Differential colour-ring loss among Dalmatian Pelican(Pelecanus crispus) colonies and its consequence on survival estimates

The analysis of bird ringing data often comes with some potential sources of error and bias,as ring wear and/or loss could affect mark-recapture analyses and produce erroneous estimates of survival.Furthermore,ring wear and loss rates may differ between and within species based on the habitat they use or the species’ life-history traits and behaviour as well as the type of the ring.In this study we use resighting data from a long-term double marking experiment to directly estimate the rate of colour-ring loss among different Dalmatian Pelican colonies over time,evaluate any possible factors that could contribute to differential ring loss and assess how it may bias the results of mark-resighting analyses.Based on 14,849 resightings from 1275 individuals and using multi-state continuous-time hidden Markov models(HMMs) we showed that probability of ring loss was markedly different among colonies,ranging from 0.10 to 0.42 within the first year of marking,whereas the cumulative probability of losing a ring after ten years ranged 0.64 to 0.99.These rates are among the highest estimated when compared to previous studies in waterbirds.Our approach assessing the intra-specific variance in ring loss provided several factors potentially involved,such as the use of glue and the fledgling age accuracy and we could further hypothesise the effect of environmental factors.Finally,our results showed that ring loss can be a significant challenge for the assessment of the species’ population dynamics using mark-recapture methods as survival was consistently underestimated when not accounting for ring loss and varied significantly among different colonies.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique, which modulates data symbols in the delayDoppler(DD) domain, presents a potential solution for supporting reliable information transmission in highmobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM). The empirical state evolution(SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Applications of artificial intelligence in geothermal resource exploration: A review

Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

Malus’ Law Derived from Deterministic Particle Behavior

A polarized beam of energy is usually interpreted as a set of particles, all having the same polarization state. Difference in behavior between the one and the other particle is then explained by a number of counter-intuitive quantum mechanical concepts like probability distribution, superposition, entanglement and quantized spin. Alternatively, I propose that a polarized beam is composed of a set of particles with a cosine distribution of polarization angles within a polarization area. I show that Malus’ law for the intensity of a beam of polarized light can be derived in a straightforward manner from this distribution. I then show that none of the above-mentioned counter-intuitive concepts are necessary to explain particle behavior and that the ontology of particles, passing through a polarizer, can be easily and intuitively understood. I conclude by formulating some questions for follow-up research.

Fermions: Spin, Hidden Variables, Violation of Bell’s Inequality and Quantum Entanglement

Using real fields instead of complex ones, it was recently claimed, that all fermions are made of pairs of coupled fields (strings) with an internal tension related to mutual attraction forces, related to Planck’s constant. Quantum mechanics is described with real fields and real operators. Schrodinger and Dirac equations then are solved. The solution to Dirac equation gives four, real, 2-vectors solutions ψ1=(U1D1)ψ2=(U2D2)ψ3=(U3D3)ψ4=(U4D4)where (ψ1,ψ4) are coupled via linear combinations to yield spin-up and spin-down fermions. Likewise, (ψ2,ψ3) are coupled via linear combinations to represent spin-up and spin-down anti-fermions. For an incoming entangled pair of fermions, the combined solution is Ψin=c1ψ1+c4ψ4where c1and c4are some hidden variables. By applying a magnetic field in +Z and +x the theoretical results of a triple Stern-Gerlach experiment are predicted correctly. Then, by repeating Bell’s and Mermin Gedanken experiment with three magnetic filters σθ, at three different inclination angles θ, the violation of Bell’s inequality is proven. It is shown that all fermions are in a mixed state of spins and the ratio between spin-up to spin-down depends on the hidden variables.

