Relative Performance Research Articles

The Effect of Dust Accumulation on Photovoltaic (PV) Panel Surface in Politeknik Mersing, Johor, Malaysia

Abstract: One of the initiatives aiming at supporting green technology sustainability education in the Politeknik Mersing is the generation of power using renewable energy sources like solar. Considered as more efficient and able to meet current power needs of the community, the generation of electricity from limitless green energy sources is Dust accumulation on the photovoltaic (PV) panel surface has been one of the main environmental elements influencing the declining sun irradiation since Politeknik Mersing located in a tropical rainforest and coastal location. Moreover, in humid environments the accumulation of dust forms mud and contamination on the PV panel surface, which subsequently reduces the relative power efficiency of PV by up to 30%. Therefore, the study has been carried out to investigate the effects of dust accumulation on PV panel surfaces on the amount of output power generated by the PV system. While the lowest relative performance of 69.6% occurred at a 30° tilt angle, the best relative performance of 97% was obtained at a 0° tilt angle during peak solar hours. The relative power efficiency was used to analyse the performance of PV panels under clean and dusty conditions since there was no pyranometer to measure solar irradiation. Several tilt angles (0°, 10°, 20°, and 30°) were assessed in order to situate the panel optimally with respect to the latitude of the location and peak solar hours (10:00 am, 11:00 am, 12:00 pm, and 1:00 pm). The performance of the PV system was evaluated using total output power that is derived from open-circuit voltage (Voc) and short-circuit current (Isc). Results showed that dust accumulation affects the efficiency of PV panels thus necessitating effective maintenance strategies within such environments.

Acute Effects of High-Intensity Resistance Exercise on Recognition of Relational Memory, Lactate, and Serum and Plasma Brain-Derived Neurotrophic Factor.

Baumgartner, NW, Belbis, MD, Kargl, C, Holmes, MJ, Gavin, TP, Hirai, DM, and Kao, S-C. Acute effects of high-intensity resistance exercise on recognition of relational memory, lactate, and serum and plasma brain-derived neurotrophic factor. J Strength Cond Res XX(X): 000-000, 2024-Acute aerobic exercise improves memory, but this phenomenon is understudied in response to resistance exercise (RE) despite evidence that RE-induced increases in lactate and brain-derived neurotrophic factor (BDNF) play mechanistic roles in memory performance. To determine the acute effect of RE on lactate, BDNF, and their associations with object and relational memory, blood lactate, and serum and plasma BDNF were taken from 36 adults (average age 23.64 ± 3.89 years; 18 woman) before and immediately after 42 minutes of high-intensity RE and a rest condition on counterbalanced days. Subjects then immediately studied a series of paired objects and completed object and relational recognition tasks. Results revealed a condition by trial interaction, previously studied objects were remembered less accurately following RE (d = 0.66) but recognition occurred faster (d = 0.28), indicating a speed-accuracy tradeoff following RE. There was no effect of either intervention on relational recognition performance. Lactate (d = 3.68) and serum BDNF (d = 0.74) increased following RE, whereas there was no time-related change in lactate and serum BDNF following rest. However, changes in lactate and BDNF did not predict any measures of object (rs < 0.25, ps > 0.16) or relation recognition (rs < 0.28, ps > 0.13). Collectively, these findings suggest that acute high-intensity RE selectively improves the processing speed of recognizing objects at the cost of less accurate recognition of previously studied objects. Furthermore, changes in object and relational memory performance are unlikely driven by acute increases in lactate or BDNF following high-intensity RE.

Cross-Validation of Lipid Structure Assignment Using Orthogonal Ion Activation Modalities on the Same Mass Spectrometer.

