Search Results (6,499)

Electricity is a significant factor in the life cycle of many products, so the reliability of greenhouse gas (GHG) emissions data is crucial. The article presents publicly available sources of emission factors representative of Poland. The aim of the study is to assess their strengths and weaknesses in the context of the calculation requirements of carbon footprint analysis in accordance with the GHG Protocol. The article presents the results of carbon footprint calculations for different ranges of emissions in the life cycle of 1 kWh of electricity delivered to a hypothetical organization. Next, a discussion on the quality of the emissions factors has been provided, taking account of data quality indicators. It was concluded that two of the emissions factors that are compared—those based on the national consumption mix and the residual mix for Poland—have been recognized as suitable for use in carbon footprint calculations. Beyond the calculation results, the research highlights the significance of the impact of the selection of emissions factors on the reliability of environmental analysis. The article identifies methodological challenges, including the risk of double counting, limited transparency, methodological inconsistency, and low correlation of data with specific locations and technologies. The insights presented contribute to improving the robustness of carbon footprint calculations. Full article

The use of opioids for cancer-related pain is essential but poses significant challenges due to the risk of misuse and the development of opioid use disorder (OUD). This review takes a multidisciplinary perspective based on the current scientific literature to analyze the pharmacological mechanisms, classification, and therapeutic roles of opioids in oncology. Key risk factors for opioid misuse—including psychiatric comorbidities, prior substance use, and insufficient clinical monitoring—are discussed in conjunction with validated tools for pain assessment and international guidelines. The review emphasizes the importance of integrating toxicological, pharmacological, physiological, and public health perspectives to promote rational opioid use. Pharmacogenetic variability is explored as a determinant of treatment response and addiction risk, underscoring the value of personalized medicine. Evidence-based strategies such as early screening, psychosocial interventions, and the use of buprenorphine-naloxone are presented as effective measures for managing OUD in cancer patients. Ultimately, this work advocates for safe, patient-centered opioid prescribing practices that ensure effective pain relief without compromising safety or quality of life. Full article

Improving occupational health and safety (OHS) in the construction industry can contribute to the advancement of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good Health and Well-being) and 8 (Decent Work and Economic Growth). Yet, workers’ risk-taking behaviors (RTBs) remain a persistent challenge. Drawing on Social Cognitive Theory and Social Information Processing Theory, this study develops and tests a social influence model to examine how foremen’s safety attitudes (SAs) shape workers’ RTBs. Drawing on survey data from 301 construction workers in China, structural equation modeling reveals that foremen’s SAs significantly and negatively predict workers’ RTBs. However, the three dimensions of SAs—cognitive, affective, and behavioral—exert their influence through different pathways. Risk perception (RP) plays a key mediating role, particularly for the cognitive and behavioral dimensions. Furthermore, interpersonal trust (IPT) functions as a significant moderator in some of these relationships. By identifying the micro-social pathways that link foremen’s attitudes to workers’ safety behaviors, this study offers a testable theoretical framework for implementing the Sustainable Development Goals (particularly Goals 3 and 8) at the frontline workplace level. The findings provide empirical support for organizations to move beyond rule-based management and instead build more resilient OHS governance systems by systematically cultivating the multidimensional attitudes of frontline leaders. Full article

As artificial intelligence (AI) technology becomes increasingly widespread in organizations, its impact on individual employees’ psychology and behavior has garnered growing attention. Existing research primarily focuses on AI’s effects on organizational performance and job design, with limited exploration of its mechanisms influencing individual employees, particularly in the critical area of risk-taking behavior, which is essential to organizational innovation. This research develops a moderated mediation model grounded in social cognitive theory (SCT) to explore how AI usage affects the willingness to take risks. A three-wave longitudinal study collected and statistically analyzed data from 442 participants. The findings reveal that (1) AI usage significantly enhances employees’ willingness to take risks; (2) self-efficacy serves as a partial mediator in the connection between AI usage and the willingness to take risks; and (3) learning goal orientation moderates both the relationship between AI usage and self-efficacy, as well as the mediating effect. This research enhances our understanding of AI’s impact on organizational behavior and provides valuable insights for human resource management in the AI era. Full article

