bluuebunny/crossref_metadata_2025 huggingface dataset:
Higher Education Teachers' Perception and Use of Project-Based Learning in Teaching English
Authors: Lengkoan Fergina , Basri Muhammad , Nur Sahril , Ali Nornazurah Nazir , Maru Mister Gidion
Published: August 2024

This study aims to examine how English professors perceive adopting project-based learning and examine the difficulties they encounter when doing so in various colleges. All the research subjects used four sources of English language education study programs. The study included all instructors who had used project-based learning in their classes. A questionnaire was used to gather quantitative data for this investigation. The study's findings indicate that English teachers positively view project-based learning. Teachers are eager to integrate Project-Based Learning if they have a favourable opinion quickly. The challenges were finding resources to help students with their projects and running out of time to finish them. The following suggestions urge more significant investigation into potential solutions to overcome project-based learning's adoption challenges, particularly in teaching English.
Analysis of Influence of Cyclone Seroja on Wave Height and Tide in the Indian Ocean
Authors: Umam Choirul Umam , Pranowo Widodo Setiyo , Azhari Ferian Azhari , Hendra Hendra , Setiyadi Johar
Published: August 2024

Tropical Cyclone Seroja, which occurred in the Indian Ocean, was characterized by maximum wind speeds of 70 knots. As part of the impact assessment associated with the cyclone, wave height observations were conducted along its track and tidal examinations were carried out at Cape Cuvier and Kupang tidal stations. Therefore, this research aims to analyze the relationship between wind speed and wave height, as well as the impact of Cyclone Seroja on waves and tide. The results showed that Cyclone Seroja reached a very high intensity with maximum wind speeds of 70 knots and maximum wave height of 14.24 meters on April 11, 2021, at 06:00 UTC. Regression analysis showed an R-squared value of 0.716, indicating that 71.6% of the variation in wave height can be explained by wind speed. The correlation value of 0.846 indicated a strong relationship between wind speed and wave height. This research also analyzed the changes in residual tide height at Cape Cuvier tidal station on April 11, 2021, with a maximum residual height of 0.57 meters. Wave height propagation from wave station 1487 significantly influenced the residual tide value at Cape Cuvier, with wave height of 11.790 meters and a propagation speed of 40.42 km/h. At Kupang tidal station, there was a residual height change of up to 0.27 meters, caused by influence of low pressure around wave station 239, with an average pressure of 1001 pHa and an average wave height of 3.89 meters, propagating towards the Kupang tidal station during the formation phase of Tropical Cyclone Seroja. Keywords: Cyclone; Seroja; Wave; Tide; Indian Ocean.
The Impact of Artificial Barriers on the Varuna litterata Migration Route in the Lower Serayu River, Central Java and Its Molecular Identification
Authors: Sinaga Sorbakti , Maimunah Yunita , Ekawati Arning Wilujeng , Widodo Maheno Sri
Published: August 2024

The Serayu River is one of the longest rivers in Central Java. Dams built in the lower reaches of the Serayu River are thought to block the migration paths of fish and aquatic macrocrustaceans. This study aimed to determine the effect of dam on Varuna litterata migration and its molecular identification. This research was conducted using the observation method. Migration observations were made on vertical paths while molecular identification of V. litterata in the lower reaches of the Serayu River was compared with existing data in GenBank. The study covered the freshwater area above the Serayu Dam, the open area with freshwater below the Serayu Dam, and the open area with brackish water at the estuary of the Serayu River, with a study length of 33.37 km. The results showed that the presence of dams in the lower reaches of the Serayu River inhibited the migration pattern of V. litterata. The vertical shape of the dam causes only a few V. litterata to migrate to the top of the dam. The dam in the lower reaches of the Serayu River resulted in not many V. litterata being able to migrate to the top of the dam, so there were very few V. litterata found in the population upstream of the dam (115 ind). The results of molecular identification of V. litratata using the cytochrome C oxidase subunit I (COI) gene show that this crab is 100% the same as the data in GenBank and is closely related to Varuna yui and other Varunidae crab species.Keywords: Dams, Molecular, Migration, Serayu River, Varuna litterata
Green Finance in Property Assessed Clean Energy Programs
Authors: Liaw K. Thomas
Published: August 2024

