Long-span steel structure trusses are widely used in factory buildings, but with the growth of service time and the increase of dynamic load fatigue, a considerable part of the long-span truss with dynamic load appears serious transverse cracks at the bottom of the middle span and oblique deformation of the abdomen in the operation process. The U-shaped cracks at the bottom and belly, as well as the mid-span down deflection of the main truss, reduced the functional function of the factory building truss structure, and had to limit the original crane load, which affected the normal safety and durability of the structure. Therefore, the application principle of the variable axial force cable system in the long-span factory building truss structure and 3D3S software modeling [1] were used. Analyzing and studying the reinforcement method of large-span powerhouse truss can provide practical experience for subsequent similar projects. In view of the above phenomenon, the large-span powerhouse trusses of Hongcheng Powerhouse 1 and No.2 located in Tonglu, Zhejiang Province are used as the research object, and the variable axial force cable method is proposed to strengthen and lift the load. Considering the span of large-span powerhouse truss, the cable system with 22m controlling force of 400kN is proposed to be selected for powerhouse 1, and the cable system with variable axial force of 24m is proposed to be selected for Powerhouse 2. The force model of large-span truss is established by using the finite element method commonly used to analyze the force of truss. Under two working conditions, The influence of reinforcement effect is analyzed and compared from three aspects: stiffness, bearing capacity and stability. And the phenomenon of uneven stress distribution is analyzed. The stress distribution characteristics of each node are understood by simulating the most disadvantageous node plates with the greatest internal force before and after reinforcement.

The aim of study was to investigate epidemiology aspects of magnetic resonance imaging (MRI) during COVID-19 pandemic. The study comprised depersonalized residents of Tomsk and Tomsk Region (n = 1714). Invitations to take online survey were sent to 50,000 residents by target SMS with response rate of 1.2% (n = 727, Cohort 1). Cohort 2 comprised retrospective patients (n = 987) who underwent contrast-enhanced cardiac MRI (CMR) in 2019-2022. Referrals, clinical characteristics, diagnosis, gender, age, past COVID-19, MRI study protocols, and MRI data were analyzed. 29% of respondents in cohort 1 received MRI examination within past two years; 26% of respondents considered MRI the most informative imaging modality for detecting COVID-19 pneumonia; 12% of respondents reported MRI unavailable. Proportion of CMR among MRI studies increased during COVID-19 pandemic, and maximum incidence of cardiac diseases detected by MRI was in 2021. Incidence of myocardial fibrosis increased from ~67% in 2019 to ~84% in 2022. The rate of outpatient MRI studies significantly increased in 2020, but returned to pre-pandemic level in 2021. COVID-19 pandemic increased the need for MRI and CMR. Patients with history of COVID-19 had persistent and newly occurring symptoms of myocardial damage suggesting chronic cardiac involvement requiring continuous follow-up.

The author's method of oligomer sums for analysis of oligomer compositions of eukaryotic and prokaryotic genomes is described. The use of this method revealed the existence of general rules for cooperative oligomeric organization of a wide list of genomes. These rules are called hyperbolic because they are associated with hyperbolic sequences including the harmonic progression 1, 1/2, 1/3, .., 1/n. These rules are demonstrated by examples of quantitative analysis of many genomes from the human genome to the genomes of archaea and bacteria. The hyperbolic (harmonic) rules, speaking about the existence of algebraic invariants in full genomic sequences, are considered as candidates for the role of universal rules for the cooperative organization of genomes. The described phenomenological results were obtained as consequences of the previously published author's quantum-information model of long DNA sequences. The oligomer sums method was also applied to the analysis of long genes and viruses including the COVID-19 virus; this revealed, in characteristics of many of them, the phenomenon of such rhythmically repeating deviations from model hyperbolic sequences, which are associated with DNA triplets. In addition, an application of the oligomer sums method are shown to the analysis of the following long sequences: 1) amino acid sequences in long proteins like the protein Titin; 2) phonetic sequences of long Russan literary texts (for checking of thoughts of many authors that phonetic organization of human languages is deeply connected with the genetic language). The topics of the algebraic harmony in living bodies and of the quantum-information approach in biology are discussed.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

Suicide dynamics form a complex system deeply influenced by interrelated factors, necessitating advanced methodologies for comprehensive analysis. This study pioneers the application of Particle Markov Chain Monte Carlo (PMCMC) methods in suicide research, recognized for their enhanced sampling efficiency over traditional techniques in complex, high-dimensional models. PMCMC is particularly adept at handling non-Gaussian distributions and navigating parameter spaces efficiently, preventing the entrapment in local optima. Utilizing PMCMC, our research simulated a broad spectrum of potential outcomes, clarifying the probable scenarios and their likelihoods, thus enriching the predictive accuracy and understanding of suicide dynamics. These methods facilitated the exploration of dynamic interactions among key risk factors such as mental health issues, trauma, substance use, social isolation, and access to harmful means, whose complex interplays challenge predictive modeling. The study extends previous applications of PMCMC in complex systems like H1N1, opioid crises, and COVID-19 to the domain of suicide, suggesting its potential in enhancing decision-making and intervention strategies. However, limitations due to the model's simplified assumptions and the specificity of the data to certain populations underscore the necessity for broader application to validate findings across varied demographics. In summary, while PMCMC offers robust capabilities for the dynamic modeling of suicide, it requires careful parameter selection and consideration of computational demands. Future research should continue to leverage this approach in more complex settings, enhancing our ability to predict and mitigate suicide risks effectively.

