<i>Background</i>: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. <i>Methods</i>: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means clustering and optimizes delivery routes through a multi-objective vehicle routing problem with time windows (MOVRPTW). This framework was applied to a case study in Phra Nakhon Si Ayutthaya, Thailand, utilizing hospital geocoordinates, demand profiles, and CO<sub>2</sub> emission factors to evaluate centralized versus decentralized network configurations. <i>Results</i>: Findings demonstrate that hub structures derived from K-means clustering achieve the highest economic efficiency, reducing the AM part cost per unit to 698.51 Baht. In contrast, a fully centralized network resulted in a significantly higher unit cost of 4759.79 Baht, while clustering based on hospital types yielded a unit cost of 959.34 Baht. Quantitative results indicate that the multi-objective approach provides a superior trade-off, achieving lead time requirements while maintaining operational costs and emissions. <i>Conclusions</i>: The results indicate that the proposed framework, particularly through spatial clustering, offers a practical decision-support tool for designing AM networks that achieve a balance between operational efficiency and sustainability objectives in healthcare logistics.
Transportation and communication, Management. Industrial management
Understanding the complex and evolving interactions between ecosystem services and socio-economic development is crucial for addressing ecological challenges and achieving harmony between humans and nature. However, multi-objective nexus optimization models that reveal the interdependencies of socio-ecological systems have received little attention. This study therefore aims to propose a species–carbon–water–food–economy functional nexus and explore trade-offs and synergies across China’s provinces from 2000 to 2020, using the InVEST model, correlation analysis, and redundancy analysis. The results revealed that the species protection function exhibited an “increase–decrease” trend, while the carbon sink, water conservation, food supply, and economic development functions increased. Both ecosystem functions and economic development functions exhibited spatial differences. Although synergies dominated the functional nexus, a trade-off was observed between species protection and food supply, with functional interactions showing spatial heterogeneity at the provincial scale. Complex interactions between social systems and ecosystems were observed in 2000, 2010, and 2020, with explanatory powers of 52.5%, 59.7%, and 59.1%, respectively. Functional policy goals exhibited both trade-offs and synergies. To achieve the sustainable development of the socio-ecological system, a “multi-goals driven, multi-sectoral linkage, and multi-policies synergy” framework should be implemented to balance the species–carbon–water–food–economy functional nexus.
Data security remains a major concern in the Internet of Things (IoT) landscape due to the inherent limitations in computational power, memory capacity, and energy availability of IoT devices. To address these challenges, lightweight encryption algorithms have emerged as alternatives to conventional cryptographic methods, aiming to balance performance and security. This study evaluates the effectiveness of five encryption algorithms—SIMON64/128, SPECK64/128, XTEA64/128, PRESENT64/128, and AES128—on IoT devices through experimental analysis of their security strength, execution time, CPU utilization, memory usage, and power efficiency. The experiments were conducted on a Raspberry Pi 3B+ using C-based implementations to emulate realistic IoT scenarios. The findings reveal that AES128 offers the strongest security characteristics, including the highest Avalanche Effect (39.29%) and Differential Resistance Score (6.76/10), but at the expense of significant resource consumption. In contrast, SIMON64/128 and SPECK64/128 deliver superior performance in terms of speed and resource efficiency, making them ideal for low-power environments, albeit with concerns about potential cryptographic backdoors. XTEA64/128 emerges as a practical compromise, delivering moderate security and low power consumption without known vulnerabilities. Based on these results, AES128 is suitable for high-capacity IoT platforms prioritizing strong encryption, while SIMON and SPECK are preferable for resource-constrained devices, with XTEA serving as a balanced alternative. This research contributes a comparative framework to guide the selection of encryption algorithms for IoT systems, ensuring an optimal trade-off between security and operational efficiency.
