Efficient Test Research Articles

Production and characterization of bioemulsifier from local isolate of Saccharomyces cerevisiae strain JZT351: Antioxidant and antimicrobial effects

AbstractThe widespread use and circulation of industrial emulsifiers pose significant health risks, compounded by their limited availability and high cost. Consequently, there is growing interest in exploring the potential of natural sources, such as microorganisms like yeast, for emulsifier production. In this study, 25 strains of Saccharomyces cerevisiae were isolated from 17 distinct local sources. The yeasts were characterized based on cell shape, size, and colony morphology using yeast malt agar, followed by morphological, microscopic, and biochemical analyzes. Among the isolates, AC1 from a vinegar starter demonstrated superior bioemulsifier production. Emulsification efficiency tests revealed that AC1 exhibited values of 7.1 cm, 0.71 nm, and 34.50%, outperforming other isolates. The biomass yield was approximately 4.35 g/L. This strain was registered in the gene bank as JZT351 (OR115510) after a 100% match with S. cerevisiae. Optimal bioemulsifier production conditions for JZT351 were identified using a liquid yeast extract peptone dextrose medium, with date juice replacing 75% of the glucose, at pH 5.5, 30°C, and 72 h. The resulting emulsification activity, index, and oil displacement were 8.69 cm, 0.95 nm, and 39.7%, respectively. Fourier‐transform infrared spectroscopy (FTIR) compared the functional groups of the bioemulsifier with those of conventional emulsifiers. Molecular characterization was confirmed by 1H NMR. FTIR spectra revealed bioemulsifier bands at 3443.28, 2929.34, 1656.559, 1534.09, 807.06, and 1656.55 cm−1. At 0.5–5 ppm, signals and spectral frequencies corresponded to a mannoprotein structure. The bioemulsifier exhibited 58.09% antioxidant activity at 10 mg/mL, as assessed by DPPH scavenging. It showed the highest inhibition against Pseudomonas aeruginosa and the least against Candida albicans.

Hybrid and on-site working: correlation between working modality and psychophysical health.

This study investigates the psychophysical health implications of hybrid and on-site working modalities among desk workers. It focuses on how hybrid work, integrating remote and on-site elements, influences physical activity, efficiency, and overall health. The research involved 57 desk workers, evaluated using the International Physical Activity Questionnaire and accelerometers for physical activity, the Cubo Fitness Test for physical efficiency, and blood sample analyses for clinical parameters. Psychosocial traits were assessed through the General Self-Efficacy Scale, the Psychological General Well-being Index, and the Job Content Questionnaire. We analyzed: 1) the correlation between physical activity, physical efficiency, and health parameters in the entire sample of desk workers (composed both by hybrid and on-site workers); 2) differences between hybrid and on-site workers; and 3) correlations between hybrid work composition (the percentage of remote working per week) and health-related parameters. Statistical analysis showed relationships between physical efficiency, amount of physical activity, and clinical health parameters in line with the literature. Moreover, hybrid workers engaged in significantly more intense physical activities outside work hours than on-site workers. In addition, hybrid working showed better results in some components of psychosocial well-being. Despite that, no significant differences were found in clinical health parameters. A correlation analysis between hybrid work composition and all considered health-related parameters highlighted that the higher the remote working, the higher the vigorous physical activity, but the worse the psychosocial status and clinical parameters. The findings suggest that a hybrid working model could effectively support the health of desk workers if adequately balanced.

Sound Metric Proximity Hearing App

Globally, hearing impairment affects over 466 million people, creating barriers to accessing audiometric testing and timely diagnosis. Conventional methods often prove challenging to access, especially for people with limited resources or any other disadvantages. The proposed innovative "Sound Metric Proximity Hearing App" aims to revolutionize auditory health through mobile technology. Utilizing advanced techniques such as frequency-specific sound generation, individual ear sound production, and obstacle detection, the app provides comprehensive hearing tests through a user-friendly platform. The application is developed using React Native for the frontend and Django for the backend. It integrates seamlessly with mobile devices and databases, ensuring efficient and reliable performance. With simple result export and tools for early detection, the application enhances the accessibility and efficiency in hearing assessments. It bridges gaps in traditional testing methods, empowering users to proactively manage their auditory health. The "Sound Metric Proximity Hearing App" addresses the essential need for accessible, efficient hearing tests, making a positive impact on global auditory health. Through its innovative approach, the app aims to reduce the stigma associated with hearing loss and promote early intervention. By providing a convenient tool for educators, the app supports classroom management and improves learning environments. With its potential to reach unprivileged people and enhance awareness, the proposed sound metric application is devised to contribute significantly to global auditory health.