The role of γδ T cells in vertebrate immunity is still an unsolved puzzle. Species such as humans and mice display a low percentage of these T lymphocytes (i.e., “γδ low species”) with a restricted diversity of γδ T cell receptors (TR). Conversely, artiodactyl species (i.e., “γδ high species”) ac-count for a high proportion of γδ T cells with large γ and δ chain repertoires. The genomic organisation of the TR γ (TRG) and δ (TRD) loci has been determined in sheep and cattle, noting that a wide number of germline genes that encode for γ and δ chains characterise their genomes. Taking advantage from the current improved version of the genome assembly, we have investigated the genomic structure and gene content of the dromedary TRD locus, which, as in the other mammalian species, is nested within the TR alpha (TRA) genes. The most remarkable finding was the identification of a very limited number of variable germline genes (TRDV) compared to sheep and cattle, which supports our previous expression analyses for which the somatic hypermutation mechanism is able to enlarge and diversify the primary repertoire of dromedary δ chains. Furthermore, the comparison between genomic and expressed sequences reveals that D genes, up to four incorporated in a transcript, greatly contribute to the increased diversity of the dromedary δ chain antigen binding-site.

Malware detection is a crucial pillar of cybersecurity and aims to identify and neutralize malware threats. This study investigates how three deep learning algorithms - Convolutional Neural Net-works with One-Dimensional Architecture (CNN1D), Neural Networks (NN), and a hybrid model of one dimensional convolutional neural networks (CNN1D) and Long Short-Term Memory (LSTM) - help detect malware in Portable Executable working (.exe) files. Using a large dataset of tagged malware and benign samples, the effectiveness of these algorithms in distinguishing dangerous from harmless executables is extensively tested. In addition, the study evaluates the computational efficiency of the proposed methods for a variety of hardware configurations, including Central Processing Units (CPU), Graphics Processing Units (GPU), Raspberry Pi (IoT device), and Tensor Processing Units (TPU). Performance metrics provide insight into the scalability and effectiveness of the model across different hardware configurations. The results of this study have significant impact on the development of malware detection, particularly in the context of IoT security. By demonstrating the capabilities and limitations of CNN1D, Neural Networks, and CNN1D with LSTM models, the study provides practitioners with efficient and scalable solutions to real-world malware detection difficulties. In addition, insights from performance tests on various hardware platforms provide important assistance in developing robust security measures in IoT contexts, strengthening cybersecurity defenses in an ever-changing digital landscape.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (<50%), compared to those presented in the related literature where the whole set of channels is used.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

We report the characterization of phenotype, genotype, and functional parameters such as CD4+ T cell, viral load, HIV-1 genotype, class I HLA genotype, CCR5 polymorphism, and IFN-γ producing cells from a cohort of 37 people living with HIV-1 (PLWH) (viremic controllers, were compared those on antiretroviral therapy (ART) from two sites in São Paulo, Brazil. Half the PLWH (51.4%) were classified as viremic controllers (Controller group - CG), compared to 48.6% were on ART. As expected, controllers showed baseline higher levels of CD4+ T cells and nadir compared to those under ART, confirming the ability of HIV-1 controllers to preserve these cells even in the absence of ART. IFN-γ-producing cells showed greater magnitude to the HIV peptides Gag in controllers (p<0.03). Our results demonstrated that HLA protective alleles are associated with strong production of IFN-γ to Gag, Nef and RT epitopes in people living with HIV controllers.

Restraint use in Australian residential aged care has been highlighted by the media, and investigated by researchers, government and advocacy bodies. In 2018, the Royal Commission into Aged Care selected ‘Restraint’ as a key focus of inquiry. Subsequently, Federal legislation was passed to ensure restraint is only used in residential aged care services as the ‘last resort’. To inform and develop Government educational resources, we conducted qualitative research to gain greater understanding of the experiences and attitudes of aged care stakeholders around restraint practice. Semi-structured interviews were held with 28 participants, comprising nurses, care staff, doctors, physiotherapists, pharmacists and relatives. Two focus groups were also conducted to ascertain the views of residential and community aged care senior management staff. Data were thematically analyzed using a pragmatic approach of inductive and deductive coding and theme development. Five themes were identified during the study: 1. Understanding of restraint; 2. Support for Legislation; 3. Restraint-free environments are not possible; 4. Low-level restraint, and 5. Restraint in the community is uncharted. Although most staff, health practitioners and relatives have a basic understanding of restraint, more education is needed at a conceptual level to enable them to identify and avoid restraint practice, particularly ‘low-level’ forms and chemical restraint. There was strong support for the new restraint regulations, but most interviewees admitted they were unsure what the legislation entailed. With regards to resources, stakeholders wanted recognition that there were times when restraint was necessary and advice on what to do in these situations, as opposed to unrealistic aspirations for restraint-free care. Stakeholders reported greater oversight of restraint in residential aged care but stated that community restraint use was largely unknown. Research is needed to investigate the extent and types of restraint practice in community aged care.

With the rise of long-read sequencers and long-range technologies, delivering high-quality plant genome assemblies is no longer reserved to large consortia. Indeed, sequencing techniques but also computer algorithms have reached a point where the reconstruction of assemblies at the chromosome-scale is now feasible at the laboratory scale. Current technologies, and especially long-range technologies, are numerous and selecting the most promising one for the genome of interest is crucial to obtain optimal results. In this study, we resequenced the genome of the yellow sarson, Brassica rapa cv. Z1, using the Oxford Nanopore PromethION sequencer and assembled the sequenced data using current assemblers. To reconstruct complete chromosomes, we used and compared three long-range techniques, optical mapping, Omni-C and Pore-C sequencing libraries commercialized by Bionano Genomics, Dovetail Genomics and Oxford Nanopore Technologies respectively, or a combination of the three, in order to evaluate the capability of each technology.

The assessment of welfare and disease progression in animal models is critical. Most tools rely on evaluating individual subjects, whereas social behaviors, also sensitive to acute illness, chronic diseases, or mental health, are scarcely monitored because of their complexity, are invasive, and time-consuming. We propose the evaluation of social nesting, a species-typical behavior naturally occurring in standard housing conditions, for such behavioral monitoring. We provide an example of its use to evaluate social deficits and the long-term effects of neonatal sensorial stimulation in male and female adult 3xTg-AD mice for Alzheimer's disease compared to sex- and age-matched NTg counterparts with normal aging. Social nesting was sensitive to genotype (worse in 3xTg-AD mice), sex (worse in males), profile, and treatment (distinct temporal patterns, time to observe the maximum score and incidence of the perfect nest). Since social nesting can be easily included in housing routines, this neuroethological approach can be useful for animal's welfare, monitoring the disease's progress, and evaluating potential risk factors and effects of preventive/therapeutical strategies. Finally, the non-invasive, painless, simple, short time and low-cost features of this home-cage monitoring are advantages that make social nesting feasible to be successfully implemented in most animal department settings.

