bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Ocular Film Containing Ofloxacin in a Chitosan Matrix
Authors: Fauziah Arsy , Suciati Tri , Julianti Elin
Published: January 2025

Background: Chitosan is a natural polymer that is widely used in pharmaceutical applications owing to its biodegradability and biocompatibility. High molecular weight chitosan, which is commonly found in the market (Sigma Aldrich), is acid soluble and thus limits its application for ocular purposes. Objective: This study aimed to improve the characteristics of high-MW chitosan ocular films by utilizing a water-soluble, low-MW chitosan modifier for the delivery of ofloxacin post-surgery. Methods: Various film formulas were prepared using high-MW chitosan as the main polymer matrix, glycerin and polyethylene glycol 400 as plasticizers, and low-MW chitosan as film modifiers. Glycerine was the best plasticizer that produced a good film appearance when added at an appropriate ratio, 8.33 times the weight of the high MW chitosan (TGc) and 6.25 times the weight of low- and high-MW chitosan blends at (1:1) ratio (MGb). The films were further developed as TGcs and MGbs were cross-linked using sodium tripolyphosphate to control the release of ofloxacin and improve its mechanical characteristics. Water absorption capability, mechanical characteristics, in vitro drug release, and antimicrobial activity were evaluated to determine the film formula. Results: The MGb formula showed the highest water absorption (approximately 230 %), while the lowest was shown by the TGcs formula (approximately 145 %). In contrast, the TGcs formula had the highest film elasticity (141.33±8.81%), and the MGb formula had the lowest (42.55±6.11%). Surprisingly, the best controlled release of ofloxacin for up to 24 h was produced by the MGbs film, which also showed the highest antimicrobial activity. MGbs also showed moderate film characteristics, which are suitable for ocular applications. Conclusion: The research concluded that The addition of water-soluble low-MW chitosan and a cross-linker agent can improve the controlled release and characteristics of chitosan-based ocular films.
Formulation and Evaluation of Transdermal Dissolving Microneedle Loaded with Ethanol Extract of Cocor Bebek Leaves (Kalanchoe pinnata)
Authors: Balapadang Rachel Noveriachristie , Sarie Aldila Divana , Rosyidah Safira , Zulqifli Iqbal , Akifah Muthia Nur , Zahra Aliya Azkia
Published: January 2025

Background: Acne is a chronic skin inflammation caused by blockage of the sebaceous glands in the skin and hypercolonization of the acne-causing bacteria Propionibacterium acnes. Cocor bebek leaves (Kalanchoe pinnata) are known to contain various secondary metabolites, including flavonoids, with antibacterial activity. Objective: In an effort to prevent side effects from using oral and topical antibiotics to treat acne, an alternative acne treatment that is safer and more effective with a strong drug delivery system is needed: microneedle patch technology containing natural ingredients. Methods: A microneedle patch formulation of Cocor bebek leaf extract was developed using a combination of HPMC and PVP polymers. The evaluation of microneedle patches included irritation tests with rat test animals and antibacterial activity tests against Propionibacterium acnes. The results showed the formation of yellow microneedle patches with uniform needles. The evaluation results showed that the microneedle patch has an irritation index value classified as non-irritant and has antibacterial activity against Propionibacterium acnes, with the highest inhibitory diameter at an extract concentration of 30% with a moderate inhibition category. Conclusion: The microneedle patch cocor bebek leaf extract shows potential as an effective drug delivery system for the treatment of acne that is safe for use on the skin.
Formulation and Characterization of Analog Rice Using Arrowroot (Maranta arundinacea L.) and Hibiscus Flower (Hibiscus rosa sinensis L.)
Authors: Antari Arlita Leniseptaria , Saraswati Indah , Eva Annisa  , Anfa Adnia Fatma
Published: January 2025

Background: Efforts to reduce high-carbohydrate consumption from rice include the development of analog rice from alternative sources such as arrowroot tubers (Maranta arundinacea L.). Proper formulation is crucial for producing analog rice with optimal quality, taste, and characteristics resembling conventional rice, while maintaining functional properties to meet carbohydrate and nutritional needs. Hibiscus (Hibiscus rosa-sinensis L.), which is rich in flavonoids, saponins, and anthocyanins, can enhance these formulations. Objective: The objective of this study was to evaluate the composition of a blend of arrowroot tubers (M. arundinacea L.) and hibiscus (H. rosa-sinensis L.) as a rice substitute. Methods: This study used 50 untrained panelists to conduct organoleptic tests on the shape, color, aroma, taste, and texture of rice and analog rice using five formulas (F1, F2, F3, F4, and F5). Furthermore, physical characteristics were tested, including the color index, 1000-grain weight, bulk density, and starch digestibility. Results: The characteristics test proved that all analog rice formulas had an average hedonic score in the range of 3.73-3.90; lower bulk Density than rice with a bulk density of 0.83 ± 0.02 g/mL; starch digestibility in the range of 0.62-0.67 g/mL; and has a yellow-red color range. Conclusion: The best and most preferred rice analog is Formula 5 (F5), with a composition of arrowroot tubers 57.8%, hibiscus 5.1%, mocaf 12.6%, GMS 0.6%, and water 23.9%.
Comparative Evaluation of Hemato-biochemical and Oxidative Parameters of Racing Horses
Authors: Atata Jamila Abdulhamid , Ajadi Abdullateef Abiodun , Adam Muhammed , Olatunji Aishat Omobolanle , Bisalla Muhammed , Adamu Sani
Published: January 2025

