bluuebunny/crossref_metadata_2025 huggingface dataset:
Improve the Bi-LSTM Model of University Financial Information Management Platform Construction
Authors: Fang Liu Fang Liu
Published: January 2024

Information Management Systems are vital tools for businesses and organizations to manage their data effectively, make informed decisions, ensure data security and compliance, and enhance operational efficiency. This paper presents a robust approach to information management through the utilization of the Optimized Probabilistic Bidirectional Long Short-Term Memory (OP_Bi-LSTM) model. The study developed into the architecture and application of Simulated Annealing (SA) for hyperparameter optimization, emphasizing the impact of fine-tuning on model performance. OP_Bi-LSTM, rooted in Bidirectional Long Short-Term Memory, exhibits superior sequential data processing capabilities, making it well-suited for a variety of information management tasks. The proposed OP_BI-LSTM  hierarchical architecture further enhances its pattern recognition capabilities in the information management system. Results from extensive experimentation demonstrate the model's versatility and adaptability in various applications, such as sentiment analysis, fraud detection, and time-series forecasting. The proposed OP_BI-LSTM model performance analysis is evaluated for the consideration of the different customers and product data. The propsoed OP_Bi-LSTM model achieves the classification accuracy of 0.98. It is stated that OP_Bi-LSTM emerges as a powerful tool for information management, offering exceptional performance and adaptability. As the field of deep learning and information management continues to evolve, this model holds great promise for addressing complex data challenges and facilitating data-driven decision-making in a variety of industries.
Evaluation model of English Informatization Teaching Quality in Universities Based on Particle Swarm Algorithm
Authors: Miao Tian Miao Tian
Published: January 2024

Teaching quality in the field of English education plays a crucial role in shaping students' language proficiency, communication skills, and overall understanding of the language.  One of the most significant contributions of deep learning to English education is its ability to personalize instruction. Deep learning algorithms analyze individual students' learning patterns, strengths, and weaknesses. This paper designed a framework for enhancing teaching quality in English education by implementation of Non-Convex Particle Swarm Optimization and Optimization (NC-PSOO) with a Generative Adversarial Network (GAN). The proposed model NC-PSOO with GAN, presents the information related to the teaching and learning process. The features are computed based on the utilization of the non-convex estimation for the analysis of the variables. Through the implementation of an effective process of data pre-processing using the Fejer filter, feature selection and extraction with NC-PSOO, and classification with GAN, this model aims to improve student performance, elevate the quality of teaching materials, increase student participation, enhance homework quality, and balance exam difficulty. The performance of NC-PSOO with GAN is compared with conventional optimization techniques like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to demonstrate its superior efficacy. The findings highlight the model's capability to achieve higher accuracy, precision, recall, and F1 score, ultimately improving teaching quality in English education.
Financial Risk Assessment Model Based on Fuzzy Logic
Authors: Xiuqiong Yang Xiuqiong Yang
Published: January 2024

In the rapidly evolving landscape of business operations, establishing a robust security domain prevention system in the Middle Office is of utmost importance. To achieve this, the integration of Choquet expectation-based methodologies offers a powerful approach. The Middle Office acts as a critical hub for various business processes, including risk management, compliance, and data protection. With the implementation of Choquet expectation theory, which encompasses the combination of multiple criteria and preferences, businesses can effectively assess and optimize their security domain prevention system. The establishment and optimization of a security domain prevention system based on Choquet’s expectations provide businesses with a comprehensive and tailored approach to protect their critical assets, maintain operational continuity, and safeguard sensitive data from emerging threats in the Middle Office environment. This paper constructed a Fuzzy Optimization Membership Estimation (FOME) for the computation of the feature vector. The proposed FOME model uses the Flemingo Optimization model for the evaluation of the feature vector in the business middle office. The FOME model effectively computes the Choquet expectation features for the analysis of the risk management of the feature vector in the middle office. Through the membership estimation with the FOME model, the model significantly computes the different attacks in the middle office. The analysis of the proposed FOME is evaluated for the conventional CICIDS dataset for the attack analysis. The simulation analysis stated that the proposed FOME model achieves a higher classification accuracy of 99.89% for attack detection.
Optimization Algorithm of Intelligent Warehouse Management System Based on Reinforcement Learning
Authors: Jianjun Zhou Jianjun Zhou
Published: January 2024

An Intelligent Warehouse Management System (IWMS) represents a technological leap forward in the realm of logistics and supply chain management. This sophisticated system integrates a suite of cutting-edge technologies, including artificial intelligence, machine learning, and the Internet of Things, to revolutionize the way warehouses operate. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of Logistics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for IWMS in Logistics applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of Logistics information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of fuzzy clustering with a reinforcement learning algorithm enhances the model's ability to handle uncertainty and imprecision in logistics management data. The advancement of Logistics in warehouses introduces the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on fuzzy clustering with a reinforcement learning algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.
Algorithm of Mental Model of Art Design Based on Virtual Reality Technology
Authors: Wenwen Li Et al.
Published: January 2024

