bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainability of Cs2AgBiBr6 and Cs2TiBr6 Perovskite Solar Cells: The Challenges and Research Roadmap for Power Conversion Efficiency Improvement
Authors: Berhe Taame Abraha , Ashebir Etsana Kiros , Abay Berhe Tadese
Published: June 2024

The stability issues in the widely known organic inorganic halide perovskite, CH3NH3PbI3, lead to the development of alternative halide double perovskite materials which get great attention in recent times. Although the stability issue seems promising, both materials and device performance of these photovoltaic materials remain inferior and challenging for improvements. Furthermore, the power conversion efficiency of single junction organic inorganic halide perovskite is now 24.2% and 29.15% for textured monolithic perovskite/silicon tandem solar cell, but for all-inorganic halide perovskite solar cell is 7.11% and halide double perovskite solar cells based on Cs2AgBiBr6 and Cs2TiBr6 is 2.8% and 3.3%, respectively, which is far less than 24.2% and 29.15%, respectively. This creates big question and concern that can both all-inorganic halide perovskite solar cell and halide double perovskites solar cells really be an acceptable alternatives to replace organic inorganic halide perovskite solar cells or lead based perovskite solar cells in the market in order to realize the sustainable practical applications? Not only this concern, but also there are many other big challenges facing by halide double perovskite solar cells. Such big challenges include: a) geometric constraints and limited integration with interfacial materials, b) dynamic disorder and a wide band gap and localized conduction band caused by cubic unit cell which restrains the interactions of orbitals, c) high processing temperature which may limit on the diverse applications, d) low electronic dimensionality making them less appropriate for single junction solar cell purpose and etc. Moreover, origin of electronic and optical properties such as the polarizability, the presence of molecular dipoles and their influence on the dynamics of the photo-excitations in the halide double perovskites remains unlock concern that need to be elucidated. Now, another big question is how to develop overcoming mechanisms for such challenges. Can we really overcome these current limitations faced by halide double perovskites so that we use them for sustainable commercialization? This research roadmap for performance improvement is suggested focusing on: materials surface and bulk engineering, band gap engineering, interfacial engineering, composition engineering, doping engineering, device architectural engineering, polar and domain order engineering. This was the reason that this review was developed in order to forward great contributions to the readers and commercial ventures.
Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds
Authors: Wernette Phillipe
Published: June 2024

Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.
The Hyper-Torus Universe Model—A New Paradigm for Understanding Reality
Authors: McCammon Chad R.
Published: June 2024

The Hyper-Torus Universe Model (HTUM) is a novel framework that unifies quantum mechanics, cosmology, and consciousness, proposing that the universe is a higher-dimensional hyper-torus containing all possible states of existence. This paper explores the fundamental concepts and implications of the HTUM, which suggests that the universe is a quantum system in which all possible outcomes are inherently connected, with consciousness playing a crucial role in actualizing reality. The HTUM addresses critical challenges in modern physics, such as the nature of quantum entanglement, the origin of the universe, and the relationship between mind and matter. By introducing concepts like singularity, quantum entanglement at a cosmic scale, and the self-actualization of the universe, the HTUM provides a comprehensive framework for understanding the fundamental nature of reality. This paper discusses the mathematical formulation of the HTUM, its implications for quantum mechanics and cosmology, and its potential to bridge the gap between science and philosophy. The philosophical implications of the HTUM are also examined, including its impact on free will, determinism, and the mind-matter relationship. The HTUM represents a significant shift in our understanding of the universe and our place within it, inviting further research and exploration into the nature of reality and consciousness.
From Cow to Climate: Tracing the Path of Dairy Sustainability: Unveiling the Impact on Sustainable Development Goals through Bibliometric and Literature Analysis
Authors: Douglas Mwirigi , Fekete-Farkas Maria , Csaba Borbély
Published: June 2024

Dairy farming dates to the early Neolithic era in Europe, Middle East, Asia, and Africa. Over time, the industry has evolved from small ranches and farmhouses milk processing units to large, intensive dairy farms and big, high-tech processing units. Despite the industry’s contribution to the economy and food production, multiple studies have tainted cattle farming as a main contributor to climate change. This study explores literature in dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of SDGs in 2015 and themes for future research. This paper argues that dairy animals are not the source of sustainability shortfalls as depicted by multiple scholarly articles, but human processes associated with dairy farming. Data was analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of literature shows that dairy farming and its contribution to sustainability has gained more intensive scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder’s management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and source of opportunities to strengthen sustainability in dairy farming sector and achieve the sustainable development goals.
Multidimensional Data Analysis of Deep–Language in J.R.R. Tolkien and C.S. Lewis Reveals Tight Mathematical Connections
Authors: Matricciani Emilio
Published: June 2024