Racing horses are prone to alterations in hemato-biochemical and oxidative stress parameters. The aim of this study was to determine the effect of racing on hemato-biochemical, and oxidative stress parameters of racing horses. Blood samples were collected from 15 control and 15 racing horses at four intervals (before, immediately after, 30 minutes after, and 24 hours after) to assay for hematological, serum biochemical, and oxidative stress markers.  The result revealed significant changes (P < 0.05) in vital parameters such as erythrocyte count, hematocrit index, hemoglobin concentration, leucocyte count, lymphocyte, neutrophil, neutrophil: lymphocyte ratio, total protein, albumin, urea, creatinine, cortisol, lactate, lactate dehydrogenase, creatinine kinase, aspartate aminotransferase, while glucose, cholesterol, triglyceride, gamma-glutamyl transferase, malondialdehyde, glutathione peroxidase, and catalase remained unaltered (P > 0.05). The alteration in hemato-biochemical parameters reported in this study only lasted for about 30 minutes after racing, with all values returning to the baseline range by 24 hours post-race. Interestingly, the comparable activities of antioxidant stress markers reported suggests that the horses have developed adaptive mechanisms that have conferred them with increased resistance to oxidative stress and reduced oxidative damage. This study demonstrates the importance of schematic monitoring of racing horses peri-racing to prevent racing-induced pathophysiological alterations and promote their general wellbeing.
Journal Review: Effectiveness of Supplementary Feed Ingredients on Egg Productivity and Body Weight Gain of Balibangtan Superior Hens
Authors: Laraswati Maria Intan Ayu , Nugraha Raden Septiarani Jati , Hidayah Nurulia , Ridlo Muhammad Rosyid
Published: January 2025

Feed is a major determining factor in the success of layer and broiler farming. The nutritional content of the feed must meet the requirements to increase livestock productivity. To improve chicken performance, we use additional feed. This review aims to determine the effectiveness of feed ingredients as a feed supplementation for chickens by using production waste, forage, household waste, forage, and enzymes to increase the growth and production of KUB chickens. Ingredients that can be used as feed additives include coconut pulp, pumpkin waste flour, papaya leaf waste, teak leaf extract, moringa leaves, tilapia plants, betel leaf juice, BS4, selenium chitosan, phytase, xylooligosaccharide, kesum leaf flour, sago pulp, spinach waste, and BSF maggot. The results of this review showed that the most effective feed ingredient was the supplementation of teak leaf extract (Tectona grandis), with a protein content of 4.9%, being the most effective feed ingredient in increasing egg productivity up to 15%. The supplementation of kesum leaf flour, with an average body weight gain of 426.97 g/head, is the most effective feed ingredient for increasing body weight growth in KUB chickens.
Indonesian Newcastle Disease Virus Field Isolate Reduces c-Jun Expression in Rat Mammary Cancer Models
Authors: Marson Fransiska Gratia Sonita , Sewoyo Palagan Senopati , Astawa I Nyoman Mantik , Adi Anak Agung Ayu Mirah , Suardana Ida Bagus Kade , Winaya Ida Bagus Oka , Berata I Ketut
Published: January 2025

c-Jun is often found to be overexpressed in various cancers, so this gene might be a target for cancer therapy. Newcastle disease virus (NDV) is recognized for its oncolytic properties and potential as a cancer virotherapy agent, with various mechanisms reported to trigger cancer cell death. This study aimed to assess the c-Jun expression in rat mammary cancer models. Rat mammary cancer models were categorized into two treatment groups: the control group (C) and the virotherapy group (V). Group C was administered with 0.5 cc of sterile PBS, while group V received 7 log 2 HAU per 0.5 cc of the Indonesian NDV field isolate Tabanan-1/ARP/2017 intratumorally. The treatment was carried out for four days in a row. Two weeks after treatment, all rats were humanely euthanized, and mammary cancer tissues were excised for further examination. Mammary cancer tissues were examined histopathologically and analyzed using immunohistochemistry to determine intranuclear c-Jun expression, quantified by the H-Score. The results demonstrated that NDV significantly reduced c-Jun expression. It can be inferred that NDV Tabanan-1/ARP/2017 holds potential as a mammary cancer therapy agent by reducing c-Jun expression. This finding is considered novel, as there have been no previous reports of decreased c-Jun expression following virotherapy with NDV.
Advancing energy efficiency: innovative technologies and strategic measures for achieving net zero emissions
Authors: Bera Mahadev , Das Sumanta , Garai Sourav , Dutta Suman , Roy Choudhury Malini , Tripathi Sudipta , Chatterjee Gautam
Published: January 2025

