Information Dimension Research Articles

The Impact of the Relationship between Company Characteristics and the Audit Committee on the Quality of Financial Reports (A Field Study on a Sample of Iraqi Private Companies)

The current study primarily aimed to determine the impact of the relationship between company characteristics and the audit committee on the quality of financial reports in its various dimensions (formal, temporal, and informational content). The study also sought to identify the influence of company characteristics on the quality of financial reports across these dimensions. The audit Committee role in improving financial report quality was the first area that the survey examined; this includes looking not just where audits. The study employed a descriptive and then analytical approach to achieve the goal of answering its research question. The study population included all senior management personnel at Iraqi private businesses, responsible auditors and members of audit commissions. From a variety of industries five companies were chosen, yielding a sample of 500 possible contacts (average 100 staff / company). A random sample of 219 participants was selected using the formula that Richard Geiger came up with. Questionnaires were sent to the participants, and all the responses accepted. After analyzing the data and testing the hypotheses using SPSS software, several key findings were revealed, the most significant being: • Company characteristics significantly affect the quality of financial reports. These characteristics provide an accurate and objective depiction of the company's financial position, aiding investors in making informed investment decisions. • Iraqi companies' unique characteristics notably affect the formal dimension of financial report information, as the company's reputation and business relationships are reflected in the reports' format, influencing investors' valuation of shares. • The timeliness of financial report information is vital in attracting investors. It reflects the company's growth and continuity in the market, significantly affecting its valuation. • Company characteristics influence the content of financial reports, including financial data and accompanying disclosures, thereby providing precise information for investors. • Audit committees play a pivotal role in ensuring the integrity and transparency of financial information, enhancing trust, and reducing errors in financial reports. They also contribute to improving the organizational structure of financial information, facilitating data comparability across different periods and entities. • The results indicate that audit committees improve the accuracy of the temporal information presented, boosting the reliability of information provided to the public. Additionally, audit committees assure investors that financial data are free from material errors, positively affecting investment decisions. • The research has shown that the relationship between company characteristics and audit committees enhances the verifiability of financial reports and also builds investor confidence. However, this research finds that if committee independence and effectiveness are not sufficiently so a very close relationship between enterprises (or groups of companies) and audit committees may have undesirable effects on quickness in presenting accountings. • The findings underscore that the unique characteristics of a company directly influence the content of its financial reports, highlighting the importance of having influential audit committees to ensure the credibility of the presented information.

Artificial Intelligence-Empowered Spectroscopic Single Molecule Localization Microscopy.

Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI-based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI-driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point-spread functions are discussed, further examining their applications in biological studies, materials science, and single-molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI-empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM.

Systematic Review of Dimensions and Indicators in Sustainable and Smart Cities: Trends, Interdependencies, and Continental Variations

Accelerated urbanization challenges cities globally. Ensuring good quality of life and environmental protection requires innovative, data-driven solutions. Sustainable and smart cities (SSCs) offer a promising response, but effective implementation depends on selecting and using appropriate dimensions and indicators. The main objective of this article is to present a systematic review within the framework of SSCs to study trends in the use of dimensions and indicators. A citation frequency index (Q) considering quartiles is used to determine the importance of the variables considered in this study. A spatiotemporal analysis of the dimensions and indicators detected is also performed. The results suggest that the multifaceted nature of SSCs necessitates a holistic approach, with environmental (Q3 = 0.536) and social (Q2 = 0.403) dimensions being principal. Despite the lower ranking (Q1 = 0.080) of the information and communication technology (ICT) dimension, its pervasive influence highlights its evolving importance. Effective ICT implementation relies on robust social and environmental foundations, illustrating their interdependence. The governance (Q1 = 0.169) dimension is also essential for the creation of transparent and equitable urban policies. Key indicators related to energy systems (Q2 = 0.379), data analysis (Q2 = 0.327), and resource management (Q2 = 0.291) are crucial for operational efficiency and sustainability. Continental differences in prioritizing SSC dimensions highlight the importance of context-specific strategies, reflecting unique challenges and opportunities faced by different continental areas. By understanding and addressing these continental differences, policymakers and urban planners can more effectively promote sustainable and resilient urban development worldwide.

Chemi-Impeditive Sensing Platform Based on Single-Walled Carbon Nanotubes.

