Search Results (131)

The Republic of Turkey established its first match factory in Sinop in 1929 but had to relocate it even before it was in operation due to severe structural damage caused by ground settlement. In July 1930, through his US-based firm the American–Turkish Investment Corporation (ATIC), the Swedish “Match King” Ivar Kreuger signed a contract with the Republic of Turkey to build and operate a factory in Büyükdere, Istanbul. By 1930, Kreuger had already established a match production monopoly in nearly every country in Europe and that year created a similar financial system for Turkey, gaining control of match production for 25 years. This article explains the events surrounding the establishment of his modern production facility in Turkey, with a particular focus on its engineering aspects. It details the strategically chosen location, the engineering solutions for the factory’s construction, its production lines, and what the country gained and lost from it. In order to determine the establishment and production processes of the facility, the authors examined domestic and foreign archival documents, firsthand news reports from the period, articles and theses, and all other available documents. After the contract was terminated by both parties, the Turkish government and ATIC, in May 1943, the factory continued its production and storage activities until May 1989. At that point, the factory and all its equipment were integrated into another existing facility in the İnegöl district of Bursa province. Almost all the buildings of the Büyükdere Match Factory were demolished, and the land was repurposed for a 450-bed regional hospital in 2012. In short, this article deploys the Büyükdere Match Factory as a case study to examine what Turkey gained and lost from the establishment and production processes of a modern industrial factory, enabled by US–Turkish collaboration, and equipped with the most advanced manufacturing and engineering technologies of the time. Full article

This paper develops an economic evaluation framework for pumped storage hydropower (PSH) projects based on real options, addressing the limitations of traditional economic evaluation methods that neglect investment flexibility and path dependence. The framework integrates an annual net cash flow model with an improved mean-reverting electricity price model to generate thousands of electricity price trajectories, while backward dynamic programming dynamically values abandonment options. The core innovation of this study lies in the dynamic pricing mechanism of abandonment options, which explicitly captures the flexibility of terminating projects under adverse conditions. A comparative analysis between the traditional NPV approach and the real options method reveals significant differences: the average NPV under base scenario is −38.35 million CNY, whereas option scenario yields an average NPV of 143.15 million CNY. The average value of real options is 181.5 million yuan, and it increases the average internal rate of return by 0.34%. These results demonstrate that incorporating real options prevents the underestimation of project value and provides more robust decision-making support under uncertainty, thereby offering methodological and policy insights for the investment appraisal of large-scale energy storage projects. Full article

Since the hydrogen-production process is not yet fully efficient, this paper proposes a poly-generation system that is driven by a geothermal energy source and utilizes a combined Kalina/organic Rankine cycle coupled with an electrolyzer unit to produce, simultaneously, power and green hydrogen in an efficient way. A comprehensive thermodynamic analysis and an exergetic evaluation are carried out to assess the effect of key system parameters (geothermal temperature, high pressure, ammonia–water concentration ratio, and terminal thermal difference) on the performance of concurrent production of power and green hydrogen. Thereby, two configurations are investigated with/without the separation of turbines. The optimal ammonia mass fraction of the basic solution in KC is identified, which leads to an overall optimal system performance in terms of exergy efficiency and green hydrogen production rate. In both configurations, the optimal evaluation is made possible by conducting a genetic algorithm optimization. The simulation results without/with the separation of turbines demonstrate the potential of the suggested cycle combination and emphasize its effectiveness and efficiency. Exemplary, for the case without the separation of turbines, it turns out that the combination of ammonia–water and MD2M provides the best performance with net power of 1470 kW, energy efficiency of 0.1184, and exergy efficiency of 0.1258 while producing a significant green hydrogen amount of 620.17 kg/day. Finally, an economic study allows to determine the total investment and payback time of $3,342,000 and 5.37 years, respectively. The levelized cost of hydrogen (LCOH) for the proposed system is estimated at 3.007 USD/kg H₂, aligning well with values reported in the literature. Full article

Investors face the challenge of how to incorporate economic and financial forecasts into their investment strategy, especially in times of financial crisis. To model this situation, we consider a financial market consisting of a risk-free asset with a constant interest rate as well as a risky asset whose drift and volatility is influenced by a stochastic process indicating the probability of potential market downturns. We use a dynamic portfolio optimization approach in continuous time to maximize the expected utility of terminal wealth and solve the corresponding HJB equations for the general class of HARA utility functions. The resulting optimal strategy can be obtained in closed form. It corresponds to a CPPI strategy with a stochastic multiplier that depends on the information from the crisis indicator. In addition to the theoretical results, a performance analysis of the derived strategy is implemented. The specified model is fitted using historic market data and the performance is compared to the optimal portfolio strategy obtained in a Black–Scholes framework without crisis information. The new strategy clearly dominates the BS-based CPPI strategy with respect to the Sharpe Ratio and Adjusted Sharpe Ratio. Full article

