Technological Advances Research Articles

Media image transmission and privacy protection for internet of things security

Internet of Things (IoT) is becoming more popular with the increase in advancements of technology and is widely used in various sectors. The data are collected from the real environment and then transmitted over the networks. Due to their restricted resources, processing capacity, and memory, IoT gadgets are susceptible to certain security risks. Several encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Assessment of Informatics Competency Among Nursing Faculty Members and Its Application in Educating Nursing Students at Abadan Nursing Faculty

Background: Nursing informatics in educational activities can serve as a strategy to bridge the gap between clinical practice and nursing education programs. Objectives: This study aimed to assess the informatics competency of nursing faculty members and its application in educating nursing students at Abadan Nursing Faculty. Methods: This descriptive-analytical research was conducted in 2023 at Abadan Nursing Faculty. The sample consisted of 73 faculty members from the nursing, operating room, and anesthesia departments, who were included in the study through census sampling. Data collection was performed using the standardized Nursing Informatics Competency Assessment Tool (NICAT) and an electronic teaching aids checklist. Data were analyzed using SPSS version 18, with independent t-tests, one-way analysis of variance (ANOVA), and Pearson’s correlation at a significance level of P < 0.05. Results: The mean score of faculty members’ nursing informatics competency at the Nursing Faculty was 110 out of a maximum of 150, placing it within the proficient range. The highest score for nursing informatics competency was related to the informatics literacy dimension (47.65), while the lowest was related to the information management skills dimension (19.54). Information management skills (r = 0.881, P < 0.001), computer literacy (r = 0.871, P < 0.001), and informatics literacy (r = 0.976, P < 0.001) were significantly correlated with nursing informatics competency. Faculty members reported the highest use of multimedia content development (mean score = 4.19) and the lowest use of robotic surgery (mean score = 1.71). Conclusions: Faculty members’ informatics competency was reported to be at a proficient level. However, due to advancements in technology and transformations in nursing care, it seems necessary to improve nursing faculty members’ abilities in all aspects, particularly in the information management skills dimension. It is recommended to hold faculty development programs regarding the integration of technology into the curriculum to elevate the quality of education.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Cardiotoxicity following thoracic radiotherapy for lung cancer.

Radiotherapy is the standard of care treatment for unresectable NSCLC, combined with concurrent chemotherapy and adjuvant immunotherapy. Despite technological advances in radiotherapy planning and delivery, the risk of damage to surrounding thoracic tissues remains high. Cardiac problems, including arrhythmia, heart failure and ischaemic events, occur in 20% of patients with lung cancer who undergo radiotherapy. As survival rates improve incrementally for this cohort, minimising the cardiovascular morbidity of RT is increasingly important. Problematically, the reporting of cardiac endpoints has been poor in thoracic radiotherapy clinical trials, and retrospective studies have been limited by the lack of standardisation of nomenclature and endpoints. How baseline cardiovascular profile and cardiac substructure radiation dose distribution impact the risk of cardiotoxicity is incompletely understood. As Thoracic Oncology departments seek to expand the indications for radiotherapy, and as the patient cohort becomes older and more comorbid, there is a pressing need for cardiotoxicity to be comprehensively characterised with sophisticated oncology, physics and cardio-oncology evaluations. This review synthesises the evidence base for cardiotoxicity in conventional radiotherapy, focusing on lung cancer, including current data, unmet clinical needs, and future scientific directions.

USO DE ENXERTOS EM CIRURGIAS RECONSTRUTIVAS APÓS TRAUMAS GRAVES: UMA REVISÃO INTEGRATIVA

This article provides a comprehensive review of the use of grafts in reconstructive surgeries for patients with severe trauma, highlighting the differences among autologous, allogeneic, and synthetic grafts and their efficacy in specific contexts. Autologous grafts are known for their high integration rates and low immune rejection, while allogeneic and synthetic grafts expand therapeutic options, albeit with higher risks of complications. Technological advances, such as 3D bioprinting, allow for the customization of grafts, enhancing aesthetic and functional outcomes. However, these methods still face limitations, such as vascularization issues and immune rejection risks. This study emphasizes the importance of a careful and personalized selection of graft type to maximize patient quality of life and satisfaction. In conclusion, while autologous grafts remain the gold standard, the development of synthetic and bioprinted materials represents a promising frontier for reconstructive medicine.

