bluuebunny/crossref_metadata_2025 huggingface dataset:
Verifiable Privacy Preservation Scheme for Outsourcing Medical Image to Cloud Through ROI Based Crypto-Watermarking
Authors: Zhou Chuan , Zhou Yi , An Xinghan , Liu Yan , Wang Min , Liu XiangZhi
Published: September 2023

A novel verifiable privacy preservation scheme for outsourcing medical image to cloud through ROI based crypto-watermarking is proposed in the paper. In the proposed scheme, data owner firstly carries out substitution of S-box for the region of interest (ROI) of medical image, and then separates the image into 4 most significant bits (MSBs) plane and 4 least significant bits (LSBs) plane images. Secondly, the hash value of ROI is embedded into the two separated bit plane images using reversible watermarking algorithm. Lastly, some selected hash values are transformed into the initial parameters of chaotic maps, and the two sharing secrets, which are produced through chaos based encryption algorithm, are finally outsourced to two different cloud servers. For authorized users, they can get shares from different cloud servers, and then can losslessly recover the original medical image through a series of decryption operations and extraction of watermarking. Furthermore, the users can verify whether the original image is completely reconstructed or not, they even can locate the tampered parts inside ROI if anyone of the sharing secrets is damaged. Some experiments analyses and comparisons are given to show the security and effectiveness of proposed scheme.
Annual Solar Geoengineering: Mitigating Yearly Global Warming Increases
Authors: Feinberg Alec
Published: September 2023

Solar geoengineering (SG) solutions have many advantages compared to the difficulty of carbon removal (CR): SG produces fast results, is shown here to have much higher efficiency than CR, is not related to fossil fuel legislation, and is something we all can participate in brightening the Earth with cool roofs, and roads. SG requirements detailed previously to mitigate global warming (GW) have been concerning primarily because of overwhelming goals and climate circulation issues. In this paper, the advantages of annual solar geoengineering (ASG) to mitigate yearly global warming increases are explored and detailed as it provides higher feasibility in geoengineering applications. ASG area modification requirements found here are generally 50 to possibly higher than 150 times less compared to the challenge of full SG GW mitigation reducing circulation concerns. Results indicate that there are mixed technologies that can help meet annual mitigation Earth brightening goals. As well, results show much higher feasibility for L1 space shading compared to prior literature estimates for full GW mitigation. However, stratosphere injections appear challenging in the annual approach. Because ASG earth brightening area requirements are much smaller than those required for full mitigation, we have concerns that worldwide negative SG would interfere with making positive advances for several reasons. Negative SG currently dominates yearly practices with the application of dark asphalt roads and roofs. This issue is discussed.
The Hidden Loopholes Undermining Photonic Quantum Nonlocality: Quantum Rayleigh Scattering and Time-Dependent Multi-Photon Pure States of Independent Photons
Authors: Vatarescu Andre
Published: September 2023

The locality condition of probabilities underpinning the derivation of Bell inequalities can be violated classically. The wave function collapse results in the factorization of quantum probabilities. It is possible to differentiate, locally, between ensemble probabilities of single detections with and without wave function collapse for the alleged quantum nonlocality. The theoretical concept of photonic quantum nonlocality cannot be implemented physically because of the quantum Rayleigh scattering of single photons. A distinction needs to be made between the correlation of individual, single measurements of pure states and the correlation of the ensemble states of the mixed states. The correlation operator of Pauli vector operators delivers the same probabilities of correlated detections of photons for both independent and multi-photon states as for ‘entangled’ states of photons. As single-photon sources are not needed, the design and implementation of quantum computing operations and other devices will be significantly streamlined.
Design and Implementation of Remote Controlled High Temperature Short Time (HTST) Pasteurization System for Healthy Milk Production
Authors: muthu Mari , j Jayakumar
Published: September 2023

The dairy industry relies heavily on milk pasteurization to ensure the safety and quality of milk products. Pasteurization is a process that involves heating milk to a specific temperature for a set period of time to kill harmful bacteria and other pathogens while preserving its nutritional value. recent years, newer techniques such as High Temperature Short Time (HTST) pasteurization have gained popularity due to their efficiency and effectiveness. HTST pasteurization involves rapidly heating milk to a high temperature for a short duration of time, followed by rapid cooling. This process helps to minimize the loss of flavor and nutrients in milk while ensuring its safety. However, there is still a need in the market for a compact and remotely controlled process plant for milk pasteurization. The proposed work aims to address this need by designing a process plant that is not only compact but also remotely controlled, allowing for efficient and convenient operation. can control the process plant remotely, allowing for flexibility and convenience in monitoring and managing the pasteurization process from anywhere with an internet connection
Exploring the Neurobiological Roots of Emotional Behavior in Tryptophan Metabolism: Insights from Novel Kynurenine Aminotransferase Knockout Mice
Authors: Szabó Ágnes , Spekker Eleonóra , Szűcs Mónika , Takeda Keiko , Ozaki Kinuyo , Inoue Hiromi , Yamamoto Sayo , Toldi József , Ono Etsuro , Vécsei László , Tanaka Masaru
Published: September 2023