Deciphering the Hidden Secrets between the Early Skin Wrinkling & the Metabolic (X) Syndrome with the Possible Reversal of This Process at the Molecular Level

The aging process is a group of degenerative changes that physiologically occur in most of the people in the elderly. This affects one or more of the human body systems. The treatment of diseases related to the aging process has a huge impact on the economy of all nations. Aging of the skin comes on the top and despite that, the results of the already present lines of treatment are not always satisfactory. This acts as a stimulus for us to dig deeper to discover the root causes of the premature aging of the skin. This was simply caused by the accumulation of repeated minute damage to the internal structure skin. In other words, if the degree of minute damage is more than the capacity of the skin to repair, the repeated micro-damage is presented in the long run as a skin wrinkling. Moreover, the skin acts as a mirror that reflects the internal structures of the human body. Thus, the more degenerative changes in the human body systems, the more the skin could become wrinkled. Our strategy to prevent or at least slow down the aging process of the skin depends on 2 main steps;the 1st is to reduce the micro-damage as can as possible, and the 2nd is to enhance the capacity of tissue regeneration to be able to reverse the already present damaged skin. As the 2 processes are synchronized with each other, this strategy would be considered the ideal for prevention of skin wrinkling especially premature ones. This not only reverses premature skin wrinkling but also protects it from future wrinklings. This review sharply pointed out the role of the functional collagen of the dermal layer of the skin in the prevention of skin wrinklings. Therefore, it would be the target to study how collagen works in the complex machinery of the dermal layer of the skin. This concept deeply believes that the recovery of dermal collagen has a much better effect than simply ingesting collagen or receiving a topical collagen booster. .

A Study on the Impact of Voice-to-Text Technology on Academic Achievement of the Hearing-Impaired

Hearing loss is a significant barrier to academic achievement,with hearing-impaired(HI)individuals often facing challenges in speech recognition,language development,and social interactions.Lip-reading,a crucial skill for HI individuals,is essential for effective communication and learning.However,the COVID-19 pandemic has exacerbated the challenges faced by HI individuals,with the face masks hindering lip-reading.This literature review explores the relationship between hearing loss and academic achievement,highlighting the importance of lip-reading and the potential of artificial intelligence(AI)techniques in mitigating these challenges.The introduction of Voice-to-Text(VtT)technology,which provides real-time text captions,can significantly improve speech recognition and academic performance for HI students.AI models,such as Hidden Markov models and Transformer models,can enhance the accuracy and robustness of VtT technology in diverse educational settings.Furthermore,VtT technology can facilitate better teacher-student interactions,provide transcripts of lectures and classroom discussions,and bridge the gap in standardized testing performance between HI and hearing students.While challenges and limitations exist,the successful implementation of VtT technology can promote inclusive education and enhance academic achievement.Future research directions include popularizing VtT technology,addressing technological barriers,and customizing VtT systems to cater to individual needs.

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular networks.In this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler.We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed algorithm.To refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)algorithm.Finally,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

Memristor hyperchaos in a generalized Kolmogorov-type system with extreme multistability

Memristor chaotic systems have aroused great attention in recent years with their potentials expected in engineering applications.In this paper,a five-dimension(5D)double-memristor hyperchaotic system(DMHS)is modeled by introducing two active magnetron memristor models into the Kolmogorov-type formula.The boundness condition of the proposed hyperchaotic system is proved.Coexisting bifurcation diagram and numerical verification explain the bistability.The rich dynamics of the system are demonstrated by the dynamic evolution map and the basin.The simulation results reveal the existence of transient hyperchaos and hidden extreme multistability in the presented DMHS.The NIST tests show that the generated signal sequence is highly random,which is feasible for encryption purposes.Furthermore,the system is implemented based on a FPGA experimental platform,which benefits the further applications of the proposed hyperchaos.

A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and apple.This paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium curve.The proposed chaotic system has two quadratic,two cubic and two quartic nonlinear terms.It is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium points.It is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium curve.We have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and multi-stability.As an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium curve.The proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)platform.Experimental results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages.The digital document needs to be evaluated physically through the Cross-Language Text Summarization(CLTS)involved in the disparate and generation of the source documents.Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents.The digital documents need to be processed with the contextual semantic data with the decoding scheme.This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents.The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning(HMMlstmRL).First,the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering.In the second stage,bi-directional long-short-term memory networks are used for key word extraction in the cross-language process.Finally,the proposed HMMlstmRL uses the voting concept in reinforcement learning for the identification and extraction of the keywords.The performance of the proposed HMMlstmRL is 2%better than that of the conventional bi-direction LSTM model.