The onset and progression of cancer is associated with changes in the composition of the lipidome. Therefore, better understanding of the molecular mechanisms of these disease states requires detailed structural characterization of the individual lipids within the complex cellular milieu. Recently, changes in the unsaturation profile of membrane lipids have been observed in cancer cells and tissues, but assigning the position(s) of carbon-carbon double bonds in fatty acyl chains carried by membrane phospholipids, including the resolution of lipid regioisomers, has proven analytically challenging. Conventional tandem mass spectrometry approaches based on collision-induced dissociation of ionized glycerophospholipids do not yield spectra that are indicative of the location(s) of carbon-carbon double bonds. Ozone-induced dissociation (OzID) and ultraviolet photodissociation (UVPD) have emerged as alternative ion activation modalities wherein diagnostic product ions can enable de novo assignment of position(s) of unsaturation based on predictable fragmentation behaviors. Here, for the first time, OzID and UVPD (193 nm) mass spectra are acquired on the same mass spectrometer to evaluate the relative performance of the two modalities for lipid identification and to interrogate the respective fragmentation pathways under comparable conditions. Based on investigations of lipid standards, fragmentation rules for each technique are expanded to increase confidence in structural assignments and exclude potential false positives. Parallel application of both methods to unsaturated phosphatidylcholines extracted from isogenic colorectal cancer cell lines provides high confidence in the assignment of multiple double bond isomers in these samples and cross-validates relative changes in isomer abundance.

The Impact of Leadership Styles on Employee Relations Performance

This paper explores the impact of leadership styles on the performance of employee relations, using various theoretical frameworks and empirical research methods for analysis. By deeply examining the definition, classification, and role of leadership styles within organizations, it reveals the direct and indirect mechanisms through which different leadership styles affect employee attitudes, emotions, and performance. The study finds that positive leadership styles can significantly enhance the quality of employee relations, thereby promoting overall organizational performance. Additionally, the paper analyzes the mediating role of leadership styles in the process of improving employee relations performance and suggests areas for future research based on current study limitations.

Gender, choice of task, and the effect of feedback on competition: An experiment

We conduct a laboratory experiment to examine gender differences in task choices and competitiveness: Individuals have the option to self-select into one of two stereotypically different tasks and subsequently decide whether or not to engage in competition while receiving one of three different feedback treatments. Compared to a control setting, we study the effect of providing relative performance feedback (rankings), and additionally the effect of providing information about the gender of the competitors. We find a significant gender gap in the choice of the male task, only when presenting the ranking in addition to the gender composition of the group. Turning to the decision to enter competition, we observe that task choice, combined with ranking feedback on performance, reduces the gender gap in competition entry in both tasks compared to the control. The dynamics over treatments reveal that men primarily respond to feedback in the male task, while women respond to feedback in the word task. These findings highlight that gender differences in task choice and competitiveness are contingent on feedback, the underlying task, and the task choice set.

Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano

Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.

Adoption of the PREVENT (Predicting Risk of Cardiovascular Disease EVENTs) Risk Algorithm: Potential International Implications

BackgroundThe PREVENT (Predicting Risk of cardiovascular disease EVENTs risk algorithm was developed to better reflect the impact of metabolic factors on cardiovascular risk. ObjectivesThe purpose of this study was to compare the relative performance of PREVENT with standard comparator algorithms (Framingham risk score, pooled cohort equation, SCORE2 [Systematic COronary Risk Evaluation2]) for risk stratification emphasizing the implications of weighing chronic kidney disease. MethodsA simulated cohort was created of males and females aged 40 to 75 years with and without other traditional risk factors and either normal estimated glomerular filtration rates (eGFR 90 or 60 ml/min/1.73 m2) or abnormal eGFR (45 or 30 ml/min/1.73 m2). The concordance and reclassification rates were calculated for each category of risk with emphasis on subjects characterized as moderate risk by the standard comparator algorithms. ResultsPREVENT demonstrated increased risk with progressive decreases in eGFR. When the standard comparator algorithms identified moderate risk, PREVENT was concordant in 6% to 88% of simulations. In simulations with normal eGFR, PREVENT identified a lower risk in 18% to 88% and a higher risk in 0% to 12% of simulations. Conversely, with abnormal eGFR, PREVENT identified lower risk in 0% to 26% and higher risk in 4% to 94% of simulations. ConclusionsPREVENT substantially reclassifies risk and has the potential to alter prevention practice patterns. The tendency to assign a lower risk compared to standard algorithms when eGFR is normal may diminish implementation of preventive therapy. National health care systems need to monitor whether such changes improve overall public health.

