Innovation in Science and Technology

Trends and Clinical Outcomes of In Vitro Fertilization in Selected Nigerian Cities: A Multi-Center Retrospective Study

2025-08-10T05:55:12+00:00

Background: The advent of In vitro fertilization (IVF) in the late 20^th century has greatly transformed reproductive medicine. It has brought hope to millions of individuals and couples facing the challenges of infertility. Assisted Reproductive Technology (ART), notably In Vitro Fertilization (IVF), has shown promise in treating infertility, yet its use in Nigeria is limited by high costs, inconsistent success rates, and a lack of standardized clinical protocols. Aim: The current study investigated the trends and clinical outcomes of IVF treatments in two Nigerian cities, Abuja and Jos, between 2016 and 2022, providing a comprehensive analysis of IVF demand, success rates, and associated risk factors. Methodology: A retrospective analysis of 26,233 medical records from four fertility centers in Abuja and Jos was conducted. Data, including clinical indications, treatment outcomes, and infertility-related variables, were extracted from patient records spanning six years. Descriptive statistics and comparative analyses were employed to examine trends and identify significant factors affecting IVF success. Results: The study revealed a notable increase in IVF cases, with Abuja Hospital showing a surge from 503 cases in 2018 to 1,665 in 2022. Success rates varied, with Abuja Hospital achieving a 94% success rate in 2021. Male infertility was a primary cause of IVF failure, and multiple pregnancies were common, especially in 2022, with 974 multiple pregnancies out of 1,552 successful IVF treatments at Abuja North Hospital. Conclusion: The findings demonstrate growing demand for IVF services in Nigeria, with a high incidence of multiple pregnancies. These trends highlight the need for improved embryo selection and management protocols. This study provides valuable insights for optimizing IVF practices and improving patient outcomes in Nigeria.

Hepatitis D and E Viruses Cause Liver Damage: Management and Prevention are the Best Policies of Elimination These

2025-08-10T05:57:32+00:00

Viral hepatitis is a term that refers to inflammation of the liver due to a viral infection. The hepatitis D virus (HDV) is a blood-borne pathogen and only occurs as either a co-infection with hepatitis B virus (HBV) or as a super-infection of persons with chronic HBV. The hepatitis E virus (HEV) is a virus that can infect both animals and humans. The HDV infection may be severe in children. On the other hand, HEV infection may be severe in pregnant women. Common symptoms of both infections are nausea and vomiting, fever, abdominal pain, fatigue, malaise, and jaundice. Both infections can cause acute for short-term infection or become a long-term chronic infection that may cause liver failure, chronic hepatitis, and liver cirrhosis. There is no vaccine of both viruses, and treatments are supportive. The pegylated interferon alpha (Peg-IFNα) is the available therapy to treat both infections associated with significant side-effects. An attempt is taken here to discuss the management and prevention strategies of both infections.

Research on Promotion Strategies for Financial Information Technology Innovation Products in Small and Medium-Sized Financial Institutions: Construction and Application of a Data-Driven Decision System

2025-08-10T05:59:33+00:00

The rapid development of financial technology has positioned financial information technology innovation products as crucial tools in enhancing the efficiency of financial services, optimizing risk management, and improving customer experience. However, small and medium-sized financial institutions (SMEFs) face significant challenges in their digital transformation journey, including inadequate technological capabilities, substantial cost pressures, and concerns over data security and privacy. These challenges have impeded the widespread adoption of financial information technology innovation products. This study constructs a Data-Driven Promotion Decision System (DDPDS) to meticulously analyze the business requirements, operational models, and market trends of SMEFs, thereby devising a targeted promotion strategy. The research initially clarifies the characteristics of financial information technology innovation products and their current application status in SMEFs. Subsequently, the DDPDS is employed to conduct a precise analysis of SMEFs’ needs, and a differentiated promotion plan is proposed in conjunction with market trends.

Big Data-Driven User Behavior Analysis and Experience Iteration Strategies for Hotel Supplier Portals

2025-08-29T09:44:19+00:00

Amidst the increasingly fierce competition in the hotel industry, user experience has emerged as a pivotal factor in enhancing supplier cooperation satisfaction and platform competitiveness. The rapid development of big data technology has provided robust support for user behavior analysis and experience optimization in hotel supplier portals. Drawing on the author’s practical experience in the hotel sector, this paper delves into big data-driven user behavior analysis methods and their application in experience iteration for hotel supplier portals. By analyzing user behavior data, this study proposes personalization-based recommendation, interface optimization, and function optimization strategies underpinned by A/B testing, and demonstrates their effectiveness through real-world cases. The findings indicate that the implementation of big data analysis and iteration strategies can significantly enhance user experience in hotel supplier portals, thereby improving supplier cooperation satisfaction and platform operational efficiency. This research not only offers theoretical support for the informatization construction of the hotel industry but also provides references for user experience optimization in other industries.

