Efficient Management Research Articles

Assessing the Role of Cultural Diversity in Shaping Organizational Performance and Customer Satisfaction in the Hospitality Sector: A Quantitative Approach

The hospitality sector staff is noted for being multicultural, as is its customer. Understanding the complexities of cultural diversity is crucial for efficient hospitality management as businesses work to establish inclusive and culturally sensitive settings. In the context of the hospitality sector, this paper aims to investigate the effects of cultural diversity on organizational performance and consumer satisfaction. A representative sample of workers in the hotel sector is given a standardized questionnaire. The survey measures demographic traits, attitudes toward cultural diversity, perceptions of inclusion, and the effects of diversity on business performance and customer happiness. Since the data are quantifiable, statistical analysis may be used to find trends, correlations, and the overall impact of cultural diversity on many aspects of hospitality management. The study's findings add to the body of knowledge on cultural diversity in the hospitality sector by providing insights into the tactics and methods that improve industry efficiency and customer happiness. The study emphasizes the need of developing a welcoming and inclusive company culture that values diversity, encourages cultural awareness, and capitalizes on the distinctive skills and viewpoints of employees from all origins.

Ips typographus vision system: a comprehensive study.

Aggressive bark beetle species such as the Eurasian spruce bark beetle Ips typographus play a fundamental role in forest ecosystems but can also lead to extensive forest mortality and massive economic damage during outbreaks. Currently I. typographus' eyes, visual perception of the world and recognition of specific targets like host plants are understudied topics. Studying its visual sense can open the way to novel efficient monitoring and management methods, particularly important in avoiding the switch from an endemic to an epidemic condition. In addition, the integration of visual cues in trapping systems may offer new opportunities for surveillance. Vision in I. typographus was investigated by means of morphological analysis, electroretinography (ERG), molecular analysis of opsin genes and behavioural tests. ERG has revealed that the compound eyes contain two classes of photoreceptors, maximally sensitive to UV and green at 370 and 530nm, respectively. The result was further supported by the identification of two relevant opsin genes. Finally, the innate wavelength sensitivity was tested in a Y-maze. Ips typographus consistently preferred UV over non-UV (VIS) light, irrespective of their intensity ratios, but preferred high over low intensity VIS light, consistent with a UV-VIS dichromatic visual system. Overall, the results may open the way to better understand the navigation pattern in tree canopies and the host selection processes of this ecologically and economically important beetle species.

Evaluating machine learning performance in predicting sodium adsorption ratio for sustainable soil-water management in the eastern Mediterranean

Soil salinization is a critical global issue for sustainable agriculture, impacting crop yields and posing a threat to achieving the Sustainable Development Goal (SDG) of ensuring food security. It is necessary to monitor it in detail and uncover its underlying factors at a regional scale. In this context, the present study aimed to evaluate soil health in the eastern Mediterranean region by using the Sodium Adsorption Ratio (SAR) as an indicator of soil salinity in three distinct soil horizons. The main objective of the research was to evaluate the performance of four machine learning (ML) models, including Random Forest (RF), Nu Support Vector Regression (NuSVR), Artificial Neural Network-Multi Layer Perceptron (ANN-MLP), and Gradient Boosting Regression (GBR), for accurate prediction of SAR following the Recursive Feature Elimination (RFE) as a feature selection method. Moreover, SHapely Additive exPlanations (SHAP) was applied as sensitivity analysis to identify the most influential covariates. Main findings of the research revealed that the average clay content in the surface horizon (H10-25cm) was 50.5% ± 10.4, which significantly increased to 57.5% ± 8.7 (p < 0.05). No significant mean differences were detected between the studied horizons for SAR and Na+. ML output revealed that NuSVR outperformed other algorithms in accurately predicting outcomes during both the training and testing stages. Moreover, Scenario 2 (SC2) with seven selected features from the RFE method facilitated highly accurate SAR predictions. Overall, the performance of ML models is ranked as NuSVR > GBR > ANN-MLP > RF. Lastly, SHAP sensitivity analysis identified CEC, Ca+2, Mg+2, and Na+ as the most influential variables for SAR prediction in both the training and testing stages. Hence, the research yielded valuable insights for efficient agricultural soil management at a regional level using state-of-the-art technology.

