Hasil untuk "Office management"

Dewi Sartika, Muhammad Arief

Tax is a primary source of funding for development in Indonesia, collected in accordance with applicable regulations. For taxpayers, taxes represent a burden that diminishes income, leading them to optimize this burden through tax planning, particularly regarding Article 21 Income Tax. Taxpayers are classified into corporate and individual categories, employing three tax planning methods the Net method, the Gross method, and the Gross Up method. This study aims to identify the impact of the Article 21 withholding method on taxpayers obligations. The findings indicate that the gross method is the most efficient approach for managing the company’s tax burden. In contrast to the net and gross-up methods, using the gross method means the company assumes the employees’ tax burden, resulting in a lower net profit for the company.

Qingkai Zhang, L. Jeff Hong, Houmin Yan

The rapid expansion of cross-border e-commerce (CBEC) has created significant opportunities for small- and medium-sized sellers, yet financing remains a critical challenge due to their limited credit histories. Third-party logistics (3PL)-led supply chain finance (SCF) has emerged as a promising solution, leveraging in-transit inventory as collateral. We propose an advanced credit risk management framework tailored for 3PL-led SCF, addressing the dual challenges of credit risk assessment and loan size determination. Specifically, we leverage conditional generative modeling of sales distributions through Quantile-Regression-based Generative Metamodeling (QRGMM) as the foundation for risk measures estimation. We propose a unified framework that enables flexible estimation of multiple risk measures while introducing a functional risk measure formulation that systematically captures the relationship between these risk measures and varying loan levels, supported by theoretical guarantees. To capture complex covariate interactions in e-commerce sales data, we integrate QRGMM with Deep Factorization Machines (DeepFM). Extensive experiments on synthetic and real-world data validate the efficacy of our model for credit risk assessment and loan size determination. This study explores the use of generative models in CBEC SCF risk management, illustrating their potential to strengthen credit assessment and support financing for small- and medium-sized sellers.

Wenjian Wu, Jijun Zhang, Pei Zhou et al.

Against the backdrop of increasingly interconnected environmental, social, and governance (ESG) challenges, enterprises must formulate sustainable strategies to achieve synergistic development among economic performance, social responsibility, and ecological conservation. As a core organizational resource, human resources serves as a critical enabler for fulfilling corporate social responsibility (CSR) and driving sustainable development. Whether enterprises can enhance the contribution of human resources to the fulfillment of corporate social responsibility and sustainable development is an important issue that currently needs to be studied in the field of human resource management. Therefore, this research follows the grounded theory method, integrates CSR and sustainable development theories, and uses systematic thinking to deeply explore the concept and structure of sustainable human resource management systems, and it develops relevant scales and combines exploratory and confirmatory factor analysis methods to revise and validate the scales. The research results show that the sustainable human resource management system is a multidimensional concept, including the following: employee rights protection, employee training and development, employee occupational health, employee relations management, and sustainable development management; its measurement scale contains five factors, with a total of 20 items. The results of factor analysis indicate that the reliability and validity tests of the developed scale have reached an ideal level. The research results enrich the concept and connotation of sustainable human resource management systems, and the development of the sustainable human resource management systems scale aims to promote the extension of the field of sustainable human resource management systems from theoretical exploration to empirical analysis research, providing a theoretical basis for Chinese enterprises to achieve sustainable development goals.

Huiying (Cynthia) Hou, Michael (Chun Pong) Sing

The COVID-19 pandemic has significantly redefined the workplace dynamics, necessitating a pivotal shift from traditional on-site work to remote work and hybrid models, leading to further transformations in office design and operational strategies. Through a systematic review of the literature published from 2020 to 2025, this study explores the multifaceted impacts of workplace management transformations. Key major trends include the following: (1) the accelerated adoption of digital technologies; (2) a shift towards hybrid work models; and (3) the integration of health, safety, and sustainability practices in workplace design and operation. By drawing these trends together, this study reveals a permanent shift in how organizations approach workplace management, prioritizing flexibility, resilience, and technology integration to support a diverse and evolving workforce. This research contributes to growing body of literature on post-pandemic workplace strategies, contributing original insights that could shape future organizational policies and enhance workplace design and functionality.