Property Assessed Clean Energy (PACE) programs finance energy efficiency and renewable energy improvements in residential and commercial properties with a special tax assessment added to property taxes. The paper surveys the literature and documents the quantitative estimates of environmental and economic benefits. The paper extends to discuss the securitization of PACE loans. The issuance of PACE green bonds frees up capacity for more PACE improvements. In addition, we summarize the concerns raised after the programs have been implemented. Those concerns include consumer protection, audit after program implementation, and lien-related risks for lenders. We highlight the challenges and discuss measures to continue to grow PACE financing. The success of PACE programs will contribute to mitigating climate change and to achieving the United Nations Sustainable Development Goals (SDGs).
Investigating the Fatigue Response of Cathodically Charged Cold-Finished Mild Steel to Varied Hydrogen Concentrations
Authors: Sey Emmanuel , Farhat and Zoheir N.
Published: August 2024

This study investigates the fatigue behavior of cold-finished mild steel subjected to electrochemical hydrogen charging under controlled conditions. Samples were hydrogenated at constant time in a fixed electrolyte pH after which they underwent fatigue testing under constant loading condition with fixed frequency. The primary objective was to assess the impact of varying hydrogen permeation levels on the number of cycles to failure. The experimental results revealed a complex relationship between hydrogen concentration and fatigue life. Initially, as hydrogen permeation increased, the number of cycles to failure substantially decreased, demonstrating a detrimental effect of diffused hydrogen on fatigue resistance of samples. This decline in fatigue life was attributed to hydrogen embrittlement (HE) and hydrogen-enhanced decohesion (HEDE) phenomena, which collectively facilitate crack initiation and propagation. However, at high hydrogen concentrations, an unexpected increase in number of cycles to failure was observed suggesting the existence of a threshold hydrogen concentration beyond which the fatigue mechanisms may be altered, potentially due to saturation of hydrogen-related defects and mechanisms such as Hydrogen Enhance Localized Plasticity (HELP). The insights gained from this research have significant implications for the material's application in hydrogen-rich environments, such as those encountered in the energy and transportation industries.
Insights into the Preparation and Evaluation of the Bactericidal Effects of Phage-Based Hydrogels
Authors: Gao Mengyuan , Wang Yuhan , Zhuang Hanyue , Zhu Yanxia , Chen Na , Teng Tieshan
Published: August 2024

The rise of antibiotic-resistant strains demands new alternatives in antibacterial treatments. Bacteriophages, with their precise host specificity and ability to target and eliminate bacteria safely, present a valuable option. Meanwhile, hydrogels, known for their excellent biodegradability and biocompatibility, serve as ideal carriers for bacteriophages. The combination of bacteriophages and hydrogels ensures heightened phage activity, concentration, controlled release, and strong antibacterial properties, making it a promising avenue for antibacterial treatment. This article provides a comprehensive review of different cross-linking methods for phage hydrogels and focuses on their application in treating infections caused by various drug-resistant bacteria.
Enhancing Air Quality Predictions on University Campuses: A Machine Learning Approach to PM2.5 Forecasting at the University of Petroșani
Authors: Panaite Fabian Arun , Rus Cosmin , Leba Monica , Ionica Andreea Cristina , Windisch Michael
Published: August 2024

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.
Enhancing Firefighter Hoods: Innovations, Challenges, and Future Directions in PPE Design
Authors: Hashemian Esfahani Bahar , Li Rui , Zhang Mengying , Eike Rachel , Song Guowen
Published: August 2024

Firefighter hoods are essential components of personal protective equipment (PPE), protecting the head, neck, and shoulders from extreme heat and hazardous particulates. This comprehensive review evaluates recent hood design and material innovations aimed at improving comfort and protective performance. Traditionally, firefighter hoods mainly focused on thermal insulation but lacked effective particulate filtration, which posed significant health risks. Recent updates to several standards set by the US National Fire Protection Association have led to advanced mul-ti-layered hoods incorporating innovative materials for improved performance. Nevertheless, challenges remain in achieving an optimal balance between comfort and protection. Firefighter hoods must not hinder movement or increase heat stress, and the process of donning and doffing must minimize contamination risks. Current standards primarily assess material properties but fail to address practical, real-world conditions and interactions with other PPE components. This review emphasizes that these knowledge gaps can only be closed through systematic performance evaluations that replicate actual firefighting scenarios and include both human trials and simu-lations. Future research should focus on refining hood designs and materials to enhance particulate filtration efficiency, improve ergonomic fit, and reduce complications associated with use. This will ensure that firefighter hoods offer comprehensive protection while maintaining high comfort levels in demanding environments.
Association between Subclinical Hypothyroidism and Adverse Pregnancy Outcomes in Assisted Reproduction Technology Singleton Pregnancies: A Retrospective Study
Authors: Narita Yuichiro , Tsuda Hiroyuki , Tsugeno Eri , Nakamura Yumi , Suzuki Miho , Ito Yumiko , Tezuka Atsuko , Ando Tomoko
Published: August 2024

