Search Results (216)

The confluence of financial constraints, climate change mitigation efforts, and evolving user expectations has significantly transformed the concept of facility management (FM). Traditional FM has now evolved to enhance sustainability in the built environment. Sustainable facility management (SFM) can add value to companies, organizations, and governments by balancing the financial, environmental, and social outcomes of the FM processes. The systematic literature review revealed a limited number of studies developing a performance measurement framework for SFM in office buildings and/or other building types in the literature. Given that the lack of this theoretical basis inhibits the effective deployment of SFM practices, this study aims to fill this gap by developing a performance measurement framework for SFM in office buildings. Accordingly, an in-depth literature review was initially conducted to synthesize sustainable performance measurement factors. Next, a series of focus group discussion (FGD) sessions were organized to refine and verify the factors and develop a novel performance measurement framework for SFM. Lastly, consistency analysis, the Bayesian best worst method (BBWM), and sensitivity analysis were implemented to determine the priorities of the factors. What the proposed framework introduces is the combined use of two performance measurement mechanisms, such as continuous performance measurement and comprehensive performance measurement. The continuous performance measurement is conducted using high-priority factors. On the other hand, the comprehensive performance measurement is conducted with all the factors proposed in this study. Also, the BBWM results showed that “Energy-efficient material usage”, “Percentage of energy generated from renewable energy resources to total energy consumption”, and “Promoting hybrid or remote work conditions” are the top three factors, with scores of 0.0741, 0.0598, and 0.0555, respectively. Moreover, experts should also pay the utmost attention to factors related to waste management, indoor air quality, thermal comfort, and H&S measures. In addition to its theoretical contributions, the paper makes practical contributions by enabling decision makers to measure the SFM performance of office buildings and test the outcomes of their managerial processes in terms of performance. Full article

Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. The integration of numerical and categorical variables, which are part of a dataset for defining ore grades, is part of the daily routine of professionals who obtain the data and manipulate the various phases of analysis in a mining project. Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. The objective of this study is the classification of gold ore or gangue through supervised machine learning methods using numerical variables represented by grade and categorical variables obtained through drillholes descriptions. Four groups of variables were selected with different variable configurations. The application of classification algorithms to different groups of variables aimed to observe the variables of importance and the impact of each one on the classification, in addition to testing the best algorithm in terms of accuracy and precision. The datasets were subjected to training, validation, and testing using the decision tree, random forest, Adaboost, XGBoost, and logistic regression methods. The evaluation was randomly divided into training (60%) and testing (40%) with 10-fold cross-validation. The results revealed that the XGBoost algorithm obtained the best performance, with an accuracy of 0.96 for scenario C1. In the SHAP analysis, the variable As was prominent in the predictions, mainly in scenarios C1 and C3. The arsenic class (Class_As), present mainly in scenario C4, had a significant positive weight in the classification. In the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) curves, the results showed that XGBoost/scenario C1 obtained the highest AUC of 0.985, indicating that the algorithm had the best performance in ore/gangue classification of the sample set. The logistic regression algorithm together with AdaBoost had the worst performance, also varying between scenarios. Full article

This in vitro study aims to investigate the fracture properties of 3D-printed resin provisional material designed with different connector dimensions for two-unit fixed dental prostheses (FDPs). The master model was digitally designed following Shillingburg’s all-ceramic restoration tooth preparation guidelines and milled from aluminum. Four two-unit FDPs with different connector dimensions were designed: 2 × 3 mm, 3 × 3 mm, 3 × 4 mm, and 4 × 4 mm (width × length) (Groups A, B, C, and D, respectively; n = 10 for each group). These specimens were printed using 3D-printed resin material (Detax FREEPRINT^® temp). Forty specimens were subjected to a three-point test using a universal testing machine until fracture. The failure mode was examined under a stereomicroscope. The Kruskal–Wallis test at α = 0.05 revealed non-significant differences in fracture resistance load but significantly different elastic modulus, yield strength, and compressive strength (p = 0.061, p < 0.001, p < 0.001, and p < 0.001, respectively) among the different groups. The 2 × 3 mm connectors had higher means of modulus, yield strength, and compressive strength compared to the other groups. The study found that the maximum load causing fractures in 3D-printed provisional material connectors was consistent, regardless of connector cross-section variations. The 2 × 3 mm group performed best, while the 4 × 4 mm group performed worst. Full article

