Comprehensive Survey Research Articles

A survey of recent approaches to form understanding in scanned documents

This paper presents a comprehensive survey of over 100 research works on the topic of form understanding in the context of scanned documents. We delve into recent advancements and breakthroughs in the field, with particular focus on transformer-based models, which have been shown to improve performance in form understanding tasks by up to 25% in accuracy compared to traditional methods. Our research methodology involves an in-depth analysis of popular documents and trends over the last decade, including 15 state-of-the-art models and 10 benchmark datasets. By examining these works, we offer novel insights into the evolution of this domain. Specifically, we highlight how transformers have revolutionized form-understanding techniques by enhancing the ability to process noisy scanned documents with significant improvements in OCR accuracy. Furthermore, we present an overview of the most relevant datasets, such as FUNSD, CORD, and SROIE, which serve as benchmarks for evaluating the performance of the models. By comparing the capabilities of these models and reporting an average improvement of 10–15% in key form extraction tasks, we aim to provide researchers and practitioners with useful guidance in selecting the most suitable solutions for their form understanding applications.

Graph neural networks for multi-view learning: a taxonomic review

With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.

Negotiation Strategies and Their Effect on Organizational Success: Analytical Research in The Iraqi Ministry of Industry and Minerals

Purpose: This study aims to determine the impact of Negotiation Strategies (NS) on Organizational Success(OS) in the Iraqi Ministry of Industry and Minerals. Conceptual Framework: The NS includes dimensions (cooperative strategy, competitive strategy, avoidance strategy, bargaining strategy and absorption strategy) measured by (Thomas, 2008), and OS includes dimensions (resources, organizational structure, management, process and knowledge management) measured by (Hamedi & Azimi, 2013)​. Design/Methodology/approach: The descriptive analytical method was adopted in completing the current research, on a sample of senior and middle leaders in the Iraqi Ministry of Industry and Minerals under study, which numbered (120) individuals, and they were selected using the comprehensive survey stratified sample method. Findings: We found that NS plays a positive role in achieving organizational success in the Iraqi Ministry of Industry and Minerals. NS enhances cooperation and coordination between parties within the ministry, which has led to improved operations. Thus, the main and sub-hypotheses of the research were proven correct. Research, Practical & Social implications: Identify the latest developments and fundamental ideas related to the variables of the study (NS - organizational success) to find interrelationships between the variables. Originality/value: Providing those concerned and interested with practical and theoretical information that explains the nature of the research variables, their sources and causes, the extent of their reflection on the research sample, and the extent of the interest of the researched organization (the Iraqi Ministry of Industry and Minerals) in their dimensions.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Micro and Small Business Growth Drivers in Nigeria: Evidence from Small Cities Small Federating State

This study involves a comprehensive survey of about 1500 micro and small businesses across three major cities in one of the smallest states in Nigeria (Ebonyi State). Based on the cross-sectional data generated, a linearized Tobit regression and a robust T-Test statistical technique are applied to comparatively examine how internal firm attributes, market attributes, and socioeconomic and macroeconomic conditions affect the growth of micro and small businesses in a typical small state and small cities. The results reveal a key challenge to be the dominance of the business space in small cities by micro and largely informal enterprises.

Controllable image synthesis methods, applications and challenges: a comprehensive survey

Controllable Image Synthesis (CIS) is a methodology that allows users to generate desired images or manipulate specific attributes of images by providing precise input conditions or modifying latent representations. In recent years, CIS has attracted considerable attention in the field of image processing, with significant advances in consistency, controllability and harmony. However, several challenges still remain, particularly regarding the fine-grained controllability and interpretability of synthesized images. In this paper, we comprehensively and systematically review the CIS from problem definition, taxonomy and evaluation systems to existing challenges and future research directions. First, the definition of CIS is given, and several representative deep generative models are introduced in detail. Second, the existing CIS methods are divided into three categories according to the different control manners used and discuss the typical work in each category critically. Furthermore, we introduce the public datasets and evaluation metrics commonly used in image synthesis and analyze the representative CIS methods. Finally, we present several open issues and discuss the future research direction of CIS.