Background and Objectives: The management of type 2 diabetes (T2D) extends beyond glycemic control, requiring a more global strategy that includes optimization of body composition, even more so in the context of sarcopenia and visceral adiposity, as they contribute to poor outcomes. Past reviews have typically been focused on weight reduction or glycemic effectiveness, with limited inclusion of new therapies’ effects on muscle and fat distribution. In addition, the emergence of incretin-based therapies and dual agonists such as tirzepatide requires an updated synthesis of their impacts on body composition. This review attempts to bridge the gap by taking a systematic approach to how current blood-glucose lowering therapies affect lean body mass, fat mass, and the risk of sarcopenia in T2D patients. Materials and Methods: Between January 2015 and March 2025, we conducted a narrative review by searching the PubMed, Scopus, and Web of Science databases for English-language articles. The keywords were combinations of the following: “type 2 diabetes,” “lean body mass,” “fat mass,” “body composition,” “sarcopenia,” “GLP-1 receptor agonists,” “SGLT2 inhibitors,” “tirzepatide,” and “antidiabetic pharmacotherapy.” Reference lists were searched manually as well. The highest precedence was assigned to studies that aimed at adult type 2 diabetic subjects and reported body composition results. Inclusion criteria for studies were: (1) type 2 diabetic mellitus adult patients and (2) reporting measures of body composition (e.g., lean body mass, fat mass, or muscle function). We prioritized randomized controlled trials and large observational studies and excluded mixed diabetic populations, non-pharmacological interventions only, and poor reporting of body composition. Results: Metformin was widely found to be weight-neutral with minimal effects on muscle mass. Insulin therapy, being an anabolic hormone, often leads to fat mass accumulation and increases the risk of sarcopenic obesity. Incretin-based therapies induced substantial weight loss, mostly from fat mass. Notable results were observed in studies with tirzepatide, demonstrating superior reduction not only in fat mass, but also in visceral fat. Sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors) promote fat loss but are associated with a small yet significant decrease in lean muscle mass. Conclusions: Blood-glucose lowering therapies demonstrated clinically relevant effects on body composition. Treatment should be personalized, balancing glycemic control, cardiovascular, and renal benefits, together with optimal impact on muscle mass along with glycemic, cardiovascular, and renal benefits. Full article

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article

Background/Objectives: An important new consideration when studying autism spectrum disorder (ASD) is the bioenergetic mechanisms underlying the relatively recent rapid evolutionary expansion of the human brain, which pose fundamental risks for mitochondrial dysfunction and calcium signaling abnormalities and their potential role in ASD, as recently highlighted by insights from the BTBR mouse model of ASD. The rapid brain expansion taking place as Homo sapiens evolved, particularly in the parietal lobe, led to increased energy demands, making the brain vulnerable to such metabolic disruptions as are seen in ASD. Methods: Mitochondrial dysfunction in ASD is characterized by impaired oxidative phosphorylation, elevated lactate and alanine levels, carnitine deficiency, abnormal reactive oxygen species (ROS), and altered calcium homeostasis. These dysfunctions are primarily functional, rather than being due to mitochondrial DNA mutations. Calcium signaling plays a crucial role in neuronal ATP production, with disruptions in inositol 1,4,5-trisphosphate receptor (ITPR)-mediated endoplasmic reticulum (ER) calcium release being observed in ASD patient-derived cells. Results: This impaired signaling affects the ER–mitochondrial calcium axis, leading to mitochondrial energy deficiency, particularly in high-energy regions of the developing brain. The BTBR mouse model, with its unique Itpr3 gene mutation, exhibits core autism-like behaviors and metabolic syndromes, providing valuable insights into ASD pathophysiology. Conclusions: Various interventions have been tested in BTBR mice, as in ASD, but none have directly targeted the Itpr3 mutation or its calcium signaling pathway. This review presents current genetic, biochemical, and neurological findings in ASD and its model systems, highlighting the need for further research into metabolic resilience and calcium signaling as potential diagnostic and therapeutic targets for ASD. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