Mohamed I. Marie, Mohamed S. Elredeny, Ahmad Essayed Yakoub
Melanoma accounts for only 1% of skin cancer diagnoses yet causes the majority of skin cancer-related deaths due to its rapid progression and high metastatic potential. Early and accurate detection is crucial for improving patient outcomes; however, existing deep learning models often struggle to balance diagnostic precision with real-time efficiency. This study presents the Melano Hybrid Model, a novel architecture that integrates the rapid detection capabilities of YOLOv9 with the boundary localization accuracy of Faster R-CNN through an adaptive feature fusion mechanism. The model was rigorously evaluated on three benchmark datasets—ISIC 2019, HAM10000, and ISIC 2020—using 5-fold cross-validation. On ISIC 2020, the hybrid model achieved a 96.2% classification accuracy (95% CI: 95.8–96.6%) and a 95.1% F1-score (95% CI: 94.7–95.5%), significantly outperforming standalone models (<inline-formula> <tex-math notation="LaTeX">$p\lt 0.001$ </tex-math></inline-formula>). The architecture delivers an average inference speed of 31.3 frames per second (FPS), surpassing clinical real-time thresholds. Additionally, computational profiling confirms its practical feasibility with 78.3 million parameters, 134.8 GFLOPs, and a 324 MB memory footprint. These results support the hybrid framework as a robust AI-assisted tool for real-world melanoma screening, offering an optimal trade-off between speed and diagnostic performance.
Fayaz Hussain Tunio, Agha Amad Nabi, Rafique Ur Rehman Memon
et al.
Environmental sustainability remains a critical challenge in the face of global economic development. This study explored the complex interactions among renewable energy consumption, urbanization, trade openness, and economic development, focusing on their effects on environmental quality in 34 high-income European and Asian economies from 1970 to 2022. Using linear Bayesian regression and the Vector Error Correction Model (VECM), the analysis examined short- and long-term impacts to uncover nuanced relationships. Results demonstrated that economic development contributed to environmental degradation over the long term while mitigating it in the short term. Renewable energy consumption supported economic growth but showed limited efficacy in reducing ecological footprints across different time frames. Urbanization and trade openness emerged as significant drivers of long-term environmental degradation, emphasizing the need for targeted policy interventions. This study examined the link among economic progress and environmental sustainability, and identified key areas for improvement in urban planning, renewable energy, and trade policies. The findings provide a framework for policymakers to balance development with environmental preservation.
The current study conducts a comparative analysis of Portfolio-Balance Models (PBM) developed by Branson, Kouri and Dornbusch to assess the role of expectations and time horizons in determining and forecasting the India–US exchange rate over the period 1996:Q2–2019:Q3. Notably, it improves the original models by integrating microstructure theory into their framework. The Autoregressive Distributed Lag Error-Correction Model (ARDL-ECM) is used to investigate both short run and long run behaviour of the models. Additionally, the study assesses out-of-sample forecasting accuracy of the modified models against the Random Walk Model (RWM) using the root mean square error metric. The estimation results reveal that models based on rational expectations are better than the static expectations model. Notably, the microeconomic determinant is counterintuitively significant only in the long run across all models. Furthermore, these modified models demonstrate superior out-of-sample forecasting abilities compared to RWM for alternative forecasting horizons. However, forecasting results over a 6-month period is better with short run models. Over 1-year and 2-year horizons, rational expectations models outperform the static expectations model. This study challenges the Meese–Rogoff puzzle, ensuring that PBM, when modified to incorporate microstructure theory, is valid and yields superior forecasting results compared to RWM. JEL Codes: F31, F32, C22
The aim of this research is to analyse the competitiveness of the agricultural sector in Romania and Serbia
according to the external trade activity of each country. For this purpose, will be used data provided by international
databases, namely the International Trade Centre, which will refer to the value of imports and exports of agricultural
products and to the total level, these data being processed quantitatively and analysed from the perspective of the trade
balance. Then, in order to determine the competitiveness of the sector in each country, certain indicators will be
calculated which can measure competitiveness, both at collective level (of the agricultural sector) and at individual
level, according to the main groups of agri-food products.