Background/Objectives: Women with subclinical hypothyroidism (SCH) were reported to be at an increased perinatal risk. We aimed to investigate the relationship between SCH and perinatal outcomes in singleton pregnancies resulting from assisted reproduction technology (ART). Methods: We retrospectively examined the perinatal outcomes of ART singleton pregnancies in women who underwent thyroid function screening before conception and delivered at our hospital from January 2020 to July 2023. We defined SCH as thyroid-stimulating hormone (TSH) levels <2.5 mU/L and normal free T4 levels. The patients were divided into three groups: normal thyroid function (group A), SCH without levothyroxine therapy (group B), and SCH with levothyroxine therapy (group C). The risks of preterm birth, preeclampsia, fetal growth restriction, manual placental removal, and blood loss at delivery were compared among the three groups. Results: Out of the 650 ART singleton deliveries, 581 were placed in group A, 34 in group B, and 35 in group C. The preterm birth rate at <34 weeks was significantly higher in group B and significantly lower in group C than in group A. The rate of preterm delivery at <34 weeks increased in correlation with TSH levels. Levothyroxine therapy was the significant preventive factor for preterm birth at <34 weeks. Conclusions: The preterm birth rate before 34 weeks was significantly higher in the SCH group. Levothyroxine therapy is a significant protective factor against preterm birth before 34 weeks. Universal screening for thyroid function and appropriate hormone therapy in pregnant women may help reduce perinatal risks, including preterm birth.
A Petri Net-based Algorithm for Solving the One-Dimensional Cutting Stock Problem
Authors: Barragan-Vite Irving , Medina-Marin Joselito , Hernandez-Romero Norberto , Anaya-Fuentes Gustavo Erick
Published: August 2024

Cutting and packing problems have been studied for decades and are of concern for a wide variety of industries to reduce costs and material utilization. This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Procedures that address this problem include linear programming methods and metaheuristics. However, linear programming methods are limited to low-complexity instances, while most metaheuristics require extensive parameter tuning. In this paper, we develop a Petri-net model to construct cutting patterns. Using the reachability tree of the Petri net, the filtered beam search algorithm is implemented to find the best solution. Our algorithm is compared with the Least Lost Algorithm and the Generate & Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal solutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.
Chemical Diversity of Ketosteroids as Potential Therapeutic Agents
Authors: Dembitsky Valery M
Published: August 2024

This article presents a comprehensive overview of recent discoveries and advancements in the field of steroid chemistry, highlighting the isolation and characterization of various steroidal compounds from natural sources. The paper discusses a wide range of steroids, including pregnane steroids, steroidal alkaloids, ketosteroids, and novel triterpenoids, derived from marine organisms, fungi, and plants. Significant findings include the isolation of bioactive compounds such as the cytotoxic erectsterates from microorganisms, soft corals, the unusual tetracyclic steroid penicillitone from a fungal culture, and innovative steroidal derivatives with potential anti-inflammatory and anticancer activities. The synthesis of steroids from microorganisms as a tool for pharmaceutical development is also explored, showcasing the role of microbial biotransformation in generating steroidal drugs. Additionally, the paper emphasizes the ecological and medicinal relevance of these compounds, which are often used in traditional medicine and have potential therapeutic applications in treating diseases like cancer and microbial infections. This article serves as a vital resource for researchers interested in the chemical diversity of steroids and their applications in drug discovery and development.
Tensile Specimen Circular Grid Pattern and AI-Based Strain Calculation Method
Authors: Yi Sarang , Hyun Daeil , Hong Seokmoo
Published: August 2024

During tensile testing of materials, strain measurement is conducted using either contact or non-contact methods. Contact methods offer high accuracy and precision but are limited by the specimen's thickness and dimensions, whereas non-contact methods minimize damage to thin specimens and allow measurements in various environments, though they require longer prep-aration and calculation times. This paper proposes a circular grid marking pattern and a strain prediction algorithm using artificial intelligence (AI), which simplifies the preparation process and allows strain prediction without additional equipment. The circular grid pattern can be ar-ranged in various configurations from 1×5 to 5×7, and a laser marker, which requires minimal time, was used to engrave the pattern on the specimen to shorten the preparation time. The AI model, trained on image-based data, enables strain calculation regardless of the specimen's gauge length and size, and allows measurement of local strain as well as gauge-length strain. The reliability of this concept was verified by applying it to tensile testing.
Tourist Intentions to Recommend Towards Gastronomy Destination: The case of Turkish Republic of North Cyprus
Authors: GUNAY TUGRUL , KILILI RUKIYE , SAH MUSTAFA
Published: August 2024

