Stages Of Life Cycle Research Articles

Climatic Influences on the Environmental Performances of Residential Buildings: A Comparative Case Study in Turkey

This study evaluates the environmental performance of residential projects in Bolu and Mardin, Turkey, by assessing the impact of climatic and architectural context on material selection, construction techniques, and environmental outcomes. Using BIM-based LCA tools, the analysis compares Bolu’s humid climate with Mardin’s hot and dry conditions across multiple environmental metrics. In the Product (A1–A3) phase, Bolu has higher CO2 emissions, accounting for 79–85% of the total environmental impact, compared to 77–82% in Mardin. However, energy consumption is higher in Mardin during the Product phase. In the Construction (A4) and End of Life (C2–C4) phases, Bolu has a higher energy consumption and environmental impact than Mardin. In terms of waste generation, the End of Life phases (C2–C4) are identified as significant contributors in both case studies. The material analysis shows that concrete, finishing materials (e.g., paint and plaster) and stone wall materials have the highest environmental impacts in both cases. This study aims to provide a detailed examination of how environmental impacts differ due to material use in two different climatic regions. BIM-based LCA methods were used to investigate the influence of regional and climatic differences on environmental performance. The impacts of material components across all life cycle stages were analyzed, and recommendations for their optimization were provided. Future research could focus on the integration of innovative materials and technologies to improve life cycle efficiency. In addition, incorporating data from different geographic regions could broaden the scope of the analysis and contribute significantly to sustainable building practices. Such approaches provide critical opportunities to develop specific strategies for reducing environmental impacts.

Important tool in our rare disease toolbox: hybrid retrospective-prospective natural history studies serve well as external comparators for rare disease studies

Natural history studies (NHS) can support regulatory decision-making at different stages of the drug product life cycle and are especially important in the context of rare diseases, which are associated with not only delayed or erroneous diagnoses but also a lack of approved treatments. Real-world evidence can fill knowledge gaps and support treatment decision-making, thereby benefiting affected patients. In this context, there are three important options for NHS design: retrospective, prospective, and cross-sectional. Each of these has been successfully used to support regulatory approval as external comparator arms (ECAs) for clinical studies, especially single-arm trials (SATs). While longitudinal data obtained from retrospective or prospective designs have been more commonly used and have been the focus of regulatory guidance documents, hybrid designs that combine retrospective and prospective data collection are particularly powerful for rare disease studies. This is due, in part, to the smaller number of patients impacted by each rare disease. In these settings, retrospective or prospective data collection alone may not be sufficient or fit-for-purpose for an external comparator. Rather, a strategic combination of all available data, regardless of timing, can deliver the right information of the desired quality and completeness to answer these important questions and support regulatory evidentiary needs. For instance, patients included in retrospective studies may differ from recently treated patients in terms of disease severity, disease variants, clinical management, or other important aspects of the disease that may impact patient outcomes. Further, retrospectively collected data may lack specific data elements required to achieve adequate comparison with the treated group in single-arm studies. In the context of prospective designs, the recruitment of sufficient new patients for prospective follow-up may not be feasible or may be prolonged due to the rarity of the disease. Further, the potential for premature truncation of patient follow-up may result in insufficient longitudinal data, or prospectively collected data alone may not provide insights into the disease course for specific groups of patients. In these situations, primary data collection in a prospective study may be supplemented with retrospectively collected data from chart reviews, registries, or electronic medical record databases, either for the same patients, in an ambispective design, or for a different set of patients. These hybrid designs allow for broader and more robust contextual information on the patient journey and the natural course of the disease to be obtained, which can improve the suitability of the data as an external comparator for SATs or studies that lack internal control in situations where a prospective design alone might not be sufficient. Because retrospective and prospective data, or any two data sources that are being combined, may differ in availability and quality, there are unique challenges alongside the strengths of these designs. In this paper, we discuss considerations for the design, analysis, and conduct of hybrid NHS intended as ECAs for single-arm studies in clinical development programs for rare diseases.

