Search Results (831)

Container liner shipping companies operate within a complex environment where they must balance profitability and service reliability. Meanwhile, evolving regulatory policies, such as surcharges imposed on ships of a particular origin or type on specific trade lanes, introduce new operational challenges. This study addresses the heterogeneous ship routing and demand acceptance problem, aiming to maximize two conflicting objectives: weekly profit and total transport volume. We formulate the problem as a bi-objective mixed-integer programming model and prove that the ship chartering constraint matrix is totally unimodular, enabling the reformulation of the model into a partially relaxed MIP that preserves optimality while improving computational efficiency. We further analyze key mathematical properties showing that the Pareto frontier consists of a finite union of continuous, piecewise linear segments but is generally non-convex with discontinuities. A case study based on a realistic liner shipping network confirms the model’s effectiveness in capturing the trade-off between profit and transport volume. Sensitivity analyses show that increasing freight rates enables higher profits without large losses in volume. Notably, this paper provides a practical risk management framework for shipping companies to enhance their adaptability under shifting regulatory landscapes. Full article

With consumers increasing in environmental awareness, manufacturers have integrated green R&D into their strategies, aiming to grasp the green market. However, manufacturers may be too bullish on the market potential of green products and maintain an optimistic attitude toward green R&D. Despite having an optimistic attitude, manufacturers often have no demand information advantage over downstream retailers due to their position in the supply chain, away from the market. It is worth exploring what impact optimistic green R&D in a sustainable supply chain with demand information asymmetry will have. Previous studies have not managed to reveal this. In this study, a stylized model is introduced to explore this question. The main findings are as follows: (1) optimistic green R&D increases the feasibility of the retailer sharing demand information, which facilitates information communication in the sustainable supply chain; (2) in most cases, optimistic green R&D does not bring higher profits for the manufacturer, yet is likely to allow the retailer to earn more, thereby resulting in a loss–win outcome; and (3) depending on the green R&D efficiency of the manufacturer and the consumer’s environmental awareness, optimistic green R&D may not generate higher environmental benefits. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

The impact of five different nets—conventional black, grey, white, and photoselective red and yellow—on the performance of “Gala Redlum” apples was evaluated over a five-year period (2020–2024) and compared to an uncovered control. The cumulative production over this period, ranked from highest to lowest, was as follows: white net (182.4 t/ha), grey net (178.5 t/ha), yellow net (175.8 t/ha), black net (175.5 t/ha), red net (169.5 t/ha), and uncovered control (138.8 t/ha). Vegetative growth results were inconsistent among the studied years. The cumulative photosynthetic rate (An) was slightly higher under the white net (57.9 µmol m⁻² s⁻¹). Fv/Fm values remained closest to optimal levels under the black and grey nets. Netting effectively protected fruits from elevated temperatures, particularly under the grey net, and reduced sunburn damage, with the grey, black, and yellow nets performing best in this regard. Overall profitability was increased by netting: the black net provided the highest cumulative income per hectare over a five-year period (EUR 72,315) alongside the second-lowest sunburn loss (0.69%), while the yellow net also showed strong economic performance (€64,742) with a moderate sunburn loss (1.26%) compared to the red net. Fruit dry matter and soluble solids content (SSC) were generally higher in the uncovered control, whereas °Hue values tended to be higher under the red and yellow nets. In summary, the black and yellow nets provided more balanced microclimatic conditions that enhanced tree performance, particularly under heat stress, leading to improved yield and profitability. However, the economic feasibility of each net type should be evaluated in relation to its installation and maintenance costs. Full article

As electric multiple unit (EMU) operations and maintenance demands have expanded, spare parts supply chain management has become increasingly crucial. This study emphasizes the supply challenges of EMU spare parts, including inadequate minimum inventory levels and prolonged response times. Redesigning the OEM–railway bureau vendor-managed inventory (VMI) model contract incentive and penalty system is the key goal. Connecting the spare parts supply system with its characteristics yields a game theory model. This study analyzes and compares the equilibrium strategies and profits of supply chain members under different mechanisms for managing critical spare parts. The findings demonstrate that mechanism contracts can enhance supply chain performance in a Pareto-improving manner. An in-depth analysis of downtime loss costs, procurement challenges, and order losses reveals their effects on supply chain coordination and profit allocation, providing railway bureaus and OEMs with a theoretical framework for supply chain decision-making. This study offers theoretical justification and a framework for decision-making on cooperation between OEMs and railroad bureaus in the management of spare parts supply chains, particularly for extensive EMU operations. Full article