Effect of water to cement ratio on mechanical properties of FRC subjected to elevated temperatures: Experimental and soft computing approaches

The deterioration of concrete is greatly dependent on the cracks and microcracks development owing to loading or environmental influences. An effective step to avoid the spread of cracks and microcracks is employing of different fibers in concrete and the manufacture of fiber reinforced concrete. On the other hand, fire is one of the cases that always threatens engineering structures. Elevated temperatures cause obvious chemical and physical changes that lead to concrete deterioration. In this study, the effect of adding various fiber with different volume contents in concrete mixture with various cement content was evaluated experimentally. Results indicate that spalling was more dominant in mixture containing steel fiber and with higher amount of cement, while there is not any spalling in mixture containing polypropylene (PP) fibers. Moreover, the reduction in tensile strength of fiber-free concrete specimens is less pronounced in mixtures with higher cement content. In addition, the positive performance of PP fibers compared to steel fibers was proved at higher temperatures and cement contents. Furthermore, by conducting a comprehensive and accurate survey, this research pinpoints gaps in the current literature. Moreover, the gathered dataset serves as input for training a machine learning approach known as Group Method of Data Handling (GMDH). The GMDH network demonstrates a satisfactory accuracy in predicting experimental results, with a MSE of 0.0044.

AI in Business and Finance: A Micro-to-Macro Perspective on Its Impact

This comprehensive survey examines the transformative impact of machine learning (ML), deep learning (DL), and natural language processing (NLP) across diverse business and finance sectors. From micro-level operational efficiencies to macro-level strategic decision-making, we delve into the multifaceted applications of AI and its influence on traditional practices. Our analysis highlights the increasing prominence of ML in enhancing decision-making and achieving greater accuracy and efficiency. We also identify key challenges such as data quality, modelinterpretability, and linguistic information processing. By addressing these challenges, we can further unlock the potential of ML in revolutionizing business and finance. We conducted a systematic literature review using the PRISMA technique to identify relevant research published within the last five years. Our findings reveal the growing adoption of ML techniques in various business and finance applications, such as fraud detection, risk assessment, customer relationship management, and algorithmic trading. We identified relevant publications through comprehensive searches in leading academic databases, including Scopus, Web of Science, and Google Scholar. We also discuss the challenges associated with implementing ML models in these domains, including data quality issues, model interpretability, and ethical considerations. Byunderstanding the applications, challenges, and opportunities associated with AI, businesses, policymakers, researchers, and investors can make informed decisions and leverage AI to drive innovation and growth in the business and finance sectors.

A Comprehensive Review of Golden Riemannian Manifolds

In differential geometry, the concept of golden structure represents a compelling area with wide-ranging applications. The exploration of golden Riemannian manifolds was initiated by C. E. Hretcanu and M. Crasmareanu in 2008, following the principles of the golden structure. Subsequently, numerous researchers have contributed significant insights with respect to golden Riemannian manifolds. The purpose of this paper is to provide a comprehensive survey of research on golden Riemannian manifolds conducted over the past decade.

A survey of intelligent multimedia forensics for internet of things communications: Approaches, strategies, perspectives, and challenges for a sustainable future

Digital forensics is a proven method for collecting, preserving, reporting, analyzing, identifying, and presenting digital evidence from the original data, and it helps find evidence of cybercrimes. Intelligent multimedia forensics is a type of digital forensics and is essential because it is used to identify fake multimedia, including images, videos, audio, and text. This paper conducted a comprehensive survey for intelligent multimedia forensics, categorized into 3 classes: deep learning forensics, multimedia forensics, and network and Internet of Things forensics. In the first class, we provided a survey of the multiple attacks breaking the DL models, such as attacks in the training step, and the testing step, and crafted. In the craft attacks, we survey the three attacks: white box, gray box, and black box. We also provided some defense methods against DL attacks, training step attacks, and testing step attacks. In the second class, we offered a survey of multimedia forensics, including passive and active manipulation. In the third class, we provide a survey of network/IoT forensics, including the attacks in five layers: physical, data link, transport, application, and network. We also provided network/IoT attack detection in the third class using deep learning models. We applied AI models in various datasets and obtained higher accuracy and performance on the datasets from various used models. Finally, we offered the challenges and future direction for the researcher in this scope.