Memory and emotion, fundamental components of our mental existence, are highly vulnerable to psychiatric disorders like post-traumatic stress disorder (PTSD). This condition has been linked to serotonin (5-HT) metabolism disruptions. Over 95% of the 5-HT precursor tryptophan (Trp) is metabolized through the Trp-kynurenine (KYN) metabolic pathway, but little is known about its role in behavior. Kynurenine aminotransferases are responsible for the production of the Trp-KYN metabolite kynurenic acid. The gene aadat codes for mitochondrial kynurenine aminotransferase isotype 2. We generated CRISPR/Cas9-induced aadat knockout (kat2−/−) mice to examine the consequence of the gene deletion on negative valence in emotion, memory, and motor function in males 8 weeks of age and compared them to their wild-type counterparts. The forced swim test showed increased depression-like behaviors in transgenic mice. Anxiety and memory tests showed no significant differences. The transgenic mice had fewer center field and corner entries, shorter ambulation distances, and fewer jumping counts in the open field test. Overall, the transgenic mice exhibit depression-like behavior in a learned helplessness model, emotional indifference, and motor deficits. Here we present the first evidence that the deletion of the aadat gene triggers depression-like behaviors, uniquely associated with despair experience rather than fear memory. These findings have profound implications, opening avenues for further exploration into the main causes of experience-based depression linked to despair. This investigation has the potential to advance our understanding of these complex conditions and pave the way for improved therapeutic strategies by elucidating the relationship between Trp metabolism and the pathogenesis of PTSD.
Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms
Authors: Devi Viswambari R , Keidar Michael
Published: September 2023

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.
Exploring the Use of the AEDET Hospital Evaluation Toolkit to Create a Better Healing Environment for Cancer Patients Beyond the Global North
Authors: Tekbiyik Tekin Bedia , Dincyurek Ozgur
Published: September 2023

Today, numerous studies have shown that the physical environment in hospitals can significantly influence patients' well-being, comfort, and recovery. However, this is currently neglected in hospitals in the Global South. Therefore, there is an urgent need to increase the awareness to make it more applicable worldwide. Thus, this study focuses on improving the healing environment standards, by exploring the impact of evidence-based design and patient-centred care in hospitals for cancer patients; particularly the architectural space quality, on patient health outcomes as well as hospital staff health and wellbeing. In Global North countries, such as the UK, the Achieving Excellence Design Evaluation Toolkit (AEDET), is used by its National Health Services, to assess the effectiveness of various environmental attributes. However, these toolkits have not been designed for and do not work well within Global South countries, such as Northern Cyprus. To examine and compare the effectiveness of different physical environmental attributes and to evaluate user responses, the Post-Occupancy Evaluation Method and the AEDET toolkit have been used in the study. These were applied to both public and private hospitals in Northern Cyprus, involving cancer patients, staff, and professionals (n=220). The findings reveal the strength and weaknesses in terms of environmental comfort based on the aspects of evidence-based design of the hospitals such as natural light, air quality, noise, view, infection control, etc., to create a more optimal physical environment for better psychological outcomes. This is the first study to propose an adaptation of the AEDET toolkit to assist architects in designing healthcare facilities that are responsive to the requirements of hospital patients and staff; and to promote the quality of healing environment for improved health and well-being outcomes.
Study on Structural Stability of ZrO2 and YSZ: Doping-Induced Phase Transitions and Fermi Level Shift
Authors: Nematov Dilshod , Burkhonzoda Amondulloi , Kholmurodov Kholmirzo , Lyubchyk Andriy , Lyubchyk Sergiy , Lyubchyk Svitlana
Published: September 2023

