l. alizadeh

The inception of artificial intelligence (AI) as a transformative force in modern society has presented novel challenges across various domains, particularly within the realm of intellectual property (IP) law. Traditionally, IP laws have been predicated on the notion that creativity and invention are inherently human attributes, thereby granting protection exclusively to works generated by human authors. However, the rapid evolution and sophistication of AI has alleviated the gap between human and machine-generated outputs, challenging the foundational principles upon which IP law is built. As AI systems increasingly demonstrate capabilities akin to human creativity, such as generating artworks, music, literature, and even innovative technological solutions, the question of ownership and protection of these AI-generated works becomes more pressing.
     Historically, the attribution of authorship in computer-generated outputs was straightforward; the person who legally utilized the computer system was considered the author. However, with AI's capacity to autonomously generate complex and creative works, this traditional approach to authorship is no longer adequate. AI’s role in the creation process varies from being a mere tool used by human creators to being an independent creator of works with minimal human intervention. As AI continues to develop and integrate into more aspects of society, the implications for IP law become increasingly complex and far-reaching.
     This paper seeks to explore these complexities and offer a comprehensive analysis of the ownership issues related to AI-generated works. The discussion delves into the theoretical underpinnings of IP law, the economic implications of AI innovations, and the potential need for legal reforms to address the challenges posed by non-human creators. By examining these issues, the article aims to provide clarity and propose solutions that balance the interests of creators, innovators, and society at large.

The central research question guiding this study is: who owns the intellectual property rights to works generated by artificial intelligence? This question is further subdivided into several key inquiries:     Can AI be recognized as the owner of the intellectual property it generates?
     If not, who should be considered the rightful owner of these AI-generated works—the developer, the user, or some other party?
     How do existing IP frameworks across different legal systems address or fail to address the issue of AI-generated works?
     What are the potential economic and commercial implications of the lack of clear ownership rights for AI-generated works?
     These questions are critical as they address the foundational elements of IP law and its application to emerging technologies. The resolution of these questions will have significant implications not only for legal theory but also for the practical aspects of innovation, investment, and the development of AI technologies.

The research is grounded in the hypothesis that the ownership of AI-generated works should be legally recognized to promote innovation and economic growth. This hypothesis is based on the premise that recognizing ownership rights in AI-generated works is essential for several reasons:     Incentivizing innovation: legal recognition of ownership rights is crucial for encouraging further investment in AI. Without such recognition, the risk of investing in AI may outweigh the potential rewards, leading to a slowdown in technological advancement.
     Economic Fairness: companies and individuals who invest significant resources into developing AI technologies should have their investments protected. If AI-generated works are not protected, these entities could face unfair competition from others who freely use their innovations without compensation.
     Legal Clarity: The current ambiguity in IP law regarding AI-generated works could lead to legal disputes and inconsistencies in court rulings. Establishing clear ownership rules will provide legal certainty and reduce the potential for litigation.

This research adopts a comparative-doctrinal methodology to analyze the treatment of AI-generated works across different legal systems. The study is structured to explore both theoretical and practical dimensions of the issue, with a focus on how various jurisdictions are addressing—or failing to address—the ownership of AI-generated intellectual property.
     Comparative Analysis: The research begins with a comparative analysis of IP laws in several jurisdictions, including the United States, European Union, Japan, and others. By examining how different legal systems approach the question of AI-generated works, the study aims to identify commonalities, differences, and potential gaps in the current legal frameworks.
     Doctrinal Approach: The doctrinal approach involves a detailed examination of legal texts, case law, and statutes relevant to IP law and AI-generated works. This approach is essential for understanding how existing laws might be interpreted or adapted to address the new challenges posed by AI. The research critically analyzes legal doctrines such as authorship, originality, and creativity, assessing their applicability to AI-generated works.
     Economic and Investment Justifications: In addition to the legal analysis, the research also considers the economic and investment implications of recognizing or not recognizing ownership rights in AI-generated works. This aspect of the study involves an analysis of market trends, investment patterns in AI technologies, and the potential economic impact of different legal approaches to AI-generated works.