Zulkarnain

This study aims to analyze the influence of reputation, culture, and knowledge on tourists' intention to visit Medan City tourism. Using a quantitative approach, this research involved 210 respondents selected through purposive sampling technique. Data were collected using a structured questionnaire with a 5-point Likert scale and analyzed using descriptive statistical methods, correlation analysis, multiple linear regression, and Structural Equation Modeling (SEM). Descriptive analysis results showed highly positive perceptions toward all research variables, with mean values above 4 out of 5. Correlation analysis revealed strong relationships between variables, with perfect correlation (1.00) between culture and knowledge variables. Regression and SEM results indicated that all independent variables had positive and significant effects on visit intention, with culture/knowledge factor having a stronger influence (β = 0.512, p < 0.001) compared to reputation (β = 0.275, p < 0.001). The research model demonstrated good fit indices (CFI = 0.956, RMSEA = 0.068) and explained 53.4% of the variation in visit intention. These findings imply the importance of tourism development strategies that emphasize cultural aspects and tourist education, while maintaining Medan City's reputation management as a tourist destination. This research contributes theoretically to understanding factors influencing tourists' visit intention and practically to the development of Medan City's tourism strategies.

Youping Tian, Qing Gu, Xiaojing Hu et al.

This study aimed to report the progress and results of the newborn screening program for congenital heart disease (CHD) in south Shanghai between 2019 and 2023, and to evaluate the accuracy of the dual-index method (pulse oximetry (POX) plus cardiac murmur auscultation) in clinical practice. Between 2019 and 2023, a total of 198,606 (99.89%) newborns were screened for CHD, of whom 3299 (1.66%) tested positive, 3043 (92.24%) underwent echocardiography for CHD diagnosis and 1109 were diagnosed with CHD in a timely manner. Among 195,307 infants with negative screening results using the dual-index method, 139 (0.07%) were later diagnosed with CHD, and none of these infants died. More than half of these false-negative infants (59.39%) were identified due to the detection of a heart murmur during routine physical examinations within six months after birth. Compared to POX testing alone, the dual-index method significantly improved the sensitivity of screening for CHD, and kept high specificity in clinical practice. This study demonstrated that newborn screening for CHD has been well conducted in Shanghai, and the dual-index method had high accuracy and reliability for neonatal CHD screening in clinical practice.

Jianling FAN, Tiejun WANG, Pengfei YANG et al.

Obesity, as a chronic recurrent disease, has become a major public health challenge in China. To implement the requirements of the Healthy China Initiative (2019—2030), under domestic guidelines or consensus statements on overweight and obesity, and in alignment with the latest scientific advances globally, the Quality control protocol for adult overweight and obesity screening in health management (examination) institutions (2025 edition) was developed. This protocol was drafted by the Health Management Center of Shanghai Changzheng Hospital and formulated through multiple rounds of deliberation by experts in China’s health examination quality control field. The protocol establishes unified standards for screening facilities, personnel qualifications, and measurement or testing procedures. It defines specific screening items, outlines a standardized screening pathway, and sets requirements for the final medical review, ensuring the scientific validity, effectiveness, and safety of the screening process. The implementation of this protocol will enhance the consistency of weight management practices for adults across health examination institutions and strengthen the quality control of overweight and obesity screening programs.

Bastien Baldacci, Philippe Bergault, Olivier Guéant

This paper introduces a novel methodology for the pricing and management of share buyback contracts, overcoming the limitations of traditional optimal control methods, which frequently encounter difficulties with high-dimensional state spaces and the intricacies of selecting appropriate risk penalty or risk aversion parameter. Our methodology applies optimized heuristic strategies to maximize the contract's value. The computation of this value utilizes classical methods typically used for pricing path-dependent options. Additionally, our approach naturally leads to the formulation of a $Δ$-hedging strategy and disentangles therefore the repurchase strategy from the hedging of the payoff.

Pablo Fondo-Ferreiro, Saber Mhiri, Cristina López-Bravo et al.