Background/Objectives: Loop diuretics are among the most commonly used drugs in patients with heart failure with reduced ejection fraction (HFrEF). Higher doses of these diuretics are associated with poorer clinical status and may contribute to malnutrition. The study aims to assess the relationship between the use of high-dose loop diuretics and nutritional status in patients with HFrEF. Methods: The study included 353 hospitalized patients with HFrEF. Nutritional status was assessed using the Mini Nutritional Assessment (MNA), the Geriatric Nutritional Index (GNRI), and the CONtrolling NUTritional status (CONUT). Patients were divided into three groups according to the daily dose of loop diuretics (defined as furosemide equivalent = 1 × furosemide dose and 2 × torsemide dose): low dose (LD), 40 mg/day or no treatment; medium dose (MD), 41–160 mg/day; or high dose (HD), >160 mg/day. Results: Of the evaluated patients, the mean MNA score was 23.31 ± 2.93 points, and 49.8% were at risk of malnutrition or malnourished. According to the MNA, patients in HD and MD groups had worse nutritional status than the LD group, similarly according to the GNRI. For CONUT, the differences were significant between all groups: nutritional status was the worst in the HD group, intermediate in the MD group, and the best in the LD group. Conclusions: The intake of loop diuretics, especially in high doses, correlates with an elevated risk of malnutrition in patients with HFrEF independently of sex, age, NYHA class, and left ventricular ejection fraction. Full article

Arguably, the most fundamental problem in Network Science is finding structure within a complex network. Often, this is achieved by partitioning the network’s nodes into communities in a way that maximizes an objective function. However, finding the maximizing partition is generally a computationally difficult NP-complete problem. Recently, a machine learning algorithmic scheme was introduced that uses information within a set of partitions to find a new partition that better maximizes an objective function. The scheme, known as RenEEL, uses Extremal Ensemble Learning. Starting with an ensemble of K partitions, it updates the ensemble by considering replacing its worst member with the best of L partitions found by analyzing a reduced network formed by collapsing nodes, which all the ensemble partitions agree should be grouped together, into super-nodes. The updating continues until consensus is achieved within the ensemble about what the best partition is. The original K ensemble partitions and each of the L partitions used for an update are found using a simple “base” partitioning algorithm. We perform an empirical study of how the effectiveness of RenEEL depends on the values of K and L and relate the results to the extreme value statistics of record-breaking. We find that increasing K is generally more effective than increasing L for finding the best partition. Full article

Background: As the rehabilitation of the upper anterior maxilla primarily requires high predictability of successful aesthetic outcomes, procedures of immediate implant placement are frequently employed. The aim of this pilot study was to retrospectively evaluate the short-term outcomes of a protocol of immediate implant placement in fresh extraction sockets, followed by immediate non-functional provisional restorations. Methods: Patients were treated for the replacement of maxillary central or lateral incisors, or cuspid teeth with a single-crown locking-taper implant. Clinical and photographic records were retrospectively compared between the teeth prior to extraction (T0) and restorations one year after prosthetic loading (T1). Outcomes were analyzed using the Pink Esthetic Score (PES), according to the patient’s phenotype (thin/thick), with or without the use of connective tissue graft (CTG). Results: The overall mean PES of 25 implants treated was 9.24 ± 2.36 at T0 and 9.60 ± 1.70 at T1. Comparison of groups between T0 and T1 revealed significant PES variations (p = 0.04), with the best and the worst scores, respectively, registered for thin + CTG group (from 7.50 ± 1.91 to 9.75 ± 2.87) and thin group (from 11.33 ± 2.33 to 10 ± 0.89); moderate increases were assessed for thick group (from 8.44 ± 2.40 to 9.44 ± 2.12) and thick + CTG group (from 9.50 ± 1.04 to 9.33 ± 0.81). Conclusions: Within the limits of a short-term analysis of a small number of patients, immediate implant rehabilitation for aesthetic areas of the upper maxilla can be assumed as a safe and predictable protocol. Concomitant use of CTG seems to provide beneficial effects in thin phenotypes, not any additional value in thick phenotypes. Full article

Health tourism is an increasingly vital sector for both Kayseri and Türkiye, contributing significantly to exports and foreign currency inflows. Recent investments in health tourism infrastructure have positioned Kayseri as one of the leading cities in the country, particularly due to its strong healthcare facilities. This study explores Kayseri’s potential in health tourism, with a focus on bariatric surgery, by employing Multi-Criteria Decision Making (MCDM) and optimization methods. The study first provides an extensive literature review to identify the key factors influencing patients’ selection of health institutions for bariatric surgery. Subsequently, the Group Best-Worst Method (G-BWM) is applied using expert input from managers of bariatric surgery centers to determine the relative importance of these factors. Based on the G-BWM findings, nine health institutions in Kayseri offering obesity surgery services are evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which generates institutional performance scores. Building on these results, a Goal Programming model is developed to assign patients to suitable health institutions while simultaneously considering the health institution’s revenue and patient satisfaction. This study offers several novel contributions. It integrates MCDM techniques with goal programming in the context of health tourism—a combination not widely explored in the literature. Additionally, it provides a comparative assessment of the factors influencing health tourists’ decision-making processes, offering policymakers a strategic framework for resource allocation. Lastly, by presenting a mathematical model for patient-institution assignment, the study offers practical guidance for health tourism organizations aiming to enhance both health institution revenue and patient satisfaction in the health tourism sector. Full article