Building Agricultural Resilience: Strategies for Climate Change Adaptation

Climate change has a significant impact on India's agriculture, influencing environmental conditions, agricultural techniques, and production. The term "climate-resilient agriculture" refers to technologies, methods, tools, and frameworks aimed at adapting to and mitigating the effects of climate change on agricultural production, food security, and livelihoods. Crop productivity and food security are greatly impacted by climate changes, which calls for creative agricultural methods to maintain food supply while reducing environmental damages. These difficulties are made worse by environmental changes and global population expansion, especially in developing countries. Technological advancements, such as geospatial analysis, and eco-friendly farming technologies, play a crucial role in optimizing land use and enhancing food security. Crop variety adaptation and management of seed, water, land, livestock, soil and nutrients, pest management, risk management, and post-harvest management are examples of effective agricultural adaptation strategies. Technological developments like geographic analysis.

Exploring Business Morals in the Cutting-Edge Corporate World

In the modern corporate world, business ethics has become a critical factor influencing the sustainability and success of organizations. With the rise of globalization, technological advancements, and increasing demands for corporate accountability, companies face complex ethical challenges that require strategic navigation. This article explores the growing importance of business ethics, focusing on key challenges such as globalization, data privacy, diversity, and profit pressures. It further outlines the role of leadership, corporate social responsibility (CSR), and transparency in maintaining ethical standards. Through case studies and real-world examples, the article provides a framework for how businesses can navigate ethical dilemmas, balance profitability with social responsibility, and foster long-term trust with stakeholders. Keywords : Corporate Ethics, Sustainability, Social Responsibility, Transparency, Stakeholder Engagement, Corporate Governance, Ethical Leadership, Fair Trade, Diversity and Inclusion, Data Privacy, Artificial Intelligence Ethics, Consumer Protection, Environmental Stewardship, Profit vs. Purpose, Whistle blower Protections, Corporate Accountability, Supply Chain Ethics, Workplace Culture, Innovation Ethics, Regulatory Compliance

Role of Technological Advancements on Customer Satisfaction in Japan’s Hospitality

Purpose: The aim of the study was to analyze the role of technological advancements on customer satisfaction in Japan’s hospitality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Technological advancements have significantly enhanced customer satisfaction in Japan’s hospitality industry by improving service efficiency, personalization, and overall guest experience. Innovations such as AI-driven customer service robots, automated check-in systems, and smart hotel rooms equipped with IoT devices offer convenience and seamless interactions for guests. These technologies reduce waiting times, streamline operations, and allow for personalized services, such as customized room settings and tailored recommendations, increasing guest satisfaction. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality model (SERVQUAL) & expectancy-confirmation theory (ECT) may be used to anchor future studies on the role of technological advancements on customer satisfaction in Japan’s hospitality. Hospitality businesses should adopt integrated management systems that combine customer relationship management (CRM), property management systems (PMS), and online booking platforms. Industry regulators should promote standards for technology integration in the hospitality sector.

Haihun Marquis Tomb in Western Han Dynasty: Social Value and Future Prospects - A Knowledge Graph Analysis with Citespace

This paper uses the CiteSpace knowledge graph visualization tool to analyze the research evolution path and trend of 583 academic papers in the CNKI database in the past ten years. The results show that the unique position of the tomb of the Haihun Marquis in the field of history and cultural heritage of the Han Dynasty, the mainstream of research in the field of Han Dynasty culture, artistic innovation and application, the evolution of cultural heritage research, and the interdisciplinary research of the deep integration of cultural heritage research and advanced technology in the future will provide strong support for promoting the cultural and museum industry and the recreation of traditional Chinese culture.

Innovative navigation: a smart cane solution with GPS, GSM, and real-time image capture

Our article presents the design, study, and implementation of a smart cane aimed at enhancing the autonomy and safety of individuals who are blind or visually impaired. The solution is built around an Arduino board, integrated with various sensors and modules, to provide real-time tactile and auditory feedback to the user. Utilizing a combination of basic and advanced technologies, this smart cane detects obstacles at varying distances, triggering sound and vibration alerts to assist the user in navigation. Beyond these essential functions, the cane offers advanced features such as real-time image capture and obstacle recognition through the ESP32-CAM module. Additionally, the system is equipped with a GSM module that facilitates emergency communication by sending SMS messages with the user’s GPS location, obtained via the Neo-6M GPS module. These technological enhancements improve user safety by streamlining obstacle detection and enabling remote assistance when needed. The primary objective is to create a comprehensive solution that not only detects and avoids obstacles but also provides visual information and supports communication with caregivers or emergency services. By doing so, the smart cane contributes to a higher degree of independence and an improved quality of life for blind and visually impaired individuals.

