This study aims to analyze the effect of education level, minimum wage, investment, and infrastructure development costs on the inequality of income distribution between districts / cities in Bali Province. The location of the study is in districts / cities in Bali Province with secondary data obtained from publications from the Central Statistics Agency, Public Works Office, Spatial Planning, Housing, and Settlement Areas of Bali Province. The number of observations of this study was 90 observations with a period of 10 years and involved 9 regencies/cities in Bali Province. Data collection method in this study through non-participant observation techniques. The analysis technique used is panel data regression analysis. The results of the panel data regression test showed that the most appropriate model was the random effect model (REM). The results of this study show that education level, minimum wage, investment, and infrastructure development costs simultaneously affect income distribution inequally. Partially, the variables of education level and minimum wage have a positive and significant effect, while investment and infrastructure development costs have a negative and insignificant effect on the inequality of income distribution between districts / cities of Bali Province.

Exploring the role of traditional medicinal plants in cancer therapy: present efficacy and future directions

Cancer has become a major global public health concern, with millions of new cases and deaths reported annually. Conventional cancer treatments, such as chemotherapy and surgery, continue to be the standard of care; however, they frequently bear significant risks and high costs, necessitating the development of more cost-effective and safe alternatives. These limitations can be overcome by nanoparticle (NPs), composed of organic or inorganic substances in the nanoscale range, which offer benefits including enhanced pharmacokinetics, selective targeting of cancer cells, reduced toxicity, and decreased drug resistance. Green nanotechnology, which integrates nanotechnology with natural compounds, has emerged as a strategy for reducing toxicity on human health and the environment by functioning as reducing, capping, and stabilising agents. Compared to other NPs, Zinc oxide NPs (ZnO NPs) possess a unique selectivity and a potent capacity to target cancer cells, in addition to being biocompatible and considered safer for both humans and the environment. Due to the physiological function of zinc, an essential micronutrient, ZnO NPs have demonstrated greater bioavailability than other metal or metal oxide NPs. NP plays a more significant role in bioavailability than particle size, making ZnO NPs an attractive option for various applications. This mini review aims to comprehensively explore the synthesis methodology of ZnO NPs and the potential mechanisms underlying their anticancer properties.

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning (LL) models capable of estimating metric (absolute) depth. LL approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to LL scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: 1) developing a model capable of addressing the depth scale variation through scale-aware depth learning; 2) devising an effective learning strategy to handle significant domain gaps; and 3) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this article, we present 1) a lightweight multihead framework that effectively tackles the depth scale imbalance; 2) an uncertainty-aware LL solution that adeptly handles significant domain gaps; and 3) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8%-15%. The code is available at https://github.com/FreeformRobotics/Lifelong-MonoDepth.

Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration. Due to the functional significance and high manufacturing costs of the catalyst layer, monitoring internal fuel cell states is crucial. For this purpose, a diagnostic-oriented multi-scale PEMFC catalytic degradation model is developed which incorporates the failure effects of catalytic degradation on cell dynamics and global stack performance. Embedded to the multi-scale model is a square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme. In this approach, multiple models are used to estimate specific internal PEMFC system parameters, such as the mass transfer coefficient of the gas diffusion layer or the exchange current density, which are treated as additional system states. Online state estimates are provided by the SRUKF, which additionally propagates model-conditioned statistical information to update a Bayesian framework for model selection. The Bayesian model selection method carries fault indication signals that are interpreted by a derived decision logic to obtain reliable information on the current-operating system regime. The proposed diagnosis scheme is evaluated through simulations using the LA 92 and NEDC driving cycles.

Introduction: More recent self-supervised deep language models, such as Bidirectional Encoder Representations from Transformers (BERT), have performed the best on some language tasks by contextualizing word embeddings for a better dynamic representation. Their proteinspecific versions, such as ProtBERT, generated dynamic protein sequence embeddings, which resulted in better performance for several bioinformatics tasks. Besides, a number of different protein post-translational modifications are prominent in cellular tasks such as development and differentiation. The current biological experiments can detect these modifications, but within a longer duration and with a significant cost. Methods: In this paper, to comprehend the accompanying biological processes concisely and more rapidly, we propose DEEPPTM to predict protein post-translational modification (PTM) sites from protein sequences more efficiently. Different than the current methods, DEEPPTM enhances the modification prediction performance by integrating specialized ProtBERT-based protein embeddings with attention-based vision transformers (ViT), and reveals the associations between different modification types and protein sequence content. Additionally, it can infer several different modifications over different species. Results: Human and mouse ROC AUCs for predicting Succinylation modifications were 0.793 and 0.661 respectively, once 10-fold cross-validation is applied. Similarly, we have obtained 0.776, 0.764, and 0.734 ROC AUC scores on inferring ubiquitination, crotonylation, and glycation sites, respectively. According to detailed computational experiments, DEEPPTM lessens the time spent in laboratory experiments while outperforming the competing methods as well as baselines on inferring all 4 modification sites. In our case, attention-based deep learning methods such as vision transformers look more favorable to learning from ProtBERT features than more traditional deep learning and machine learning techniques. Conclusion: Additionally, the protein-specific ProtBERT model is more effective than the original BERT embeddings for PTM prediction tasks. Our code and datasets can be found at https://github.com/seferlab/deepptm.