The classical Rotational Isomeric State (RIS) model, originally proposed by Flory, has been used to rationalize a wide range of physicochemical properties of neutral polymers. However, many weak polyelectrolytes of interest are able to regulate their charge depending on the conformational state of the bonds. Recently, it has been shown that the RIS model can be coupled with the Site Binding (SB) model, for which the ionizable sites can adopt two states: protonated or deprotonated. The resulting combined scheme, the SBRIS model, allows to analyse ionization and conformational equilibria on the same foot. In the present work this approach is extended to include pH-dependent electrostatic Long Range (LR) interactions, ubiquitous in weak polyelectrolytes at moderate and low ionic strengths. With this aim the original LR interactions are taken into account by defining effective Short Range (SR) and pH-dependent parameters, such as effective microscopic protonation constants and rotational bond energies. The new parameters are systematically calculated using variational methods. The machinery of statistical mechanics for SR interactions, including the powerful and fast transfer matrix methods, can then be applied. The resulting technique, to which we will refer as Local Effective Interaction Parameters (LEIP) method, is illustrated with a minimal model of a flexible linear polyelectrolyte containing only one type of rotating bonds. LEIP reproduces very well the pH dependence of the degree of protonation and bond probabilities obtained by semi-grand canonical Monte Carlo simulations, where LR interactions are taken explicitly into account. The reduction in the computational time in several orders of magnitude suggests that the LEIP technique could be useful in a range of areas involving linear weak polyelectrolytes, allowing direct fitting of the relevant physical parameters to the experimental quantities.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

The rapid evolution of technology has profoundly reshaped supply chain operations across various industries, compelling organizations to adapt swiftly to maintain competitive advantage. This qualitative study delves into the adaptive strategies that organizations employ to navigate the intricate landscape of digital transformation within their supply chains. By conducting comprehensive in-depth interviews with key stakeholders, including supply chain managers, technology officers, and operational staff, this research aims to uncover the multifaceted challenges, opportunities, and best practices associated with integrating digital technologies into supply chain operations. The findings of this study reveal that successful digital transformation in supply chains hinges on several critical factors. Leadership plays a pivotal role in steering the organization through the complexities of technological change, emphasizing the need for visionary and adaptive leaders who can drive strategic initiatives. Organizational culture, characterized by a willingness to embrace change and foster innovation, emerges as a significant enabler of digital transformation. Additionally, strategic planning and a clear roadmap are essential for aligning technological initiatives with business objectives and ensuring seamless integration. Employee training and development are identified as crucial elements in overcoming resistance to change and enhancing digital literacy among the workforce. The study highlights the importance of continuous learning and skill development programs to equip employees with the necessary competencies to leverage new technologies effectively. Furthermore, the research underscores the significance of collaboration and communication across different levels of the organization to foster a cohesive approach to digital transformation. Challenges such as data security concerns, substantial financial investments, and the complexity of integrating diverse technologies are also explored. The insights derived from the interviews provide a nuanced understanding of how organizations can address these challenges by adopting robust cybersecurity measures, securing adequate funding, and leveraging modular and scalable technological solutions. This research contributes valuable empirical insights into the digital transformation journey of supply chain operations, offering practical recommendations for practitioners and scholars. The implications of this study are far-reaching, providing a framework for organizations to navigate the digital landscape successfully. Key recommendations include fostering a culture of innovation, investing in leadership development, prioritizing employee training, and adopting a strategic and holistic approach to digital transformation.

UN Convention on Climate Change 2015 mandates reducing carbon footprint to reduce global warming. Considering environmental concerns, Electric Vehicles (EVs) spearhead the move towards green mobility. Niti Aayog (Indian Government) has envisaged an “EV only” scenario by 2030. Two wheelers dominate the Indian automobile industry with almost 80% market share. With the transportation sector accounting for 70% of world’s fossil fuels use, the need for a sustainable supply chain which addresses environment, economy and societal issues, has, of late, aroused interest in the business academic community and industries. Sustainability in business decision-making promotes efficiency, controls expenditure, enhances customer delight, increases sales and market share, optimises risk management strategy, and promotes profitability. This study intends to develop a conceptual framework after examining research gaps in sustainable supply chain management (SSCM) and allied topics like sustainability, green supply chain management, and vendor partnerships through collaborative efforts. Even the UN vide Goal 9 of sustainable goals pertaining to industry, highlights the importance of this framework.

Fixed offshore wind turbines continue to be developed for high latitude areas where not only wind and wave loads need to be considered, but also moving sea ice. Current rules and regulations for the design of fixed offshore structures in ice-covered waters do not adequately consider effects of ice loading and its stochastic nature on fatigue life of the structure. Ice crushing on such structures results in ice-induced vibrations, which can be represented by loading the structure using a variable-amplitude loading (VAL) sequence. Typical offshore load spectra are developed for wave and wind loading. Thus, a combined VAL spectrum is developed for wind, wave, and ice action. To this goal, numerical models are used to simulate the dynamic ice-, wind-, and wave-structure interaction. The stress time-history at an exemplarily selected critical point in an offshore wind energy monopile support structure is extracted from the model and translated into a VAL sequence, which can then be used as a loading sequence for the fatigue assessment or fatigue testing of welded joints of offshore wind turbine support structures. This study presents the approach to determine combined load spectra and standardized time series for wind, wave, and ice action.

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11^th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.