The rapid growth of e-commerce and advances in information and communication technologies have placed increasing pressure on last-mile delivery companies to enhance operational productivity. As investments in logistics infrastructure require long-term planning, maximizing the efficiency of existing terminal operations has become a critical priority. This study proposes a mathematical model for inbound truck scheduling that simultaneously minimizes truck waiting times and balances workload across temporary inventory storage located at outbound chutes in cross-docking terminals. The model incorporates a dynamic rescheduling strategy that updates the assignment of inbound trucks in real time, based on the latest terminal conditions. Numerical experiments, based on real operational data, demonstrate that the proposed approach significantly outperforms conventional strategies such as First-In First-Out (FIFO) and Random assignment in terms of both load balancing and truck turnaround efficiency. In particular, the proposed model improves workload balance by approximately 10% and 12% compared to the FIFO and Random strategies, respectively, and it reduces average truck waiting time by 17% and 18%, thereby contributing to more efficient workflow and alleviating bottlenecks. The findings highlight the practical potential of the proposed strategy for improving the responsiveness and efficiency of parcel distribution centers operating under fixed infrastructure constraints. Future research may extend the proposed approach by incorporating realistic operational factors, such as cargo heterogeneity, uncertain arrivals, and terminal shutdowns due to limited chute storage. Full article

In wet subtropical environments, perennial groundcovers are common in horticultural plantations to protect the soil from erosion. However, there has been little investigation into whether seeding annual cover crops into the perennial groundcovers provides additional soil services including carbon and nutrient cycling in these systems. To investigate this, farmer participatory field trials were conducted in commercial avocado, macadamia, and coffee plantations in the wet Australian subtropics. Cover crops were direct-seeded into existing inter-row groundcovers in winter (cool season cover crops), and into the same plots the following summer (warm season cover crops). Inter-row biomass was quantified at the end of winter and summer in the control (no cover crop) and cover crops treatments. Soil carbon and nutrient cycling parameters including hot water extractable carbon, water soluble carbon, autoclavable citrate-extractable protein and soil enzyme activities were quantified every two months from early spring (September) 2021 to late autumn (May) 2022. Seeded cover crops produced 500 to 800 kg ha⁻¹ more total inter-row biomass over winter at the avocado coffee sites, and 3000 kg ha⁻¹ biomass in summer at the coffee site. However, they had no effect on biomass production in either season at the macadamia site. Soil functional parameters changed with season (i.e., time of sampling), with few significant effects of cover crop treatments on soil function parameters across the three sits. Growing a highly productive annual summer cover crop at the coffee site led to suppression and death of perennial groundcovers, exposing bare soil in the inter-row by 3 weeks after termination of the summer cover crop. Annual cover crops seeded into existing perennial groundcovers in tree crop systems had few significant impacts on soil biological function over the 12-month period, and their integration needs careful management to avoid investment losses and exacerbating the risk of soil erosion on sloping lands in the wet subtropics. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

Background: The Port of Santa Marta, located on Colombia’s northern Caribbean coast, plays a vital role in the country’s maritime trade, particularly in the export of agricultural and perishable goods. This raises the question: how competitive is Santa Marta’s container terminal compared to national and regional ports, and what strategic factors shape its performance within the Colombia and Latin American maritime logistics system? Methods: This study evaluates the port’s competitiveness by applying Porter’s Extended Diamond Model. A mixed-methods ap-proach was employed, combining structured surveys and interviews with port stakeholders and operational data analysis. A competitiveness matrix was developed and examined using standardized residuals and L1 regression to identify critical performance gaps and strengths. Results: The analysis reveals several competitive advantages, including the port’s strategic location, natural deep-water access, and advanced infrastructure for refrigerated cargo. It also benefits from skilled labour and proximity to global shipping routes, such as the Panama Canal. Nonetheless, challenges remain in storage capacity, limited road connectivity, and insufficient public investment in hinterland infrastructure. Conclusions: While the Port of Santa Marta shows strong maritime capabilities and spe-cialized services, addressing its land-side and institutional constraints is essential for positioning it as a resilient, competitive logistics hub in the Latin American and Caribbean region. Full article

The southern Baltic Sea is characterized by highly variable weather conditions, particularly in autumn and winter, when storms, strong westerly winds, and temporary sea ice formation disrupt maritime operations. This study presents a climatographic overview and evaluates key hydrometeorological factors that influence the safe and efficient use of liquefied natural gas (LNG) as bunkering fuel in the region. The analysis draws on long-term meteorological and hydrological datasets (1971–2020), including satellite observations and in situ measurements. It identifies operational constraints, such as wind speed, wave height, visibility, and ice cover, and assesses their impact on LNG logistics and terminal functionality. Thresholds for safe operations are evaluated in accordance with IMO and ISO safety standards. An ice severity forecast for 2011–2030 was developed using the ECHAM5 global climate model under the A1B emission scenario, indicating potential seasonal risks to LNG operations. While baseline safety criteria are generally met, environmental variability in the region may still cause temporary disruptions. Findings underscore the need for resilient port infrastructure, including anti-icing systems, heated transfer equipment, and real-time environmental monitoring, to ensure operational continuity. Integrating weather forecasting into LNG logistics supports uninterrupted deliveries and contributes to EU goals for energy diversification and emissions reduction. The study concludes that strategic investments in LNG infrastructure—tailored to regional climatic conditions—can enhance energy security in the southern Baltic, provided environmental risks are systematically accounted for in operational planning. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