Scholars of the English Literature unanimously say that J.R.R. Tolkien and C.S. Lewis influenced each other writings. For the first time we have investigated this issue mathematically by using an original multi–dimensional analysis of linguistic parameters, based on surface deep–language variables and linguistic channels. To set our investigation in the framework of the English Liter-ature, we have considered some novels written by earlier authors, such as C. Dickens, G. Mac-Donald and others. The deep–language variables and the linguistic channels, discussed in the paper, are likely due to writers’ unconscious design and have revealed connections between texts far beyond writers’ awareness. In summary, the capacity of the extended short–term memory required to readers, the universal readability index of texts, the geometrical representation of texts and the fine tuning of linguistic channels within texts – all tools largely discussed in the paper – have revealed strong connections between The Lord of the Rings (Tolkien), The Chronicles of Narnia, The Space Trilogy (Lewis) and novels by MacDonald, therefore in agreement with what scholars of the English Literature say.
Promoting urban corridors in Saudi city center to enhance walkability feature using multi-criteria decision analysis methods.
Authors: Aloshan Mohammed , Gharieb Moustafa , Heba Khaled Mahmoud , Khalil Ragab , Alhumaid Mohammed Humaid , Ezz Mohamed salah
Published: June 2024

Saudi Arabian cities have experienced a rapid expansion of their urban areas - particularly their city centers - over the last four decades. This has resulted to an increase in vehicular usage, which has adversely affected the daily walking experience of the residents. Walkability has several positive effects on people's health as well as the environment in cities. In addition to being a means of transportation, it also helps people create a sense of place, enhancing their image ability to be easily understood as a legible urban structure, and deepen their emotional bond with the city. Using the medium-sized Saudi Arabian city of Onaizah as a case study, this study looks into the feasibility of creating urban walking corridors to encourage users to walk more. According to Saudi Arabia's Vision 2030, which emphasizes the value of sustainable urban development and raising quality of life for residents, the study addresses walkability as a generator to maintain and improve the urban scene of the city center. Geographic Information Systems (GIS) was used to analyze the data and generate the urban corridors in the city center. The results showed the possibility of improving walking in Onaizah through three urban corridors to the city center, measuring 1,401 meters, 1,283 meters, and 1,507 meters, respectively. These corridors represent urban, commercial and heritage corridors, giving an opportunity for planners and decision makers to challenge prioritize pedestrian connectivity and enhance the physical environment, leading to sustainable urban development. This research paper used various criteria analysis methods to provide a comprehensive assessment of various factors to conclude these urban corridors; for example, assessments of land use, transportation and environmental considerations. The study aligns with Saudi Arabia's vision 2030 by promoting walking and enhancing the city's overall walkability while offering Onaizah society by creating and suggesting urban corridors to the city center a sustainable and livable urban environment.
Why It Remains Statistically Probable That SARS-CoV-2 Was Isolated and Then Accidentally Lab-Leaked before the First COVID-19 Outbreak
Authors: Theodor-Nicolae Carp
Published: June 2024

