How to fill the biodiversity data gap:Is it better to invest in fieldwork or curation?

Data gaps and biases are two important issues that affect the quality of biodiversity information and downstream results.Understanding how best to fill existing gaps and account for biases is necessary to improve our current information most effectively.Two current main approaches for obtaining and improving data include(1)curation of biological collections,and(2)fieldwork.However,the comparative effectiveness of these approaches in improving biodiversity data remains little explored.We used the Flora de Bogota project to study the magnitude of change in species richness,spatial coverage,and sample coverage of plant records based on curation versus fieldwork.The process of curation resulted in a decrease in species richness(synonym and error removal),but it significantly increased the number of records per species.Fieldwork contributed to a slight increase in species richness,via accumulation of new records.Additionally,curation led to increases in spatial coverage,species observed by locality,the number of plant records by species,and localities by species compared to fieldwork.Overall,curationwas more efficient in producing new information compared to fieldwork,mainly because of the large number of records available in herbaria.We recommend intensive curatorial work as the first step in increasing biodiversity data quality and quantity,to identify bias and gaps at the regional scale that can then be targeted with fieldwork.The stepwise strategy would enable fieldwork to be planned more costeffectively given the limited resources for biodiversity exploration and characterization.

Numerical three-dimensional modeling of earthen dam piping failure

A physically-based numerical three-dimensional earthen dam piping failure model is developed for homogeneous and zoned soil dams.This model is an erosion model,coupled with force/moment equilibrium analyses.Orifice flow and two-dimensional(2D)shallow water equations(SWE)are solved to simulate dam break flows at different breaching stages.Erosion rates of different soils with different construction compaction efforts are calculated using corresponding erosion formulae.The dam's real shape,soil properties,and surrounding area are programmed.Large outer 2D-SWE grids are used to control upstream and downstream hydraulic conditions and control the boundary conditions of orifice flow,and inner 2D-SWE flow is used to scour soil and perform force/moment equilibrium analyses.This model is validated using the European Commission IMPACT(Investigation of Extreme Flood Processes and Uncertainty)Test#5 in Norway,Teton Dam failure in Idaho,USA,and Quail Creek Dike failure in Utah,USA.All calculated peak outflows are within 10%errors of observed values.Simulation results show that,for a V-shaped dam like Teton Dam,a piping breach location at the abutment tends to result in a smaller peak breach outflow than the piping breach location at the dam's center;and if Teton Dam had broken from its center for internal erosion,a peak outflow of 117851 m'/s,which is 81%larger than the peak outflow of 65120 m3/s released from its right abutment,would have been released from Teton Dam.A lower piping inlet elevation tends to cause a faster/earlier piping breach than a higher piping inlet elevation.

Period–Luminosity Relationship forδScuti Stars Revisited

The Gaia DR3 parallax approach was used to estimate the absolute parameters of 2375δScuti stars from the ASAS catalog.The selected stars have a variety of observational characteristics,with a higher than 80%probability of beingδScuti stars.We have displayed all the stars in the Hertzsprung-Russell diagram along with theδScuti instability strip,the Zero Age Main Sequence and the Terminal Age Main Sequence.Then,we determined which fundamental and overtone modes each star belongs to using pulsation constant(Q)calculations.In addition,we evaluated the parameters in the Q calculation equation using three machine learning methods,which showed that surface gravity and temperature have the greatest effect on its calculation.The Period-Luminosity(P-L)relationship of theδScuti stars was also revisited.Eventually,using least squares linear regression,we made four linear fits for fundamental and overtone modes and updated their relationships.