To evaluate the essential role of energy efficiency in achieving net-zero emissions and combating climate change, this comprehensive review was conducted based on a thorough analysis of academic literature, diverse case studies, and industry reports, focusing on key energy efficiency sectors and technological advancements. It begins by outlining global efforts, including the Paris Agreement and the Sustainable Development Goals, which have driven initiatives to enhance energy efficiency across various sectors. The review examines key technological innovations, such as advanced manufacturing, energy-efficient building designs, transportation electrification, and smart grid integration contributing to energy savings and decarbonization. It also discusses policy frameworks, regulatory incentives, and financial mechanisms that encourage energy efficiency. The study provides the detailed outcomes of the different strategies and initiatives, showcasing successful case studies from different regions in transportation, urban planning, power generation, and industry. It illustrates how countries have effectively implemented energy efficiency measures to reduce emissions and meet climate goals. Additionally, the review explores the potential of emerging technologies, including artificial intelligence, big data, and blockchain, to optimize energy management, enhance grid flexibility, and promote further efficiencies. Despite these advancements, the review identifies several key barriers - financial, technological, and behavioral challenges that impede the widespread adoption of energy-efficient practices. Finally, it offers strategies to overcome these obstacles, emphasizing the need for tailored policies, innovative business models, and greater consumer engagement. This review underscores the importance of energy efficiency in achieving a sustainable, net-zero future by synthesizing successful case studies and highlighting emerging solutions.
Insecticidal and Repellent Effects of Some Local Plants Against the Rice Weevil (Sitophilus oryzae L.) Infesting Pearl Millet Stored in Northern Benin
Authors: Obédatou Alagbe Tayé , Estelle Loko Yeyinou Laura
Published: January 2025

The rice weevil, Sitophilus oryzae L. is a widely distributed storage insect pest that causes tremendous damage to stored pearl millet. Farmers employ synthetic chemical insecticides to overcome this constraint, which have significant impacts on human health and the environment. The use of plant extracts and plants can be an alternative to these insecticides. The objective of this study is to evaluate the insecticidal and insect repellent properties of powders and ethanoic extracts of Ocimum gratissimum L., Ocimum basilicium L., Parkia biglobosa (Jacq.) R.Br. ex G.Don., and Azadirachta indica A. Juss. against S.oryzae using antixenosis and antibiosis methods. Deltamethrine insecticide and Bextoxin served as positive controls for antibiosis and antixenosis tests respectively. Untreated pearl millet were used as negative control. According to the results, the leaf powders and ethanolic extract of the four plants had repellent properties and were more repellent than synthetic insecticides. The leaf powder of O. basilicium was more repellent after 1 h (88.24%) and 24 h (80.11%), while that of P. bligobosa was more repellent after 12 h of exposure (78.57%).The P. biglobosa extract exhibited the highest rate of repulsion against S. oryzae at 5% concentration after 2 h of exposure. Deltamethrine had a greater insecticidal effect than powders and resulted in complete mortality (100%) of S. oryzae adults from the first day of treatment. The ethanol extract of O. gratissimum caused the greatest contact toxicity (100% mortality) at 2.5% concentration after 21 days of treatment. Similarly O. gratissimum had a high fumigant toxicity (92.5% mortality) at 7.5 % (160 µL/L of air) of concentration after 24 h after exposure. The results of this study demonstrated that the powders and extracts of the four tested medicinal plants could be utilized as biopesticides, a potential alternative for controlling S. oryzae in stored pearl millet.
Phytochemical Screening, Anti-bacterial and Anti-fungi Activities of Leafs, Stems and Roots of C. parviflorus Lam and C. salviifolius L Plants
Authors: Adress. Hasan Hamad. M. , Ben. Areos Najat. , M. Emaza Naser.
Published: January 2025

This study was carried out on leafs and steams and roots of C. parviflorus Lam and C. salviifolius plants, which growing at Al–Gabal Al–Akhder region, Libya. The phytochemical screening, anti-oxidant and total phenol contents were determined. Two different solvents were used in this study (Aqueous and Acoholl). The anti-microbial investigation was carried out on the plant extracts against different types of pathogenic bacteria including (Bacilli, Streptococcus, E. coli and Pseudomonas), beside three types of Fungi including (Aspergillus niger, Rhizopus and Fusarium). The results recorded that, there are variations in phytochemical screening, where the detected compounds of: carbohydrates and/or glycosides, tannins, flavonoids, sterols and/or triterpenes, saponins, and anthraquinone, showed variations in both extracts of the selected plant samples. Also the extracts were used for antibacterial and antifungal application studies, the results showed different effects on the selected microbial and fungi species which selected in this study, where some of extracts gave inhibition zones compared with those which no gave the effect on the studied bacteria and fungi species.
Evaluation of the impact of vine pruning periods on grape production and composition: an integrated approach considering different years and cultivars
Authors: Ghiglieno Isabella , Facciano Luca , Valenti Leonardo , Amari Federico , Cola Gabriele
Published: January 2025