This study endeavors to elucidate the intricate interplay between gastronomic involvement, gastronomic knowledge, past gastronomic experiences, gastronomic attractiveness, destination attractiveness, tourist satisfaction, and the intention to revisit. In the context of this investigation, the research population comprises 300 international culinary tourists who have ventured to the Turkish Republic of Northern Cyprus (TRNC). Employing SPSS software, a confirmatory factor analysis (CFA) was conducted to rigorously assess construct validity, reliability, and the discriminant and convergent validity of the research model. The findings of this research study underscore the positive relationships that exist between various dimensions of gastronomy, namely involvement in gastronomy, knowledge of gastronomy, and previous gastronomic experiences, and the perceived attractiveness of gastronomy. These findings enhance our understanding of how culinary elements affect tourists' perceptions and experiences, highlighting the crucial role of gastronomy in destination appeal and visitor satisfaction. This research is a significant step towards enhancing our comprehension of the dynamic nexus between gastronomy and the tourism industry, ultimately benefiting destination management and strategy development.
Exploring the Adoption of Digital Payment Systems in Retail
Authors: Brown William , Wilson George , Johnson Oliver
Published: August 2024

The rapid evolution of digital payment systems has significantly transformed the retail industry, reshaping how transactions are processed and enhancing the overall shopping experience. This research explores the adoption of digital payment technologies in retail settings, focusing on the motivations, challenges, benefits, and impacts associated with their implementation. Retailers are increasingly driven by the need to meet consumer demands for convenience and speed, as well as to gain a competitive edge in a saturated market. Digital payment systems, including mobile wallets, contactless payments, and online payment gateways, have become essential tools for retailers seeking to streamline operations and improve customer satisfaction. The study highlights several key findings, including the challenges related to technical integration, security concerns, and cost implications. Technical difficulties in integrating new payment systems with existing point-of-sale infrastructure, coupled with the need for robust security measures, present significant obstacles. Despite these challenges, the long-term benefits of digital payments, such as increased transaction volumes, higher average transaction values, and valuable data insights, often outweigh the initial investment and ongoing costs. The research also underscores the positive impact of digital payments on customer behavior, noting improvements in transaction speed, convenience, and overall satisfaction. Furthermore, the findings reveal variations in the impact of digital payment systems across different retail settings, including large department stores, independent retailers, and e-commerce businesses. The ability to tailor digital payment solutions to the specific needs of each retail environment is crucial for maximizing benefits and ensuring successful implementation. Overall, the study provides a comprehensive understanding of the role of digital payment systems in modern retail and offers insights into the future trajectory of these technologies.
An Efficiency Examination of the DJI Mavic 2 Pro’s Precision Landings
Authors: Kiss Barnabás , Ballagi Áron , Kuczmann Miklós
Published: August 2024

In recent times, the application areas of unmanned aerial vehicles (UAVs) are expanding, and autonomous functions are becoming increasingly prominent, eliminating the need for remote pilot control. Certain applications require the drone to use a docking station autonomously, even for wireless charging. This involves performing autonomous takeoff and landing, where high preci-sion is demanded. These maneuvers can be significantly influenced by environmental factors. The effectiveness of the precision landing function of the DJI Mavic 2 Pro UAV in strong winds is in-vestigated in this article based on experimental measurements conducted outdoors. New findings on the precision landing capabilities of the DJI Mavic 2 Pro UAV are presented in this paper, par-ticularly under windy conditions. The efficiency of precision landing is compared in cases when the precision landing function is enabled versus when it is disabled. It is investigated whether there is a statistically significant difference between the two landing modes in strong winds and it is examined whether the DJI Mavic 2 Pro can perform precision landing on a surface of a specific size in windy conditions. This helps to predict the suitability of the DJI Mavic 2 Pro for precise landing and wireless charging at a docking station in strong winds and high gusts.
Navigating Change and Driving Innovation: Leveraging Big Data for Enhanced User Behavior Analysis and Strategic Decision-Making
Authors: Spadacini Daniel
Published: August 2024

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.
Ensemble Learning Algorithms for Solar Radiation Prediction in Santo Domingo: Measurements and Evaluation
Authors: Ramírez-Rivera Francisco A. , Guerrero-Rodríguez Néstor F.
Published: August 2024

