Performance Data Research Articles

Safety, Dosimetry, and Feasibility of [68Ga]Ga-PSMA-R2 as an Imaging Agent in Patients with Biochemical Recurrence or Metastatic Prostate Cancer.

Prostate-specific membrane antigen (PSMA) is highly expressed in most prostate cancers (PCs). PET and CT imaging studies using 68Ga-labeled PSMA ligands demonstrated the specific localization of 68Ga in PC lesions and distant metastatic lesions. [68Ga]Ga-PSMA-R2 (68Ga-PSMA-R2) is a PSMA-targeted PET/CT radiotracer with potential diagnostic applications. Methods: PROfind (NCT03490032) was a phase 1/2, open-label, multicenter study of administration of 3 MBq/kg of 68Ga-PSMA-R2 (from >150 to ≤250 MBq) in patients with biochemical recurrence (BCR) or metastatic PC (mPC). Participants underwent baseline conventional imaging (CT/MRI or bone scan) and PET/CT. Whole-body PET/CT imaging sequences were obtained between 20 min and 4 h after injection. Primary endpoints were safety and tolerability; secondary endpoints included biodistribution, potential lesion identification, pharmacokinetics, and dosimetry. Potential lesions were identified by 2 masked expert panels; a third panel evaluated the identified lesions. Results: Six patients with BCR were enrolled into phase 1, and 24 patients with BCR or mPC (n = 12 each) into phase 2. Thirteen treatment-emergent adverse events were reported, including 1 serious adverse event (ileus), unrelated to drug administration. All adverse events were mild or moderate and deemed not related to 68Ga-PSMA-R2. Peak blood concentration of 68Ga-PSMA-R2 was typically observed approximately 5 min after injection, steadily decreasing over 6 h. Mean absorbed radiation dose was highest in the urinary bladder wall (0.120 mGy/MBq) and kidney (0.061 mGy/MBq). No other organ mean absorbed radiation dose exceeded 0.020 mGy/MBq. Mean absorbed radiation doses in the salivary and lacrimal glands were 0.016 and 0.008 mGy/MBq, respectively. Mean total body absorbed radiation dose was 0.014 mGy/MBq. Mean effective total body dose was 0.015 mSv/MBq (range, 0.012-0.018 mSv/MBq). 68Ga-PSMA-R2 PET/CT detected 85 lesions in 22 participants at 1 h after injection and 103 lesions in 22 participants at 2 h after injection. Conventional imaging detected 49 lesions in 8 participants with mPC but none in participants with BCR. Conclusion: 68Ga-PSMA-R2 was well tolerated, with no drug-related treatment-emergent adverse events. Safety and preliminary imaging performance data support further development of 68Ga-PSMA-R2 as a diagnostic agent in patients with PC.

Impact of Automation on Retail Logistics: AI-Powered Solutions for Efficient Supply Chains

This research explores the transformative impact of automation on retail logistics, focusing specifically on AI-powered solutions that enhance supply chain efficiency. As e-commerce continues to grow, retailers face increasing pressure to streamline logistics processes to meet rising consumer expectations for fast, reliable deliveries and cost-effective operations. AI technologies, such as machine learning, predictive analytics, and autonomous systems, have emerged as critical tools in addressing these challenges by improving various logistics functions, including demand forecasting, inventory management, route planning, and warehouse automation. This study investigates the role of AI-powered solutions in enhancing operational performance, reducing costs, and boosting customer satisfaction across retail logistics networks. A mixed-methods approach is employed, combining quantitative analysis of logistics performance data and qualitative case studies from retail companies that have integrated AI technologies into their supply chains. The quantitative analysis examines key performance indicators such as delivery time, cost per order, inventory turnover, and customer satisfaction before and after the adoption of AI solutions. The qualitative analysis draws on case studies from companies like Amazon, Walmart, and JD.com to explore the real-world applications and challenges of AI in retail logistics. The results indicate that AI automation improves forecasting accuracy, reduces operational costs, enhances inventory management, and improves delivery efficiency. However, challenges such as system integration, data quality, and workforce adaptation are also identified. This study contributes to understanding the current state of AI applications in retail logistics and offers practical insights for industry leaders aiming to optimize their supply chains. Keywords: Automation, Artificial Intelligence, Retail Logistics, Supply Chain Efficiency, Machine Learning, Inventory Management, Predictive Analytics, E-commerce, Cost Reduction, Customer Satisfaction

