Hasil untuk "Office management"

Rezvi Shahariar, Chris Phillips

Vehicular Ad Hoc Networks (VANETs) support the information dissemination among vehicles, Roadside Units (RSUs), and a Trust Authority (TA). A trust model evaluates an entity or data or both to determine truthfulness. A security model confirms authentication, integrity, availability, non repudiation issues. With these aspects in mind, many models have been proposed in literature. Furthermore, many information dissemination approaches are proposed. However, the lack of a model that can manage traffic incidents completely inspires this work. This paper details how and when a message needs to be generated and relayed so that the incidents can be reported and managed in a timely manner. This paper addresses this challenge by providing a traffic incident management model to manage several traffic incidents efficiently. Additionally, we simulate this model using the VEINS simulator with vehicles, RSUs, and a TA. From the experiments, we measure the average number of transmissions required for reporting a single traffic incident while varying the vehicle density and relaying considerations. We consider two types of relaying. In one series of experiments, messages from regular vehicles and RSUs are relayed up to four hops. In another series of experiments, messages from the regular vehicles and RSUs are relayed until their generation time reaches sixty seconds. Additionally, messages from the official vehicles are relayed when they approach an incident or when the incident is cleared. Results from the simulations show that more vehicles are informed with four-hop relaying than sixty-second relaying in both cases.

Meirani Harsasi , M Muzammil , Olivia Idrus

As a large Open University system, Universitas Terbuka (UT) has consistently implemented an effective performance management and reward system to ensure that performance is objectively measured, improvement can be effectively made, and people are fairly rewarded based on performance. This paper addresses the influence of emotional intelligence on management performance in an open and distance learning (ODL) institution. Emotional intelligence is defined as managing feelings so that they are expressed appropriately and effectively, enabling people to work together smoothly toward their common goals. This study aims to investigate the influence of emotional intelligence of top management staff in the 37 UT Regional Offices, in Indonesia. Besides UT’s headquarters in Jakarta, as an ODL institution, UT also has 37 Regional Offices all over Indonesia that are responsible for students’ academic and non-academic services. The participants were therefore all the 37 Heads of Regional Offices of UT. The data was obtained in two ways, namely primary data about emotional intelligence obtained through the administration of a questionnaire and secondary data about performance obtained through the office of the Vice Rector III. The results of the study showed that four elements of emotional intelligence (self-awareness, self-regulation, motivation, and social skills) strongly influenced management performance. One element of emotional intelligence, namely empathy, did not significantly affect management performance. Overall, the results suggested that it is important for an ODL institution to have leaders and employees with high emotional intelligence to achieve the goal effectively.

Nahshon Mokua Obiri, Kristof Van Laerhoven

This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen, City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO$_2$) concentration, barometric pressure, and particulate matter levels (PM$_{2.5}$). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R$^2$) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication.

Jessica E. Christian, Richard M. Buzard, Katie L. Spellman et al.

Arctic amplification of climate change has resulted in increased coastal hazards impacts to remote rural coastal communities in Alaska where conducting research can be difficult, requiring alternate methods for measuring change. The pilot program, Stakes for Stakeholders, was initially planned to be funded from 2016–2018. Upon project completion the work has shifted to individual community’s partnering with several agencies to continue the work. This research showcases a successful long-term community-based erosion monitoring program in two rural communities in Southwest Alaska. The resulting outputs from the workflow we developed were (1) locally prioritized data products, such as a hazard assessment report for Chignik Bay and (2) evaluation rubrics used to assess the suitability of future sites and the efficacy of the program. Our model of two-way communication, responsiveness to individual community needs, and attention to efficiency and effectiveness of the program workflow, can serve as a model for universities, for-profit, non-profit, Tribal, city, state, and federal research agencies and communities partnering to respond to global climate change.

YAO Weimin, ZHENG Yungu, LIU Bo et al.