Solar radiation corresponds to fundamental parameters for solar photovoltaic (PV) technology. Reliable solar radiation prediction became valuable to design solar PV systems, performance, operatively efficient planning, safety operation, grid dispatch and financial characteristics. However, high quality ground-based solar radiation measurements are scarce, especially for very short-term time horizon. Most of the existent studies trained the machine learning (ML) model used dataset with time horizon of 1-hour or day, very fewer studies have been reported using a dataset with a 1-mitute time horizon. In this study, a comprehensive evaluation of nine ensemble learning algorithms (ELA) is performed to estimate the solar radiation in Santo Domingo with a 1-minute time horizon dataset collected from a local weather station. The ensemble learning evaluated included seven homogeneous ensembles; Random Forest (RF), Extra Tree (ET), Adaptive Gradient Boosting (AGB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light Gradient Boosting (LGBM), Histogram-based Gradient Boosting (HGB) and heterogeneous ensemble named as Voting and Stacking. RF, ET, GB, HGB were combined to develop Voting and Stacking and Linear Regression (LR) was adopted in the second layer of the Stacking. Five technical metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) were used as criteria to determine the prediction quality of the developed ensemble algorithms. Comparison of the results indicates that the HGB algorithm offers superior prediction performance among the homogeneous ensemble learning and overall, the Stacking provides the best accuracy with metric values of MSE=3218.27, RMSE= 56.73, MAE=29.87, MAPE=10.60, R2=0.964.
Resources Optimization Using Pareto Analysis for Sea Water Desalination Plants
Authors: Chirinza Nicolau , Muguirrima Paulino , Zerpa Federico A. Leon , Ramos-Martin Alejandro , Pino Carlos Alberto Mendieta
Published: August 2024

This study examines the potential of implementing a systematic approach to piloting desalination plants, with the objective of evaluating energy improvements in water desalination processes. The global scarcity of freshwater resources has significantly influenced the development of desalination technologies, with reverse osmosis emerging as one of the most prevalent and highly regarded methods. Pilot tests with the latest generation of reverse osmosis membranes in large desalination plants are becoming increasingly common. This is due to the fact that small-scale experiments are being carried out on the operation of new generation membranes with better salt rejection and lower energy consumption. The objective of this approach is to achieve the optimal water quality standard at the lowest possible cost. In this manner, pilot tests are being conducted prior to the determination of whether to alter the reverse osmosis membranes, as this represents a substantial investment. The objective of these tests is to minimise the risk of making an erroneous decision and to ensure optimal results in terms of energy consumption, operating costs and reduction of environmental impact, while complying with the requisite water quality standards. In this case, the Pareto analysis is employed to identify the two or three most significant causes whose treatment affects more than 80% of the potential energy savings to be implemented. This article introduces a novel approach to studying a total of 180 desalination plants at the territorial level in the Canaries.
The Importance of Bioactive Compounds and Plant Extracts in the Treatment of Pancreatic Cancer
Authors: Lin Xingda
Published: August 2024

Pancreatic cancer is a serious and aggressive type of cancer that affects the digestive system and has a low chance of survival. Although traditional treatment methods have achieved some efficacy, the survival rate of patients remains low. Therefore, exploring novel treatment strategies is important. This article looks at the research done on bioactive compounds and plant extracts for treating pancreatic cancer. Bioactive compounds, such as naringenin and ziyuglycoside II, have significant anticancer activity and exert anti-pancreatic-cancer effects by inducing cell apoptosis, inhibiting the cell cycle, and blocking signaling pathways. Plant extracts, such as epigallocatechin-3-gallate and resveratrol, also demonstrate good anti-pancreatic cancer activity and inhibit tumor growth and metastasis. Additionally, the combined application of bioactive compounds and plant extracts may result in improved therapeutic effects. This article aims to provide a theoretical basis for the application of bioactive compounds and plant extracts in the treatment of pancreatic cancer and to help guide future medical research and practice.
Sublethal Sodium Hypochlorite Exposure: Impact on Resistance-Nodulation-cell Division (RND) Efflux Pump Overexpression, and Cross-Resistance to Imipenem
Authors: Nam Ji-Hyun , Yoo Jung Sik
Published: August 2024