Evaluation of Ohio Agricultural Education Students’ Performance and User Experience in a Virtual Reality Machinery Safety Experience

Agriculture is one of the most hazardous industries in the United States for all workers, and even more so for young workers. In the U.S., legislation prescribes training for youth under the age of 16 working in hazardous situations in production agriculture. Virtual Reality (VR) technology has become an increasingly popular means of deploying training for various disciplines. The purpose of this study was to determine the feasibility of a VR curriculum to provide a realistic and positive user experience for students in tractor and machinery safety operation lessons. The VR curriculum developed for the study was based on the National Safe Tractor and Machinery Operation Program. Ohio Agricultural Education students’ (n = 132) user experience data were analyzed, and found students had a positive experience in the virtual reality training. Results indicated that students had a positive user experience from the VR experience. Performance data from agricultural education students resulted in poor scores during the precheck and driving course. Finally, two groups of students comparing traditional training versus training using the VR experience resulted in a non-significant difference between the pass/fail rates. The implications of these findings suggest VR can provide a supplemental training method for tractor and machinery programs. It is recommended that this study be replicated with a larger sample of students as well as the evaluation of other VR-based educational experiences.

A data-and-knowledge-driven framework for automated generation of asphalt pavement maintenance strategies

ABSTRACT Due to the inefficiency of manual decision-making, automation of pavement maintenance strategy-making (PMSM) has become a focal point of attention in pavement engineering. The current automated PMSM processes lack a framework capable of integrating engineering prior knowledge into decision. In this paper, a data-and-knowledge-driven (DKD) framework was proposed to achieve the automated generation of asphalt pavement maintenance strategies. A pavement performance prediction model Convolutional Neural Network (CNN) was trained considering multiple influencing factors to analyse pavement deterioration, and maintenance thresholds were set according to predicted performance data distribution. The model incorporating Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long-Short-Term Memory (Bi-LSTM) and Conditional Random Field (CRF) was employed to extract the name entities from pavement maintenance domain knowledge, constructing the knowledge graph to achieve maintenance treatment determination. A freeway in Eastern China was selected for case study. The result demonstrated that 87 one-hundred-meter pavement sections on the freeway need preventive maintenance totally, utilising micro-surfacing in 2023. Furthermore, a level of automation assessment method and a reasonableness of decision process assessment method were proposed to assess this framework. The results indicate that this framework significantly enhances the level of automation compared to traditional PMSM methods while maintaining decision reasonableness.

Reach of the Instagram profile @microbioworld in popularizing mycology and microbiology.

Online social networks have revolutionized scientific communication, making platforms like Instagram indispensable for sharing complex topics, including mycology. This study evaluated three key factors in assessing the impact of social media on scientific dissemination: follower profiles, reach and engagement levels. We used the professional Instagram account @microbioworld as a case study. Account performance data was collected over a 90-day period (August 12-November 9, 2024). Post performance was evaluated using data from selected posts published between January 11 and November 11, 2024. By the end of our sampling period, the account reached a total of 45 959 followers, with the majority aged 25-34 years (44.8%). It reached 108 631 unique accounts, with 22.4% being followers and 77.6% non-followers, generating 236 860 impressions and 15 750 interactions. Likes accounted for 83.3% of engagement. Posts featuring microorganism cultures achieved the highest engagement and reach. Sentiment analysis, using Bing and AFINN lexicons, revealed that over 89% of sentiments expressed in comments were positive. These findings demonstrate how Instagram can disseminate microbial content, foster positive perceptions of microorganisms, and emphasize their ecological importance, encouraging audience involvement with mycology and microbiology.