The control of Spartina alterniflora has become a significant issue for coastal and island nations worldwide. The choice of chemical control and herbicides has a direct impact on the health of wetland ecosystems, and thus possesses significant scientific research value. To better understand the research progress in chemical control and herbicides for Spartina alterniflora, this study utilized bibliometric methods and VOSviewer software to visualize literature data retrieved from the Web of Science. Preliminary screening using “Spartina alterniflora herbicides” as keywords primarily resulted in three themes: invasive plant management, wetland weeding and preservation, and the working principles of those herbicides. The overlay visualization of co-occurrence analysis showed that research foci had gradually shifted towards the exploration and application of environmentally friendly novel herbicides. Based on this foundation, this study conducted further literature retrieves on chemical control of Spartina alterniflora and its novel herbicides, with the following results: during the period from 2000 to 2024, the number of publications and citation frequency in this research field showed a similar upward trend, characterized by “an initial slow increase followed by a rapid rise”. The disciplines involved in this research field were primarily plant science, agriculture, and environmental science. Correspondingly, the main journals publishing in this area also focused more on weed management technology and invasive plant management. The main contributing countries were concentrated in coastal and island nations, with the United States, Brazil, and China demonstrating the strongest research capacity in this research field. The main hotspots included the mechanism of action and the resistance of herbicides, the analysis and degradation of herbicides, and management and ecological restoration of herbicides. Synthesizing the above analysis, this study provided a prospective outlook on the research of chemical control of Spartina alterniflora and its novel herbicides. Future research may further explore the mechanisms of environmental biological adaptive responses, the scientific management and sustainable development, as well as interdisciplinary research and technological innovation.

Xuan Zeng, Jibo Wang, Ning Liu et al.

ImportanceColorectal cancer (CRC) constitutes a significant segment of the global cancer burden, thereby warranting an in-depth epidemiological appraisal to inform strategic public health interventions and resource allocation. Previous studies, such as those based on the GBD 2019 dataset, have provided valuable insights into the CRC burden. However, they have limitations in terms of data recency, regional granularity, and comprehensive risk factor analysis.ObjectiveThis research seeks to undertake a thorough analysis of the burden of CRC at global, regional, and national levels, along with its associated risk factors, spanning the period from 1990 to 2021. This analysis will employ data sourced from the Global Burden of Disease (GBD) 2021 study, addressing limitations in previous research by providing a more detailed and updated assessment.MethodsWe assessed the distribution of CRC across 204 countries and territories, focusing on age, gender, and geographic variations. The impact of key risk factors (including behavioral risks, metabolic risks, behavioral risks, metabolic risks) on disability-adjusted life years (DALYs) was evaluated across 21 GBD regions. A Bayesian age-period-cohort (BAPC) model was employed to project CRC trends over the next three decades.FindingsIn 2021, global CRC incidence was approximately 2,194,143 cases, with a prevalence of 11,679,120 and 24,401,100 DALYs. Central Europe exhibited the highest burden, with incidence peaking among individuals aged 84 to 94 years. From 1990 to 2021, age-standardized incidence, mortality, and DALY rates for CRC showed upward trends, particularly among males. The analysis of risk factors across 21 GBD regions reveals significant regional disparities in the colorectal cancer (CRC) burden, with Central Europe showing the highest contribution from risk factors (305.66). Behavioral risks, such as smoking and high alcohol use, have the greatest impact, followed by dietary risks (particularly low whole grain intake and high processed meat consumption) and metabolic risks (including high BMI and high fasting plasma glucose). By 2051, the global ASIR, ASMR, and ASDR of CRC are projected to reach 18.21 (95% UI: 10.83–25.59), 7.10 (95% UI: 4.36–9.84), and 165.21 (95% UI: 102.48–227.93) per 100,000 population, respectively, with the burden remaining higher in males than in females.ConclusionThis study provides the most granular assessment of CRC burden to date, highlighting dietary policies and sex-specific interventions as priorities. Methodological advancements in projection modeling offer actionable insights for long-term public health planning.

SI Tailai, ZHANG Shengmao, DAI Yang et al.

