance (efficacy). Adverse events were recorded (safety). The primary end-point was clinical improvement (reduction in the Partial Mayo score) and no serious adverse events, while the secondary end-points were the evaluation of different efficacies and safeties between the two doses of LGG. The patients with disease flares dropped out of the study and went back to standard therapy. The efficacy data were analyzed in an intention-to-treat (ITT) and per-protocol (PP) analysis. Out of the 76 patients included in the study, 75 started the probiotic therapy (n = 38 and 37 per group). In the ITT analysis, 32/76 (42%) responded to treatment, 21/76 (28%) remained stable, and 23/76 (30%) had a worsening of their clinical condition; 55 (72%) completed the treatment and were analyzed in a PP analysis: 32/55 (58%) had a clinical response, 21 (38%) remained stable, and 2 (4%) had a light worsening of their clinical condition (p < 0.0001). Overall, 37% of the patients had a disease remission. No severe adverse event was recorded, and only one patient stopped therapy due to obstinate constipation. No difference in the clinical efficacy and safety has been recorded between groups treated with different doses of LGG. The present prospective clinical trial demonstrates, for the first time, that LGG in monotherapy is safe and effective for the induction of remission in UC patients with mild–moderate disease activity (ClinicalTrials.gov identifier: NCT04102852).
Effect of Acidic Strength of Surface Ligands on the Carrier Relaxation Dynamics of Hybrid Perovskite Nanocrystals
Authors: Narra Sudhakar , Liao Po-Sen , Bhosale Sumit S. , Diau Eric Wei-Guang
Published: May 2023

Perovskite nanocrystals (PeNCs) are known for their use in numerous optoelectronic applications. Surface ligands are critical for passivating surface defects to enhance the charge transport and photoluminescence quantum yields of the PeNCs. Herein, we investigated the dual functional abilities of bulky cyclic organic ammonium cations as surface-passivating agents and charge scavengers to overcome the lability and insulating nature of conventional long-chain type oleyl amine and oleic acid ligands. Here, red-emitting hybrid PeNCs of the composition CsxFA(1−x)PbBryI(3−y) are chosen as the standard (Std) sample, where cyclohexylammonium (CHA), phenylethylammonium (PEA) and (trifuluoromethyl)benzylamonium (TFB) cations were chosen as the bifunctional surface-passivating ligands. Photoluminescence decay dynamics showed that the chosen cyclic ligands could successfully eliminate the shallow defect-mediated decay process. Further, femtosecond transient absorption spectral (TAS) studies uncovered the rapidly decaying non-radiative pathways; i.e., charge extraction (trapping) by the surface ligands. The charge extraction rates of the bulky cyclic organic ammonium cations were shown to depend on their acid dissociation constant (pKa) values and actinic excitation energies. Excitation wavelength-dependent TAS studies indicate that the exciton trapping rate is slower than the carrier trapping rate of these surface ligands.
Tree-based Multicast Routing and Channel Assignment for Enhanced Throughout in Emerging Cognitive Radio Networks
Authors: Bany Salameh Haythem , Ali Mustafa
Published: May 2023

In future multi-hop wireless networks like 5G and B5G, efficient large-scale video sharing and data dissemination are expected to rely heavily on multicast routing and Cognitive Radio (CR) technology. While multicast routing is efficient when the network always has access to the spectrum, the dynamic nature of Primary User (PU) activities, heterogeneous spectrum across the CR Network (CRN), and PU access priority make it challenging to implement efficient multicast routing protocols in CRNs. This paper proposes a hierarchical multicast routing mechanism for multi-hop CRNs that exploits the Shortest Path Tree (SPT) and Minimum Spanning Tree (MST) concepts. The proposed multicast routing mechanism consists of tree construction and channel assignment algorithms. The tree-construction algorithm models the network topology as a multicast tree rooted at the CR source and spanning all the CR nodes. Based on the constructed tree, the channel assignment algorithm employs the Probability Of Success (POS) metric to assign channels to the various layers defined by the constructed SPT or MST, ensuring that the most reliable channel is used for the multi-hop multicast transmissions. Simulation experiments are conducted to evaluate the mechanism’s effectiveness, revealing significant improvements in throughput and Packet Delivery Rate (PDR) compared to state-of-the-art protocols under different network conditions. The simulations also show that the SPT-based mechanism outperforms the MST-based mechanism in terms of throughput but has a higher tree construction complexity.
MIRNA Adaptive 3D Game to assist children's Distance Learning Difficulties Design and Teachers Intention to use
Authors: Mustafa Dheya , Mustafa Intisar , Zriqat Samah , AlThebyan Qutaibah
Published: May 2023