In this study, we considered the structural stability, electronic properties, and phonon dispersion of the cubic (c-ZrO2), tetragonal (t-ZrO2), and monoclinic (m-ZrO2) phases of ZrO2. We found that the monoclinic phase of zirconium dioxide is the most stable among the three phases in terms of total energy, lowest enthalpy, highest entropy, and other thermodynamic properties. The smallest negative modes were found for m-ZrO2. Our analysis of the electronic properties showed that during the m–t phase transformation of ZrO2, the Fermi level first shifts by 0.125 eV toward higher energies and then decreases by 0.08 eV in the t–c cross-section. The band gaps for c-ZrO2, t-ZrO2, and m-ZrO2 are 5.140 eV, 5.898 eV, and 5.288 eV, respectively. Calculations based on the analysis of the influence of doping 3.23, 6.67, 10.35, and 16.15 mol. %Y2O3 onto the m-ZrO2 structure showed that the enthalpy of m-YSZ decreases linearly, which accompanies further stabilization of monoclinic ZrO2 and an increase in its defectiveness. A doping-induced and concentration-dependent phase transition in ZrO2 under the influence of Y2O3 was discovered, due to which the position of the Fermi level changes and the energy gap decreases. It has been established that, not only for pure systems but including those doped with Y2O3, the main contribution to the formation of the conduction band is made by the p-states of electrons. The t-ZrO2 (101) and t-YSZ (101) surface models were selected as optimal surfaces for water adsorption based on a comparison of their surface energies. An analysis of the mechanism of water adsorption on the surface of t-ZrO2 (101) and t-YSZ (101) showed that H2O on unstabilized t-ZrO2 (101) is adsorbed dissociatively with an energy of -1.22 eV, as well as by the method of molecular chemisorption with an energy of - 0.69 eV and the formation of a hydrogen bond with a bond length of 1.01 Å. In the case of t-YSZ (101), water is molecularly adsorbed onto the surface with an energy of -1.84 eV. Dissociative adsorption of water occurs at an energy of -1.23 eV, near the yttrium atom. The results show that ab initio approaches are able to describe the mechanism of doping-induced phase transitions in (ZrO2+Y2O3)-like systems, based on which it can be assumed that DFT calculations can also flawlessly evaluate other physical and chemical properties of YSZ, which have not yet been studied quantum chemical research. The obtained results complement the database of research works carried out in the field of the application of biocompatible zirconium dioxide crystals and ceramics in green energy generation and can be used in designing humidity-to-electricity converters and creating solid oxide fuel cells based on ZrO2.
Effect of SrO Addition on Recrystallization of ZrSiO4 in Raw Glass-Ceramic Glazes from the SiO2-Al2O3-CaO-MgO-K2O-ZrO2 System
Authors: Partyka Janusz , Pasiut Katarzyna , Kozien Dawid
Published: September 2023

The aim of the experiment was to determine the effect of strontium oxide addition on zirconium silicate recrystallization when adding strontium oxide to the glaze composition. Zirconia glazes (4 different contents) were prepared to which strontium oxide was added in amounts of 0, 1, 3, 6, and 12 mass%. SrO. The characteristic temperatures of the raw glazes were measured, based on which the maximum firing temperatures were determined. The fired glazes were subjected to study of the phase composition and observation of the microstructure. Analysis of the characteristic temperatures showed a fluxing effect, but it was not as strong for all glazes. Differences in the amount of the crystalline phase of zirconium silicate obtained in fired glazes and the partial transition of zirconium silicate to the amorphous phase were observed. Observations of the microstructure clearly indicated an increase in the homogeneity of the distribution of zirconium silicate.
The Sustainable Material Culture (SMC): A Review
Authors: Fucà Romina , Cubico Serena , Ardolino Piermatteo , Rosiello Francesco , Leitão João , Favretto Giuseppe
Published: September 2023

We confidently approach current issues in Green Finance (GF) from the perspective of an international organization, where the first author has previous experience as a support assistant. In fact, the economy of the EU (European Union) is deeply involved in the changing needs related to the dramatic consequences of climate change and environmental degradation, which has placed the needs of a more sustainable economy at the top of the international agenda. The environmental, social and governance (ESG) aspects are marginally addressed to arouse a major interest in questions raised by green financial products which require long-term profit expectations, and which above all contain a margin of risk that is difficult to calculate in the landscape of traditional financial instruments. Through a careful approach akin to artificial intelligence research methods, we attempt to “materialize” at least the most visible clues of this fashionable economic shift through what we call SMC (sustainable material culture) objects. The presented SMC objects serve as tangible representations of sustainable practices and can promote awareness and action incorporating into everyday life and business practices a powerful reminder and catalyst for a shift towards more sustainable investment and consumption patterns.
Dark Energy and Dark Matter from Extra Dimensional Symmetry as Different Manifestations of Vacuum Energy
Authors: Umar Kabir
Published: September 2023