Achieving the best aesthetic result is a key objective of implant-prosthetic therapy. To maximize the aesthetic outcome, ensuring that implant-supported restorations are in harmony with the peri-implant soft tissues and bone is essential. A comparative analysis of zirconia and titanium dental implants revealed that both materials offer high success rates; however, they present distinct advantages and limitations in clinical practice. Titanium implants, with surface modifications but without chemical composition changes, boast remarkable long-term survival. In contrast, zirconia implants, while presenting high aesthetic and biocompatibility qualities, have a relatively high incidence of material-specific failure, ranging from 4% to 2% after 5-10 years. While titanium implants generally show superior long-term performance, zirconia implants benefit from ongoing advancements aimed at improving their osseointegration and longevity. This study emphasizes the need for short-term and long-term studies, complemented by randomized controlled trials (RCTs), which are essential for optimizing implant materials and ensuring patient satisfaction. Continued innovation and research are crucial for advancing dental implant technology and enhancing the quality of life for patients requiring dental restorations. Finally, it is important to understand that all materials have both advantages and disadvantages, so the choice of the optimal material should be based on the specific purpose.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

This proposal was prepared in the very first weeks of 2020 because of the outbreak of COVID-19.There is good reason to suppose that rotavirus vaccine can be used as protection tool to effectively and safely fight and mitigate SARS-CoV-2 infection and the impact caused by COVID-19 in adult humans, due to the development of cross and trained immunity following rotavirus vaccination. Up-to-date, some rotavirus vaccines are available and approved, two of them have a large experience in results and safety. Little experience has been achieved in the use of rotavirus vaccine in adults. However, it can be expected that it would be safe and effective in adults and in the elderly as well. This proposal explains the background.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

Integrated photovoltaic-fuel cell (IPVFC) systems have the potential to contribute to sustainable energy production for grid and off-grid applications. While there are studies focusing on fundamental science and designs for IPVFC systems, there are scarcely studies that have focused on the manufacturability of IPVFC systems, which is certainly the pathway for commercialisation of the systems. This study explores manufacturing strategies that can be considered for exploiting a family of eleven IPVFC systems. A survey and systems thinking were used to investigate the potential modularisation and supply chain management to achieve optimal lean and agile manufacturing strategy. Results show that the Photovoltaic-Thermal-Separate Converter/Inverter-Battery System received 25% of the responses. The optimal manufacturing strategy depends on the overall business strategy of the firm. Although 17% preference for System 1 is significant compared to four members of the family of IPVFC systems (Systems 2, 6, 8 and 9) that received only 2% of the responses, it indicates that there is a likelihood that the demand for System 1 will be among the top 42% of the total demand. Overall, this study provides new insights on how the family of IPVFC systems can contribute to realising greater access to clean energy, by extension contributing to the net-zero efforts using solar energy and solar hydrogen.

The lack of accurate and feasible diagnostic tests poses a significant challenge to visceral leish-maniasis (VL) healthcare services in endemic areas. To date, various VL diagnostic tests have been or are being developed, and their diagnostic performance needs to be assessed.. In the present study, the diagnostic performance of rk39RDT, Direct Agglutination Test (DAT), Microscopy, Loop Mediated Isothermal Amplification (LAMP), and miniature direct-on-blood polymerase chain reaction - nucleic acid lateral flow immunoassay (mini-dbPCR-NALFIA) was assessed using quantitative polymerase chain reaction (qPCR) as reference test in an endemic region of Ethiopia. In this study, 235 patients VL suspected patients and 104 non-endemic healthy controls (NEHC) were recruited. Among the VL-suspected patients, 144(61.28%) tested positive with qPCR. Sensi-tivities for rk39 RDT, DAT, Microscopy, LAMP assay, and mini-dbPCR-NALFIA were 88.11%, 96.50%, 76.58%, 94.33%, and 95.80%, respectively. Specificities were 83.33%, 97.96%, 100%, 97.38%, and 98.92% for rk39 RDT, DAT, Microscopy, LAMP assay, and mini-dbPCR-NALFIA, respectively. In conclusion, Rk39 RDT and microscopy exhibited lower sensitivity, while DAT demonstrated excellent performance. LAMP and mini-dbPCR-NALFIA showed excellent perfor-mance with feasibility to implement in remote endemic areas, although the latter requires further evaluation in such regions.

The results of numerical experiments with the chemistry-climate model of the Institute of Numerical Mathematics of the Russian Academy of Sciences - Russian State Hydro-Meteorological University (INM-RSHU CCM) in comparison with the MERRA2 reanalysis data are presented. To assess the impact of sea surface temperature (SST) on stratospheric processes and the ozone layer, numerical experiments were carried out for scenarios corresponding to the El Niño and La Niña Southern Oscillation phases. It is shown that the El Niño phase contributes to the enhancement of heat and mass transfer to the polar stratosphere, which strengthen the Brewer-Dobson circulation and the flux of wave activity that affect the zonal wind. As a result, during El Niño, the probability of sudden stratospheric warming increases and the stratospheric polar vortex (SPV) weakens, re-sulting in an increase in total polar stratospheric ozone. The La Niña phase, on the contrary, is accompanied by a weakening of heat and mass transfer from the troposphere to the stratosphere, as a result of which the stratospheric polar vortex strengthens and the total column ozone decreases. It is shown that the effect associated with the Southern Oscillation is more pronounced in the Northern Hemisphere than in the Southern Hemisphere. Numerical experiments were also carried out for scenarios in which the sea surface temperature was set corresponding to the early 1980s, and the results were compared with calculations in which the SST was set corresponding to the late 2010s. This allowed us to assess the influence of the multiyear trend in the SST increase on stra-tospheric processes and the ozone layer. It was shown that the SST increase is most pronounced in the Arctic, which corresponds to the phenomenon of "Arctic amplification", and therefore, the heat and mass transfer to the stratosphere increases in the Northern Hemisphere, which contributes to some weakening of the SPV and increase of the total column ozone. But, compared to the Southern Oscillation, the influence of the SST trend on the SPV and total column ozone is much weaker and is observed mainly in the Northern Hemisphere.