Robotic Technology Enables Automated Nondestructive Weld Inspection in Subsea Zone

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215588, “Automated Nondestructive Weld Inspection in Splash/Subsea Zone,” by Karsten Husby, Magnus Bjerkeng, and Jens T. Thieleman, SINTEF, et al. The paper has not been peer reviewed. _ Jacket structures all over the world are now reaching the limit of their estimated service life, but new discoveries and improved production methods require lifetime extension of the assets. The complete paper presents an automated underwater robot and a nondestructive testing (NDT) eddy-current probe for alternating-current field-measurement analysis of jacket main welds. This robot makes integrity testing, which often is required for lifetime extensions, possible. Introduction Jacket lifetime calculations are based on long service intervals. Some welded braces in the splash zone often are considered unserviceable because of the hostile environment. Thus, divers or remotely operated vehicles cannot be used under normal weather conditions and wave heights. Structural integrity is ensured with long service intervals, often long enough for the lifetime of the jacket. However, multiple jacket platforms are approaching their certified lifespan limit. The question, then, is whether they should be decommissioned, reused, or have their lifespan extended. New discoveries and improved production methods make extended operation desirable for certain fields. For other platforms, repurposing for offshore wind, aquaculture, or geothermal energy may be a cost-effective alternative. Regardless, before any action can be taken, the integrity of the jackets must be tested. There is, therefore, a growing need for subsea condition monitoring of aging offshore jacket steel platforms. To speed up operations and to handle complicated health, safety, and environment (HSE) issues in the splash zone, robots can be used. A robot for NDT inspection in the hostile splash zone is pictured in Fig. 1. The robot clamps itself to the circular braces and is equipped with 3D vision camera, kinematics algorithms, and an NDT probe. Integrity Testing Under Rough Conditions Testing for integrity of critical infrastructure often is performed by different NDT methods. In this study, eddy-current testing was chosen as the preferred method because it is fast and can penetrate layers of marine growth on the surface. The normal procedure is to use lightweight hand-held equipment. The splash zone, however, is often inaccessible under modern HSE standards. In this project, an automated NDT robot helped conduct efficient NDT testing for longer periods in challenging conditions. This robot offloads the NDT operator and makes testing more efficient.

Development and application of a rapid research prioritisation process for identifying health research priorities in low- and middle-income countries: the RAPID-RP stakeholder analysis