Background and Objectives: Lateral epicondylitis, commonly known as tennis elbow, is a prevalent condition characterized by pain and tenderness over the lateral epicondyle. Various treatment options, including corticosteroids, platelet-rich plasma (PRP), and saline injections, are utilized, yet their comparative efficacy remains unclear. Hypothesis: This study hypothesizes that PRP injections result in superior functional and clinical outcomes compared to corticosteroid and saline treatments, as assessed by clinical scoring systems and radiological findings. Materials and Methods: The study enrolled patients aged 18 years and older with pain and tenderness over the lateral epicondyle persisting for at least three months and no prior treatment. Patients with comorbidities affecting the upper extremity were excluded. Fifty-five elbows from 50 patients were randomized into three groups (glucocorticoid, PRP, and saline). Functional outcomes were assessed using the Visual Analog Scale (VAS), Patient-Rated Tennis Elbow Evaluation (PRTEE), and Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire. Radiological evaluations included vascularity and superb microvascular imaging (SMI) indices via ultrasonography before injection and three months post-injection. Results: Fourteen patients were lost to follow-up, leaving 36 patients (36 elbows, 16 males and 20 females; mean age 42.4 ± 6.15 years) for analysis. The glucocorticoid group included 13 elbows, PRP group 14 elbows, and saline group 14 elbows. Baseline functional and radiological scores were similar across groups. At three months, PRP and glucocorticoid groups showed no significant differences in VAS scores (p = 0.7), but PRP outperformed both of the other groups in DASH and PRTEE scores, with the saline group performing the worst (p < 0.001). PRP consistently achieved the best outcomes at both three and six months. Radiological assessments revealed no significant group differences in vascularity or SMI indices (p = 0.3 and p = 0.2, respectively). Conclusions: PRP treatment demonstrated superior functional outcomes in early and mid-term evaluations compared to glucocorticoid and saline. However, ultrasonographic measures of vascularity and SMI did not correlate with functional outcomes. Clinical Relevance: PRP offers a promising treatment option for lateral epicondylitis, with superior functional improvements over other commonly used injections. Radiological assessments of vascularity and SMI may not reliably predict clinical outcomes. Full article

The integration of Industry 4.0 and Artificial Intelligence (AI) technologies has redefined global supply chain operations, with increasing emphasis on sustainability as a strategic priority. Despite this evolution, there remains a significant gap in the literature regarding the structured prioritization of sustainability-related indicators influenced by digital transformation. This study addresses that gap by identifying and ranking key sustainability enablers across environmental, operational, strategic, and social dimensions using the Best–Worst Method (BWM), a robust multi-criteria decision-making (MCDM) technique. Based on expert input from 37 professionals in the fields of supply chain management, sustainability, and digital technologies, twenty indicators were evaluated within four separate thematic groups. Results reveal that Emissions Monitoring and Reduction and Energy Efficiency are the most critical in the environmental dimension, while Supply Chain Traceability and Smart Inventory Management dominate the operational category. Supply Chain Resilience is identified as the top strategic factor, and Ethical Sourcing is deemed most vital from a social sustainability standpoint. These findings provide actionable insights for policymakers and practitioners, supporting data-driven decision-making and strategic alignment of digital investments with sustainability goals. This research contributes to both academic discourse and practical frameworks by offering a replicable approach to prioritizing sustainability indicators in the context of digital transformation. This study also identifies limitations and proposes future research directions to enhance the integration of digital and sustainable development in global supply chains. Full article