The global transition from traditional classroom instruction to online learning has been hastened through COVID-19. Notwithstanding its benefits, students were unable to quickly adjust to the difficulties of change. This research offers MIRNA, an assistive 3D instructional tool for slow learners in primary school, mostly for Arabic, English, and Math topics, to address academic issues that develop with online education. The proposed tool can be fully integrated with remedial programs to assist students who struggled to adjust to distant learning during the pandemic, slow learners, or even those who are unable to attend school. This application categorizes students based on academic performance rather than age and automatically adjusts to their limitations. Additionally, MIRNA offers a simple interface that allows teachers to personalize it with their own content and game scenarios. We carried out an empirical investigation to assess teachers' intentions to use MIRNA as an online learning platform in the learning process. The findings of the study show that teachers’ attitude towards the game was positive, and they intended to use the game in the learning process in the future
Utilizing Artificial Bee Colony Algorithm as Feature Selection Method in Arabic Text Classification
Authors: Hijazi Musab , Zeki Akram , Ismail Amelia
Published: May 2023

A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance
On Rapid Transitioning to Online Learning Under COVID-19: Challenges and Solutions at Al al-Bayt University
Authors: Bani-Mohammad Saad , Ababneh Ismail
Published: May 2023

The aim of this research work is to study the issues and challenges that Al al-Bayt University faculty members faced in the transition from face-to-face learning to online learning during the COVID-19 pandemic, and to highlight successful online learning strategies adopted. These issues and challenges are identified using question and solution-based analysis covering several issues that include the procedures and mechanisms adopted for the rapid transition from face-to-face learning to online learning, the online learning environment used, the impact of the transition to online learning on faculty members, courses’ content and students, and the challenges of online learning and the impacts it had on teaching and scientific research. The successful strategies adopted provide many practical methods for faculty members and leaders to follow for future online learning. In addition, the results of this work are expected to provide faculty members with a clear and insightful view on how to successfully integrate online learning and traditional learning into a blended learning approach.
New Model of Feature Selection based Chaotic Firefly Algorithm for Arabic Text Categorization
Authors: Hadni Meryeme , Hjiaj Hassane
Published: May 2023

The dimensionality reduction is a type of problem that appear in the most classification processes. It contains a large number of features; these features may contain unreliable data which may lead the categorization process to unwanted results. Feature selection can be used for reducing dimensionality of datasets and find interesting relevant information. In Arabic language, the number of works applies a meta-heuristic algorithm for feature selection is still limited due to the complex nature of Arabic inflectional and derivational rules as well as its intricate grammatical rules and its rich morphology. This paper proposes a new model for Arabic Feature Selection that combines the chaotic method in the Firefly Algorithm (CFA). The Chaotic Algorithm replaces the attractiveness coefficient in firefly algorithm by the outputs of chaotic application. The enhancement of the new approach involves introducing a novel search strategy which is able to obtain a good ratio between exploitation and exploration abilities of the algorithm. In terms In terms of performance, the experiments of the proposed method are tested using classifiers, namely Naive Bayes (NB), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) and three evaluation measures, including precision, recall, and F-measure. The experimental findings show that the combining of CFA and SVM classifiers outperforms other combinations in terms of precision.
Temporal residual network based multi-head attention model for Arabic handwriting recognition
Authors: Zouari Ramzi , Othmen Dalila , Boubaker Houcine , Kherallah Monji
Published: May 2023

In this study, we developed a new system for online Arabic handwriting recognition based on temporal residual networks with multi-head attention model. The main idea behind the application of attention mechanism was to focus on the most relevant parts of the data by a weighted combination of all input sequences. Moreover, we applied beta elliptic approach to represent both kinematic and geometric aspects of the handwriting motion. This approach consists of representing the neuromuscular impulses involving during the writing act. In the dynamic profile, the curvilinear velocity can be fitted by an algebraic sum of overlapped beta functions, while the original trajectory can be rebuilt by elliptic arcs delimited between successive extremum velocity instants. The experiments were conducted on LMCA database containing the trajectory coordinates of 23141 Arabic handwriting letters, and showed very promising results that achieved the recognition rate of 97,12%
A proposed model to integrate drone technology in accounting for long term contracts: a cash flow management perspictive
Authors: Qasim Amer , El Refae Ghaleb , Eletter Shorouq
Published: May 2023