Background Health research should be locally prioritised by key stakeholders to ensure the best use of available resources, maximum likelihood of research leading to uptake into policy and practice and relevance to the needs of the potential beneficiaries. Established approaches are complex and time-consuming, therefore not feasible in settings where prioritisation is uncommon and time is limited. Objectives We developed and applied a simple, rapid research prioritisation approach to elicit the views of stakeholders. Setting and participants This was a collaboration between a multidisciplinary group of United Kingdom academics, the International Primary Care Respiratory Group and primary care respiratory researchers from four low- and middle-income countries: Brazil, China, Georgia and North Macedonia. We identified 10 topics for research prioritisation through workshops involving 26 topic, methods and local context experts, and considering the programme remit, to develop, adapt and evaluate culturally appropriate community and behavioural approaches for the prevention, early identification and management of chronic obstructive pulmonary disease in primary care. Design and methods In each setting, local research teams convened stakeholder groups of patients, clinicians, managers/policymakers and researchers. Each group briefly discussed the 10 potential research study topics. Discussions were facilitated using short vignettes for each topic, and participants were encouraged to consider importance, feasibility and equity. Individual participants rated each study on a three-point traffic light scale, then ranked them following a facilitated discussion on what drove the ratings. The research team rated then ranked each study considering three further criteria (international novelty, potential for future funding and capacity building on a five-point scale). Within each group, ranks were summed to create a final rank order which guided our research programme and provided insight for future projects. Results In each country, four to eight members attended each stakeholder group. The engagement process was completed in less than 4 hours and feedback was very positive, especially from patients who valued the opportunity to contribute to research decision-making about their own condition. Ranking varied to some extent between groups and settings, but there was consistency around topics that were prioritised among the top five in all groups (identifying efficient chronic obstructive pulmonary disease screening test strategies, evaluations of lung age to aid smoking cessation, feasibility of locally adapted pulmonary rehabilitation, clinical education for primary care staff) and study topics that consistently ranked low (use of e-cigarettes for smoking cessation, weight management to improve chronic obstructive pulmonary disease symptoms and handwashing to reduce infections). Limitations Despite attempts to maximise inclusivity and diversity, stakeholders were mainly limited geographically to the centres where researchers were based, potentially limiting generalisability of views across the countries. Facilitator styles varied and may have influenced some of the discussions and potentially the ranking. Conclusions Despite some limitations, we demonstrated the feasibility and acceptability of the rapid research prioritisation approach stakeholder analysis for identifying locally relevant research priorities in low- and middle-income country settings. Future work Further validation is needed for aspects of the process. We have identified some useful lessons from our evaluation of the process, to facilitate future use of this approach. Funding This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research programme as award number 16/137/95.

A general consonance principle for closure tests based on -values.

The closure principle is a powerful approach to constructing efficient testing procedures controlling the familywise error rate in the strong sense. For small numbers of hypotheses and the setting of independent elementary -values we consider closed tests where each intersection hypothesis is tested with a -value combination test. Examples of such combination tests are the Fisher combination test, the Stouffer test, the Omnibus test, the truncated test, or the Wilson test. Some of these tests, such as the Fisher combination, the Stouffer, or the Omnibus test, are not consonant and rejection of the global null hypothesis does not always lead to rejection of at least one elementary null hypothesis. We develop a general principle to uniformly improve closed tests based on -value combination tests by modifying the rejection regions such that the new procedure becomes consonant. For the Fisher combination test and the Stouffer test, we show by simulations that this improvement can lead to a substantial increase in power.

Optimized Frame Design for Head Loss Testing Equipment Through Material Strength Analysis

This article presents the design and analysis of a frame for head loss testing equipment, crucial for evaluating flow losses in pipe installations. The objective was to develop a robust yet lightweight frame that could withstand the operational loads imposed by the testing equipment. The frame, which supports essential components such as pipes, venturi meters, elbows, and reducers, was constructed using ASTM A500 hollow sections with dimensions of 20 x 20 x 1.6 mm and 35 x 35 x 1.6 mm. These dimensions were selected for their balance between strength and weight, validated through strength analysis and SolidWorks simulations. Conducted at Universitas Mercu Buana, the project involved the design, manufacturing, and testing of the frame to determine its load-bearing capacity. The results from the SolidWorks simulations confirmed the frame's structural integrity, which was further validated by its successful application in a practical setup. This study demonstrates the effectiveness of a systematic design approach, integrating material selection, load analysis, and simulation to achieve an optimal solution. The findings contribute valuable insights into the use of ASTM A500 hollow sections in structural applications, particularly where both strength and weight are critical. This work sets a precedent for future designs in mechanical engineering, offering a reliable framework for developing durable and efficient testing equipment.

Aging Effects Observed in Automotive Fuel Cell Stacks by Applying a New Realistic Test Protocol and Humidity Control