Switchable hidden spin polarization and negative Poisson's ratio in two-dimensional antiferroelectric wurtzite crystals

Two-dimensional(2D)antiferroelectric materials have raised great research interest over the last decade.Here,we reveal a type of 2D antiferroelectric(AFE)crystal where the AFE polarization direction can be switched by a certain degree in the 2D plane.Such 2D functional materials are realized by stacking the exfoliated wurtzite(wz)monolayers with“self-healable”nature,which host strongly coupled ferroelasticity/antiferroelectricity and benign stability.The AFE candidates,i.e.,Zn X and Cd X(X=S,Se,Te),are all semiconductors with direct bandgap atΓpoint,which harbors switchable antiferroelectricity and ferroelasticity with low transition barriers,hidden spin polarization,as well as giant in-plane negative Poisson's ratio(NPR),enabling the co-tunability of hidden spin characteristics and auxetic magnitudes via AFE switching.The 2D AFE wz crystals provide a platform to probe the interplay of 2D antiferroelectricity,ferroelasticity,NPR,and spin effects,shedding new light on the rich physics and device design in wz semiconductors.

Existence of hidden attractors in nonlinear hydro-turbine governing systems and its stability analysis

This work studies the stability and hidden dynamics of the nonlinear hydro-turbine governing system with an output limiting link,and propose a new six-dimensional system,which exhibits some hidden attractors.The parameter switching algorithm is used to numerically study the dynamic behaviors of the system.Moreover,it is investigated that for some parameters the system with a stable equilibrium point can generate strange hidden attractors.A self-excited attractor with the change of its parameters is also recognized.In addition,numerical simulations are carried out to analyze the dynamic behaviors of the proposed system by using the Lyapunov exponent spectra,Lyapunov dimensions,bifurcation diagrams,phase space orbits,and basins of attraction.Consequently,the findings in this work show that the basins of hidden attractors are tiny for which the standard computational procedure for localization is unavailable.These simulation results are conducive to better understanding of hidden chaotic attractors in higher-dimensional dynamical systems,and are also of great significance in revealing chaotic oscillations such as uncontrolled speed adjustment in the operation of hydropower station due to small changes of initial values.

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’Response Against COVID Variants

TheCOVID-19 outbreak began in December 2019 andwas declared a global health emergency by the World Health Organization.The four most dominating variants are Beta,Gamma,Delta,and Omicron.After the administration of vaccine doses,an eminent decline in new cases has been observed.The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies.However,strong variants likeDelta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination.Therefore,it is indispensable to study,analyze and most importantly,predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons.In this regard,machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes.In this study,prediction of T-cells Epitopes’response was conducted for vaccinated and unvaccinated people for Beta,Gamma,Delta,and Omicron variants.The dataset was divided into two classes,i.e.,vaccinated and unvaccinated,and the predicted response of T-cell Epitopes was divided into three categories,i.e.,Strong,Impaired,and Over-activated.For the aforementioned prediction purposes,a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers.Furthermore,the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach.Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

Suspicious mass traffic constantly evolves,making network behaviour tracing and structure more complex.Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them.They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results.Artificial neural network(ANN)offers optimal solutions in classifying and clustering the various reels of data,and the results obtained purely depend on identifying a problem.In this research work,the design of optimized applications is presented in an organized manner.In addition,this research work examines theoretical approaches to achieving optimized results using ANN.It mainly focuses on designing rules.The optimizing design approach of neural networks analyzes the internal process of the neural networks.Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters.The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues.The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors.The proposed ANN offered optimal results in real-world application problems,and the results were obtained using MATLAB.

Reliability and validity of the hidden hunger assessment scale in China-revised for high school students

