The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020 and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the three analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last few years. These situations have strongly influenced the spatial-temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management.

We are witnessing the revival of the CAM model, which was already used in the past by some authors studying angiogenesis and anti-cancer drugs, that now offers a refined model to fill, in the translational meaning, the gap between in vitro-in vivo studies. It can be used for a wide range of purposes, from testing cytotoxicity, pharmacokinetics, tumorigenesis, invasion, to action mechanisms of molecules, and validation of new materials from tissue engineering research. CAM model is easy to use with a fast outcome and makes experimental research more sustainable since allowing to replace, reduce and refine pre-clinical experimentation ("3Rs" rules [1]). This re-view aims to highlight some unique potentials that CAM-assay presents; in particular, the authors intend to use in the next future the CAM model to verify, in a microenvironment comparable to in vivo conditions, albeit simplified, the angiogenic ability of functionalized 3D constructs to be used in regenerative medicine strategies in the recovery of skeletal injuries of critical size (CSD) that do not repair spontaneously. For this purpose, organotypic cultures will be planned on sever-al CAMs set up in temporal sequence: a sort of organ model for assessing CSD conducted in the CAM bioreactor, rather than in vivo.

Insulin resistance, as a common pathological process of many metabolic diseases, including diabetes and obesity, has attracted much attention due to its relevant influencing factors. To date, studies have mainly focused on the mechanisms between mitochondrial stress and insulin resistance, and they are now pursued as a very attractive therapeutic target due to their extensive involvement in many human clinical settings. In view of the complex pathogenesis of diabetes, natural drugs have become new players in diabetes prevention and treatment because of their wide targets and few side effects. In particular, plant phenolics have been paid more attention because of their close relationship with oxidative stress. In this review, we briefly review the mechanisms by which mitochondrial stress leads to insulin resistance. Moreover, we list some cytokines and genes that have recently been found to play roles in mitochondrial stress and insulin resistance. Furthermore, we describe several natural drugs that are currently widely used and give a brief overview of their therapeutic mechanisms. Finally, we suggest possible ideas for future research related to the unique role that natural drugs play in the treatment of insulin resistance through the above targets.

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

Methotrexate (MTX) is a potent drug for the treatment of various diseases globally amidst being a chemotherapeutic and immunosuppressant agent. However, hepatotoxicity induced by MTX could be life-threatening if left untreated. Folate supplementation is concurrently applied to reduce the adverse effects of MTX, albeit efficacy compromise. Therefore, there is the need to understand the process for the prevention and treatment strategies for MTX induced hepatotoxicity (MIH). In recent times, preliminary preclinical and clinical findings indicate the potential of natural phytobioactive compounds for MIH prevention and treatment. This mini review therefore summarizes proposed mechanisms of MIH and recent advances in the prevention and treatment prospects of natural phytobioactive compounds on MIH.

Cytokines are a family of proteins which play a major role in the regulation of immune system, and enter the development of several diseases from rheumatoid arthritis to cancer and, more recently, COVID-19. Therefore, many efforts are currently being spent in therapy and diagnosis, producing inhibitory drugs, as well as biosensors for a rapid, poor invasive, and effective detection. In this regards, even more efficient cytokine receptors are under investigation. In this paper we analyze a set of receptors of cytokine IL-6, investigating their topological features by means of a theoretical approach. Our results suggest a topological indicator that may help in the identification of those receptors having the highest complementarity with the protein, a feature expected to ensure a stable binding. Furthermore, we propose and discuss the use of these receptors in an ideal experiment.

Coronavirus and COVID-19 infections continue to wreak havoc across the world. Interestingly, the COVID-19 infections and deaths display a clear seven-day cycles. Mathematical analysis using linear mixed-effects models show that this periodicity is not due to reporting errors. We hypothesize that these COVID-19 cycles are related to natural immune cycles which also oscillate every seven days. These immune cycles are regulated by stress and mediated through the endocrine and the central nervous systems. Our routine activities and lifestyle of more stressful weekdays flanked by less stressful, relaxing weekends define the seven-day immune cycles. The synchronized low immunity levels in the population is responsible for repeated seven-day waves of pathogenic infections such as COVID-19. The new understanding of the role of immune oscillations will help in developing strategies to enhance our immunity through modified lifestyle and better, innovative prophylactic and therapeutic approaches against infectious diseases.

The present investigation deals with the determination of the physio-chemical properties of two commercial grade samples of local gums (Gum Arabic and Natural Rubber Latex (NRL)). The results revealed that the gum samples have high melting point that indicate thermal stability at room and moderate temperatures. The gum samples have about 95 % carbohydrate content and a corresponding high internal energy and can serve as a source of energy. The rheology of the samples revealed shear-thickening characteristics with gum Arabic being thixotropic and pseudo-plastic in nature while NRL was observed to be anti-thixotropic and rheopectic. Further results from the moisture absorption, contact angle and Fourier Transform Infrared Radioscopy (FTIR) analyses gave better insight into their hydroscopic behaviors. Gum Arabic has excellent water absorption capacity with less wettability as it consists mainly of more water-soluble compounds in comparison to Natural Rubber Latex. These insights from the study will enhance wider application of the gums with increased value-addition to the gums and the communities where they (can) thrive.

The effect of the addition of maleated polyethylene (MAPE) to compounds of natural rubber (NR) and Teline monspessulana flour (TMF) previously mercerized was investigated. Two factors were analyzed: A. concentration of MAPE with five levels 2; 4; 6; 8; 10 phr (parts per hundred rubber), B. concentration of TMF with two levels 25 and 40 phr. The effect of MAPE on compatibility between NR and HTM was evaluated by tensile testing the compounds. The mixing was performed in a laboratory scale mill. The test tubes were obtained by cutting or die-cutting crosslinked peroxide sheets, these were obtained during the compression molding process. Analysis indicate that the MAPE coupling agent improved the compatibility between HTM and NR, this effect was evidenced by the values of tensile strength and elongation at break. However, the gel content determination indicates that the addition of 10 phr of MAPE crosslinking decreases due to competition with coupling reaction MAPE - HTM.

Transient analysis of gas flow in pipeline was studied. Finite Element Method based on ANSYS FLOTRAN was used to account for changes in pressure, temperature and flow rate. Compressibility factor function of temperature and pressure was considered. For non-isothermal transient results, the pressure and the flowrate gave wave propagation as a result of slow transients created by demand condition at the outlet end of custody transfer. Results obtained were in agreement with the demand restrictions at the outlet end of custody transfer indicating that the predictions are accurate and reliable. The results demonstrated that the Finite Element Method gave accurate prediction of pressure, temperature and flowrate in transient gas studies. For steady state non-isothermal model, results showed that the magnitude of the average pressure drop was higher when pressure was predicted with constant compressibility factor, but the same average pressure drop was reduced when the pressure was predicted with variable compressibility factor, z. Since compressibility factor is a function of temperature and pressure, the above findings signifies that in the case when gas temperature does not stabilize, the prediction of pressure with isothermal model and constant compressibility factor will lead to significant errors.

Dengue virus (DENV) and chikungunya virus (CHIKV) are reemergent arboviruses that are transmitted by mosquitoes of the Aedes genus. During the last several decades, these viruses have been responsible for millions of cases of infection and thousands of deaths worldwide. Therefore, several investigations were conducted over the past few years to find antiviral compounds for the treatment of DENV and CHIKV infections. One attractive strategy is the screening of compounds that target enzymes involved in the replication of both DENV and CHIKV. In this review, we describe advances in the evaluation of natural products targeting the enzymes involved in the replication of these viruses.

We have developed an application aiming at federated search for EU and Hungarian legislation and jurisdiction. It now contains above 1 million documents, with daily updates. The database holds documents downloaded from the EU sources EUR-Lex and Curia Online as well as public jurisdiction documents from the Constitutional Court of Hungary and The National Office for The Judiciary. The application is termed Justeus. Justeus provides comprehensible search possibilities. Besides free text and metadata (dropdown list) searches, it features hierarchical data structures (concept hierarchy trees) of directory codes and classification as well as subject terms. Justeus collects all links of a particular document to other documents (court judgements citing other case law documents as well as legislation, national court decisions referring to EU regulation etc.) as tables and directed graph networks. Choosing a document, its relations to other documents are visualized in real time as a network. Network graphs help in identifying key documents influencing or referred by many other documents (legislative and/or jurisdictive) and sets of documents predominantly referring to each other (citation networks).

