Search Results (371)

This paper mainly studies tax inspection decision-making technology, aiming to improve the accuracy and robustness of target recognition, state estimation, and autonomous decision making in complex environments by constructing an application that integrates visual, radar, and inertial navigation information. Tax inspection is a universally complex phenomenon, but little is known about the use of innovative technology to arm tax auditors with tools in monitoring it. Thus, based on the legitimacy theory, there is an agreement between taxpayers and the tax authorities regarding adequate compliance with tax legislation. The use of systemic controls by tax authorities is essential to track stakeholders’ contracts and ensure the upholding of this mandate. The case study is exploratory, using participant observation, and interventionist approach to a tax auditing. The results indicated that partnership between experienced tax auditors and IT tax auditors offered several tangible benefits to the in-house development and monitoring of an innovative application. It also indicates that OCR supports a data lake for inspectors in which stored information is available on standby during inspection. Furthermore, auditors’ use of mobile applications programmed with intelligent perception and tracking resources instead of using searches on mainframes streamlined the inspection process. The integration of professional skepticism, empathy among users, and technological innovation created a surge in independence among tax auditors and ensured focus. This paper’s contribution lies in the discussion of the enhancement of tax inspection through target recognition, drawing on legitimacy theory to rethink the relationship between taxpayers and tax authorities regarding adequate compliance with tax legislation, and presenting an exploratory case study using a participant observation, interventionist approach focused on a tax auditor. The implications of this study for policy makers, auditors, and academics are only the peak of the iceberg, as innovation in public administration presupposes efficiency. As a suggestion for future dimensions of research, we recommend the infusion of AI into these tools for further efficacy and effectiveness to mitigate fraud in the undue appropriation of taxes and undue competition. Full article

Background and Objectives: GLP-1 receptor agonists (GLP-1 RAs; ATC A10BJ) and dual GLP-1/GIP agonist (ATC A10BX16) have expanded rapidly due to strong evidence in type 2 diabetes, obesity and metabolic dysfunction-associated steatotic liver disease (MASLD). Their high acquisition costs and accelerating uptake make them key drivers of pharmaceutical expenditure. This study quantified national utilization and expenditure trends for antihyperglycemic drugs in Croatia (2017–2024), with a focus on GLP-1 RA and dual GLP-1/GIP agonists. Materials and Methods: Aggregate national data on dispensed medicines, valued at wholesale pharmacy prices, were obtained from HALMED’s annual ATC/DDD reports. Utilization was expressed as defined daily doses per 1000 inhabitants per day (DDD/1000/day). We analyzed the total A10 and key subclasses. The dual GLP-1/GIP agonist was only marketed in 2024. Compound annual growth rates (CAGR) were calculated. Results: The total antihyperglycemic utilization increased from 66.9 to 96.8 DDD/1000/day (a 44.7% rise), while the total A10 expenditure increased from EUR 54.2 million to EUR 96.5 million, indicating that expenditure growth outpaced utilization growth. GLP-1 receptor agonist expenditure increased from EUR 5.2 million (2018) to EUR 28.6 million (2024) (CAGR 33.0%), reaching 29.8% of total A10 expenditure in 2024. Expenditure for GLP-1-based agents grew faster than their DDD volume because per-DDD acquisition costs are substantially higher than for SGLT-2 inhibitors. These growth patterns are consistent with trajectories reported in higher-uptake EU health systems, suggesting convergence rather than an outlier position for Croatia. Conclusions: Croatia experienced a rapid shift towards GLP-1 RA-based antihyperglycemic pharmacotherapy, with GLP-1-based therapies exerting a disproportionate budget impact. For payers, these surveillance data support budget forecasting and negotiation of pricing and reimbursement conditions; clinicians can use them to benchmark and optimize evidence-aligned prescribing; and policymakers can apply them to monitor the diffusion and fiscal impact of high-cost therapies. Routine national ATC/DDD analysis, complemented by HZZO claims and primary-care datasets, is essential for guiding future pricing, reimbursement and formulary decisions. Full article