Several studies have already highlighted how late pruning, conducted after bud break, can result in a delay in phenology and, consequently, in ripening. However, the effects of pruning periods on vegetative and productive parameters are less well known. The aim of this study was to evaluate the effect of pruning periods on ripening kinetics, grape composition at harvest and yield parameters, considering the grapevine cultivar and year. The experimentation was conducted from 2011 to 2013 in two of the main Italian wine-growing areas: Franciacorta, in Northern Italy, and Montefalco, in Central Italy. In Franciacorta: Chardonnay and Pinot Noir were considered while in Montefalco, the varieties included were Sauvignon Blanc, Grechetto di Orvieto, Sagrantino, and Sangiovese. Six different pruning periods were compared: at leaf fall (NOV), during dormancy (DEC, JAN, FEB), in the pre-bud break period (MAR) and late, post-bud break (APR).  Results obtained showed that the effects of the pruning period are clear and sufficiently replicable only considering pre-bud brake pruning (APR). The observation of the ripening kinetics highlighted in fact a delay in grape ripening for APR in both contexts as well as a lower values for the sugar-acidity ratio at harvest. In terms of grape yield, a moderately negative effect of APR was observed in Montefalco, while a more severe effect was observed in Franciacorta. This analysis gives a comprehensive understanding of the effects of pruning timing on grape yield and composition, providing viticulturists with transversally applicable information, useful for an aware management of the vineyard.
Cryptographically Upgrading TOR Network to Enforce Anonymity by Enhancing Security and Improving Performances
Authors: ghanem mohamed chahine
Published: January 2025

The Onion Route Network (also called TOR) is by far the most efficient and widely used anonymity platform with millions of users daily and an expanding size and capacities. Since its public deployment in 2002, the Onion Routing network (also known as TOR) has maintained its leading position and dozens of propositions aiming to improve its performance and enhance the security (anonymity and privacy) have been made. Given the significance of this research area, this work seek to contribute into the improvement of TOR by investigating and testing revolutionary cryptographic and routing mechanism. This work is justified by the current TOR vulnerability and observed weaknesses, and set the challenging aim of covering these security flaws by proposing the relevant security and performances improvement such as the authenticated-encryption for onion construction, the lightened onion encapsulation approach and the secured circuit selection and cell routing mechanisms. The obtained results from implementing the proposed improvements and testing them into a TOR-like simulation platform permit to validate not only, the performances and security contributions brought by the improvement, but also the suitability of their potential implementation into the real TOR network.
Structure Performance of Integrity Through On-Site: Decision Making Analysis for a Jacket-Type Offshore Platform
Authors: Martínez Rodrigo Daniel Álvarez Bello , Álvarez-Arellano Juan Antonio , El  Hamzaoui Youness
Published: January 2025

This paper presents a comprehensive on-site decision-making framework for assessing the structural integrity of a jacket-type offshore platform in the Gulf of Mexico, installed at a water depth of 50 m. Six critical analyses—(i) static operation and storm, (ii) dynamic storm, (iii) strength-level seismic, (iv) maximum wave resistance (pushover), (v) seismic ductility (pushover), and (vi) spec-tral fatigue—are performed using SACS software to capture both linear and nonlinear interactions among the soil, piles, and superstructure. The environmental conditions include multi-directional wind, waves, currents, and seismic loads. In the static-linear analyses (i, ii, iii), the overall results confirm that the unity checks (UC) for struc-tural members, tubular joints, and piles remain below allowable thresholds (UC
Recipe Based Anomaly Detection with Adaptable Learning: Implications on Sustainable Smart Manufacturing
Authors: Lee Junhee , Jang Jaeseok , Tang Qing , Jung Hail
Published: January 2025

The advent of Industry 4.0 has significantly transformed the manufacturing sector, bringing advancements in quality control efficiency, environmental sustainability, and production development etc. These changes have led to the development of intelligent technologies such as artificial intelligence(AI). However, implementing AI solutions in manufacturing processes still faces challenges in many aspects, particularly in handling irregular datasets influenced by diverse manufacturing settings. Especially in the field of injection molding, quality inspection often occurs in a batch level rather than singular data, providing only the overall defective ratio of the batch production instead of each individual labeling result. These issues limit the general application of AI and data-driven decision making. To address these limitations and promote product efficiency, this study proposes a novel anomaly detection framework for a specific manufacturing process. For Recipe-Based Learning, we first apply K-Means to take the flexible manufacturing process relying on diverse settings into account. The injection molding data are classified into specific-setting based recipes to secure data normality and uniqueness. Then, autoencoders for anomaly detection are trained with normal data from each recipe. With this data-driven AI approach, 61 defective products are predicted compared to the existing 41 defects. Meanwhile, the integrated model without considering the change of settings only predicted 2 defects which imply a poor and distorted quality inspection. For Adaptable Learning focused on new inputs with unseen settings, we apply KL-Divergence to find the closest trained recipe data and its model. In this case, it also exceeded the predictivity compared to the integrated and additionally trained models. This leads to continuous prediction without further training, which also successfully implies the enhancement of process optimization. In the aspect of smart factory in the injection molding field, such improvement of process management can first improve the overall productivity and decision making mainly with data-driven AI approach. Furthermore, it is expected to enhance energy efficiency such as reduction of environmental waste and computational cost, ensuring sustainable manufacturing.
Orbiting Obstacles: Navigating Space Debris with Advanced Surveillance and Tracking Solutions
Authors: Wani Irfan Nazir , Kumar Manoj , Kumar Shukla Pushpendra
Published: January 2025