Loan default prediction is a critical task for financial institutions, directly influencing risk management, loan approval decisions, and profitability. This study evaluates the effectiveness of machine learning models, specifically XGBoost, Gradient Boosting, Random Forest, and LightGBM, in predicting loan defaults. The research investigates the following question: How effective are machine learning models in predicting loan defaults compared to traditional approaches? A structured machine learning pipeline is developed, including data preprocessing, feature engineering, class imbalance handling (SMOTE and class weighting), model training, hyperparameter tuning, and evaluation. Models are assessed using accuracy, F1-score, ROC AUC, precision–recall curves, and confusion matrices. The results show that Gradient Boosting achieves the highest overall classification performance (accuracy = 0.8887, F1-score = 0.8084, recall = 0.8021), making it the most effective model for identifying defaulters. XGBoost exhibits superior discriminatory power with the highest ROC AUC (0.9714). A cost-sensitive threshold-tuning procedure is embedded to align predictions with regulatory loss weights to support audit requirements. Full article

This study examines the deferred tax valuation allowance disclosures of a sample of venture-capital-backed IPO firms that incurred a net operating loss (NOL) in the period prior to their public offering (IPO). I find that 82 percent of these firms record an allowance that reduces the associated deferred tax asset to zero, that the choice to record the allowance is largely driven by a firm’s history of losses, and that the allowance is associated with lower future book income. I further propose a new explanation for the presence of the allowance: the Section 382 ownership change limitation, which can cause firms to record an allowance independent of their past profitability or expectations about future earnings. I find that firms consider this limitation when recording the allowance, and that controlling for it can enhance the signal regarding future income. Full article

The change in the socio-political formation of Russia from a socialist planned system to a capitalist market system significantly influenced agriculture and one of its components—arable land. The loss of the sustainability of land management for arable land led to a reduction in sown areas by 38% (from 119.7 to 74.7 million ha) and a synchronous drop in gross harvests of grain and leguminous crops by 48% (from 117 to 61 million tons). The situation stabilized in 2020, with a sowing area of 80.2 million ha and gross harvests of grain and leguminous crops of 120–150 million tons. This process was not formalized legally, and the official (legal) area of arable land decreased by only 8% from 132.8 to 122.3 million ha. Legal conflict arose for 35 million ha for unused arable land, for which there was no classification of its condition categories and no monitoring of the withdrawal time of the arable land from actual agricultural use. The aim of this study was to resolve the challenges in the method of retrospective monitoring of soil–land cover, which allowed for the achievement of the aims of the investigation—to elucidate the history of land use on arable lands from 1985 to 2025 with a time step of 5 years and to obtain a detailed classification of the arable lands’ abandonment degrees. It was also established that on most of the abandoned arable land, carbon sequestration occurs in the form of secondary forests. In the course of this work, it was shown that the reasons for the formation of an array of abandoned arable land and the stabilization of agricultural production turned out to be interrelated. The abandonment of arable land occurred proportionally to changes in the soil’s natural fertility and the degree of land degradation. Economically unprofitable lands spontaneously (without centralized planning) left the sowing zone. The efficiency of land use on the remaining lands has increased and has allowed for the mass application of modern farming systems (smart, precise, landscape-adaptive, differentiated, no-till, strip-till, etc.), which has further increased the profitability of crop production. The prospect of using abandoned lands as a carbon sequestration zone in areas of forest overgrowth has arisen. Full article