The mental model of art design in the realm of virtual reality (VR) technology is a dynamic fusion of creativity, immersion, and technological innovation. In this paradigm, artists leverage the immersive capabilities of VR to transcend traditional boundaries, creating a multisensory experience that goes beyond the confines of a two-dimensional canvas. The artist's mental canvas extends into a three-dimensional space where users can engage with art on a profound level, exploring and interacting with the artwork in ways previously unimaginable. The core objective is to redefine art design design through the lens of machine vision cognition into the ethical and social implications inherent in Art Design. With the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO) to amplify the capabilities of VR systems. At the forefront of this study is the reimagination of art design design, characterized by aesthetics, ergonomics, and functionality. The infusion of machine vision cognition into these designs not only enhances user experience but also prompts contemplation of ethical considerations surrounding privacy, accessibility, and informed consent. Ethical and social implications are scrutinized comprehensively, acknowledging the profound impact of VRs on individual rights, security, and privacy. The research probes into equitable access to VR technologies, ethical data utilization in art design, identity verification, and surveillance contexts. Central to this multidisciplinary inquiry is the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO). With swarm intelligence and probabilistic modelling, HMPSO enhances the efficiency, accuracy, and reliability of Mental Model VR systems. It addresses the critical challenge of reducing false positives and false negatives in VR authentication. The research methodology comprises performance evaluations, ethical analyses, and socio-cultural investigations, offering a comprehensive view of the interplay between design innovation, machine vision cognition, ethical awareness, and the application of HMPSO in the Art Design.
Learning Experience of University Music Course Based on Emotional Computing
Authors: Liying Huang Liying Huang
Published: January 2024

Identification and analysis of piano performance music are essential activities in the study of music knowledge and enjoyment. The goal of this study is to employ recurrent neural networks (RNNs) to create a multimedia identification and analysis method for piano performance music. RNNs excel at capturing the temporal relationships and dynamics seen in musical performances. The project entails gathering a large dataset of piano performance recordings that spans a variety of genres, performers, and playing techniques. The audio and video components of the performances are pre-processed in order to extract pertinent information. Long Short-Term Memory (LSTM), an RNN architecture, is used to mimic the sequential nature of the performances. The RNN is trained on the features that were retrieved in order to discover the patterns and traits connected to various piano performances. The similarity between the performance representations may be measured using similarity measures using Euclidean distance. The RNN-based system may be further developed to do tasks like score following, expressive performance analysis, and stylistic variation creation to aid in performance analysis. The system may provide insights into timing accuracy, dynamics, phrasing, and other expressive qualities of the piano performances by matching performance data with related musical scores. The proposed method RNN-LSTM provides accuracy about 99%, precision about 97% , recall about 98.9% and F1 Score of 97.6%.
Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments
Authors: Suresh Limkar Et al.
Published: January 2024

For Wireless Sensor Networks (WSNs) to operate as efficiently as possible in Indoor Internet of Things (IoT) environments, energy-efficient localization approaches are essential. We investigate several localization approaches, such as trilateration based on Received Signal Strength Indicator (RSSI), Proximity Based Technique, Inertial Navigation, Ultrasound-based, and Magnetic Field-based approaches, in the context of energy efficiency. RSSI-based trilateration, which provides good accuracy with little energy consumption, uses measurements of signal intensity to infer device positions. In cases where there are limitations on line of sight, technologies based on ultrasound measure signal travel durations. Although calibration and sensitivity to interference are taken into account, magnetic field-based approaches use magnetic field anomalies to determine positions. Accuracy, energy usage, scalability, robustness, and calibration effort are some of the factors that these techniques are evaluated against in order to fulfil the demands of indoor IoT environments. A thoughtful choice of localization methods can increase energy efficiency, increase the lifespan of sensor networks, and enable precise location-aware IoT applications. In order to meet the increasing demand for energy-efficient localisation in Indoor IoT environments, more research in this field is still being conducted.
Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning
Authors: Torana Kamble Et al.
Published: January 2024

Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing.   Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency.   IT operations and service delivery have changed due to widespread computational resource access.  Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation.   Resource allocation impacts efficiency, cost, performance, and SLAs.   Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning.   IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities.   Reactive strategies allocate resources only when shortages or surpluses occur at demand.   This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance.   Predictive analysis and workload forecasting predict resource needs in proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning.  Attention has been drawn to proactive predictive resource allocation.   These methods predict resource needs using historical data, machine learning, and predictive analytics.   Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs.   Matching resource distribution to workloads optimizes cloud resource management.  Resource allocation prediction improves with deep learning.   CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising.   New tools for accurate and flexible workload predictions have come from their ability to spot intricate patterns in historical data. This paper compares CNN, LSTM, and Transformer deep learning algorithms for cloud computing resource allocation forecasting.   This study determines the best predictive accuracy and workload ada[1]ptability algorithm using Google Cluster Data (GCD). The study evaluates upgrading cloud computing resource allocation with the Transformer model.   This study advances predictive resource allocation strategies, which can help cloud service providers and organizations improve resource utilization, cost-effectiveness, and performance in the face of rapid technological change.
ACTIVE POWER FILTER IMPACT ON POWER QUALITY IMPROVEMENT FOR INDUSTRUAL PLANT SUPPLIED BY PHOTOVOLTAIQUE STATION
Authors: Bourourou F. Et al.
Published: January 2024