Innovations in manual waste sorting have stagnated for decades, despite the increasing global demand for efficient recycling solutions. The recAIcle system introduces an innovative AI-powered assistance system designed to modernise manual waste sorting processes. By integrating machine learning, continual learning, and projection-based augmentation, the system supports sorting workers by highlighting relevant waste objects on the conveyor belt in real time. The system learns from the decision-making patterns of experienced sorting workers, enabling it to adapt to operational realities and improve classification accuracy over time. Various hardware and software configurations were tested with and without active tracking and continual learning capabilities to ensure scalability and adaptability. The system was validated in initial trials, demonstrating its ability to detect and classify waste objects and providing augmented support for sorting workers with high precision under realistic recycling conditions. A survey complemented the trials and assessed industry interest in AI-based assistance systems. Survey results indicated that 82% of participating companies expressed interest in supporting their staff in manual sorting by using AI-based technologies. The recAIcle system represents a significant step toward digitising manual waste sorting, offering a scalable and sustainable solution for the recycling industry. Full article

Background and Objectives: There is a worldwide increase in the demand for gender-affirming surgical treatments among transgender and gender-diverse (TGD) adults and adolescents. In Poland, transgender people generally lack trust in healthcare providers, which makes it more difficult for them to begin their transition process. This patient population is not well understood by many of the specialists who may potentially be involved in their care, in some way, reinforcing their concerns. The aim of this study is to present the sociodemographic characteristics of a group of female-to-male transgender patients who were admitted to a privately based plastic surgery center to undergo chest wall reconstruction. Materials and Methods: This study comprises a statistical analysis of data retrospectively obtained from the medical records of 100 patients from across the country undergoing female-to-male transition, who were operated on between 2021 and 2025 at a specialized private clinic in Poland. All individuals had already started gender-affirming medical treatment with testosterone at the time of first consultation. Results: The results show a trend toward a decreasing age at the time of the decision to undergo gender-affirming surgery. In the study group, 100% of patients were already undergoing hormone therapy. In our group of transgender individuals, we did not observe a correlation between cultural or social background, religion, and gender dysphoria. It is encouraging that more than half of the patients reported no longer needing psychiatric support, and that those who were still under specialist supervision stated that they experienced a significant improvement in their overall well-being. Conclusions: The rising demand for transgender healthcare highlights the need for studying and analyzing this group of patients in order to provide the best patient-centered care throughout the gender transition process by all specialists involved. Gender-affirming mastectomy, when combined with testosterone therapy, has a positive mental health impact on transgender individuals. Full article

Background: This study was conducted within the Qassim Health Cluster as part of efforts to operationalize the Saudi Model of Care (SMoC). It aims to evaluate the outcomes and value of palliative care services in alignment with SMoC by applying patient-centered measurement tools. In Saudi Arabia, Vision 2030 has spurred significant expansion in palliative care, yet challenges persist in home-based care and consistent outcome measurement. Current Quality-Adjusted Life Year (QALY) methodologies often fail to capture patient-centered dimensions (emotional, spiritual, and social support), leading to gaps in outcome assessments. Methods: This cross-sectional descriptive study characterized 147 oncology palliative care patients in Qassim, Saudi Arabia, from January to December 2024. Data on demographics, diagnoses, care duration, health-related quality of life (EQ-5D-5L), and patient experiences were collected via face-to-face interviews. Although QALY was conceptually referenced, the study utilized EQ-5D-5L as a proxy measure for patient-perceived health status. Results: The cohort was predominantly older females (64.63%) with various cancer diagnoses, primarily breast cancer (29.25%), and a long duration of palliative care. Patients reported significant impairments in mobility (21.77%), self-care, daily activities, and a high prevalence of pain and anxiety/depression. However, most felt respected by their care team (85.71%), experienced effective symptom management (68.03%), and were consistently involved in decision-making (68.03%). Conclusions: This study provides baseline data on oncology palliative care in Qassim, highlighting complex patient needs alongside positive perceptions of care. QALY methodologies must be refined to better capture patient-centered benefits and inform resource allocation, contributing to more responsive and effective palliative care services. However, due to the cross-sectional design, causal relationships between care exposure and outcomes cannot be inferred; hence, the findings should be interpreted as descriptive rather than causal. Full article