Objective:Hidden hunger remains a severe public health problem that affects millions of people worldwide.In China,challenges related to dietary imbalance and hidden hunger persist.Micronutrient inadequacy deserves more attention among adolescents,given its vital role in their growth and development;however,this problem appears to have been largely ignored.High school students,in particular,are often at a high risk of hidden hunger but have limited assessment tools available.Therefore,this study aims to revise the hidden hunger assessment scale for high school students(HHAS-HSS)in China and assess its reliability and validity.Methods:Based on a literature review,expert consultation,pre-experiment,and formal survey,a hidden hunger assessment scale was revised for high school students.The formal survey involved 9336 high school students in 11 of the 16 cities in Anhui Province,China,and 9038 valid questionnaires were collected and included in the analysis.The item analysis,internal consistency reliability,test-retest reliability,content validity,exploratory factor analysis,and confirmatory factor analysis of the HHAS-HSS were examined.Results:The HHAS-HSS included a total of 4 dimensions and 12 items:"vegetables and food diversity"(three items),"fruits and dairy products"(three items),"micronutrient-dense foods"(four items),and"health condition and eating habits"(two items).The results showed a Cronbach's alpha of 0.758,a split-half reliability of 0.829,and a test-retest reliability of o.793,indicating good internal consistency.Using the Bartlett's test and Kaiser-Meyer-Olkin test(KMO)to test the exploratory factor analysis presented a four-factor model of the HHAS-HSS,the KMO0 value was 0.820(P<0.001),which indicated the possibility for factor confirmatory factor analysis.Using the maximum variance rotation method,four factors were obtained,and the cumulative variance explained rate was 57.974%.Confirmatory factor analysis also supported the division of the scale into four dimensions,and the fitting indices were x^(2)=1417.656,x^(2)/df=29.534,goodness-of-fit index=0.974,adjusted goodnessof-fit index=0.958,parsimonious goodness-of-fit index=0.600,normed fit index=0.938,incremental fit index=0.940,Tucker-Lewis index=0.917,comparative fit index=0.939,and root mean square error of approximation=0.056.Except for x^(2)/df,all the indices reached the fitting standard,and the above results showed that the construct validity of the scale reached an acceptable level.Conclusions:The HHAS-HSS has good validity and reliability for Chinese high school students.It is a convenient self-report measure of hidden hunger risk.

Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Hidden Hierarchy Based on Cipher-Text Attribute Encryption for IoT Data Privacy in Cloud

Most research works nowadays deal with real-time Internetof Things (IoT) data. However, with exponential data volume increases,organizations need help storing such humongous amounts of IoT data incloud storage systems. Moreover, such systems create security issues whileefficiently using IoT and Cloud Computing technologies. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has the potential to make IoT datamore secure and reliable in various cloud storage services. Cloud-assisted IoTssuffer from two privacy issues: access policies (public) and super polynomialdecryption times (attributed mainly to complex access structures). We havedeveloped a CP-ABE scheme in alignment with a Hidden HierarchyCiphertext-Policy Attribute-Based Encryption (HH-CP-ABE) access structure embedded within two policies, i.e., public policy and sensitive policy.In this proposed scheme, information is only revealed when the user’sinformation is satisfactory to the public policy. Furthermore, the proposedscheme applies to resource-constrained devices already contracted tasks totrusted servers (especially encryption/decryption/searching). Implementingthe method and keywords search resulted in higher access policy privacy andincreased security. The new scheme introduces superior storage in comparisonto existing systems (CP-ABE, H-CP-ABE), while also decreasing storage costsin HH-CP-ABE. Furthermore, a reduction in time for key generation canalso be noted.Moreover, the scheme proved secure, even in handling IoT datathreats in the Decisional Bilinear Diffie-Hellman (DBDH) case.

Data-Driven Probabilistic S for Batsman Performance Prediction in a Cricket Match

Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success.A good batsman not only scores run but also provides stability to the team’s innings.The most important factor in selecting a batsman is their ability to score runs.It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record.This hypothesis is based on the fact that a player’s batting aver-age is generally considered to be a good indicator of their future performance.We proposed a data-driven probabilistic system for batsman performance prediction in the game of cricket.It captures the dependencies between the runs scored by a batsman in consecutive balls.The system is evaluated using a dataset extracted from the Cricinfo website.The system is based on a Hidden Markov model(HMM).HMM is used to generate the prediction model to foresee players’upcoming performances.The first-order Markov chain assumes that the probabil-ity of a batsman scoring runs in the next ball is only dependent on how many runs he scored in the current ball.We use a data-driven approach to learn the para-meters of the HMM from data.A probabilistic matrix is made that predicts what scores the batter can do on the upcoming balls.The results show that the system can accurately predict the runs scored by a batsman in a ball.