MXenes for Bioinspired Soft Actuators:Advancements in Angle-Independent Structural Colors and Beyond

Soft actuators have garnered substantial attention in current years in view of their potential appliances in diverse domains like robotics,biomedical devices,and biomimetic systems.These actuators mimic the natural movements of living organisms,aiming to attain enhanced flexibility,adaptability,and versatility.On the other hand,angle-independent structural color has been achieved through innovative design strategies and engineering approaches.By carefully controlling the size,shape,and arrangement of nanostructures,researchers have been able to create materials exhibiting consistent colors regardless of the viewing angle.One promising class of materials that holds great potential for bioinspired soft actuators is MXenes in view of their exceptional mechanical,electrical,and optical properties.The integration of MXenes for bioinspired soft actuators with angle-independent structural color offers exciting possibilities.Overcoming material compatibility issues,improving color reproducibility,scalability,durability,power supply efficiency,and cost-effectiveness will play vital roles in advancing these technologies.This perspective appraises the development of bioinspired MXene-centered soft actuators with angleindependent structural color in soft robotics.

A Harmonic Approach to Handwriting Style Synthesis Using Deep Learning

The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

Alzheimer’s visual variant: a report of a diagnosis easily missed on ophthalmic examination

Dear Editor,We present a case of posterior cortical atrophy(PCA),which is a syndromic entity caused by different neurodegenerative diseases,mainly Alzheimer’s disease,but it has been described in several other entities.Its frequency is reported to be as high as 5%of all cases;however,it remains widely under-recognized among ophthalmologists and optometrists due to scarce reports in visual health-related journals[1].

The Impact and Intervention of Handheld Tai Chi Water-Resistance Fitness Ball on Middle-Aged and Elderly Patients with Parkinson’s Disease

Through the use of a survey and statistical methods, this study explores the effects and interventions of handheld Tai Chi water resistance fitness balls on the elderly with Parkinson’s disease. Firstly, a questionnaire on exercise compliance for patients with Parkinson’s disease was developed, and its reliability and validity were tested. Then, a survey was conducted to investigate the current status of exercise compliance among Parkinson’s disease patients, including general information, scoring status, and single and multiple factor analyses of influencing factors [1]. The results of the study show that through qualitative research, the dimensions and item pools of the questionnaire were initially constructed, and the reliability analysis of the questionnaire was conducted through Delphi expert consultation, with favorable results in terms of its reliability and validity [2]. Regarding the current status of exercise compliance among Parkinson’s disease patients, the study found that the level of exercise compliance needs improvement, and there are significant differences in exercise compliance levels among patients under different circumstances. Finally, the research results were discussed and conclusions were drawn. The innovation of this study lies in the development of a questionnaire on exercise compliance for patients with Parkinson’s disease and the preliminary qualitative research and Delphi expert consultation conducted on it, providing new ideas and methods for the study of exercise compliance. However, the study also has limitations as it did not examine the effects of other interventions on Parkinson’s disease, so further research should be conducted [3].

Impact of D-dimer on in-hospital mortality following aortic dissection:A systematic review and meta-analysis

BACKGROUND The utility of D-dimer(DD)as a biomarker for acute aortic dissection(AD)is recognized.Yet,its predictive value for in-hospital mortality remains uncertain and subject to conflicting evidence.AIM To conduct a meta-analysis of AD-related in-hospital mortality(ADIM)with elevated DD levels.METHODS We searched PubMed,Scopus,Embase,and Google Scholar for AD and ADIM literature through May 2022.Heterogeneity was assessed using I2 statistics and effect size(hazard or odds ratio)analysis with random-effects models.Sample size,study type,and patients’mean age were used for subgroup analysis.The significance threshold was P<0.05.RESULTS Thirteen studies(3628 patients)were included in our study.The pooled prevalence of ADIM was 20%(95%CI:15%-25%).Despite comparable demographic characteristics and comorbidities,elevated DD values were associated with higher ADIM risk(unadjusted effect size:1.94,95%CI:1.34-2.8;adjusted effect size:1.12,95%CI:1.05-1.19,P<0.01).Studies involving patients with a mean age of<60 years exhibited an increased mortality risk(effect size:1.43,95%CI:1.23-1.67,P<0.01),whereas no significant difference was observed in studies with a mean age>60 years.Prospective and larger sample size studies(n>250)demonstrated a heightened likelihood of ADIM associated with elevated DD levels(effect size:2.57,95%CI:1.30-5.08,P<0.01 vs effect size:1.05,95%CI:1.00-1.11,P=0.05,respectively).CONCLUSION Our meta-analysis shows elevated DD increases in-hospital mortality risk in AD patients,highlighting the need for larger,prospective studies to improve risk prediction models.