Международные инновационно-инвестиционные проекты в сфере образования как объект менеджмента и маркетинга

The aim of this article is to examine the specifics of project and marketing management necessary for the effective implementation of new forms of international innovative investment projects (IIIPs) under modern conditions. Current trends and key issues of this process are revealed at the present time. The methodological foundations of managing international innovative investment projects (IIIPs) have been explored and refined, highlighting their characteristics, features, and stages of the project life cycle. Conditions for the effectiveness of such projects are summarized and formulated. Recommendations for improving the management mechanisms of IIIPs have been developed based on the integration of project and marketing approaches. A classification of project costs and a methodology for their assessment have been proposed, illustrated through a joint Russian-Chinese project in the field of higher education. The necessity of algorithmizing the management of IIIPs based on a structural approach is substantiated.

Importin α inhibitors act against the differentiated stages of apicomplexan parasites Plasmodium falciparum and Toxoplasma gondii.

Nuclear import, dependent on the transporter importin α (IMPα), is a drug target for apicomplexan parasites Plasmodium falciparum and Toxoplasma gondii. Indeed, a panel of small molecule inhibit interactions between IMPα and nuclear localization signals (NLSs) in vitro and the growth of rapidly dividing stages (P. falciparum blood stages and T. gondii tachyzoites) in culture. As new drugs targeting multiple life cycle stages of both parasites are required, the panel of IMPα inhibitors was tested for their ability to inhibit nuclear transport in the rapidly dividing stages and the maturation of differentiated stages (P. falciparum gametocytes and T. gondii bradyzoites). Using biophysical assays, Bay 11-7082, a Bay 11-7085 structural analogue, was tested for inhibition of IMPα:NLS interactions. The effect of the panel of inhibitors on the nuclear localization of reporter proteins was analysed in both parasites using transfections and microscopy. Also, using microscopy, the effect of inhibitors on differentiated stages of both parasites was tested. Bay 11-7085 can inhibit nuclear transport in tachyzoites, while GW5074 and Caffeic Acid Phenethyl Ester (CAPE) can inhibit nuclear transport in the blood stages. Interestingly, CAPE can strongly inhibit gametocyte maturation, and Bay 11-7082 and Bay 11-7085 weakly inhibit bradyzoite differentiation. As differentiation of gametocytes and bradyzoites is dependent on the activation of gene expression triggered by the nuclear translocation of transcription factors, our work provides a 'proof of concept' that targeting nuclear import is a viable strategy for the development of therapeutics against multiple stages of apicomplexan parasites, some of which are recalcitrant to existing drugs.

How do firms adapt their portfolios of external collaborations to changing internal organizational attributes? The moderating role of firm age

Objective of the study: Given the benefits of external collaborations in technology-intensive industries, we explore how firms adapt their portfolios of external collaborations to internal uncertainties. Using the behavioral theory of the firm, this study examines how firms adapt based on innovation performance feedback. Methodology/approach: We built a panel dataset from three sources. Patent data came from The National Bureau of Economic Research (NBER) paper (Kogan, Papanikolaou, Seru, & Stoffman, 2017), which used extensive name-matching tools to link USPTO patents to firms. External collaborations data were collected from the SDC Platinum Joint Ventures and Alliances database, focusing on R&D agreements. Financial data were sourced from Compustat. After processing, the dataset included nearly 900 publicly listed firms from 12 high-tech industries (1990-2010). We tested our hypotheses using two probit models, each predicting a different dependent variable. Originality/Relevance: We shift the focus from the traditional dyad perspective, which centers on individual partnerships, to how firms adjust their entire portfolios of external collaborations in response to their internal dynamics, like innovation performance feedback. While real options and transaction cost theories emphasize the need to maximize efficiency in individual partnerships, we explore how firms adapt their broader set of external collaborations to changing internal conditions. Main results: We find that firms performing above innovation aspirations are more likely to form equity alliances (i.e., joint ventures). However, the magnitude of this effect greatly differs between mature and young firms. Young firms are about four times more likely than their mature counterparts to form equity alliances when they significantly surpass their innovation targets. Theoretical/methodological contributions: Our study contributes to the research on alliance portfolio adaptation by showing that, beyond external factors like technological change and market uncertainty, firms also adjust their portfolios of external collaborations in response to internal factors, specifically innovation performance feedback. Moreover, our findings also demonstrate that firms’ responses to innovation performance relative to aspirations vary based on their lifecycle stage. Social /management contributions: Our study also has several implications for managers. First, managers of well-performing firms should view exceeding innovation aspirations as a signal to pursue collaborations to scale new technologies and knowledge. Their performance gives them a stronger negotiating position for forming joint ventures, allowing them to secure more favorable terms. Additionally, they should, in this case, seek partnerships to share risks associated with cutting-edge innovation projects rather than solely investing in internal innovation prospects. For young firms, which tend to respond more aggressively when outperforming their innovation aspirations, managers should adopt bold partnership strategies. In contrast, managers in mature firms should focus their resources on internal development, mergers and acquisitions, or partnerships that help maintain the autonomy of these firms.