Sodium hypochlorite (NaOCl) is widely used in public healthcare facilities; this exposure can result in the development of bacterial tolerance to disinfectant, which has known links to antibiotic cross-resistance. However, the mechanism through which cross-resistance to antibiotics and disinfectants develops remains ambiguous. Therefore, this study aimed to examine the phenotypic, and transcriptomic changes caused by disinfectant exposure in gram-negative bacteria and determine the cause of cross-resistance to antibiotics. The results demonstrated that the misuse of disinfectants plays an important role in the emergence of disinfectant resistance and in the increase in antibiotic resistance. Antibiotic resistance may occur from exposure of gram-negative bacteria to subminimal inhibitory concentrations (MICs) of NaOCl. Ten passages of gram-negative bacteria in increasingly higher subMICs of NaOCl disinfectant was sufficient to increase the MIC to >2,500 µg/ml NaOCl, particularly in K. pneumoniae and P. aeruginosa. To determine the development of cross-resistance to antibiotics due to NaOCl exposure, the MICs for each antibiotic before and after exposure of each strain to sublethal concentrations of NaOCl were compared. After overnight incubation with a sublethal concentration of NaOCl, a statistically significant increase in MIC was only observed for imipenem (P<0.01). Investigation of the mechanism of cross-resistance by means of transcriptome analysis revealed that 1,250 µg/ml NaOCl-adapted K. pneumoniae and P. aeruginosa strains increased resistance to imipenem due to increased expression of the resistance-nodulation-cell division (RND) efflux pumps, such as AcrAB-TolC and MexAB/XY-OprM. Therefore, we suggest that exposure to NaOCl can influence the expression of RND efflux pump genes, contributing to imipenem cross-resistance.
Harnessing Large Language Models for Next-Generation Recommender Systems
Authors: Kristensen Alexander , van der Berg Charlotte , Hofmann Matthias
Published: August 2024

Recommender Systems (RecSys) are crucial in managing information overload and enhancing user satisfaction across various digital platforms, including e-commerce and entertainment. Evolving from traditional models to Deep Neural Networks (DNNs) and, more recently, Large Language Models (LLMs), these systems leverage sophisticated algorithms to analyze user behaviors and preferences. LLMs, such as GPT-4, are trained on extensive datasets to comprehend and generate natural language, significantly advancing their ability to deliver personalized recommendations. This tutorial explores the transformative impact of LLMs on RecSys, discussing their development, application in handling complex datasets, and the integration of contextual insights. Real-world examples illustrate how LLMs enhance recommendation accuracy and user experience, highlighting challenges and future directions in the field.
A Platform Technology for VR Biofeedback Training under Operant Conditioning for Functional Lower Limb Weakness
Authors: Dutta Anirban , Das Abhijit
Published: August 2024

Functional neurological disorder (FND) is a complex condition that involves a disruption in the brain's ability to properly integrate sensory input and prior expectations, leading to motor dysfunction in functional motor disorders (FMD). This study investigates the role of sensory attenuation and the sense of agency in FMD, focusing on how these mechanisms can be leveraged for therapeutic interventions. Sensory attenuation, the brain's ability to predict and reduce the intensity of self-generated sensory inputs, is impaired in FMD, leading to a reduced sense of agency. This impairment disrupts motor control and contributes to the perception of involuntary movements.We propose using a bipedal reaching virtual reality (VR) task combined with a haptic ankle device to explore and potentially rehabilitate these impairments. In this task, foot positions are represented by VR objects, with initial weighting favoring the unaffected limb. This weighting gradually shifts to the affected limb as its involvement increases under operant conditioning. The task occasionally introduces perturbations to probe motor-related processing and feedback correction, providing a sensitive measure of visuomotor control. Additionally, we incorporate neuroimaging techniques, specifically functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to monitor brain activity and assess the effectiveness of these interventions. This approach aims to enhance understanding of the neural mechanisms underlying FND and develop targeted therapies that improve motor control by leveraging the interplay between sensory attenuation and amplification.Our findings could provide valuable insights into the treatment of FND, emphasizing the importance of feedback modulation and the integration of modern technologies like VR and haptic feedback in therapeutic interventions. This research has the potential to significantly improve the prognosis and quality of life for individuals with FND.
Development of Reconfigurable High-Frequency Devices Using Liquid Crystal in Substrate-Integrated Groove Gap Waveguide Technology
Authors: Voronov Aleksandr Andreyevich , Bachiller María Carmen Martín , Boria Vicente Esbert , Villacampa Belén
Published: August 2024

This article presents the theoretical study, numerical simulation and the fabrication of a phase shifter and stub resonator in microstrip ridge gap waveguide (MGCW) technology with the use of liquid crystal (LC) in its substrate as a reconfigurable material. The phase shifter and the stub resonator are filled with LC and thanks to LC's dielectric anisotropy properties, the phase shift and resonance response can be easily controlled using an external electric or magnetic bias field. The phase shifter was designed to operate in the range of 10 to 20 GHz and the resonator was designed to operate in the range of 7.8 to 8.8 GHz. The phase shifter's insertion losses, associated with both parallel and perpendicular LC's permittivity, as well as the phase response were computed and measured. Finally, the figure of merit (FoM) was extracted. The resonator's frequency response, associated with both parallel and perpendicular LC's permittivity, was computed. The resonator's frequency responses, provided different polarization voltages, were measured and compared to the simulation results. The technological issues were discussed. The good agreement between simulation and measurement results establishes this technology as a viable approach to the development of RF reconfigurable devices.
The Biological Characteristics of Mycobacterium Phage Henu3 and the Fitness Cost Associated with Its Resistant Strains
Authors: Li Xinyu , Xu Junge , Wang Yuhan , Gomaa Salwa E , Teng Tieshan
Published: August 2024