Dark energy and dark matter are described as different manifestations of vacuum energy within the framework of Extra Dimensional Symmetry (EDS) that doubles large spatial dimensions with microscopic partners. In this framework, the bare vacuum energy component exists in a gravitationally inert state where actual gravitational constant Go= 0G, while real particles oscillate between this inert state and the active state where Go = 2G. Neutrinos induce small component of the gravitational constant in the inert state, causing the gravitation of virtual particles as dark matter. A small component of vacuum energy from the inert state also spills into the gravitationally active state as dark energy due to an energy density constraint and a speed limit asymmetry. This form of dark energy and dark matter, are parameterized by a gravitational potential suppressed asymmetry parameter. The dynamics of dark matter is further parameterized by the flavour, phase and kinetic energy parameters of its neutrino substrate. This results in the existence of a dominant light form of dark matter and a detectable heavy form of dark matter. EDS predicts a curvature suppressed Gravitational Wave Reheating (GWR) mechanism, enhanced by negative pressure. It converts gravitational wave energy to photon energy.
Phytovirus Vectors, Detection Techniques, and Future Directions
Authors: Wendimu Gebissa Yigezu
Published: September 2023

The phytophagous insect pests were vectors and could transmit the majority of the phytoviruses to their host plants. The orders of Hemiptera and Thysanoptera were the most common vectors of phytoviruses. The orders Orthoptera, Dermaptera, Coleoptera, Lepidoptera, Diptera, Thysanoptera, and Hemiptera were also the vectors of phytoviruses. Furthermore, aphids, whiteflies, cicadas, spittlebugs, leafhoppers, planthoppers, assassin bugs, plant bugs, stink bugs, lygaeid bugs, and thrips were among the most phytophagous insects that vector and transmit phytoviruses to healthy plants. The occurrence of a single species of these phytophagous insects resulted in one or more phytoviruses in general, and the Hemipteran order, in particular, vectored a lot of phytoviruses species. This review manuscript is focused on vectors of phytoviruses, techniques for their detection, and future directions. It will play a vital role in exploring scientific information concerning the interactions of phytoviruses and vector insects, the effect of phytoviruses on host behavior, mediators of phytoviruses transmission, persistent phytoviruses, some other insect vectors of the phytopathogen, mechanisms of host plant resistance against phytoviruses, and techniques of phytoviruses detection, as well as some important points to be considered in the future sustainably.
Effects of Foliar Spraying Organic Selenium and Nano-Selenium Fertilizer on Pak Choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’) Under Low Temperature Stress
Authors: WANG Yanyan , TAN Guozhang , CHEN Jiao , WU Jianfu , LIU Shiyu , HE Xiaowu
Published: September 2023

In recent years, frequent occurrences of low temperature disasters due to global climate change have significantly impacted the normal growth of crops. Research has indicated that inorganic selenium (which is more toxic) can not only enhance the effects of low temperature stress but also increase selenium content on plants; however, there is a scarcity of studies investigating the impacts of organic selenium and nano-selenium on crops. In order to investigate whether organic selenium and nano-selenium can enhance low-temperature tolerance and increase selenium content in pak choi (Brassica chinensis var. pekinensis. cv. ‘Suzhouqing’), different concentrations (0, 5, 10, 20 mg L-1) of exogenous selenium were applied in this experiment to assess their effects on plant growth, nutritional quality and antioxidant properties. RNA-Seq technology was used to sequence the transcriptome of the leaves. Based on the transcriptome data, a Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a network that associates with physiological traits related to stress resistance. Two highly correlated gene co-expression modules were identified, and within them, nine hub genes associated with endocytosis, antioxidant stress, absorption, transport, and metabolism of selenium were discovered. The results indicated that the beneficial effects on yield and total selenium content under low temperature were attributed to (1) protection of photosynthetic pigments for enhancing photosynthetic capacity by the up-regulation of LHca2, LHcb1, LHca1, LHcb4 in KEGG pathway: photosynthesis-antenna proteins; (2) activation of antioxidant system for efficient ROS homeostasis such as SOD, POD and CAT by the genes such as Superoxide dismutase, Monodehydroascorbate and (3) selenium absorption by endocytosis, seleninum transportation by ABC transporter gene family and selenium metabolism related genes such as Cysteine synthase, Glutaredoxin. These findings provide a foundation for further investigation into the molecular mechanisms underlying the effects of organic selenium and nano-selenium on cyanochloride production at low temperatures.
Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors
Authors: Ahmad Iftikhar
Published: September 2023

Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.
Pharmacological Modulation of the Ca2+/cAMP/Adenosine Signaling in Cardiac Cells as a New Cardioprotective Strategy to Reduce Severe Arrhythmias in Myocardial Infarction
Authors: Tallo Fernando Sabia , Santana Patrícia Oliveira , Pinto Sandra Augusta Gordinho , Lima Rildo Yamaguti , De Araújo Erisvaldo Amarante , Tavares José Gustavo Padrão , Pires-Oliveira Marcelo , Nicolau Lucas Antonio Duarte , Medeiros Jand Venes Rolim , Taha Murched Omar , David André Ibrahim , Luna-Filho Bráulio , Filho Carlos Eduardo Braga , Barbosa Adriano Henrique Pereira , Silva Célia Maria Camelo , Wanderley Almir Gonçalves , Caixeta Adriano Mendes , Caricat-Neto Afonso , Menezes-Rodrigues Francisco Sandro
Published: September 2023