Mapping critical soil moisture thresholds of water stress for global grasslands

The critical soil moisture threshold (SMth) of plant water stress distinguishes whether vegetation is in an energy-limited or water-limited regime. Accurate quantification of SMth for grasslands holds significant implications for drought monitoring and efficient grassland management. However, our understanding of SMth within global grasslands remains limited. In this study, we investigated the relationship between SMth and environmental factors and mapped the global SMth for grasslands based on the estimated SMth across FLUXNET sites. Firstly, SMth was identified and estimated for 18 distinct grassland sites. Then, the factors significantly affecting SMth were investigated and the global SMth for grasslands was predicted using an empirical linear model. Our results showed that SMth was significantly positively correlated with mean annual soil moisture (SM), mean annual precipitation (PRE), and maximum annual LAI (LAImax), while negatively correlated with vapor pressure deficit (VPD) and shortwave radiation (RAD). These factors collectively constrain SMth well, with the coefficient of determination of 0.79. The estimated mean SMth for global grasslands from 2000 to 2021 is 27.8%, with a range from 1.3% to 55.2%. The SMth values are lower in the northern hemisphere than that in the southern hemisphere. The highest values of SMth, exceeding 40.0%, are mainly distributed in Brazil, Peru, Southern Europe, New Zealand and the Tibetan Plateau. In contrast, the lowest values, failing below 20.0%, are mainly distributed in Northern Canada, Greenland, Central African Savannah and Kazakhstan. The SMth is markedly lower in the arid region than that in the tropical and temperate regions. The quantified SMth in this study could provide a direct way to determine whether and to what extent grasslands are subjected to water stress, which would greatly facilitate the study of drought or water stress effects on vegetation growth for grassland ecosystems.

Empowering the circular economy practices: Lifecycle assessment and machine learning-driven residual value prediction in IoT-enabled microwave oven

In an era of resource scarcity and environmental concerns, integrating Internet of Things (IoT) technology into the circular economy (CE), particularly for household appliances like microwaves, is crucial. The lack of systematic assessment of their post-use residual values often reduces utilization and shortens lifespans. Inadequate disposal and management contribute to electronic waste and environmental pollution. Addressing these challenges is vital for efficient appliance management within resource constraints, ensuring meaningful contributions to sustainable resource management. Thus, this study addresses these concerns by integrating IoT technology into microwave ovens, enabling real-time monitoring of key parameters such as voltage, current, door closures, and motor/blade rotations. Data from integrated sensors enables performance analysis and trend tracking, offering potential for advancing CE practices and sustainable product management. Subsequently, utilizing the insights stored from IoT data analysis and tailored surveys, a predictive maintenance model is developed, aiming to predict the life cycles of microwave oven components and categorize them within the CE principles, including reuse, repair, remanufacturing, and cascade. Finally, to mitigate the challenges of lower effective utilization and shortened operating lifespans observed in household appliances, this research employs machine learning models such as Random Forest, Gradient Boosting, and Decision Tree to accurately predict the residual values of IoT-enabled microwaves. Notably, Random Forest demonstrates superior accuracy compared to the other models. Therefore, these technological advancements allow household appliances to be utilized more effectively, thereby enhancing resource utilization.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

SUPPLY CHAIN DISRUPTION AND SUSTAINABILITY OF PHARMACEUTICAL FIRMS IN ANAMBRA STATE, NIGERIA

&lt;p&gt;The pharmaceutical sector, in Nigeria plays a role in the nations’ healthcare system by supplying medications to the people. However pharmaceutical companies in the Anambra State have been facing obstacles recently due to disruptions in their supply chains. This research investigates how supply chain disruptions impact the sustainability of firms in Anambra State. A mixed-methods approach, involving qualitative interviews with industry experts and quantitative analysis of operational data, identifies the main causes of supply chain disruptions and their effects on this firm’s long-term success. The research reveals that supply chain disruptions result from factors such as delays in customs clearance, inconsistent regulatory policies, currency fluctuations, inadequate transportation infrastructure and issues with supplier reliability. Interviews with industry experts highlighted customs delays as a disruption while quantitative analysis showed a 20% increase in times for imported pharmaceuticals along with a 15% rise in delivery delays and a 10% increase in product returns due to transportation challenges. Moreover, inefficiencies in inventory management were observed, leading to a 5% rise in holding costs and a 3% decrease in turnover rates due to supply chains. Challenges related to suppliers involved issues, with delivery reliability and product quality leading to a 10% extension in supplier lead times and a 5% uptick, in returns. The findings highlighted a direct correlation between these supply chain issues and decreased profitability, operational efficiency, and the ability to meet patient needs, emphasizing the urgent need for improvements to enhance the resilience of the pharmaceutical supply chain in Anambra State.&lt;/p&gt;