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

Cultural Tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists the tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for Riyadh for peak visitor time. Primarily, the map data and cultural event dataset are processed for location, such as grouping with Kriging Interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes are processed with word embedding. Meanwhile, the Social Network Sites (SNS) data like reviews and news are extracted with an external Application Programming Interface (API). The review data is processed with keyword extraction and word embedding, whereas the news data is processed with score value estimation. Lastly, the data are fused corresponding to a historical site and given to the Multi-Quadratic-Long Short Term Memory (MQ-LSTM) Recommendation System (RS); also, the recommended result with the map is stored in a database. Lastly, the database security is maintained with Locality Sensitive Hashing (LSH); moreover, by attaining higher performance values, the proposed model is experimentally verified.

In the face of the upcoming 30th anniversary of econophysics, we review our contributions and other related works on the modeling of the long–range memory phenomenon in physical, economic, and other social complex systems. Our group has shown that the long–range memory phenomenon can be reproduced using various Markov processes, such as point processes, stochastic differential equations and agent–based models. Reproduced well enough to match other statistical properties of the financial markets, such as return and trading activity distributions and first–passage time distributions. Research has lead us to question whether the observed long–range memory is a result of actual long–range memory process or just a consequence of non–linearity of Markov processes. As our most recent result we discuss the long–range memory of the order flow data in the financial markets and other social systems from the perspective of the fractional Lèvy stable motion. We test widely used long-range memory estimators on discrete fractional Lèvy stable motion represented by the ARFIMA sample series. Our newly obtained results seem indicate that new estimators of self–similarity and long–range memory for analyzing systems with non–Gaussian distributions have to be developed.

Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

The literature lacks consensus on the minimum microbial density required for diagnosing urinary tract infections (UTIs). This study categorized the microbial densities of urine specimens from symptomatic presumptive UTI patients and correlated them with detected levels of immune response biomarkers NGAL, IL-8, and IL-1β. The objective was to identify changes in immune response at different microbial densities and determine an optimal threshold for diagnosing symptomatic UTIs. Midstream clean-catch urine samples were analyzed for microbial identification and quantification using SUC and M-PCR, while NGAL, IL-8, and IL-1β levels were measured with ELISA. NGAL, IL-8, and IL-1β levels were all significantly higher in symptomatic subjects with microbial densities ≥ 10,000 cells/mL by M-PCR (p < 0.0069) or equivalent CFUs/mL by SUC (p < 0.0104) compared to samples with no detectable microbes. With both PCR and SUC, a consensus of two or more elevated biomarkers correlated well with microbial densities > 10,000 cells/mL or CFU/mL, respectively. The association between ≥ 10,000 cells and CFU per mL with elevated biomarkers in symptomatic patients suggests that this lower threshold may be more suitable for diagnosing UTIs. These findings challenge the widely used threshold of 100,000 CFU per mL, which may overlook many active UTI cases.

BTB domain and CNC homology 1 (BACH1) is a highly expressed transcription factor in tumors including breast and lung, relative to their non-tumor tissues. BACH1 is known to regulate multiple physiological processes including heme homeostasis, oxidative stress response, senescence, cell cycle, and mitosis. In a tumor, BACH1 promotes invasion and metastasis of cancer cells, and the expression of BACH1 presents a poor outcome for cancer patients including breast cancer patients. Recent studies identified novel functional roles of BACH1 in the regulation of metabolic pathways in cancer cells. BACH1 inhibits mitochondrial metabolism through transcriptional suppression of mitochondrial membrane genes. In addition, BACH1 suppresses activity of pyruvate dehydrogenase (PDH), a key enzyme that converts pyruvate to acetyl-CoA for the citric acid (TCA) cycle through transcriptional activation of pyruvate dehydrogenase kinase (PDK). Moreover, BACH1 increases glucose uptake and lactate secretion in aerobic glycolysis through the expression of metabolic enzymes involved such as hexokinase 2 (HK2) and glyceraldehyde 3- phosphate dehydrogenase (GAPDH). Pharmacological or genetic inhibition of BACH1 could reprogramme metabolic pathways, subsequently rendering metabolic vulnerability of cancer cells. Furthermore, inhibition of BACH1 decreased antioxidant-induced glycolysis rates as well as reduced migration and invasion of cancer cells, suggesting BACH1 as a potentially useful cancer therapeutic target.

1) Background: Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges are acknowledged; however, a comprehensive understanding of the status and the remedies remained unexplored. 2) Objective: This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. 3) Methods: A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history-taking, and attitudes toward adopting digital solutions for managing patient histories; 4) Results: 92.67% of the physicians face workloads, 88.46% struggle in medical history-taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. An AI-based Smart Health Gantt Chart (SHGC) system has been introduced for their instant feedback. 93.27% of physicians expressed their willingness to use such a system. 5) Conclusions: The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

A novel cytoplasmic dye decolorizing peroxidase from Dictyostelium discoideum was investigated that oxidizes anthraquinone dyes, lignin model compounds and general peroxidase substrates like ABTS efficiently. Unlike related enzymes, an aspartate residue replaces the first glycine of the conserved GXXDG motif in Dictyostelium DyPA. In solution, Dictyostelium DyPA exists as a stable dimer with the side chain of Asp146 contributing to the stabilization of the dimer interface by extending the hydrogen bond network connecting two monomers. To gain mechanistic insights, we solved the Dictyostelium DyPA structures in the absence of substrate as well as in the presence of potassium cyanide and veratryl alcohol to 1.7, 1.85, and 1.6 Å resolution, respectively. The active site of Dictyostelium DyPA has a hexa-coordinated heme iron with a histidine residue at the proximal axial position and either an activated oxygen or CN^- molecule at the distal axial position. Asp149 is in an optimal conformation to accept a proton from H₂O₂ during the formation of compound I. Two potential distal solvent channels and a conserved shallow pocket leading to the heme molecule were found in Dictyostelium DyPA. Further, we identified two substrate-binding pockets per monomer in Dictyostelium DyPA at the dimer interface. Long-range electron transfer pathways associated with a hydrogen-bonding network that connects the substrate-binding sites with the heme moiety are described.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