Accuracy of PVSyst Simulations in the Reproduction of the Yield Performance of Multicrystalline, Monocrsytalline and Monocasting Modules in Outdoor Conditions

In this study we performed field tests on three small installations consisting of 12 modules each and next to each other, all of them using exactly the same manufacturing technology for cells and modules and employing the same manufacturing equipment and raw materials. The only difference between the systems was the wafer technology used. Two of them were manufactured through the same casting technology (Direct solidification system-DSS) and the same equipment: in one of them we grew mc-Si and in the other one CM-Si (mono casting). In the third system we used traditional Czochralski monocrystalline wafers as technology. Two of the facilities (the DSS based ones) have been closely monitored for three years, and the traditional monocrystalline one for 17 months. For the three installations the performance data is shared and compared with PVsyst simulations and correlated well to each other.

Ranking with ties based on noisy performance data

Abstract We consider the problem of ranking a set of objects based on their performance when the measurement of said performance is subject to noise. In this scenario, the performance is measured repeatedly, resulting in a range of measurements for each object. If the ranges of two objects do not overlap, then we consider one object as ‘better’ than the other, and we expect it to receive a higher rank; if, however, the ranges overlap, then the objects are incomparable, and we wish them to be assigned the same rank. Unfortunately, the incomparability relation of ranges is in general not transitive; as a consequence, in general the two requirements cannot be satisfied simultaneously, i.e., it is not possible to guarantee both distinct ranks for objects with separated ranges, and same rank for objects with overlapping ranges. This conflict leads to more than one reasonable way to rank a set of objects. Although the problem of ranking with ties has been widely studied, there remains a lack of clarity regarding what constitutes a set of reasonable rankings. In this paper, we explore the ambiguities that arise when ranking with ties, and define a set of reasonable rankings, which we call partial rankings. We develop and analyze three different methodologies to compute a partial ranking. Finally, we show how performance differences among objects can be investigated with the help of partial ranking.

Effects of water acidifiers on the growth performance, intestinal function and gut microflora in broilers

ABSTRACT 1. This study evaluated the effect of acidified drinking water on the gastrointestinal function and intestinal health of broilers. 2. A total of 630 one-day-old male broilers (Arbor Acre) were randomly assigned to one of three treatment groups: drinking water treatment (CON), drinking water + 0.5 ml Selko pH®/L (Selko pH), or + 0.85 ml Forticoat®/L (Forticoat) treated groups. Performance data, gut and digesta samples were collected from the broilers at the age of 21 and 42 d. 3. The results showed that acidifying drinking water had no significant effect on body weight or average daily gain (ADG). However, addition of Forticoat significantly increased (p < 0.05) feed conversion ratio (FCR) throughout the experimental period and significantly increased (p < 0.05) pepsin activity on d 21. The Selko pH supplemented drinking water significantly increased (p < 0.05) the relative length of the duodenum and jejunum on d 21. The relative length of the jejunum and caecum on d 42 compared to birds receiving CON. The addition of the Forticoat to drinking water significantly increased (p < 0.05) the relative length of the jejunum and caecum on d 42 than for samples from birds in the CON group. In the caecal chyme, abundance of Blautia, Bifidobasterium, Faecalibacterium, Limosilactobacillus and Akkermania spp. on d 21 were significantly higher (p < 0.05) in the caecum of birds receiving Selko pH than those in CON group and the number of Escherichia Shigella in Selko pH and Forticoat group were significantly lower (p < 0.05). 4. Overall, adding Seiko pH and Forticoat to drinking water improved pepsin activity, reduced the number of caecal pathogens, increased the number of beneficial bacteria and improved intestinal health in broilers.

Age and Sex Differences in IRONMAN World Championship Performance.