This work deals on industrial electrical power quality improvement using an active power filter (APF) based on fuzzy logic controller. The studied system is a PV conversion chain used to supply linear and non-linear industrial loads of 200 kw plant. The PV station is modeled then simulated under Matlab Simulink, with standard conditions consideration in the first time then under real irradiation and sun power variation during the day hours, to obtain the true parameters design of the PV station to be able to give the needed power during the climatic conditions changing. APF parameters design and control loop structure are well studied to have an optimal control. MPPT technique is used in PV station control with the P&O algorithm. Also PV panel fault diagnosis has been done by using alarm for fault indicator and protection over under power. Simulation results are analyzed and discussed comparison with real signals, produced by the FLUK 6001B reference source with the use of the PRS 600 and the Teledyne 3,5 GHz-40Gs/s oscilloscope as measurement instrument, to improve the effectiveness of APF on electrical power quality, THD, amelioration and harmonic minimization in the industrial installation.
Sexting among young people: Perceptions and practices
Authors: Lee Murray , Crofts Thomas , McGovern Alyce , Milivojevic Sanja
Published: January 2024

The rapid development and adoption of online digital technologies has had a profound effect on the way young people conduct their social relationships. The emergence of sexting, or the distribution of sexually explicit photos and videos, has gained widespread attention and raised moral concerns. However, there remains little policy-relevant research on the prevalence of sexting and its impact on young people. This study provides a valuable contribution to the evidence base. In a survey of over 2,000 respondents, almost half reported having sent a sexual picture or video of themselves to another party, while two-thirds had received a sexual image. Sexting was prevalent among all age groups, with 13 to 15 year olds particularly likely to receive sexual images. Sexting was prominent among homosexual and bisexual respondents. Most sexting occurred between partners in committed relationships. The study found very little evidence of peer pressure or coercion to engage in sexting. Rather, young people reported engaging in the practice as a consensual and enjoyable part of their intimate relationships. The paper considers the implications of this for legal and policy responses to sexting.
Understanding the local government role in crime prevention
Authors: Homel Peter , Fuller Georgina
Published: January 2024

In Australia, crime prevention is primarily the responsibility of state and territory governments. What is less well understood is the significant role of local government in developing and delivering crime prevention at the community level, although councils have long been involved in helping to create safer communities. This research offers one of the first detailed insights into the valuable contribution made by local government within the multi-layered crime prevention strategies and initiatives which keep Australian communities safe. The Drugs and Crime Prevention Committee of the Parliament of Victoria carried out this research as part of an investigation into locally-based approaches to community safety and crime prevention in 2011. The results of a comprehensive survey of the crime prevention activities of local government authorities across Victoria are examined. This study reveals the issues local government prioritises, the responses they deploy and the challenges that they face, such as gaps in capacity and the need to manage complex relationships between participants who work on local community safety. Findings reveal a system that, while highly variable in sophistication and reach, provides an important platform for improving local community safety. The study also identifies important gaps and opportunities to improve collaboration between government and the private and NGO sectors.
Preventing the onset of youth offending: The impact of the Pathways to Prevention Project on child behaviour and wellbeing
Authors: Homel Ross , Freiberg Kate , Branch Sara , Le Huong
Published: January 2024

There are big gaps in our knowledge about how well civil-society initiatives work, including the effectiveness of family-support services that are often available in socially disadvantaged communities. This paper addresses this knowledge gap through an analysis of data from the child longitudinal database constructed through the Pathways to Prevention Project.
Misuse of information and communications technology within the public sector
Authors: Hutchings Alice , Jorna Penny
Published: January 2024

Information and communications technology (ICT) may be the target of criminal activity, as well as a tool used to facilitate criminal acts. Trust is placed in government employees to ensure that personal data are handled appropriately. However, there have been instances where this trust has been abused and data have been inappropriately accessed or otherwise used by insiders for illegitimate purposes. The misuse of ICT may also provide opportunities for financial gain, such as where accounting systems are manipulated by persons both within and external to agencies to commit fraud. Using the findings from the Fraud against the Commonwealth Surveys conducted by the Australian Institute of Criminology, data is presented regarding the misuse of ICT within the Australian Government over the three year period between 1 July 2008 and 30 June 2011. It is concluded that although the ICT environment is rapidly changing, fraud control plans, organisational policies and technical standards for data security minimise the risk of ICT misuse and identify intervention points at which prevention and detection methods may be focused.
Parental sexual offending: Managing risk through diversion
Authors: Goodman-Delahunty Jane , O'Brien Kate
Published: January 2024