The production of Moringa oleifera capsules has emerged as a promising agricultural activity in Mexico, driven by the plant’s well-documented health and nutritional benefits. In response, farmers have begun producing Moringa oleifera as a dietary supplement, using artisanal methods with minimal production controls. This study aimed to estimate the processing times of the Moringa oleifera production system using simulation modeling with Arena® software, and to evaluate the economic feasibility of capsule production. Methodologically, it was approached as a case study in the state of Veracruz, processing times were estimated through systematic sampling and modeled with Arena simulation software. Three simulation scenarios were considered to select a technically and economically viable alternative for agricultural producers: one with a 200-capsule encapsulator (1E200C), another with two 200-capsule encapsulators (2E200C) and a third with an 800-capsule encapsulator (1E800C). For the initial diagnosis a research stay was conducted for 180 days, and the production capacity was set at 24,000 capsules due to raw material limitations. Results indicated that the 1E800C scenario reduced processing time by 60% compared to the1E200C scenario, and by 35% compared to the 2E200C scenario. Additionally, the 1E800C model required only 2 days (16 hours) of work, while the 2E200C required 4 days (32 hours), and the 1E200C required 7 days (56 hours). In terms of production costs per unit of 90 capsules, the 1E200C was 3.93 USD the 2E200C 3.64 USD and the 1E800C 3.45 USD The study concluded that due to raw material limitations, the company can produce 12,800 units per year of 90 capsules. It is recommended to adopt the 1E800C encapsulator, which would lower overall production costs by 12.23%, representing 0.48 USD per unit produced, resulting in an additional profit of 6,150.50 USD Over a five-year evaluation period, the benefit-cost ratio was 5.03, the NPV was 922,370.11 and the IRR was 42.09%, indicating that this type of agribusiness in Mexico is both technically and economically feasible.

Real-time data processing and decision-making are increasingly crucial in various applications, driven by the continuous influx of data streams. Event-driven AI workflows within serverless computing environments offer a promising approach to handle these real-time demands efficiently. This paper presents a framework for simulating and analyzing the performance characteristics of such workflows. Our proposed approach utilizes simulated data with varying event rates and durations to investigate the impact on key performance metrics like latency, throughput, and resource utilization. This enables a comprehensive evaluation of the inherent trade-offs within event-driven AI systems. The key findings reveal a trade-off between latency and throughput. As the event rate increases, average processing latency generally increases while average throughput increases. Resource utilization remains relatively stable across different event rates in the simulated scenarios (e.g., 75.55\% at 2 events/second, 74.51\% at 10 events/second). This framework provides a valuable tool for understanding the performance characteristics of event-driven AI workflows and optimizing resource allocation strategies.

Structural evolution, mechanical and tribological properties of Al-Mg alloy admixed with 5, 10 and 15 vol.% of iron powder using multi-pass friction stir processing (FSP) have been studied. Homogeneous distribution of intermetallic compound (IMC) particles were achieved after 4 FSP passes what resulted in increasing the stir zone hardness and ultimate tensile stress with simultaneous loss in the ductility. The reinforcement of the aluminum alloy matrix with IMCs allowed reducing wear at the same magnitude of friction. Subsurface composite tribological layer was formed on the FSPed samples as a result of rubbing against a steel counterbody.

There is a growing notion that biomass are the best resource to replace the declining fossil fuels, yet both share the long lived human threat: toxic combustion emissions. Among the toxic combustion products is carbon monoxide (CO) that not only causes acute but also chronic ailments. This brief review discusses the solid fuel processing technologies from combustion, thermochemical and biochemical processing to kinetics and thermodynamics including the mechanism for release of CO. It further expounds on the burden that CO has caused England and Wales in the last 25 years. The main gist are the systems that have been developed to minimize human exposure to CO including cooking, heating, catalytic and detection systems. Finally, alternative technologies are discussed that work by changing the chemical nature of solid fuels as a way to minimize CO emissions.

Skin pigmentation ensures efficient photoprotection and relies on the pigment melanin, which is produced by epidermal melanocytes and transferred to surrounding keratinocytes. While the molecular mechanisms of melanin synthesis and transport in melanocytes are now well characterized, much less is known about melanin transfer and processing within keratinocytes. Over the past few decades, distinct models have been proposed to explain how melanin transfer occurs at the cellular and molecular levels. However, this remains a debated topic as up to four different models have been proposed, with evidence presented supporting each. Here, we review the current knowledge on the regulation of melanin exocytosis, internalization, processing and polarization. Regarding the different transfer models, we discuss how these might co-exist to regulate skin pigmentation under different conditions, i.e. constitutive and facultative skin pigmentation or physiological and pathological conditions. Moreover, we discuss recent evidence that sheds light on the regulation of melanin exocytosis by melanocytes and internalization by keratinocytes, as well as how melanin is stored within these cells in a compartment that we proposed to be named melanokerasome. Finally, we review the state of the art on the molecular mechanisms that lead to melanokerasome positioning above the nuclei of keratinocytes, forming supranuclear caps that shield the nuclear DNA from UV radiation. Thus, we provide a comprehensive overview of the current knowledge on the molecular mechanisms regulating skin pigmentation, from melanin exocytosis by melanocytes and internalization by keratinocytes, to processing and polarization within keratinocytes. A better knowledge of these molecular mechanisms will clarify long lasting questions in the field that are crucial for the understanding of skin pigmentation and can shed light on fundamental aspects of organelle biology. Ultimately, this knowledge can lead to novel therapeutic strategies to treat hypo- or hyper-pigmentation disorders, which have a high socio-economic burden to patients and healthcare systems worldwide, as well as having cosmetic applications.

So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.

Human beatboxing is a vocal art making use of speech organs to produce vocal drum sounds and imitate musical instruments. Beatbox sound classification is a current challenge that can be used for automatic database annotation and music-information retrieval. In this study, a large-vocabulary humanbeatbox sound recognition system was developed with an adaptation of Kaldi toolbox, a widely-used tool for automatic speech recognition. The corpus consisted of eighty boxemes, which were recorded repeatedly by two beatboxers. The sounds were annotated and transcribed to the system by means of a beatbox specific morphographic writing system (Vocal Grammatics). The image processing techniques plays vital role on image Acquisition, image pre-processing, Clustering, Segmentation and Classification techniques with different kind of images such as Fruits, Medical, Vehicle and Digital text images etc. In this study the various images to remove unwanted noise and performs enhancement techniques such as contrast limited adaptive histogram equalization, Laplacian and Harr filtering, unsharp masking, sharpening, high boost filtering and color models then the Clustering algorithms are useful for data logically and extract pattern analysis, grouping, decision-making, and machine-learning techniques and Segment the regions using binary, K-means and OTSU segmentation algorithm. It Classifying the images with the help of SVM and K-Nearest Neighbour(KNN) Classifier to produce good results for those images.

Hydrothermal /super critical processes are important process to synthesize materials which are otherwise difficult to form under normal conditions. A mathematical model is developed using standard transport equations to calculate the time for heating of hydrothermal reactor and computer simulation of the model was carried out in SOLIDWORKS® to validate it. The materials used to form reactor vessel were stainless steel (outer body) and TEFLON (PFA) (inner vessel). It was shown that composite wall, its geometry, construction & properties greatly affect the time and pattern of heat transfer. The time calculated and pattern generated were found to be in good agreement with experimental values.

It is becoming clear that in addition to gap junctions, playing a role in cell-cell communication, gap junction proteins, connexins, located in cytoplasmic-compartments may have other important functions. Mitochondrial connexin 43 (Cx43) is increased after ischemic preconditioning and has been suggested to play a protective role in the heart. How Cx43 traffics to the mitochondria and the interactions of mitochondria with other Cx43-containing structures are unknown. In this study, immunocytochemical, super-resolution and transmission electron microscopy were used to detect cytoplasmic Cx43-containing structure and to demonstrate their interactions with other cytoplasmic organelles. The most prominent cytoplasmic Cx43-containing structures, annular gap junctions, were demonstrated to form intimate associations with lysosomes as well as with mitochondria. Surprisingly, the frequency of associations between mitochondria and annular gap junctions was greater than that between lysosomes and annular gap junctions. The benefits of annular gap junction/mitochondrial associations are not known. However, it is tempting to suggest that the contact between annular gap junction vesicles and mitochondria facilitates Cx43 deliver to the mitochondria. Furthermore, it points to the need for investigating trafficking of Cx43 to cytoplasmic compartments and annular gap junction as more than only a vesicle destined for degradation.