Prediction of Heavy Oil Production Based on Geomechanical Analysis in Entire Lifecycle of SAGD

ABSTRACTSteam‐assisted gravity drainage (SAGD) technology is an essential means of efficient development of heavy oil, super heavy oil, oil sands, and other unconventional resources in the world. Accurate prediction and evaluation of heavy oil output during SAGD production is a key step of construction optimization design and economic evaluation. The traditional prediction model of heavy oil production does not fully consider many geomechanical factors, such as rock deformation and permeability dynamic evolution. In this paper, a new mathematical model of crude oil production‐geomechanical coupling was established for three stages of the SAGD life cycle (steam chamber breakthrough stage, rising stage, and lateral dilation stage). The influence of the dynamic evolution of rock porosity and permeability on production was fully considered through the sensitivity coefficient of rock strain and permeability stress. It is found that the gap between the new model and the traditional model is larger when the strain and stress sensitivity of the reservoir body is larger. The value calculated by the conventional model is small when the reservoir dilates and large when the reservoir compresses. For Karamay heavy oil in Xinjiang, China, the steam breakout time predicted by the new model is 0.72, 1.50, 1.37, and 1.44 times the conventional model when the volumetric strain is 6%. The heavy oil production of Karamay, Xinjiang, China, Athabasca, and Cold Lake SAGD production areas in Canada was predicted. In the lateral dilation stage of the steam chamber, the predicted values of the model considering geomechanical factors were 1.44, 1.28, and 1.15 times the traditional model, respectively. This model can help field engineers obtain more accurate production of heavy oil and evaluate the significance of reservoir geomechanics in SAGD production.

Трансформация механизмов корпоративного управления на различных этапах жизненного цикла организации

In the material of the work, a study of the features of corporate governance of an organization at its various stages of its life cycle is carried out. The analysis is based on the theoretical provisions of the concept of the life cycle. The key characteristics and objectives of corporate governance at each stage of the organization's development life cycle are analyzed. Special attention is paid to the practice of corporate governance in Russian companies, taking into account the specifics of their life cycle. In conclusion, effective approaches are proposed to adapt the corporate governance system to various stages of the organization's life cycle.

Robustness-Based Evaluation of GHG Emissions and Energy Use at Neighborhood Level