Incoming feeder calf production potential can be predicted based on phenotypic appearance, and nutrient supply can be optimized to match each animal’s specific production profile. Tailoring the supply, quality, and quantity of nutrients to the requirements for maximum profit could further support the economic pillar of sustainable livestock farming. Feeder calves (n = 104) were categorized into the production profiles (PP 1; PP 2; PP 3). Within each PP category, the allocated pens were subsequently randomized into three diets (high-, medium-, and low-production diets). Economic important traits were measured, and a deterministic model was created to evaluate economic implications. There was a significant interaction between the incoming feeder calf production profile and diet on the profit margin, with the PP 2 calves being most profitable when fed on the medium-production diet (profit margin = 4.81%). This was in stark contrast of the profit made by PP 2 calves fed on the low- or high-production diets (profit margin = 0.21% and −2.97%, respectively). PP 3 calves made a loss on all diets; however, this loss was reduced by 14% when fed on the low- compared to the medium-production diet (profit margin = −1.45% and −1.68%, respectively). PP 1 calves were profitable on all three diets although the margin was highest on the medium-production diet. In conclusion, the medium-production diet, representing the current industry norm in South Africa, is financially suitable for feeding calves with average production potential (PP 2), whereas the loss made by calves with low production potential (PP 3) can be reduced by adjusting the feed formulation for low production. More work is required to formulate diets that will maximize the profit made by calves with above average production potential (PP 1). Full article

The Portuguese forest plays a crucial role in maintaining ecological balance and fostering socio-economic sustainability within rural areas. Nonetheless, it is currently facing significant challenges due to the increasing intensity and frequency of forest fires observed in recent decades. The deterioration of traditional agricultural practices, the proliferation of monocultures, and alterations in land use patterns have significantly exacerbated these challenges. Consequently, the landscape has undergone considerable transformations, resulting in a decline in biodiversity and a weakening of local economies. This study examines land use in mainland Portugal from 1995 to 2018, utilising data on land occupation, land cover, and burnt areas from the Institute for Nature Conservation and Forests. The cartographic analysis of three periods—1995, 2007, and 2018—along with the fire data recorded between 1996 and 2018, enabled the observation of changes in the predominant land use and land cover (LULC) classes, particularly among forests, scrubland, and agricultural areas. The results highlight a significant increase in forested areas, especially eucalyptus, as well as urbanisation, while scrubland and agricultural areas have decreased. Using specific LULC level 4, and burnt (BA) and unburnt (NB) areas, temporary crops decreased substantially (−14% NB/−4% BA 1995–2007; −23% NB by 2018). Eucalyptus showed strong continuous growth (16% NB/35% BA 1995–2007; 23% NB/47% BA 2007–2018). Maritime pine suffered severe losses, especially in burnt areas (−42%/−28%). Cork oak remained stable (1–4% growth). Other oaks showed minimal changes. Bushes (scrubland) declined sharply post-2007 (−31% BA/−6% NB). The most significant transformation occurred between 1995 and 2007, particularly in the south of Portugal, where wildfires promoted the replacement of maritime pine with eucalyptus, a species that offers greater profitability, leading to agricultural abandonment in burned areas. Full article

The growing human population has increased the need for food beyond what terrestrial sources can provide. This boosts aquaculture demand for molluscs, fish, and crustaceans. Molluscs are popular for their nutritional benefits, making them a profitable industry. Despite a 3% annual growth in mollusc populations, recent high mortality rates and population losses due to poor feeding practices and water pollution have made them more disease-prone. Limited treatment options exist for mollusc diseases in aquaculture systems. Hence, developing rapid, sensitive, and cost-effective diagnostic tools for field use is essential to identify and prevent infections promptly. Recently developed isothermal nucleic acid amplification technologies, like loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA), offer rapid results within an hour. This review examines these isothermal diagnostic techniques for mollusc pathogens and their potential for field application. Full article