An automated social media manager based on artificial intelligence

In today's world of social media, where brands and companies constantly interact with their audiences, the automation of content management processes is becoming increasingly important. Manual content creation, publication, and analysis on social networks require significant resources and time. Automated social media management systems based on artificial intelligence help simplify these processes, enhancing efficiency and optimizing audience engagement strategies. Artificial intelligence in social media marketing systems allows for the automation of tasks such as creating text posts, scheduling publications across different platforms, and analyzing user interactions. Artificial intelligence can analyze audience behavior, predict the optimal time for posts, and recommend changes to content strategies for maximum reach. This not only saves time but also improves the effectiveness of marketing campaigns through more accurate analytical data. However, the automation of social media marketing also faces challenges. The primary issues include the complexity of integrating different social networks into a single management system and the limitations in generating high-quality content that meets the emotional and stylistic requirements of the brand. Additionally, a major challenge is content personalization based on data analysis, which must account for each audience segment's unique needs and behavioral characteristics. Despite these challenges, the prospects for developing automated social media marketing managers are promising due to ongoing innovations in artificial intelligence and machine learning. Future research and development will focus on improving the quality of automated content generation, enhancing user interfaces, and integrating new social platforms to expand multi-channel marketing campaign management capabilities. Thus, the automation of social media marketing is a promising field that will enable brands and companies to optimize their audience engagement strategies while significantly reducing the time and resources required for content creation, publication, and analysis. This work presents the structure of the main entities of an automated social media management system based on artificial intelligence, including users, professions, questions, answers, settings, and integration with Telegram. The developed system enables efficient content management, automates publications, and personalizes audience interaction based on user responses. The system's described key components, attributes, and relationships provide scalability and flexibility, allowing brands and companies to optimize their marketing strategies and client interactions.

A Network Security Transmission Model Based on Improved Sparrow Search Algorithm

With the development of the socio-economic landscape, the demand for electricity supply is steadily increasing. The application of 5G networks in this field has gradually alleviated the supply-demand response issues associated with large-scale distributed power sources. However, the broadcast nature of 5G channels raises concerns about secure data transmission. Addressing these challenges, this paper establishes a collaborative communication model for large-scale Demand Response (DR). Using the distributed power source network as a foundation, a communication system based on Power Line Communication (PLC) and Generic State Machine (GenSM) is proposed. Building on this, a self-adaptive data transmission algorithm based on the sparrow algorithm is proposed. This algorithm uses swarm intelligence for efficient energy management and secure transmission. Simulation results show that, compared to traditional Multiple-Input Multiple-Output (MIMO) models, under specific conditions, the proposed model achieves an energy efficiency improvement of 131% and 69%.

Spatial impulse response analysis and ensemble learning for efficient precision level sensing

In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution.

Data Lakehouse: A survey and experimental study

Efficient big data management is a dire necessity to manage the exponential growth in data generated by digital information systems to produce usable knowledge. Structured databases, data lakes, and warehouses have each provided a solution with varying degrees of success. However, a new and superior solution, the data Lakehouse, has emerged to extract actionable insights from unstructured data ingested from distributed sources. By combining the strengths of data warehouses and data lakes, the data Lakehouse can process and merge data quickly while ingesting and storing high-speed unstructured data with post-storage transformation and analytics capabilities. The Lakehouse architecture offers the necessary features for optimal functionality and has gained significant attention in the big data management research community. In this paper, we compare data lake, warehouse, and lakehouse systems, highlight their strengths and shortcomings, identify the desired features to handle the evolving challenges in big data management and analysis and propose an advanced data Lakehouse architecture. We also demonstrate the performance of three state-of-the-art data management systems namely HDFS data lake, Hive data warehouse, and Delta lakehouse in managing data for analytical query responses through an experimental study.

Method Development and Validation of Metformin and Gliclazide in Tablet dosage form by Micellar Liquid Chromatography

Micellar Liquid Chromatography (MLC) has emerged as a promising technique for the analysis of Pharmaceutical compounds due to its versatility, efficiency, and cost-effectiveness. In this experimental research paper, we explore the application of MLC in the simultaneous analysis of two commonly prescribed antidiabetic drugs, Metformin and gliclazide. A combination of these medicines is widely used to treat type 2 diabetes mellitus, so accurate quantification of them is essential for efficient therapeutic management. To separate the drug formulation, reversed phase liquid chromatography (RP-HPLC) was employed using surfactant solutions above Critical Miceller concentration (CMC). The mobile phase included 0.10M SDS and 10% Propanol-1, with a pH of 3.50 maintained by o-Phosphoric Acid and analysis was done using Deoxyprobe C18 column (4.6 x 150 mm, 5 µm). The separation was carried out at room temperature with a flow rate of 1.0 ml/min, and detection was performed at 228 nm. Gliclazide has a retention time of 4.46 minutes, compared to 5.57 minutes for metformin. The percentage composition of the sample under analysis ranged from 90 to 110 percent in accordance with schedule V of the Drug and Cosmetic Act. The suggested practice met ICH requirements. The method was also low-cost, easy to use, secure, and ecologically benign. It can be used to conduct standard quantitative analyses on pills that include metformin and gliclazide.