Evaluating neighborhood performance is crucial for achieving long-term zero-carbon goals, enabling efficient energy, cost, and resource sharing among buildings. This task requires balancing multiple criteria and managing uncertainties, emphasizing the importance of performance robustness alongside high performance. This article introduces a flexible multi-criteria approach for evaluating neighborhood performance, focusing on greenhouse gas (GHG) emissions and energy use across different life cycle stages. Flytårnet, a Norwegian neighborhood with zero-emission ambitions, serves as a case study. The methodology incorporates the T-robust method, an established robustness-based approach, to select high-performance, resilient neighborhood designs under various uncertainties. Results indicate that when assessing buildings as key components and considering energy delivered during the operational phase, including photovoltaic generation, the supplied energy ranges from 25 to 80 kWh/m2/year. Over a 60-year period, life cycle GHG emissions span from 4 to 12 kg CO2-eq./m2/year, accounting for uncertainties and encompassing material production and replacement, as well as energy consumption and generation. However, the optimal design choice varies based on whether life cycle stages beyond the use stage are considered. This research provides valuable insights for decision-makers and designers seeking effective neighborhood designs in early-stage planning, considering diverse and conflicting performance criteria to achieve zero-emission goals.

Continuous learning in SMES: the mediating role of resilient dynamic capabilities across the firm life cycle

Purpose Small and medium-sized enterprises (SMEs) are critical in the global business environment, yet they face significant challenges of survival. This study aims to investigate the role of continuous learning and resilience dynamic capabilities (RDC) on SME performance. Design/methodology/approach The authors conducted a quantitative survey of leaders from 290 SMEs to examine the relationship between continuous learning and performance. Structural equation modeling (SEM) is used to test the dependent relationships in this study. In addition, the Monte Carlo Method is used to test the mediating effect of RDC. Findings The results suggest that there is a direct effect of continuous learning on firm performance, as well as an indirect effect through RDC. In other words, the effect of continuous learning on firm performance was partially mediated through RDC. Originality/value The authors also explored whether firm life cycle stage matters regarding the role of continuous learning and RDC in SMEs. The mediating role of RDC becomes more pronounced (fully mediated) as SMEs transition from the growth stage to maturity stage of their life cycle.

LCA-TOPSIS Integration for Minimizing Material Waste in the Construction Sector: A BIM-Based Decision-Making

The construction sector is one of the industries with the highest environmental impact due to resource consumption and waste generation. Material waste exacerbates these impacts by increasing carbon emissions and energy consumption. This study introduces an innovative approach by integrating Life Cycle Assessment (LCA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate material waste and environmental impacts simultaneously. By analyzing scenarios of material use in the design and construction phases, this study explores their effects on material efficiency and environmental performance while addressing a notable research gap. Existing studies on the integration of LCA and TOPSIS in evaluating material waste and its environmental impacts remain limited. This research not only demonstrates the applicability of these methods but also contributes to filling this gap. Material waste and efficiency were assessed through Building Information Modeling (BIM), while BIM-LCA integration was used to evaluate environmental impacts. The findings were examined in two stages: LCA and TOPSIS. The TOPSIS analysis considered two scenarios—material waste and environmental impacts. In the first scenario, cast-in-place concrete (5000 psi) and stone and ceramic tiles emerged as priorities. In the second scenario, where carbon emissions and environmental impacts were emphasized, cast-in-place concrete (5000 psi), laminated timber, and stone tiles were identified as critical materials. The results reveal that reducing material waste significantly enhances environmental performance, lowers costs, and promotes sustainability. These findings provide practical insights for developing sustainable strategies in diverse cultural and geographical contexts, particularly for residential projects. The integration of LCA and TOPSIS offers a robust decision-making framework, enabling targeted actions to minimize environmental footprints across all life cycle stages. This study contributes to the literature by providing actionable recommendations for optimizing resource use and improving sustainability in construction practices.

Systematic Risks of the Global Lithium Supply Chain Network: From Static Topological Structures to Cascading Failure Dynamics.