The SARS-CoV-2-induced acute COVID-19 pandemic disorder seems to have begun all of the sudden in 2020, with very little data available regarding prior times in which people experienced mysterious flu-like illness symptoms characteristic of the pandemic disease. The scientific community seems to be divided into a few parts with regards to the belief of the kind of viral origins. A significant number of scientists believe that the virus solely has natural origins and that it underwent a thorough process of zoonosis, independent of laboratory research-based research that would only catalyse zoonosis due to helping the virus gain additional function of transmission and virulence. Many other scientists believe that the virus has wide origins characteristic of “gain-of-function” laboratory research, whilst many other scientists believe that most of the viral genome has natural origins, with the zoonotic process having been artificially catalysed only in its latter part of transmission to humans. Considerable extent of scientific evidence suggests that the virus underwent a restricted, but considerable extent of “gain-of-function” research, prior to having been accidentally leaked into the nearby environment, whilst its process of zoonotic spillover into humans was influenced by such events, given for example the multidimensional pathogenetic process displayed by the virus and its main antigen of action, the spike glycoprotein, causing all kinds of immune pathophysiology - including transient immunosuppression. Investigations into the origins of the novel coronavirus have been lasting for years, seemingly with no concrete end in sight, and the available molecular testing methods to detect SARS-CoV-2 infection displaying less than perfect results have not significantly helped the process. Evidence even started pointing toward the existence of artificial, laboratory research-based viral origins, exposing a probable accidentally-induced artificial process of “accelerated viral evolution”, which could be particularly dangerous because of the nature of the SARS-CoV-2 pathogenic agent. Likewise, it is possible that the prevention of distribution of scientific data regarding scenarios as such in fact constitutes censorship, rather than regulation of data that may be considered not to be significantly evidence based. Simultaneously, it is important not to regard all hypotheses and preliminary reports of scientific research as evidence-based, but to ensure that due diligence is performed in all cases. The integrity of scientific research and ultimately of the innovation process of medical solutions depends on the integrity of democracy and the rule of law, as well as on regulating partnerships with scientific companies from world nations where autocracy rules their society.
Hamiltonian Model for Electron Heating by Lower Hybrid Drift Waves during Magnetic Reconnection with a Strong Guide Field
Authors: Sattin Fabio
Published: June 2024

Recent measurements in the MRX laboratory plasma [J.Yoo et al, Phys. Rev. Lett. 132, 145101 (2024)] have evidenced the role of lower hybrid drift waves produced during magnetic reconnections in producing an effective anomalous resistivity and heating of electrons. A detailed modelization of the energy transfer from the waves to electrons is missing, though. In this work, we attempt such an exercise using a Hamiltonian test-particle model for wave-electron interaction, valid for low-β weakly collisional plasmas. Quantitative agreement with MRX results is found.
Therapeutic Effect of Nerium oleander in Acetic Acid-Induced Ulcerative Colitis Model in BALB-C Mice
Authors: Ghosh Swapnadeep
Published: June 2024

Inflammatory bowel disease (IBD) is a chronic inflammatory intestinal disorder that poses significant challenges in terms of prevention and treatment due to its complex etiology. The multifaceted nature of IBD's underlying causes means that developing effective interventions requires a nuanced understanding and innovative approaches. As a result, research into new drugs and treatment strategies has become a critical area of focus in the scientific community.Among the promising areas of research are phenolic acids, which are abundant in plant-based diets. These compounds have garnered attention because of their potent antioxidant and anti-inflammatory properties, which have shown potential in alleviating colitis. This review aims to provide a comprehensive overview of phenolic acids, highlighting their structures and primary dietary sources, specifically focusing on benzoic acid and cinnamic acid.We delve into the role of phenolic acids as a nutritional therapy for the prevention and treatment of IBD. The review synthesizes findings from animal and cell experiments, which have demonstrated that phenolic acids can effectively mitigate IBD symptoms induced by drug exposure or genetic predispositions. The mechanisms through which phenolic acids exert their beneficial effects include enhancing intestinal mucosal barrier function, reducing oxidative stress, inhibiting the overactivation of the immune response, and regulating the balance of intestinal microbiota.The evidence from these studies underscores the therapeutic potential of Nerium oleander in Acetic acid-induced Ulcerative Colitis in BALB-C mice and its amelioration. However, our observations also highlight the necessity for further basic and clinical research to fully understand the capabilities and limitations of phenolic acids and their derivatives as novel therapeutic agents for IBD. Comprehensive investigations will be essential to translate these promising findings into practical treatments for individuals suffering from this debilitating condition.
Impact of Combined Neuromuscular Electrical Stimulation (Comb-NMES) on Glucose Signaling and Muscle Myofiber Distribution in a Patient with Acute Spinal Cord Injury and Lower Motor Neuron Lesion
Authors: Alharbi Amal , Womack Erika , Yarar-Fisher Ceren
Published: June 2024