UNNECESSARY USE OF ANTIMICROBIALS – COMPARISON OF THE PREDICTIVE PERFORMANCE OF QSOFA AND APACHE SCORE FOR EVALUATION OF DISEASE PROGNOSIS PATTERNS IN CRITICAL ILLNESS

In critical care, accurate prognosis assessment, including antimicrobial therapy, is vital for guiding treatment decisions. The Acute Physiology and Chronic Health Evaluation (APACHE) and quick Sequential Organ Failure Assessment (qSOFA) scores are widely used for predicting patient outcomes. Yet, their relative performance and potential implications for antimicrobial stewardship need further exploration. Objective: To evaluate the disease prognosis patterns in critically ill patients by comparing the predictive performance of APACHE and qSOFA scores and to explore their correlation with antimicrobial misuse. Methods: A comparative study was conducted at the National Hospital and Medical Center in Lahore from January to November 2023. The study included 138 critically ill patients. Demographic information, clinical parameters, and medical history were collected, explicitly focusing on APACHE and qSOFA scores recorded at admission and during follow-up. Statistical analysis, including Pearson's and Spearman's correlation coefficients and logistic regression models, was performed using RStudio to assess the predictive ability of these scores for disease progression and patient outcomes. Results: The analysis revealed a moderate positive correlation (r=0.51) between qSOFA and APACHE-II scores, suggesting that both scores align in assessing the severity of illness in critically ill patients. The predictive models indicated that combining qSOFA and APACHE-II scores enhanced the accuracy of predicting critical outcomes, such as shortness of breath (SOB), with a combined model AUC of 0.659 compared to 0.601 for the APACHE-II model alone. Conclusion: The findings underscore the value of integrating multiple clinical scoring systems in managing critically ill patients. Such integration can aid in more judicious use of antibiotics, potentially mitigating the risk of antimicrobial resistance. The study advocates incorporating these insights into clinical guidelines and decision-making processes in critical care settings.

Formal education as a contested pastoral adaptation pathway: insights from southern Kenya

In dryland pastoral environments, political and geographical marginalization has historically led to development strategies that poorly account for individual and communities’ spatial and socio-economic realities. These development legacies, including long-standing epistemic biases in defining what should be adapted, are often insufficiently considered within adaptation research and practice. This article sets out to analyze the historical emergence and enactment of formal education as a contested adaptation pathway in southern Kenya. For this, I combine the strengths of the historically situated analyses of the pathways scholarship and feminist political ecology’s attention to the performance of intersectional relations of power in everyday livelihood practices. I bring together both archival data and qualitative primary data from focus group discussions (n = 16) and individual interviews (n = 122) conducted in three pastoral communities. The results exemplify the ways that non-climatic factors, such as increased formal school enrolment, (re)shape everyday livelihood practices and social aspirations, molding the current adaptation space. Notably, enacting formal education as an adaptation pathway requires one to navigate increasing cash pressures, mobility, and labor constraints. Wealth disparities, gendered norms, and geographies intersect to shape patterns of vulnerability, with poorer pastoralists residing further away from school centers facing difficult trade-offs on their time and resources. Understanding pathways enactments contributes to problematizing current logics of development and adaptation needs, while yielding important information on socio-spatial differentiation processes in pastoral systems. It also opens the space for further research to use these critical insights to identify alternative adaptation pathways that support more just transformations towards sustainability.

Rozwiązania instytucjonalne na rzecz rozwoju konkurencyjności sektora gazu ziemnego w UE

The study explores the subject of sectoral competitiveness on the example of gas sectors in different European countries. Literature review on sectoral competitiveness suggests that the concept has evolved considerably over time and that contemporary definitions of competitiveness span beyond traditional economic factors. Studies on gas sector in this context to date, however, have largely focused on export competitiveness of upstream activities. To address the research gap, a targeted approach is suggested to study and compare competitiveness of the entire national gas sectors, spanning beyond standard productivity measures. A dedicated synthetic indicator is designed to perform a comparative analysis of contemporary gas sectors, taking account of the gas price offered to the industry, institutional performance, infrastructure readiness and job creation potential. Then, the case study is presented and it examines four countries that have liberalized their gas sectors and are reliant on gas imports to a different degree. Results confirm that the application of a synthetic indicator factoring in different aspects of sectoral competitiveness is appropriate to arrive at a holistic view of gas sector’s relative performance. The significance of the institutional setup is also confirmed from the perspective of inducing investment, enhancing market liquidity, as well as creating sustainable jobs in the gas sector.

A comprehensive in vitro characterization of non-crosslinked, diverse tissue-derived collagen-based membranes intended for assisting bone regeneration.