ABSTRACTTraditional automotive proton exchange membrane fuel cell (PEMFC) endurance testing relies on the fuel cell (FC) dynamic load cycle (FC‐DLC) protocol, which inadequately reflects real‐world driving conditions. To address this limitation the “Investigations on degradation mechanisms and Definition of protocols for PEM Fuel cells Accelerated Stress Testing” (ID‐FAST) consortium defined the new representative “ID‐FAST driving load cycle,” a novel approach capturing the load distribution, transitions, temperature variations, and humidity fluctuations experienced by FCs in real‐world operation. We demonstrate the ID‐FAST driving cycle itself and the integration into a realistic durability test program for FC test benches and present the resulting test data. Furthermore, we showcase its implementation within an accelerated stress testing (AST) protocol, highlighting its potential to significantly reduce testing time by accelerating degradation. Additionally, a novel method for highly dynamic humidity adjustment within test benches is introduced. By overcoming limitations of existing methods and incorporating the ID‐FAST driving cycle, this work paves the way for a new era of efficient and realistic FC endurance testing, ultimately contributing to the development of more robust and durable automotive FC stacks.

Development of a Multiplexed Sphingolipids Method for Diagnosis of Inborn Errors of Ceramide Metabolism.

Sphingolipids play a crucial role in cellular functions and are essential components of cell membranes, signaling molecules, and lipid metabolism. In particular, ceramide is a key intermediate in sphingolipid metabolism and defects in ceramide metabolism can lead to various inborn errors of metabolism, making ceramides important targets for clinical screening and diagnosis. Detecting altered concentration patterns of sphingolipids is desirable for distinguishing related inborn errors of metabolism for diagnosis and treatment monitoring. We developed a liquid chromatography-tandem mass spectrometry method with a pathway-oriented approach to focus on sphingolipids involved in ceramide metabolism. A total of 47 sphingolipids bearing different head groups and side chains were targeted. Precision/reproducibility, linearity, and spike recovery extraction efficiency tests were performed on plasma and serum samples from confirmed cases of sphingolipidosis. Linearity of the method showed the coefficient of determination (r2) for all standards to be >0.99 with a slope of 1.00 ± 0.01. Intra- and interday reproducibility of standards spiked into plasma and serum revealed a coefficient of variation <20%. Spike and recovery assessment showed recovery values of 80%-120% for all standards. Altered levels of sphingolipids from patients with hereditary sensory and autonomic neuropathy caused by pathogenic variants in SPTLC2 and hypomyelinating leukodystrophy related to variants in DEGS1 were detected, in agreement with trends reported in earlier studies confirming the utility of this pathway-centric method. This method can serve as a useful tool to simultaneously monitor sphingolipids, enabling screening and diagnosis of inborn errors of ceramide metabolism.

Development of Multi-Walled Carbon Nanotube-Integrated Recycled Polyethylene Terephthalate Electrospun Antibacterial Face Mask

In this study, electrospun nanofiber membranes were successfully fabricated by modifying recycled polyethylene terephthalate (r-PET) with multi-walled carbon nanotubes (MWCNTs) for face mask applications. The recycling of PET bottles ensures effective waste management, while the electrospinning process facilitates the creation of delicate nano-range fibers. The inclusion of MWCNTs aimed to assess the antibacterial activity of the resulting nanomembranes. The fabricated samples underwent comprehensive characterization, including scanning electron microscopy (SEM), ultimate tensile testing, particulate filtration efficiency (PFE) testing, Fourier transform infrared spectroscopy (FTIR), and antibacterial sensitivity assays, revealing significant findings. MWCNTs incorporation led to minor bead formation in the membranes, with fiber diameters ranging from 446 to 862 nm. However, MWCNTs resulted in reduced stiffness and tensile properties due to the disruption of polymer chain alignment. The nanofiber membranes demonstrated effective filtration of 0.3 µm particulates, achieving filtration rates between 96 to 97%Functional group analysis confirmed the presence of both r-PET and MWCNTs in the composites. Although the r-PET/MWCNTs 12 kV membrane lacked an antibacterial zone of inhibition, it exhibited reductions of 28.64% for S. aureus and 31.9% for E. coli. These results collectively underscore the potential of r-PET/MWCNTs samples as an alternative nanofibrous membrane material for face masks. J. of Sci. and Tech. Res. 5(1): 63-74, 2023

Synthesis, testing, and evaluation of efficiency and emissions properties of tamarind-based biodiesel with magnetite nanoparticles

Synthesis, testing, and evaluation of efficiency and emissions properties of tamarind-based biodiesel with magnetite nanoparticles