Gries, KJ and Witto, PE. Age and sex differences in IRONMAN world championship performance. J Strength Cond Res 39(2): 269-276, 2025-There has been an increasing number of masters athletes, particularly women, who have been competing in ultra-endurance events. With this in mind, we sought to determine the influence of age and sex on IRONMAN World Championship performances from 2014 to 2023. The final times of each component (swim, bike, and run) and overall time were collected for places 1st to 10th in each age group (18-69 years). Sex differences in performance were calculated for each age group. Depth of field (time of n th place/first place), a surrogate for participation, was also calculated for each age group and sex. Significance was set at p < 0.05. These data show a ∼12% sex difference in the performance of the swim, bike, run, and final ( p < 0.05), which increased to 20% in the 65-69 years age group ( p < 0.05). The differences appeared driven by the bike and run. Depth of field was greater in men than in women ( p < 0.05), and the sex difference increased with age. Sex differences in performance trended to be related to depth of field ( p = 0.08; R2 = 0.32), suggesting a moderate role of participation in performance sex differences. Although speculative, the performance data can give us an insight into the physiology and sociology of these elite athletes and warrants future research.

Exercise-induced effects on the metabolome of endurance and strength-trained athletes in comparison with sedentary subjects: A pilot study.

Little is known about the exercise-induced adaptations of the metabolome in endurance and strength athletes in comparison with sedentary subjects. In order to analyze exercise-induced effects, quantitative, targeted metabolomics (Biocrates MxP® Quant 500) were performed in plasma samples before and after one bout of endurance or resistance exercise (RE) in 12 strength-trained weightlifters (ST), 10 endurance-trained runners (ET) and 12 sedentary controls (CG) at the end of each of three characteristic training phases. Performance and anthropometric data were significantly different between CG and athletes. A significant exercise-induced increase in lactate (Lac) was observed in all groups after all exercise tests. After endurance exercise (EE), there were significant increases in acetylcarnitine, arachidonic acid, and docosahexaenoic acid in CG and ET while aconitic acid, hippuric acid, glutamate, hexoses, xanthine were significantly increased in ET only. Only CG showed increases in several triglycerides following EE. RE, however, induced significant increases in Lac only. In summary, EE induces distinct increases in some metabolites of the fatty acid metabolism and the oxidative defense system in ET and CG. There are some indications for specific adaptations of the energy metabolism after long lasting endurance training with a distinct exercise-induced response of the metabolome in ET.

Video conferencing algorithms for enhanced access to mental healthcare services in cloud-powered telepsychiatry

Exploring the video conferencing algorithms for cloud-powered telepsychiatry to improve mental healthcare access. The goal is to evaluate and optimise these algorithms' latency, bandwidth utilisation, packet loss, and jitter across worldwide locations. To provide a smooth and high-quality virtual consultation between patients and mental health providers. Using performance data to identify areas for development, the effort aims to lower technological hurdles and increase telepsychiatry session dependability. Findings will help create strong, efficient algorithms that can handle different network situations, increasing patient outcomes and extending mental healthcare services. In the 1st instance latent analysis in a sample of 5 cities, the average latency (ms) is 45, the peak latency is 120, the off-peak latency is 30, and the packet loss is 0.5. In another instance, bandwidth utilisation in a sample of 5 sessions ranged from 30 to 120 minutes, with data supplied in MB - 150-600 and received in MB - 160-620, with average bandwidth (Mbps) - 5-15 and maximum bandwidth: 10-20.

Analysis of Method Performance for Quantitative Assessment of Listeria monocytogenes in Queso Fresco Cheese.

A Proficiency Test (PT) was conducted for Food Emergency Response Network (FERN) laboratories for quantitative assessment of Listeria monocytogenes (L. monocytogenes) in queso fresco cheese. The Moffett Proficiency Test Laboratory: MPTL (organizer) prepared test samples for each participating laboratory with 10CFU/g of L. monocytogenes. Statistical analysis was conducted to analyze test sample preparation and evaluate the performance of the quantitative method by utilizing participant's data. Although individually inoculated, the samples were considered replicates based on the standard deviation values obtained from the Q/Hampel method. A similar observation was noted on pre- and postshipment data, which permitted a combined analysis as per Q/Hampel and ISO 5725-2 methods. Based on the results, it can be summarized that MPTL prepared homogenized and stable samples for L. monocytogenes enumeration in cheese. The participant results indicated that the repeatability of standard deviation, which reflects the scatter of results within a laboratory (participant), was mainly driven by the MPN procedure. Out of forty reported results from twenty laboratories, 85% were satisfactory. A few laboratories reported different MPN results (2.5% unsatisfactory, and 12.5% warning) indicating a lack of understanding in handling the MPN dilutions. Since BAM MPN-based method performance data for L. monocytogenes is not widely available in the literature, more PT events like these can help generate quantitative data for the evaluation of the MPN method.