Enhancement in the strength of austenitic steels with a small content of carbon can be achieved by a limited number of methods, among which is the ultrafine-grained (UFG) structure formation, especially efficient with the use of severe plastic deformation (SPD) processing that enables increasing significantly the contribution of grain-boundary strengthening, and also involves a combination of other strengthening factors (work hardening, twins, etc.). In this paper, we demonstrate that the use of SPD processing combined with conventional methods of deformation treatment of metals, such as rolling, may lead to an additional strengthening of UFG steel. Analysis of the results of the study on the change of the microstructure and mechanical properties of the Cr-Ni stainless austenitic steel after a combined deformation reveals a substantial increase in the strength properties of this steel, resulting from a consecutive application of SPD processing via equal-channel angular pressing and rolling at a temperature of 400 С, yielding a strength more than 1.5 times higher that that produced by any of these two methods used separately.

The ability to decode words accurately at an early age strongly predicts reading competence later on. Decoding-challenged children may find it difficult to understand texts, which might impede their overall academic development. Strong word decoding skills accelerate the process of learning vocabulary and grammar rules in a variety of languages. The paper aimed to collect relevant literature regarding word decoding ability in the world of the language acquisition process. Policymakers and educators will be greatly impacted by realizing how crucial word decoding is to language learning. Providing targeted interventions for struggling readers, encouraging a positive reading culture, and explicitly teaching phonological and orthographic abilities are all essential components of effective reading education. Promoting decoding proficiency also requires making sure that people have access to excellent reading materials and tools. In order to enable learners to become great readers and communicators across linguistic and cultural boundaries, educators and policymakers should prioritize phonemic awareness from an early age and use effective teaching methodologies.

This study introduces a specialized Automatic Speech Recognition (ASR) system, leveraging the Whisper Large-v2 model, specifically adapted for radiological applications in French language. Our methodology focused on adapting the model to accurately transcribe medical terminology and diverse accents within the French language context, achieving a notable Word Error Rate (WER) of 17.121%. The research involved extensive data collection and preprocessing, utilizing a wide range of French medical audio content. The results demonstrate the system's effectiveness in transcribing complex radiological data, underscoring its potential to enhance medical documentation efficiency in French-speaking clinical settings. The discussion extends to the broader implications of this technology in healthcare, including its potential integration with Electronic Health Records (EHRs) and its utility in medical education. The study also explores future research directions, such as tailoring ASR systems to specific medical specialties and languages. Overall, this research contributes significantly to the field of medical ASR systems, presenting a robust tool for radiological transcription in the French language and paving the way for advanced technology-enhanced healthcare solutions.

Humans have a unique cognitive ability known as relational abstraction, which allows us to identify logical rules and patterns beyond basic perceptual characteristics. This ability represents a key difference between how humans and other animals learn and interact with the world. With current large language models rivaling human intelligence in many regards, we investigated whether relational abstraction was an emergent chat completion capacity in various models. We find that despite their impressive language processing skills, all tested language models failed the relational match-to-sample (RMTS) test, a benchmark for assessing relational abstraction. These results challenge the assumption that advanced language skills inherently confer the capacity for complex relational reasoning. The paper highlights the need for a broader evaluation of AI cognitive abilities, emphasizing that language proficiency alone may not be indicative of certain higher-order cognitive processes thought to be supported by language.

According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as Generative Pretrained Transformers (GPTs), may have potential implications on the job market, specifically regarding job occupations that demand writing or programming skills. The research points out Interpreters and Translators as one of the main job occupations with greater exposure to AI in the US job market (76.5%). This article, following a mixed-methods survey-based research, provides insights into the Portuguese Language Service Providers (LSPs) awareness and knowledge about AI, specifically regarding neural machine translation (NMT) and large language models (LLM), its actual use and usefulness, as well as their potential influence on work performance and labour market. The results show that most professionals are unable to identify if AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in the profession, expressing as main concerns the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors.

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs) such as ChatGPT has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.

Sign Language Recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. Researchers still face challenges developing efficient SLR systems because of the video’s complex background, light illumination, and subject structures. Recently many researchers developed a skeleton-based sign language recognition system to overcome the subject and background variation of hand gesture signs. However, skeleton-based SLR is still under exploration due to the lack of information and annotations on hand key points. More recently, researchers included body and face information with the hand gesture for the SLR, but their performance and efficiency are unsatisfactory. We proposed a Multi-Stream Graph-based Deep Neural Network (SL-GDN) for a skeleton-based SLR system to overcome the problems. The main purpose of the proposed SL-GDN approach is to improve the efficiency and performance of the SLR system with a low computational cost based on the human body pose in the form of 2D landmark locations. In the procedure, firstly, we constructed a skeleton graph based on the selected 27 whole-body key points among 67 key points to solve the inefficiency problems. Then we proposed multi-stream SL-GDN to extract features from the whole-body skeleton graph for four streams. Finally, we concatenated the four different features and applied a classification module to refine the feature and recognize corresponding sign classes. Our data-driven and graph construction method increases the system’s flexibility and brings high generability to adapt various data samples. We used three large-scale benchmark SLR datasets to evaluate the proposed model: WLASL, AUTSL and CSL. The demonstrated performance accuracy table proved the superiority of the proposed model, and we believe this will be considered a great invention in the SLR domain.

The development in the field of ceramics has been increasing over the past several decades. Among all of the dental ceramics, zirconia ceramics show very good mechanical properties and a significant improvement in mechanical function. However, it is properly established that each step of the fabrication process of zirconia ceramics must be controlled to achieve the expected mechanical properties. These steps include the block constructing, sintering process, machining and surface processing. Recent advances have focused on zirconia ceramics in forming and sintering processes, mechanical properties, combining with various materials, and on surface processing. All these items provide conceptions about the formability and machinability of the zirconia. Until now, very excellent studies have been done to specify the evolution of microstructures, phase and mechanical properties of zirconia ceramics during the sintering process. However, more studies are required in this field. This paper aims to demonstrate general views and various studies in the field of zirconia ceramics. The properties, applications and tests performed and the recent developments in the field of zirconia bioceramics are discussed.

This comprehensive scientific review provides an in-depth analysis of both the natural compounds, pyrethrins, and their synthetic derivatives, pyrethroids, focusing on their classification, biosynthesis, mechanism of action, general and pharmaceutical uses, as well as their toxicity and environmental impact. Pyrethrins, derived from certain plant species, have long been recognized for their potent insecticidal properties. The review begins by examining the classification of pyrethrins and pyrethroids, elucidating their structural characteristics and unique features within the field of natural and synthetic compounds. The biosynthetic pathways responsible for producing pyrethrins in plants are discussed, highlighting the enzymatic reactions and genetic regulation involved. In addition, the synthesis of pyrethroid derivatives is explored, including both natural and synthetic sources and potential optimization strategies. Understanding the mechanisms of action by which pyrethrins and pyrethroids exert their insecticidal effects is a crucial aspect of this review. Complex interactions with the nervous systems of target organisms are examined, providing insights into their selective toxicity and modes of action. In addition, the various applications of these compounds are explored, from their use in agriculture for pest control to their incorporation into household insecticides and potential pharmaceutical applications. The review also critically evaluates the potential toxicity of pyrethrins and pyrethroids to human health. By consolidating current knowledge and research findings, this review provides a comprehensive understanding of the properties and applications of pyrethrins and pyrethroids, highlighting their benefits and risks, and the importance of responsible and sustainable use in various areas.

This study investigated the presence and characteristics of Juglans neotropica in three ecosystems in Southern Ecuador: Montane Evergreen Forest, Evergreen Seasonal Lower Montane Forest, and Semideciduous Foot Montane Forest. The main focus was on the species' multipurpose nature as both a fruit and timber source. Six study sites, totaling at least 0.5 hectares each, were established, with four in Loja province and two in Zamora Chinchipe province.The results showed significant differences in dendrometric variables across the sites, with the most favorable growth recorded in The Tundo, where trees exhibited an average diameter at breast height (DBH) of 45.16 cm, basal area (G) of 1.41 m2, total height (TH) of 19.22 m, canopy height (CH) of 13 m, cubic volume (CV) of 3.55 m3, and total volume (TV) of 5.22 m3. The species displayed a clumped distribution pattern, as indicated by a Morisita index greater than 1. Regarding abundance, the highest density of 297 trees per hectare was found in Argelia, while Victoria had the lowest density of 46 trees per hectare. This research highlights the importance of Juglans neotropica as a multipurpose species, providing both valuable fruits and wood resources in the studied ecosystems of Southern Ecuador.