Immunocytochemistry of bone marrow aspirates: a tool in the diagnosis of feline leukemia virus infection in cats

Feline leukemia virus (FeLV) is a highly debilitating cat pathogen due to its ability to cause many pathological changes. Therefore, identifying the virus directly in bone marrow can be a highly relevant diagnostic tool even in the absence of viraemia. The aim of this study was to compare the diagnostic efficiency of immunocytochemistry (ICC) of bone marrow aspirates with enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). Blood samples were collected from 188 cats and separated into aliquots of whole blood for nested PCR using the U3 LTR region and the gag gene of FeLV-A as reference and serum for detection of the p27 antigen by ELISA. Bone marrow samples from these cats were placed on silanized slides for anti-FeLV ICC using gp70 as primary antibody. A total of 28.2% of the cats tested for FeLV were positive in at least one of the tests, with 26.6% positive by PCR, 18.1% by ICC and 11.2% by ELISA. Cohen's kappa agreement test revealed moderate agreement between ELISA and PCR results and substantial agreement between ICC and ELISA and between ICC and PCR. The results indicated that ICC of bone marrow is an efficient novel diagnostic test for FeLV infection.

Over 10% efficient Cu2ZnSn(S, Se)4 Thin-Film cells prepared by aluminium doping

Cu2ZnSn(S,Se)4(CZTSSe) solar cell is of excellent development tendency because of lower pollution and outstanding power conversion efficiency(PCE). Yet, current PCE of CZTSSe is limited because of a high open-circuit voltage deficit, primarily put down to severe band tailing and carrier recombination. In this paper, absorber films were obtained by solution method and the influences of different aluminum doping concentrations on the properties of CZTSSe thin film cell were studied. Analysis about X-ray diffraction (XRD) and Raman spectroscopy confirms which Al has incorporated into CZTSSe thin film lattice and replaced Sn. Hall effect measurements reveal the improvement of carrier concentration on thin film absorber layer attribute to the formation of AlSn acceptor defects. Furthermore, X-ray photoelectron spectroscopy (XPS) reveals that Al enter into the CZTSSe lattice and Al is in the appropriate state of + 3 valence without generating additional harmful defects. More important, the generation of deep energy level defects and defect clusters associated with Sn were suppressed by replacing Sn with Al. Finally, the photovoltaic performance of different Al doped CZTSSe cells were characterized by conducting J-V and external quantum efficiency (EQE) tests. Remarkably, the CZTSSe solar cell with PCE as high as 10.59 % is obtained under a doping level of 6 % Al, which has an open circuit voltage of 522 mv. Compared with reference CZTSSe solar cells with an efficiency of 7.29 %, which indicates with improvement of 45 % in efficiency. Consequently, Al doping process is considered as a prospective tactic to elevate the PCE of CZTSSe solar cells.

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish

BackgroundAlthough zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications have empowered automated image recognition and video-tracking to ensure more efficient behavioral testing. New methodCapitalizing on several AI tools that most recently became available, here we present a novel open-access AI-driven platform to analyze tracks of adult zebrafish collected from in vivo neuropharmacological experiments. For this, we trained the AI system to distinguish zebrafish behavioral patterns following systemic treatment with several well-studied psychoactive drugs - nicotine, caffeine and ethanol. ResultsExperiment 1 showed the ability of the AI system to distinguish nicotine and caffeine with 75 % and ethanol with 88 % probability and high (81 %) accuracy following a post-training exposure to these drugs. Experiment 2 further validated our system with additional, previously unexposed compounds (cholinergic arecoline and varenicline, and serotonergic fluoxetine), used as positive and negative controls, respectively. Comparison with existing methodsThe present study introduces a novel open-access AI-driven approach to analyze locomotor activity of adult zebrafish. ConclusionsTaken together, these findings support the value of custom-made AI tools for unlocking full potential of zebrafish CNS drug research by monitoring, processing and interpreting the results of in vivo experiments.