The LED fish aggregation light (Light Emitting Diode fish lamp) serves as a crucial device in modern light-attracting fisheries, playing a pivotal role in advancing the marine fishing industry. By analyzing the configurations of various light-attracting fishing vessel fish aggregation lights, the number and types of such lights on offshore and distant-water fishing vessels, as well as catch types and operational styles, this study summarizes the current state of China's marine fishing industry. It further compares the historical development of fish aggregation lights and the characteristics of LED surface fish aggregation lights, LED underwater fish aggregation lights, and high-pressure sodium lamps. A detailed analysis is provided on the specific LED fish aggregation light configurations aboard stern trawlers. Finally, the development trends of LED fish aggregation lights are examined. Although research on LED fish aggregation light equipment in China began relatively late, challenges persist in offshore fishing and specialized species harvesting. However, against the backdrop of achieving "carbon peak and carbon neutrality", supported by national policies and driven by production development needs, LED fish aggregation lights for light-attracting fisheries exhibit significant growth potential and market prospects. In the future, they are expected to advance in high efficiency, precise catches, stable illumination, product upgrades, and intelligent integration.

Aref Ayati, Mohammad Mahdi Hashemi, Mohsen Saffar et al.

Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field.

Bao-Thien Nguyen-Tat, Minh-Quoc Bui, Vuong M. Ngo

Haar Cascade is a cost-effective and user-friendly machine learning-based algorithm for detecting objects in images and videos. Unlike Deep Learning algorithms, which typically require significant resources and expensive computing costs, it uses simple image processing techniques like edge detection and Haar features that are easy to comprehend and implement. By combining Haar Cascade with OpenCV2 on an embedded computer like the NVIDIA Jetson Nano, this system can accurately detect and match faces in a database for attendance tracking. This system aims to achieve several specific objectives that set it apart from existing solutions. It leverages Haar Cascade, enriched with carefully selected Haar features, such as Haar-like wavelets, and employs advanced edge detection techniques. These techniques enable precise face detection and matching in both images and videos, contributing to high accuracy and robust performance. By doing so, it minimizes manual intervention and reduces errors, thereby strengthening accountability. Additionally, the integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing efficiency, making it suitable for resource-constrained environments. This system caters to a diverse range of educational institutions, including schools, colleges, vocational training centers, and various workplace settings such as small businesses, offices, and factories. ... The system's affordability and efficiency democratize attendance management technology, making it accessible to a broader audience. Consequently, it has the potential to transform attendance tracking and management practices, ultimately leading to heightened productivity and accountability. In conclusion, this system represents a groundbreaking approach to attendance tracking and management...

Yohny Anwar, Indra Muis

The relationship among knowledge management, organizational ambidexterity, innovation, and marketing performance is crucial in driving organizational success and competitive advantage in today's rapidly changing business environment. The aim of the study is to look at relationship knowledge management, organizational ambidexterity, innovation, and marketing performance. Besides, it examines the effects of knowledge management and organizational ambidexterity on marketing performance as intervened by innovation as a mediator. The unit analysis is the owners of SMEs in Medan Municipality, North Sumatra Province, Indonesia. This research applies quantitative methods. The research population was 478 SMEs in technology and internet businesses registered in the Government Office of Cooperatives and Micro, Small, and Medium Enterprises (MSME) of Medan, North Sumatra, Indonesia. The respondents were 217 owners of SMEs. The sampling technique is simple random sampling. The data analysis uses the Partial Least Square technique. The research finds that both knowledge management and organizational ambidexterity have positive impacts on innovation. Innovation mediates the knowledge management-marketing performance relationship and organizational ambidexterity-marketing performance relationship. It is advisable for Small Medium Business owners to implement knowledge management, organizational ambidexterity, and innovation to increase their marketing performance.

Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic et al.

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data -- so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning and job shop scheduling, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision-making under distributional shifts.

Chi-Lin Li, Chung-Han Hsieh

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.