Chemical sensing methodology based on electrochemical impedance spectroscopy (EIS) targeting analytes in aqueous samples on functionalized single-walled carbon nanotube (SWCNT) is reported. The SWCNT in contact with electrolyte shows unique impedance spectra that cannot be analyzed with classical equivalent circuit models. Inspired by the charge transport property of mixed ionic-electronic conductors, we propose an equivalent circuit based on transmission line model (TLM), by which the impedance of the CNT-electrolyte system can be analyzed to track down all the equivalent circuit parameters. By combining multiple pieces of information, which are technically immeasurable with conventional chemiresistive or chemicapacitive techniques, several analyte species responding to the sensor can be differentiated from each other. We demonstrate the "chemi-impeditive" concept on chemically modified SWCNTs for detecting perfluoroalkyl substances (PFAS) in aqueous solutions. The EIS coupled with a fluorination chemistry on SWCNT surface provides unique changes in equivalent circuit components for each PFAS, i.e., changes in CNT and solution resistances, as well as interfacial CNT-solution capacitance, through which perfluorooctanesulfonic acid, perfluorooctanoic acid, hexafluoropropylene oxide dimer acid, and perfluorobutanesulfonic acid are detected in a discriminative manner. The new impedimetric method opens up new vistas in chemical sensing in that the EIS analysis provides an additional dimension of information beyond the single resistance or capacitance typically measured by many conventional types of sensors.

A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination

The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption.

Quality Analysis Of Seameo Recfon Website Using The Webqual Method

his study aims to analyze and measure the quality of the SEAMEO RECFON website. The method used in this research is the Webqual method, which is a method or technique for measuring website quality based on end user perceptions which is used as the main reference with indicators of usability, information quality, and service interaction and the Importance Performance Analysis method. Data collection was carried out using a questionnaire of 50 questionnaires. The results showed that the level of user satisfaction with functionality has been achieved, while the level of user satisfaction with information quality has not been achieved, and the quality-of-service interaction has not been achieved. Based on the assessment of the three measurement dimensions, the usability dimension has a value of (0.245), the service interaction dimension has a difference or gap value of - (0.39) and the information dimension has a difference or gap value of - (0.26). Based on this, it can be concluded that the actual perception of quality has not been able to meet the ideal quality desired by SEAMEO RECFON website users, especially from quality attributes related to information on the website.

Machine Learning techniques for predicting inpatient probability and LOS from EU Injury Database

Abstract Background Unlike statistical inference Machine Learning (ML) makes repeatable predictions without prior assumptions about the underlying relationships among variables. The Full Data Set (FDS) of the EU-IDB (European Injury DataBase) consent to explore risk of hospitalization due to injuries using many predictors. The Length Of Stay of patients in hospital (LOS) is a relevant proxy of complexity of treatment and resources consumption. Methods The IDB-FDS provides more than 3.800.000 ED records, in years 2008-19 for 19 Countries. LASSO (Least Absolute Shrinkage and Selection Operator) cross-validated linearized regression technique (linear for LOS) was used for variable selection and parameter regularization. Inpatients are those admitted or transferred to hospital. Days of hospitalization were used for LOS. A cross-validated Generalized Linear Model was performed on 5 folds randomly sampled assigning 80% of records to training and 20% to testing samples. Results The strongest predictors of hospital admission risk, selected by the model were in order of importance: EUROCOST-39 diagnoses categories, Age Group, Intent, Mechanism Of Injury, Activity When Injured, Transport Injury Event, Sex Of Patient, Place Of Occurrence. EUROCOST-39 represents 61,9% of explained variability and age group 19,4%. The strongest predictors of LOS were substantially the same: EUROCOST-39 86,2% of explained variability and age group 8,6%. Conclusions The main part of variability in the ML model is explained by diagnoses reclassified according to a disability standardization method. For instance, in the maximum training sample risk of hospitalization ranges from odd 0,76% for hand/fingers sprain up to 154,02% for brain concussion. In the median sample LOS ranges from 0,45 days for strain of hand/fingers up to 11,65 for multi-trauma. A combination of more disabling injury, older age and mechanism of injury (i.e. threat to breathing) increases enormously the risk of hospitalization and LOS. Key messages • Machine Learning techniques applied on EU-IDB can provide identification of relevant risk factors of hospital admission from injuries for targeting preventive measures and organizing health care. • Artificial Intelligence consents to analyse big amount of data as dimension or analytical detail of information for identifying patterns and predictors of specific indicators for injuries or diseases.