Global optimization of access point (AP) assignment to user terminals requires efficient monitoring of user behavior, fast decision algorithms, efficient control signaling, and fast AP reassignment mechanisms. In this scenario, software defined networking (SDN) technology may be suitable for network monitoring, signaling, and control. We recently proposed embedding virtual switches in user terminals for direct management by an SDN controller, further contributing to SDN-oriented access network optimization. However, since users may restrict terminal-side traffic monitoring for privacy reasons (a common assumption by previous authors), we infer user traffic classes at the APs. On the other hand, since handovers will be more frequent in dense small-cell networks (e.g., mmWave-based 5G deployments will require dense network topologies with inter-site distances of ~150-200 m), the delay to take assignment decisions should be minimal. To this end, we propose taking fast decisions based exclusively on extremely simple network-side application flow-type predictions based on past user behavior. Using real data we show that a centralized allocation algorithm based on those predictions achieves network utilization levels that approximate those of optimal allocations. We also test a distributed version of this algorithm. Finally, we quantify the elapsed time since a user traffic event takes place until its terminal is assigned an AP, when needed.

Christopher M. Graney

Did the papacy tacitly permit the circulation of an explicitly Copernican book in 1660? One scholar has recently argued that it did. A close analysis of a unique illustration from that book, Andreas Cellarius's atlas Harmonia Macrocosmica, illuminates this argument. This is because the illustration, a diagram showing the relative sizes of the sun, moon, planets, and stars, was among the material reviewed (at the request of the book's publisher) by the Holy Office prior to the book's publication and was pro-Copernican.

Arsham Farzinnia, Corine Boon

Investigating the optimal nature of social interactions among actors (e.g., people or firms), who seek to achieve certain mutually-agreed objectives, has been the subject of extensive academic research. Using the relational models theory (describing all social interactions as combinations of four basic sociality ingredients: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing), the common approach revolves around qualitative arguments for determining sociality configurations most effective in realizing specific purposes, at times supplemented by empirical data. In the current treatment, we formulate this question as a mathematical optimization problem, in order to quantitatively derive the most suitable combination of sociality forms for dyadic actors, which optimizes their mutually-agreed objective. For this purpose, we develop an analytical framework of the (meta)relational models theory, and demonstrate that combining the four sociality forms to define a specific meaningful social situation inevitably prompts an inherent tension among them, codified by a single elementary and universal metarelation. In analogy with financial portfolio management, we subsequently introduce the concept of Social Relations Portfolio (SRP) management, and propose a generalizable methodology capable of quantitatively identifying the efficient SRP, which, in turn, enables effective stakeholder and change management initiatives. As an important illustration, the methodology is applied to the Triple Bottom Line (Profit, People, Planet) paradigm to derive its efficient SRP. This serves as a guide to practitioners for precisely measuring, monitoring, reporting and steering stakeholder and change management efforts concerning Corporate Social Responsibility (CSR) and Environmental, Social and Governance (ESG) within and / or across organizations.

Ling Chen

This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model performance. Two specialized GANs models, the Wasserstein Generative Adversarial Network (WGAN) and the Tail Generative Adversarial Network (Tail-GAN), were evaluated under the FE-GAN framework. The results demonstrate that FE-GAN significantly outperforms traditional architectures in both VaR and ES estimation. Tail-GAN, leveraging its task-specific loss function, consistently outperforms WGAN in ES estimation, while both models exhibit similar performance in VaR estimation. Despite these promising results, the study acknowledges limitations, including reliance on highly correlated temporal data and restricted applicability to other domains. Future research directions include exploring alternative input generation methods, dynamic forecasting models, and advanced neural network architectures to further enhance GANs-based financial risk estimation.

Mitch Arkeen Salvador, Khavee Agustus Botangen, Mary Camille Rabang et al.

The Central Luzon Agriculture, Aquatic and Natural Resources Research and Development Consortium (CLAARRDEC), comprising 29 member institutions, faces challenges in effectively monitoring and evaluating their R&D activities. To address these challenges, they seek to harness digital technology for data management and real-time monitoring. This paper presents the development of a web-based database and real-time monitoring system aimed at enhancing data collection, storage, retrieval, and utilization within the consortium. The system consists of two key components: i) a data management module, designed to facilitate project data collection from member institutions, and ii) a real-time monitoring module for report generation and analytics at the CLAARRDEC main office. Successful deployment of the system not only fosters information sharing, collaboration, and informed decision-making but also empowers member institutions to monitor their own R&D engagements. Furthermore, the system's potential extends beyond CLAARRDEC, as it could be utilized by other research consortia in the Philippines.