Commercial geography. Economic geography, Economics as a science
Labor force is a key driver of economic growth and productivity. The more labor force, the more production, trade and services can be created. In addition, the quality and efficiency of the labor force directly affects the level of productivity and competitiveness of the region and countries. Although the workforce is a key resource, managing it can be challenging. One of the main challenges in workforce management is retaining highly qualified employees, as the labor market is constantly changing and opening up new opportunities. This article is devoted to the study of the labor force in the Arkhangelsk Oblast, which is of great importance for understanding the current situation of the region and determining directions for development. The object of the study is the labor force in the specified territory, and the subject is its essence, state, structure and movement. In the course of the study, the following goals were set: determining factors affecting the quality of the labor force, analyzing the dynamics and structure of the labor force, studying the level of employment and unemployment, identifying the impact of the labor force on the economic indicators of the region. The methodological basis of the study is general scientific statistical methods of data analysis: absolute and relative statistical indicators, series of dynamics, correlation and regression analysis. The main conclusion of the article is that the labor force of the Arkhangelsk Oblast is steadily declining, but the balance between the share of men and women in it is preserved. However, the problem of unemployment during the period under review becomes more relevant for women than for men. These changes reflect the need for key measures that will stimulate not only economic development, but also the creation of favorable working conditions.
Public administration frequently deals with geographically scattered personal data between multiple government locations and organizations. As digital technologies advance, public administration is increasingly relying on collaborative intelligence while protecting individual privacy. In this context, federated learning has become known as a potential technique to train machine learning models on private and distributed data while maintaining data privacy. This work looks at the trade-off between privacy assurances and vulnerability to membership inference attacks in differential private federated learning in the context of public administration applications. Real-world data from collaborating organizations, concretely, the payroll data from the Ministry of Education and the public opinion survey data from Asia Foundation in Afghanistan, were used to evaluate the effectiveness of noise injection, a typical defense strategy against membership inference attacks, at different noise levels. The investigation focused on the impact of noise on model performance and selected privacy metrics applicable to public administration data. The findings highlight the importance of a balanced compromise between data privacy and model utility because excessive noise can reduce the accuracy of the model. They also highlight the need for careful consideration of noise levels in differential private federated learning for public administration tasks to provide a well-calibrated balance between data privacy and model utility, contributing toward transparent government practices.
Russia and Portugal established diplomatic relations relatively late. Some researchers indicate geography as the main cause; however, geography alone could hardly explain why, for instance, the Russian Empire established official relations with Spain approximately sixty years before it did with the Portuguese Royal Court. As it seems, the international situation and factors related to both states’ domestic development were at play. Back in the late 15th and early 16th centuries, Portugal considered regions far beyond Europe, such as South America, Africa, India and the Far East to be its sphere of interests. As a result, the country’s limited resources, combined with the need to ensure the preservation of the vast colonial empire, predetermined its dependence on Great Britain for defense. In the 18th century, the costs of such dependence outweighed the potential benefits. Meanwhile, Russia in the 18th century was a powerful and dynamic state which sought to change the traditional balance of power in Europe and develop maritime trade. The idea of establishing relations with Portugal was already considered under Peter I, yet it was not until late 18th century that the Russian Empire succeeded under Catherine II. The geographical factor did have a significant impact on delaying mutual recognition; still it was the two countries’ converging interests and the favorable international situation that proved to be crucial.
Abdullah Altamimi, Muhammad Bilal Ali, Syed Ali Abbas Kazmi
et al.
Rapid growth in a number of developing nations’ mobile telecommunications sectors presents network operators with difficulties such as poor service quality and congestion, mostly because these locations lack a dependable and reasonably priced electrical source. In order to provide a sustainable and reasonably priced energy alternative for the developing world, this study provides a detailed examination of the core ideas behind renewable energy technology (RET). A multi-agent-based small-scaled smart base transceiver station (BTS) site reinforcement strategy is presented to manage energy resources by boosting resilience so to supply power to essential loads in peak demand periods by leveraging demand-side management (DSM). Diverse energy sources are combined to create interconnected BTS sites, which enable energy sharing to balance fluctuations by establishing a market that promotes economical energy. A MATLAB simulation model was developed to assess the effectiveness of the proposed system by using real load data and fast electric vehicle charging loads from five different base transceiver stations (BTSs) located throughout Pakistan’s southern area. In this proposed study, the base transceiver station (BTS) sites can share their energy through a multi-agent-based system. From the results, it is observed that, after optimization, the base transceiver station (BTS) sites trade their energy with the grid at rate of 0.08 USD/kWh and with other sites at a rate of 0.04 USD/kWh. Therefore, grid dependency is decreased by 44.3% and carbon emissions are reduced by 71.4% after the optimization of the base transceiver station (BTS) sites.