Public policy initiatives to manage parental child sexual offending have been hindered by the absence of risk instruments sensitive to unique factors associated with the distinctive profile of this core group of offenders. Using an Australian sample of 172 male parental offenders referred to community-based treatment designed for low-risk offenders, this study retrospectively compared risk levels and reoffence rates of offenders accepted into treatment (46%) with those who returned to court for standard criminal prosecution (54%). Overall, the results indicated that offenders with low risk scores were significantly more likely to be accepted into treatment, spend longer in treatment and complete treatment than offenders with high risk scores. Low-risk offenders who experienced standard criminal prosecution and sentencing reoffended 11 times faster than their counterparts who attended the community-based program (after controlling for pre-treatment dynamic risk levels). This diversion program was an effective preventive intervention that increased offender desistance and reduced threats to the safety and welfare young children and their families.
Risky facilities: Analysis of crime concentration in high-rise buildings
Authors: Townsley Michael , Reid Sacha , Reynald Danielle , Rynne John , Hutchins Benjamin
Published: January 2024

Current town planning and housing policies suggest that in the very near future, housing density in major Australian cities will be much higher than current levels. To date, little attention has been paid to how these policy shifts will impact levels of crime and fear of crime. The aim of this research is to contribute to the development of strategic policy for the secure management of high-density housing. By analysing actual rates and types of crime, guardianship levels, building management styles and perceptions of fear of crime, the research will reveal how planning policies and high-rise building management styles can coalesce to create safer vertical communities. The research focuses on high-rise apartments and touristic buildings on the Gold Coast (specifically Surfers Paradise) and identifies the disproportionate concentration of crimes among a handful of buildings. Results may help state and local governments in Australia to avoid repeating the housing policy mistakes experienced by other countries.
Caffeine consumption and its effect among medical students in Anbar, Iraq
Authors: Al Shawi Ameel F. , Al Diwan Jawad K. , Saleem Alaa , Alaa Hazim , Ali Eman , Amer Haneen , Khalid Russel
Published: January 2024

Background: Caffeine consumption is considered a global issue, and the prevalence of caffeine consumption has dramatically increased among young adults.

Objectives: To assess the frequency of caffeine consumption, its sources, attitudes, and adverse effects among medical students in Fallujah and Anbar Universities in the west of Iraq.

Methods: A cross-sectional study was conducted, and a convenience sample was used to choose medical students from Anbar and Fallujah universities in the Anbar district in June 2022. The questionnaire consisted of questions about social characteristics such as age, gender, academic study level, and caffeine consumption-related questions.

Results: Out of the total (415), female students comprised 63.1% of the participants. The findings revealed that 34% of the participants consumed a caffeinated beverage once a day, 40% drank two to three times a day, 15.4% consumed caffeine once a week, and 57.6% had at least one cup each time. There was no statistically significant association between the adverse effects of caffeine and gender and the frequency of caffeine intake.

Conclusion: The findings of the current study reveal that caffeine consumption is common among medical students in the Anbar district.
Rezension von: Glasbrenner, Fritz, Journey of a lifetime
Authors: Trefz Bernhard , Glasbrenner Fritz
Published: January 2024

Fred Glasbrenner: Journey of a lifetime. Point Cook: Selbstverlag des Autors 2019. 581 S., zahlr. Abb.
Lightweight Medical Image Encrypting and Decrypting Algorithm Based on the 3D Intertwining Logistic Map
Authors: BOUREKOUCHE Hadjer , BELKACEM Samia , MESSAOUDİ Noureddine
Published: January 2024

The quantity of medical image data accessible for analysis is increasing because of advancements in telehealth services. Therefore, effective cryptographic solutions must be developed to prevent data manipulation by unauthorized users in insecure networks. This paper focuses on developing a lightweight symmetric cryptosystem algorithm with decreasing memory and power consumption at high speed for standard and medical images based on 3D intertwining logistic map-cosine (ILM-cosine), which is a powerful chaotic system in contemporary cryptography. The motivation of this paper is to reduce the memory space required for storing program data while minimizing execution time for lower implementation complexity in telehealth applications. Our proposed scheme consists of five main steps: ILM-cosine map key generation with histogram normalization, row rotation, column rotation, and exclusive-OR (XOR) logic operation. Various normal and medical images were used as samples for the simulation. The results showed that cipher images have good visual quality, high information entropy, large key space, and low computational complexity.
Breast Cancer Image Classification using Convolutional Neural Networks (CNN) Models
Authors: BOUKAACHE Abdelnour , NASSER EDİNNE Benhassıne , BOUDJEHEM Djalil
Published: January 2024

Breast cancer can progress silently in its early stages and frequently without noticeable symptoms. However, it poses a serious risk to women. It is imperative to recognize this potential health concern to mitigate it early. In the last few years, Convolutional Neural Networks (CNNs) have advanced significantly in their ability to classify images of breast cancer. Their capacity to automatically extract discriminant features from images has enhanced the performances and accuracy of image classification tasks. They outperform state-of-the-art techniques in this area. Furthermore, complicated models that were first learned for certain tasks can be easily adapted to complete new tasks by using transfer-learning approaches. However, deep learning-based categorization techniques could experience overfitting issues, particularly in cases where the dataset is small. The primary goal of this work is to investigate the performances of certain deep learning models to classify breast cancer images and to study the effects of data augmentation techniques, such as image rotation or displacement when utilizing a transfer learning approach. Using certain image datasets, the ResNet18, Resnet50, and VGG16 models demonstrated accuracy improvements, according to our experimental results.
Conventional and Enzyme-Assisted Extraction of Total Flavonoids from Rotten Onion (Allium cepa L.)
Authors: Hooda Ashish , Masih Dorcus , Vaibav
Published: January 2024