Different types of disasters, whether natural or human character, lead to the significant loss of human lives. In the latter case, the quick action of identification of corpses and human remains is mandatory. There are a variety of protocols to identify victims, however, genetics is one of the tools that allow an exact identification of the victim. However, several factors may interfere with this identification, from the biological samples’ degradation not allowing the analysis of nuclear information, to failure to dispose of biological samples from family members. Access to certain family members could be a determinant of the proper choice of genetic markers that allow the identification of the victim, or his/her inclusion in a given genetic maternal or paternal lineage. With the new advances in the genetic field, it is expected to allow soon the identification of victims from disasters only with his/her biological postmortem samples, being possible to draw a robot portrait and its most likely physical characteristics. In all cases, genetics is the only modern tool with universal character and can be used in essentially all biological samples, giving and identification of more or less accurate statistical character, depending on whether nuclear or lineage markers are used.

The Sustainable Development Goals (SDGs) purport to report holistically on progress towards sustainability and do so using more than 231 discrete indicators with a primary objective to achieve a balance between the environment, social and economic aspects of development. The research question underpinning the analyses presented in this paper is: are the indicators in the SDGs sufficient and fit-for-purpose to assess the trajectory of natural resources towards sustainability? We extracted the SDG indicators that monitor the state of natural resources, or alternately support policy or governance for their protection, and determined if these are adequate to provide the essential data on natural resources to achieve the aims of the SDGs. The indicators are clustered into four natural resource categories; land, water (both marine and freshwater), air and biodiversity. Indicators for monitoring land resources show that the most comprehensive land resource indicator, for degraded land, is not fully implemented and that missing from land monitoring is an evaluation of vegetation health outside of forests and mountains, the condition of soils, and most importantly the overall health of terrestrial ecosystems. Indicators for monitoring water resources have substantial gaps, unable to properly monitor water quality, water stress, many aspects of marine resources and most significantly, the health of fresh and salt water ecosystems. Indicators for monitoring of air have recently become more comprehensive, but linkage to IPCC results would benefit both programmes. Monitoring of biodiversity is perhaps the greatest weakness of the SDG Agenda, having no comprehensive assessment even though narrow aspects are monitored. Again, deliberate linkages to other global biodiversity programmes (e.g. CBD and the Post 2020 Biodiversity Framework; IPBES; Living Planet, etc.) are recommended on condition that data can be defined at a country level. While the SDG list of indicators in support of natural resource is moderately comprehensive, it lacks holistic monitoring in relation to evaluation of ecosystems and biodiversity to the extent that these missing but vital measures of sustainability threaten the entire SDG Agenda. In addition, an emerging issue is that even where there are appropriate indicators, the amount of country-level data remains inadequate to fully evaluate sustainability. This signals the delicate balance between the extent and complexity of the SDG Agenda and uptake at a country level

Signal preprocessing and feature extraction are two important and necessary steps of data analysis. In signal analysis, typically these two steps are implemented through multiple algorithms. Moreover, the significant parameters in most algorithms are set empirically. We have developed an algorithm that can preserve and highlight characteristic waveforms while attenuating non-feature waveforms. The parameters in the algorithm are set through difference analysis. We have conducted experiments on both clinical and open-source ECG data, obtaining effective test results.

Ferns and lycophytes, now known as monilophytes, have received scant molecular attention in comparison to angiosperms. The advent of high-throughput technologies allowed an advance towards a greater knowledge of their elusive genomes. In this work, proteomic analyses of heart-shaped gametophytes of two ferns were performed: the apomictic species Dryopteris affinis ssp. affinis and its sexual relative Dryopteris oreades. In total, a set of 218 proteins shared by these two gametophytes were analyzed using the STRING database, and the proteome associated with metabolism, genetic information processing, and responses to abiotic stress is discussed. Specifi-cally, we report proteins involved in the metabolism of carbohydrates, lipids, and nucleotides, the biosynthesis of amino acids, and secondary compounds, energy, oxido-reduction, transcrip-tion, translation, protein folding, sorting and degradation, and responses to abiotic stress. Look-ing at the interactome of this set of proteins, it represents a total network composed of 218 nodes and 1,792 interactions, obtained mostly from databases and textmining. The interactions among the identified proteins of the ferns D. affinis and D. oreades, together with the description of their biological functions, might contribute to a better understanding of the function and develop-ment of ferns as well as to fill knowledge gaps in plant evolution.

Precipitation processes are technologies commonly used in hydrometallurgical plants to recover metals or to treat wastewaters. Moreover, solid-liquid separation technologies, such as thickening or filtering, are relevant unit operations, included in the precipitation technologies. These methods are strongly dependent on the characteristics of the solid precipitates formed during the specific precipitation reaction. One of these characteristics is the particle size distribution (PSD) of the solid precipitates which are fed into a solid-liquid separation process. Therefore, PSD determination is a typical practice for the characterization of the slurries generated in a precipitation plant. Furthermore, the precipitates generated in these processes have a colloidal or aggregation behavior, depending on the operational conditions. Nevertheless, the conventional methods used to estimate PSD (e.g., laser diffraction and/or ciclosizer) have not been designed to measure particles that tend to aggregate or disaggregate, since they include external forces (e.g., centrifugal, agitation, pumping and sonication). These forces affect the true size of the aggregates formed in a unit operation, thereby losing representativity in terms of aggregates particle size. This study presents an alternative method of measuring the size distribution of particles with aggregation behavior, particularly, by using non-invasive microscopy and image processing and analysis. The samples used have been obtained from an experimental precipitation process by applying sulfidization to treat the cyanide-copper complexes contained in a cyanidation solution. This method has been validated with statistical tools and compared with a conventional analysis based on laser diffraction. Our results show significant differences between the methods analyzed, demonstrating that image processing and analysis by microscopy is an excellent and non-invasive alternative to obtaining size distribution of aggregates in precipitation processes.

It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the semantic aspects of GIS-BIM integration. We present here the results of the GeoBIM project, in which we tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models, (ii) the integration of existing GIS subsoil data in BIM, and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in the Netherlands. For each problem, we expose in detail the issues we faced, our proposed solutions, and our recommendations for a more successful integration.

Larus michahellis increased its population throughout the 20th century in its worldwide distribution area. The increase in the Salinas de San Pedro del Pinatar has produced changes in the habitats due to the contribution of guano, predation on birds, incidents with workers, and the production of salt. The objective is to analyze the impacts of L. michahellis on the landscape, habitats, waterfowl, salt production, and workers, as well as to evaluate the effectiveness of control activities. Censuses of wintering L. michahellis have been carried out between 1990-2021, of nesting aquatic birds between 1994-2021, and nests and eggs of L. michahellis have been eliminated between 2000-2021. The result has been a decrease in pairs of L. michahellis, recovery of waterfowl populations, colonization of others, absence of incidents with workers, and reduction of damage to salt production. However, to reach a definitive solution, measures should be adopted to prevent L. michahellis from accessing the main sources of human food: urban solid waste dumps, aquaculture farms, and fish discards.

Alcoholic Liver Disease (ALD) and Non-Alcoholic Fatty Liver Disease (NAFLD) are the most common causes of chronic liver disease and are increasingly emerging as a global health problem. Such disorders can lead to liver damage, resulting in the release of pro-inflammatory cytokines and the activation of infiltrating immune cells. These are some of the common features of ALD progression in ASH (alcoholic steatohepatitis) and NAFLD to NASH (non-alcoholic steatohepa-titis). Hepatic steatosis and subsequent fibrosis, whose continuous progression is accompanied by angiogenesis, lead to hypoxia, inducing the activation of vascular factors, which in turn triggers pathological angiogenesis and subsequent fibrosis, resulting in a vicious cycle. This condition further exacerbates liver injury and may contribute to the development of comorbidities, such as metabolic syndrome as well as hepatocellular carcinoma. Increasing evidence suggests that antiangiogenic therapy may have beneficial effects on these hepatic disorders and their exacerbation. Therefore, there is a great interest to deepen the knowledge of the molecular mechanisms of natural antiangiogenic products that could both prevent and control liver diseases. In this review, we focus on the role of major natural antian-giogenic compounds against steatohepatitis and determine their potential therapeutic benefits in the treatment of liver inflammation.

Leishmaniasis is considered one of the different neglected tropical diseases by the World Health Organization. Over the past few decades to tackle leishmaniasis, effective and novel drugs have progressed. But few are expensive some other medication shows poor effects and few drugs with long treatment lead to cause resistance. But it is very important to start a better medication against leishmania so researchers came to the front of the utilization of natural products which are considered a better option. Finding active compounds in medicinal plants is another alternative to currently accessible medications. Materials and methods: This study examined and reported the far more potential natural products used to treat disease caused by Leishmania spp. Leishmaniasis, plant metabolites, in vivo, in vitro, and treatment against leishmaniasis have been used as search terms in the Google Scholar, PubMed, and Science Direct databases, and only papers published between 2015 and 2021 have been chosen for further analysis. Results: The use of novel natural compounds with leishmanicidal action as well as the leishmanicidal activity of natural compounds against promastigote, axenic, and intracellular amastigote forms were included in roughly 20 research papers that were reviewed.Conclusion: Due to their capacity to selectively target parasites without harming host cell viability, herbal plants are a possible source of new anti-leishmanial medication. Future leishmaniasis treatments will draw on the isolated compounds as a source, completing those already offered in clinics.