The aggregate aftermath of persistent inflammation in patients with inflammatory bowel disease (IBD) places them at increased risk for the advancement to colitis-associated colorectal cancer (CACRC). CACRC is preceded by IBD, the highly heterogenous, pharmacologically incurable, pertinacious, reverting/worsening, and immune-mediated inflammatory pathologies of the colon and rectum. The molecular and immunological basis of CACRC is highly correlated with the length/duration and stringency/severity of colon inflammation predisposed by the exogenous/free hemoglobin alpha chain (HbαC) the byproduct of infiltrating immune cells, extravasated erythrocytes, and macrophage erythrophagocytosis. The exogenous free HbαC prompts oxygen-free radical-arbitrated DNA damage (DNAD) through increased cellular reactive oxygen species (ROS) exacerbated by decreased tissue antioxidant defenses. Mitigation of Fenton reaction via pharmaceutical therapy would attenuate the ROS, promote apoptosis, DNAD repair, and subsequent prevent the incidence of CACRC. Three pharmaceutical options that attenuate hemoglobin toxicity include haptoglobin, deferoxamine, and flavonoids (vitamins C/E). Haptoglobin’s clearance rare from plasma is inversely correlated with its size; the smaller the size, the faster the clearance. Thus, the administration of Hp1-1 may prove to be beneficial. Furthermore, deferoxamine’s hydrophilic structure limits its ability to cross cell membranes. Thus, it may be beneficial if administered intracellularly to avoid the higher plasma concentrations and longer incubation periods associated with extracellular administration. Finally, the effectiveness of flavonoids and natural herb antioxidants is associated with high reactivity of hydroxyl substituents. Multiple analyses are currently underway to assess the clinical context of CACRC and outline the molecular basis of HbαC-induced ROS pathogenesis by exposing colonocytes and/or colonoids to HbαC. These cells are then treated with haptoglobin, deferoxamine (DFO), and flavonoids in order to separate free HbαC and measure their impact on hydroxyl radical formation therapies. The molecular pathogenesis of sporadic colorectal cancer (SCRC) i.e., “inflammation-dysplasia-carcinoma” progression sequence is well described, but the immunopathogenesis of CACRC herein reviewed is broadly still in prodromal stage/phase to be validated and understood. Therefore this timely review outlines the molecular and immunological basis of disease pathogenesis and the pharmaceutical intervention as a protective measure for CACRC.

Neck circumference (NC), neck circumference to height ratio (NHtR) and neck circumference to weight ratio (NWtR) appear to be good candidates for the non-invasive management of non-alcoholic fatty liver disease (NAFLD). This study aimed to evaluate the ability of routine variables to assess and manage NAFLD in participants with obesity and NAFLD included in a 2-year nutritional intervention program. Anthropometric measurements, biochemical variables and imaging techniques were performed at different study time-points (baseline, 6, 12 and 24 months). The nutritional intervention significantly improved all anthropometric measurements as well as the glucose profile and the hepatic enzymes. NC and neck ratios combined with ALT levels and HOMA-IR showed good prediction ability for the hepatic fat content and hepatic steatosis at all the study time-points in a ROC analysis. The prediction ability of the combination panels improved when the weight loss variable was also considered. NC and neck ratios are easy anthropometric measurements that in combination with routine biochemical variables (ALT and HOMA-IR) showed good prediction ability of NAFLD. More research studies are necessary to validate the utility of these simple and easy variables as surrogate markers of NAFLD since their application could improve the prevention and management of this prevalent disease.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Long-range interactions are relevant for a large variety of quantum systems in quantum optics and condensed matter physics. In particular, the control of quantum-optical platforms promises to gain deep insights in quantum-critical properties induced by the long-range nature of interactions. From a theoretical perspective, long-range interactions are notoriously complicated to treat. Here, we give an overview of recent advancements to investigate quantum magnets with long-range interactions focusing on two techniques based on Monte Carlo integration. First, the method of perturbative continuous unitary transformations where classical Monte Carlo integration is applied within the embedding scheme of white graphs. This linked-cluster expansion allows to extract high-order series expansions of energies and observables in the thermodynamic limit. Second, stochastic series expansion quantum Monte Carlo which enables calculations on large finite systems. Finite-size scaling can then be used to determine physical properties of the infinite system. In recent years, both techniques have been applied successfully to one- and two-dimensional quantum magnets involving long-range Ising, XY, and Heisenberg interactions on various bipartite and non-bipartite lattices. Here, we summarise the obtained quantum-critical properties including critical exponents for all these systems in a coherent way. Further, we review how long-range interactions are used to study quantum phase transitions above the upper critical dimension and the scaling techniques to extract these quantum critical properties from the numerical calculations.

Spinocerebellar ataxia type 2 (SCA2) is caused by polyglutamine expansion in Ataxin-2 (ATXN2). This factor binds RNA/proteins to modify metabolism after stress, and to control calcium (Ca2+) homeostasis after stimuli, thus exerting crucial neuroprotection for cerebellar ataxias and corticospinal motor neuron degeneration. Our Atxn2-CAG100-Knock-In mouse faithfully models features observed in patients at pre-onset, early and terminal stages. Here, its cerebellar global RNA profiling revealed downregulation of signaling cascades to precede motor deficits. Validation work at mRNA/protein level defined alterations that were independent of constant physiological ATXN2 functions, but specific for RNA/aggregation toxicity, and progressive across the short lifespan. Earliest changes were detected at 3 months among Ca2+ channels/transporters (Itpr1, Ryr3, Atp2a2, Atp2a3, Trpc3), IP3 metabolism (Plcg1, Inpp5a, Itpka), and Ca2+-Calmodulin dependent kinases (Camk2a, Camk4). CaMKIV–Sam68 control over alternative splicing of Nrxn1, an adhesion component of glutamatergic synapses between granule and Purkinje neurons, was found affected. Systematic screening of pre/post-synapse components, with dendrite morphology assessment, suggested early impairment of CamKIIα abundance together with weakening of parallel fiber connectivity. These data reveal molecular changes due to ATXN2 pathology, impacting communication and excitability of cerebellar neurons. Discovery of such risk versus progression markers improves the assessment of pre-symptomatic treatments in SCA2 and related disorders.