One of the richest sources of dietary flavonoids is the bulb of the onion (Allium cepa L.), which greatly contributes to the daily intake of flavonoids. This research aimed to optimize the yield of Total flavonoids from Onion Solid Waste (OSW). A comparison was drawn between Conventional and Enzyme Assisted extraction methods in terms of the yield of total flavonoids present in OSW extract. The results showed that the highest TFC were obtained at Time/Temp 60 °C for 180 minutes and Pectinase Conc./Time 2g for 120 minutes for Conventional and Enzyme assisted extraction respectively. The yield of TFC by both methods showed that the yield for both was higher in case of enzyme assisted extraction for T13, i.e., 0.472 mg/g respectively than that for conventional extraction for T4 i.e., 0.352 mg/g.
Impact of Mixed Biofertilizers on growth, yield and soil of Groundnut
Authors: Reddy Chinthakuntla Sudharshan , Kumari Gayatri , Yadav Sonu
Published: January 2024

A field experiment on Bio-Efficiency of Incryl on Groundnut (Arachis hypogaea L.) in Punjab Conditions was planned with an objective to know the effect of Incryl & biofertilizers on various chemical properties of groundnut. Groundnut is an important cash crop and most widely grown oil seed crop in India. During the kharif season of 2022, the field experiment was conducted on an agricultural farm at the School of Agriculture of Lovely Professional University in Phagwara, Punjab, to examine the various chemical properties of groundnut. The randomized block design (RBD) of the experiment consisted of seven treatments in five replications (T1 : control + (recommended dose of fertilizer i.e., RDF), (T2 : Incryl @ 1.5kg/acre + recommended dose of fertilizer RDF+ ), (T3 : Incryl @ 3kg/acre + recommended dose of fertilizer + RDF), (T4 : Incryl @ 6kg/ acre + recommended dose of fertilizer + RDF), (T5 : Incryl @ 1.5kg/acre + recommended dose of fertilizer + Rhizobium + RDF), (T6 : Incryl @ 1.5 kg/acre + recommended dose of fertilizer + Trichoderma enriched vermicompost + RDF), T7 : Incryl @ 1.5kg/acre + Rhizobium + Trichoderma enriched vermicompost + RDF) and application of Incryl @ 1.5kg/acre + Rhizobium + Trichoderma enriched vermicompost + recommended dose of fertilizer ) (T7) has shown better results among other treatments.
Influence of Phytohormones on Growth, Yield and Quality of tomato (Solanum lycopersicon L.) in irrigated ecosystems of Punjab, India
Authors: Rani Shalu , Komalpreet  , Kachroo Dileep , Sarthi
Published: January 2024

The present study was conducted at the research farm and Horticulture lab of Sant Baba Bhag Singh University, Punjab during the year 2020 and 2021. The experiment was conducted in a randomized block design comprising of 9 treatments (T0 - Control, T1 - GA3 @ 20 ppm, T2 - GA3 @ 30 ppm, T3 - GA3 @ 40 ppm, T4 - NAA @ 15 ppm, T5 - NAA @ 20 ppm, T6 - NAA @ 25 ppm, T7 - GA3 @ 30 ppm + NAA @ 20 ppm, T8 - GA3 @ 40 ppm + NAA @ 25 ppm) with three replications. Findings of the present study revealed that treatment T8 - GA3 @ 40 ppm + NAA @ 25 ppm resulted in highest number of leaves per plant (56), plant height (82.46 cm), number of flowers per plant (44.8), number of fruits (22.9), fruit diameter (5.83 cm), fruit weight (50.27 g), TSS (5.32 o Brix), fruit yield per plot (20.68 kg/plot) and fruit yield per hectare (344.69 q/ha). Hence, it is successfully established that the application of GA3 + NAA can be helpful in enhancing the yield and quality parameters of tomato crop thereby, improving the socio-economic status of the tomato growers.
Genetic Variability Studies in Chilli (Capsicum annuum L.) Genotypes under the Mid Hill region of Himachal Pradesh, India
Authors: Rao K Sai Timma , Sharma Hemraj , Dogra Rajesh Kumar
Published: January 2024