Deep Knowledge Tracing Integrating Temporal Causal Inference and PINN

Knowledge tracing predicts students’ future performance based on their historical performance data, which is significant for students’ learning resource recommendation, learning path prediction, and other aspects. Students’ knowledge mastery, learning ability, and question difficulty all influence the performance metrics of knowledge tracing. This paper proposes a deep knowledge tracing model that integrates temporal causal inference and the PINN (Physics-Informed Neural Network) model. The model first uses the temporal causality model to explores the causal relationships between students’ knowledge points, which is then combined with the deep learning-based knowledge tracing model for prediction. Next, it treats the logical model as a ’physical model’, adds a loss term, considers the confounding factors caused by students’ answer preferences, and adjusts students’ learning ability through backdoors to obtain more accurate predictions. In the public education datasets ASSISTment2012 and ASSISTchall, the predictive performance of the TLPKT-PINN model is superior to some classical models and LPKT. From the experimental results, we can conclude that considering the degree of mastery of causal knowledge points and adjusting the loss term can improve the accuracy of predicting student grades.

Practical Implications of Value Based Care in the Setting of Ambulatory Surgery Center

Ambulatory surgery centers (ASCs) are increasingly replacing traditional hospital settings for orthopaedic procedures, driven by advancements in surgical techniques, anesthesia, and enhanced recovery protocols. These innovations enable complex procedures to be performed safely in outpatient settings. ASCs align with value-based care (VBC) principles by offering cost-effective, high-quality, and patient-centered care. This article explores strategies to integrate VBC into ASCs, focusing on operational, financial, and clinical enhancements. This analysis evaluates strategies for preparing ASCs for alternative payment models (APMs) like bundled payments and Accountable Care Organizations. It highlights the importance of standardizing practices, such as lean methodologies and Six Sigma approaches, to improve efficiency and outcomes. Data collection is targeted at actionable metrics, and cost management includes strategies like standardized implant selection and vendor negotiations. Financial alignment through performance-based compensation structures was also assessed. ASCs have demonstrated cost savings of up to 45% compared to hospital outpatient departments, driven by streamlined operations and patient-centric approaches. Adopting APMs fosters improved care coordination and efficiency while aligning financial incentives with clinical outcomes. Standardizing practices, such as implant selection and clinical protocols, has reduced variability, enhanced surgical outcomes, and controlled costs. Performance monitoring using metrics like patient-reported outcome measures and surgical complication rates has facilitated continuous improvement. ASCs represent a significant opportunity to implement VBC principles in orthopaedic surgery. By preparing for APMs, standardizing practices, and leveraging performance data, ASCs can achieve cost savings and improve patient outcomes. Continuous improvement and alignment between financial and clinical goals are essential for ASCs to remain leaders in orthopaedic care delivery. These strategies position ASCs to meet the evolving demands of health care while maintaining high-quality, patient-centered care.

Battery Sentiment Analysis and Recommention System

- The increasing reliance on battery technology across industries such as renewable energy, electric vehicles, and portable electronics underscores the importance of robust analysis and optimization of battery systems. Battery-centric analysis focuses on understanding key parameters such as temperature, current, voltage, state of charge (SOC), and state of health (SOH) to enhance performance, safety, and longevity. These parameters are critical in identifying potential failures, improving efficiency, and predicting battery life cycles. This study employs advanced methodologies, including machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN), to analyse battery performance data. By training and testing these models on extensive datasets, the research provides accurate predictions of battery behavior under diverse operating conditions. Key metrics, including accuracy, root mean square error (RMSE), and mean absolute error (MAE), are used to evaluate the effectiveness of these algorithms, ensuring reliable insights into battery health and operation. The integration of real-time monitoring systems with predictive analytics enhances the safety and efficiency of batteries, reducing risks such as overheating, overcharging, and premature failure. Additionally, this approach supports the development of sustainable energy solutions by identifying areas for material optimization and improving the design of energy storage systems. Key Words: Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Gradient Boosting, Neural Networks (NN), and Deep Neural Networks (DNN).