The analysis of AI-generated works under current intellectual property (IP) laws reveals a significant gap between the traditional legal frameworks and the challenges posed by emerging technologies. The results of this study indicate that existing IP laws are inadequate to address the complexities associated with works created by artificial intelligence, primarily because these laws are predicated on the notion of human authorship. The inability of traditional IP law to recognize non-human entities as creators or authors leads to significant ambiguity regarding the ownership and protection of AI-generated works.
     Ownership and Authorship of AI-Generated Works: One of the key findings of this paper is that the traditional concept of authorship, which ties the creation of a work to human ingenuity and effort, is becoming increasingly problematic in the context of AI-generated works. As AI systems become more autonomous in their creative processes, the distinction between human-created and machine-generated works blurs. This blurring raises the question of whether the law should adapt to recognize AI as a potential author or whether the law should continue to focus on the human elements in the creation process.
     It is shown that the British model, which grants ownership rights to the person who enables the operation of the AI, appears to be the most practical and effective approach. This model ensures that the entities investing in AI technology are rewarded for their contributions, thereby encouraging continued innovation and investment. By recognizing the programmer, user, or entity that initiates the AI’s creative process as the owner of the resulting work, the law can maintain the incentive structures that underpin IP law.
     However, this approach is challenging. One of the key concerns is determining the extent of human involvement necessary to claim ownership. In scenarios where the human contribution is minimal—such as merely pressing a button to initiate the AI’s creative process—there is debate over whether this should be sufficient to warrant full ownership rights. This issue becomes even more complex when considering AI systems that are capable of learning and evolving independently of human input, potentially leading to the creation of works without any direct human intervention.
     Economic and Legal Implications: The research also highlights the significant economic and legal implications of not adequately addressing the issue of AI-generated works. Without clear ownership rights, the economic incentives for investing in AI technologies could be undermined. Companies and individuals may be less willing to invest in AI research and development if the outputs of their investments are not protected under IP law. This could slow down technological progress and innovation, particularly in sectors where AI has the potential to drive significant advancements.
     Additionally, the lack of clear legal guidelines could lead to an increase in litigation as parties seek to assert ownership over AI-generated works. The study suggests that resolving disputes on a case-by-case basis, as seen in judicial practices like the "Nova Productions Ltd. v. Mazooma Games Ltd." case, may provide a temporary solution. However, relying on case-by-case adjudication is likely to result in inconsistent outcomes and could contribute to legal uncertainty, making it difficult for businesses to navigate the landscape of AI-generated works.
     Judicial and Legislative Approaches: The research finds that judicial approaches to AI-generated works have thus far been limited and inconsistent. Courts have generally been hesitant to extend IP protection to non-human creators, often defaulting to traditional interpretations of authorship and creativity. However, as AI becomes more prevalent, there is a growing recognition that legislative reform may be necessary to address these challenges systematically.
     One possible legislative approach is to create a new category of IP that specifically addresses AI-generated works. This new category could establish criteria for determining ownership based on the level of human involvement, the nature of the AI’s creative process, and the economic impact of the work. Such legislation would need to balance the need for innovation and investment with the principles of fairness and equity, ensuring that all stakeholders—developers, users, and society at large—are adequately protected and rewarded.

In conclusion, the rapid development of AI technologies presents significant challenges to the current intellectual property framework, particularly in the context of ownership and authorship of AI-generated works. The study underscores the necessity of adapting IP laws to address these challenges, as the traditional concept of human authorship becomes increasingly difficult to apply in the digital age.
     The British model, which attributes ownership to the person or entity that enables the AI’s creative process, emerges as a viable solution. This approach balances the need to protect investments in AI technology with the recognition that human involvement in the creative process may vary significantly. By granting ownership rights to those who initiate the AI’s operations, the law can ensure that innovation continues to be incentivized while also providing a clear legal framework for AI-generated works.
     However, the study also recognizes that this approach has its limitations. The minimal human intervention required in some AI-generated works raises questions about the adequacy of this model in all cases. There is a need for further refinement of the legal framework to address scenarios where AI operates with significant autonomy, potentially creating works with little to no human input.
     The economic implications of failing to address these issues are considerable. Without clear ownership rights, the incentive for investment in AI technologies could be diminished, slowing the pace of innovation and potentially stifling the development of new technologies that have the potential to benefit society as a whole. Moreover, the lack of legal clarity could lead to increased litigation, creating further uncertainty and potentially hindering the growth of industries reliant on AI.
     As AI continues to evolve, so too must the laws that govern the ownership and protection of the works it generates. The study suggests that a combination of legislative reform and judicial interpretation will be necessary to create a robust and adaptable IP framework capable of addressing the unique challenges posed by AI-generated works. By doing so, the law can ensure that the benefits of AI technology are realized while also protecting the rights and interests of all stakeholders involved.

Various measures are considered to stabilize the erodible beds such as planting. Environmental variables in the canopy cover of plants, including soil temperature and moisture, are readily indicators of planting projects success evaluation. Therefore, this study was planned to evaluate the success of Tamarix (Tamarix ramosissima Ledeb.) plantation in modulating modifying soil temperature and moisture indices in the dried-up beds of Lake Urmia. To this endFor this purpose, 15 stands of seven-year-old Tamarix shrubs were randomly systematically selected and their heights and canopy area were measured. Additionally, in four cardinal directions, the top-soil temperature and humidity were also measured in the inside and outside of the under of canopy zones (120 points in total) following the prevailing erosive winds. Afterward, tThe results were then statistically analyzed. Two-way analysis of variance showed significant differences (p<0.01) for soil temperature and humidity among the sampling points (inside and outside of the microclimate zones) and significant differences (p<0.01) for soil temperature among the cardinal directions of sampling. The mean temperature and moisture content of the soil surface in the inside and outside of the Tamarix shrubs canopy were 23.77 and 34.78 °C, and 2.95 and 1.82%, respectively. So that, a significant decrease (p<0.01; 45%) in the soil temperature and a significant increase (p<0.01; 62%) in the soil moisture content were recorded in the inside of the canopy zones in comparison to the outside zones. The findings showed that the selection of Tamarix shrubs with larger canopy and planting with higher density should be considered to improve the environmental conditions in erosion-prone deserts management programs.