A field experiment was carried out at the Vegetable farm of Dr Y S Parmar University of Horticulture and Forestry, Nauni to evaluate variability parameters of fifteen yield and yield contributing characters in twenty-five chilli genotypes during the Kharif season 2020-21. The analysis of variance revealed significant differences among all the genotypes studied. High phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were observed for nine characters except for days to 50% flowering, fruit length and fruit diameter, plant height, days to maturity (mature green) and days to maturity (red ripe stage). High estimates of PCV and GCV coupled with high heritability and genetic gain were recorded for nine traits indicating the existence of additive gene action and these traits can be improved through selection.
Konaklama işletmeleri yöneticilerinin yeşil örgütsel davranışına küresel sosyal sorumluluğun etkisi
Authors: Güdül Ayşegül , Sağır Mehmet
Published: January 2024

Günümüzde çevresel sorunların artmasıyla birlikte, neredeyse tüm sektörler çevresel sorunlara karşı önlem almaya başlamıştır. Özellikle çevreyle en çok etkileşim halinde ve varlığı çevreye bağlı olduğu halde zarar da verebilen sektörlerden biri olan turizm de çevreye karşı önlemler almaktadır. Çalışan sayısının ve çevreye verilen zararın da fazla olması göz önüne alındığında, sektör çalışanlarının küresel sosyal sorumluluk kapsamında hem kendi ülkesi hem diğer ülkeler için yapabileceklerinin farkında olarak eyleme geçmesi ve yeşil örgütsel davranış sergilemesi beklenmektedir. İşletmede yürütülen küresel sosyal sorumluluk faaliyetlerine çalışanların katılım sağlaması, yeşil örgütsel davranış sergilemesine de yardımcı olacaktır. Çalışmada, çevre dostu 5 yıldızlı konaklama işletmelerinin yöneticilerinin küresel sosyal sorumluluk ve yeşil örgütsel davranışları arasındaki ilişki düzeyleri araştırılmış, aralarında pozitif ve anlamlı bir ilişki olduğu sonucuna ulaşılmıştır. Aynı zamanda, küresel sosyal sorumluluğun yeşil örgütsel davranış üzerinde anlamlı ve pozitif etkisi olduğu ortaya konmuştur. Demografik özelliklerin küresel sosyal sorumluluk algısında anlamlı fark ortaya koyup koymadığının da araştırıldığı bu çalışmada, yaş, medeni durum, eğitim düzeyi ve departman değişkenlerinde anlamlı farklılık olduğu, cinsiyet, yönetici düzeyi ve mevcut iş yerinde toplam çalışma süresine göre anlamlı farklılık olmadığı belirlenmiştir.
Ethnicity, physical activity, and the dietary intake of boarding high school students: A photovoice mixed method study
Authors: Aqilah Syarifah , Taneepanichskul Nutta
Published: January 2024

This study explores the dietary intake of female students and its associated factors to understand their meal experiences in a boarding high school setting through a photovoice study. A mixed-methods study included 60 students in paper-and-pencil assessment and 8 students in photovoice activities. The nutritionist measured the anthropometrics and trained the students to keep multiple 24-hour food records. A linear regression model was used to examine the associations and was complemented by a thematic analysis of the story from the produced photos. The result shows most students were at normal weight, and overnutrition was 26.7%. The mean of students’ total energy, protein, fat, and carbohydrate intake was 1182 kcal, 38.7 g, 64.2 g, and 141.7 g, respectively, lower than national recommendations. Ethnicity and physical activity were found to be associated with students’ dietary intake. In the photovoice study, 14 photos were produced, and six major themes emerged during focus group discussions. Sundanese and less physically active students tended to have higher dietary intakes. To conclude, students were aware of food shortages at school and made efforts to supplement their meals, but appropriate food choices were not made. It is necessary for schools to address students’ meal preferences on school menus, promote healthy food, and provide practical nutritional information.
Estimating the effects of electronic money on the income velocity of money in Indonesia
Authors: Anwar Cep Jandi , Ayunda Vida Tri , Suhendra Indra , Ginanjar Rah Adi Fahmi , Kholishoh Lilis Nur
Published: January 2024

This paper investigates the effects of electronic money, exchange rate, inflation, money supply, economic growth, and interest rate on the income velocity of money. We perform the Autoregressive Distributed Lag (ARDL) estimation for Indonesian data from 2009 to 2022 to examine the short-run and long-run estimations. The findings of this paper show that the income velocity of money requires 1.2 years of adjustment time to reach long-run equilibrium. We discover that electronic money and money supply have a considerable negative impact on the income velocity of money in the long term. Meanwhile, the interest rate, exchange rate, and economic growth have a significant positive effect on the income velocity of money in the long run. On the other hand, the findings indicate that in the short run, economic expansion and electronic money have a significant and positive impact on the income velocity of money. Whereas, inflation, interest rate, and money supply have a significant negative effect on the income velocity of money in the short run. Based on these findings, it is absolutely necessary for policymakers to encourage the use of electronic money to boost the income velocity of money and create a more efficient economy. A future study will likely employ cross-country data to compare the findings across samples of countries.
Othering the Baha’is in Their Homeland: Reviewing Baha’i Persecution in Iran (1979-Present)
Authors: Sadeghian Saghar
Published: January 2024

