Limitations In Terms Research Articles

Can term limits reduce political sabotage? Evidence from negative campaigning in gubernatorial races

Can term limits reduce political sabotage? Evidence from negative campaigning in gubernatorial races

Are Term Limits for Congress the Best Way To ‘Drain the Swamp’?

Are Term Limits for Congress the Best Way To ‘Drain the Swamp’?

Analysis of the Principal, Fabrication and Application of AI Chips

As a matter of fact, artificial intelligence (AI) technology has developed by leaps and bounds with the rapid development computation ability and improvements of machine learning scenarios (e.g., deep learning neural network). On this basis, its applications have appeared in various fields in recent years. In order to support complex artificial intelligence computing, AI chips have gradually appeared in people's vision, as well as have played an essential role in the future development of life. With this in mind, based on the development of AI chips in recent years, this paper discusses the definition and classification, principles and design, preparation, and application of AI chips. According to the analysis, the current limitations in terms of the state-of-art facilities will be demonstrated. At the same time, this study predicts AI chips’ future development prospects, so that readers can have a more comprehensive understanding of AI chips and the field of artificial intelligence.

Dynamics of travel decision-making between organized packaged tourists and backpackers

Tourists are different in terms of their purpose of travel, the tourist type, the number of days spent, and how they spend time in a destination. Tourist decision-making models attempt to conceptualize how tourists make decisions during their pre-travel, onsite experiences, and post-travel stages. These models, including the grand models of tourist behavior, have paved the way to broaden our understanding of this complex behavioral phenomenon. Despite their remarkable contributions to the existing knowledge, these models display some limitations in terms of complexity, general nature of explanations, poor empirical support, and lack of consideration of different traveler types. Further examinations are necessary to understand the dynamic nature of decisions that take place under each broader stage. This study attempted to address some of these limitations through a qualitative inquiry with a comparison of tourist decision-making between two mainstream traveler types i.e. organized package tourists and backpackers. The findings uncovered empirical evidence to propose a research proposition that a general travel decision-making model cannot be derived for different traveler types and certain alterations need to be done with reference to different traveler types. These findings contribute to the literature on tourist behavior by inviting scholars to revisit the existing models in light of different traveler types.

Application of Artificial Intelligence to Diagnosis of Laryngeal Lesions Using Laryngoscopy

Laryngeal diseases have a significant impact on quality of life and often require timely and accurate diagnosis for effective management. Conventional methods of diagnosis, such as manual inspection of laryngoscopic images, have limitations in terms of accuracy and efficiency. The integration of artificial intelligence (AI) and machine learning techniques in laryngoscopic image analysis has emerged as a promising approach to enhance diagnostic accuracy, streamline workflow, and improve patient outcomes. This review paper provides an in-depth analysis of the recent advancements in AI-driven laryngoscopic image analysis for the diagnosis of laryngeal diseases, also covering methodologies, challenges, and future prospects.

Balancing Democracy and Adz-Dzari'ah Principles: Legal Reasoning on Term Limits for Political Party Chairpersons in Indonesia

This study explores the issue of term limits for political party chairpersons from the perspective of democracy and the principles of az-zari'ah. In 2022-2023, there was a surge in lawsuits to the Constitutional Court regarding the absence of rules regarding the term of office of political party chairpersons in Indonesia. The number of lawsuits shows that the absence of such rules has caused considerable problems, both within and outside political parties and the state. Various political diseases have become increasingly apparent, such as the perception that public officials only follow the direction of party leaders (bossism), and this has the potential to lead to abuse of power by political party chairmen in the context of public policy. Using a qualitative literature review methodology, this study uses a comparative philosophical approach, namely by paying attention to the principles of democracy and applying methods derived from az-zari'ah. Both of these are used because, on the one hand, Indonesia is a country that applies the principles of democracy; on the other hand, many Indonesian people believe in the truth of Islamic law, including the principles of az-zari'ah. It was found that, from the perspective of az-zari'ah, the term of office of the party chairman must be limited (closed) so as not to become a vehicle for political disease. By Az-zari'ah, limiting the term of party leaders becomes a crucial matter, as it helps mitigate potential harms that may arise in the absence of such limitations. Meanwhile, from a democratic perspective, it also has the same value implications, namely the need to limit the term of office of the chairman. Democracy has the basic function of limiting political power in a person and not expanding or giving unlimited power like a monarchical system. This is because democracy believes that power tends to corrupt, and absolute power corrupts. So, since Indonesia uses the philosophy of democracy in its country, every political institution, in this case, political parties, must abide by the same rules.