Collagen-based membranes are class III-medical devices widely used in dental surgical procedures to favour bone regeneration. Here, we aimed to provide biophysical and biochemical data on this type of devices to support their optimal use and design/manufacturing. To the purpose, four commercial, non-crosslinked collagen-based-membranes, obtained from various sources (equine tendon, pericardium or cortical bone tissues, and porcine skin), were characterized in vitro. The main chemical, biophysical and biochemical properties, that have significant clinical implications, were evaluated. Membranes showed similar chemical features. They greatly differed in morphology as well as in porosity and density and showed a diverse ranking in relation to these latter two parameters. Samples highly hydrated in physiological medium (swelling-ratio values in the 2.5-6.0 range) and, for some membranes, an anisotropic expansion during hydration was, for the first time, highlighted. Rheological analyses revealed great differences in deformability (150-1500kPa G') also alerting about the marked variation in membrane mechanical behaviour upon hydration. Samples proved diverse sensitivity to collagenase, with the cortical-derived membrane showing the highest stability. Biological studies, using human-bone-derived cells, supported sample ability to allow cell proliferation and to prompt bone regeneration, while no relevant differences among membranes were recorded. Prediction of relative performance based on the findings was discussed. Overall, results represent a first wide panel of chemical/biophysical/biochemical data on collagen-based-membranes that 1) enhances our knowledge of these products, 2) aids their optimal use by providing clinicians with scientific basis for selecting products based on the specific clinical situation and 3) represents a valuable reference for optimizing their manufacturing.

Optimization method for capacity of BESS considering charge‐discharge cycle and renewable energy penetration rate

AbstractIn order to achieve the “carbon peaking and carbon neutrality” goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance.

Evaluating Business Efficiency: The Role of Digital Transfor-mation in Modern Enterprises

Digital transformation, renowned for its capacity to stimulate economic expansion and enhance business landscapes, requires a supportive ecosystem comprising universal digital infrastructure, skilled workforce, appropriate legal frameworks, adequate investment, effective governance, educational initiatives, robust security measures, and other conducive environments. This transformation presents governments with strategic opportunities to shape various economic sectors, encompassing finance, retail, healthcare, agriculture, manufacturing, education, tourism, media, and culture. Existing literature extensively investi-gates digital transformation in academic and practical spheres, yet a consensus on its fundamental principles remains elusive. This study contributes by summarizing the effects of digital technologies on business and management, stressing the need to broaden existing business domains and explore novel areas. Advocating for pro-social objectives, sustainable business mod-els, and widespread adoption of artificial intelligence (AI) are suggested strategies for navigating digital transformation. Fur-thermore, the research scrutinizes the concept of digital disruption, focusing on how emerging digital technologies and inno-vative business models reshape established value propositions. Business process management (BPM) is examined for its role in facilitating these changes, despite historical challenges in terminology and methodological coherence. The study un-derscores the importance of a structured approach to change management, advocating for flexibility and real-time decision-making to address complex business activities. Additionally, the research evaluates the performance of multiple companies using key performance indicators such as customer satisfaction, operational efficiency, employee productivity, and IT infra-structure cost reduction. Employing the Weighted Sum Method (WSM) to rank these companies, the study offers insights in-to their relative performance. These findings aim to guide stakeholders in strategic decision-making by providing a holistic assessment of company performance across various dimensions, identifying areas for potential enhancement, and deepening understanding of digital transformation's ramifications.

Statistical Signal Detection Algorithm in Safety Data: A Proprietary Method Compared to Industry Standard Methods.

