Search Results (1,520)

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

Pimelea is one of the highly toxic plants in Australia, particularly affecting cattle. It contains simplexin, a potent toxin that can cause Pimelea poisoning (St. George Disease) in livestock. A survey was conducted to assess the current impact of Pimelea on livestock production, pasture systems, and financial losses among agricultural producers. In addition, information was also sought about the environmental conditions that facilitate its growth and the effectiveness of existing management strategies. The survey responses were obtained from producers affected by Pimelea across nine different Local Government Areas, through three States, viz., Queensland, New South Wales, and South Australia. Pimelea was reported to significantly affect animal production, with 97% of producers surveyed acknowledging its detrimental effects. Among livestock, cattle were the most severely affected (94%), when compared to sheep (13%), goats (3%), and horses (3%). The presence of Pimelea was mostly observed in spring (65%) and winter (48%), although 29% of respondents indicated that it could be present all year-round under favorable rainfall conditions. Germination was associated with light to moderate rainfall (52%), while only 24% linked it to heavy rainfall. Pimelea simplex F. Muell. was the most frequently encountered species (71%), followed by Pimelea trichostachya Lindl. (26%). Infestations were reported to occur annually by 47% of producers, with 41% noting occurrences every 2 to 5 years. Financially, producers estimated average annual losses of AUD 67,000, with 50% reporting an average of 26 cattle deaths per year, reaching up to 105 deaths in severe years. Some producers were spending up to AUD 2100 per annum to manage Pimelea. While chemical and physical controls were commonly employed, integrating competitive pastures and alternative livestock, such as sheep and goats, was considered as a potential management strategy. This study reiterates the need for further research on sustainable pasture management practices to reduce Pimelea-related risks to livestock and agricultural production systems. Full article

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

The ongoing degradation of global forests has severely weakened ecosystem service functions, and traditional judicial remedies have struggled to quantify intangible ecological losses. China has become an important testing ground for restorative justice through the establishment of specialized environmental courts and the practice of environmental public interest litigation. Since 2015, China has actively explored and institutionalized the application of the concept of restorative justice in its environmental justice reform. This concept emphasizes compensating environmental damages through actual ecological restoration acts rather than relying solely on financial compensation. This shift reflects a deep understanding of the limitations of traditional environmental justice and an institutional response to China’s ecological civilization construction, providing critical support for forest ecosystem restoration and enabling ecological restoration activities, such as replanting and re-greening, habitat reconstruction, etc., to be enforced through judicial decisions. This study conducts a qualitative analysis of judicial rulings in forest restoration cases to systematically evaluate the effectiveness of restorative justice in compensating for losses in forest ecosystem service functions. The findings reveal the following: (1) restoration measures in judicial practice are disconnected from the types of ecosystem services available; (2) non-market values and long-term cumulative damages are systematically underestimated, with monitoring mechanisms exhibiting fragmented implementation and insufficient effectiveness; (3) management cycles are set in violation of ecological restoration principles, and acceptance standards lack function-oriented indicators; (4) participation of key stakeholders is severely lacking, and local knowledge and professional expertise have not been integrated. In response, this study proposes a restorative judicial framework oriented toward forest ecosystem services, utilizing four mechanisms: independent recognition of legal interests, function-matched restoration, application of scientific assessment tools, and multi-stakeholder collaboration. This framework aims to drive a paradigm shift from formal restoration to substantive functional recovery, providing theoretical support and practical pathways for environmental judicial reform and global forest governance. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

Despite growing recognition of the role that social determinants of health (SDOHs) and health-related social needs (HRSNs) play in chronic disease, limited research has examined their associations with Chronic Obstructive Pulmonary Disease (COPD) in population-based studies. This cross-sectional study analyzed 2022 Behavioral Risk Factor Surveillance System (BRFSS) data from 37 U.S. states and territories to determine how financial hardship, food insecurity, employment loss, healthcare access barriers, and psychosocial stressors influence the prevalence of COPD. Weighted logistic regression models were used to assess the associations between COPD and specific SDOHs and HRSNs. Several individual SDOH and HRSN factors were significantly associated with COPD prevalence, with financial strain emerging as a particularly strong predictor. In models examining specific SDOH factors, economic hardships like inability to afford medical care were strongly linked to higher COPD odds. Psychosocial HRSN risks, such as experiencing mental stress, also showed moderate associations with increased COPD prevalence. These findings suggest that addressing both structural and individual-level social risks may be critical for reducing the prevalence of COPD in populations experiencing financial challenges. Full article