Sewage Treatment Equipment Supply Chain Collaboration and Resilience Improvement Path Analysis: Collaborative Decision-Making, Information Sharing, Risk Management

Since the dawn of the 21st century, supply chains (SCs) have faced an array of unprecedented challenges, encompassing economic volatility, escalating geopolitical conflicts, intensified trade disputes, and persistent environmental degradation. These challenges have imposed immense pressures on SCs, amplifying both the risks and occurrences of disruptions. In response to these critical demands, there has been a substantial increase in academic, industrial, and governmental focus on the resilience and recovery capabilities of SCs. In this context, the supply chain (SC) for sewage treatment equipment has been significantly affected. This study aims to systematically investigate collaborative strategies and mechanisms for bolstering the resilience of manufacturing SCs within the sewage treatment sector, with particular attention to collaborative decision-making, information sharing, and risk mitigation throughout the SC lifecycle. Following a comparative analysis of various methodologies, this paper employed the structural equation model (SEM) and subsequently conducted a comprehensive survey of manufacturers specializing in glass fiber sewage treatment systems, yielding 385 valid responses. Through this data, a structural equation model was developed to analyze pathways for resilience enhancement. A thorough analysis of the results indicates that Supply Chain Collaboration (SCC) and collaborative decision-making are instrumental in strengthening Glass Fiber Sewage Treatment Supply Chain Resilience (SCR) through the implementation of effective information sharing and risk management practices. These findings contribute to the theoretical framework of SCC and SCR by clarifying the influence of collaborative practices while also providing practical guidance for the governance and strategic management of sewage treatment equipment enterprises and their associated SCs. The conclusions drawn from this study can guide us in establishing a more resilient and sustainable SC ecosystem, making it more directive and capable of mitigating complex disruptions.

A Survey of Joint Security-Safety for Function, Information and Human in Industry 5.0

Industry 5.0 blows the whistle on a new industrial revolution, aiming to refocus industrial development by reintegrating the human factor into the technological equation. On the eve of the revolution, the comprehensive surveys for Industry 5.0 would provide important support for future development. However, current surveys for Industry 5.0 are still in their infancy and some gaps remain. (i) Current work lacks a comprehensive technical architecture for Industry 5.0 and an in-depth analysis of the enabling technologies that will drive Industry 5.0; (ii) There is no comprehensive survey on security issues of Industry 5.0, which will directly hinder its development; (iii) As Industry 5.0 introduces people into the technological equation, then it will also further consider the broader human interests in its security equation. It is an open issue that traditional security classifications cannot summarize these new security threats in Industry 5.0. Therefore, this survey starts by reviewing the latest key enabling technologies and proposing an overall technology hierarchical structure for Industry 5.0. Second, we investigate the triad of security issues in Industry 5.0, which includes threats and countermeasures for functional safety, information security, and humanized security. Among them, we define the third security issue in Industry 5.0, humanized security, which includes safeguarding the broader interests and rights of individuals, machines, and society. Finally, we summarize future challenges and research trends. To the best of our knowledge, this is the first comprehensive overview of security in Industry 5.0, in which humanized security is defined for the first time.

Harmonizing Power and Economy: A Comprehensive Survey On Optimizing Heterogeneous Platforms For High-Performance Computing

Harmonizing Power and Economy: A Comprehensive Survey On Optimizing Heterogeneous Platforms For High-Performance Computing

Readiness of the health system to provide non-communicable disease services in Nepal: a comparison between the 2015 and 2021 comprehensive health facility surveys