Health Literacy Levels of Vocational School of Health Services Students and Related Factors

Introduction: The concept of health literacy requires individuals to have basic health knowledge in order to use health services correctly and effectively, and this is very important. Considering that research in the field of health literacy in our country is not yet at a sufficient level, it was aimed to determine the level of the concept of health literacy, which is gaining importance, among students of vocational schools of health services and related factors. Methods: The research is a descriptive cross-sectional type, and the population of the research consists of students studying at vocational schools of health services at Izmir Tinaztepe University, Ege University, Pamukkale University and Bolu Abant Izzet Baysal University. Individual Introduction Form and Health Literacy Scale were used as data collection forms. Results: 1370 students participated in the research. The average age of the students is 20.29±2.86 years, 78.5% are female and 21.5% are male. It was determined that 24.6% of the students smoked and 22.4% consumed alcohol. The proportion of students participating in the research who read books frequently is only 31.4%. It was determined that 46.7% of the students in the research group knew the concept of health literacy. It was determined that the average score of the students in the research group on the health literacy scale was 110.24±12.22. In addition, it was determined that the average of the subscales, the access to information dimension, was 21.93±2.96, the average of the information understanding dimension was 31.17±3.58, the average of the evaluation/evaluation dimension was 35.40±4.57, and the average of the application/use dimension was 21.74±3.13. According to the findings of the research, it was determined that there was a statistically significant difference between the health literacy scale scores of the students according to their gender, family structure and literacy level, income level, smoking status, book reading frequency and general health status (p

Enhanced stimulated Raman and fluorescence imaging by single-frame trained BDN.

Hyperspectral and multispectral imaging capture an expanded dimension of information that facilitates discoveries. However, image features are frequently obscured by noise generated from the limited photodamage threshold of the specimen. Although machine learning approaches demonstrate considerable promise in addressing this challenge, they typically require extensive datasets, which can be difficult to obtain. Here, we introduce BiFormer denoising network (BDN), designed to effectively and efficiently extract image features by utilizing both local and global level connections, sparse architectures, and fine-tuning. Experimental results indicate that BDN enhances the quality of stimulated Raman scattering (SRS) images by up to 16-fold in signal-to-noise ratio (SNR), particularly improving subtle features at higher spatial frequencies. Furthermore, BDN is successfully adapted to fluorescence imaging, achieving significant improvements in SNR and order-of-magnitude reduction in exposure time, thereby showcasing its versatility across various imaging modalities. Collectively, BDN exhibits substantial potential for spectroscopic imaging applications in the fields of biomedicine and materials science.

Memories on “Chronicle of Chervona Kalyna” pages: source, information and communication dimension

Memories on “Chronicle of Chervona Kalyna” pages: source, information and communication dimension

Fractal analysis of urban growth in complex systems

A complex network consists of number of elements which may interact with each other to form a complex system. The fractal feature is one of the most important characteristics in complex networks. This study examines the fractal analysis of Madaba City Centre as a complex system through the period to 1953-2022. In order to reveal the fractal characteristics of the study area box-counting (Db) and information (Di) dimensions were calculated using Benoit for five satellite images for years 1953, 1981, 1992, 2000, and 2022. Results showed that Madaba road networks have fractal characteristics for the study period, and the calculated fractal dimensions are between 1 and 2. Db values for years 1953, 1981, 1992, 2000, and 2022 are 1.38, 1.51, 1.55, 1.58, and 1.65, while Di values are 1.37, 1.52, 1.57, 1.60, and 1.67 respectively. The difference between Db and Di values for the same year was small, indicating the similarity between the two methods. Increasing the complexity of the system components and dynamic activities are other indicators of its growth, and there is a significant positive relationship between the fractal dimension value and the complexity level of the city in terms of population growth and area expansion; which increase the importance of fractal theory in studying the growth of the complex system like cities.

An unsupervised machine learning-based profile system of Chinese researchers

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

Collective Wisdom in Constitutional, Informational and Digital Dimensions: on the Concept and Nature of Popular Deliberative Constitutionalism for Public Law

The article discusses the scientific and intellectual origins of collective wisdom in the constitutional, information and digital dimensions, reveals the doctrinal approaches and normative foundations of popular deliberative constitutionalism, the development of an inclusive constitutional paradigm and its significance for the use of procedures and technologies for citizen participation, expert opinion and civil society institutions in the constitutional development, in social transformations, in constitutional changes brought to life by the needs of society and the state. Popular deliberative constitutionalism is identified in the article as a public legal meta-concept that explains the process and need for constitutional involvement of citizens. The author concludes that popular deliberative constitutionalism allows us to better understand the problems of implementing the principle of popular sovereignty in modern democratic states, create an optimal model and consider the forms of exercising the constituent power of the people when implementing constitutional changes. In the article, the author notes that the future of democratic constitutionalism is associated with the creation and use of collective intelligence technologies in the public legal sphere, which, in the context of the development of an algorithmic and information society, combine classical procedures for discussing constitutional issues and the use of aggregated preferences. The practical application of the achievements of natural and artificial intelligence is intended to discuss and resolve issues of public legal significance, is based on co-evolutionary development and leads to the emergence of mini-publics, civil assemblies, and constitutional crowdsourcing.