Testing the ratio of two Poisson means based on an inferential model

The ratio of two Poisson means is commonly used in biological, epidemiological, and medical. In this article, we consider the problem of testing the ratio of two Poisson means and propose a valid and efficient test based on the inference model (IM). The simulation studies show that the type I error and power of the IM-based test are competitive or even better than the six recommended tests: The likelihood ratio, mid-p, logarithmic transformation, and three tests based on the method of variance estimates recovery (MOVER). For R < 1 , the Rao-MOVER and fiducial-MOVER tests are slightly liberal, the mid-p test is somewhat conservative. For R > 1 , the Rao-MOVER and fiducial-MOVER tests are slightly conservative. The likelihood ratio and logarithmic transformation tests cannot control the type I error well. Therefore, the IM test could be recommended for practical applications. A real numerical example is presented to illustrate the flexibility of the proposed test.

AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review

About 70 million people globally have been diagnosed with epilepsy. Electroencephalogram (EEG) devices are the primary method for identifying and monitoring seizures. The use of EEG expands the preclinical research involving the long-term recording of neuro-activities in rodent models of epilepsy targeted towards the efficient testing of prospective antiseizure medications. Typically, trained epileptologists visually analyse long-term EEG recordings, which is time-consuming and subject to expert variability. Automated epileptiform discharge detection using machine learning or deep learning methods is an effective approach to tackling these challenges. This systematic review examined and summarised the last 30 years of research on detecting epileptiform discharge in rodent models of epilepsy using machine learning and deep learning methods. A comprehensive literature search was conducted on two databases, PubMed and Google Scholar. Following the PRISMA protocol, the 3021 retrieved articles were filtered to 21 based on inclusion and exclusion criteria. An additional article was obtained through the reference list. Hence, 22 articles were selected for critical analysis in this review. These articles revealed the seizure type, features and feature engineering, machine learning and deep learning methods, training methodologies, evaluation metrics so far explored, and models deployed for real-world validation. Although these studies have advanced the field of epilepsy research, the majority of the models are experimental. Further studies are required to fill in the identified gaps and expedite preclinical research in epilepsy, ultimately leading to translational research.

Design of gravity assisted heat exchanger and its application on enhanced waste heat recuperation utilizing organic Rankine and LNG system

Waste heat recovery involves capturing and reusing heat from a system that would typically be discarded. In industries like manufacturing and power generation, this method is gaining importance due to its potential to reduce greenhouse gas emissions and fuel consumption. This paper discusses a novel heat exchanger system that utilizes gravity to extract heat from exhaust gas, providing an alternative solution to the difficulties encountered by the conventional heat recovery techniques. Moreover, by adding both the ORC (Organic Rankine Cycle) and LNG (Liquefied Natural Gas) cycle to this system, excess heat can be used more efficiently, allowing for better energy recovery. The gravity-pipe heat exchanger and two cycles are used in the combined energy recovery system to extract useful heat from a low-grade waste stream. Energy performance is measured by using heat transfer analysis, energy efficiency testing, and exergy analysis followed by a comprehensive parametric analysis. To identify the optimal operating parameters for maximizing energy recovery and minimizing energy losses including economic analysis of GPHE with conventional HE, mathematical optimization models are developed. The heat exchanger demonstrates good effectiveness, close to 52.3 %, at an optimum temperature of approximately 275 °C to 280 °C for a 35 kg/s air flow rate. The ORC cycle is most efficient with optimum operating condition when pentane's mass flow rate is 3.3 kg/s. The maximum work output is obtained at a condenser pressure of 0.21 MPa, reaching 280 kW. When using pentane, the cycle's maximum efficiency is 36.8 %. However, the system's exergy efficiency drops by 4.94 % when the pinch temperature goes up by 7 °C. The output of ORC turbine increases from 220 kW to 240 kW, and the output of LNG turbine increases from 25 kW to 40 kW, as the condenser pressure rises. From economic analysis it's attain that the designed GPHE is economically viable for waste heat recovery from dirty exhaust gas. This paper develops a theoretical model to evaluate several cycles for extracting energy from waste heat, which could reduce fuel use and greenhouse gas emissions in industries.