Background: Despite growing interest in the literature on Parkinson’s disease (PD) on cognitive functioning, financial incompetence—a crucial aspect of daily living—and its modulation susceptibility by PD treatment regimens remains relatively understudied. Objective: This systematic review and meta-analysis aimed to synthesize existing evidence on how PD treatments affect financial capacity, assessing both direct financial competence and cognitive or behavioral proxies of financial decision-making. Methods: A comprehensive literature search according to PRISMA protocol was conducted across major biomedical databases, supplemented by gray literature and manual reference list checks. Eligible studies assessed financial capacity directly or indirectly through cognitive proxies (e.g., executive function, decision-making) or financial risk behaviors (e.g., impulse control disorders). Two separate meta-analyses were performed. Heterogeneity (I²), publication bias (Egger’s test), and sensitivity analyses were conducted to assess robustness. Results: Twenty-three studies met inclusion criteria. One study directly measured financial capacity and was analyzed narratively, reporting diminished competence in patients on levodopa therapy. A meta-analysis of cognitive proxies (10 studies) showed a moderate effect size (Hedges’ g = 0.70, 95% CI [0.45, 0.92], p < 0.001), indicating that PD treatments negatively affect executive function and financial decision-making. A second meta-analysis of impulse control and financial risk behaviors (12 studies) revealed a larger effect size (Hedges’ g = 0.98, 95% CI [0.75, 1.22], p < 0.001), strongly linking dopamine agonists to increased financial risk-taking. Moderate heterogeneity (I² = 45.8–60.5%) and potential publication bias (Egger’s test p = 0.027) were noted. Conclusions: These findings suggest that PD treatments negatively impact financial decision-making both directly and indirectly through cognitive and behavioral pathways. Integrating financial decision-making assessments into PD care, particularly for patients on dopamine agonists, is recommended. Future research should prioritize longitudinal studies and standardized neuropsychological measures to guide clinical practice and optimize patient outcomes. Full article

Background: Type 2 diabetes (T2D) has been placed among the risk factors for fragility (osteoporotic) fractures, particularly in menopausal women amid modern clinical practice. Objective: We aimed to analyze the bone status in terms of mineral metabolism assays, blood bone turnover markers (BTM), and bone mineral density (DXA-BMD), respectively, to assess the 10-year fracture probability of major osteoporotic fractures (MOF) and hip fracture (HF) upon using conventional FRAX without/with femoral neck BMD (MOF-FN/HF-FN and MOF+FN/HF+FN) and the novel model (FRAXplus) with adjustments for T2D (MOF+T2D/HF+T2D) and lumbar spine BMD (MOF+LS/HF+LS). Methods: This retrospective, cross-sectional, pilot study, from January 2023 until January 2024, in menopausal women (aged: 50–80 years) with/without T2D (group DM/nonDM). Inclusion criteria (group DM): prior T2D under diet ± oral medication or novel T2D (OGTT diagnostic). Exclusion criteria: previous anti-osteoporotic medication, prediabetes, insulin therapy, non-T2D. Results: The cohort (N = 136; mean age: 61.36 ± 8.2y) included T2D (22.06%). Groups DM vs. non-DM were age- and years since menopause (YSM)-matched; they had a similar osteoporosis rate (16.67% vs. 23.58%) and fracture prevalence (6.66% vs. 9.43%). In T2D, body mass index (BMI) was higher (31.80 ± 5.31 vs. 26.54 ± 4.87 kg/m²; p < 0.001), while osteocalcin and CrossLaps were lower (18.09 ± 8.35 vs. 25.62 ± 12.78 ng/mL, p = 0.002; 0.39 ± 0.18 vs. 0.48 ± 0.22 ng/mL, p = 0.048), as well as 25-hydroxyvitamin D (16.96 ± 6.76 vs. 21.29 ± 9.84, p = 0.013). FN-BMD and TH-BMD were increased in T2D (p = 0.007, p = 0.002). MOF+LS/HF+LS were statistically significant lower than MOF-FN/HF-FN, respectively, MOF+FN/HF+FN (N = 136). In T2D: MOF+T2D was higher (p < 0.05) than MOF-FN, respectively, MOF+FN [median(IQR) of 3.7(2.5, 5.6) vs. 3.4(2.1, 5.8), respectively, 3.1(2.3, 4.39)], but MOF+LS was lower [2.75(1.9, 3.25)]. HF+T2D was higher (p < 0.05) than HF-FN, respectively, HF+FN [0.8(0.2, 2.4) vs. 0.5(0.2, 1.5), respectively, 0.35(0.13, 0.8)] but HF+LS was lower [0.2(0.1, 0.45)]. Conclusion: Type 2 diabetic menopausal women when compared to age- and YSM-match controls had a lower 25OHD and BTM (osteocalcin, CrossLaps), increased TH-BMD and FN-BMD (with loss of significance upon BMI adjustment). When applying novel FRAX model, LS-BMD adjustment showed lower MOF and HF as estimated by the conventional FRAX (in either subgroup or entire cohort) or as found by T2D adjustment using FRAXplus (in diabetic subgroup). To date, all four types of 10-year fracture probabilities displayed a strong correlation, but taking into consideration the presence of T2D, statistically significant higher risks than calculated by the traditional FRAX were found, hence, the current model might underestimate the condition-related fracture risk. Addressing the practical aspects of fracture risk assessment in diabetic menopausal women might improve the bone health and further offers a prompt tailored strategy to reduce the fracture risk, thus, reducing the overall disease burden. Full article