Efficient Solution of Fokker–Planck Equations in Two Dimensions

Finite element analysis (FEA) of the Fokker–Planck equation governing the nonstationary joint probability density function of the responses of a dynamical system produces a large set of ordinary differential equations, and computations become impractical for systems with as few as four states. Nonetheless, FEA remains of interest for small systems—for example, for the generation of baseline performance data and reference solutions for the evaluation of machine learning-based methods. We examine the effectiveness of two techniques which, while they are well established, have not to our knowledge been applied to this problem previously: reduction of the equations onto a smaller basis comprising selected eigenvectors of one of the coefficient matrices, and splitting of the other coefficient matrix. The reduction was only moderately effective, requiring a much larger basis than was expected and producing solutions with clear artifacts. Operator splitting, however, performed very well. While the methods can be combined, our results indicate that splitting alone is an effective and generally preferable approach.

EFFECTS OF DIETS CONTAINING RED AND WHITE SORGHUM SUPPLEMENTED WITH TANNASE ON THE GROWTH PERFORMANCE AND NUTRIENT DIGESTIBILITY OF BROILER CHICKENS

A 56 - day feeding trial was conducted to determine the growth performance and nutrient digestibility of broiler chickens fed diets containing red sorghum (RS) and white Sorghum (WS) with or without tannase supplementation. A total of 240 one day old unsexed Arbor acre broiler chicks were randomly assigned to eight (8) dietary treatments which were divided into 3 replicates each of 10 birds in a randomized completely block design 4x2 factorial arrangement. Eight (8) diets were formulated in which sorghum replaced maize at 0, 100 RS, 100 WS and 50RS:50WS% at the starter and finisher phases. The first 4 diets (diets 1, 2, 3 and 4) were formulated without tannase then the other 4 diets (diets 5, 6, 7 and 8) had the same composition as diets 1, 2, 3 and 4 but supplemented with tannase. Growth performance data was collected on weekly basis and nutrient digestibility was determined at the 28th and 56th day for starter and finisher phases respectively. Starter birds fed 100%WS diet and those fed 50RS:50WS tannase supplemented diet had the highest (P&lt;0.05) final weight and weight gain. There was no significant (P&gt;0.05) difference in the growth indices of the finisher birds except the total feed intake which was highest (P &lt; 0.05) in birds fed 50RS:50WS% diet without tannase. Although, finisher birds fed the control diet with tannase had the least feed intake, those fed the control diet without tannase had the best FCR. None of the nutrient digestibility indices were significantly (P&gt;0.05) influenced at the starter phase except the crude protein retention which was highest (P&lt; 0.05) in finisher birds fed 100%RS diet supplemented with tannase. In conclusion, 100% WS without tannase and 50RS:50WS with tannase may replace maize in starter broilers diet while 100% RS and 100% WS with or without tannase may replace maize in finisher broilers diet for optimum performance and nutrient digestibility without any deleterious effect. Similarly, the addition of tannase to the control diet also enhanced performance and nutrient digestibility.