Cellular therapy has emerged as an attractive option for the treatment of cancer, and adoptive transfer of chimeric antigen receptor (CAR) expressing T cells has gained FDA approval in hematologic malignancy. However, limited efficacy has been observed utilizing CAR-T therapy in solid tumors. Natural killer (NK) cells are crucial for tumor surveillance and exhibit potent killing capacity of aberrant cells in an antigen-independent manner. Adoptive transfer of unmodified allogeneic or autologous NK cells has shown limited clinical benefit due to factors including low cell number, low cytotoxicity and failure to migrate to tumor sites. To address these problems, immortalized and autologous NK cells have been genetically engineered to express high affinity receptors (CD16), CARs directed against surface proteins (PD-L1, CD19, Her2, etc.) and endogenous cytokines (IL-2 and IL-15) that are crucial for NK cell survival and cytotoxicity, with positive outcomes reported by several groups both preclinically and clinically. With a multitude of NK cell-based therapies currently in clinic trials, it is likely they will play a crucial role in next-generation cell therapy-based treatment. In this review we will highlight the recent advances and limitations of allogeneic, autologous and genetically enhanced NK cells used in adoptive cell therapy.

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains such as psychiatry and cardiology. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, a number of commercial digital devices have been developed that record physiological data which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether outputs from four sensors, specifically FitBit, Withings Aura, Hexoskin, and Oura Ring, were related to known cognitive and psychological metrics, including the PSQI and N-back test. We found that sleep metrics extracted from these devices did not predict cognitive and psychological metrics well in our pilot data. However, we did identify certain signification associations, specifically the Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure with p=0.004 and p=0.033, respectively. Additionally, correlation of various sleep features among the devices across the sleep cycle was almost uniformly low. These findings can hopefully be used to guide future sensor-based sleep research.

SARS-CoV-2 (2019-nCoV), a novel coronavirus, caused the pneumonia outbreak in China and continue to expand. The host receptor for 2019-nCoV Angiotensin-converting enzyme 2 (ACE2), is the same as the host receptor for SARS-CoV. Targeting ACE2 holds the promise for preventing and inhibiting 2019-nCoV infection. Chinese Medicine herbs could be a valuable pool for identifying active compounds for treating infection of 2019-nCoV. In this study, we summarize several active compounds, including baicalin, Scutellarin, Hesperetin, Nicotianamine and glycyrrhizin that could have potential anti-2019-nCoV effects. We conduct molecular docking to predict their capacity for binding ACE2, which may prevent the 2019-nCoV infection. We propose that these selected compounds worth further investigation for preventing 2019-nCoV.

In this review, we discuss novel natural products discovered within the last decade that are reported to have antifungal activity against pathogenic species. Nearly a hundred natural products were identified that originate from bacteria, alga, fungi, sponges and plants. Fungi were the most prolific source of antifungal compounds discovered during the period of review. The structural diversity of these antifungal leads encompasses all the major classes of natural products including polyketides, shikimate metabolites, terpenoids, alkaloids and peptides.

Cyanobacteria are photosynthetic microorganisms that colonize diverse environments worldwide, ranging from ocean to freshwaters, soils, and extreme environments. Their adaptation capacities and the diversity of natural products (molecules, metabolites, or compounds) that they synthesize support the cyanobacterial success for the colonization of their respective ecological niches. Although cyanobacteria are well-known for their toxin production and their relative deleterious consequences, they also produce a large variety of molecules that exhibit beneficial properties with high potential for various fields of application (e.g., synthetic analog of the dolastatin 10 used against Hodgkin lymphoma). The present review specially focuses on the beneficial activities of cyanobacterial molecules described so far. Based on an analysis of 670 papers, it appears that more than 90 genera of cyanobacteria have been found to produce compounds with potential beneficial activities, most of them belonging to the orders Oscillatoriales, Nostocales Chroococcales, and Synechococcales. The rest of the cyanobacterial orders (i.e., Pleurocapsales, Chroococcidiopsales, and Gloeobacterales) remain poorly explored in terms of their molecular diversity and relative bioactivity. The diverse cyanobacterial molecules presenting beneficial bioactivities belong to 10 different chemical classes (alkaloids, depsipeptides, lipopeptides, macrolides/lactones, peptides, terpenes, polysaccharides, lipids, polyketides, and others) that exhibit 14 major kinds of bioactivity. However, no direct relation between the chemical class and the bioactivity of these molecules has been demonstrated. We further selected and specifically described 50 molecule families according to their specific bioactivities and their potential uses in pharmacology, cosmetology, agriculture, or other specific fields of interest. This up-to-date review takes advantage of the recent progresses in genome sequencing and biosynthetic pathway elucidation, and presents new perspectives for the rational discovery of new cyanobacterial metabolites with beneficial bioactivity.

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds from natural origin is increasing. Several countries have initiatives in place to construct and maintain compound databases that are representative of their diversity. Examples are Brazil, France, Panama and recently Vietnam. Herein, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. It is reported the profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints. BIOFACQUIM is freely available.

Fresh water springs are unique natural resources in Florida, currently under threat from increasing groundwater pumping and pollution resulting from a variety of sources. This paper estimates current recreation benefits from visiting springs using the travel cost method and elicits residents’ willingness to contribute for springs restoration using the contingent valuation method. It further compares the performance of count data models correcting for endogenous stratification and truncation, and finds that the annual consumer surplus per person per trip is between $20 and $43. Furthermore, visitors are willing to contribute $12 to $14 per person per trip for springs restoration without reducing trip demand.

Abstract: Experiments to measure a single molecule/particle, i.e. an individual molecule/particle, at room temperature or under physiological conditions without immobilization, for example on a surface, or without significant hydrodynamic flow have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastics due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Experiments to measure a single molecule/particle, i.e. an individual molecule/particle, at room temperature or under physiological conditions without immobilization, for example on a surface, or without significant hydrodynamic flow have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastics due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

The theory of fractional calculus extends the order of classical integer calculus from integer to non-integer. As a new engineering application tool, it has made many important research achievements in many fields, including image processing. This paper mainly studies the application of fractional calculus theory in image enhancement and denoising, including the basic theory of fractional calculus and its amplitude frequency characteristics, the application of fractional differential operator in image enhancement, and the application of fractional integral operator in image denoising. The experimental results show that the fractional calculus theory has more special advantages in image enhancement and denoising. Compared with the existing integer order image enhancement operators, the fractional differential operator can more effectively enhance the "weak edge" and "strong texture" details of the image. The fractional order integral image denoising operator can not only improve the signal-to-noise ratio of the image compared to traditional denoising methods, but also better preserve detailed information such as edges and textures of the image.

Salmon aquaculture generates 80% of the total revenue of finfish aquaculture across Canada. Salmon farming is carried out in a multilevel process, and at least 60% of the total production is considered as by-products, including skin, head, viscera, trimming, frames, bones, and roes. These by-products are an excellent source of protein, which can be converted to protein hydrolysates through enzymatic hydrolysis and non-enzymatic processes such as chemical hydrolysis (acid and alkaline) and salt extraction. Several studies have reported that peptides from salmon protein hydrolysates possess bioactivities, including antihypertensive, antioxidant, anticancer, antimicrobial, antidiabetic, anti-allergic, and cholesterol-lowering effects. Incorporating in-silico computational methods is gaining more attention to identify potential peptides from source protein. The in-silico methods can be used to predict the properties of the peptides and thereby predetermine the processing, isolation, and purification steps that can be used for the peptides of interest. Therefore, it is essential to implement robust, standardized, and cost-effective processing techniques that can easily be transferrable and scale up for industrial applications. This contribution summarizes the latest research information on Atlantic salmon, production statistics, growth lifecycle, processing, protein production techniques, nutritional and functional properties, peptide production and purification processes, as well as potential health benefits as a nutraceutical product.