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

Abstract:The digital landscape is rapidly reshaping under the influence of AI-powered cloud-based e-commerce platforms (PCEPs). These platforms leverage the transformative power of AI to personalize customer experiences, optimize pricing strategies, enhance security, and streamline operations, offering businesses a significant competitive edge. However, challenges surrounding data privacy, algorithmic bias, and potential job displacement raise crucial ethical questions regarding widespread AI adoption in e-commerce.This study delves into the untapped potential and ethical considerations of PCEPs, distinguishing itself by proposing practical strategies for responsible development and ethical implementation.Objectives: Investigate the transformative potential of PCEPs: Personalized product recommendations:Analyze effectiveness and potential bias. Dynamic pricing strategies:Explore impact on consumer fairness and market stability. Enhanced fraud detection:Evaluate accuracy and privacy implications. Optimized supply chain management:Assess efficiency gains and job displacement risks. Critically analyze the ethical considerations of AI in e-commerce: Data privacy:Assess current practices and develop mitigation strategies. Algorithmic bias:Identify potential sources and propose solutions for fairer decision-making. Job displacement:Evaluate impacts and propose reskilling initiatives. Propose comprehensive and practical strategies for responsible PCEP development and implementation: Transparency:Enhance user awareness and control over data usage. Fairness:Design AI systems to mitigate bias and uphold consumer rights. Accountability:Establish clear guidelines and oversight mechanisms for ethical AI development.Conclusion: While PCEPs offer a plethora of transformative benefits, their ethical implementation is paramount. By adhering to principles of transparency, fairness, and accountability, businesses can unlock the full potential of PCEPs while minimizing potential risks and shaping the future of online commerce in a sustainable and equitable manner

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.

The official recommendations to manage COVID-19 in the outpatient setting focus on high-cost drugs (remdesivir, nirmatrelvir-ritonavir, molnupiravir), suffering from many problems. Therefore, one may consider also other treatments meeting these criteria: • promising effectiveness, based on favorable randomized controlled trials (RCTs) of sufficient validity. Even without definitive evidence, physicians and informed patients can consider them if they are also: • safe, to the state of knowledge • with a very favorable opportunity cost • biologically plausible/not implausible • accessible • without major commercial sponsors in the trials promoting them, and with researchers without major conflicts of interests. The main focus is on a limited group of functional foods and some their active ingredients, with further mention of other substances of different origin, yet fully meeting all the aforementioned criteria. The functional foods and substances described and discussed certainly do not cover all the known effective therapies for COVID-19 (in one case also for Long-COVID), but the only therapies proposed in this paper currently satisfy all the above criteria. The supporting scientific evidence will favor meta-analyses of RCTs that include the most definitive outcome: all-cause mortality, i.e. the outcome that most fully informed people tend to prioritize. Furthermore, the reported evidence favors clinical studies of the most valid design, i.e. RCTs.

The morphology of virgin reactor powder (RP) of high-density polyethylene HDPE with MW = 160,000 g/mol was investigated with the help of DSC, SEM, SAXS and WAXS methods. The morphological SEM analysis showed, that the main morphological units of RP are macro- and micro-shish kebab structures with significantly different geometric dimensions, as well as individual lamellae of folded chain crystals. Quantitative analysis of an asymmetric SAXS reflection made it possible to reveal the presence of several periodic morphoses in the RP with long periods from 20 nm to 60 nm and to correlate them with the observed powder morphology. According to the DSC crystallinity data, the thickness of the lamellae in each long period was estimated. Their surface energy was calculated in the framework of the Gibbs-Thompson theory. The presence of regular and irregular folds on the surface of the different shish-kebab lamellae is discussed. The percentage of identified morphoses in the RP was calculated. It has been suggested that the specific structure of HDPE RP is due to the peculiarity of polymer crystallization during suspension synthesis in a quasi-stationary regime, in which local overheating and inhomogeneous distribution of shear stresses in a chemical reactor are possible.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

Monitoring seaweed growth rates and biomass is crucial for optimizing harvest strategies in aquaculture systems. While such a task can be performed manually on a small farm, such as the Integrated Multi-Trophic Aquaculture (IMTA) system at Harbor Branch Oceanographic Institute at Florida Atlantic University (HBOI), a commercial farm will have to rely on the automated sensor to perform such a task. This study introduces an advanced LSTM-based approach for forecasting seaweed growth and biomass. Utilizing a combination of real and synthetic data, LSTM models are trained and evaluated for their predictive performance. Synthetic sensor data was generated using mathematical equations that simulate realistic aquaculture conditions, with added noise to reflect sensor variability. Building on the foundation of the Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor deployed at the seaweed tank in the HBOI IMTA system, we refine the process of biomass estimation by introducing non-linear regression models for predicting seaweed growth and biomass. The results showed that the LSTM model trained with a loss function under physics constraint, combining MSE and physical laws, outperformed models trained with MSE alone, achieving a significantly lower error in predicting seaweed growth. The variation trend of the predicted biomass from the network matched well with the sensor measurement after moving average preprocessing, demonstrating the robustness of the proposed technique in handling noisy sensor data. This study highlights the potential of integrating machine learning with physical models to optimize seaweed cultivation and support sustainable aquaculture practices.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. Chain stiffness is controlled by a tunable harmonic potential for the bending angle whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range from acute to obtuse ones, reaching the limit of rod-like polymers. We analyze how packing density and chain stiffness affect the ability of chains to self-organize at the local and global levels. The former corresponds to crystallinity as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures is obtained, ranging from random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, to nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, hysteresis is observed between the establishment of long-range nematic order and crystallization, while for right-angle chains both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres.