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is an infectious disease that seriously affects human life and health. Despite centuries of efforts to control it, in recent years the emergence of multidrug-resistant bacterial pathogens of M. tuberculosis due to various factors has exacerbated the disease, posing a serious threat to global health. Therefore, a new method to control M. tuberculosis is urgently needed. Phages, viruses that specifically infect bacteria, have emerged as potential biocontrol agents for bacterial pathogens due to their host specificity. In this study, a Mycobacterium phage, Henu3, was isolated from soil around a hospital. The particle morphology, biological characteristics, genomics and phylogeny of Henu3 were characterized. Additionally, to explore the balance between phage resistance and stress response, phage Henu3-resistant strains 0G10 and 2E1 were screened by sequence passage and bidirectional validation methods, which significantly improved the sensitivity of phage to antibiotics (cefotaxime and kanamycin). By whole genome resequencing of strains 0G10 and 2E1, 12 genes involved in cell wall synthesis, transporter-encoded genes, two-component regulatory proteins and transcriptional regulatory factor-encoded genes were found to have mutations. These results suggest that phage Henu3 has the potential to control M. tuberculosis pathogens, and phage Henu3 has the potential to be a new potential solution for the treatment of M. tuberculosis infection.
Study of the Imbalance and Trading Settings on the Impact of the Intraday Electricity Trading on the Czech Transmission System Operation
Authors: Šrom Jakub , Chemišinec Igor , Muller Zdenek , Fandi Ghaeth
Published: August 2024

The intraday electricity market is increasing its importance within the energy markets. This article introduces a case study, basing on real examples of the contribution electricity markets make to prevent and/or solve emergencies in electric system.
The case study was conducted in four possible states on short-term markets – different variants of trading period lengths and intervals in which the system imbalance is evaluated. The outage of the biggest power plant´s unit in the transmission system was always considered, and together with the situation in the short-term markets, the impact on the operation and maintenance on a transmission system was evaluated from that point-of view, system imbalance, and necessity to activate necessary ancillary services by an operator.
Study of Contactless Computer Vision-based Road Condition Estimation Methods within the Framework of an Operational Design Domain Monitoring System
Authors: Subramanian Ramakrishnan , Büker Ulrich
Published: August 2024

Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.
Primordial Axion Stars and Galaxy Halos Formation
Authors: Nesterov Alexander I
Published: August 2024

Primordial axion stars, hypothetical stars formed from axions, could play an essential role in forming galaxy halos. These stars could have originated in the early universe shortly after the Big Bang. We show that while “normal” axions coalesce into a gravitationally bound Bose-Einstein condensate, forming a dense object - an axion star, the ultralight axions could act as the initial seeds for galaxy halos.
Confrontation of Capitalism and Socialism in Wikipedia Networks
Authors: Ermann Leonardo , Shepelyansky Dima L.
Published: August 2024

We introduce the Ising Network Opinion Formation (INOF) model and apply it for the analysis of networks of 6 Wikipedia language editions. In the model, Ising spins are placed at network nodes/articles and the steady-state opinion polarization of spins is determined from the Monte Carlo iterations in which a given spin orientation is determined by in-going links from other spins. The main consideration is done for opinion confrontation between capitalism, imperialism (blue opinion) and socialism, communism (red opinion). These nodes have fixed spin/opinion orientation while other nodes achieve their steady-state opinions in the process of Monte Carlo iterations. We find that the global network opinion favors socialism, communism for all 6 editions. The model also determines the opinion preferences for world countries and political leaders, showing good agreement with heuristic expectations. We also present results for opinion competition between Christianity and Islam, and USA Democratic and Republican parties. We argue that the INOF approach can find numerous applications for directed complex networks.
PDAM-FAQ: Paraphrasing-based Data Augmentation and Mixed-Feature Semantic Matching for Low-Resource FAQs
Authors: Wang Dongsheng , Wang Liming , Tang Kun , Bo Qile , Han Bin
Published: August 2024