Space Debris or orbital debris, also called Space waste or junk present in Earth's orbit, offers a substantial challenge to space exploration. Satellite operations and other space-based activities. This research paper delves into the causes and effects of space debris accumulation, explores current mitigation techniques, and presents a hopeful outlook on the potential for future sustainable space activities. The paper emphasizes the necessity of addressing planetary fragments to ensure durable sustainability in universe exploration and utilization. It examines various strategies for mitigating space debris, including debris removal technologies, spacecraft design improvements, and international collaboration efforts. Additionally, the paper highlights the importance of space debris monitoring and tracking systems in preventing collisions and minimizing the growth of orbital debris. By comprehending the complexities of space debris and implementing effective mitigation measures, the space industry can work towards a future where sustainable space activities are achievable.
Development and Validation of a Scale for Perceived Health Information Fog Among Social Media Users from the Perspective of Information Ecology
Authors: An Qi , Bi Xinhua , Wu Jiage
Published: January 2025

The phenomenon of health information fog on social media has become increasingly prevalent, posing significant challenges to users’ health literacy, decision-making, and the sustainable development of social media information ecosystems. This study aims to develop a robust and effective scale to measure users’ perception of health information fog and to uncover its underlying mechanisms. Grounded in information ecology theory, the initial scale was developed through a literature review, semi-structured interviews, and grounded theory. A pilot survey involving 155 respondents was conducted, followed by a formal survey with 561 participants. Exploratory and confirmatory factor analyses were employed to validate and refine the scale, resulting in a final version comprising 46 items across eight dimensions: health information overload, low-quality information, unclear information sources, group blind following, cognitive conflict, anxiety, group polarization, and urgent demand for health information. A normative model was further constructed to categorize users into five levels of perception intensity: low, relatively low, moderate, relatively high, and high. This study provides targeted intervention strategies for stakeholders and offers a scientific foundation for optimizing information dissemination while promoting the sustainable development of social media ecosystems.
FreeMix: Open Vocabulary Domain Generalization of Remote Sensing Images for Semantic Segmentation
Authors: Wu Jingi , Shi Jingye , Zhao Zeyong , Liu Ziyang , Zhi Ruicong
Published: January 2025

In this study, we present a novel concept termed open vocabulary domain generalization (OVDG), which we investigate within the context of semantic segmentation. OVDG presents greater difficulty compared to conventional domain generalization, yet it offers greater practicality. It jointly considers 1) recognizing both base and novel classes and 2) generalizing to unseen domains. In OVDG, only the labels of base classes and the images from source domains are available to learn a robust model. Then the model could be generalized to images from novel classes and target domains directly. In this paper, we propose a dual-branch FreeMix module to implement the OVDG task effectively in a universal framework: the Base Segmentation Branch (BSB) and the Entity Segmentation Branch (ESB). First, the entity mask is proposed for the first time for segmentation generalization, and the semantic logits are learned for both base mask and entity mask, so that to enhance the diversity and completeness of masks for both base classes and novel classes. Second, the FreeMix utilizes pre-trained self-supervised learning on large-scale remote sensing data (RS_SSL) to extract domain-agnostic visual features for decoding masks and semantic logits. Third, a training tactic called dataset-aware sampling (DAS) is introduced for multi-source domain learning, aimed at improving the overall performance. In summary, RS_SSL, ESB and DAS can significantly improve the generalization ability of model on both class-level and domain-level. Experiments demonstrate that our method produces state-of-the-art results on several remote sensing semantic segmentation datasets, including Potsdam, GID5, DeepGlobe, and URUR, for OVDG.
A Comparative Study and Thermophysical Characterization of Wool Fiber from Different Regions of Morocco
Authors: Aazou Nabil , Zaim Faical , Gmouh Said
Published: January 2025

Wool fibers have long been used in home textiles and clothing, but their future looks even more promising with the growing consumer demand for natural, renewable, and sustainable materials. Beyond traditional clothing, wool is gaining increasing attention in the field of technical textiles due to its unique properties that make it suitable for a wide range of applications. As a natural and renewable fiber, wool offers benefits including tremendous moisture absorption, temperature law, and flame resistance, which make it a perfect material for technical textiles. In this study, two styles of Moroccan wool (Sardi and Timahdite), sourced from one-of-a-kind areas become accrued to explore their bodily and chemical homes and to investigate their capacity use within the manufacturing of nonwoven textiles for technical applications. Various analyses were carried out, inclusive of Fourier-remodel infrared spectroscopy and the Optical Fiber Diameter Analyzer, together with numerous check techniques based totally on global standards. These tests evaluated parameters which include grease content material, alkali content, acid content, solubility in alkali, and tensile strength. The results showed that Timahdite wool is the most suitable for nonwovens, way to its fineness and excessive absorbency compared with the Sardi wool.
Effect of Surface Treatments and Thermal Aging on Bond Strength Between Veneering Resin and CAD/CAM Provisional Materials
Authors: Robaian Ali , AlShehri Abdullah , Alqhtani Nasser Raqe , Almudahi Abdulellah , Alanazi Khalid K , Abuelqomsan Mohammed A S , Raffat Eman Mohamed , Elkaffas Ali , Hashem Qamar Mohammadziad , Soliman Tarek Ahmed
Published: January 2025