Ko Hosoda, Mitsuharu Nishikawa, Yasutaka Yasui et al.

Compounds such as 2-methylisoborneol (2-MIB) and geosmin present in the final effluents of wastewater treatment plants may affect water treatment in downstream areas. To reduce the concentrations of 2-MIB and geosmin, we investigated the influence of the returned water from sludge treatment on their concentrations and behaviors during wastewater treatment in the reactor. The inflow of returned water from sludge treatment increased the geosmin concentration in the primary effluent (PE) making it higher than that in the influent. The dissolved 2-MIB concentration in the mixed liquor was higher than that in the PE. However, the geosmin concentration in the mixed liquor tended to be lower than that in the PE as the biological treatment progressed. Laboratory experiments indicated that the 2-MIB and geosmin concentrations varied in the reactor due to their release from activated sludge and removal by aeration and agitation. Our analyses revealed that the return activated sludge substantially impacts 2-MIB concentrations in secondary effluents. Moreover, the geosmin concentration in the reactor decreased, possibly due to removal by aeration and agitation.

Oluwatosin Wuraola Akande, Babak Afrough, Maria Amante et al.

The world has seen unprecedented gains in the global genomic surveillance capacities for pathogens with pandemic and epidemic potential within the last 4 years. To strengthen and sustain the gains made, WHO is working with countries and partners to implement the Global Genomic Surveillance Strategy for Pathogens with Pandemic and Epidemic Potential 2022–2032. A key technical product developed through these multi-agency collaborative efforts is a genomics costing tool (GCT), as sought by many countries. This tool was developed by five institutions – Association of Public Health Laboratories, FIND, The Global Fund to Fight AIDS, Tuberculosis and Malaria, UK Health Security Agency, and the World Health Organization. These institutions developed the GCT to support financial planning and budgeting for SARS-CoV-2 next-generation sequencing activities, including bioinformatic analysis. The tool costs infrastructure, consumables and reagents, human resources, facility and quality management. It is being used by countries to (1) obtain costs of routine sequencing and bioinformatics activities, (2) optimize available resources, and (3) build an investment case for the scale-up or establishment of sequencing and bioinformatics activities. The tool has been validated and is available in English and Russian at https://www.who.int/publications/i/item/9789240090866. This paper aims to highlight the rationale for developing the tool, describe the process of the collaborative effort in developing the tool, and describe the utility of the tool to countries.

Shabani M, Beye SA, Traore A et al.

Majaliwa Shabani,1 Seydina Alioune Beye,2,&ast; Abdoulaye Traore,3 Pablo Echave,4,&ast; Xavier Raingeval,5,&ast; Daouda Coulibaly,6 Sophie Crespo7,&ast; 1Health_unit, International Committee of the Red Cross, Bamako, Mali; 2Anesthesia Department, Clinique Périnatale Mohamed VI, Bamako, Mali; 3Anesthesia Department, Hôpital Somine Dolo de Mopti, Mopti, Mali; 4Anesthesia Department, Université de Sherbrooke, Sherbrooke, Quebec, Canada; 5Association de Développement et de Recherche en Anesthésie Locorégionale Echoguidée (ADRALE), Paris, France; 6Surgery Department, Centre de Santé de Référence de Kidal, Kidal, Mali; 7Health Unit, International Committee of the Red Cross, Geneva, Switzerland&ast;These authors contributed equally to this workCorrespondence: Majaliwa Shabani, Health Unit, International Committee of the Red Cross, HAMDALLAYE RUE 239, Postal Office Box: 58, Bamako, Mali, Email mshabani2001@gmail.comPurpose: The posterior femoral cutaneous nerve (PFCN) block is used in regional anesthesia for lower extremity surgery. This study introduces a new ultrasound-guided technique called the “Gluteus-Deep Investing Fascia compartment Block (GDIF block)“ for blocking the PFCN. This approach involves injecting local anesthetic into the potential space between the gluteus maximus muscle and the deep investing fascia, named the ‘Gluteus Deep Investing Fascia Compartment’. The study discusses the anatomical and sonographic features crucial for identifying this compartment and explores the potential benefits of this approach for achieving effective PFCN block. Additionally, it examines the clinical application of the GDIF block for PFCN block as part of the Complete Lower Extremity Fascia Tri-compartment Block technique, named ”CLEFT Block.” This technique combines the suprainguinal fascia iliaca block with GDIF compartment block for PFCN and a sciatic nerve block as exclusive anesthesia technique.Patients and Methods: Nine patients with weapon-related lower limb injuries underwent surgery at district hospitals supported by the International Committee of the Red Cross. Between October and December 2023, seventeen above-knee procedures were performed for the nine patients using the GDIF block as part of a CLEFT block technique. Anesthesia was performed with a CLEFT block technique using a volume ratio of 1:1 of 1% lidocaine and 0.5% levobupivacaine.Results: The GDIF block technique for PFCN blockade was performed successfully in all patients without complications, achieving complete PFCN blockade. The CLEFT block technique proved effective as the sole anesthetic technique for seventeen above-knee procedures. All surgeries were completed successfully without additional pain medication or conversion to general anesthesia.Conclusion: The GDIF block appears to be a promising technique for anesthetic management, alone or as part of the CLEFT block. Further research with a larger patient population is necessary to validate these findings.Keywords: posterior femoral cutaneous nerve block, deep investing fascia, sciatic nerve block, gluteal deep investing fascia compartment block, GDIF block, CLEFT block