Several quantitative methods have been established, in pharmacovigilance, to detect signals of disproportionate reporting (SDRs) from databases containing reports of adverse drug reactions (ADRs). The signal detection algorithms (SDAs) and the source of the reporting per product vary, but it is unclear whether any algorithm can provide satisfactory performance using data with such large variance factors. Determine the appropriate SDA for Biogen's internal Global Safety Database (GSD) given the characteristics of the database including frequencies of events, data skewness, outliers, and missing information. Compare performance of standard approaches (EBGM, EB05, PRR, and ROR), well accepted by industry, to a Biogen-developed Machine Learning (ML) Regression Decision Tree (RDT) model, across several Biogen products, to determine a champion SDA. All data associated with seven marketed Biogen products were chosen and a historical subset of reported ADRs were considered. Six SDAs (five common industry disproportionality methods) and RDT were evaluated. The SDRs were calculated on training and test data composed of quarterly reporting intervals from 2004-2019. The performance measures used were sensitivity, precision, time to detect new events, and frequency of detected cases for each algorithm for each product. Outcomes in the test data are known a priori and easily compared to predicted outcomes. Validation was performed via rates of misclassification. This work solely represents Biogen's internal information, intentionally chosen to serve the performance review of its signal detection systems, and results will not necessarily be generalizable to other external sources. Several algorithms performed differently among products, but no one method dominated any other. Performance was dependent on the thresholds used to define a signal according to different criteria. However, those different statistics subtly influenced the achievable performance. The relative performance of RDT and Medicines and Healthcare products Regulatory Agency (MHRA) algorithms were superior and paired across products. A reduction in precision for all methods spanning the products was present. Hence, companies evaluating signal detection approaches, search for innovative methods to minimize this effect. In designing signal detection systems, careful consideration should be given to the criteria that are used to define SDRs. The choice of disproportionality statistics does not affect the achievable range of signal detection performance. These choices should consider mainly ease of implementation and interpretation. The implementation of a method is specific to its accuracy. The RDT attempted to take advantage of known methods and compare results on a per-product basis. Many factors influencing ADRs may improve RDT in future efforts. In this experiment, RDT demonstrated superiority in terms of quickest time to detect and capturing of the highest number of ADRs. Next steps include expansion of data for products representing other indications and testing models in external databases to investigate generalizability of estimates when comparing SDAs.

A proposed feedback model for the objective structured clinical examination (OSCE) in health and medical education

What was the educational challenge? The objective structured clinical examination (OSCE) is used extensively in health and medical education to assess clinical competence. While OSCEs are not primarily designed for individual feedback, they provide an opportunity to generate meaningful and constructive feedback that students can use to reflect on areas of weakness (and strength). The most timely and effective way to incorporate individual feedback following a summative OSCE continues to be explored. What was the solution? This paper describes a novel OSCE feedback model which uses readily available summative assessment data to calculate 10% index scores. This provides information about relative station difficulty and relative individual student performance. How was the solution implemented? An individualised feedback report was provided to every student after the OSCEs. This enables identification of stronger and weaker performance areas and composite skills, which can be utilised by all students to direct future learning. What lessons were learned that are relevant to a wider global audience? Providing timely, individual actionable feedback to every student following a summative OSCE is possible without impacting the examination process or examiner burden. What are the next steps? A study is planned to determine the utilisation of this feedback by students and it’s impact as a qualitative self-assessment exercise.

Can leagues mitigate the demoralization effect of rank feedback? A randomized controlled trial

In a randomized controlled trial involving hundreds of university students, we provide relative performance feedback specifically designed to reduce low performers’ demoralization, by dynamically assigning students to small leaderboard groups that share a similar score in a semester-long online assignment. Treated students appear 2.6% more likely to go beyond the call-of-duty on their assignment by mid-semester. For low performers, this translates in 0.27 SDs higher exam grades, more stress, increased effort and lower procrastination. High performers are happier, procrastinate less and overachieve in the assignment on which they are ranked, but ultimately also score 0.25 SDs lower exam grades.

Morpho-physiological characterization and selection of heat tolerant wheat lines using selection indices

Heat stress is one of the major constraints in wheat production due to its adverse effect on grain yield and component traits. Moreover, it negatively affects the physiological, biochemical and quality traits of wheat. Therefore, selection of heat tolerant wheat lines is one of the major breeding goals for wheat scientist. In present study, 238 recombinant inbred lines were evaluated along with their parents WH 711 × WH 542 under timely and late sown (heat stress) condition. The heat tolerance indices were calculated based on grain yield under normal and stress conditions. The mean grain yield of 238 wheat lines was reduced by 20.54% suggesting critical effect of heat stress on grain yield. The harmonic mean (HM), stress tolerance index (STI), mean productivity (MP), geometric mean productivity (GMP), and mean relative performance (MRP) were correlated significantly and positively with grain yield under stress and normal conditions. Whereas, Tolerance index (TOL) was correlated negatively with grain yield under stress conditions and positively under normal condition. Based on STI, MP, HM, GMP and MRP, wheat lines WL 92, WL 119, WL 114, WL 110, WL 6 were identified as heat tolerant and could be utilized as potential lines for increasing heat stress tolerance of future wheat breeding programs.