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions. Full article

In this paper, we aim to provide a new algorithm for managing financial risk in portfolios containing multiple high-volatility assets. We assess the variability of volatility with the Volterra model, and we construct an estimator of the Value-at-Risk (VaR) function using quantile regression. Because of its long-memory property, the Volterra model is particularly useful in this domain of financial time series data analysis. It constitutes a good alternative to the standard approach of Black–Scholes models. From the weighted asymmetric loss function, we construct a new estimator of the VaR function usable in Multi-Functional Volterra Time Series Model (MFVTSM). The constructed estimator highlights the multi-functional nature of the Volterra–Gaussian process. Mathematically, we derive the asymptotic consistency of the estimator through the precision of the leading term of its convergence rate. Through an empirical experiment, we examine the applicability of the proposed algorithm. We further demonstrate the effectiveness of the estimator through an application to real financial data. Full article

Dietary protein is an essential macronutrient derived from both plant and animal sources required for muscle building, immune function, and wound healing. However, in the United States, protein consumption worsens as individuals age, with 30% of men and 50% of women over 71 consuming inadequate dietary protein due to a variety of factors, including changes in gut function, loss of appetite, tooth loss, financial concerns, and social isolation. The aim of this review is to underscore the need for increased protein requirements in aging populations, highlight potential barriers, synthesize these protein requirements, and also recommend strategies to meet these increased protein needs. Achieving adequate protein status, especially when facing chronic or acute health concerns, is essential to promote muscle and bone strength (because aging is associated with significant decreases in postprandial muscle protein synthesis), to support immune health (due to immunosenescence), and to maintain a good quality of life. For older adults, the literature suggests that a dietary protein intake of at least 1.0–1.2 g/kg/day is required in healthy, aging populations, and intakes of 1.2–1.5 g/kg/day are necessary for those with chronic or acute conditions. These protein intake recommendations can increase to 2.0 g/kg/day in more severe cases of illness, malnutrition, and chronic conditions. The reviewed literature also suggests that evenly balanced protein distributions of 25–30 g of dietary protein (0.4 g/kg) per meal from animal and plant protein sources alike are sufficient to maximize muscle protein synthesis (MPS) rates in older populations. Additionally, pre-sleep protein feeds of 40 g/night may be another strategy to improve daily MPS and amino acid utilization. Full article

The recent losses and damages due to climate change have destabilized the insurance industry. As global warming is one of the most critical aspects of climate change, it is essential to investigate to what extent greenhouse gas emissions affect the financial stability of insurers. Insurers typically do not emit substantial greenhouse gases directly, while their underwriting and investment activities play a substantial role in enabling companies that do. This article uses panel data regressions to analyze companies in all insurance segments and in all geographic regions of the world from 2004 to 2023. The main finding is that insurers that increase their greenhouse gas emissions become financially unstable. This result is consistent in all three scopes (scope 1, scope 2, and scope 3) of emissions. Furthermore, the findings reveal that this impact is related to reserves and reinsurance. Specifically, reserves increase with greenhouse gas emissions, while premiums ceded to reinsurers decline. Thus, high-emissions insurers retain a significant share of carbon risk and eventually become financially weak. The results encourage several policy recommendations, highlighting the need for instruments that improve the assessment and disclosure of insurers’ carbon footprints. This is crucial to achieving environmental targets and improving the stability of both the insurance market and the economic system. Full article

Mabroom dates (Phoenix dactylifera L.) are recognized as one of the most important crops in Qatar. Fresh fruit dates are susceptible to mould and post-harvest spoilage, resulting in a significant financial loss. Octanoic fatty acid (OFA) has been shown to regulate the growth of mould-causing organisms such as fungi and bacteria. It is known to have antibacterial properties. The objective of the current study was to evaluate the in vitro effect of OFA on the post-harvest pathogens of Mabroom fruits. Fresh, apparently healthy, and fully ripe Mabroom dates were obtained from the National Agriculture and Food Corporation (NAFCO). The chosen fruits were packed in sterile, well-ventilated plastic boxes and transported to the lab under controlled conditions. The fruits were distributed into five groups (G1 to G5). The groups G1, G2, and G3 received 1%, 2%, and 3.5% OFA, respectively, while G4 was left untreated and G5 was washed only with tap water as a positive control treatment. Each group contained 200 g of fresh and healthy semi-soft dates. The samples were then dried and incubated in a humidity chamber at 25 °C ± 2 for seven days. The signs and symptoms of decay were monitored and recorded. The presence of pathogens was confirmed via phenotypic and microscopic-based methods. The results showed a significant difference (p ≤ 0.05) among the groups. OFA at 3.5% had the strongest inhibitory action against post-harvest pathogens, followed by OFA2%. However, there were no differences (p ≤ 0.05) between OFA1% and the control groups. Aspergillus spp., Penicillium spp., Rhizopus spp., and Botrytis spp. were most abundant in the control group, followed by OFA2% and OFA1%, respectively. In conclusion, octanoic fatty acid at 3.5% may improve the quality of date fruits through its high antimicrobial activity, reduce the effect of post-harvest decay, minimize the loss of date fruits during storage, and improve the sustainability of date fruits. Further experiments are necessary to confirm the effectiveness of OFA as a green solution for sustainable date fruit production. Full article

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article