BackgroundIn Nepal, despite the escalating burden of non-communicable diseases (NCDs), there is a gap in the continuum of care for prevention, diagnosis, treatment, and care services for NCDs. The study aimed at assessing the changes in availability and readiness scores of health facilities between two consecutive health facility surveys.MethodsWe compared NCD readiness scores between 2015 and 2021, using data from two nationally representative cross-sectional Nepal Health Facility Surveys (NHFS). Both consecutive surveys used globally validated standard tools of Demographic and Health Surveys (DHS)’s service provision assessment. Both surveys were undertaken using World Health Organization’s (WHO) service availability and readiness assessment (SARA) tools. Data were collected using the Census and Survey Processing System on tablets, with validation performed through field check tables. Trained enumerators with a medical background collected data for the surveys, and we analyzed the information from a de-identified dataset downloaded from the DHS website upon request. Both the NHFS protocols were reviewed and approved by the Nepal Health Research Council and the institutional review board of ICF. We calculated the readiness scores based on WHO SARA indicators for diabetes, cardiovascular disease (CVD), and chronic respiratory disease (CRD) using a additive procedure. Multivariate linear regression analysis was undertaken to assess associated factors, with complex sampling design accounting for both surveys.ResultsThe overall availability of all three services has improved between 2015 and 2021 NHFS. Although the availability of diabetes-related services increased significantly between 2015 and 2021, this does not correspond to the increase in the readiness score. The readiness score increased by 10% points for CVDs related services and 9% points for CRDs. Compared to public hospitals, primary healthcare facilities experienced greater increase in readiness scores (11.5% versus 20.9%). Interestingly, those health facilities without quality assurance systems experienced a lower increase or even decrease in readiness scores than those with quality assurance systems. For the factors associated with readiness scores, health facilities charging additional or separate fees to the patients had a higher readiness score than those not charging any user fee for all three services. Compared to 2015, the readiness scores in 2021 improved for diabetes [β = 11.01 (95% CI 9.02 to 12.96)], CVD [β = 10.70 (95% CI 9.61 to 11.80)], and CRD [β = 8.41 (95% CI 7.20 to 9.62)].ConclusionThe improvement in NCD service availability does not correspond to the proportional increase in readiness scores, which is crucial for delivering quality care. Regular staff meetings and feedback systems are crucial for improving all services including NCD-related service readiness and should be prioritized by local and provincial governments.

A INTEGRAÇÃO DE EDUCAÇÃO INCLUSIVA NA FORMAÇÃO DE PROFESSORES

The global movement for inclusive education is a social, cultural, pedagogical and political revolution, born in defense of the rights of all students, so that they can be together, sharing and learning without any discrimination. In this sense, it is worth highlighting that educational inclusion is a gradual, dynamic and constantly changing process. Paths full of challenges are being followed so that inclusive education becomes an effective reality, with teacher training standing out among these challenges. In this context, teacher training is a fundamental commitment of educational institutions, both public and private, to develop the skills and competencies necessary for teaching to be carried out successfully, with quality and efficiency. To this end, the research for this study was conducted based on a comprehensive survey of existing materials on the subject, including books, dissertations, theses, national articles and websites of government agencies and scientific databases available on the internet.

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Addressing HLA Laboratory Vacancy Challenges: Insights from a Survey-Based Analysis

Abstract Introduction/Objective Laboratory vacancies pose significant challenges to institutions across diverse sectors. Understanding the landscape of these vacancies is crucial for developing effective strategies on how best to address them. The American Society of Histocompatibility & Immunogenetics (ASHI) Laboratory Professionals Recruitment Initiative (LPRI) was created to help address these issues by promoting and raising awareness of Histocompatibility and Immunogenetics (HLA) labs. This poster presents findings from a comprehensive survey conducted among various types of institutions to explore the prevailing dynamics surrounding HLA laboratory job vacancies. The primary purpose of this study is to provide a comprehensive overview of the HLA laboratory job vacancy landscape, including current vacancy rates, time taken to fill vacancies, and the perceived underlying causes attributed to these vacancies. Methods/Case Report The ASHI LPRI developed structured survey was administered to HLA laboratories represented in different service sectors, including hospital based, reference, organ procurement and independent laboratories. The survey collected data on the type of institution, current laboratory job vacancy rates, average time taken to fill vacancies, and perceived primary causes for vacancies. Data analysis was conducted to identify patterns and insights into the HLA laboratory vacancy challenges. Results (if a Case Study enter NA) The majority of respondents were from hospital-based or independent HLA laboratories, with few identifying as reference or organ procurement laboratories. Licensure or required certification was not a restricting factor in filling the vacancies. Vacancy rates for the varying institutions range between 10%- 25% with few reporting greater than 25% vacancy rate. A majority (54%) report their vacancies remain open for > 6 months. Predominant vacancy perpetuation was most attributed to lack of applicants (58%) and insufficient pay offered (41%). Conclusion The findings of this study offer valuable insights into the complexities surrounding laboratory job vacancies in the clinical HLA space. Understanding the underlying causes and dynamics of these vacancies is essential for developing targeted strategies to mitigate the problem. By addressing these challenges, institutions can enhance workforce stability and ensure optimal laboratory operations. This poster serves as a foundation for further research and policy interventions aimed at addressing laboratory vacancy challenges.