Exploring high-performance photonic crystal slow light waveguides through deep reinforcement learning

Supervised deep learning is not really suitable for automatic inverse design of photonic crystal waveguides. There are three reasons for this:(1) Pre-training of the network requires a large number of datasets, which leads to a lot of time spent in the physical simulation process. (2) The one-to-many mapping problem often arises during the reverse design process. (3) It is difficult to realize the inverse design of photonic structures beyond the performance of the dataset. These problems can be well addressed by deep reinforcement learning (DRL). Here, deep reinforcement learning is proposed for the first time for the design of complex slow-light photonic crystal waveguides. The design using DRL took less than 72 h to obtain a photonic crystal waveguide with a normalized delay bandwidth product of 0.449 and group index of 48.72. By increasing the scaling factor of bandwidth in the reward function to 0.25, the bandwidth value of the optimized slow-light waveguide increases to 50.01 nm with an NDBP value of 0.39. The slow-light performance of both structures is verified by FDTD. In addition, the NDBP value is further optimized to 0.46 if the size of the structural parameters is not constrained. Compared with deep learning, DRL can improve the sampling efficiency by more than an order of magnitude and is not plagued by the three problems mentioned above. This work confirms the effectiveness of DRL in the inverse design of energy band engineering. DRL can be widely used in the optimization of energy band related performance.

Correlation between Yokohama Cytological Coding and Radiological Findings and Their Diagnostic Accuracies against Histopathology: A Retrospective Study of Palpable Breast Lesions

Background: Breast carcinoma is the most common malignancy and demands quick and accurate diagnosis and treatment. Precise diagnosis of breast lesions is made using a triple-test approach: clinical, radiological and cytological. However, multiple steps make the process time-consuming and expensive. In developing countries like India, trained and certified radiologists are extremely overburdened. Fine needle aspiration cytology (FNAC) along with clinical examination can fill the gap. This study aims to correlate cytological, radiological and histological findings and measure their relative accuracies. Based on these findings, a new approach will be proposed to address the above shortcomings.Materials and methods: The FNAC was performed on all cases and reported as per Yokohama cytology. The cytological findings were correlated &amp; validated against radiological and histopathological findings respectively. Relative performance of cytological and radiological findings were established using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. A chi-square test for independence between cytological and radiological findings was performed.Results: The sensitivity, specificity, PPV, NPV, and accuracy for cytological findings come out as 97.60, 90.08, 90.37, 97.52, and 93.75, respectively. Meanwhile, the Radiological findings come out as 96.61, 82.20, 84.44, 96.04, and 89.41, respectively. The chi-square test demonstrates strong interdependence between cytological and radiological findings.Conclusion: FNAC is more accurate, quicker, and cheaper than radiological tests. Hence, FNAC based on the Yokohama system, along with clinical observations, can be used as a primary diagnosis tool in developing countries with limited health resources without making significant compromises on incorrect treatment. If needed, radiology and histopathology can be used for precise diagnosis and treatment.Keywords: FNAC, cytology, breast lesions, Yokohama, radiology, histopathology

A systematic approach for learning imbalanced data: enhancing zero-inflated models through boosting

In this paper, we propose systematic approaches for learning imbalanced data based on a two-regime process: regime 0, which generates excess zeros (majority class), and regime 1, which contributes to generating an outcome of one (minority class). The proposed model contains two latent equations: a split probit (logit) equation in the first stage and an ordinary probit (logit) equation in the second stage. Because boosting improves the accuracy of prediction versus using a single classifier, we combined a boosting strategy with the two-regime process. Thus, we developed the zero-inflated probit boost (ZIPBoost) and zero-inflated logit boost (ZILBoost) methods. We show that the weight functions of ZIPBoost have the desired properties for good predictive performance. Like AdaBoost, the weight functions upweight misclassified examples and downweight correctly classified examples. We show that the weight functions of ZILBoost have similar properties to those of LogitBoost. The algorithm will focus more on examples that are hard to classify in the next iteration, resulting in improved prediction accuracy. We provide the relative performance of ZIPBoost and ZILBoost, which rely on the excess kurtosis of the data distribution. Furthermore, we show the convergence and time complexity of our proposed methods. We demonstrate the performance of our proposed methods using a Monte Carlo simulation, mergers and acquisitions (M&A) data application, and imbalanced datasets from the Keel repository. The results of the experiments show that our proposed methods yield better prediction accuracy compared to other learning algorithms.