Trends in Democratic Attitudes: Bolivia 2004-2021

After more than a decade of political stability, in 2019 Bolivia suffered a major political crisis that ended with the resignation of a long-standing President, amidst popular unrest after a questioned election. While the crisis was the immediate result of a questionable attempt by the President to skip constitutional term limits, its roots can be found in the declining rates of support and satisfaction with democracy among citizens and in the polarization reaching democratic support. Using survey data from LAPOPs AmericasBarometer, this article finds that national averages for different measures for democratic support show declining trends, and, perhaps more importantly, they also show high levels of polarization, with supporters of the president showing radically different attitudes that those who are critics of the government, particularly during critical times. Cluster analyses performed on the same data indicate that the proportion of individuals who share attitudes that can be considered as “institutionalists” has been declining, while the proportion of those who support extra-powers for the executive and authoritarian alternatives, has increased. The article concludes discussing some risks for Bolivian democracy within its citizens’ attitudes towards democratic institutions.

Prime Ministerial Term Limit: Adopted, Abused, and Abandoned

Since 2007, Thailand has introduced a prime ministerial term limit into its constitutional system. However, the purpose of this term limit appears not to prevent an elected tyranny but rather to obstruct enemies of the conservative establishment who have been entrenching their presence in Thailand’s volatile politics. The measure was put to the test when Prayuth Chan-ocha, the junta leader and one of Thailand’s longest serving prime ministers, hit the eight-years term limit. The Constitutional Court was willing to ignore the law and save him. This article traces the adoption of the prime ministerial term limit and highlights the constitutional court decision as a case study of an abusive constitutional borrowing.

Bull Breeding Soundness Assessment Using Artificial Neural Network-Based Predictive Models

For years, efforts have been devoted to establishing an effective bull breeding soundness evaluation procedure; usual research on this subject is based on bull breeding soundness examination (BBSE) methodologies, which have significant limitations in terms of their evaluation procedure, such as their high cost, time consumption, and administrative difficulty, as well as a lack of diagnostic laboratories equipped to handle the more difficult cases. This research focused on the creation of a prediction model to supplement and/or improve the BBSE approach through the study of two algorithms, namely, clustering and artificial neural networks (ANNs), to find the optimum machine learning (ML) approach for our application, with an emphasis on data categorization accuracy. This tool was designed to assist veterinary medicine and farmers in identifying key factors and increasing certainty in their decision-making during the selection of bulls for breeding purposes, providing data from a limited number of factors generated from a deep pairing study of bulls. Zebu, European, and crossbred bulls were the general groupings. The data utilized in the model’s creation (N = 359) considered five variables that influence improvement decisions. This approach enhanced decision-making by 12% compared to traditional breeding bull management. ANN obtained an accuracy of 90%, with precision rates of 97% for satisfactory, 92% for unsatisfactory, and 85% for bad. These results indicate that the proposed method can be considered an effective alternative for innovative decision-making in traditional BBSE.

A Method for Riverway Erosion and Deposition Calculation Using Measured Cross-Sections

The calculation of erosion and deposition in riverways plays a pivotal role in river morphology studies, comprehensive river management and flood safety. Some existing methods have certain limitations in terms of accuracy and applicability. To address these challenges, we propose a method for calculating riverway erosion and deposition based on measured cross-sectional terrain data. This method not only enables the calculation of changes in erosion and deposition along the riverway but also provides information on the spatial distribution of these changes. The validity of the proposed calculation method was assessed using measured bathymetric data. The results indicate a relative error of only 5.6% between the calculated and measured values for the total volume of erosion and deposition. A comparison with the results obtained using a cross-section method reveals that, with an average distance between adjacent sections of 1.0 km, the proposed method generally outperforms the cross-section method. The relative error in the total volume of erosion and deposition decreases from 19.2% with the cross-sectional method to 5.6% with our proposed method. When facing the need to calculate changes in riverway erosion and deposition, our approach offers a more accurate and flexible computational method.