Non-Uniform Pion Tetrahedron Aether and Electron Tetrahedron Model

We propose that the QCD vacuum pion tetrahedron condensate density vary in space and drops to extremely low values in the Kennan, Barger and Cowie (KBC) void in analogy to earth’s atmospheric density drop with elevation from earth. We propose a formula for the gravitation acceleration based on the non-uniform pion tetrahedron condensate. Gravity may be due to the underlying microscopic attraction between quarks and antiquarks, which are part of the vacuum pion tetrahedron condensate. We propose an electron tetrahedron model, where electrons are comprised of tetraquark tetrahedrons, and . The quarks determine the negative electron charge and the or quarks determine the electron two spin states. The electron tetrahedron may perform a high frequency quark exchange reactions with the pion tetrahedron condensate by tunneling through the condensation gap creating a delocalized electron cloud with a fixed spin. The pion tetrahedron may act as a QCD glue bonding electron pairs in atoms and molecules and protons to neutrons in the nuclei. Conservation of valence quarks and antiquarks is proposed.

Chemotherapy and radiotherapy:Could they contribute to the development of new tumors and metastases?

Objective: Both chemotherapy and radiotherapy have demonstrated high effectiveness as the best mechanisms in the fight against cancer;however, various studies seem to confirm that they could also favor the development of other unwanted effects of great importance for the patient. The main objective of this study is to find out the possible existence of this type of links. Method: This is a systematic literature review that seeks to find out which and how long cases of late interactions related to chemotherapy and radiotherapy treatments have been known. The bibliographic review was carried out based on references published in the last five years. Results: Various studies confirm the possible relationship between chemotherapy and radiotherapy treatments with the development of new undesirable side effects, especially as a consequence of the hepatotoxicity generated in the case of chemotherapy and radiation in radiotherapy. However, in this last type of treatment, the problems raised are really few. Conclusions: The existence of a risk of suffering new unwanted side effects after different types of treatment seems to have been demonstrated, especially in the case of chemotherapy. In the case of radiotherapy, adverse effects are practically non-existent, although they are no less important.

Proposal of a Deuterium-Deuterium Fusion Reactor Intended for a Large Power Plant

This article looks for the necessary conditions to use Deuterium-Deuterium (D-D) fusion for a large power plant. At the moment, for nearly all the projects (JET, ITER…) only the Deuterium-Tritium (D-T) fuel is considered for a power plant. However, as shown in this article, even if a D-D reactor would be necessarily much bigger than a D-T reactor due to the much weaker fusion reactivity of the D-D fusion compared to the D-T fusion, a D-D reactor size would remain under an acceptable size. Indeed, a D-D power plant would be necessarily large and powerful, i.e. the net electric power would be equal to a minimum of 1.2 GWe and preferably above 10 GWe. A D-D reactor would be less complex than a D-T reactor as it is not necessary to obtain Tritium from the reactor itself. It is proposed the same type of reactor yet proposed by the author in a previous article, i.e. a Stellarator “racetrack” magnetic loop. The working of this reactor is continuous. It is reminded that the Deuterium is relatively abundant on the sea water, and so it constitutes an almost inexhaustible source of energy. Thanks to secondary fusions (D-T and D-He3) which both occur at an appreciable level above 100 keV, plasma can stabilize around such high equilibrium energy (i.e. between 100 and 150 keV). The mechanical gain (Q) of such reactor increases with the internal pipe radius, up to 4.5 m. A radius of 4.5 m permits a mechanical gain (Q) of about 17 which thanks to a modern thermo-dynamical conversion would lead to convert about 21% of the thermal power issued from the D-D reactor in a net electric power of 20 GWe. The goal of the article is to create a physical model of the D-D reactor so as to estimate this one without the need of a simulator and finally to estimate the dimensions, power and yield of such D-D reactor for different net electrical powers. The difficulties of the modeling of such reactor are listed in this article and would certainly be applicable to a future D-He3 reactor, if any.

Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset Fine-Tuning

As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.

On the Number of Primes in the Interval (x, 2x) by an Elementary Method

An elementary formula to know the number of primes in the interval (x, 2x) close to the exact figure for a fixed x is given here. A new elementary equation is derived (a relation between prime numbers and composite numbers distributed in the interval [1, 2x]). An elementary method to know the number of primes in a given magnitude is suitably placed in the form of a general formula, and we have proved it. The general formula is applied to the terms of the equation, and a tactical simplification of the terms gives rise to an expression whose verification envisages scope for its further studies.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

Interrelationships between Length of the Day, Moon Distance, Phanerozoic Geodynamic Cycles, Tidal Dissipation and Earth’s Core: Review and Analysis

The rotation of the Earth and the related length of the day (LOD) are predominantly affected by tidal dissipation through the Moon and the growth of the Earth’s core. Due to the increased concentration of mass around the rotation axis of the spinning Earth during the growth of the core the rotation should have been accelerated. Controversially the tidal dissipation by the Moon, which is mainly dependent on the availability of open shallow seas and the kind of Moon escape from a nearby position, acts towards a deceleration of the rotating Earth. Measurements of LOD for Phanerozoic and Precambrian times open ways to solve questions concerning the geodynamical history of the Earth. These measurements encompass investigations of growth patterns in fossils and depositional patterns in sediments (Cyclostratigraphy, Tidalites, Stromatolites, Rhythmites). These patterns contain information on the LOD and on the changing distance between Earth and Moon and can be used as well for a discussion about the growth of the Earth’s core. By updating an older paper with its simple approach as well as incorporating newly published results provided by the geoscientific community, a moderate to fast growth of the core in a hot early Earth will be favored controversially to the assumption of a delayed development of the core in an originally cold Earth. Core development with acceleration of Earth’s rotation and the contemporaneous slowing down due to tidal dissipation during the filling of the ocean may significantly interrelate.

Solving the Olbers’s Paradox, Explaining the “Red-Shift”, and Challenging the Relativities by “Sun Matters Theory” and “Sun Model of Universe”, an Evolution of the Einstein’s Static Universe Model

Olbers’s paradox, known as the dark night paradox, is an argument in astrophysics that the darkness of the night sky conflicts with the assumption of an infinite and eternal static universe. Big-Bang theory was used to partially explain this paradox, while introducing new problems. Hereby, we propose a better theory, named Sun Matters Theory, to explain this paradox. Moreover, this unique theory supports and extended the Einstein’s static universe model proposed by Albert Einstein in 1917. Further, we proposed our new universe model, “Sun Model of Universe”. Based on the new model and novel theory, we generated innovative field equation by upgrading Einstein’s Field Equation through adding back the cosmological constant, introducing a new variable and modifying the gravitationally-related concepts. According to the Sun Model of Universe, the dark matter and dark energy comprise the so-called “Sun Matters”. The observed phenomenon like the red shift is explained as due to the interaction of ordinary light with Sun Matters leading to its energy and frequency decrease. In Sun Model, our big universe consists of many universes with ordinary matter at the core mixed and surrounded with the Sun Matters. In those universes, the laws of physics may be completely or partially different from that of our ordinary universe with parallel civilizations. The darkness of night can be easily explained as resulting from the interaction of light with the Sun Matters leading to the sharp decrease in the light intensity. Sun Matters also scatter the light from a star, which makes it shining as observed by Hubble. Further, there is a kind of Sun Matters named “Sun Waters”, surrounding every starts. When lights pass by the sun, the Sun Waters deflect the lights to bend the light path. According to the Sun Model, it is the light bent not the space bent that was proposed in the theory of relativities.