The accounting profession is undergoing a significant transformation due to the impact of artificial intelligence, robotic process automation, and big data. One of the latest areas of research in this field is exploring the potential use of drones for accounting and auditing tasks. However, this study takes a different approach by proposing a theoretical framework that utilizes drones for cash flow management in long-term construction projects. The proposed framework suggests that drones can be utilized as a supplementary tool to remotely conduct project site inspections and monitor construction progress. The framework addresses the percentage of completion method for recognizing revenues from long-term contracts and highlights the benefits of using drones, such as improved data quality, cost and time efficiency, increased safety during site inspections, and overall effectiveness
Deep learning based MobileNet and multi-head attention model for facial expression recognition
Authors: Nouisser Aicha , Zouari Ramzi , Kherallah Monji
Published: May 2023

Facial expressions is an intuitive reflection of a person’s emotional state, and it is one of the most important forms of interpersonal communication. Due to the complexity and variability of human facial expressions, traditional methods based on handcrafted feature extraction have shown insufficient performances. For this purpose, we proposed a new system of facial expression recognition based on MobileNet model with the addition of skip connections to prevent the degradation in performance in deeper architectures. Moreover, multi-head attention mechanism was applied to concentrate the processing on the most relevant parts of the image. The experiments were conducted on FER2013 database, which is imbalanced and includes ambiguities in some images containing synthetic faces. We applied a pre-processing step of face detection to eliminate wrong images, and we implemented both SMOTE and Near-Miss algorithms to get a balanced dataset and prevent the model to being biased. The experimental results showed the effectiveness of the proposed framework which achieved the recognition rate of 96.02% when applying multi-head attention mechanism
Navigating the Complex Landscape of IoT Forensics: Challenges and Emerging Solutions
Authors: Musa Nura , Mirza Nada , Ali Adnan
Published: May 2023

With the increasing proliferation of the Internet of Things (IoT) devices, digital forensics professionals face numerous challenges whilst investigating cybercrimes. The vast number of IoT devices, the heterogeneity of their formats, and the diversity of the data they generate make the identification and collection of relevant evidence a daunting task. In this research paper, we explore the complex landscape of IoT forensics, highlighting the major challenges and emerging solutions. We start by listing the available digital forensics models and frameworks. We then delve into evidence management during different IoT forensic investigation stages such as Identification, Acquisition, Preservation and Protection, Analysis and Correlation, Attack and Deficit Attribution and lastly Presentation. Furthermore, we highlight the current challenges, open issues and major security and privacy concerns related to IoT forensics. Finally, we review the state-of-the-art in IoT forensics, exploring the possible solutions proposed in recent literature. Overall, this paper provides a comprehensive overview of the current IoT forensics ecosystem, the challenges, and proposes the latest possible solutions, which is critical for ensuring the security and integrity of IoT-enabled critical infrastructures and can serves as a valuable resource for researchers and practitioners in the field
Machine Learning Models for Statistical Analysis
Authors: Grebovic Marko , Filipovic Luka , Katnic Ivana , Vukotic Milica , Popovic Tomo
Published: May 2023

Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results
A Comparative Study of Different Pre-Trained Deep Learning Models and Custom CNN for Pancreatic Tumor Detection
Authors: Zavalsız Muhammed , Alhajj Sleiman , Sailunaz Kashfia , Ozyer Tansel , Alhajj Reda
Published: May 2023

Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging is briefly making the internal structure of the human body visible with various methods. With deep learning models, cancer detection, which is one of the most lethal diseases in the world, can be made possible with high accuracy. Pancreatic Tumor detection, which is one of the cancer types with the highest fatality rate, is one of the main targets of this project, together with the data set of Computed Tomography images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. In the field of image classification, which is a computer vision task, the transfer learning technique, which has gained popularity in recent years, has been applied quite frequently. Using pre-trained models were previously trained on a fairly large dataset and using them on medical images is common nowadays. The main objective of this article is to use this method, which is very popular in the medical imaging field, in the detection of PDAC, one of the deadliest types of pancreatic cancer, and to investigate how it per- forms compared to the custom model created and trained from scratch. The pre-trained models which are used in this project are VGG-16 and ResNet, which are popular Convolutional Neutral Network models, for Pancreatic Tumor Detection task. With the use of these models, early diagnosis of pancreatic cancer, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started, may be possible. Due to the abundance of medical images reviewed by medical professionals, which is one of the main causes for heavy workload of healthcare systems, this application can assist radiologists and other specialists in Pancreatic Tumor detection by providing faster and more accurate method