Validation of the Activ8 Activity Monitor for Monitoring Postures, Motions, Transfers, and Steps of Hospitalized Patients.

Sedentary behaviors and low physical activity among hospitalized patients have detrimental effects on health and recovery. Wearable activity monitors are a promising tool to promote mobilization and physical activity. However, existing devices have limitations in terms of their outcomes and validity. The Activ8 device was optimized for the hospital setting. This study assessed the concurrent validity of the modified Activ8. Hospital patients performed an activity protocol that included basic (e.g., walking) and functional activities (e.g., room activities), with video recordings serving as the criterion method. The assessed outcomes were time spent walking, standing, upright, sedentary, and newly added elements of steps and transfers. Absolute and relative time differences were calculated, and Wilcoxon and Bland-Altman analyses were conducted. Overall, the observed relative time differences were lower than 2.9% for the basic protocol and 9.6% for the functional protocol. Statistically significant differences were detected in specific categories, including basic standing (p < 0.05), upright time (p < 0.01), and sedentary time (p < 0.01), but they did not exceed the predetermined 10% acceptable threshold. The modified Activ8 device is a valid tool for assessing body postures, motions, steps, and transfer counts in hospitalized patients. This study highlights the potential of wearable activity monitors to accurately monitor and promote PA among hospital patients.

Accessibility of Virtual Museum Spaces in the 21st Century in Turkey

Museums, which are the embodiment of art, exhibition, culture, and science, have started to offer virtual space experiences as well as traditional physical space experiences in recent years. In addition to the development of technology, the pandemic, which started in 2019 and affected the whole world, has also been effective in accelerating the transformation of museums from physical space to virtual space. The increasing use of virtual museums directly concerns different user groups in terms of accessibility. In a physical museum experience, solutions that appeal to different types of users are generally produced. Thanks to these solutions, an individual with any disability can be included in the space with all senses such as sight, touch, smell, and hearing during a museum visit, while the lack of inclusion of all senses in the virtual space experience causes the experience to be more limited. This study discusses this limitation in terms of accessibility. How can the virtual space experience be more accessible for different types of users? Can different solutions be offered to all user groups for a better understanding of the space and the art product? These questions constitute the research questions of this study. In this context, this study aims to examine how accessible virtual museums are. The study has conducted this inquiry through 59 virtual museums in Turkey. As a method, virtual museums were classified and tabulated in terms of their accessibility levels in line with their characteristics. It was concluded that there are deficiencies in the experience of different types of users and various suggestions were made in terms of virtual accessibility.

Exploring Intercultural Insights Through the Critical Incident Research Method

The Critical Incident Technique (CIT) has become a renowned approach in qualitative research, offering a systematic framework for exploring interactions, revealing human perception and conduct in different scenarios. The technique analyses significant incidents that emerge from either observations or narrations within specific contexts and helps to scrutinise thoughts, emotions, attitudes or actions. CIT uncovers cultural discrepancies within intercultural communication and has been implemented in various disciplines. Originating from aviation research, CIT has proved to be adaptable to other areas and has thus been successfully integrated into intercultural training and research. Regardless of its utility and popularity, some critics suggest it can oversimplify complexity. Nevertheless, recent publications have demonstrated the ongoing significance of CIT. These publications use linguistic approaches that broaden its horizons, encompassing narrative methodologies and multi-perspective analyses. While CIT provides significant insights, it is important to acknowledge its limitations in terms of subjectivity and generalisability. In conclusion, CIT continues to be a valuable qualitative research approach that enhances our understanding of human behaviour and experiences through the systematic analysis of key events.