Energetic and Entropic Changes in Volume Work and Chemical Reactions

In the present study, energetic and entropic changes are investigated on a comparative basis, as they occur in the volume changes of an ideal gas in the Carnot cycle and in the course of the chemical reaction in a lead-acid battery. Differences between reversible and irreversible processes have been worked out, in particular between reversibly exchanged entropy (∆eS) and irreversibly produced entropy (∆iS). In the partially irreversible case, ∆eS and ∆iS add up to the sum ∆S for the volume changes of a gas, and only this function has an exact differential. In a chemical reaction, however, ∆eS is independent on reversibility. It arises from the different intramolecular energy contents between products and reactants. Entropy production in a partially irreversible Carnot cycle is brought about through work-free expansions, whereas in the irreversible battery reaction entropy is produced via activated complexes, whereby a certain, variable fraction of the available chemical energy becomes transformed into electrical energy and the remaining fraction dissipated into heat. The irreversible reaction process via activated complexes has been explained phenomenologically. For a sufficiently high power output of coupled reactions, it is essential that the input energy is not completely reversibly transformed, but rather partially dissipated, because this can increase the process velocity and consequently its power output. A reduction of the counter potential is necessary for this purpose. This is not only important for man-made machines, but also for the viability of cells.

Gravitation, Density Upper Limit and Quantization of Space

The singularity at distance r → 0 at the center of a spherically symmetric non-rotating, uncharged mass of radius R, is considered here. Under inverse square law force, the Schwarzschild metric, needs to be modified, to include Newton’s Shell Theorem (NST). By including NST for r, both Schwarzschild singularity at r = 2GM/c2 and at r → 0 singularities are removed from the metric. Near R → 0, the question of maximal density is considered based on Schwarzschild’s modified metric, and compared to the quantum limit of maximal mass density put by Planck’s quantum-based universal units. It is asserted, that General relativity, when combined with Planck’s universal units, inevitably leads to quantization of gravity.

Decoupling CO2 from Climate Change

This study determines if there is a correlation between rising carbon dioxide levels and global warming. Historical data were reviewed from three different time periods spanning 500 million years. It showed that the curves and trends were too dissimilar to establish a connection. Observations from CO2/temp ratios showed that the CO2 and the temperature moved in opposite directions 42% of the time. Many ratios displayed zero or near zero values, reflecting a lack of response. As much as 87% of the ratios revealed negative or near zero values, which strongly negate a correlation. The infrared spectra showed the Greenhouse Gases had an exceptionally low absorption band between 11.67 μm to 9.1 μm, which is a zone called the infrared atmospheric window. Most of the Greenhouse Gases absorb little infrared inside that zone. And that zone is where the Earth’s surface emits almost all infrared radiation. Even with minimal absorbance, water vapor captures the most infrared radiation. It absorbs 84 times more than CO2, 407 thousand times more than methane, 452 thousand times more than ozone and 2.3 million times more than nitrous oxide. The Intergovernmental Panel on Climate Change (IPCC) and the United States EPA excluded water vapor because it was not associated with man-made activities. They reported that water vapor and clouds were simply feedback mechanisms from CO2. Clouds reflect radiation from the sun. The Northern Hemisphere is 2.7°F warmer than the Southern Hemisphere because of clouds. The world cloud cover has gone down 4.1% from 1982 to 2018. Calculations show that this could be responsible for 2.4°F of the 2.7°F. The research shows that most of the recent increase in temperature (89.9%) is because of fewer clouds.