Practical Exploration of Digital Governance in Smart Apartment Communities from the Perspective of Data Interoperability

2025-08-29T09:47:33+00:00

In the context of accelerating urbanization, the frequent mobility of residents in apartment communities poses significant challenges to traditional governance models, such as large personnel turnover and difficulties in service coordination. This study focuses on the digital governance of smart apartment communities, exploring the technical implementation and application value of data interoperability between “apartment management systems and community governance platforms”. Taking the integration practice of the independently developed “Tenant Information Intelligent Filing System” and the community governance platform as a case study, this paper in-depth analyzes the key technologies and security standards of data interface development, and demonstrates the actual effectiveness of the digital governance model in improving tenant filing efficiency and shortening safety hazard response time. The results indicate that through data interoperability technology, information silos can be effectively broken down, community governance processes can be optimized, and governance efficiency can be enhanced, providing practical references and theoretical support for the governance of new-type urban communities.

Digital Twin-Driven Intelligent Collaborative Automation Model for Global Warehouse Networks and Application Validation

2025-08-29T09:54:41+00:00

In the context of accelerated globalization, multinational logistics companies are confronted with significant challenges in global warehousing networks, including information latency, resource misallocation, and inefficient collaboration. This study addresses these issues by introducing digital twin technology to construct a virtual mirroring system for the overseas warehouse layouts of DongGuan Kreen Import and Export Co., Ltd. (with branches in the United States, Canada, Germany, and Vietnam). The digital twin system enables real-time collaboration and automated scheduling across global warehousing nodes, thereby enhancing the resilience of the global supply chain. The research is centered on three main aspects: First, a dynamic digital twin model is developed based on the physical space, equipment status, and business data of global warehousing nodes, achieving millisecond-level synchronization between physical and virtual warehouses. Second, intelligent resource allocation algorithms, automated cross-continental transfer decision mechanisms, and autonomous abnormal event response processes are designed to upgrade warehouse collaboration from a “passive execution” to an “active prediction” mode. Third, the digital twin model is validated in the context of “Amazon FBA headhaul logistics,” focusing on its collaborative efficiency in global warehouse stocking, replenishment, and return/exchange processes, with an emphasis on improving the response speed of cross-border e-commerce supply chains. The study demonstrates that the digital twin-driven intelligent collaborative automation model for global warehouse networks significantly enhances collaborative efficiency and supply chain resilience, forming a technical standard and practical paradigm for digital twin automation collaboration in global warehouses. This provides technological support for multinational logistics companies to address global warehousing collaboration challenges and enhances China’s scheduling discourse power in the global supply chain.

Protective and Curative Effect of Clove Isolates on Trichloroacetic Acid-Induced Sperm-Endocrine Deficit, Redox Imbalance and Histomorphology in Adult Rat

2025-08-29T09:57:18+00:00

Environmental pollutants, such as Trichloroacetic Acid (TCA), have been implicated in compromising male reproductive health by inducing sperm-endocrine deficits, redox imbalances, and histomorphological alterations. This research investigates the protective and curative effects of clove isolates, particularly Eugenol, on TCA-induced reproductive toxicity in adult male rats. The study employs various parameters, including body and testes weight, reproductive hormone levels, oxidative stress markers, sperm analysis, and histological profiles. Sixty adult male Wistar rats were divided into ten groups, with treatments involving TCA, Eugenol Isolates from Clove (EIC), and combinations of both. The results indicate significant changes in body weight and testes weight, with TCA-treated groups showing pronounced effects. EIC supplementation, however, mitigated these changes, emphasizing the potential protective role of clove isolates. Reproductive hormone analysis revealed a decrease in testosterone levels induced by TCA, while EIC treatment demonstrated a capacity to elevate testosterone levels. The interaction of Eugenol with luteinizing hormone (LH) underscored the complexity of hormonal effects, with dose-independent impacts requiring further exploration. Oxidative stress marker analysis demonstrated TCA-induced alterations, while EIC treatment exhibited synergistic effects, particularly in reducing oxidative stress. Sperm analysis revealed TCA-induced adverse effects, which were mitigated by EIC, suggesting a potential role in preserving reproductive health. Histological examination delineated distinct profiles, with EIC showing potential therapeutic benefits. The study concludes that Eugenol from clove isolates may have protective and curative effects against TCA-induced reproductive deficits, redox imbalance, and histomorphological abnormalities. These findings contribute valuable insights into the potential applications of clove isolates in mitigating the detrimental effects of environmental toxins on male reproductive health. Further investigations are warranted to elucidate underlying mechanisms and optimize the therapeutic potential of Eugenol in safeguarding male reproductive function.

Fairness Assessment and Ethical Governance of Insurance AI: A Patch Approach for Vulnerable Groups

2025-08-29T09:59:42+00:00

The widespread application of artificial intelligence (AI) in the insurance industry has brought numerous conveniences, but it has also triggered a host of ethical controversies, with the unfair treatment of vulnerable groups becoming increasingly prominent. This paper focuses on the fairness assessment and ethical governance of insurance AI, delving into the adverse effects of insurance AI on vulnerable groups (such as the elderly and low-income populations) in practical applications, such as the refusal of insurance by smart underwriting for the elderly and premium differences caused by algorithmic discrimination. To address these issues, this paper constructs a fairness assessment index system for insurance AI, covering five core indicators: explainability, coverage of vulnerable groups, premium fairness, complaint rate and claims efficiency. It also proposes the design logic of “algorithmic patches” for vulnerable groups, such as optimizing the weight of health data for the elderly and simplifying the language of smart recommendations.