Characterized by severe and fatal arrhythmias induced by cardiac ischemia/reperfusion (CIR), acute myocardial infarction (AMI) is the main cause of morbidity and mortality worldwide. To investigate the cardioprotective role of cardiac Ca2+/cAMP/adenosine signaling in AMI, the effects of L-type Ca2+ channels (LTCC) blocker Nifedipine (NIF) and Verapamil (VER), in the absence and presence of A1-adenosine receptors (A1R) blocker (DPCPX), on the incidence of ventricular arrhythmias (VA), atrioventricular block (AVB) and lethality (LET) induced by CIR in rats were evaluated. CIR was induced in adult male Wistar rats (290-320 g) by occlusion of the left anterior descendent coronary artery (10 min) followed by reperfusion (75 min). VA, AVB and LET incidences were evaluated by ECG analysis and compared between control (CIR group) and intravenously treated 5 min before CIR with NIF 1, 10, and 30 mg/kg (NIF+CIR groups) or DPCPX 100 µg/kg plus NIF 10 mg/kg (DPCPX+NIF+CIR group) and VER 1 mg/kg (VER+CIR group) or DPCPX 100 µg/kg plus VER 1 mg/kg (DPCPX+VER+CIR group). Serum levels of cardiac injury biomarkers (TCK and CK-MB) were quantified. In CIR group, VA, AVB and LET incidences were 90, 80 and 70%, respectively. In NIF+CIR group, incidence of VA was reduced to 50, 30 and 30%, AVB to 25, 10 and 20%, and LET to 25, 20 and 20%, and respectively. In DPCPX+NIF+CIR group, these incidences were not different from CIR group. TCK and CK-MB levels were similar in all groups. These results indicate that pharmacological modulation of the Ca2+/cAMP/adenosine signaling in cardiac cells by LTCC blockade and A1R stimulation could be a new strategy cardioprotective for human AMI therapy.
Nicotinamide Riboside and Phycocyanin Oligopeptides Affect Stress Susceptibility in Chronic Corticosterone-Exposed Rats
Authors: Orhan Cemal , Sahin Emre , Tuzcu Mehmet , Sahin Nurhan , Celik Abdullah , Ojalvo Sara Perez , Sylla Sarah , Komorowski James R , Sahin Kazim
Published: September 2023

Nicotinamide riboside (NR) is an NAD+ precursor capable of regulating mammalian cellular metabolism. Phycocyanin oligopeptide (PC), a phytonutrient found in blue-green algae, has antioxidant and anti-inflammatory properties. This study explored the effects of NR, PC, and their combination on telomere length, inflammatory and antioxidant status in rats under chronic stressed conditions (CS). Forty-nine rats were allocated into seven groups: Control, chronic stress (CS), CS with NR (26.44 mg/kg), a low dose of 2.64 mg/kg of PC (PC-LD), or a high dose of 26.44 mg/kg PC (PC-HD), NR+PC-LD and NR+PC-HF. Rats were given daily corticosterone injections (40 mg/kg) to induce stressed conditions, or NR and PC were orally administered for 21 days. NR and PC supplementation, particularly NR plus PC, increased the serum antioxidant enzyme activities, hepatic nicotinamide adenine (NAD+) content, and telomere length (P<0.001 for all) compared with the CS group. The levels of serum malondialdehyde (MDA), liver interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), IL-1β, and IL-8 were reduced under CS condition (P<0.001). In addition, CS decreased the levels of hepatic telomere-related proteins and sirtuin1 (SIRT1-3), while supplementation of NR and PC alone or in combination increased the levels of telomere-related proteins (e.g., POT1b, TRF1, and TRF2), and SIRT3, NAMPT P<0.05. In conclusion, NR and PC, especially their combination, can alleviate metabolic abnormalities by enhancing hepatic cytokines, SIRT3, NAMPT, and NAD+ levels in CS-exposed rats. More research is needed to elucidate further the potential health effects of the combination of NR and PC in humans.
Analysis of Green Innovation of the E-Tailer and Supplier with a Drop Shipping Option in E-Commerce
Authors: Cheng Yuepeng , Li Bo
Published: September 2023