This paper investigates the ultimate socioeconomic causes underlying the termination of parental investment in humans by analyzing the relationship between socioeconomic status and various forms of child mortality, including live births, stillbirths, infant deaths, and infanticide. Utilizing theoretical foundations from human behavioral ecology, the study illustrates how different forms of termination of parental investment can be viewed as points along a continuum of adaptive strategies aimed at optimizing reproductive fitness. The research emphasizes that technical and cognitive limitations lead to many instances of infanticide being concealed as natural child deaths, such as Sudden Infant Death Syndrome (SIDS), thus complicating the accurate detection of true causes of death. However, addressing common ultimate causes—specifically socioeconomic factors such as healthcare accessibility, nutritional quality, social support, and stress reduction—can simultaneously prevent or reduce all forms of investment termination. The paper further analyzes demographic data from Zagreb and surrounding municipalities. Ultimately, the study advocates a holistic approach to public health interventions and policies aimed at improving socioeconomic conditions as a crucial step toward reducing all forms of child mortality. Full article

As essential components of arid region ecosystems, terminal lakes play a critical role in enhancing the functions of ecosystem services (ESs) and improving ecological structure. Despite the increasing degradation of ESs and landscape stability due to climate and human pressures, comprehensive assessments of water provision, carbon storage, soil conservation, and habitat integrity in arid terminal lake regions are still lacking. Focusing on the Ebinur Lake Basin (ELB), this study employed the InVEST model to quantify ES changes from 2000 to 2020, combined with univariate regression, Pearson, and Spearman correlation analyses to explore their dynamic evolution. Landscape pattern indices calculated via Fragstats 4.2 further revealed trends in fragmentation, boundary complexity, and diversity. Results show that most ESs exhibited synergistic relationships, particularly between carbon sequestration and habitat quality (r = 0.45), observed clear trade-offs, such as between water yield and carbon sequestration (r = −0.47), underscoring the complexity of ecosystem interactions. Enhanced ES functions were associated with increased patch number, density, and shape complexity, while landscape diversity fluctuated. NDVI growth improved ES performance and reduced fragmentation, though changes in landscape metrics were largely driven by climate variability and socio-economic pressures, exacerbating fragmentation and weakening ecological stability. Overall, understanding the trade-offs and synergies among ESs in the ELB is crucial for informing sustainable development strategies. Full article

This paper introduces a novel framework for multi-period portfolio optimization that incorporates intertemporal spectral risk measures (ISRMs). The model dynamically manages risk by considering both tail risk, through spectral risk measures, and overall portfolio volatility, through variance, across multiple time periods. This approach allows investors to specify time-varying risk preferences via a spectral function, making it particularly suitable for investors with evolving risk management needs. We develop an efficient solution methodology based on the Progressive Hedging Algorithm (PHA), enhanced with specialized reformulations to handle linkage objectives and constraints inherent in the multi-period setting. We establish the theoretical convergence properties of our algorithm, demonstrating a q-linear convergence rate under mild conditions. Numerical experiments validate the effectiveness of our approach, showing that the intertemporal weighting scheme provides more consistent risk management across the investment horizon compared to terminal-focused strategies. Notably, our approach exhibits superior downside risk protection, as evidenced by improved Sortino and Omega ratios, and generates more balanced wealth distributions with moderate tails. These findings offer valuable insights and practical tools for investors seeking to implement dynamic risk-management strategies that account for both intermediate and terminal objectives. Full article

This paper presents a robust multi-period portfolio optimization framework that integrates interval analysis, entropy-based diversification, and downside risk control. In contrast to classical models relying on precise probabilistic assumptions, our approach captures uncertainty through interval-valued parameters for asset returns, risk, and liquidity—particularly suitable for volatile markets such as cryptocurrencies. The model seeks to maximize terminal portfolio wealth over a finite investment horizon while ensuring compliance with return, risk, liquidity, and diversification constraints at each rebalancing stage. Risk is modeled using semi-absolute deviation, which better reflects investor sensitivity to downside outcomes than variance-based measures, and diversification is promoted through Shannon entropy to prevent excessive concentration. A nonlinear multi-objective formulation ensures computational tractability while preserving decision realism. To illustrate the practical applicability of the proposed framework, a simulated case study is conducted on four major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB). The model evaluates three strategic profiles based on investor risk attitude: pessimistic (lower return bounds and upper risk bounds), optimistic (upper return bounds and lower risk bounds), and mixed (average values). The resulting final terminal wealth intervals are [1085.32, 1163.77] for the pessimistic strategy, [1123.89, 1245.16] for the mixed strategy, and [1167.42, 1323.55] for the optimistic strategy. These results demonstrate the model’s adaptability to different investor preferences and its empirical relevance in managing uncertainty under real-world volatility conditions. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article