This study compared two groups of furniture joints, i.e., a so-called ready-to-assemble (RTA) plastic biscuit joint from Lamello©, while the second group consists of four types of eccentric joints with beech dowels. L-shaped specimens were prepared with the help of the selected joints and a three-layer particleboard with dimensions of 150 × 150 × 400 mm. These L-shaped specimens were tested for bending moment capacity under compression and under tension. Cam joints with wooden dowels can withstand high stress. If Lamello© Bisco P-15 joints are added to the plastic Clamex P-14 joint, this joint will achieve 13% higher values for bending moment capacity under compression and 22% under tension. During testing, the worst result was achieved by the Tenso P-14 joint. The best values achieved during the testing of bending moment capacity under compression and under tension were for an eccentric joint with the use of a metal-capped bolt and Euro screw. This joint achieved 147% higher values for bending moment capacity under compression than a standard eccentric joint with a euro screw bolt and 213% higher values for bending moment capacity under compression than the Lamello© and Clamex P-14 joints. This study aimed to determine how the joints differ, how they behave during testing, and what deformations occur. Full article

This paper investigates wine tourists’ preferences for the attributes of the wine tourism experience (WTEXP) in Italy and Turkey, exploring cross-cultural differences and similarities in two countries with diverse wine tourism development. Data were collected through an online survey of 253 wine consumers, and the Best–Worst Scaling method was employed in both countries to assess the perceived importance of selected WTEXP attributes that influence tourists’ choices. The samples were then segmented using cluster analysis based on key attitudinal scales (e.g., wine involvement), with BWS applied to each segment to further examine visitor preferences. The results show that both Italian and Turkish wine tourists prioritized expert-led tours but differed in other preferences. Italian tourists valued the winery’s aesthetic appeal, while Turkish tourists favored pre-visit informative sessions. Additionally, Italians placed less importance on accompanying events, while Turks considered the reputation of the wine, winery, or wine region the least significant factor. These preferences also varied within the clusters identified in each sample. This research represents the first comparison of consumer preferences in wine tourism between Turkey, a developing market, and Italy, a traditional Old World wine producer, while considering the diversity within each group. The findings provide key insights for wine tourism stakeholders, such as wineries and tourism managers, offering actionable recommendations to tailor their offerings for specific tourist segments to attract a larger number of wine tourists, enhance their experience, and foster sustainable growth of wine tourism. Full article

Background: In this study, we evaluated the changes in clinicopathological features and prognosis in patients with thyroid cancer in the last two decades using the Surveillance, Epidemiology, and End Results Database (SEER) data. Methods: Data from the SEER-12 registry (1992–2021) were analyzed, focusing on patients diagnosed with malignant thyroid cancer between 2001 and 2020. The study population was divided into Cohort 1 (2001–2010) and Cohort 2 (2011–2020). Cohorts 1 and 2 were compared regarding clinicopathological features and prognosis. Results: The study included 94,892 patients diagnosed with thyroid cancer between 2001 and 2020, with 39,265 patients in Cohort 1 and 55,627 in Cohort 2. Compared to Cohort 1, in Cohort 2 showed a statistically significant increase in the proportion of patients aged 60+ (+4.2%), male patients (+2.1%), and cases of papillary cancer (+5.3%) and regional disease (+3.7%) (all p < 0.001). Although Cohort 2 demonstrated an 8% improvement in survival compared to Cohort 1, this result was not statistically significant (p = 0.057). Prognostic factors were identified, such as disease stage at diagnosis, age, gender, and origin. Among pathological subtypes, the patients with papillary + FV had the best prognosis (HR: 0.78), compared to patients in the other group, mainly comprising anaplastic tumors and sarcomas, which had the worst prognosis (HR: 9.61). Conclusions: In this large-scale study of thyroid cancer patients, we found significant differences between the two cohorts. In Cohort 2, the proportion of patients aged ≥60 years, male, and with papillary thyroid cancer was increased. We found that age, sex, origin, histopathological subtype, and stage at diagnosis were prognostic factors in patients with thyroid cancer. Also, we observed a trend toward improved survival in Cohort 2. Full article

Supplier selection in the shipbuilding industry is a typical multicriteria group decision-making (MCGDM) problem, often characterized by significant uncertainty and fuzziness. To address this issue effectively, this paper proposes a novel integrated approach for supplier selection in shipbuilding enterprises by combining intuitionistic fuzzy sets (IFSs) with the weighted aggregated sum product assessment (WASPAS) method. The proposed method utilizes IFS operators alongside an innovative process for evaluating indicator weights. Initially, an intuitionistic fuzzy number approach is employed to obtain indicator data, which effectively captures the uncertainty of linguistic variables and ensures accurate reflection of real-world conditions. Subsequently, the indicator weights are evaluated by integrating subjective weights, derived through the best–worst method, with objective weights, calculated using an entropy-based approach, resulting in more balanced and realistic weight assignments. Subsequently, the WASPAS method is used to prioritize alternative suppliers, and a shipbuilding enterprise in Shanghai is taken as an example to verify the effectiveness of the model. In addition, to evaluate the stability of the proposed method, sensitivity analyses were performed for varying attribute values. The results demonstrate that the combination of subjective and objective weights enhances the stability of the method under varying attribute weights. Finally, a comparison with various existing methods based on intuitionistic fuzzy information proves that the proposed method exhibits certain advantages in solving the MCGDM problem under uncertain environments. Full article