The holy town of Joshimath, gateway to the India-China border and religious places like Badrinath, Hemkunt Sahab, Valley of flowers in the Uttarakhand state of India experienced cracks in many of the residential and commercial buildings in the month of November-December 2022 which created panic and relocation of people. The reason(s) for the subsidence is still not known but using the InSAR technology and data from Sentinel-1, SAR data for the region was processed using HyP3 and MintPy for January-December 2022 to understand when the phenomenon started and how much uplift/subsidence the area has underwent in the last one year, as no such study is available till today. The entire town can be classified into two zones with respect to the annual subsidence values. Severe subsidence has been observed in the North and East regions whereas most of the southern region experienced moderate to low subsidence. Since the town is built on the debris of an old landslide, the subsidence may be attributed to the change in the course of the underground water channels due to heavy and continuous construction happening in the region and also due to drop in the water table. The results shows that the subsidence in the region was ongoing since June (from January till May, almost negligible vertical movement was recorded) which accelerated after September (when an uplift was recorded which lasted till October), while the subsidence peaked in the month of December with recorded subsidence of as much as 10 cm in some areas located around the Joshimath town, where most of the residential and commercial buildings are located.

Modern hyperspectral imaging technologies generate enormous datasets that could potentially transmit a wealth of information, but such a resource presents numerous difficulties for data analysis and interpretation. Deep learning techniques undoubtedly provide a wide range of potential for solving both traditional imaging tasks and exciting new problems in the spatial-spectral domain. This is true in the primary application area of remote sensing, where hyperspectral technology originated and has made the majority of its progress, but it may be even more true in the vast array of now existing and developing application areas that make use of these imaging technologies. The current review advances on two fronts: on the one hand, it is directed at domain experts who desire an updated overview of how deep learning architectures might work in conjunction with hyperspectral acquisition techniques to address specific tasks in various application sectors. On the other hand, by providing them with a picture of how deep learning technologies are applied to hyperspectral data from (near)real-time perspective. The contributions of this review include the existence of these two points of view and the inclusion of opportunities and important problems associated with the development of future CHIME mission to be launched by European Space Agency (ESA).

Additive manufacturing or 3D printing applying polycaprolactone-(PCL)-based medical devices represents an important branch of tissue engineering, where the sterilization method is a key process for further safe application in vitro and in vivo. In this study, the authors intend to access the most suitable gamma radiation conditions to sterilize PCL-based scaffolds in a preliminary biocompatibility assessment, envisioning future studies for airway obstruction conditions. Three radiation levels were considered, 25 kGy, 35 kGy and 45 kGy and evaluated as to their cyto- and biocompatibility. All three groups presented biocompatible properties, indicating an adequate sterility condition. As for the cytocompatibility analysis, devices sterilized by 35 kGy and 45 kGy showed better results, with the 45 kGy showing overall improved outcomes. This study allowed to select the most suitable sterilization condition for PCL-based scaffolds, aiming at immediate future assays, by applying 3D-customized printing techniques to specific airway obstruction lesions of the trachea.

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.

The determination of crack propagation velocities can provide valuable information for a better understanding of damage processes of concrete. The spatio-temporal analysis of crack patterns developing at a speed of several hundred meters per second is a rather challenging task. In the paper, a photogrammetric procedure for the determination of crack propagation velocities in concrete specimens using high-speed camera image sequences is presented. A cascaded image sequence processing which starts with the computation of displacement vector fields for a dense pattern of points on the specimen’s surface between consecutive time steps of the image sequence chain has been developed. These surface points are triangulated into a mesh, and as representations of cracks, discontinuities in the displacement vector fields are found by a deformation analysis applied to all triangles of the mesh. Connected components of the deformed triangles are computed using region-growing techniques. Then, the crack tips are determined using principal component analysis. The tips are tracked in the image sequence and the velocities between the time stamps of the images are derived. A major advantage of this method as compared to established techniques is in the fact of its allowing for spatio-temporally resolved, full-field measurements rather than point-wise measurements and that information on crack width can be obtained simultaneously. To validate the experimentation, the authors processed image sequences of tests on four compact-tension specimens performed on a split-Hopkinson tension bar. The images were taken by a high-speed camera at a frame rate of 160,000 images per second. By applying to these datasets the image sequence processing procedure as developed, crack propagation velocities of about 800 m/s were determined with a precision in the order of 50 m/s.

Over the past 30 years, the use of field spectroscopy has risen in importance in remote sensing studies for the characterization of the surface reflectance of materials in situ within a broad range of applications. Potential uses range from measurements of individual targets of interest (e.g. vegetation, soils, validation targets etc.), to characterizing the contributions of different materials within larger spatially-mixed areas as would be representative of the spatial resolution captured by a sensor pixel (UAV to satellite scale). As such, it is essential that a complete and rigorous assessment of both the data-acquisition procedures, and the suitability of the derived data product be carried out. The measured energy from solar-reflected range spectroradiometers is influenced by the viewing and illumination geometries and the illumination conditions which vary due to changes in solar position and atmospheric conditions. By applying corrections, the estimated absolute reflectance (Rabs) of targets can be calculated. This property is independent of illumination intensity or conditions and is the metric commonly suggested to be used to compare spectra even when data are collected by different sensors or acquired under different conditions. By standardizing the process of estimated Rabs, as is provided in the described toolkit, consistency and repeatability in processing are ensured and the otherwise labor intensive and error-prone processing steps are streamlined. The resultant end data product (Rabs) represents our best current effort to generate consistent and comparable ground spectra which have been corrected for viewing and illumination geometries as well as other factors such as the individual characteristics of the reference panel used during acquisition.

Tree nuts confer many health benefits because of their high content of vitamins and antioxidants and they are increasingly consumed in the last the years. Food processing is an important industrial tool to modify allergenic properties of foods, in addition to ensure safety and to enhance organoleptic characteristics. The effect of high pressure, without and with heating, on SDS-PAGE and immunodetection profile of potential allergenic proteins (anti-11S, anti-2S and anti-LTP) of pistachio, cashew, peanut, hazelnut, almond and chestnut was investigated. Processing based on heat and /or pressure and Ultra high pressure (HHP, 300-600 MPa) without heating was applied. After treating the six tree nuts with pressure combined with heat a progressive diminution of proteins with potential allergenic properties was observed. Moreover, some tree nuts proteins (pistachio, cashew and peanut) seemed to be more resistant to technological processing than others (hazelnut and chestnut). Differences among tree nut varieties were found regarding to protein content, SDS and immunoblotting profile. High pressure combined with heating processing markedly reduce tree nut allergenic potential as the pressure and treatment time increases. HHP do not alter hazelnut and almond immunoreactivity

Solar pasteurization, as a means of treating milk in remote rural areas without electrical power, is based on using solar energy to thermally inactivate pathogenic microorganisms at temperatures below the boiling point to maintain or improve milk quality. A solar milk pasteurizer of flat‐plate water‐heating glass acting as the solar collector, connected to a stainless-steel cylindrical milk vat, was constructed and evaluated in Navrongo, in the tropical savanna zone of Ghana, West Africa. The novel approach taken in this project is to integrate the solar collection and heat exchanger into a single unit using materials and fabrication techniques readily available in the developing world. The vat comprises a 1.5 mm thick stainless steel cylindrical tank, a 2.2 cm wide hot water jacket, and an outer layer of 5.0 mm thick aluminium foil insulation. Hot water produced by the collector was used for pasteurizing milk. The optimum quantity of milk that this device could pasteurize under the study conditions was 8 L. The device could pasteurize raw milk to a maximum temperature of 74 °C at 14:00 h GMT. The ambient temperature during the pasteurization ranged from 30 °C to 43 °C. The microbial analysis before and after the pasteurization was done, and the result shows that the total bacteria count before pasteurization was 6.6x10⁶ log CFU/mL per unit volume, and the total bacteria count after pasteurization was 1.0x10² log CFU/mL. By acceptable standards, the total bacteria count after pasteurization is within a safe range for consumption, and coliform counts were negative. The solar milk pasteurization system is cost-effective. Hence, it is appropriate for milk producers in arid pastoral areas without electricity to adopt the solar pasteurizer as an alternative to firewood as a fuel source for milk pasteurization. This approach can potentially create a cottage industry that may also play a crucial role as a water treatment technology that will improve the health of rural populations.

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal Large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality GPT (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4, requiring no human efforts. SQ-GPT approaches GPT-4’s performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjin, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

This review paper aimed at reviewing English language teaching articles on EFL teachers’ perceptions, practices and challenges towards communicative language teaching. In this review, two ELT articles, which have been conducted on teachers’ perceptions, practices and challenges towards communicative language teaching in EFL classes, were reviewed in detail. While reviewing the articles, the researcher adapted Rideley’s (2008) checklist to evaluate elements of article. Thus, articles were reviewed by focusing on the title, abstract, introduction, research questions, objectives, research design, participants, sample and sampling techniques, data collecting instruments, methods of data analyses, findings and references based on the checklist provided above. The review revealed that the articles had clear objectives, researchable questions, appropriate methods and inevitable conclusions. They tried to answer the questions which were raised in each article, and the objectives went along with the overall research findings. The review, on the other hand, revealed that the participants in the articles were not representative so as to conclude and generalize the overall population by taking only few participants which were selected even by using non probability sampling.