As customer demand for green products increases in the digital economic era, this study analyses the green innovation of the e-tailer and supplier in drop shipping models. Moreover, drop shipping e-tailers and suppliers with a drop shipping option need to make choices regarding whether to provide green or normal products to the market. When a supplier with a drop shipping option produces green products, more fees may be invested on the production of green products than on normal products. The drop shipping e-tailers and suppliers with a drop shipping option can also choose to sell normal products at a low cost, as before. This study designs four models of drop shipping e-teilers and suppliers with a drop shipping option under different choices, analyzes their operational process in drop shipping models, and investigates five theorems. The optimal pricings decisions and green degree of drop shipping e-tailers and suppliers with a drop shipping option were evaluated in this study. The impacts of the green innovation factor, green elasticity coefficient, manufacturing and distribution costs on the drop shipping e-tailers and suppliers with a drop shipping option, and the effect of other environmental parameters on the green degree of green products are also analyzed through computer simulation. The findings of the simulation analysis provide valuable guidance for e-tailers and suppliers with green innovation in drop shipping models and offer important academic and practical implications for e-commerce and the digital economy.
AI Enhancements for Linguistic E-learning System
Authors: Liu Jueting , Li Sicheng , Ren Chang , Lyu Yibo , Xu Tingting , Wang Zehua , Chen Wei
Published: September 2023

The E-learning system has achieved great development after the pandemic. In this work, we proposed three artificial intelligence-based enhancements to our linguistic interactive E-learning system from different aspects. Compared with the original phonetic transcription exam system, our enhancements include an MFCC+CNN-based disordered speech classification module, a Transformer-based Grapheme-to-Phoneme converter, and a Tacotron2-based IPA-to-Speech speech synthesis system. This work not only provides a better experience for the users of this system but also explores the utilization of artificial intelligence technologies in the E-learning field and linguistic field.
D1-like and D2-like Dopamine Receptor of Rat Prefrontal Cortex: Impacts of Genetic Generalized Epilepsies and Social Behavioral Deficits
Authors: Birioukova Lidia M , Van Luijtelaar Gilles , Midzyanovskaya Inna S
Published: September 2023

The involvement of the prefrontal cortical dopaminergic system in psychopathology of epilepsies and comorbid conditions such as autism spectrum disorder (ASD) still needs to be explored. We used autoradiography to study the D1-like (D1DR) and D2-like (D2DR) receptor binding density in the prefrontal cortex of normal Wistar rats and Wistar-derived strains with generalized convulsive and/or nonconvulsive epilepsies. WAG/Rij rats served as a model for non-convulsive absence epilepsy, WAG/Rij-AGS as a model of mixed convulsive/non-convulsive forms, and KM strain was a model for convulsive epilepsy comorbiding with ASD-like behavioral phenotype. Prefrontal cortex of rats with any of epileptic pathology studied, demonstrated profound decreases in binding densities to both D1DR, D2DR; the effects were localized in the primary and secondary anterior cingulate cortexi, and adjacent regions. The local decreased D1DR and D2DR binding densities were independent (not correlated) from each other. The particular group of epileptic rats with ASD-like phenotype (KM strain), displayed changes in the lateral prefrontal cortex and adjacent regions: D1DR were lowered, but those to D2DR elevated, in anterior dysgranular insular cortex and adjacent regions. Thus, epilepsy-related changes in the dopaminergic system of rat archeocortex were localized in the medial regions, whereas ASD-related candidates were seen in the lateral aspects. The findings point to putative local dopaminergic dysfunctions, associated with generalized epilepsies and/or ASD.
Management of Agri-Food Waste Based on Thermochemical Processes towards a Circular Bioeconomy Concept. The Case Study of Portuguese Industry
Authors: Berenguer Cristina V. , Perestrelo Rosa , Pereira Jorge A. M. , Câmara José S.
Published: September 2023

Sustainable biomass production has significant potential for mitigating greenhouse gas emissions and is a promising alternative for the production of eco-friendly biofuels, biochemicals, and carbonaceous materials for biological, energetic, and environmental applications. Biomass from agroforestry and agricultural wastes is the richest natural carbon source and a sustainable option for woody biomass from a circular economic perspective. Portugal has several residues and by-products of agriculture, food processing, and forestry origin, and has a high availability of residual biomass. Portugal intends to reduce its dependence on energy imports and the use of fossil fuels, thereby increasing its economic performance while using biomass waste to produce high-value-added products. In this context, this review aims to explore the potential of agri-food wastes obtained from Portuguese industries via thermochemical conversion technologies as a promising sustainable alternative to woody biomass for the development of eco-friendly biofuels, biochemicals, and high-value carbonaceous materials and their applications. This strategy based on the circular bioeconomy concept can contribute to reducing the dependence on fossil fuels and minimising greenhouse gas emissions while meeting the demands of the growing population and providing a sustainable waste management solution.
Modeling Requirements for Collaborative Robotic Services
Authors: Zapata Oscar Stiven Morales , Correa Yaney Gomez , Yoshioka Leopoldo Rideki , Silva Jose Reinaldo
Published: September 2023