MANAGEMENT STRATEGY: CASE STUDY IN NAVA DHAMMASEKHA, BETING VILLAGE, MERANTI ISLANDS, RIAU PROVINCE

This study aims to describe the management strategy for the development of Dhammasekha as a need for a community in forming human beings who are critical, creative, innovative, and dynamic in Buddhist values, with a case study mix combine research design, respondents selected based on purposive sampling and analysis with interactive model. The results of the study show that from the strategic management model in the development of NDS Maitreya has been carried out well, despite various limitations in terms of infrastructure, human resources, funds and other resources, because schools are basically a community, serving students and the nation's children; The implementation of strategic management both short and long term has been well formulated through the school's vision, mission, goals and objectives, the school is able to make programs, activities and standard procedures and is evaluated periodically.

A novel method for the evaluation of ship berthing risk using AIS data

The assessment of ship berthing risk, encompassing the potential for berthing collisions and unforeseen events, holds paramount importance in the realm of waterway traffic management and maritime surveillance. However, existing methods for analyzing ship berthing risk suffer from limitations in terms of timeliness, comprehensiveness and data accessibility. Therefore, this paper presents a novel approach to ship berthing risk assessment. The proposed method relies on Automatic Identification System (AIS) data and takes into consideration information related to the ship, berth, and environmental factors. It calculates crucial parameters, including the vertical distance between the ship and the berth, berthing speed, berthing angle and real-time distance between the ship and the berth, utilizing the AIS data and the berth location. Furthermore, environmental disturbance data pertaining to the ship's berthing environment is integrated with AIS data. Subsequently, we introduce the Improved Bossel Model considering Catastrophe (IBM-CC) to evaluate ship berthing risk in real-time. Finally, the proposed method was validated using actual ship berthing data and various simulation scenarios. The results demonstrate that our proposed method accurately assesses real-time ship berthing risk under diverse scenarios, offering a novel approach for the real-time and precise quantitative assessment of ship berthing risk.

The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models

Deep learning technology is widely used for recognizing character images, including various regional characters and diverse ancient scripts. Deep learning models require large data sets to recognize images accurately. However, creating a dataset has limitations in terms of quantity, including the Komering script dataset used in this study. Data augmentation techniques can be applied to expand the dataset by modifying existing images to increase data diversity. This study aims to investigate the impact of augmentation techniques on the performance of deep learning models in the case of Komering script recognition. The dataset consists of 500 images for five classes of Komering script characters. Three augmentation techniques, namely random rotation, height shift, and width shift, were applied to the five characters, which were then used to test the model trained to recognize characters in the Komering dataset. This research contributes to providing insights into the effect of augmentation techniques on robust confidence prediction of deep learning models for recognizing newly augmented data. The results demonstrate that the deep learning model can recognize modified data using augmentation techniques with an average accuracy of 80.05%.

Carbon foam directly synthesized from industrial lignin powder as featured material for high efficiency solar evaporation

Interfacial solar evaporation has emerged as a promising method for clean water production. Among the various types of evaporators, biomass-based carbon evaporators have garnered significant attention due to their renewability and low cost. However, biomass-based carbon materials still face certain challenges, including limitations in terms of their morphology, size adjustability, light absorption capability, and mechanical strength. To overcome these issues, a novel carbon foam evaporator has been developed and extensively optimized. The carbon foam can be directly synthesized from industrial lignin powder without the need for any additives, using a simple and environmentally friendly methodology that involves foaming, carbonization, and activation processes. The evaporator demonstrates remarkable attributes, encompassing swift water transport capacity, broad-spectrum sunlight absorption, expeditious photo-thermal conversion capability, low density (0.281 g cm−3), low thermal conductivity (0.0473 W m−1 K−1), and a profusion of micro-pores (1430.49 m2/g of SBET). The developed lignin-based solar evaporator achieves a high water evaporation rate of 2.11 kg m−2h−1, coupled with an impressive evaporation efficiency of 93.37 % under 1 sun. Furthermore, the evaporator demonstrates exceptional resistance to salt accumulation and exhibits efficient recycling capabilities. Notably, the evaporator's flexible and plastic nature enhances its versatility and holds great promise for its application in the field of solar-powered seawater desalination.