The global secondhand apparel industry, valued at USD 256B in 2025, is expanding rapidly. The growing acceptance of secondhand fashion and advancements in retail technology have driven millions of individuals to resell, yet little research has analyzed the motivations behind these decisions. Guided by Consumption Values Theory and Goal-Framing Theory, this qualitative study uses ten in-depth interviews with experienced resellers to examine why individuals participate in apparel reselling. Analysis of the participants’ narratives indicates that financial gain is the dominant driver of participation, followed by the convenience provided by reselling platforms and channels, emotional satisfaction, and contributing to sustainability. Conceptually, the study integrates value-based and goal-based lenses to offer an extensive explanation of reseller motivations, shifting focus from the buyer perspective that has dominated prior research. Practically, the findings suggest that resale platforms can encourage participation by reducing visible fees, enabling faster payout, and simplifying the reselling process, while also making community and environmental benefits more visible. In all, these insights help retailers and sustainability advocates better design approaches that support individual resellers and sustain growth in apparel resale. Full article

Background/Objectives: Autofluorescence (AF) is a widely used adjunctive tool in the detection of oral potentially malignant disorders (OPMDs) and malignant lesions, but its performance can be influenced by clinicians’ experiences. This study aimed to examine how AF influences diagnostic decision-making and performances of a novice clinician compared with those of an experienced examiner. Methods: A total of 80 patients with oral lesions participated in this cross-sectional study. Each underwent a standard oral examination (OE) followed by an assessment with the VELscope^® System Vx (LED Medical Diagnostics Inc., Burnaby, BC, Canada), independently conducted by an expert clinician (E) and a postgraduate dentist (PD), both blinded to each other’s results. Biopsy and histopathological analysis provided the reference diagnosis. After every examination, lesions were categorized as either “Risk of Malignancy” (RM) or “No Risk of Malignancy” (NRM). Results: Based on OE, PD identified 39 RM lesions, while E 29. AF with VELscope^® identified an additional 12 RM lesions for the PD and 7 for the E that were not suspected on OE alone. Combining OE with VELscope^® improved sensitivity (PD: 90.9%; E: 95.4%) and negative predictive value (PD: 91.7%; E: 97.6%), while decreasing specificity (PD: 37.9%; E: 70.7%) and positive predictive value (PD: 35.7%; E: 55.3%) compared with OE alone. Conclusions: AF increases diagnostic sensitivity, particularly for less experienced clinicians, while offering moderate advantages for experts. Nevertheless, the corresponding decline in specificity emphasizes the need for cautious interpretation. AF should be incorporated as a complementary tool within structured diagnostic pathways, accompanied by adequate training, and cannot replace histopathological confirmation or clinical expertise. Full article

Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and public health risks. This study develops a site-specific water quality index (WQI) for a gold mining area in Bam Province, Burkina Faso, with the objective of improving pollution monitoring and management in relation to tailing dams. Surface and groundwater samples were collected between 2021 and 2024. Physico-chemical and bacteriological analyses of groundwater sources including wells, piezometers and boreholes revealed that several parameters such as pH, turbidity, nitrates, sulphates, total iron, aluminium, arsenic, cadmium, cyanide and total and faecal coliforms exceeded international drinking water standards. Geospatial techniques were employed to identify the main contamination sources: domestic wastewater, industrial and artisanal mining and agricultural runoff. The evolution of these parameters in relation to the dynamics of soil occupation and the influence of geological structure has enabled the distinction of key parameters associated with discharges. Although individual contaminant levels were mostly moderate, their combined effects pose a significant long-term risk to ecosystems and human health. The tailored WQI is suitable for both surface water and groundwater. It provides an integrated tool for classifying and monitoring water quality in mining environments, supporting evidence-based decision making in the management of tailing dams, environmental protection and public health. Full article