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.
Hybrid Additive Manufacturing of Parts with Relatively Complex Geometry by 3D Printing in Segments with Uniform Thickness, Variable Height Per Radius, and Constant Filament Feed Rate
Authors: Claudiu Negrău Dan , Pancu Rareș , Grebenișan Gavril
Published: September 2023

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.
Clinical Rationale for Dietary Lutein Supplementation in Post COVID-19 and mRNA Vaccine Injury Syndromes
Authors: Kyriakopoulos Anthony M , Nigh Greg , McCullough Peter A , Seneff Stephanie
Published: September 2023

Lutein, a plant-derived xanthophyl-carotenoid, is an exceptional antioxidant and anti-inflammatory constituent found in food. Elevated concentrations of lutein found in human blood and plasma, due to high dietary intake, are beneficial against eye disease and improve cardiometabolic health. Lutein plays an important protective role against the development of neurological disorders, including Alzheimer’s disease (AD), multiple sclerosis (MS) and Parkinson’s disease (PD). It has also been shown to be beneficial for liver, kidney and respiratory health. Lutein, acting as a very strong antioxidant, can alleviate oxidative stress and downgrade reactive oxygen species (ROS). Oxidative stress is one of the key pathogenic mechanisms in post-COVID and mRNA vaccine injury syndromes. Recent in silico studies suggest that lutein and other naturally derived antioxidants, by docking at the site where the SARS-CoV-2 spike protein (SP) binds to the angiotensin enzyme type 2 (ACE2) receptor, may neutralize the SP-ACE2 interactions. Lutein can be added to a detoxification regimen to aid in clearing Spike protein and relieving symptoms. In agreement with Hippocrates’ dictum to “Let food be thy medicine,” this review establishes dietary lutein as a valuable therapy in the treatment of post-COVID syndrome, mRNA vaccine injury syndromes, and a wide range of other chronic illnesses.
Climate and Food Insecurity Risks: Identifying Exposure and Vulnerabilities in the Post-Food Production System of Northern Ghana
Authors: Osman Balikisu
Published: September 2023

Evidence shows how food system activities from production to consumption underpin food security. However, studies exploring climate impacts on food security in northern Ghana have overly focused on production systems, neglecting post-production activities that loom large in food security. This paper addresses the research need to comprehensively analyze how climate change affects post-production activities and exacerbates food insecurity risks in northern Ghana. The study collects and analyzes data on climate hazards, impacts, and food system vulnerabilities using questionnaires and participatory engagement with farming households in northern Ghana. Results show that climate-induced food insecurity risks in northern Ghana are not just products of persistent climate impacts on food production in the region. Instead, risks are inextricably connected to the vulnerability contexts within which food is harvested, processed, stored, and marketed. Specifically, the results reveal that climate hazard events such as floods, extreme temperatures, and droughts damage stored grain, disrupt food supply to the market, and cause seasonal volatilities in food prices. However, these impacts are not solely externally generated circumstances. The food system is highly vulnerable; most households lack access to threshing and grinding machines, warehouse storage, post-harvest management information, and transportation services. These underlying characteristics of the post-food-production system of northern Ghana, which is ultimately quite remote from climate change, exacerbate household-level food insecurity risks.
Numerical Aperture Dependent Spatial Scaling of Plasma Channels in HPHT Diamond
Authors: Gulina Yulia , Zhu Jiaqi , Krasin George , Kuzmin Evgeny , Kudryashov Sergey
Published: September 2023

The investigation of plasma channels induced by focused ultra-short 1030-nm laser pulses in bulk of synthetic HPHT diamond revealed strong dependence of their size and shape on the used numerical aperture of the lens (NA=0.15-0.45). It was shown that at loose focusing conditions, it is possible to significantly increase the length of the plasma channel with a slight increase in pulse power, while tight focusing allows to obtain more compact structures in the same range of used powers. Such dependence paves the way to new possibilities in 3D processing of transparent dielectrics, allowing, for example, to vary the spatial parameters of modified regions without changing the setup, but only by controlling the lens aperture, which seems very promising for industrial applications.
Design and Analysis of the High-Speed Underwater Glider with a Bladder-Type Buoyancy Engine
Authors: Ji Dae-Hyeong , Lee Jung-Han , Ko Sung-Hyub , Hyeon Jong-Wu , Lee Ji-Hyeong , Choi Hyeung-Sik , Jeong Sang-Ki
Published: September 2023