Recent years have seen increasing concerns on supply chain risks of lithium, a critical material for achieving e-mobility transition and climate ambitions. These risks propagate both along the life cycle and across national boundaries in a multilayer network. However, most previous studies are either only based on static network measures or focused on individual layers, ignoring dynamic cascading risks and interconnected and interdependent relationships along life cycle stages and across economies. Here, we integrated trade-linked material flow and complex network analyses to investigate intricate interconnections, interdependencies, and systematic risks of the global lithium supply chain. Both static and dynamic measures of the global lithium supply chain network exhibit a "robust-yet-fragile" property: robust for random shocks yet fragile for targeted shocks and robust for small or local disruptions yet fragile for large or cascading failures. Portugal, Brazil, Singapore, Canada, Finland, Norway, South Africa, Israel, Hungary, and the United Arab Emirates are most likely to be affected by supply disruptions. A hypothetical USA-China trade decoupling will increase the severity and susceptibility of network-wide failures by around 5%. Our results call for global collaborations and collective efforts to balance efficiency and security and avoid a "zero-sum game" in securing the lithium supply chain.

The fourth-stage autoinfective larva of Strongyloides stercoralis: redescription and diagnostic implications.

Human strongyloidiasis is often underdiagnosed or misdiagnosed, which can relate to a lack of knowledge or recognition of the importance of particular developmental/larval stages of Strongyloides stercoralis in making an accurate diagnosis using parasitological methods (a morphological approach or morphological features/characters). Here, we report the identification of S. stercoralis autoinfective fourth-stage larvae (L4a) in naturally infected humans, encountered in two clinical cases in Australia. These larvae were identified in sputum (Case 1) and bronchoalveolar lavage (Case 2) specimens by direct wet-mount microscopy. The L4a of S. stercoralis can be morphologically differentiated from autoinfective third-stage larvae by its conical and pointed tail and a relatively mature genital primordium with an enlarged genital rudiment and the formation of a vulva within cuticle layers. This study emphasizes the need to consider these morphological features of the L4a stage for an accurate diagnosis of S. stercoralis infection. A detailed morphological description of this stage is given to guide laboratory practitioners and researchers in the identification and differentiation of this unique but neglected life-cycle stage of S. stercoralis.

PfMORC protein regulates chromatin accessibility and transcriptional repression in the human malaria parasite, Plasmodium falciparum.

The environmental challenges the human malaria parasite, Plasmodium falciparum, faces during its progression into its various lifecycle stages warrant the use of effective and highly regulated access to chromatin for transcriptional regulation. Microrchidia (MORC) proteins have been implicated in DNA compaction and gene silencing across plant and animal kingdoms. Accumulating evidence has shed light on the role MORC protein plays as a transcriptional switch in apicomplexan parasites. In this study, using the CRISPR/Cas9 genome editing tool along with complementary molecular and genomics approaches, we demonstrate that PfMORC not only modulates chromatin structure and heterochromatin formation throughout the parasite erythrocytic cycle, but is also essential to the parasite survival. Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments suggests that PfMORC binds to not only sub-telomeric regions and genes involved in antigenic variation but may also play a role in modulating stage transition. Protein knockdown experiments followed by chromatin conformation capture (Hi-C) studies indicate that downregulation of PfMORC impairs key histone marks and induces the collapse of the parasite heterochromatin structure leading to its death. All together these findings confirm that PfMORC plays a crucial role in chromatin structure and gene regulation, validating this factor as a strong candidate for novel antimalarial strategies.