Enhancing the fatigue property of high entropy alloy by regulating characteristics of grains and precipitates

High entropy alloys (HEAs) are a new class of alloys with many excellent mechanical properties, having the potential as structural materials. One advantage of HEAs is the vast space for designing compositions and microstructures, which provides more possibilities to develop fatigue-resistant materials. In this study, a multi-phase fine-grained microstructure was engineered through cold rolling and annealing treatment in Al0.3CoCrFeNi HEA to obtain an enhanced fatigue property. This alloy consists of FCC matrix phase with an average grain size of ∼ 1.13 ± 0.84 μm, and B2 and σ precipitates with sub-micron scale. Their phase fractions are ∼ 90 %, ∼ 9 % and ∼ 1 %, respectively. Stress-life (S-N) data indicate that fatigue endurance limit and ratio in terms of stress amplitude are ∼ 281 MPa and ∼ 0.3, based on stress ratio of 0.1. Good fatigue property is related to high ultimate tensile strength derived from good work-hardening ability and fine microstructure. In addition, activation of deformation twins, blunting of microcracks and deflection of fatigue cracks associated with precipitates are beneficial for enhancement of fatigue resistance. The above results provide theoretical guidance on how to enhance fatigue resistance of HEAs by regulating microstructures.

Stem cutting: A novel introduction site for transporting water-insoluble particles into tomato (Solanum lycopersicum) seedlings

The introduction of exogenous particles into plants has promising applications in agriculture and biotechnology. Nanoparticles can be transported into plants through foliar application or root uptake. However, both methods have limitations in terms of the size of the particles (<40 nm) that can be transported due to the barriers of the cell wall and cuticle. In the present study, we proposed a novel method to deliver particles of up to 110 nm into plants by cutting the stem of tomato seedlings. We demonstrated for the first time, using water-insoluble silica colloids, that not only nanoparticles but also submicron particles can be transported toward the leaves when the plant stem is used as the entry point of particles. Thirty-five-day-old tomato seedlings were used as the target plants. When the cut stem seedlings were immersed in the colloidal particle suspension for up to 24 h, significant particle accumulation was observed in the nodes and leaves. The relatively low particle concentrations (10 mg/L) allowed effective transport throughout the plants. Silica particles with average diameters of 10 nm and 110 nm were both well transported and moved through the stem. Even after the particles entered the plant, adventitious roots were formed, resulting in the formation of whole plants with roots, stems, and leaves. This method can be applied not only to tomatoes but also to other food crops for various applications in plant biotechnology.

Adversarial Defense: A GAN-IF Based Cyber-security Model for Intrusion Detection in Software Piracy

Software-piracy continues to be most critical distress, posing grave threats to digital-assets and financial stability. Traditional Intrusion Detection systems (IDS) often battles hard to identify latest piracy attempts owing to their dependence on pre-established patterns. To effectively address this we attempt to suggest innovative approach leveraging DL based Generative Adversarial Networks (GANs) and ML based Isolation Forest (IF) for detecting software piracies. Our proposed GAN-IF based cyber-security model performs its functions by training a Generator network to mimic the behavior of genuine software applications. Discriminator network discriminates between legitimate and pirated software. Isolation Forests assists in detecting anomalies in diverse conditions, including unseen attacks. Integrated training based on DL and ML framework enables efficient learning and adaptation with respect to piracy challenges, making it highly-successful against prior known threats. There are several DL models which are utilized in IDS operations having limitations in terms of robustness, interpretability. Utilizing GAN in the context of cyber-security to combat software-piracy can have noteworthy merits since GANs can precisely identify forged software as they are skilled at generating fake content resembling actual. Training a GAN on legitimate software, helps to learn and identify disparities in pirated versions. Isolation-Forest can detect anomalies in software distribution networks or user behavior with respect to software usage by recognizing abnormal patterns indicating software piracy, like illegal access or sharing of software licenses. Our proposed model combines GANs and Isolation Forests, excels at accurately detecting subtle indicators of software piracy, a capability that traditional methods may fail to recognize. ML-DL integrated model continuously learns and updates its detection capabilities in response to evolving piracy tactics, making it resilient against zero-day attacks, polymorphic malware. Through adversarial training, ml-model minimizes false alarms and focuses only on genuine threats. In our evaluation, we demonstrate the effectiveness of GAN-IF based cyber-security model in detecting software piracy attempts across various scenarios. Results indicate that our approach outperforms traditional solutions in terms of detection accuracy and adaptability.