Insights from “Advanced Technologies and Practices in the Built Environment and Cultural Heritage”

Insights from “Advanced Technologies and Practices in the Built Environment and Cultural Heritage”

Biomechanical and machine learning approaches to automating the identification of musical styles and emotions through human motion analysis

This study explores the intricate relationship between biomechanical movements and musical expression, focusing on the identification of musical styles and emotions. Violin performance is characterized by complex interactions between physical actions—such as bowing techniques, finger placements, and posture—and the resulting acoustic output. Recent advances in motion capture technology and sound analysis have enabled a more objective examination of these processes. However, the current literature frequently addresses biomechanics and acoustic features in isolation, lacking an integrated understanding of how physical movements translate into specific musical expressions. Machine Learning (ML), particularly Long Short-Term Memory (LSTM) networks, provides a promising avenue for bridging this gap. LSTM models are adept at capturing temporal dependencies in sequential data, making them suitable for analyzing the dynamic nature of violin performance. In this work, they have proposed a comprehensive model that combines biomechanical analysis with Mel-spectrogram-based LSTM modeling to automate the identification of musical styles and emotions in violin performances. Using motion capture systems, Inertial Measurement Units (IMUs), and high-fidelity audio recordings, we collected synchronized biomechanical and acoustic data from violinists performing various musical excerpts. The LSTM model was trained on this dataset to learn the intricate connections between physical movements and the acoustic features of each performance. Key findings from the study demonstrate the effectiveness of this integrated approach. The LSTM model achieved a validation accuracy of 92.5% in classifying musical styles and emotions, with precision, recall, and F1-score reaching 94.3%, 92.6%, and 93.4%, respectively, by the 100th epoch. The analysis also revealed strong correlations between specific biomechanical parameters, such as shoulder joint angle and bowing velocity, and acoustic features, like sound intensity and vibrato amplitude.

Into the Revolution of NanoFusion: Merging High Performance and Aesthetics by Nanomaterials in Textile Finishes

AbstractThe field of technical textiles has grown significantly during the last two decades, with a focus on functionality rather than aesthetics. However, the advancement of NanoFusion technology provides a novel potential to combine better functionality and aesthetic value in textile finishes. NanoFusion incorporates nanoparticles into textile treatments to improve waterproofing, stain resistance, durability, and breathability. This is performed without affecting the textile's visual appeal or aesthetics and may even improve them. This textile finishing revolution is expected to impact industries such as athletics, outdoor clothing, car upholstery, and luxury fashion. It offers cutting‐edge functionality while maintaining style and design integrity. Furthermore, the use of nanoparticle textile coatings opens up new opportunities for personalization and modification. Manufacturers and designers can now experiment with different color combinations, patterns, and textured finishes while maintaining performance characteristics. NanoFusion technology has the potential to transform the textile industry by providing hitherto unattainable levels of performance and aesthetics. This study reviews the current state of the art in nanofinishes for garment textiles, focusing on their many varieties, techniques, mechanisms, and applications. In addition, it addresses significant concerns such as sustainability and the environmental footprint, paving the way for a new era in textile manufacturing.

Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment

Objective. Vision restoration approaches, such as prosthetics and optogenetics, provide visual perception to blind individuals in clinical settings. Yet their effectiveness in daily life remains a challenge. Stereotyped quantitative tests used in clinical trials often fail to translate into practical, everyday applications. On the one hand, assessing real-life benefits during clinical trials is complicated by environmental complexity, reproducibility issues, and safety concerns. On the other hand, predicting behavioral benefits of restorative therapies in naturalistic environments may be a crucial step before starting clinical trials to minimize patient discomfort and unmet expectations.Approach. To address this, we leverage advancements in virtual reality technology to conduct a fully immersive and ecologically valid task within a physical artificial street environment. As a case study, we assess the impact of the visual field size in simulated artificial vision for common outdoor tasks.Main results. We show that a wide visual angle (45°) enhances participants' ability to navigate and solve tasks more effectively, safely, and efficiently. Moreover, it promotes their learning and generalization capability. Concurrently, it changes the visual exploration behavior and facilitates a more accurate mental representation of the environment. Further increasing the visual angle beyond this value does not yield significant additional improvements in most metrics.Significance. We present a methodology combining augmented reality with a naturalistic environment, enabling participants to perceive the world as patients with retinal implants would and to interact physically with it. Combining augmented reality in naturalistic environments is a valuable framework for low vision and vision restoration research.

The Evolution of Identity and Access Management: Integrating Biometric and Behavioral Authentication

This article explores the transformative role of biometric and behavioral authentication in the evolving landscape of Identity and Access Management (IAM). As traditional password-based systems increasingly fail to meet the challenges posed by sophisticated cyber threats, these advanced authentication methods offer a promising solution. The article provides a comprehensive overview of physical and behavioral biometrics, examining their integration into multi-factor authentication systems and their capacity for continuous, passive user verification. It delves into the significant security benefits these technologies offer, including enhanced resistance to phishing, social engineering, and insider threats, while also highlighting the improvements in user experience and convenience. The article critically analyzes the challenges and considerations associated with implementing biometric and behavioral authentication, including privacy concerns, data security, accuracy issues, and scalability. Furthermore, it explores future directions and research opportunities in the field, such as advancements in biometric technologies, the application of machine learning and AI in behavioral analysis, and the development of context-aware authentication systems. By synthesizing current research and industry trends, this article provides valuable insights for cybersecurity professionals, IT decision-makers, and researchers interested in the future of secure and user-friendly authentication methods. It underscores the potential of biometric and behavioral authentication to significantly enhance organizational security postures while improving user satisfaction in an increasingly digital world.