This paper introduces an innovative nanotechnology-based approach that provides a pathway to enhance the energy release efficiency of nanoenergetic materials (nEMs) by harnessing self-synchronized collective atomic vibrations and phonon wave resonance within the transition domains between nanocomponents, without altering the material composition. The key innovation involves incorporating finely-tuned 2D-ordered linear-chain carbon-based multilayer nano-enhanced interfaces as programmable nanodevices into the transition domains using advanced multistage processing and assembly techniques. These programmable nanodevices enable precise control over self-synchronized collective atomic vibrations and phonon wave propagation, leading to synergistic effects. To activate and optimize these effects, a combination of various methods is employed, including energy-driven initiation of allotropic phase transformations, surface acoustic wave-assisted micro/nano-manipulation, heteroatom doping, directed self-assembly using high-frequency electromagnetic fields, and data-driven inverse design approaches. By leveraging a data-driven inverse design strategy and uncovering hidden structure-property relationships, we maximize energy release efficiency using the carbon nano-materials genome approach derived from multifactorial neural network-based predictive models. This approach not only unlocks new functionalities in nEMs but also improves environmental performance and safety levels. By pioneering transformative pathways for nEMs through harnessing phonon wave resonance in low-dimensional nanocarbon transition interfaces, this research brings significant advancements in the field.

The document sets out three differentiated proposals for the transport of medicines through RPAS with their respective regulations, adapted to different operational categories: open category, special category and military scope. In the open category, an economical and efficient solution is prioritized for urban and rural areas, complying with weight and height regulations, in addition to guaranteeing the stability and quality of medications during transportation. The special category allows more complex operations and in more adverse conditions, such as densely populated areas and long routes, incorporating advanced security measures and training for personnel. In the military field, RPAS are used in conflict zones and hard-to-reach areas, ensuring the safe and rapid delivery of medical supplies under extreme conditions. Success stories in different countries are highlighted, demonstrating significant improvements in delivery times and risk reduction for health personnel.

Power sources that can consistently deliver high levels of energy over extended periods are crucial for various space applications. Among the many methods of energy generation, direct heat-to-electricity conversion without intermediate steps or moving components shows particular promise but also presents significant challenges. The aim of this research is to address these challenges by enhancing the energy efficiency of thermionic conversion, which transforms thermal energy into electrical energy, through the development and utilization of a novel class of emission nanomaterials known as multilayer carbyne-enriched 3D-shaped nano-interfaces. By manipulating the topological, physicochemical, and functional characteristics of these interfaces, we seek to optimize their thermionic conversion capabilities. Building on the recent discovery of collective atomic vibrations called phonon waves within transition domains of multilayer nanostructures, we have devised a new approach to enhance the efficiency of thermionic energy converters. This involves unlocking the predictive functionality of 2D-ordered linear-chain carbon-based multilayer emitters through the excitation and fine-tuning of collective atomic vibrations and nanoarchitecture. To achieve this, we propose employing a combination of techniques that leverage interface effects, enable phonon wave propagation, facilitate energy exchange, and capitalize on synergistic effects offered by the multilayer nano-enhanced interfaces. The resulting devices for thermionic energy conversion have the potential to significantly surpass the efficiency of existing plasma thermionic converters by several orders of magnitude. Moreover, these electricity-generating sources based on thermionic conversion represent fundamental options for the advancement of Lunar and Martian research missions, opening up possibilities for sustainable energy production in extraterrestrial environments.

High-resolution forecasting of vegetation type shifts may prove essential for anticipating and mitigating the impacts of future climate change on bird populations. Here, we used the US Forest Service Ecological Response Unit (ERU) classification to develop and assess vegetation-based breeding habitat profiles for eight owl species occurring in foothills and mountains of the southwestern US. Shifts in mapped habitat were forecast using an ecosystem vulnerability model based on the pre-1990 climate envelopes of ERUs and the A1B moderate IPCC emission scenario of future climate. For five of the eight owl species, regional breeding habitat extent was projected to decline by at least 60% by 2090. Three species, the boreal owl (Aegolius funereus; at the trailing edge of its distribution), flammulated owl (Psiloscops flammeolus), and northern pygmy-owl (Glaucidium gnoma) were projected to experience the steepest habitat-loss rates, or 85%, 85%, and 76%, respectively. Projected vegetation shifts overlaid with well-documented flammulated owl breeding populations showed complete or near complete loss of habitat by 2090 in areas of montane forest currently supporting dense aggregations of owl territories. Generalist or lower-elevation owl species were predicted to be less impacted, while for the whiskered screech-owl (Megascops trichopsis), the contraction of current habitat was nearly offset by a projected northward expansion. In general, the results of this study suggest high exposure to climate change impacts for upper-elevation forest owls of semi-arid southwestern North America. Long distance migration and low natal philopatry may prove important to some montane owl populations for adapting to regional loss of habitat.

This literature review provides a comprehensive analysis of the functional morphology and paleoecology of Pteranodon, a prominent genus of Pterosaur from the Late Cretaceous period. By synthesizing recent research findings with classic studies, this paper elucidates the aerodynamic and ecological adaptations of Pteranodon, which was one of the largest pterosaurs to ever soar the skies. The review examines the distinctive cranial features, including its elongated cranial crest and its implications for sexual dimorphism and species identification. Detailed analysis of wing morphology and limb proportions reveals insights into its flight mechanics, suggesting that Pteranodon was adapted for dynamic soaring, leveraging its expansive wingspan for efficient long-distance travel. Additionally, the paper explores Pteranodon's feeding habits and dietary preferences based on dental and jaw structure, indicating a diet primarily consisting of fish and other aquatic prey. The review also integrates fossil evidence with ecological models to reconstruct the paleoenvironment of the Late Cretaceous, highlighting the role of Pteranodon in its ecosystem as both a predator and a competitor. This synthesis not only enhances our understanding of Pteranodon’s physiological and ecological roles but also contributes to broader discussions on pterosaur evolution and behavior.

We introduce the linear subgroup of volume-preserving diffeomorphism as the underlying symmetry to construct an action within the effective field theory framework and the Schwinger-Keldish formalism. The formulated action is posited to effectively incorporate dissipative effects into the Navier-Stokes equations.