The heterogeneity of breast tumors might reflect biological complexity and provide prediction clues for the sensitivity of treatment. This study aimed to construct a model based on tumor heterogeneity in magnetic resonance imaging (MRI) for predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). This retrospective study involved 217 patients with biopsy-confirmed invasive breast cancer who underwent MR before and after NAC. Patients were randomly divided into the training cohort and the validation cohort at a 1:1 ratio. MR images were processed by algorithms to quantify the heterogeneity of tumors. Models incorporating heterogeneity and clinical characteristics were constructed to predict pCR. The patterns of heterogeneity variation during NAC were classified into four categories abbreviated as the heterogeneity high-keep group (H_keep group), heterogeneity low-keep group (L_keep group), heterogeneity rising group, and decrease group. The average heterogeneity in patients achieving pCR was significantly lower than in those who did not (p = 0.029). Lower heterogeneity was independently associated with pCR (OR, 0.401 [95%CI: 0.21, 0.76]; p = 0.007). The model combining heterogeneity and clinical characteristics demonstrated improved specificity (True Negative Rate 0.857 vs. 0.698) and accuracy (Accuracy 0.828 vs. 0.753) compared to the clinical model. Survival outcomes were best for the L_keep group and worst for the rising group (Log-rank p = 0.031). Patients with increased heterogeneity exhibited a higher risk of recurrence approaching two years post-surgery, particularly within the non-pCR population. The quantified heterogeneity of breast cancer in MRI offers a non-invasive method for predicting pCR to NAC and evaluating the implementation of precision medicine. Full article

This study was conducted to evaluate the protective efficacy of modified live vaccines (MLVs) against porcine reproductive and respiratory syndrome (PRRS) in nursery pigs in a worst case scenario where MLV does not match the genetic profile of the field isolate, different MLVs are used for sows and piglets, and piglets are naturally exposed to genetically distinct heterologous PRRS virus (PRRSV) isolates. We divided 76,075, 2-week-old piglets from a seropositive sow herd vaccinated with US1-MLV into four groups. US1-MLV, US2-MLV, and US3-MLV groups were vaccinated with PRRSV-2 MLV including Ingelvac^® PRRS MLV (Boehringer Ingelheim, Ingelheim am Rhein, Germany), HP-PRRSV-2 based MLV (Harbin Veterinary Research Institute, CAAS, Harbin, China), and Prime Pac^® PRRS (MSD Animal Health, Rahway, NJ, USA), respectively. The NonVac group was left unvaccinated. At 0, 14, 28, and 56 days post-vaccination (DPV), sera were assayed for the presence of PRRSV-specific antibodies using ELISA and serum neutralization (SN), and PRRSV RNA using PCR. Average daily gain (ADG) and survival rates were compared between treatment groups. The results demonstrated vaccinated groups significantly improved in ADG compared to the non-vaccinated control group. Only US1-MLV and US3-MLV were able to significantly reduce mortality associated with field PRRSV infection in nursery pigs. Pigs vaccinated with US3-MLV displayed significantly lower mortality and higher ADG compared to all other groups. Field isolates were isolated and genetically compared to all three MLV vaccines at the start of the trial. The MLV with closest genetic similarity to the field isolate was US2-MLV by ORF5 gene comparison. This provided the lowest protection judging by ADG improvement and mortality reduction, as compared to US1-MLV and US3-MLV. Separately, strains of Thai PRRSV-2 isolates collected in 2017, 2019, and 2020 in the study area were investigated for evolutionary changes. Over time, we observed a shift in PRRSV-2 isolates from lineage 8.7 to lineage 1. The field isolates found shared 82.59–84.42%, 83.75–85.74%, and 84.25–85.90% nucleotide identity with the US1-MLV, US3-MLV and US2-MLV based vaccine, respectively. Our findings suggest genetic similarity between field viruses and vaccine strains should not be used as a predictor of field performance. We found that zootechnical performance of piglets was best in US3-MLV, despite sows being treated with a different vaccine The results also support that different MLVs can be used at different stages of production. Finally, we concluded that the shift from lineage 8.7 to lineage 1 was due to shifts in the worldwide prevalence of PRRSV isolates during that period of time and not due to vaccine recombination between isolates. Overall, MLV vaccine selection should be based on production performance and safety profile. Full article