Candidates resume analyzer using artificial intelligence

In the modern recruitment process, where the number of candidates and job vacancies constantly increases, automating resume analysis becomes critically important for efficient selection. This paper focuses on developing a system for automated resume analysis using artificial intelligence, specifically the OpenAI model (ChatGPT). The paper thoroughly examines the system's components, structure, and functional capabilities. The system consists of several key modules: the user interface ensures interaction with users, the Application Programming Interface Gateway facilitates data exchange between the interface and the server, and the authentication module controls system access, ensuring user security. The main part of the system's business logic is implemented on the server, which interacts with the natural language processing module, utilizing the ChatGPT model for processing resume text data. The use of natural language processing technologies allows the system to automatically extract and analyse key elements of resumes, such as education, work experience, skills, and certifications of candidates. This significantly speeds up the processing of large volumes of resumes, improving selection efficiency and accuracy. Data obtained during the analysis is stored in a PostgreSQL database, ensuring reliable storage and quick access for further processing. The paper also emphasizes the importance of integrating the system with other recruiting platforms and databases for more efficient management of the hiring process. Such integration allows for automatic synchronization of job vacancies and candidates, optimizing the search and selection process. Additionally, the paper highlights the key challenges associated with automating resume processing, such as the diversity of resume formats submitted by candidates, differences in data structure, and potential errors in automatically recognizing important information. Despite these challenges, the use of artificial intelligence and natural language processing methods significantly enhances the productivity and accuracy of recruitment processes. Thus, the developed automated resume analysis system based on Artificial Intelligence can become a significant tool in the modern candidate selection process. Its functionality not only enables quick and accurate resume analysis but also allows integration with other HR systems, simplifying the recruitment process, reducing data processing time, and improving selection accuracy.

Correlation of Neuroinflammation and Therapeutic Targets in Perioperative Neurocognitive Disorders.

Perioperative Neurocognitive (PND) disorders represent a prevalent complication among geriatric patients, manifested in diverse forms of cognitive impairment following anesthesia and surgical procedures. Even though the exact origin of PND disorders is still unknown, neuroinflammation has been identified as a significant contributing factor, particularly in older patients. Hence, this review aims to provide a deeper insight into the underlying mechanism and associated potent therapeutic targets for the efficient management of perioperative neurocognitive disorders. Many factors, such as PRRs, chemokine receptors, immunoglobulin superfamily receptors, and purinergic receptors, are involved in the development and occurrence of perioperative neurocognitive disorders to varying degrees and may be valuable biomarkers for their effective management. Here, we present a comprehensive overview of the involvement of neuroinflammation in PND disorders, including their onset and possible therapeutic targets. This review would benefit future researchers in elucidating a better therapeutic approach for the management of perioperative neurocognitive disorders. We have also briefly outlined the clinical trials associated with Postoperative neurocognitive disorders in the last section of the review. Altogether, this review would help the researchers investigate better therapeutics for the management of PND disorders.

The Application of Dewey Decimal System in Traditional Libraries

The Dewey Decimal System, created by Melvil Dewey in the late 19th century, has been a linchpin of traditional library classification systems for well over a century. This abstract provides a concise overview of the historical significance, principles, and enduring relevance of the Dewey Decimal System in traditional library settings. The Dewey Decimal System revolutionized the way libraries organized their collections, simplifying the process of locating and accessing books and materials. This paper traces the historical development of the Dewey Decimal System and its profound impact on traditional library cataloging and information retrieval. Key principles, such as the use of decimal notation and hierarchical classification, are explored in detail, highlighting their pivotal role in enhancing library management efficiency. Despite its enduring utility, this abstract also acknowledges the system's limitations. Traditional libraries have faced significant challenges in adapting the Dewey Decimal System to accommodate digital resources, multimedia materials, and non - traditional formats. Furthermore, questions have arisen regarding the system's cultural biases and its ability to adequately represent the diversity of knowledge and perspectives. In today's digital age, traditional libraries confront a unique set of challenges, necessitating a reevaluation of the Dewey Decimal System's role and adaptability. This abstract underscores the ongoing dialogue surrounding the system's relevance and its capacity to meet the evolving needs of modern libraries and information seekers. In conclusion, the Dewey Decimal System remains a cornerstone of traditional library organization, albeit with notable adaptational challenges. This abstract serves as a foundation for understanding the historical and contemporary significance of the Dewey Decimal System within the context of traditional libraries, setting the stage for a more comprehensive examination of its impact and future prospects in the full paper.