Anticausativization in Gyalrongic languages

Abstract This article presents a comprehensive survey of anticausativization within Gyalrongic languages, a group of Sino-Tibetan languages spoken in Sichuan, China, contributing significantly to our understanding of this phenomenon’s theoretical and diachronic underpinnings. The research affirms that anticausative alternation is predominantly associated with verbs that exhibit a change of state semantics. This categorization includes decausative and autocausative verbs, further subdivided in Gyalrongic languages into specific semantic groups such as Separation, Removal, Physical Transformation, etc. Certain verbs with agent-oriented meanings also undergo anticausative alternation, calling for a revision of previous claims. Gyalrongic languages demonstrate a unique trend in their use of anticausative marking, deviating from global patterns by avoiding the polyfunctional employment of these markers. The article traces the origins of anticausative marking to a spontaneous / non-volitional action prefix and identifies the emergence of a new non-volitional prefix that has overtones of anticausative marking, descending from an orientational / TAME prefix.

Global health training opportunities during pediatric hematology/oncology fellowship: Results from a survey of program leaders.

There is growing interest in global health (GH) among medical trainees in the United States. However, providing GH training opportunities at the fellowship level presents several challenges. Understanding of barriers and facilitators to implementing GH training is essential for addressing these challenges. We developed a comprehensive survey of 65 Likert-scale multiple-choice and open-ended questions to assess perspectives of pediatric hematology/oncology fellowships leaders on GH training. The survey was electronically distributed to program leaders at all ACGME-accredited pediatric hematology/oncology fellowships programs, and data were summarized using descriptive analysis. Of 73 eligible programs, we had a 45.2% response rate with 27 complete and 6 partial responses. Respondents represented programs across the United States, including 19 (57.6%) affiliated with NCI-Designated Cancer Centers. Fourteen programs (43.8%) currently offer GH training opportunities, whereas 18 programs expressed interest in future opportunities (15, 83.3%) or plan to offer them soon (3, 16.7%). Major barriers identified include competing training priorities (23, 82.1%), lack of faculty mentors in the division (23, 82.1%), and lack of dedicated institutional funding (20, 71.4%). Key facilitators include the interest and initiative of current fellows (27, 96.4%), dedicated institutional funding (26, 92.8%), and having established international partnerships (26, 92.8%). The study reveals strong interest in GH training in pediatric hematology/oncology fellowships. Despite challenges such as competing priorities and a lack of mentors, significant facilitators include current fellows' initiatives, dedicated funding, and established international partnerships. These insights can help shape future initiatives to incorporate GH into pediatric oncology/hematology fellowship training.

Surveying ecolinguistics

Abstract This article presents a comprehensive and detailed survey of ecolinguistics, defined as an enterprise oriented to how language plays a role in the interactions between human beings, other species, and the natural environment. Since the early 1990s, ecolinguistics has been driven by a concern for life on Earth and as such it comprises the linguistic study of the current ecological crisis. Through a detailed close reading of the literature, in combination with the bibliometric tool of VOSviewer, it surveys eleven subfields of contemporary ecolinguistics. The eleven surveyed subfields of ecolinguistics are: discourse-oriented ecolinguistics, corpus-assisted ecolinguistics, ecostylistics, narratological ecolinguistics, identity-oriented ecolinguistics, ethno-lexical ecolinguistics, ecological discourse analysis, harmonious discourse analysis, cognitive ecolinguistics, educational ecolinguistics, and decolonial/transdisciplinary ecolinguistics. In the conclusion, the article discusses two challenges that face contemporary ecolinguistics: the repetition of certain tropes and narratives about the field, even in the absence of empirical evidence, and the lack of internal debate and critique.