The oral environment significantly influences the aesthetic appearance of CAD/CAM provisional restorative materials. Therefore, a veneering layer is required. However, bonding veneering resin composites to these materials is challenging, especially when exposed to thermal aging. This study assessed the effect of different surface treatments and thermal aging on bond strength between veneering resin and CAD/CAM provisional materials. Provisional materials specimens including CAD-Temp, Everest C-Temp, and PEEK were randomly allocated to five groups. Group C: without any surface treatments. DB: mechanically roughened using a diamond bur; DB+TC: DB group with 5000 cycles of thermocycling; SB: aluminum oxide airborne abraded; SB+TC: SB group, along with 5000 cycles of thermocycling. Following the surface treatments, the specimens received primer and resin veneering composite. The bond strength (SBS) was evaluated. C-Temp in the SB group exhibited the highest SBS values (MPa), while CAD-Temp in the C group showed the lowest values. PEEK recorded significantly higher SBS in DB+TC and SB+TC groups than CAD-Temp in DB+TC and SB+TC. C-Temp exhibited higher SBS without surface treatment, whereas PEEK showed higher SBS after diamond bur roughening and air particle abrasion. Thermocycling significantly reduced bond strength in all materials except for PK material in the air particle abrasion group.
Regional and Climatic Seasonality in Avian Species Richness
Authors: Özdemir Serkan , Uyar Çağdan , Perkumienė Dalia , Aleinikovas Marius , Šilinskas Benas
Published: January 2025

This study highlights the spatial, seasonal, and climatic variations in bird species richness across Türkiye, a country with rich avian richness situated at the intersection of major migratory routes. Bird species richness was calculated for each province. Differences between regions, Köppen-Geiger climate classes, and seasons were analyzed using the Kruskal-Wallis method. Nonparametric Analysis of Longitudinal Data in Factorial Experiments was also employed to determine seasonal differences within regions and climate classes. The results revealed significant spatial variations in species richness, particularly between temperate and cold climate regions. While seasonal differences were generally less pronounced, they were critical for both migratory and resident bird species. Wetlands, coastal areas, and transitional habitats were identified as biodiversity hotspots for both resident and migratory birds. This study underscores the need to integrate regional, climatic, and seasonal variations into ecosystem-based management plans. Protecting critical habitats, enhancing connectivity through ecological corridors, and adopting adaptive conservation strategies are essential for sustaining Türkiye’s rich avian diversity. These results provide valuable insights for conservation planning and emphasize the importance of addressing spatial and seasonal dynamics to ensure long-term biodiversity preservation.
A Novel Approach to Autonomous Driving Using DDQN-Based Deep Reinforcement Learning
Authors: Khlifi Ahmed , Othmani Mohamed , Kherallah Monji
Published: January 2025

Deep reinforcement learning (DRL) trains agents to make decisions by learning from rewards and penalties, using trial and error. It combines reinforcement learning with deep neural networks, enabling agents to process large datasets and learn from complex environments. DRL has achieved notable success in gaming, robotics, decision-making, etc. However, real-world applications, such as self-driving cars, face challenges due to complex state and action spaces, requiring precise control. Researchers continue to develop new algorithms to improve performance in dynamic settings. A key algorithm, Deep Q-Network (DQN), uses neural networks to approximate the Q-value function, but suffers from overestimation, leading to suboptimal outcomes. To address this, Double Deep Q-Network (DDQN) was introduced to reduce bias by separating action selection from evaluation, resulting in more stable learning. This work examines the effectiveness of DQN and DDQN in autonomous driving using the CARLA simulator, highlighting DDQN's benefits in reducing bias and enhancing policy performance.
Genetic Resistance of Solanum lycopersicum L. (Tomato) to Phytophthora infestans and Aspergillus niger
Authors: Anakaa Tavershima Moses , Olawuyi Odunayo Joseph , Jonathan Segun Gbolagade
Published: January 2025

There is limited information on genetic response of tomato cultivars to fungi. This study investigated genetic resistance of tomato accessions, chromosomes and morphological characters to Phytophtora infestans and Aspergillus niger infection. Thirty tomato seed accessions were grown in screen house using complete randomised design in triplicates, and evaluated for resistance to both fungi using disease severity rating scale of 1 (highly resistant) to 6 (highly susceptible), while chromosome assessment was done using Carnoys protocol. Morphological data of 28 characters were collected using IPGRI descriptor, while Variance components, Genetic Advance (GA) and Heritability were estimated for treated tomato and control using R statistical program. Accession NHT0254b that was highly resistant to P.infestans (1.00) and A.niger (1.33) were diploid (2n=2x=24). Phytophtora infestans treated NHT343a was moderately susceptible (3.67) with 2n=2x=23, while NGB00711 was moderately susceptible (4.33) with 2n=2x=22 after A.niger treatment, indicating aneuploidy. The Environmental variance (1509.57) and genotypic variance (8.22) were highest for Cumulative Fruit Weight (CFW) and Number of Seeds (NS), respectively for P.infestans treatment, while GA (2.97) and phenotypic variance (754.91) were higher for NS and CFW, respectively in A.niger treatment. Heritability estimate showed that fruit length (63.0%), size of core (65.0%), and number of chambers (60.0%) were resistant to P.infestans, while only fruit length (56.0%) was resistant to A.niger for yield characters. Therefore, accessions and characters that showed resistance to P.infestans and A.niger are recommended for selection and genetic improvement.
A Hybrid PINNs-FNO Approach for Advanced Option Pricing Models
Authors: Elbayed Zakaria , El Idrissi Abdelmjid Qadi
Published: January 2025