Marine pollution from plastic pellets, small granules used as a raw material for plastic production, is a growing environmental problem with grave consequences for marine ecosystems, biodiversity, and human health. This form of primary microplastic is increasingly becoming the focus of environmental policies, owing to its frequent release into the marine environment during handling, storage, and marine transportation, all of which play a crucial role in global trade. The aim of this paper is to contribute to the ongoing discussions by highlighting the environmental risks associated with plastic pellets, which are recognized as a significant source of microplastics in the marine environment. It will also explore how targeted education and awareness-raising within the maritime sector can serve as key tools to address this environmental challenge. The study is based on a survey conducted among seafarers and maritime students to raise their awareness and assess their knowledge of the issue. Given their operational role in ensuring safe and responsible shipping, seafarers and maritime students are in a key position to prevent the release of plastic pellets into the marine environment through increased awareness and initiative-taking practices. The results show that awareness is moderate, but there is a significant lack of knowledge, particularly in relation to the environmental impact and regulatory aspects of plastic pellet pollution. These results underline the need for improved education and training in this area, especially among future and active maritime professionals. Full article

Public administrators are empirical experimenters by nature. This paper argues that policy experiments are functions of administrative institutional settings. These administrative traditions influence bureaucrat-led policy experiments. This argument is explored through the case of nature-based solutions in the European Union; a field that is reporting increasing policy experimentation across diverse geographies and across four of the archetypal administrative traditions. Our review of this nascent literature revealed 19 sources across multiple different disciplinary sources. These revealed that the Nordic tradition is effective due to its culture of discretionary risk taking in experimentation; the Rechtsstaat tradition supports longevity in policy experiments; and the Westminster tradition facilitates broad and inclusive experimental spaces. This offers significant new contributions to the tradition and policy experimentalist literature. By drawing out the relevance of heterogeneous institutional administrative settings on experiments, it adds to the growing discourse evidencing the policy impact of administrative tradition literature and showing why nature-based solutions in the EU have significant empirical value to these policy and administration literatures. Full article

Maternal and child health during pregnancy is an important issue in global public health, and the classification accuracy of fetal cardiotocography (CTG), as a key tool for monitoring fetal health during pregnancy, is directly related to the effectiveness of early diagnosis and intervention. Due to the serious category imbalance problem of CTG data, traditional models find it challenging to take into account a small number of categories of samples, increasing the risk of leakage and misdiagnosis. To solve this problem, this paper proposes a two-step innovation: firstly, we design a method of adaptive adjustment of misclassification loss function weights (MAAL), which dynamically identifies and increases the focus on misclassified samples based on misclassification rates. Secondly, a primary and secondary correction network model (MAC-NET) is constructed to carry out secondary correction for the misclassified samples of the primary model. Experimental results show that the method proposed in this paper achieves 99.39% accuracy on the UCI publicly available fetal health dataset, and also obtains excellent performance on other domain imbalance datasets. This demonstrates that the model is not only effective in alleviating the problem of category imbalance, but also has very high clinical utility. Full article

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article