Exploring the Impact of E-WOM Information via Social Media on Customer Purchasing Decision: A Mediating Role of Customer Satisfaction

The study investigates the impact of E-WOM information on purchasing decisions, using customer satisfaction as a mediator. It examines E-WOM information as independent variables, such as quality, quantity, and credibility, and as dependent variables, such as consumer satisfaction and purchasing decisions, with customer satisfaction serving as a mediator to investigate the relationship between E-WOM information and purchasing decisions. A questionnaire was issued to 307 social media-active clients, and the hypotheses were tested using quantitative methods. Data analysis comprised descriptive statistics, Cronbach's alpha, skewness and kurtosis, and Pearson correlation coefficient, as well as a fit model for measuring questionnaire reliability and validity, regression for sub-hypotheses, and a path model for evaluating main hypotheses. The findings revealed that all three dimensions of E-WOM information had a positive impact on customer satisfaction and purchase decisions, both individually and jointly. Customer satisfaction has a positive influence on purchasing decisions. Furthermore, E-WOM information has been shown to positively impact purchasing decisions via consumer satisfaction. The study suggests that organizations should understand the dimensions that impact customer satisfaction and purchasing decisions in order to fulfill their goals, remain ahead of the competition, and obtain a competitive advantage. Proper tracking of social media reviews, comments, and recommendations may help organizations deliver answers, increase customer satisfaction, and aid in making purchasing decisions

Optimised GRS coded cooperation: sliding relay selection design and improved joint decoding

ABSTRACT In this paper, the novel optimised generalised Reed-Solomon coded cooperative (OGRSCC) scheme is developed with a flexible selection of the code length and information dimension. In the OGRSCC scheme, two GRS codes with different dimensions are deployed at the source and relay, respectively, where the information symbols of the relay are dependent on those of the source. At the relay, different symbol selections may contribute to distinct joint codes at the destination. As a result, to achieve a maximum free distance (FD) and the optimised weight distribution of the final code, the global search method is first proposed to obtain the optimal selection pattern. However, as the code grows longer, its complexity rises dramatically. To make the selection more practical, the low-complexity sliding search method is then investigated to obtain the local optimal symbol selection pattern. Monte-Carlo simulated results indicate the OGRSCC scheme with two selection methods demonstrates almost identical performance. Furthermore, based on this special selection method and the unique algebraic structure of GRS code, the improved joint decoding algorithm is first proposed to boost the overall system performance. In addition, the theoretical upper bound is analysed, verifying the effectiveness of the proposed OGRSCC scheme. Numerical simulations reveal that the proposed OGRSCC scheme outperforms its corresponding non-cooperative scheme by a gain of over 3.2 dB and its existing comparable schemes by a margin of approximately more than 4 dB at high signal-to-noise ratio (SNR), respectively.

Textual Dimensions of Sustainability Information, Stock Price Informativeness, and Proprietary Costs: Evidence from Integrated Reports

We examine whether integrated report quality, IRQ, is negatively associated with stock price synchronicity, an inverse measure of firm-specific information, and the extent to which the relation between IRQ and synchronicity is attenuated by proprietary costs. We measure IRQ using machine-based textual analysis along four dimensions: textual attributes, topical content, integrated reporting capitals, and financial versus sustainability information. We find that measures of IRQ based on seven textual attributes are negatively related to synchronicity, which is consistent with higher quality text containing more firm-specific content. Using PhraseLDA to identify topics in integrated reports, we find that contents related to the three most common categories—governance, performance, and risks and opportunities—are negatively associated with synchronicity. We find similar results for all integrated report capitals, except manufactured capital. Further, we find that sustainability information has a larger negative association with synchronicity than financial information. We also find that proprietary costs stemming from product market competition attenuate the association between IRQ and synchronicity, which suggests the informativeness of integrated reports varies with a firm’s competitive environment. Our results may inform the International Sustainability Standards Board as it considers the role of the Integrated Reporting Framework in developing sustainability standards.

Comparing the effects of premarital booklet- and video-based educations on the reproductive health literacy of engaged couples.