Cracking the Code: What Drives Dental Students to Specialize? Unveiling the Impact of Final Year Performance, Gender, and Geography

Purpose/Objectives: This study investigates how final year performance at dental school, gender, and geographic factors influence the likelihood of pursuing dental specialization in Sri Lanka. Methods: We analyzed a 33-year retrospective cohort, using final year performance data from the country’s sole dental school. Data on dental specialization were obtained from the Postgraduate Institute of Medicine (PGIM), University of Colombo, covering 1990 to 2023. Data were compiled in Excel and analyzed with SPSS-26. Results: Graduates who achieved a ‘class’ in their Final BDS had 4.53 times higher odds (95% CI 3.15-6.53; p=0.001) of becoming dental specialists compared to those who did not. The mean time from graduation to specialization was 9.48 years (95% CI 8.78-10.18) for ‘class’ graduates, compared to 12.57 years (95% CI 11.25-15.89) for ‘pass’ and 12.88 years (95% CI 11.12-14.63) for ‘referred’ graduates, with these differences being statistically significant (p=0.0001). Females constituted 60% of dental specialists, with notable representation in Restorative Dentistry, Orthodontics, and Oral Pathology. Most specialists came from Colombo district, the country’s most affluent area. Conclusions: High performance in Final BDS correlates strongly with pursuing dental specialization. There is a significant female dominance among specialists and notable geographic disparities in dental school entry. These insights are valuable for dental education and workforce planning.

Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review.

Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production. We analyze the potential strengths and weaknesses of various ML models, identify important features for favorable outcomes, and suggest potential future directions of ML applications for CI-related clinical and research purposes. We conducted a systematic literature search with Web of Science, Scopus, MEDLINE, EMBASE, CENTRAL, and CINAHL from the date of inception through September 2024. We included studies with ML models predicting a CI functional outcome, defined as those pertaining to sound perception and production, and excluded simulation studies and those involving patients without CIs. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we extracted participant population, CI characteristics, ML model, and performance data. Sixteen studies examining 5058 pediatric and adult CI users (range: 4 to 2489) were included from an initial 1442 publications. Studies predicted heterogeneous outcome measures pertaining to sound production (5 studies), sound perception (12 studies), and language (2 studies). ML models use a variety of prediction features, including demographic, audiological, imaging, and subjective measures. Some studies highlighted predictors beyond traditional CI audiometric outcomes, such as anatomical and imaging characteristics (e.g., vestibulocochlear nerve area, brain regions unaffected by auditory deprivation), health system factors (e.g., wait time to referral), and patient-reported measures (e.g., dizziness and tinnitus questionnaires). Used ML models were tree-based, kernel-based, instance-based, probabilistic, or neural networks, with validation and test methods most commonly being k-fold cross-validation and train-test split. Various statistical measures were used to evaluate model performance, however, for studies reporting accuracy, the best-performing models for each study ranged from 71.0% to 98.83%. ML models demonstrate high predictive performance and illuminate factors that contribute to CI user functional outcomes. While many models showed favorable evaluation statistics, the majority were not adequately reported with regard to dataset characteristics, model creation, and validation. Furthermore, the extent of overfitting in these models is unclear and will likely result in poor generalization to new data. This suggests the need for more robust validation procedures and standardization in reporting, with the ultimate hope that the iterative improvement of these models will allow for their adoption as a future clinical tool.

Analisis penerapan sistem akuntabilitas kinerja instansi pemerintah berdasarkan Peraturan Presiden Nomor 29 Tahun 2014 pada Badan Pendapatan Daerah Provinsi Sulawesi Utara

The Government Agency Performance Accountability System is a system whose application is used to measure performance against the level of success and failure in programs and activities. In this case, Presidential Regulation Number 29 of 2014 concerning the Performance Accountability System of Government Agencies which is the basis for the regulation on the implementation of SAKIP. In this regulation, the implementation of SAKIP consists of Strategic Plan, Performance Agreement, Performance Measurement, Performance Data Management, Performance Data Reporting, Performance Reporting, and Review and Evaluation. This study aims to determine the suitability of the implementation of SAKIP based on Presidential Regulation No. 29 of 2014 and to determine the level of success and failure of programs and activities that have been determined. The methods used in this study are qualitative descriptive and data collection methods through interviews and documentation. The results of this study show that the implementation of the Government Agency Performance Accountability System at the North Sulawesi Provincial Regional Revenue Agency has been good and in accordance with Presidential Regulation Number 29 of 2014.