This case report examines the impact of a novel Combined Neuromuscular Electrical Stimulation (Comb-NMES) regimen on muscle glucose signaling, fiber type distribution, and metabolic function in a patient with acute spinal cord injury (SCI) and lower motor neuron lesions (LMNLs). A 32-year-old male with complete T9 SCI underwent a ten-session Comb-NMES intervention targeting the quadriceps. Muscle biopsies and blood samples were analyzed pre- and post-intervention to evaluate changes in muscle fiber types, key metabolic proteins, fasting insulin, glucose, and lipid profiles. The intervention led to a 74.7%, and 28.2% reduction in fasting insulin and glucose, respectively. Muscle analysis showed significant increases in CaMK II, Hexokinase II, and IRS-1, indicating improved glucose metabolism. Comb-NMES training markedly improved metabolic control and muscle glucose metabolism in a patient with acute SCI and LMNLs. Enhanced insulin sensitivity and glucose utilization were evidenced by upregulated metabolic proteins which suggests Comb-NMES is a promising intervention for improving muscle and metabolic health in SCI patients with LMNLs. Further studies are needed to confirm these benefits and explore long-term effects.
Targeting divergent pathways in the nutritional management of depression
Authors: Tobin Derek , Vuckovic Alex , Sarris Jerome
Published: June 2024

Nutritional management of depression has long been discussed due to the perceived benefit of a nutritional product having less side effects than pharmaceutical agents. Candidate nutrients for managing depression include Vitamin D, B-vitamins, tryptophan, branch chain amino acids, probiotics, omega-3 fatty acids, folate/methylfolate (aka Vitamin B9), and s-adenosylmethionine.
This paper reviews three nutrients which have significant scientific support for the management of depression and have evidence of low levels in subjects with depression, and that correction of the nutritional deficiency provides clinical benefit. We present epidemiological evidence, mechanistic explanation and a review of interventional studies for these nutrients. Finally, relevant nutritional guidelines are presented with their conclusion for the role of each nutrient in the management of depression.
Identification of New Polymorphic Patterns for Elacestrant Dihydrochloride
Authors: Masum Zia Uddin , Spoors P Grant , Burke Matt D , Gupta Vivek
Published: June 2024

Extensive polymorphism screening of elacestrant dihydrochloride, an Active Pharmaceutical Ingredient (API), was conducted using various methods and solvents. The solubility assessment and polymorph screening of elacestrant dihydrochloride (referred to as elacestrant throughout the paper) utilized both crystalline and amorphous forms of the API as input material with 40 non-conventional solvents, employing slow cooling, maturation, and/or evaporation. Crystalline material underwent further screening via anti-solvent addition, polymer templating, and solvent-drop grinding. A total of 7 new polymorphs were identified. No solvated forms were identified, but some samples contained notable water content, tentatively classified as hydrated forms. Comprehensive solid-state characterization data for representative crystalline samples of each polymorph is presented. Polymorph A exhibited the highest melt (225-226 °C, onset). Thermal analysis of polymorphs B, C, D, E, F and G showed an endothermic-exothermic event (with an onset temperature between 136 and 184 °C), indicating a form conversion, followed by a melt at 205-222 °C (onset), consistent with the previously identified and most stable form of elacestrant. This suggests that these materials convert to the most stable form prior to melting. Pattern G does not show an endothermic-exothermic event, but after desolvation, the material melts at 222 °C (onset temperature).
An Innovative Aggregation-Induced Emission-Based NIR Fluorescent Probe for Visualizing Carboxylesterases in Living Cells, Zebrafish and Tumor-Bearing Mice
Authors: Gao Chao , Chen Dandan , Liu Hu-Wei , Ma Minglan , Zhang Lin , Cui Hai-Rong
Published: June 2024

In human body, carboxylesterases (CEs) play the crucial roles in xenobiotic metabolism and lipid homeostasis. But the abnormal CEs expression is highly associated with some diseases, such as hyperlipidemia, diabetes and liver cancer. Therefore, it is of great significance to develop an efficient tool for the accurate detection of CEs in living organisms. Herein, an innovative near-infrared (NIR) fluorescent probe TTAP−AB has been designed for CEs detection based on aggregation-induced emission (AIE) mechanism. This probe exhibits rapid response (2 min), excellent sensitivity (limit of detection = 8.14×10−6 U/mL), and high selectivity towards CEs. Additionally, owing to the good biocompatibility, probe TTAP−AB enables to monitor the dynamic changes of CEs level under drug-induced modulation in living cells and zebrafish. More importantly, probe TTAP−AB is successfully employed to image the liver tumor and assist tumor resection by the real-time monitoring of CEs, indicating that TTAP−AB is promising to guide liver cancer surgery. Therefore, probe TTAP−AB can not only enrich the strategies for CEs detection in biological systems, but also has great potential for some clinical imaging applications, including medical diagnosis, preclinical research, and imaging-guided surgery.
Optimizing Traffic Flow Through Congestion Management at Peshawar Toll Plaza: A Data-Driven Approach
Authors: Hussain Adil , Afzal Muhammad , Tariq Tariq
Published: June 2024