Complexity of High-Dimensional Identity Testing with Coordinate Conditional Sampling

We study the identity testing problem for high-dimensional distributions. Given as input an explicit distribution \(\mu\) , an \(\varepsilon \gt 0\) , and access to sampling oracle(s) for a hidden distribution \(\pi\) , the goal in identity testing is to distinguish whether the two distributions \(\mu\) and \(\pi\) are identical or are at least \(\varepsilon\) -far apart. When there is only access to full samples from the hidden distribution \(\pi\) , it is known that exponentially many samples (in the dimension) may be needed for identity testing, and hence previous works have studied identity testing with additional access to various “conditional” sampling oracles. We consider a significantly weaker conditional sampling oracle, which we call the \(\mathsf{Coordinate\ Oracle}\) , and provide a computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for an \(n\) -dimensional visible distribution \(\mu\) , then there is an efficient identity testing algorithm for any hidden distribution \(\pi\) using \(\widetilde{O}(n/\varepsilon)\) queries to the \(\mathsf{Coordinate\ Oracle}\) . Approximate tensorization of entropy is a pertinent condition as recent works have established it for a large class of high-dimensional distributions. We also prove a computational phase transition: for a well-studied class of \(n\) -dimensional distributions, specifically sparse antiferromagnetic Ising models over \(\{+1,-1\}^{n}\) , we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless \(\mathsf{RP}=\mathsf{NP}\) . We complement our results with a matching \(\Omega(n/\varepsilon)\) statistical lower bound for the sample complexity of identity testing in the \(\mathsf{Coordinate\ Oracle}\) model.

An Entropic Analysis of Efficiency in the West Texas Intermediate Crude Oil Futures Market

For the last 50 years or so, the efficient market hypothesis (EMH) has been the central pillar of economic thought and the building block of portfolio theory. However, the validity of the EMH still remains controversial, and the methods of testing for market efficiency have been criticised. This study utilises entropy analysis combined with generalised autoregressive conditional heteroscedasticity (GARCH) regressions to provide extensive empirical evidence on informational efficiency. This study develops an innovative method for studying market efficiency by investigating the complexity of information saturated in the market. To this end, we have used daily West Texas Intermediate (WTI) crude oil futures contracts from June 1983 to February 2016, giving a total data set of 282,427 observations. The results of the Jensen–Shannon informational criteria indicate that the informational efficiency measure has never reached the maximum value of 1 which indicates weak-form efficiency. Our study has further found that the informational efficiency measure tends to decrease consistently during recessionary periods and to increase in subsequent periods. We have also found that the entropy measure tends to increase over time, supporting the adaptive market hypothesis over the EMH.

Utilization of Machine Learning Algorithms for the Strengthening of HIV Testing: A Systematic Review

Several machine learning (ML) techniques have demonstrated efficacy in precisely forecasting HIV risk and identifying the most eligible individuals for HIV testing in various countries. Nevertheless, there is a data gap on the utility of ML algorithms in strengthening HIV testing worldwide. This systematic review aimed to evaluate how effectively ML algorithms can enhance the efficiency and accuracy of HIV testing interventions and to identify key outcomes, successes, gaps, opportunities, and limitations in their implementation. This review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A comprehensive literature search was conducted via PubMed, Google Scholar, Web of Science, Science Direct, Scopus, and Gale OneFile databases. Out of the 845 identified articles, 51 studies were eligible. More than 75% of the articles included in this review were conducted in the Americas and various parts of Sub-Saharan Africa, and a few were from Europe, Asia, and Australia. The most common algorithms applied were logistic regression, deep learning, support vector machine, random forest, extreme gradient booster, decision tree, and the least absolute shrinkage selection operator model. The findings demonstrate that ML techniques exhibit higher accuracy in predicting HIV risk/testing compared to traditional approaches. Machine learning models enhance early prediction of HIV transmission, facilitate viable testing strategies to improve the efficiency of testing services, and optimize resource allocation, ultimately leading to improved HIV testing. This review points to the positive impact of ML in enhancing early prediction of HIV spread, optimizing HIV testing approaches, improving efficiency, and eventually enhancing the accuracy of HIV diagnosis. We strongly recommend the integration of ML into HIV testing programs for efficient and accurate HIV testing.