Abstract By collecting and sorting the energy demand data of developing and developed countries, this paper makes a comprehensive analysis of their energy demand, including the change of energy demand and the change trend of energy load in various sectors. The survey scope of the article includes the overall change trend of energy supply, natural gas, oil, electricity, coal, renewable energy (such as wind energy, solar energy, geothermal energy, tidal energy, etc.), and the data change of global carbon dioxide emission. Besides, this paper selects a variety of energy sources for comprehensive analysis to analyze the existing change trend in chronological order. The analysis methods include data statistics of primary energy production and consumption, energy intensity analysis of gross domestic product (GDP), production, and demand balance of oil, natural gas, and coal, and study the trade balance between different types of energy in different countries and regions. The regions examined in this review include the organization for economic cooperation and development (OECD); the group of seven (G7); Brazil, Russia, India, China and South Africa (BRICs); the European Union; Europe; North America; the Commonwealth of Independent States (CIS); Asia; Latin America; the Pacific Ocean; the Middle East and Africa. By studying these data, we can make a better summary of the current energy use, so as to conveniently grasp the context of energy development and have a general understanding of the current energy structure. Therefore, individuals and policymakers in the fields of energy trade can think more deeply about the future situation and draw conclusions.
Bichaye Tesfaye, Monica Lengoiboni, Jaap Zevenbergen
et al.
Land degradation, food and tenure insecurity are significant problems in the northern highlands of Ethiopia, particularly in the region known as the country’s famine corridor. Addressing these twine issues in the region has become a focal point for both local and international organizations, underscoring the significance of preventive measures. Since 2000, the Government of Ethiopia (GoE) has been implementing sustainable land management and certification programs. This study aims on households involved in these programs, specifically in Dessie Zuria and Kutaber Woredas, South Wello Zone (SWZ). The primary objectives of the research were to assess households’ current food security status, identify factors influencing their food security, and classify coping and survival strategies employed by households during food shortages. Primary and secondary sources have been used to collect both qualitative and quantitative data. Quantitative data were collected from surveyed households and analyzed USING SPSS software version 26, whereas qualitative data were transcribed, grouped, and interpreted in line with the aim of the research. Three food security models, namely the Household Food Balance Model, Months of Adequate Household Food Provisioning, and Household Dietary Diversity Score, were employed to evaluate food security. Consequently, a significant percentage of the surveyed households, amounting to 88.3%, 35.6%, and 93.8%, were found to experience food insecurity according to the respective models. Rainfall shortages and variability, crop pests and diseases, shrinking farm plots, and land degradation are among the identified food security determinants. During dearth periods, households deploy a variety of coping and survival strategies. To mitigate food insecurity stemming from both natural and socio-economic factors, the research suggests several recommendations. These include advocating for tenure policy reforms by the GoE, and the local governments should promote the adoption of efficient land management practices, instituting a land certification system based on cadasters, encouraging family planning, boosting investments in education and literacy, raising awareness and providing training in climate-smart agriculture techniques, educating communities on optimal grain utilization, saving, trade, and storage methods, facilitating opportunities for income generation through off-farm and non-farm activities, and offering support for crop and livestock diversification.
Nelson Manolo Chávez Muñoz, Nidya Gómez Ramírez, Jorge Eliécer Vigoya Casas
et al.
The study aims to contribute to the discussion of the economic variables that determine the functional distribution
of income –measured as the share of wages in value added– in the three main sectors of Colombia, based on the theoretical position of the Post-Keynesian school. This school argues that this measure of income distribution may present a better measurement approximation than traditional distribution measures such as the Gini –since wage compensation and value
added would be obtained from taxpayers’ information and not from surveys such as the Gini. Therefore, a sectoral analysis
of the functional income distribution is presented, and a panel data model is estimated for the period 2000-2020. The results allow us to infer that the functional income distribution is explained and in fact improves with an increase in output, trade balance, taxes, and investment; however, the individual effect of each of these variables on the distribution is inelastic. Thus, it is concluded that the Post-Keynesian postulate is partially fulfilled in Colombia in the period under study.