Tjeerd de Vries, Alexis Akira Toda

How should financial institutions hedge their balance sheets against interest rate risk when managing long-term assets and liabilities? We address this question by proposing a bond portfolio solution based on ambiguity-averse preferences, which generalizes classical immunization and accommodates arbitrary liability structures, portfolio constraints, and interest rate perturbations. In a further extension, we show that the optimal portfolio can be computed as a simple generalized least squares problem, making the solution both transparent and computationally efficient. The resulting portfolio also reduces leverage by implicitly regularizing the portfolio weights, which enhances out-of-sample performance. Numerical evaluations using both empirical and simulated yield curves support the feasibility and accuracy of our approach relative to existing methods.

Adi Hirshberg, K. Downes, S. Srinivas

shuvrangshu Jana, Rudrashis Majumder, Aashay Bhise et al.

This paper discusses a Decision Support System (DSS) for cases prediction, allocation of resources, and lockdown management for managing COVID-19 at different levels of a government authority. Algorithms incorporated in the DSS are based on a data-driven modeling approach and independent of physical parameters of the region, and hence the proposed DSS is applicable to any area. Based on predicted active cases, the demand of lower-level units and total availability, allocation, and lockdown decision is made. A MATLAB-based GUI is developed based on the proposed DSS and could be implemented by the local authority.

Helena Dudycz, Marcin Hernes, Zdzislaw Kes et al.

The utilization of management control systems in university management poses a considerable challenge because university's strategic goals are not identical to those applied in profit-oriented management. A university's management control system should take into account the processing of management information for management purposes, allowing for the relationships between different groups of stakeholders. The specificity of the university operation assumes conducting long-term scientific research and educational programmes. Therefore, the controlling approach to university management should considerat long-term performance measurement as well as management in key areas such as research, provision of education to students, and interaction with the tertiary institution's socioeconomic environment.This paper aims to develop a conceptual framework of the Intelligent Management Control System for Higher Education (IMCSHE) based on cognitive agents. The main findings are related to developing the assumption, model, and technological basis including the artificial intelligence method.

Mesele Damte Argaw, Binyam Fekadu Desta, Zergu Taffesse Tsegaye et al.

Abstract Background The aim of this study was to investigate the quality of immunization data and monitoring systems in the Dara Malo District (Woreda) of the Gamo Administrative Zone, within the Southern Nations, Nationalities, and Peoples’ Region (SNNPR) of Ethiopia. Methods A cross-sectional study was conducted from August 4 to September 27, 2019, in Dara Malo District. The district was purposively selected during the management of a pertussis outbreak, based on a hypothesis of ‘there is no difference in reported and recounted immunization status of children 7 to 23 months in Dara Malo District of Ethiopia’. The study used the World Health Organization (WHO) recommended Data Quality Self-Assessment (DQS) tools. The accuracy ratio was determined using data from routine Expanded Program of Immunization (EPI) and household surveys. Facility data spanning the course of 336 months were abstracted from EPI registers, tally sheets, and monthly routine reports. In addition, household surveys collected data from caretakers, immunization cards, or oral reports. Trained DQS assessors collected the data to explore the quality of monitoring systems at health posts, health centers, and at the district health office level. A quality index (QI) and proportions of completeness, timeliness, and accuracy ratio of the first and third doses of pentavalent vaccines and the first dose of measles-containing vaccines (MCV) were formulated. Results In this study, facility data spanning 336 months were extracted. In addition, 595 children aged 7 to 23 months, with a response rate of 94.3% were assessed and compared for immunization status, using register and immunization cards or caretakers’ oral reports through the household survey. At the district level, the proportion of the re-counted vaccination data on EPI registers for first dose pentavalent was 95.20%, three doses of pentavalent were 104.2% and the first dose of measles was 98.6%. However, the ratio of vaccination data compared using tallies against the reports showed evidence of overreporting with 50.8%, 45.1%, and 46.5% for first pentavalent, third pentavalent, and the first dose of measles vaccinations, respectively. The completeness of the third dose of pentavalent vaccinations was 95.3%, 95.6%, and 100.0% at health posts, health centers, and the district health office, respectively. The timeliness of the immunization reports was 56.5% and 64.6% at health posts and health centers, respectively, while the district health office does not have timely submitted on time to the next higher level for twelve months. The QI scores ranged between 61.0% and 80.5% for all five categories, namely, 73.0% for recording, 71.4% for archiving and reporting, 70.4% for demographic information, 69.7% for core outputs, and 70.4% for data use and were assessed as suboptimal at all levels. The district health office had an emergency preparedness plan. However, pertussis was not on the list of anticipated outbreaks. Conclusion Immunization data completeness was found to be optimal. However, in the study area, the accuracy, consistency, timeliness, and quality of the monitoring system were found to be suboptimal. Therefore, poor data quality has led to incorrect decision making during the reported pertussis outbreak management. Availing essential supplies, including tally sheets, monitoring charts, and stock management tools, should be prioritized in Daro Malo District. Enhancing the capacity of healthcare providers on planning, recording, archiving, and reporting, analyzing, and using immunization data for evidence-based decision making is recommended. Improving the availability of recording and reporting tools is also likely to enhance the data accuracy and completeness of the community health information system. Adapting pertussis outbreak management guidelines and conducting regular data quality assessments with knowledge sharing events to all stakeholders is recommended.