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement
Microwave Corona Breakdown Suppression of Microstrip Coupled-Line Filter Using Lacquer Coating
Authors: Ye Ming , Hu Shaoguang , Wang Rui , Zhang Yong , He Yongning
Published: June 2024

Due to its potential harm to space payload, microwave corona breakdown of microstrip circuits has attracted much attention. This work describes an efficient way to suppress corona breakdown. Since corona breakdown threshold is determined by the highest electric field intensity at the surface of microstrip circuits, lacquer coating with thickness of tens of microns is sprayed on the top of microstrip circuits. The applied dielectric coating is used to move the discharge location away from circuit’s surface which is equivalent to reduce the highest electric field intensity on the in-terface of solid/air of the circuit and thus results into higher breakdown threshold. Two designs of classic coupled-line band-pass filter were used for verification. Corona experimental results at 2.5 GHz show that, in the interested low pressure range (100 to 4500 Pa), 5.3 dB improvement of mi-crowave corona breakdown threshold can be achieved for a filter with narrowest gap of 0.2 mm while its electrical performances like insertion loss, Q-factor are still acceptable. A threshold improvement prediction method is also presented and validated.
Chemical Characterization and Biotechnological Potential of Bioactive Compounds in Salicornia neei Lag.
Authors: Melo Camila Freire de , Souza Adriana Ferreira , Macedo Bezerra Filho Clovis , Andrade Rosileide Fontenele da S. , Lima Marcos Antônio Barbosa , Franco Luciana Oliveira , Fernandes Josimar Gurgel , Campos-Takaki Galba M.
Published: June 2024

.Halophyte species are rich in bioactive compounds, antioxidant, and antimicrobial properties. However, due to the limited tech-nical-scientific data regarding these properties in the halophyte Salicornia neei, this research aimed to analyze micronutrients, chlorophyll, carotenoids, and activities of S. neei extracts using solvents of different polarities and plant organs (leaf, stem, and root) as free radical scavengers and antimicrobial agents. The fresh plant was collected and subsequently, pigments and micro-nutrients were determined by spectrophotometry and ICP, respectively. From the prepared extracts, antioxidant activity was de-termined by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging method and antimicrobial activity by the disc diffusion method for fungi and bacteria. The results obtained demonstrated that the native Brazilian halophyte S. neei can be considered a food rich in minerals and pigments with excellent antioxidant properties, showing inhibition 96.72% and IC50 (4.15 mg/mL). For free radical scavenging capacity, the most effective solvent was ethanol: acetone (1:1) and there was no significant difference (p
Enhancing Language Learning and Intergroup Empathy through Multi-user Interaction and Simulations in a Virtual World
Authors: Hoter Elaine , Yazbak Abu Ahmad Manal , Azulay Hannah
Published: June 2024

This study explores the impact of multi-user interaction, group work, and simulations within virtual worlds on language learning and the development of intergroup empathy. Two distinct research projects were conducted, involving a total of 241 participants aged 19-45. The language learning study engaged 116 participants in diverse interactive experiences, while the intercultural study had 125 participants collaborating in multicultural groups and participating in perspective-taking simulations. Both studies employed qualitative data collection methods, including surveys, interviews, and observations. The findings suggest that the combination of networking strategies, collaborative learning, and simulations within virtual worlds contributes to improvements in learners' language proficiency, confidence, and empathy towards diverse social groups. Participants reported increased motivation and engagement, attributed to the immersive and interactive nature of the virtual environments. The studies highlight the importance of collaboration and reflection in facilitating language acquisition and intercultural understanding. Technical challenges were identified as potential barriers to implementation. The results demonstrate the potential of virtual worlds to enhance language education and foster empathy in diverse societies, offering valuable insights for educators and researchers. However, the findings may be limited by the specific contexts and sample sizes of the studies, warranting further research to explore the generalizability and long-term impact of virtual world interventions. not exaggerate the main conclusions.
CWT-AM-CNN: A Lightweight Feature Extraction Network with Attention Mechanism Based on CWT Peak Seeking Method for Aero-Engine Hot Jet FT-IR Classification
Authors: Du Shuhan , Han Wei , Kang Zhenping , Lu Xiangning , Liao Yurong , Li Zhaoming
Published: June 2024