Diagnostic Techniques for Infantile Subglottic Hemangiomas: A Scoping Review.

Infantile subglottic hemangioma (SGH) poses a risk of airway compromise if untreated. Traditionally, operative endoscopy (OH) diagnoses SGH, but since the discovery of beta-blockers' efficacy in treating infantile hemangiomas (IHs) in 2008, and advances in endoscopic technology, nonoperative methods have emerged. This review identifies endoscopic practices for diagnosing and monitoring infantile SGH during the oral beta-blocker treatment era. A comprehensive literature search in October 2022 and August 2023 covered PubMed, Embase, Cochrane Library, SCOPUS, and Web of Science. The search was limited to English-language studies published since 2008, considering this when propranolol was demonstrated as an effective treatment option for IH. The articles were screened for relevance based on predefined inclusion and exclusion criteria. After inclusion and exclusion criteria, sixty final studies were identified, describing 240 cases of infantile SGH. Most children were diagnosed using OE alone (73.3%; n = 176/240), 23.3% (n = 56/240) using office-based laryngoscopy procedures (OBPs) followed by OE, 3.3% using OBP alone (n = 8/240). There were no reported diagnostic endoscopy-related complications. Twenty-nine studies described using endoscopy plus diagnostic imaging to either confirm an SGH lesion, characterize the extent of disease spread, or rule out other causes of presenting symptoms. The proportion of infants diagnosed with OE alone decreased from 2008 to 2023. Operative endoscopy remains the SGH diagnostic standard, but OBP adoption is increasing. Further research is needed to determine the optimal SGH diagnosis and management approach. NA Laryngoscope, 2024.

Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: “(audiology) AND (“artificial intelligence” OR “machine learning” OR “deep learning”)”. The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology.

CD137 Protein Expression Pattern Determines the Functional Role of Galectin-9 in Colorectal Cancer.

The rapid advancement of single-cell sequencing technology has generated extensive data, providing critical resources for colorectal cancer (CRC) research. This study conducts a detailed analysis of CRC single-cell sequencing data to develop a novel clinical prognostic tool and explore potential therapeutic targets for the LGALS9 gene. Using the Scissor algorithm, we created a CRC prognostic scoring system (SDRS) based on 13 key genes, with particular focus on LGALS9 and its protein, Galectin-9, in mice CRC model with altered CD137 expression. Our findings demonstrate that the SDRS accurately reflects clinical and pathological features of CRC patients, acting as an independent predictor of outcomes. LGALS9 expression is generally reduced in CRC tissues and is associated with poorer prognosis. We also observed a strong positive correlation between LGALS9 and CD137 expression, with CD137 showing significant variability in CRC tissues. In mouse models with CD137 overexpression, Galectin-9 treatment led to notable antitumor effects and increased infiltration of activated T cells. In contrast, in CD137-deficient models, Galectin-9 promoted tumor growth with limited T cell presence. These results suggest that the role of LGALS9 in CRC may depend on CD137 expression, highlighting the potential of LGALS9 as a therapeutic target. CD137 levels may serve as a key indicator for predicting the effectiveness of this treatment strategy.

Internet terminology in modern German: semantics and structure

Abstract The article presents an in-depth analysis of the formation of terminology in the professional language of the Internet in German and offers predictions about its future development, in line with the evolution of this area of knowledge. Drawing from a corpus of about 8,000 terms and term combinations, the authors explore the dynamics of German Internet terminology, propose a timeline for the emergence of these terms in relation to advancements in science and technology, and identify key methods of term formation along with their semantic characteristics. The primary source of expansion in German Internet terminology comes from Anglo-American borrowings, which, during their adaptation into German, have undergone structural and semantic assimilation. The main methods of term formation within the examined terminology are compounding (with a predominance of binary structures), derivation, and abbreviation. German Internet terminology is characterized by synonymy and polysemy, reflecting the dynamic nature of network technology development. A distinctive feature of this terminology is the formal redundancy in expressing a single concept, leading to the formation of synonymous sets. The uniqueness of the semantic paradigm of these terms lies in the extralinguistic factors that influence terminological synonymy, polysemy, and the broader system of structural relations in German Internet terminology.

Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.