To date, the complex picture of atopic dermatitis (AD) has not yet been fully clarified, despite the important prevalence of this disease in the pediatric population (20%) and the possibility of persistence into adulthood, with important implications for the quality of life of those affected, as well as significant social and financial costs. The most recent scientific evidence suggests a new interpretation of AD, highlighting the important role of the environment, particularly that of nutrition in the early stages of development. In fact, the new indications seem to point out the harmful effect of elimination diets, except in rare cases, the uselessness of chrono-insertions during complementary feeding and some benefits, albeit weak, of breastfeeding in those at greater risk. In this context, metabolomics and lipidomics can be necessary for a more in-depth knowledge of the complex metabolic network underlying this pathology. In fact, an alteration of the metabolic contents in children with AD has been highlighted, especially in correlation to the intestinal microbiota. While preliminary lipidomic studies showed the usefulness of a more in-depth knowledge of the alterations of the skin barrier to improve the development of baby skin care products. Therefore, investigating the response of different allergic phenotypes could be useful for better patient management and understanding, thus providing an early intervention on dysbiosis necessary to regulate the immune response from the earliest stages of development.

As small and medium-sized enterprises (SMEs) navigate the complexities of the digital economy, adopting advanced technologies such as SAP S/4HANA has become crucial for maintaining competitiveness. This paper examines the role of SAP S/4HANA in transforming business proces-ses for small and medium-sized enterprises (SMEs) within the manufacturing industry. SAP S/4HANA, an ERP system leveraging the HANA in-memory database, offers real-time data processing and advanced analytics capabili-ties, streamlining operations and improving decision-making. The research investigates the benefits of using SAP S4HANA, such as enhanced process efficiency, data integration, and competitive advantage, alongside challenges including signifi-cant implementation costs and technical complexities. Through a review of existing studies and expert interviews, the paper explo-res how SAP S/4HANA supports digital transformation by integrating ad-vanced technologies like AI and IoT. It emphasizes the need for strategic alignment, readiness for digitalization, and strong change manage-ment for successful ERP implementation. Ultimately, this research provides valuable insights into how ERP systems can foster innovation and long-term growth for SMEs, contributing to the broader discourse on digital transformation in the manufacturing sector. JEL Codes: C80, D2, L10, L23, L60, M11, M15, O33, Q55

CONTEXT: Blood stream infection (BSI) is a serious clinical condition often associated with morbidity, hospital admission, significant health care costs and sometimes mortality. AIMS: To investigate the correlation between suspected or confirmed BSIs with length of hospital stay (LOS) and cost of health care. SETTINGS AND DESIGN: Retrospective study done in a cancer hospital in eastern India. MATERIALS AND METHODS: Blood culture from patients admitted was used as a surrogate for suspected or confirmed BSI. Study was done over a 40 day period (in July-August 2015) involving 683 patients. STATISTICAL ANALYSIS USED: Welch's unpaired t-test has been used to compare means between data groups. RESULTS: The overall mean LOS, cost of management per patient admission, and all-cause mortality were 5.9 days (range: 0–64 days), Rs. 95,208 (USD 1,413) (range INR 220-27,50,653) and 5.7% (39 out of 683 patients) respectively. The LOS and the average health care costs increased progressively between the following patients cohorts: No blood cultures taken CONCLUSIONS: The data from this study would be useful to clinicians and hospital administrators, as well as help in counseling patients regarding approximate LOS and cost of health care related to infections.

Abstract Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits. Significance Statement The results of this paper are significant because they inform decision-makers about the future configuration of the NOAA’s environmental satellite constellation, which serves millions of diverse users.