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.
Antioxidant Activity and UHPLC-MS/MS Characterization of Polyphenol and Nicotine Content of Nicotiana glauca Leaf Extracts: A Comparative Study of Conventional and Deep Eutectic Solvent Extraction Methods
Authors: Issa Reem , Al-Akayleh Faisal , Alnsour Lilian , Alsammaraie Tabarak , Omari Khaled W. , Awwad Shady Helmi
Published: June 2024

The leaves of Nicotiana glauca (Solanaceae) plant, are known for its major cause of health concern. This study investigated the antioxidant activity and polyphenols composition of aerial parts of N.glauca collected from its wild habitat in Jordan, using Methanol-Conventional (MC) and deep eutectic solvents (DESs) extraction methods in addition to nicotine content determination using UHPLC. The results have shown that the MC extract contains fewer total phenols and flavonoid content than the DES extract at ratio of 90% (0.1194 ±0.009 and 0.311± 0.020 mg/mL equivalent to gallic acid) and (0.01084±0.005 and 0.928 ± 0.09 mg/mL equivalent to rutin), respectively. Moreover, the study showed that the MC extract contains 635.07 ppm nicotine compared to the DES extraction method, which contains 1194.91 ppm. Both the MC and the DES extracts exhibited weak antioxidant activities with the highest was 33%inhibition equivalent to ascorbic acid was found for DES at (90%) ratio. The UHPLC-MS/MS analysis revealed the presence of variations in the detected compounds between the two extraction methods used. This study found that en-vironmentally friendly DES extraction of N. glauca produced higher phenol and flavonoid content than MC method, highlighting green chemistry methods' superior efficiency and environmental benefits for extracting valuable phytoconstituents.
Post-Acute Sequelae And Mitochondrial Aberration In SARS-CoV-2 Infection
Authors: Ward Charles , Schlichtholz Beata
Published: June 2024

This review investigates links between post-acute sequelae of SARS-CoV-2 infection (PASC), post-infection viral persistence, mitochondrial involvement and aberrant innate immune response and cellular metabolism during SARS-CoV-2 infection. Advancement of proteomic and metabolomic studies now allows deeper investigation of alterations to cellular metabolism, autophagic processes and mitochondrial dysfunction caused by SARS-CoV-2 infection while computational biology and machine learning have advanced methodologies of predicting virus-host gene and protein interactions. Particular focus is given to interaction between viral genes and proteins with mitochondrial function and that of the innate immune system. Finally, the authors hypothesise that viral persistence may be a function of mitochondrial involvement in sequestration of viral genetic material. While further work is necessary to understand the mechanisms definitively, a number of studies now point to resolution of questions regarding the pathogenesis of PASC.
Exploring the Mechanism of Topical Application of Clematis Florida in the Treatment of Rheumatoid Arthritis through Network Pharmacology and Experimental Validation
Authors: Lei Ting , Jiang Chang , Zhao Li , Zhang Jizhou , Xiao Qing , Chen Yanhong , Zhang Jie , Zhou Chunquan , Wang Gong , Han Jing
Published: June 2024

Clematis Florida (CF) is a folk medicinal herb in the southeast of China, which is traditionally used for treating osteoarticular diseases. However, the mechanism of action remains unclear. The present study used network pharmacology and experimental validation to explore the mechanism of CF in the treatment of rheumatoid arthritis (RA). Liquid chromatography-mass spectrometry (LC-MS/MS) identified 50 main compounds of CF, then the targets of them were obtained from TCMSP, ETCM, ITCM and Swiss TargetPrediction databases. RA disease related targets were obtained from DisGenet, OMIM and GeneCards databases. 99 overlapped targets were obtained using Venn diagram. The protein-protein interaction network (PPI)、the compound-target network (CT) and the compound-potential target genes-signaling pathways network (CPS) were built and analyzed. The results showed that the core compounds were screened as oleanolic acid, oleic acid, ferulic acid, caffeic acid, and syringic acid. And the core therapeutic targets were identified as PTGS2 (COX-2), MAPK1, NF-κB1, TNF, RELA by network pharmacology analysis, which belong to the MAPK signaling pathway and NF-κB signaling pathway. The animal experiments indicated that topical application of CF showed significant anti-inflammatory activity in mice model of xylene-induced ear edema and strong analgesic effect on acetic acid-induced withing. Furthermore, in the rat model of adjuvant arthritis (AA), topical administration of CF could alleviate toe swelling and ameliorate joint damage. The elevation of serum content of IL-6, COX-2, TNF-α, IL-1β, and RF caused by adjuvant arthritis were reduced by CF treatment. Western blotting tests showed that CF may exert regulation on the ERK and NF-κB pathway. The results provided a new perspective for the topical application of CF on RA.
A proof of the infinitude of prime numbers
Authors: Iqbal Md. Shouvik
Published: June 2024