In the wake of globalization and the revolutionary impact of Artificial Intelligence (AI), literature and language education play a pivotal role in preparing the next generation to navigate an ever-changing world. This reflective concept paper explores the profound insights shared by educators, focusing on literature and language education amidst globalization and AI. Drawing on interviews and observations, teachers' reflections reveal the enduring value of literature in shaping young minds, fostering critical thinking, and promoting empathy. Additionally, they emphasize the importance of embracing multilingualism and cultural diversity to equip students for an interconnected world. In the face of challenges, educators showcase their resilience and adaptability, harnessing technology, particularly AI-driven tools, to foster future-ready learners. Identifying gaps and implications in literature and language education, this paper sets the stage for collective efforts among stakeholders to shape the future of education. Ultimately, teachers hold the key to nurturing a generation capable of embracing the complexities of the AI-driven global landscape with confidence and compassion, empowering students as transformative agents in a rapidly changing world.

In the intricate tapestry of our ever-evolving world, where complexities abound and change unfurls with unyielding speed, the amalgamation of values, critical thinking, and moral reasoning within the realm of language and literature education emerges as an imperious necessity. For it is through this harmonious integration that we fortify the minds of our students, bestowing upon them the indispensable tools to traverse the labyrinthine paths of multifaceted ethical quandaries, while shaping them into steadfast guardians of our shared global community. This article highlights the connection between language, literature, critical thinking, moral reasoning, and values integration in education. Through language and literature, teachers can incorporate values education in their classroom, which can benefit not only individual students but also society as a whole. Theoretical frameworks such as character education, moral education, and values education provide the basis for this approach. The researcher recommends strategies for incorporating values integration in language and literature curricula, including the use of literary texts that promote critical thinking and moral reasoning skills. The limitations of this approach, including the challenges in assessing the effectiveness of values integration and potential resistance from some stakeholders, are also discussed. Ultimately, this article aims to call on educators to take a proactive role in promoting values integration in language and literature education, guided by the principles of Jesus Christ and the belief that education is not only about acquiring knowledge but developing character as well. Moreover, this concept has a significant and lasting impact on students' overall development that highlights the importance of values and moral reasoning needed to live a better life.

Rivers across the globe experience and respond to changes within the riparian corridor. Disturbance of the riparian corridor can affect warmwater, intermediate, and coldwater streams, which can negatively influence instream physical structure and biological communities. This study focused on assessing the influence of riparian habitat on instream structure within the Whitewater River, a coldwater stream system within an agricultural watershed in southeastern Minnesota, USA. Twenty variables (riparian, n = 9; instream, n = 11) were measured at 57 sites across three forks of the Whitewater. Canonical correlation detected significant associations between riparian and instream variables across the river forks, and indicated that wider riparian buffers, more bank grass and shrubs, longer overhanging vegetation, limited bare soil and more rocks on banks were significantly associated with increased instream cover, high levels of coarse substrates with reduced embeddedness, increased pool habitats, and reduced fine sediments. In contrast, excessive fine sediments, lack of riffle habitat, reduced coarse substrates, and high width to depth ratios indicative of impaired instream habitat were associated with narrow riparian buffers and high percentages of bare soil on banks. Riparian corridors have the capacity to enhance and protect physical instream habitat and overall ecosystem health when managed properly. Wide, grassy riparian corridors with stable banks, overhanging vegetation, and limited shade from trees should protect and/or enhance instream physical habitat, providing the structural diversity favored by aquatic communities.

Hox gene clusters are crucial in Embryogenesis. It was observed that some Hox genes are located in order along the telomeric to centromeric direction of the DNA sequence: Hox1, Hox2, Hox3…. These genes are expressed in the same order in the ontogenetic units of the Drosophila embryo along the Anterior-Posterior axis. The two entities (genome and embryo) differ significantly in linear size and in-between distance. This strange phenomenon was named Spatial Collinearity (SP). Later, it was observed that, particularly in the Vertebrates, a Temporal Collinearity (TC) coexists: first is Hox1 expressed, later Hox2 and even later Hox3,…,. According to a Biophysical Model (BM), pulling forces act at the anterior end of the cluster while a cluster fastening applies at the posterior end. Hox clusters are irreversibly elongated along the force direction. During Evolution, the elongated Hox clusters are broken at variable lengths thus split clusters may be created. An Empirical Rule was formulated distinguishing development due to a complete Hox cluster from development due to split Hox clusters. BM can ‘explain’ this Empirical Rule. In a spontaneous mutation where the cluster fastening is dismantled, a minimal pulling force can automatically shift the cluster inside the Hox activation domain. This cluster translocation can probably explain the absence of Temporal Collinearity in Drosophila.

Inflammation is a vital aspect of the body's immune response, appearing either as an acute reaction that swiftly develops (e.g., after injury or during an infection) or as a chronic condition that persists over a prolonged period, often linked with autoimmune disorders or cancer. This study introduces potential TNF-alpha inhibitors investigated by Molecular Docking investigation. They are Dactolisib with a binding energy of -10.1 kcal/mol, Nilotinib with -9.6 kcal/mol, Eltrombopag with -9.7 kcal/mol, Radotinib with -9.6 kcal/mol, and Nilotinib again with -9.7 kcal/mol. Additionally, Hypericin exhibited a notable binding energy of -9.9 kcal/mol, suggesting its potential as a natural TNF-alpha inhibitor. While these computational results are preliminary, they provide a foundation for further biological studies to confirm their efficacy. The significance of these findings extends to the scientific community, offering potential applications in the field of inflammatory diseases.

The peculiar physicochemical features of deep eutectic solvents (DESs), in particular their tunability, make them ideal media for various applications. Despite their ability to solubilize metal oxides, their use as rust removers from valuable substrates has not yet been thoroughly investigated. In this study we chosen three known DESs, consisting of choline chloride and acetic, oxalic or citric acid for evaluating their ability to remove corrosion products from a cellulose-based material as linen fabric and two different lithotypes, as travertine and granite. The artificial staining was achieved by placing a rusty iron grid on their surfaces. The DESs were applied by means of cellulose poultice on the linen fabrics, while on the rusted stone surfaces with a cotton swab. Macro- and microscopic observations, colorimetry and SEM/EDS analysis were employed to ascertain the cleaning effectiveness and the absence of side effects on the samples after treatment. Oxalic acid-based DES was capable of removing rust stains from both stone and cellulose-based samples, while choline chloride/citric acid DES was effective only on stone specimens. The results suggest a new practical application of DESs for the elimination of rust from lithic and cellulosic substrates of precious and artistic value.

Wound healing is a process through which skin maintains itself. Once a wound occurs, the inflammatory and proliferative stages are instigated in reaction to injury. It is established that wound restorative comprises four stages including haemostasis, inflammation, proliferation, and remodeling. The amelioration of wound healing is very challenging as tumors can develop at the site of chronic injury. There are numerous plants, plant extracts and plant based natural products were widely used by tribal communities from ancient times for the treatment of cuts, burns, scars, burns and wounds. The therapeutic potential of these plants is recognized due to the presence of phytomolecules such as phenolic compounds, flavonoids, triterpenoids, saponins, tannins, alkaloids and glycosides. The plant used for the treatments of wound healing includes Achillea millefolium, Andrographis paniculata, Boswellia sacra, Calendula officinalis, Crocus sativus, Curcuma longa, Ehretia laevis, Ehretia microphylla, Glycyrrhiza glabra, Malva sylvestris, Rosmarinus officinalis and Salvia officinalis. This assemblage comprises the structures of phytomolecules isolated from the different extracts of these plants, mechanistic insights and important key findings responsible for wound healing. The mechanistic insights involved in wound healing are similar to cytotoxic, anti-inflammatory and antioxidant agents such as ROS generation, DNA fragmentation and western blotting. This review article is an effort to bridge the gaps in the prevailing literature and thus offers gigantic scope for researchers and academicians betrothed in validation of the customary claims and development of safer and efficient and worldwide recognized natural potential candidates as drugs for healing of wounds, burns and cuts.