Artificial Intelligence Life Cycle: The Detection and Mitigation of Bias

The rapid expansion of Artificial Intelligence(AI) has outpaced the development of ethical guidelines and regulations, raising concerns about the potential for bias in AI systems. These biases in AI can manifest in real-world applications leading to unfair or discriminatory outcomes in areas like job hiring, loan approvals or criminal justice predictions. For example, a biased AI model used for loan prediction may deny loans to qualified applicants based on demographic factors such as race or gender. This paper investigates the presence and mitigation of bias in Machine Learning(ML) models trained on the Adult Census Income dataset, known to have limitations in gender and race. Through comprehensive data analysis, focusing on sensitive attributes like gender, race and relationship status, this research sheds light on complex relationships between societal biases and algorithmic outcomes and how societal biases can be rooted and amplified by ML algorithms. Utilising fairness metrics like demographic parity(DP) and equalised odds(EO), this paper quantifies the impact of bias on model predictions. The results demonstrated that biased datasets often lead to biased models even after applying pre-processing techniques. The effectiveness of mitigation techniques such as reweighting(Exponential Gradient(EG)) to reduce disparities was examined, resulting in a measurable reduction in bias disparities. However, these improvements came with trade-offs in accuracy and sometimes in other fairness metrics, identified the complex nature of bias mitigation and the need for precise consideration of ethical implications. The findings of this research highlight the critical importance of addressing bias at all stages of the AI life cycle, from data collection to model deployment. The limitation of this research, especially the use of EG, demonstrates the need for further development of bias mitigation techniques that can address complex relationships while maintaining accuracy. This paper concludes with recommendations for best practices in Artificial Intelligence development, emphasising the need for ongoing research and collaboration to mitigate bias by prioritising ethical considerations, transparency, explainability, and accountability to ensure fairness in AI systems.

Firm life cycle and accrual quality

We examine the role of firms' life cycle stages on accrual quality (AQ). Our findings are consistent with an inverted U-shaped pattern in AQ across life cycle stages, where mature firms report the highest quality of accruals, followed by growth and shake-out firms, with firms in the introduction and decline stages reporting the lowest AQ. We further find that AQ worsens when firms move to the decline stage or when they move out of the mature stage to the shake-out and/or decline stage. Our findings are robust to several alternative specifications, subsample analyses, AQ measures, and firm life cycle proxies. We contribute to the literature on accrual quality by providing a broad view of how firm life cycle and accrual quality are intrinsically linked.

Voice health throughout the entire lifespan

Objective:Voice is a vital tool for human communication, and its health spans across various stages of an individual's life cycle. This article discuss the importance of voice health from a lifecycle perspective, exploring the needs of voice, phonatory organ changes, the main etiologies and their incidence, the impact on social function, and treatment of voice disorders at different stages （childhood, adolescence, adulthood, and old age）. During childhood, due to incomplete vocal cords development and weak behavioral control, the incidence of voice disorders is high, which adversely affects children's psychological and social function development. The most common condition is vocal nodules, and there has been insufficient attention to the treatment of voice disorders in this stage. Voice disorders during adolescence are mainly related to physiological development and hormonal changes. In the elderly, the larynx undergoes structural and functional aging, which can be delayed with active intervention and treatment. Therefor, maintaining voice health should focus on preventing and managing voice problems throughout the entire life cycle, with targeted voice care and intervention at each stage. Establishing a comprehensive voice health management framework thereby upholds an individual's optimal health status and social function.

ESG rating, corporate dividends policy, and the moderating role of corporate life cycle: Cross country study

AbstractThis article investigates the link between environmental, social, and governance performance (ESG) and dividend policy, as well as how likely the corporate life cycle might moderate this association. Using cross‐country data from 2010 to 2020, the findings of this study reveal that ESG has a favorable influence on corporate dividend payments as measured by dividend payout ratio, dividend yield, and dichotomous variable. This conclusion holds true when the three ESG pillars are examined independently on dividend policy measurements. Furthermore, this analysis reveals that the firm's life cycle stage moderates the association between ESG and corporate dividend policy, exhibiting a negative moderating impact. This study specifically reveals that the relationship between ESG and dividends is stronger for firms in the early stages of their life cycle than for those in the mature stages. This relationship applies to firms operating in developed economies compared to developing economies. The study findings particularly highlight the dynamic nature of the link between ESG and dividends, underlining that this relationship is dependent on the stage of a company's life cycle. Understanding this relationship may assist stakeholders, such as investors and management, in making educated decisions about dividend expectations and sustainable practices depending on the life cycle of a firm.