Simulation of indoor space thermal energy circulation in architectural environment design detail features based on visual object tracking algorithm

The heat circulation of interior space plays a crucial role in the design of the built environment, affecting energy efficiency and residential comfort. With the development of science and technology, visual target tracking algorithm provides a new possibility for optimizing thermal energy cycle. The purpose of this paper is to study the simulation method of indoor space thermal energy cycle based on visual target tracking algorithm to improve the efficiency of thermal energy management in building environment design and ensure indoor comfort. The research uses laboratory Settings, combined with high-performance cameras and advanced visual target tracking algorithms, to monitor indoor heat source distribution and personnel movement in real time. Through data acquisition and analysis, the indoor heat cycle model was constructed and simulated by computational fluid dynamics (CFD) software to evaluate the influence of different design parameters on the heat cycle. Through the optimization of the design scheme, the efficiency of indoor heat use is significantly improved, and the comfort score of residents is significantly improved. Therefore, the simulation of indoor space thermal energy cycle based on visual target tracking algorithm provides an effective tool for building environment design, which can realize efficient energy management and improve living comfort.

THE IMPACT OF PRICING SCHEMES ON CLOUD COMPUTING AND DISTRIBUTED SYSTEMS

This article investigates the economic implications of pricing models in cloud and distributed computing systems, emphasizing their influence on system performance and user cost efficiency. We analyze the efficacy of various pricing structures, including pay-as-you-go and resource-consumption-based models, and their impact on both operational efficiency and financial management. Our findings highlight significant challenges in optimizing cost-efficiency without compromising system effectiveness and call for the development of more sophisticated pricing strategies. By examining the limitations of current economic models and the evolution of dynamic and auction-based pricing mechanisms, the study offers insights into future research directions aimed at enhancing fairness and competitiveness in cloud computing environments.

THE IMPACT OF PRICING SCHEMES ON CLOUD COMPUTING AND DISTRIBUTED SYSTEMS

This article investigates the economic implications of pricing models in cloud and distributed computing systems, emphasizing their influence on system performance and user cost efficiency. We analyze the efficacy of various pricing structures, including pay-as-you-go and resource-consumption-based models, and their impact on both operational efficiency and financial management. Our findings highlight significant challenges in optimizing cost-efficiency without compromising system effectiveness and call for the development of more sophisticated pricing strategies. By examining the limitations of current economic models and the evolution of dynamic and auction-based pricing mechanisms, the study offers insights into future research directions aimed at enhancing fairness and competitiveness in cloud computing environments.

Sustainability Reporting and Financial Performance: Examine the Correlation between Sustainability Disclosures Financial Performance in Publicly Traded Companies

Sustainability reporting has increasingly gained prominence in academia and practice over the past few decades. With growing stakeholder expectations for companies to report on environmental, social, and governance issues, sustainability reporting allows companies to communicate non-financial impacts. While sustainability reporting aims to enhance accountability and transparency, questions remain regarding its influence on corporate financial performance. Some studies have found a positive correlation between sustainability reporting and financial metrics like profitability and share price, arguing reporting leads to more efficient resource management and risk mitigation. However, other research has found no significant relationship or negative impact, noting that reporting requirements incur costs that may outweigh benefits. This comprehensive review aims to provide an in-depth analysis of the existing literature on the correlation between sustainability disclosures and the financial performance of publicly traded companies. Relevant studies from various regions and sectors published between 1990 and 2023 will be reviewed. The methodologies applied, measurement variables, sample sizes, and time frames considered will be evaluated to understand the scope and limitations of each study. Key findings related to the direction and strength of the reported relationship between the two parameters will be summarized and compared. Moderating factors such as company size, industry, cultural context, and disclosure type will also be examined to determine their influence on research outcomes. The review will conclude by outlining existing knowledge gaps, inconsistencies in current empirical evidence, and areas that require further research.

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia. The pathway from conceptualisation to deployment for the HOTspots digital surveillance platform is described.