The aim of this study was to compare the effects of premarital booklet-based education (BBE) and video-based education (VBE) on engaged couples' RHL. This quasi-experimental study was conducted in 2021-2022. The study population consisted of engaged couples who referred to Amirkola Premarital Counseling Center, Babol, Iran, to receive premarital counseling. One hundred and ten couples were purposefully recruited and randomly allocated to a 55-couple BBE group and a 55-couple VBE group. Participants in the BBE group received RH-related education through an educational booklet, while participants in the VBE group received RH-related education through 4 weekly RH-related educational videos shared through WhatsApp. The Sexual and Reproductive Health Literacy questionnaire was used to assess RHL at three measurement time points, i.e., before, 4 weeks after, and 2 months after the intervention onset. The data were analyzed using the SPSS software (v. 23.0). The mean score of RHL significantly increased in both BBE and VBE groups (p < 0.001). The time-group interaction was also significant for the mean scores of the access to information, evaluation of information, and decision-making and application dimensions (p < 0.05) and insignificant for the reading and understanding dimension of RHL (p = 0.56). Both premarital BBE and VBE are effective in significantly improving RHL among engaged couples, while the effects of VBE on some RHL dimensions are significantly more than BBE.

Effects of Information Providers, Channels and Types on the Adoption of Climate – Resilient Practices in Lesotho

This study examined the effects of information providers, channels and types on the adoption of climate-resilient practices in Lesotho, deviating from the usual separate analysis of the relationships between information provider, channels and types. Previous studies have generated only partial insights into the influence of different information variables on adoption behaviour, neglecting a holistic representation of the interactive effects of all dimensions of information and adoption. Using a sample of 1,659 farmers from the Bureau of Statistics (BOS) database on the 2019/2020 agricultural production survey, the data was analysed using frequency counts, percentages and Probit regression. The results show that the majority of the farmers are male (53%), less than 30 years of age (59%), possess an education level between High School form 1 to 5 (59%), have between 5 and 10 persons per household (50%) and rely on subsistence farming as their main source of income (36%). The extension services provided, as indicated by at least 90% of the farmers, include information on farm management, crop selection, input use, credit, farm machinery, livestock, crop protection, conservation, marketing, irrigation and nutrition. In contrast, the information received is more focused on marketing, livestock production, agronomic practices, irrigation and fisheries production. The major extension service providers and sources of information are public service providers and radio. Agricultural extension information providers, channels and types influence the adoption of climate-resilient practices. The study recommends that extension information providers, channels and types be matched to specific contexts for improved effectiveness.

Aspects of effective communication (european experience): digital, informational, and sociolinguistic dimensions

Introduction: The modern era of digital technologies and globalization has brought about significant transformations in communication methods. This article explores how digital, informational, and sociolinguistic dimensions influence the processes of effective communication, focusing particularly on European experiences.Objective: The objective of this study is to analyze the impact of digital transformation, information transparency, linguistic diversity, and cultural adaptation on effective communication in modern European society. The research aims to uncover both the benefits and challenges posed by these factors in the context of communication processes.Methods: This research employed a theoretical approach, synthesizing a wide range of literature and conducting psychological and sociolinguistic analyses. Comparative methods were used to assess different strategies of communication in European contexts, while particular attention was paid to understanding how digitalization and sociolinguistic factors interact.Results: The findings suggest that digitalization enhances accessibility and the speed of communication but introduces challenges, such as the digital divide and data protection issues. Information transparency and accessibility have been identified as crucial in building trust and ensuring the effective exchange of information. Moreover, sociolinguistic factors, especially cultural and language adaptation, have proven essential for effective intercultural communication in Europe.Conclusions: The study concludes that successful communication strategies must incorporate digital, informational, and sociolinguistic aspects, particularly in multilingual and culturally diverse contexts. Awareness and adaptation to cultural norms and linguistic nuances are vital for creating more effective communication practices in both international and local interactions. Practical recommendations include focusing on cultural sensitivity and data transparency to further enhance communication processes

Quantum ghost imaging microscopy depth-of-field study

Quantum ghost imaging approaches have been proposed to enhance biological microscopy, for example, using 2D visible detectors to provide IR images or providing additional dimensions of spatial or spectral information. Toward the goal of making such imaging schemes practical, we compare image quality and depth-of-field between traditional images and ghost images at the same excitation levels. We measure how image quality and depth-of-field depend on the parameters of the entangled light produced using type-I spontaneous parametric down-conversion (SPDC). We use a pair of time-synchronized, photon-timing single-photon avalanche diode (SPAD) array detectors to capture two distinct microscope imaging paths simultaneously on a photon-pair-by-photon-pair basis: one in a traditional imaging pathway and the other a quantum ghost imaging pathway. We calculate the depth-of-field, resolution, contrast, and signal-to-noise ratio (SNR) through the parameter space of a β-Barium Borate (BBO) type-I bulk non-linear crystal length and angle. Our results provide a basis for choosing parameters for quantum ghost imaging with type-I SPDC sources.