A system dynamics model for predicting African armyworm occurrence and population dynamics

This study develops a comprehensive system dynamics model to predict and manage African armyworm (Spodoptera exempta) outbreaks, a major threat to cereal crops across Africa. We applied system dynamics approache with its archetypes (causal loop diagram (CLD), reinforcing (R) and balancing (B)) to analyse the population dynamics of the pest. The VENSIM modelling platform (Ventana Systems Inc., DSS 8.2) was used to implement the models and carry out the simulations. The research integrates extensive data from 1980 to 2023, encompassing the African armyworm's life cycle stages, climatic variables, and intervention strategies, to simulate potential outbreak scenarios and evaluate the impacts of various control measures. The model demonstrates the ability to accurately capture the solitary and gregarious phases of the armyworm, showing how different climatic conditions influence these phases and affect the outbreak patterns across various African regions. The findings reveal that precipitation and humidity are critical factors affecting African armyworm outbreaks, with variations in these elements significantly influencing the pest population dynamics. Scenario analysis within the model indicates that integrated pest management (IPM) strategies, which combine biological control, cultural practices, and chemical methods, can effectively reduce armyworm populations, and mitigate crop damage. This approach not only helps manage current infestations but also contributes to sustainable agricultural practices by reducing reliance on chemical pesticides. The simulations of the model provide insights into the timing and intensity of armyworm outbreaks and illustrate how different interventions can alter these dynamics. For instance, the study highlights the effectiveness of early intervention and the potential consequences of delayed action, underscoring the importance of timely and informed decision-making in pest management. This research advances the understanding of African armyworm ecology and management by providing an approach that can predict outbreaks and evaluate the effectiveness of various control strategies under different climatic conditions. By incorporating real-world data and simulating realistic scenarios, the model offers a valuable resource for researchers, policymakers, and farmers in developing targeted, effective, and sustainable pest management strategies. This study stands out for its unique integration of biological, ecological, and IPM strategies, providing a holistic approach to addressing the challenges posed by S. exempta outbreaks in Africa. The implications of this work are significant, offering potential to enhance food security and economic stability in regions affected by the African armyworm, thereby supporting broader efforts to manage agricultural pests in a changing global climate.

The Role of Artificial Intelligence in Modern Software Engineering

Abstract. The rapid advancement of Artificial Intelligence (AI) has significantly influenced various industries, including software engineering. This paper explores the integration of AI into software engineering, focusing on its applications across different stages of the software development life cycle, including design, development, testing, project management, and maintenance. AI's ability to automate tasks, enhance efficiency, and improve code quality is revolutionizing how software is built and maintained. The paper also addresses the challenges and risks associated with AI-driven software engineering, such as dependency on AI tools, ethical concerns, and security vulnerabilities. Finally, the paper highlights future trends in AI-powered software engineering, including adaptive and self-healing systems, AI-enhanced collaboration, and full software automation. The role of AI in shaping the future of software engineering is both profound and transformative, making it a critical area of study.

A Review of 3D Human Pose Estimation

Abstract. Visual perception and human body recognition are fundamental capabilities required for effective and safe interactions between artificial intelligence (AI), computer vision, and humans in real-world scenarios. Recent groundbreaking developments in AI and computer vision have resulted in major advancements in human body recognition technology. However, research in human body recognition is still in the early stages of the product lifecycle. Identifying the three-dimensional locations of the joints in the human body from pictures or videos is known as 3D posture estimation. Although it is widely used in areas like human motion analysis and robotics, it continues to be a difficult task due to challenges such as depth ambiguity and the scarcity of robust datasets. Over the past decade, numerous methods have been developed, many of which are based on deep learning, significantly improving the performance of existing benchmarks. A comprehensive literature review of this field is crucial for future development. However, in nowadays,more and more such research has mainly concentrated on traditional techniques, requirement for a comprehensive examination of tools based on deep learning. This paper delivers a thorough overview of current deep learning-based 3D pose estimation algorithms, outlining their advantages and limitations while providing a detailed understanding of the field. It also explores commonly used benchmark datasets and methods for analyzing human poses in unlabeled field images, providing a thorough comparative analysis. Finally, insights are provided to aid in the design of future models and algorithms.