Irritable bowel syndrome (IBS) is a functional disorder that causes chronic abdominal pain without a known cause. It is a common, chronic gastrointestinal (GI) motility disorder with bothersome symptoms that often lower quality of life and activity. In addition, Patients and healthcare facilities also face significant financial costs. COVID-19 directly damages the digestive system and alters the complex interaction of physical, mental, and social factors that cause digestive problems. SARS-CoV-2 survivors in personal isolation will be examined for IBS prevalence. The dates of this descriptive cross-sectional study are January through April 2023. Rome IV criteria and an online questionnaire were used to confirm the diagnosis of IBS. The principal location where polls have been sent is East Java, Indonesia. The 96 COVID-19 survivors aged 18–60 of both genders was included during self-quarantine. There were 59 females (61.46%) and 37 males (38.54%). The prevalence of IBS was discovered to be 19 (19.79%) among a total of 96 patients. This could be because self-quarantined people have more stable living conditions than hospitalized people. Based on these findings, it is suggested that future research consider gender as the primary proxy for identifying irritable bowel syndrome (IBS).

The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs. Pruning neural networks has thus gained interest since it effectively lowers storage and computational costs. In contrast to weight pruning, which results in unstructured models, structured pruning provides the benefit of realistic acceleration by producing models that are friendly to hardware implementation. The special requirements of structured pruning have led to the discovery of numerous new challenges and the development of innovative solutions. This article surveys the recent progress towards structured pruning of deep CNNs. We summarize and compare the state-of-the-art structured pruning techniques with respect to filter ranking methods, regularization methods, dynamic execution, neural architecture search, the lottery ticket hypothesis, and the applications of pruning. While discussing structured pruning algorithms, we briefly introduce the unstructured pruning counterpart to emphasize their differences. Furthermore, we provide insights into potential research opportunities in the field of structured pruning. A curated list of neural network pruning papers can be found at: https://github.com/he-y/Awesome-Pruning. A dedicated website offering a more interactive comparison of structured pruning methods can be found at: https://huggingface.co/spaces/he-yang/Structured-Pruning-Survey.

Wind turbine blades (WTBs) are susceptible to lightning damage, resulting in significant costs for repair and replacement, which poses a considerable economic burden on wind farms. Therefore, this study investigates the mechanism of lightning damage of glass fiber composite (GFC)-WTBs to reduce the risk of such damage. The damage of GFC-WTBs caused by lightning strikes was analyzed using a numerical simulation method. The lightning pilot was simulated using high-voltage rod electrodes, and subsequently, the electric field strength and damage area on the blade surface under different conditions were measured. Additionally, a simulation model based on finite element analysis was developed to further predict and validate the experimental findings. The results reveal that the maximum electric field intensity was observed in the blade tip. Notably, the electric field intensity was found to be over 798,000 V/m when the rod electrode was positioned at a 0-degree angle with the blade tip. Further investigation revealed a non-linear and positive correlation between the damage area of GFC and lightning current amplitude, charge, and specific energy. In conclusion, the study provides a comprehensive examination of the relationship between lightning current and the damage to GFC-WTBs, as well as elucidates the mechanism of lightning damage to GFC-WTBs.

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

Newer defects in memories arising from shrinking manufacturing technologies demand improved memory testing methodologies. The percentage of memories on chips continues to rise. With shrinking technologies (10 nm up to 1.8 nm), the structure of memories is becoming denser. Due to the dense structure and significant portion of a chip, the nanometer memories are highly susceptible to defects. High-frequency specifications, the complexity of internal connections, and the process variation due to newer manufacturing technology further increased the probability of the physical failure of memories to a great extent. Memories need to be defect-free for the chip to operate successfully. Therefore, testing embedded memories has become crucial and is taking significant test costs. Researchers have proposed multiple approaches considering these factors to test the nanometer memories. They include using new fault models, march algorithms, memory built-in self-test (MBIST) architectures, and validation strategies. This paper surveys the methodologies presented in recent times. It discusses the core principles used in them, along with benefits. Finally, it discusses key opens in each and offers the scope for future research.