This paper presents a new proof of the infinite nature of prime numbers. Grounded in fundamental Set theory and Analysis, the proof uses the method of contradiction to demonstrate the absurdity of assuming the set of all prime numbers to be finite.
Integrating High-Performance Flexible Wires with Strain Sensors for Wearable Human Motion Detection
Authors: Wu Pucheng , He Hu
Published: June 2024

Flexible electronics have revolutionized the field by overcoming the rigid limitations of traditional devices, offering superior flexibility and adaptability. Conductive ink performance is crucial, directly impacting the stability of flexible electronics. While metal filler-based inks exhibit excellent conductivity, they often lack mechanical stability. To address this challenge, we present a novel conductive ink utilizing a ternary composite filler system: liquid metal and two micron-sized silver morphologies (particles and flakes). We systematically investigated the influence of filler type, mass ratio, and sintering process parameters on the composite ink's conductivity and mechanical stability. Our results demonstrate that flexible wires fabricated with the liquid metal/micron silver particle/micron silver flake composite filler exhibit remarkable conductivity and exceptional bending stability. Interestingly, increasing the liquid metal content results in a trade-off, compromising conductivity while enhancing mechanical performance. After enduring 5000 bending cycles, the resistance change in wires formulated with a 4:1 mass ratio of micron silver particles to flakes is only half that of wires with a 1:1 ratio. This study further investigates the mechanism governing resistance variation during flexible wire bending. Additionally, we observed a positive correlation between sintering temperature and pressure with the conductivity of flexible wires. The significance of sintering parameters on conductivity follows a descending order: sintering temperature, sintering pressure, and sintering time. Finally, we demonstrate the practical application of this technology by integrating the composite ink-based flexible wires with conductive polymer-based strain sensors. This combination successfully achieved the detection of human movements, including finger and wrist bending.
Next generation blockchain technology: The Entropic Blockchain
Authors: Vopson Melvin M. , Lepadatu Serban G. , Vopson Anna , Łukaszyk Szymon
Published: June 2024

Storage, transmission and processing of data become a significant problem when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here we propose a novel method that allows the scalability of digital assets, including blockchain databases in the download, validation and confidentiality process, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method to generate an information-entropic numerical representation of a digital asset, which can be utilized on its own, or as an optically machine-readable entropic barcode, for data secure transmission, processing, labelling, identification, one-way encryption and compression, validation, and digital tamper-proof check.
Novel Pan-Coronavirus 3CL Protease Inhibitor MK-7845: Bio-Logical and Pharmacological Profiling
Authors: Alvarez Nadine , Adam Gregory C , Howe John A , Sharma Vijeta , Zimmerman Matthew D , Dolgov Enriko , Rasheed Risha , Nizar Fatima , Sahay Khushboo , Nelson Andrew M , Park Steven , Zhou Xiaoyan , Burlein Christine , Fay John F , Iwamoto Daniel V , Bahnck-Teets Carolyn M , Getty Krista L , Lin Goh Shih , Salhab Imad , Smith Keith , Boyce Christopher W , Cabalu Tamara D , Murgolo Nicholas , Fox Nicholas G , Mayhood Todd W , Shurtleff Valerie , Layton Mark E , Parish Craig A , McCauley John A , Olsen David B , Perlin David S
Published: June 2024