Udit Agarwal, Vinay Rishiwal, Sudeep Tanwar et al.

Supply chain management (SCM) is a core corporate activity responsible for moving commodities and services from one point to another through a variety of stakeholders. The traditional SCM is based on a centralized approach managed at the central headquarter, and all other sub-offices get instructions from the main office. Some major issues with present SCM systems are security, transactional transparency, traceability, stakeholder involvement, product counterfeiting, additional delays, fraud, and instabilities. Blockchain (BC) emerges as a technology that can manage the data and build trust efficiently and transparently. It can also aid in transaction authorization and verification in the supply chain or payments without a third party. To address the present SCM issues, BC technology is a feasible solution. Motivated by the aforementioned considerations, in this paper, we present a survey on the adoption of BC in SCM. This paper undertakes a comprehensive analysis of the literature on BC characteristics, implementations, and business consequences in various SCM. This Blockchain-centered study, in particular, discloses the research state and delineates future research directions by studying and analyzing 97 up-to-date publications highlighting BC’s supply chain uses. Transparency and traceability, information sharing, product anti-counterfeiting, and building trust are the major aspects propelling BC’s implementation in SCM. Further, we analyzed various applications of SCM in which BC can be used as a probable technology to secure all transactions. Then, we have highlighted open issues and research challenges for adopting BC technology in SCM that open the doors for beginners eager to start work in this amazing area.

Sung Hwan Hwang

Recent trends in benign anal disease treatment are minimizing surgery to preserve normal anorectal anatomical unit and its functions. However, some surgeons still prefer and are confident with the use of conventional solid surgical methods. In this report, we will investigate the recent trends in the treatment for hemorrhoids, fistula, and anal fissure. The practice guidelines of advanced countries, including UK, Italy, France, USA, Japan, and ESCP, are referred to in this review. Opinions suggested in international meetings were also added. In the management of hemorrhoids, surgical treatments and office procedures were recommended according to a patient's status and preference. For the management of complex anal fistula, novel sphincter-preserving surgical techniques are more widely accepted than a sphincter-dividing procedure of immediate repair following fistulectomy. The treatment of anal fissures is well covered in the guidelines of the ASCRS.

Thierry Roncalli, Amina Cherief, Fatma Karray-Meziou et al.

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. The review of the academic literature and professional research studies shows that there is a lack of standardized and analytical models. The aim of this research project is then to fill the gap with the goal of developing mathematical and statistical approaches, and providing appropriate answers. In this second article focused on asset liquidity risk modeling, we propose a market impact model to estimate transaction costs. After presenting a toy model that helps to understand the main concepts of asset liquidity, we consider a two-regime model, which is based on the power-law property of price impact. Then, we define several asset liquidity measures such as liquidity cost, liquidation ratio and shortfall or time to liquidation in order to assess the different dimensions of asset liquidity. Finally, we apply this asset liquidity framework to stocks and bonds and discuss the issues of calibrating the transaction cost model.