آیین بهائی زاده‌ی ایران و دارای چندین میلیون پیرو در سراسر جهان است. بهائیان بزرگترین اقلیت دینی ایران هستند. با این وجود، از زمان پیدایش تا کنون، پیروان این آیین را در ایران آزرده اند و زیر فشار گذاشته اند. این نوشتار می‌کوشد با نگاهی به تاریخ و روش‌های بهائی آزاری، دلایل چنین رویکردی را واکاود. تاکید بیشتر بر دوران پس از انقلاب ۱۳۵۷ است که فشارها چندین برابر و سازمانی شد. همچنین پرسش اصلی آن بود که گروه‌های گوناگون اجتماعی، به ویژه جامعه‌ی روشنفکر ایرانی، چه دلایلی برای چنین دفع و سرکوبی دارند. این مقاله نشان می‌دهد که بهائیان ایران از آغاز سپر بلای جامعه بوده اند که می‌بایست تقصیر مشکلات و بحران‌ها را گردن گیرند. در عین حال، ایرانیان حقوق انسانی بهائیان را منوط به اثبات برحق بودن این آیین می‌شمارند و می‌کوشند با رد باورهای این مذهب، سرکوبی پیروان آن را توجیه کنند. در پایان مقاله، گفتگویی در باب تغییرات جامعه‌ی امروز آمده است که گویای رشد آگاهی و حمایت جامعه‌ی ایرانی از هم میهنان بهائی خویش در عین شدت گرفتن سرکوبی‌هاست. مبنای این نوشتار اسناد تاریخی، نظریه‌های چندی از اندیشمندان، اخبار روز و مصاحبه با کسانی است که این سرکوب‌ها را تجربه کرده‌اند.
School Rebellers in Iran
Authors: Paivandi Saeed
Published: January 2024

اسلامی کردن نظام آموزشی و دانشگاه در ایران در دوران پس از ۱۳۵۷ پرسش‌های مهمی درباره تاثیر این روندها بروی آموزش زنان و جایگاه آنها در جامعه را به میان کشید. تحولات دهه ۱۳۶۰ و ۱۳۷۰ در ایران نشان دادند که از نظر کمی رشد آموزش در ایران متوقف نشد و نسبت زنان در جمعیت دانشجویی هم بطور منظم افزایش یافت. این رشد کمی چشمگیر اما به معنای وجود شرایط مطلوب در فضاهای آموزشی برای دختران نسل جوان نبود چرا که اسلامی کردن مدارس و دانشگاه‌ها گسترش گفتمان تبعیض آمیز علیه زنان و تبلیغ آشکار مردسالاری را هم در پی داشت. مقاله حاضر تلاش می کند با تکیه بر تجربه زیسته و دیدگاه دختران جوان در نظام آموزشی به بررسی این تناقض مهم میان گفتمان جنسیت‌زده آموزش و موفقیت دختران در درون این نهاد بپردازد. نتایج این کار میدانی نشان می دهد که دختران با وجود تلاش برای دسترسی به سطوح بالاتر آموزش از درون به مقاومت در برابر گفتمان ایدئولوژیک آن دست می‌زنند.
Discovery Elbow System arthroplasty polyethylene bearing exchange: outcomes and experience
Authors: Morris Daniel L J , Walstow Katherine , Pitt Lisa , Morgan Marie , Tambe Amol A , Clark DI , Cresswell Timothy , Espag Marius P
Published: January 2024

Background: The Discovery Elbow System (DES) utilizes a polyethylene bearing within the ulnar component. An exchange bearing requires preoperative freezing and implantation within 2 minutes of freezer removal to allow insertion. We report our outcomes and experience using this technique.Methods: This was an analysis of a two-surgeon consecutive series of DES bearing exchange. Inclusion criteria included patients in which exchange was attempted with a minimum 1-year follow-up. Clinical and radiographic review was performed 1, 2, 3, 5, 8 and 10 years postoperative. Outcome measures included range of movement, Oxford Elbow Score (OES), Mayo Elbow Performance Score (MEPS), complications and requirement for revision surgery.Results: Eleven DESs in 10 patients were included. Indications were bearing wear encountered during humeral component revision (n=5); bearing failure (n=4); and infection treated with debridement, antibiotics and implant retention (DAIR; n=2). Bearing exchange was conducted on the first attempt in 10 cases. One case required a second attempt. One patient developed infection postoperatively managed with two-stage revision. Mean follow-up of the bearing exchange DES was 3 years. No further surgery was required, with no infection recurrence in DAIR cases. Mean elbow flexion-extension and pronosupination arcs were 107° (±22°) and 140° (±26°). Mean OES was 36/48 (±12) and MEPS was 83/100 (±19).Conclusions: Our results support the use of DES bearing exchange in cases of bearing wear with well-fixed stems or acute infection. This series provides surgeons managing DES arthroplasty with management principles, successful and reproducible surgical techniques and expected clinical outcomes in performing DES polyethylene bearing exchange.Level of evidence: IV.
Estágio curricular supervisionado e a formação em gestão do agronegócio: um relato de experiência
Authors: Dos Santos Jerliane de Melo , De Morais Bárbara Oliveira
Published: January 2024