Jaturong Som-ard, Savittri Ratanopad Suwanlee, Dusadee Pinasu et al.

Updated and accurate crop yield maps play a key role in the agricultural environment. Their application enables the support for sustainable agricultural practices and the formulation of effective strategies to mitigate the impacts of climate change. Farmers can apply the maps to gain an overview of the yield variability, improving farm management practices and optimizing inputs to increase productivity and sustainability such as fertilizers. Earth observation (EO) data make it possible to map crop yield estimations over large areas, although this will remain challenging for specific crops such as sugarcane. Yield data collection is an expensive and time-consuming practice that often limits the number of samples collected. In this study, the sugarcane yield estimation based on a small number of training datasets within smallholder crop systems in the Tha Khan Tho District, Thailand for the year 2022 was assessed. Specifically, multi-temporal satellite datasets from multiple sensors, including Sentinel-2 and Landsat 8/9, were involved. Moreover, in order to generate the sugarcane yield estimation maps, only 75 sampling plots were selected and surveyed to provide training and validation data for several powerful machine-learning algorithms, including multiple linear regression (MLR), stepwise multiple regression (SMR), partial least squares regression (PLS), random forest regression (RFR), and support vector regression (SVR). Among these algorithms, the RFR model demonstrated outstanding performance, yielding an excellent result compared to existing techniques, achieving an R-squared (R²) value of 0.79 and a root mean square error (RMSE) of 3.93 t/ha (per 10 m × 10 m pixel). Furthermore, the mapped yields across the region closely aligned with the official statistical data from the Office of the Cane and Sugar Board (with a range value of 36,000 ton). Finally, the sugarcane yield estimation model was applied to over 2100 sugarcane fields in order to provide an overview of the current state of the yield and total production in the area. In this work, the different yield rates at the field level were highlighted, providing a powerful workflow for mapping sugarcane yields across large regions, supporting sugarcane crop management and facilitating decision-making processes.

Bharathan Balaji, Jian Xu, A. Nwokafor et al.

B. Sherehiy, W. Karwowski

Paraskevi Nousi, Loukia Avramelou, Georgios Rodinos et al.

Financial portfolio management describes the task of distributing funds and conducting trading operations on a set of financial assets, such as stocks, index funds, foreign exchange or cryptocurrencies, aiming to maximize the profit while minimizing the loss incurred by said operations. Deep Learning (DL) methods have been consistently excelling at various tasks and automated financial trading is one of the most complex one of those. This paper aims to provide insight into various DL methods for financial trading, under both the supervised and reinforcement learning schemes. At the same time, taking into consideration sentiment information regarding the traded assets, we discuss and demonstrate their usefulness through corresponding research studies. Finally, we discuss commonly found problems in training such financial agents and equip the reader with the necessary knowledge to avoid these problems and apply the discussed methods in practice.