The global adoption of solar-powered streetlights has grown significantly, driven by their cost-effectiveness and potential to reduce dependence on fossil fuels associated with conventional street lighting. Battery storage represents a substantial portion of the total capital cost in solar-powered streetlight systems. Therefore, selecting an efficient charge regulator is crucial to protect battery lifespan and reduce energy losses. In this context, the choice of an appropriate charge regulator plays a vital role in enhancing system reliability and overall performance. This study presents a practical approach for evaluating three commercially available 6 A-rated Pulse Width Modulation (PWM) solar charge regulators intended for recharging lead-acid batteries in a proposed 12 V off-grid solar photovoltaic (PV) street lighting system. The regulators were evaluated concurrently in separate circuits, each experiencing similar meteorological conditions, including similar temperature and solar irradiance. The measured data for each regulator were acquired using LabVIEW-based virtual instruments. The performance comparison was conducted using the Linear Regression Algorithm (LRA) to support decision-making. Based on the analysis, the most suitable PWM charge regulator was identified as the one offering the best charging performance due to low internal losses. Hence, solar battery charge regulators with identical load current ratings do not necessarily deliver equivalent charge/discharge performance. Full article

Background. West Nile Virus (WNV) represents a significant public health concern in Europe, with Italy—particularly its northern regions—experiencing recurrent outbreaks. Climate variables and vector dynamics are known to significantly influence transmission patterns, highlighting the need for reliable predictive models to enable timely outbreak detection and response. Methods. We integrated epidemiological data on human WNV infections in Italy (2012–2024) with high-resolution climate variables (temperature, humidity, and precipitation). Using advanced feature engineering and a gradient boosting framework (XGBoost), we developed a predictive model optimized through time-series cross-validation. Results. The model achieved high predictive accuracy at the national level (R² = 0.994, MAPE = 5.16%) and maintained robust performance across the five most affected provinces, with R² values ranging from 0.896 to 0.996. SHAP analysis identified minimum temperature as the most influential climate predictor, while maximum temperature and rainfall demonstrated considerably weaker associations with case incidence. Conclusions. This machine learning approach provides a reliable framework for forecasting WNV outbreaks and supports evidence-based public health responses. The integration of climate and epidemiological data enhances surveillance capabilities and enables informed decision-making at regional and local levels. Full article

Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning. Full article

With an aging population and the widespread adoption of smart technologies, elderly residents’ perceived safety in smart apartments has become a critical determinant of their quality of life and their acceptance of technology. However, much of the current research remains confined to either technical or psychological dimensions, with insufficient attention to the systematic interactions among multiple factors as experienced by elderly populations. This study aims to systematically evaluate and optimize the living environments of older adults, with the goal of enhancing their overall quality of life and subjective well-being. This study employs the DANP–mV model to empirically analyze the safety perception of older adults in smart apartments, integrating case-based investigation and evaluation to propose targeted optimization strategies and improvement pathways. Unlike traditional approaches that treat criteria as independent, this hybrid model reveals the interdependencies among factors and establishes a more realistic prioritization of improvement actions. The study found that, compared with merely reinforcing physical security measures, factors such as enhanced remote security support, a stronger sense of control and coping confidence, and higher satisfaction with the protective system exert a more fundamental influence on the overall safety perception. These results demonstrate that adopting a systems-thinking approach shifts the focus of decision-making from superficial safety risks to underlying causal drivers, thereby mitigating resource allocation imbalances and enhancing the effectiveness and sustainability of safety improvement measures. Full article