Lester G. De la Noval Gómez, Jesús Chía Garzón, María de los Ángeles Ruiz González
et al.
El concepto de tecnología comprende la incorporada en bienes de capital (maquinarias, equipamiento y producciones físicas) y la constituida por los servicios tecnológicos intangibles (derechos de propiedad industrial, know-how, la prestación de servicios con contenido técnico y los servicios intelectuales), por lo que las transacciones comerciales de tecnología, consideran ambos tipos. Los ingresos y egresos derivados del comercio de tecnología del segundo tipo, por su naturaleza intangible, forma parte de la Balanza de Pagos de los países bajo la denominación de Balanza de Pagos Tecnológica (BPT). En los años noventa del pasado siglo, se elaboró un manual metodológico sobre este tema, aplicado por países de alto desarrollo económico. El presente artículo, expone los elementos conceptuales y aspectos metodológicos de mayor relevancia a tener en cuenta para su implementación en Cuba. Se explican los componentes, métodos de compilación de datos, factores a tener en cuenta, sistemas de clasificación y la identificación de los indicadores básicos para la medición de la BPT.
English abstract
The concept of technology includes both the one incorporated in capital goods (machinery, equipment and physical productions) and also the intangible technological services (industrial property rights, know-how, the provision of services with technical content and intellectual services), therefore, commercial technology transactions consider both types. Due to its intangible nature, revenues and expenses derived from trade in technology of the second type form part of the Balance of Payments of the countries under the denomination of Balance of Technological Payments (BTP). In the nineties of the last century, a methodological manual on this subject was developed, applied by countries with high economic development. This article exposes the most relevant conceptual elements and methodological aspects to be taken into account for its implementation in Cuba. The components, data compilation methods, factors to consider, classification systems and the identification of the basic indicators for the measurement of BTP are explained.
Abstract The Government of Indonesia has been promoting the advancement of the biodiesel sector to fulfill its commitment to support clean energy, energy security, and rural development. This paper examines the economic impact of the biodiesel sector using a computable general equilibrium model. Besides analyzing the impacts on the national macroeconomic conditions, other sectors, and household incomes, our model has also included a regional block to capture the impact of the biodiesel mandate on regional growth. Two simulations were performed: (1) fulfillment of the 30% biodiesel blending target (B30 mandate), and (2) Simulation 1 combined with the European Union's biodiesel trade ban resulting in an export reduction of 5.18%. The results show that the two simulations provide positive impacts on macroeconomic variables, including real gross domestic product and real wages. However, the B30 mandate and the combined effect of the EU trade ban still yield an inflationary effect in the short term. They also potentially reduce the production of several agricultural products—such as sugarcane, fruits, vegetables, and soybeans—leading to an increase in food prices. The policy implications highlight that the current B30 mandate and EU ban cannot automatically improve the fuel trade balance.
Economic growth, development, planning, Economics as a science
Practical cancelable biometrics (CB) schemes should satisfy the requirements of revocability, non-invertibility, and non-linkability without deteriorating the matching accuracy of the underlying biometric recognition system. In order to bridge the gap between theory and practice, it is important to prove that new CB schemes can achieve a balance between the conflicting goals of security and matching accuracy. This paper investigates the security and accuracy trade-off of a recently proposed local ranking-based cancelable biometrics (LRCB) scheme for protecting iris-codes. First, the irreversibility of the LRCB is revisited and a new attack for reversing the LRCB transform is proposed. The proposed attack utilizes the distribution of order statistics for discrete random variables to reverse the protected rank values and obtain a close approximation of the original iris template. This ranking-inversion attack is then utilized to realize authentication, record multiplicity, and correlation attacks against the LRCB transform. Our theoretical analysis shows that the proposed reversibility attack can recover more than 95% of the original iris-code bits and the proposed correlation attack can correctly correlate two templates 100% of the time for the parameter setting that retain the recognition accuracy of the underlying iris recognition system. The validity of the proposed attacks is verified using the same iris dataset adopted by the authors of the LRCB scheme. Experimental results support our theoretical findings and demonstrate that the security properties of irreversibility and non-linkability can only be fulfilled at the expense of significant and impractical degradation of the matching accuracy.