Saturated and trans fatty acids are used to provide desirable texture, high oxidative stability and extended shelf life in lipid-based food products such as margarine. In recent years, concerns on the use of trans and saturated fatty acids have increased due to the reasons such as cardiovascular diseases and obesity. Therefore, novel methods such as oleogel have been used to produce desired food structures without using harmful compounds.

In the current study, melting point and rheological and textural characteristics of four oleogel samples prepared with beeswax (2.5, 5, 7.5 and 10 wt%) were studied and compared with those of palm oil. To formulate low-saturated fat margarine, an optimized ologel sample was selected as the substitute for palm oil and hydrogenated palm olein oil. Margarines (70% fat) were assessed for their solid fat content, melting point and rheological and textural characteristics and results were compared with those of commercial samples.

The sample containing 10% beeswax included the most similar characteristics to those of palm oil. Replacement of 10% beeswax oleogel with 100% hydrogenated palm olein oil and 25% palm oil resulted in the production of margarines with rheological and textural characteristics, similar to those of commercial margarines. Furthermore, these samples included lower solid fat contents and higher melting points than those the commercial samples did.

The results of this study showed that the replacement of palm oil and partially hydrogenated palm olein oil with beeswax oleogel in margarine led to a reduction of  28% and 80% saturated and trans fatty acids, respectively.

Motivation and its conditions are among the most important environmental factors affecting learning. Non-generic feedback implies that performance is malleable, while generic feedback implies that task performance reflects an inherent ability. Therefore, each one has different motivational effects. This quasi-experimental study examined the motivational effects of generic and non-generic feedback on motor learning in twelve-year-old girls. The participants were 40 female students randomly assigned to two different groups-generic feedback (G-FB) and non-generic feedback (NG-FB). The task required participants to perform low kicks at a squared target area consisting of a piece of cardboard (measuring 50 cm wide and 50 cm high) attached to a wall and touching the ground, and placed at a distance of seven meters from the participants. Accuracy scores were analyzed in 2 x 4 (group*blocks) analysis of variance with repeated measures for the first experimental phase, and in 2 x 2 (group*blocks) analysis of variance with repeated measures for the negative feedback phase and the retention test. The findings show that both groups performed with similar accuracy across practice blocks. But on the second phase and no-feedback retention test, the NG-FB group indicated higher accuracy than the G-FB group. Therefore, it seems that that children’s motor performance, learning and motivation can be affected by the kind of augmented feedback provided during practice.

With the dramatic increase in private institutions and hospitals، health services market has become somewhat a competitive market; in this position one of the most important duties of hospitals is to struggle to attract customer. So، the purpose of this research was to determine the most important factors that patients adopt to choose a public a or private hospital.

This research was a descriptive – analyical study. In this study، participants included patients who were in-patients in Shams and Emam Reza hospitals in Tabriz. By the sampling method، 376patients were chosen. Data gathering tool was a researcher-made questionnaire which was made in 2 parts and its validity and reliability was confirmed. Finally Data were analyzed by Chi-Square، T-Test، fisher test.

The results showed that among the factors influencing choice of hospital، emergency، doctor’s recommendation، family income، kind of inssurance، quality of hospital، and education presented to patient about illness، were the most important faehors.

According to the finding، al though factors such as family income، kind of inssurance، quality of hospital services and … were effecive on hospital choice، the role of patients was meager in choosing hospital.

Background: 

Electrocardiography is one of the oldest, easiest, simplest and most ‎important inexpensive paraclinical tools in cardiology. Being able to use this easy ‎method with enough sensitivity would be a great aid to the cardiologist in evaluating a ‎common problem like congestive heart failure (CHF‏).

Method: 

Two hundred cases with left bundle branch block (LBBB) were chosen and ‎divided into two groups: QRS duration less than 120msec in the first group, and more ‎than 120msec in the second group. Left ventricular ejection fraction (LVEF) was ‎studied by 2D, M-mode and Doppler echocardiography as an index of LV systolic ‎function‏.‏

Results: 

In cases with QRS duration less than 120msec, LVEF averaged between 40-‎‎50%. In the other group with QRS duration more than or equal 120mseconds, LVEF ‎was between 30-40%. Finally, when QRS duration exceeded 160msec, almost all of ‎the patients had LVEF less than 30%‎‏

.‏Conclusion: 

LV systolic function could be estimated with an acceptable sensitivity by ‎observing surface ECG in LBBB cases. This observation may lead to conceptual ‎support for attempts at normalizing QRS duration by biventricular or multi-site ‎ventricular pacing in patients with severe LV systolic dysfunction (Iranian Heart ‎Journal 2003; 4 (4):31-34‎‏).