Pelvic organ prolapse (POP) affects millions of women around the world, with age-standardized prevalence rates of 2769 per 100,000 women in 2021. Although it greatly affects quality of life (QoL), only 18–50% of women experiencing this issue seek medical attention, largely due to a lack of knowledge, misunderstandings about the condition, and obstacles to accessing healthcare. This narrative review explores the progression of POP management towards a focus on patient-centered care, highlighting the importance of personalized treatment strategies that prioritize patient-reported outcomes (PROs) over solely anatomical factors. The approach to treatment has transitioned from being centered on anatomy to focusing on the patient, emphasizing the relief of symptoms and enhancement in QoL. Existing research indicates that monitoring without intervention is advisable for asymptomatic patients, as long-term studies have revealed that up to 40% of women experience stable or improved prolapse over a period up to 60 months. Pessary treatment has a fitting success rate above 90% and a treatment persistence rate of 60%, providing an effective non-surgical option for management. The approach to selecting surgical treatments has progressed to prioritize sufficient apical support as a key factor for achieving lasting results. For primary POP, native tissue repair (NTR) is now recommended as the first-line surgical option. Mesh-augmented repairs are used only in certain high-risk situations, whereas sacrocolpopexy offers the best anatomical stability for particular cases, such as those involving post-hysterectomy prolapse and recurrences. Contemporary POP management involves personalized, patient-focused decision-making that emphasizes addressing symptom severity and functional objectives rather than solely aiming for anatomical precision. The evidence suggests that NTR should be the primary surgical approach, while other procedures should be reserved for specially chosen patients. Success should primarily be evaluated based on PROs instead of anatomical factors, ensuring that treatments align with each patient’s preferences and expectations while reducing complications. Full article

The rational delineation of open-pit mining areas constitutes the core foundation for achieving safe, efficient, economical, and sustainable mining operations. The quality of this decision-making directly impacts the economic benefits experienced throughout the mine’s entire lifecycle and the efficiency of resource recovery. Traditional open-pit mining area delineation relies on an experience-driven manual process that is inefficient and incapable of real-time dynamic data optimization. Thus, there is an urgent need to establish an intelligent decision-making model integrating multi-source data and multi-objective optimization. To this end, this study proposes an intelligent mining area division algorithm. First, a geological complexity quantification model is constructed, incorporating innovative adaptive discretisation resolution technology to achieve precise quantification of coal seam distribution. Second, based on the quantified stripping-to-mining ratio within grids, a block-growing algorithm generates block grids, ensuring uniformity of the stripping-to-mining ratio within each block. Subsequently, a matrix of primary directional variations in the stripping-to-mining ratio is constructed to determine the principal orientation for merging blocks into mining areas. Finally, intelligent open-pit mining area delineation is achieved by comprehensively considering factors such as the principal direction of mining areas, geological conditions, boundary shapes, and economic scale. Practical validation was conducted using the Shitoumei No. 1 Open-Pit Coal Mine in Xinjiang as a case study in engineering. Engineering practice demonstrates that adopting this methodology transforms mining area delineation from an experience-driven to a data-driven approach, significantly enhancing delineation efficiency. Manual simulation of a single scheme previously required approximately 15 days. Applying the methodology proposed herein reduces this to just 0.5 days per scheme, representing a 96% increase in efficiency. Design costs were reduced by approximately CNY 190,000 per iteration. Crucially, the intelligently recommended scheme matched the original design, validating the algorithm’s reliability. This research provides crucial support for theoretical and technological innovation in intelligent open-pit coal mining design, offering technical underpinnings for the sustainable development of open-pit coal mines. Full article

In a significant transition from classical theories of efficient markets and perfectly rational investors, the recent literature has increasingly acknowledged the importance of the human element and external market conditions in decision-making. However, the application of adaptive market frameworks in the property sector remains underexplored. This gap is particularly pronounced in the commercial property market, where structural inefficiencies, such as information asymmetry and illiquidity, amplify decision-making complexity. Given that investor rationality tends to diminish as uncertainty and complexity increase, this study explored how private commercial property investors adapt their strategies amid heightened uncertainty. The perspectives of seven experienced property experts were thematically analysed to highlight recurring patterns, which were then integrated into a conceptual mind map. The findings reveal that while economic fundamentals are the constant drivers of capital allocation decisions, investors process these signals through the lens of adaptive behaviour based on intuition, experience, risk perceptions, and herding. This relationship becomes more pronounced under conditions of heightened uncertainty, where investors seek to supplement available information with sentiment due to weaker signals and declining confidence in fundamentals. Sustainable investing and technology integration also emerged as core considerations, but interest among private investors is subdued due to ambiguous value propositions regarding the long-term economic benefits of a green premium. These findings offer practical insights into how external market conditions influence property investment decisions and provide a platform for operational models of investment decision-making that integrate adaptive behaviour. Full article