We examined how bilinguals process language switches between their first (L1) and second language (L2). Language switching costs (slower responses to language switch than nonswitch trials) appear to arise more systematically in production than in comprehension, possibly because the latter context might sometimes elicit less language coactivation (Declerck et al., 2019). This might reduce language competition and in turn the need for bilinguals to apply language control when processing language switches. Yet even in comprehension, language coactivation may vary depending on variables such as the accent of the speaker (e.g., whether the L2 words are pronounced with an L1 or L2 accent) and input modality (spoken or written). In three experiments conducted during 2021-2022, we tested how unbalanced Mandarin-English bilinguals processed language switches during comprehension and the potential influence of a speaker's accent and input modality. Overall, across settings, participants experienced significant language switching costs. In some conditions, switching costs were larger to L1-Mandarin than to L2-English, an asymmetry consistent with the participants' dominance in L1-Mandarin and the application of language control. However, manipulating accent and input modality did not influence language switches, suggesting they did not impact language coactivation sufficiently to modulate language control. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The concept paper explores the potential of artificial intelligence (AI) and machine learning (ML) in revolutionizing food quality control processes. In response to the growing challenges faced by the food industry in ensuring consistent quality and safety standards, this paper proposes leveraging advanced technologies to enhance predictive analysis. The traditional methods of food quality control are often reactive and time-consuming, leading to inefficiencies and increased risks of contamination or spoilage. By harnessing AI and ML algorithms, businesses can shift towards proactive strategies, predicting potential issues before they arise and implementing preventive measures accordingly. Key components of the proposed approach include data collection from various sources such as sensors, supply chain records, and historical quality data. Through sophisticated data analysis techniques, AI systems can identify patterns, anomalies, and correlations that might indicate deviations from expected quality standards. Moreover, ML models can continuously learn and adapt based on new data, improving prediction accuracy over time. Implementation of AI-driven predictive analysis in food quality control offers several benefits. Automation of quality control processes reduces manual effort and enables real-time monitoring, enabling timely interventions to maintain product quality. By minimizing the likelihood of product recalls, waste, and rework, businesses can achieve significant cost savings associated with quality control measures. Consistently delivering high-quality products strengthens consumer trust and loyalty, leading to increased market competitiveness and brand reputation. AI-powered systems can assist in ensuring compliance with stringent food safety regulations by providing comprehensive documentation of quality control measures and outcomes. However, successful adoption of AI and ML technologies in food quality control requires overcoming various challenges, including data privacy concerns, integration with existing systems, and ensuring the reliability and interpretability of AI-driven insights. the integration of AI and ML for predictive analysis represents a transformative opportunity for the food industry to modernize quality control practices and uphold the highest standards of safety and excellence. Embracing innovation in this domain is essential for staying competitive in a rapidly evolving market landscape and meeting the evolving expectations of consumers and regulatory bodies alike.

In this paper, we present SasWOT, the first training-free Semantic segmentation Architecture Search (SAS) framework via an auto-discovery proxy. Semantic segmentation is widely used in many real-time applications. For fast inference and memory efficiency, Previous SAS seeks the optimal segmenter by differentiable or RL Search. However, the significant computational costs of these training-based SAS limit their practical usage. To improve the search efficiency, we explore the training-free route but empirically observe that the existing zero-cost proxies designed on the classification task are sub-optimal on the segmentation benchmark. To address this challenge, we develop a customized proxy search framework for SAS tasks to augment its predictive capabilities. Specifically, we design the proxy search space based on the some observations: (1) different inputs of segmenter statistics can be well combined; (2) some basic operators can effectively improve the correlation. Thus, we build computational graphs with multiple statistics as inputs and different advanced basis arithmetic as the primary operations to represent candidate proxies. Then, we employ an evolutionary algorithm to crossover and mutate the superior candidates in the population based on correlation evaluation. Finally, based on the searched proxy, we perform the segmenter search without candidate training. In this way, SasWOT not only enables automated proxy optimization for SAS tasks but also achieves significant search acceleration before the retrain stage. Extensive experiments on Cityscapes and CamVid datasets demonstrate that SasWOT achieves superior trade-off between accuracy and speed over several state-of-the-art techniques. More remarkably, on Cityscapes dataset, SasWOT achieves the performance of 71.3% mIoU with the speed of 162 FPS.

Whistleblowing plays a critical role in building healthy transparent and responsible organizations. Whistleblowing can be challenging, however, as it may result in significant costs for the whistleblower. Facilitating whistleblowing that resists illegal and unethical behaviors within organizations requires management to enhance behavior while protecting whistleblowers. From the perspective of risk-taking, the current research examines which factors are associated with whistleblowing intention using an original approach, the gamification method. Accordingly, in addition to the effects of respondents’ characteristics including Big Five personalities, this research tests how the immediate supervisor’s sex and organizational size, which are randomly assigned by interactive webcomics using hypothetical situations and experimental questionnaires, affect whistleblowing intention. Logistic regression analysis shows openness is significantly associated with whistleblowing intention. In the hypothetical situation, whistleblowers are more likely to come forward if they have a supervisor of a different sex and/or are in a larger organization.

Application of aggregation operators for forecasting [formula omitted] fluctuations: From available Caribbean data sites to unequipped ones