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC) which first emerged in 2012, persists and continues to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction of viral burdens by >6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).
Data Fusion Using Medical Records and Clinical Data to Support TB Diagnosis
Authors: Romero-Gómez Andrés F. , Orjuela-Cañón Alvaro D. , Jutinico Andrés L. , Awad Carlos E. , Vergara Erika , Palencia María A.
Published: June 2024

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.
An Evolutionary Hybrid Convolutional Deep Learning Network to Classify Paddy Leaf Disease
Authors: Jesie R Sherline , Premi M S Godwin
Published: June 2024

Rice has become the second major food among rural people. It is one of the most nutrient and low-cost food available in Asia. Rice is composed of two major subspecies as Japonica and Indica and it belongs to the Poaceae family. The World Bank predicts that rice demand will grow 51% faster than population growth in 2025. It is high time to develop a model which predicts the type of infected paddy leaf and avoids the loss of grains. In this paper, the proposed hybrid CNN model, three CNN networks with different layer properties are trained parallel. The extracted feature from these CNN networks is combined to provide the classification of the type of infected leaf. Fieldwork was carried and about 340 samples are collected from five different types of classes. The proposed hybrid model was tested with both the fieldwork dataset and also with an available dataset on the internet. The experimental results show that the proposed model outperforms the other CNN network and yields an accuracy value of 98%, a precision value of 95%, and a misclassification rate of 4% respectively.
Role of Vaccination on the Co-Infection Model with COVID-19 Associated with Diabetes
Authors: Hye Md. Abdul , Biswas Md. Haider Ali , Uddin Mohammed Forhad
Published: June 2024

COVID-19 infection is extremely dangerous for persons with comorbidities such as kidney disease and diabetes. Although many people of all ages and socioeconomic backgrounds have suffered due to this pandemic, those with underlying medical conditions like kidney disease and diabetes are more vulnerable to becoming infected with COVID-19 related to weak immunity. However, the role of vaccination in the co-infection of COVID-19 with diabetics is not available in the literature. Therefore, we examine the unique challenges presented by the co-infection of COVID-19 in individuals with diabetes, focusing on its disease transmission dynamics. A mathematical modelling technique is to be employed in this work to analyse the dynamics of COVID-19 outbreaks with seven compartments, including vaccination with comorbidities such as diabetes. We also investigated analytically by displaying the solutions' existence, boundedness, positivity and sensitivity. After calculating the basic reproduction numbers, the stability analysis of model equilibrium points is also covered. If the reproduction numbers are less than unity, the disease-free equilibrium points of the model appear to be both locally and globally stable. The findings of this study are that as the vaccination rate rises, the incidence of COVID-19 and its co-infections with diabetes decreases. Effective disease treatment strategies may be based partly on the possible impact of vaccination on the co-infection of COVID-19 related to diabetic disease insights.
From Traditionally Extensive to Sustainably Intensive: A Review on the Path to a Sustainable and Inclusive Beef Farming in Brazil
Authors: Pereira Mariana A. , Bungenstab Davi J. , Euclides Valéria P. B. , Malafaia Guilherme C. , Biscola Paulo H. N. , Menezes Gilberto R. O. , Abreu Urbano G. P. , Laura Valdemir A. , Nogueira Ériklis , Mauro Rodiney de A. , da Silva Marta P. , Nicacio Alessandra C. , Almeida Roberto G. de , Gomes Rodrigo da C. , Silva Juliana C. B. , de Souza Vanessa F.
Published: June 2024

Brazil became a major player in the global beef market, after the market liberalization in the 1990’s, and experiencing a substantial development. Nonetheless, the environmental impact of beef production associated with land use and greenhouse gas emissions has been under discussion. The necessity of increased adoption of sustainable technologies, however, may preclude some farmers to keep up to date with technological advances. This article describes the major historical changes in beef farming and how they have been shifting production systems toward a sustainable intensification of grass-fed beef, with likely impacts for farmers. We combined an extensive literature review, public data and our own insights, as senior researchers, to achieve that. The trajectory shown here evidenced the substantial technological development underway in Brazilian beef farming, with strong support of public policies for decarbonizing agriculture. Nonetheless, the pace of this sustainable transition may affect small to medium farmers unable to cope. Our recommendations, therefore, involve a broad program of technical assistance and training on sustainable technologies and other supportive practices, including financial and digital literacy. Additionally, we suggest an alternative credit system, with progressive borrowing allowances to ensure financial resources to support a sustainable and inclusive transition of Brazilian beef farming.