Frequently Asked Questions (FAQs) systems rely on semantic symmetry similarity measuring between two sentences. To address the challenges of insufficient training data and limited domain-specific understanding in low-resource FAQs, this paper proposes a general framework, PDAMF-FAQ, to solve these issues. Firstly, we propose a paraphrasing-based data augmentation model that integrates syntactic information and edit vectors. Using a rule-based approach, it retrieves template sentences from a corpus and masks relevant words with special characters. Edit vectors between the original and paraphrase sentences are added to a pre-trained model's encoding layer to enhance the model's ability to learn the differences between the original and reference paraphrase sentences. Additionally, this paper presents a mixed-feature semantic matching model based on SimBERT. The model extracts keyword features from the text, replacing these keywords with special characters to construct intent features. These intent features, along with the user question and keyword features, are then concatenated to form the model's input. Experiments were conducted respectively on the paraphrasing-based data augmentation model, mixed-feature semantic matching model and their comprehensive application in a low-resource domain-specific FAQ. The experimental results show that the proposed framework effectively improve the performance of domain-specific FAQ system.
SSIM-based Autoencoder Modeling to Defeat Adversarial Patch Attacks
Authors: Lee Seungyeol , Hong Seongwoo , Kim Gwangyeol , Ha Jaecheol
Published: August 2024

Object detection systems are widely used in various fields such as autonomous vehicles and facial recognition. In particular, object detection using deep learning networks enables real-time processing in low-performance edge devices, and can maintain high detection rates. However, edge devices that operate far from administrators are vulnerable to various physical attacks by malicious adversaries. In this paper, we implement a function for detecting traffic signs by using three versions of You Only Look Once (YOLO) and Faster-RCNN, which can be adopted by edge devices of autonomous vehicles. Then, assuming the role of a malicious attacker, we executed adversarial patch attacks with Adv-Patch and Dpatch. As a result of experimenting with misdetection of traffic stop signs using Adv-Patch and Dpatch, we confirmed the attacks can succeed with a high probability. To defeat these attacks, we propose an image reconstruction method using an autoencoder and the Structural Similarity Index Measure (SSIM). We confirm that the proposed method can sufficiently defend against an attack, attaining an AP above 91.46% even when two adversarial attacks are launched.
MMSE Estimation and Mutual Information Gain
Authors: Gibson Jerry
Published: August 2024

Information theoretic quantities such as entropy, entropy rate, information gain, and relative entropy are often used to understand the performance of intelligent agents in learning applications. Mean squared error has not played a role in these analyses, primarily because it is not felt to be a viable performance indicator in these scenarios. We build on a new quantity, the log ratio of entropy powers, to establish that minimum mean squared error (MMSE) estimation, prediction, and smoothing are directly connected to Mutual Information Gain or Loss in an agent learning system modeled by a Markov chain for many probability distributions of interest. Expressions for mutual information gain or loss are developed for MMSE estimation, prediction, and smoothing and an example for fixed lag smoothing is presented.
Analysis of the Kerr-Newman Diagram. Unravelling the Interior of a Black Hole
Authors: Flores Hector Gerardo , Jain Harshit , Mahapatra Premachand , Gonçalves de Souza Maria Isabel
Published: August 2024

Here we will explain the correlation between the Kerr-Newman diagram and the theory: RLC electrical modelling of a black hole and the early universe. We will develop a black hole model that describes and explains both theories. In the development of our analysis, we will emphasize the following conditions: M² > Q² + a², M² = Q² + a² and M² < Q² + a² and we will also analyse the condition in which the mass of a black hole reaches its critical mass M = Mc. We will demonstrate that the interpretation of the Kerr-Newman diagram is equivalent to the interpretation of the theory: RLC electrical modelling of a black hole and the early universe. Finally, we will generalize the proposed model for a black hole and propose a black hole model formed by negative particles, neutroniumd, which after reaching critical conditions of mass, pressure, volume, temperature, density, etc.; decays into a positively charged black hole, protoniu. Let us remember that both models are formed by charged particles of matter (they do not contain antimatter), but their vector configuration means that the net charges of both proposed black holes are zero.
Efficiency of Image Processing with Parallel Techniques
Authors: PACCO ZUVIETA HENRY ANGEL , TORRES CRUZ FRED
Published: August 2024

Image processing is a computationally intensive task that can benefit significantly from parallel techniques. In this study, we evaluate the efficiency of image processing using parallel techniques compared to sequential techniques. We use Dask to parallelize the loading and processing of images and compare the execution times for various image processing techniques, including smoothing, edge detection, rotation, and thresholding. The results show that parallel processing significantly reduces execution time compared to sequential processing.