This study entailed the design and analysis of a 400 m-class underwater glider operated by a bladder-type buoyancy engine. The underwater glider was designed for high-speed movement with a maximum velocity of 2 knots. The shape of the hull was designed to reduce water resistance using the Myring hull profile equation. The reliability was verified by performing simulations using resistance coefficients. The relationship between the control value of the ballast discharged from the buoyancy engine and the glider's speed according to the path angle was analyzed. Further, the relationship between the optimal glide angle and the design control value of the ballast was derived, and the optimal glider speed was estimated accordingly. Based on the analysis results, a bladder-type buoyancy engine was developed, and the maximum speed of the tested underwater glider was measured via sea trials.
Developing an Advanced Software Requirements Classification Model Using BERT: an Empirical Evaluation Study on Newly Generated Turkish Data
Authors: Yucalar Fatih
Published: September 2023

Requirements Engineering (RE) is an important step in the whole software development lifecycle. The problem in RE is to determine the class of the software requirements as functional and non-functional. Proper and early identification of these requirements is vital for the entire development cycle. On the other hand, manual government of requirement classes is a timewasting task, and it needs intensive effort. Automatic requirement classification techniques through advanced Machine Learning (ML) strategies are started to be developed to address this problem. Basically, software requirements are generated as natural language explanations and therefore requirement classification may be handled as a particular Natural Language Processing (NLP) problem. From ML point of view, classification strategies require dataset to be used in the training/learning phase. For this goal, we generated a unique Turkish dataset having collected the requiremenst from real-world software projects with 4600 samples. Of these requirements, 3000 and 1600 were labeled as functional and non-functional, respectively. The data set has been evaluated using (i) traditional machine learning algorithms, (ii) deep learning algorithms, and (iii) transformer models respectively. As a result BERTurk was found to be the most successful algorithm to discriminate functional and non-functional requirements in terms of 95% f-score metric.
Virtual Worlds, Video Games and the COVID-19 Pandemic
Authors: Somoza Medina Xosé , Somoza Medina Marta
Published: September 2023

The expansion of Open World Video Games (OWVGs) has seen the emergence of multiple new fictional worlds. This type of video game is characterized by total immersion in a world built for the players entertainment. What videogames developers want when players enter an OWVG is that they could feel like if they were open a door to an entirely new world and leave the real world behind. These virtual worlds manifest as a complement, an alternative, or the successor to the physical universe, and in some way continue the philosophical tradition of possible worlds begun by Leibniz. Accustomed to ‘surviving’ in hostile environments, millions of people continued to improve their skills in the midst of the COVID-19 pandemic while locked in their homes. Once the pandemic seems to start to be left behind, the paper analyzes the virtual worlds created by the video games and the relations between the pandemic and these fictional worlds.
Unveiling the Railway Traffic Knowledge in Tibet: An Advanced Model for Relational Triplet Extraction
Authors: Luo Weiqun , Wang Jiabao , Yan Xiangwei , Jiang Guiyuan
Published: September 2023

To address the deficiency of existing relation extraction models in effectively extracting relational triplets pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieves an F1-score of 93.3\%, surpassing the baseline model CasRel by 0.8\%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1\% and 92.6\% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5\% and 0.8\%, which highlights the good generalization ability of the proposed model.
Quantized Newton and General Relativity Theory
Authors: Haug Espen
Published: September 2023

Recent advances in understanding the Planck scale have led to a new, straightforward method for quantizing the general theory of relativity. This results in a theory of gravity that predicts the same outcomes as general relativity with greater fundamental comprehension connected directly to the Planck scale.
Global and Local Approaches for Forecasting of Long-Term Natural Gas Consumption in Poland Based on Hierarchical Short Time Series
Authors: Gaweł Bartłomiej , Paliński Andrzej
Published: September 2023

This study presents a novel approach for predicting hierarchical short time series. In this article, our objective was to formulate long-term forecasts for household natural gas consumption by considering the hierarchical structure of territorial units within a country's administrative divisions. For this purpose, we utilized natural gas consumption data from Poland. The length of the time series was an important determinant of the data set. We contrast global techniques, which employ a uniform method across all time series, with local methods that fit a distinct method for each time series. Furthermore, we compare the conventional statistical approach with a machine learning (ML) approach. Based on our analyses, we devised forecasting methods for short time series that exhibit exceptional performance. We have demonstrated that global models provide better forecasts than local models. Among ML models, neural networks yielded the best results, with the MLP network achieving comparable performance to the LSTM network while requiring significantly less computational time.