Este trabalho buscou demonstrar a relevância do estágio na formação de gestores do agronegócio, considerando aspectos teóricos, práticos e a integração entre eles. A discussão transcendeu a simples aplicação de conceitos, explorando a função essencial do estágio como uma ferramenta de integração do projeto pedagógico do curso, a partir do relato de experiência. Para nos auxiliar, trouxemos as contribuições de autores que discutiram a relevância do estágio para diferentes áreas profissionais, incluindo De Andrade (2005), Lopes (2006), Brito (2017), Rodrigues (2019), Lima e Pimenta (2018), Silva; Gaspar (2018), Pires; Farias; Batista (2019) e Dos Santos; Morais (2023). No âmbito do agronegócio, há uma lacuna nas discussões sobre o tema. Assim, dedicamo-nos à prática reflexiva como elemento crucial no processo de aprendizagem, trazendo a narrativa de experiências durante o estágio que serviu não apenas como uma ferramenta de autoanálise, mas permitiu com que a discussão fosse ampliada ao dialogar com a literatura.
Analysis of Line Intensity Mapping, Dust Grain Radiation, Lyman-α Emission and Transmission
Authors: Chang George , Yuan Yijin , Zhang Daxun
Published: January 2024

Existing models of the universe consider the combined effects of neutrons and dark matter. While the properties of neutrons are well understood, depicting galaxy formation and understanding the universe through spectral images remains a major research direction. This study can be classified into two disciplines: Cosmology and Astrophysics. Astrophysicists focus on the evolution of galaxies, while cosmology studies the universe on a larger scale, including interactions of dark matter and dark energy. Cosmologists mainly obtain data through cosmological simulations, which play an important role in cosmology research. However, existing computers struggle with the complex calculations involved. One possible solution is simulating cosmic fluid dynamics, which can effectively simulate large volumes but cannot simulate detailed information. The THESAN project has achieved important research results but also has some defects. Existing articles have summarized THESAN's characteristics and shortcomings by comparing its expected simulations with observational data. This paper summarizes the existing problems and introduces solutions, focusing on THESAN's improved simulation method for the Lyman-alpha emission spectrum and its simulation of cosmic dust. We talk about 3 new techniques used in the simulation of THESAN.
Comparison of High Voltage Device Based on Si and SiC
Authors: Li Ruihan , Zhang Shucun
Published: January 2024

High voltage devices play a crucial role in various power electronic applications. Silicon (Si) has been the dominant material for such devices, but silicon carbide (SiC) is emerging as a promising alternative due to its superior properties. The objective of this study is to comprehensively compare high voltage devices based on Si and SiC. This study will focus on their different characteristics and performances in individual environments. Based on the analysis, we have observed that SiC devices exhibit superior performance in several key aspects. Notably, SiC devices demonstrate lower power losses attributed to reduced line and switching losses. These findings underline the immense potential of SiC as the preferred material for high voltage applications. The research outcomes offer valuable insights into the performance disparities between silicon-based and SiC-based high voltage devices. This knowledge is crucial for guiding future advancements in the field of power electronics, enabling the development of more efficient and reliable high voltage systems. By embracing SiC devices, one can enhance power transmission, improve energy efficiency, and advance sustainable energy solutions.
Fuzzy Julia Sets and Fuzzy Superior Julia Sets
Authors: Mu Beining
Published: January 2024

This article examine the past study of fuzzy Mandelbrot set and fuzzy superior Mandelbrot set, then give the definition of fuzzy Julia sets and fuzzy superior Julia sets with the idea of utilizing membership functions to represent the escape velocity of Julia sets or superior Julia sets of each complex number in the definition of fuzzy Mandelbrot set and fuzzy superior Mandelbrot set inherited while the membership functions are selected to distinguish complex numbers with different escape velocity and same orbit. Then some examples of fuzzy Julia sets and fuzzy superior Julia sets are presented. With some observation of the examples, some analytical and topological properties of fuzzy Julia sets are demonstrated and sketches of the proofs are also presented. In the part of fuzzy superior Julia sets, some trivial conclusions as well as an example is presented where more complicated properties which involves the study into very chaotic behaviors are left open.
Research on the Influencing Factors that May Lead to Obesity
Authors: Qiu Nike
Published: January 2024

The global obesity problem is getting more and more serious, which not only affects the quality of life but also induces a series of complications. In this study, multiple linear regression method was used to study the influencing factors of obesity, and their answers were linked to the literature review to draw conclusions. And their responses were linked to literature Review to draw conclusions. Calorie consumption monitoring, physical activity frequency, and vehicle use also have significant positive effects on obesity levels. Frequent consumption of vegetables, main meals, smoking times, and alcohol consumption will also have a significant negative impact on the obesity level. Due to the limitations of this data set and other reasons, the model test results will be inaccurate. In the future, people from other regions may be included, making the research more scientific. The research in this paper has a positive effect on the treatment of obesity, which will help people to find it earlier, treat it as soon as possible, and improve the survival rate and quality of life of patients.