Accurate option pricing remains a cornerstone of quantitative finance, where capturing the intricate dynamics of financial markets poses a persistent challenge. Traditional approaches, like the Black-Scholes framework, suffer from restrictive assumptions, while emerging deep learning techniques often prioritize either physical consistency or data-driven flexibility, rarely achieving both. This study proposes a novel hybrid framework that combines Physics-Informed Neural Networks (PINNs) and Fourier Neural Operators (FNOs) to overcome these limitations. By leveraging the complementary strengths of PINNs for embedding the governing equations of the Black-Scholes model and FNOs for capturing complex, global relationships, the hybrid model achieves remarkable accuracy and robustness. Our methodology is validated on synthetic datasets representing local volatility surfaces, demonstrating significant performance improvements. The hybrid approach outperforms standalone PINNs and FNOs across key metrics, achieving a Mean Squared Error (MSE) reduction of up to 50% and enhanced generalization capabilities. Furthermore, extensive visualizations confirm the model’s ability to reconcile mathematical consistency with the adaptability required for real-world applications. This work not only advances the state-of-the-art in option pricing but also establishes a scalable and extensible framework applicable to broader domains governed by partial differential equations. By bridging the gap between theoretical finance and modern machine learning, the proposed approach opens new horizons for practical applications in risk management, hedging strategies, and derivative pricing.
Quantum-Enhanced Conformal Methods for Multi-Output Uncertainty: A Holistic Exploration and Experimental Analysis
Authors: Tasar Davut Emre
Published: January 2025

Quantum computing introduces unique forms of randomness arising from measurement processes, gate noise, and hardware imperfections. Ensuring reliable uncertainty quantification in such quantum-driven or quantum-derived predictions is an emerging challenge. In classical machine learning, conformal prediction has proven to be a robust framework for distribution-free uncertainty calibration, often focusing on univariate or low-dimensional outputs. Recent advances (e.g., [1–3]) have extended conformal methods to handle multi-output or multi-dimensional responses, addressing sophisticated tasks such as time-series, image classification sets, and quantum-generated probability distributions. However, bridging the gap between these powerful conformal frameworks and the high-dimensional, noise-prone distributions typical of quantum measurement scenarios remains largely open. In this paper, we propose a unified approach to harness quantum conformal methods for multi-output distributions, with a particular emphasis on two experimental paradigms: (i) a standard 2-qubit circuit scenario producing a four-dimensional outcome distribution, and (ii) a multi-basis measurement setting that concatenates measurement probabilities in different bases (Z, X, Y) into a twelve-dimensional output space. By combining a multi-output regression model (e.g., random forests) with distributional conformal prediction, we validate coverage and interval-set sizes on both simulated quantum data and multi-basis measurement data. Our results confirm that classical conformal prediction can effectively provide coverage guarantees even when the target probabilities derive from inherently quantum processes. Such synergy opens the door to next-generation quantum-classical hybrid frameworks, providing both improved interpretability and rigorous coverage for quantum machine learning tasks. All codes and full reproducible Colab notebooks are made available at https://github.com/detasar/QECMMOU.
Community-Based Telehealth Approach Improves Specialist Access for Individuals with Increased Cancer Risk in Low-Resource Settings
Authors: Alp Aksel , Doud Winston , Doud Christian , Takesh Thair , Wink Cherie , Miranda-Hoover Annachristine , Foote Joseph , Liang Rongguang , Messadi Diana V , Le Anh , Wilder-Smith Petra
Published: January 2025

The low-resource, minority and underserved populations (LRMU) that carry the highest risk of oral cancer (OC) experience many barriers to early detection and treatment, resulting in disproportionately poor out-comes. One major barrier to better outcomes is poor compliance with specialist referral for diagnosis and treatment. The goal of this study was to compare specialist referral compliance for Telehealth vs. in-person visits in LRMU individuals screening positive for increased OC risk. Forty subjects who had screened posi-tive for oral potentially malignant lesions (OPMLs) were recruited from community clinics. The subjects indi-cated whether they would prefer an in-person or Telehealth specialist visit. They were offered assistance with all aspects of the visit, and then tracked over 3 months for referral compliance. A novel, very low-cost, simple Telehealth platform located within the community clinic was used for the remote specialist visit. In the Telehealth group, 16/24 subjects attended the first scheduled remote specialist visit; 4/24 attended resched-uled visits within 3 months, and 4/24 did not comply at all. All attendees and specialists were able to com-plete the remote visit in full. Of the 7/16 subjects who completed the in person visit, 3/16 attended the first scheduled visit, 4/16 complied within 3 months. 9/16 did not comply at all with specialist referral. Significantly more individuals complied with telehealth specialist referral at 1 month (p=0.0006) and after 3 months (p=0.0154). A novel Telehealth platform may improve compliance with specialist referral in low-resource in-dividuals with OPMLs.
Construction and Optimization of Intelligent Gateway Software Management Platform Based on Jenkins Cluster Management Under Cloud Edge Integration Architecture in Industrial Internet of Things
Authors: Wu Wei
Published: January 2025