Establishing a partnership necessitates agreeing on how to divide future profits or losses. We consider parties who wish to contract on the division of uncertain future profits. We propose to divide profits according to the Raiffa–Kalai–Smorodinsky (K-S) solution, which is the intersection point of the feasible region’s boundary and the line connecting the disagreement and ideal points. It is the only function which satisfies invariance to linear transformations, symmetry, strong Pareto optimality, and monotonicity. We formulate the general problem of designing a contract which divides uncertain future profit between the partners and determines shares of each partner. We first focus on linear and, later, non-linear contracts between two partners, providing analytical and numerical solutions for various special cases in terms of the utility functions of the partners, their beliefs, and the disagreement point. We then generalize the analysis to any number of partners. We also consider a contract which is partially based on the parties’ financial contribution to the partnership, which have a positive impact on profit. Finally, we address asymmetric K-S solutions. K-S solutions are seen to be a useful predictor of the outcome of negotiations, similar to Nash’s bargaining solution. Full article

In emerging economies, where the legislative and economic landscapes may significantly differ from those of advanced economies, accrual-based earnings management (AEM) is especially problematic for financial disclosure and investor trust. This paper conducts a systematic literature review and a bibliometric analysis to evaluate the incentives for AEM in developing countries and to understand the evolution of the AEM domain within emerging countries. For this purpose, 312 journal articles from ResearchGate, Google Scholar, ScienceDirect, Google, and Scopus, covering the period from 2000 to 2024, were reviewed under various thematic areas. The findings highlighted multiple significant motivators for AEM within developing markets, encompassing financial distress, loss avoidance, profitability pressures, high leverage, weak corporate governance structures and processes, diverse ownership structures (such as concentrated ownership, family ownership, institutional ownership, government ownership, and insider ownership), market performance indicators, political ties, weak regulatory systems, as well as factors such as executive compensation, tenure, career retention, agency issues, investor expectations, audit quality, economic crises, and firm-specific characteristics like size, reputation, and age. This research contributes to existing knowledge by examining the motivations behind AEM in emerging economies, underscoring the need for tailored regulatory frameworks and strong governance structures and processes to address the unique challenges developing nations face. For regulators and policymakers, these findings emphasize the need for robust regulatory frameworks, more stringent auditing protocols, and improved corporate governance structures to discourage business executives from engaging in AEM practices. Full article

The higher integration of photovoltaic (PV) units is an inevitable component of smart city development. Thanks to smart meter devices that can record the exchange of power between the grid and customers, it is expected that homeowners and businesses will tend to install PV arrays on their rooftops and parking lots to benefit from selling power back to the grid. However, the overvoltage issue resulting from high PV penetration is a major challenge that necessitates the active power curtailment of PV units to ensure power grid stability. Fairness-oriented methods aim to minimize the active power of PV units as much as possible, adopting a fairer approach, and then address the PV owner’s satisfaction with fair profit and loss. Maintaining voltage within a limited standard range under very low load conditions while prioritizing PV inverters’ participation in reactive power contribution and attempting to ensure fairer curtailment of active power presents challenges to existing control design approaches. This paper presents twelve new volt–watt curve design methods to achieve these goals and address the challenges. The methods yield polynomial curves, piecewise linear curves, and single linear curves. A unique voltage sensitivity value for each PV inverter is used to determine the control region area and the slope of the curve at the starting point in certain instances. The effectiveness of the proposed methods is discussed by evaluating their capabilities on the 37-bus IEEE system. Full article

Environmental and temporal conditions, particularly dust accumulation, can significantly impact the performance of photovoltaic solar panels, potentially reducing their efficiency by up to 20%, and thereby affecting profitability. Accurately estimating these losses is crucial for optimising maintenance and avoiding unforeseen losses. Various models have been proposed in the literature for this purpose. In this context, four machine learning models were developed using meteorological and air quality data from the Solar Energy Research Center (CIESOL). A Gradient-Boosting model (LightGBM) and a neural network achieved RMSE values of 0.68% and 0.88% of soiling loss, and $R^2$ values of 0.86 and 0.76 between measured and estimated values, respectively, on their test sets. The generalisation capability of these models was tested by extrapolating them to other regions in Spain. To enhance robustness across locations, a global artificial neural network (ANN) model was trained using combined data from two sites, achieving an RMSE of 1.02% when estimating soiling losses. This result highlights a significant improvement over models trained on a single location and tested elsewhere, demonstrating the global model’s stronger ability to generalise across different geographic settings. Full article