With the rapid development of artificial intelligence and the fifth generation mobile communication technology, as well as the in-depth promotion of the concept of cloud-edge collaboration, the construction of the "cloud-edge-end" integration architecture in the field of industrial Internet of things has made remarkable progress, which has brought profound changes to the traditional manufacturing industry. In the practical application process of this architecture, the complexity of end-side device management and data interaction exposes many shortcomings of the existing technology, especially for the software management of industrial intelligent gateway, there is still a lack of systematic solutions. As a key node connecting the cloud and industrial field devices, industrial intelligent gateway plays an important role in data collection, protocol conversion, edge computing and device linkage, etc. However, the current management platform generally has problems such as single strategy, low efficiency and insufficient compatibility when dealing with the upgrade requirements of highly customized devices. This study proposed and optimized an intelligent gateway software management platform based on the "cloud-edge-end" architecture, combined with the typical scenarios of small and medium-sized manufacturing enterprises, designed the platform architecture with strong versatility, defined the functional modules and task division, and established the node organization form of "one gateway and multiple devices" through the industrial visual quality inspection project to verify its rationality. In order to solve the bottleneck problem of software upgrade, this paper innovatively proposes the large file transfer mechanism combining block and breakpoint continuation, the upgrade rollback strategy based on "three-mesh backup", and the efficient transmission algorithm of dynamically adjusting bandwidth, and the optimization scheme supporting differential update, which greatly improves the upgrade efficiency and stability. With Jenkins cluster management tool, the platform realizes the automation of task construction and batch deployment, and ensures the reliability of the upgrade process through the end-to-end security check script. The practical application and test results show that the platform has excellent performance in functional perfection, operational reliability and scenario adaptation, which provides strong support for the popularization of industrial intelligent gateway and the further development of "cloud-edge-end" collaborative architecture.
Miroslav Volf’s Theology of Memory in Relation to Zimbabwean Social Narratives
Authors: Useni Joseph Chifuniro
Published: January 2025

‘The End of Memory’ by Miroslav Volf, which begins with a detailed account of his terrible memories of being interrogated by ‘Captain G’ in Yugoslavia, is the source of his public theological concept of memory. In light of Volf’s public theology of memory, the article addressed the issue of what should be done for Zimbabweans who witnessed the public tragedies of Gukurahundi, Fast Tracked Land Reform Program, Murambatsvina, election-related conflicts and death. Every one of these people has a slightly unique story to tell. Stories on social injustice in Zimbabwe can be told from different kinds of angles. Aspects of Volf’s theology of memory that encourage the development of both new narratives and memories of social injustice narratives are discussed in the article’s conclusion.
The article examines social justice in Zimbabwe through an interpretation of Miroslav Volf's public theology of memory. Miroslav Volf's theology will be used as a point of departure and critique in this article, along with a mixed approach to qualitative research and the gathering and analysis of relevant research data.
Antimicrobial Activity of the Peptide C14R Against Ab Initio Growing and Preformed Biofilms of Candida albicans, C. auris and C. parapsilosis
Authors: Walter Jan-Christoph , Kissmann Ann-Kathrin , Gruber Daniel , Alpízar-Pedraza Daniel , Martell-Huguet Ernesto M. , Preising Nico , Rodriguez-Alfonso Armando , Ständker Ludger , Kleber Christoph , Knoll Wolfgang , Stenger Steffen , Firacative Carolina , Rosenau Frank
Published: January 2025

Biofilms are the predominant lifestyle for microorganisms, contributing to over 80% of infections, including those caused by Candida species like C. albicans, C. auris, and C. parapsilosis. These species form biofilms on medical devices, making infections challenging to treat, especially with the rise of drug-resistant strains. Candida infections, particularly hospital-acquired ones, are a significant health threat due to their resistance to antifungals and the risk of developing systemic infections (i.e. sepsis). C14R, a classical pore-forming antimicrobial peptide (AMP), shows promising activities against both planktonic and biofilm forms of Candida. The 16-amino acid peptide disrupts cell membranes by integrating into lipid bilayers and forming pore-like structures. Studies demonstrate that C14R reduces the viability of C. albicans, C. auris, and C. parapsilosis, including clinical isolates. Here, we show that C14R not only inhibits biofilm growth, but also halts biofilm maturation and significantly reduces the biomass of preformed biofilms by over 70%. These findings suggest C14R could be an effective option for treating severe fungal infections, offering a potential new treatment approach for biofilm-related diseases. Further research is needed to fully understand its biofilm dispersal potential and to optimize its use for future applications as an antifungal in clinical settings.
Momentum Contrast for Unsupervised Visual Representation Learning
Authors: Sowe Ebou A
Published: January 2025

This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum optimisation step to improve representation learning performance and generate better quality representations. Experiments on various datasets demonstrate that momentum contrast is able to learn high-quality representations, allowing us to directly use them to achieve competitive performance with fewer labelled examples.