The purpose of this study is to examine the potential of the sacred groves and natural sites for tourism in local communities in Nigeria. Three communities in Inyi town were chosen as case studies: Umuome, Enugwu-Inyi, and Alum, using a purposive sampling technique. An ethnographic data collection method was adopted, using in-depth interviews and direct observation. Grove locations were located and mapped using a geographic information system (GIS) throughout the research area. The secondary data was obtained from scholarly journals. The findings showed that the sacred groves and natural sites had exceptional value for cultural and eco-tourism. There are not many works that have studied the tourism dimension of the sacred groves in the study domain. The implication of the study is that tourism aids in the preservation of the sites' integrity. The research helps the government and policymakers to adopt a policy that promotes tourism in the sacred groves. This research is important to researchers at the universities and research institutions that are seeking to carry out a related research. Conclusively, utilizing the area for tourism is a superior conservation alternative.

Pronematus ubiquitus (McGregor) is a small iolinid mite that is capable of establishing on tomato plants. Once established, this mite has been shown to control both tomato russet mite, Aculops lycopersici (Tryon) (Acari: Eriophyidae) and tomato powdery mildew (Oidium neolycopersici L. Kiss). In the present study, we explored the nutritional value of various food sources in the laboratory. First, we assessed the reproduction of two food sources that P. ubiquitus can encounter on a tomato crop: tomato pollen and powdery mildew. In a second laboratory experiment, we evaluated the nutritional value of two types of prey mites: the astigmatid Carpoglyphus lactis L. (Acari: Carpoglyphidae) and the tarsonemid Tarsonemus fusarii (Acari: Tarsonemidae). Powdery mildew and C. lactis did not contribute to the reproduction, whereas tomato pollen and T. fusarii did allow egg-laying. However, Typha angustifolia pollen was a superior food source in both experiments. In a greenhouse trial on individual caged tomato plants, we evaluated the impact of pollen supplementation frequency on establishment of P. ubiquitus. Here, a pollen addition frequency of every other week was required to allow populations of P. ubiquitus to establish.

Biodiesel has caught the attention of many researchers because it has great potential to be sustainable fossil fuel substitute. Biodiesel has non-toxic and renewable nature and has been proven to emit less amount of environmentally harmful emissions such as hydrocarbons (HC), and carbon monoxide (CO), as well as smoke particles during combustion. Problems related to global warming caused by greenhouse gas (GHG) emissions could also be solved by utilizing biodiesel as a daily energy source. However, the expensive cost of biodiesel production, mainly because of the cost of natural feedstock, holds the potential of biodiesel commercialization. The selection of natural sources of biodiesel should be made with observations from economic, agricultural, and technical perspectives to obtain one feasible biodiesel with superior characteristics. This review paper presents a detailed overview of various natural sources, their physicochemical properties, as well as the performance, emission, and combustion characteristics of biodiesel when used in a diesel engine. The recent progress in studies about natural feedstocks and manufacturing methods used in biodiesel production were evaluated in detail. Finally, the findings of the present work reveal that transesterification is currently the most superior and commonly used biodiesel production method compared to other methods available.

Background: Twitter is a major tool for communication in emergencies such as natural disasters. This online social network allows the user to produce content, and it is not designed exclusively for news releases, as opposed to other service providers. Aim: The aim of this study is to investigate Twitter uses in natural disasters and pandemics. Methods: The included studies reported the role of Twitter in natural disasters. The studies that report in settings other than the natural disasters (such as man-made disasters) and other social media were excluded. Electronic databases for a comprehensive literature search including MEDLINE, Web of Science, CINAHL, PsycINFO, Cochrane Register of Controlled Trials (CENTRAL) and EMBASE were used to identify the records that match the mentioned inclusion criteria published till May 2020. The study characteristics were extracted from the qualified studies including year of publication, findings, and geographical location of the study conduct. A narrative synthesis for this literature review was used. Results: The search identified 822 articles of which 780 articles were removed, 256 were not available, 311 papers were not relevant, 16 were duplicated articles, and 197 were non-related to the emergencies. 45 articles met the selection criteria and were included in the review. eleven themes were found in the narrative synthesis including early warning, disseminating information and misinformation, advocacy, personal gains, assessment, various roles of organizations, public mood, geographical analysis, charity, using influencers, and trust. Conclusions: It is recommended that influential individuals be identified in each country and community before disasters occur so that the necessary information can be disseminated in response to disasters. Preventing the spread of misinformation is one of the most important issues in times of disaster, especially pandemics. Disseminating accurate, transparent, and prompt information from relief organizations and governments can help. Also, analyzing Twitter data can be a good source for understanding the mental state of the community, estimating the number of injured people, estimating the points affected by natural disasters, and modeling the prevalence of epidemics. Therefore, various groups such as politicians, the government, non-governmental organizations, aid workers, and the health system can use this information to plan and implement interventions.

Molecular engineering research is the fundamental way and the only way for the development of biomaterials. Based on molecular engineering, the biocompatibility of natural conformation and polymer biomaterials was studied. In this paper, we discuss that natural conformation is the basis of protein biological function, and that the synergistic action of peptide chain and side group is the motive force for protein to construct natural conformation and complete biological function. On the basis of the influence of the adsorption of polymer biomaterials on the natural conformation of proteins, the relationship between biocompatibility of biomaterials and protein conformation is further explained. Studies have shown that bismuth molecular materials can only be applied in the market and have their functionality if they have good biocompatibility. Therefore, the biocompatibility evaluation of new materials has important practical significance.

The operation of electricity and natural gas transmission networks in the U.S. are increasingly interdependent, due to the growing number of installations of gas fired generators and the penetration of renewable energy sources. This development suggests the need for closer communication and coordination between gas and power transmission system operators in order to improve the efficiency and reliability of the combined energy system. In this paper, we present a co-simulation platform for examining the interdependence between natural gas and electricity transmission networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We analyze the value of day-ahead coordination of power and natural gas network operations and show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and renewable energy sources. Results show that day-ahead coordination contributes to a reduction in curtailed gas during high stress periods (e.g., large gas offtake ramps) and a reduction in energy consumption of gas compressor stations.

Deadwood represents a source of nutrients, carbon and water for metabolism within forest ecosystem. Nutrients are mobilized due to the decomposition of wood, which is a long-term process that can be best studied by analysing environmental data on a temporary scale. Our study provides physico-temporal data on the downed logs of three major tree species in European temperate forests: Abies alba Mill., Fagus sylvatica L. and Picea abies (L.) Karst. Time since death was obtained using tree censuses (repeated for 40 years) and dendrochronology for each single downed log, the oldest being 75 years old. Standard laboratory methods were used for the determination of wood density and moisture changes. F. sylvatica was decomposed rapidly in the initial phase – mass loss was 50% during the 5 years after death, while A. alba and P. abies lost 13% and 16%, respectively. Downed logs of F. sylvatica contained 391 kg of water per m³, while these of P. abies 279 kg. A log-transformed linear model was created that shows the dependence of time since death on mass loss. According to the model, F. sylvatica had the shortest total decomposition time (39 years), followed by A. alba (58 years) and P. abies (86 years).

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).

Accurate estimation of livestock carrying capacity (LCC) and implementing an appropriate actual stocking rate (ASR) is key to sustainable management of grazing adapted alpine grassland ecosystems. Reliable determination of aboveground biomass is fundamental to these determinations. Peak aboveground biomass (AGBP) captured from satellite data at the peak of the growing season (POS) is widely used as a proxy for annual aboveground biomass (AGBA) to estimate LCC of grasslands. Here we demonstrate limitations of this approach and highlight the ability of POS in the estimation of ASR. We develop and trail new approaches that incorporate remote sensing phenology timings of grassland response to grazing activity, considering relations between biomass growth and consumption dynamics, in efforts to support more accurate and reliable estimation of LCC and ASR. Results show that based on averaged values from large-scale studies of alpine grassland on the Qinghai-Tibet Plateau (QTP), differences between AGBP and AGBA underestimate LCC by about 31%. Findings from a smaller-scale study that incorporate phenology timings into estimation of annual aboveground biomass reveal that Haibei alpine meadows were overgrazed by 11.5% during the study period from 2000-2005. The methods proposed can be extended to map grassland grazing pressure by predicting LCC and tracking ASR, thereby improving sustainable resource use in alpine grasslands.

Papain-like protease PLpro, a domain within a large polyfunctional protein nsp3, plays key roles in the life cycle of SARS-CoV-2, being responsible for the first events of cleavage of a polyprotein into individual proteins (nsp1-4) as well as for the suppression of cellular immunity. Here, we developed a new genetically encoded fluorescent sensor, named PLpro-ERNuc, for detection of PLpro activity in living cells using translocation-based readout. The sensor was designed as follows. A fragment of nsp3 protein was used to direct the sensor on the cytoplasmic surface of endoplasmic reticulum (ER) membrane, thus closely mimicking the natural target of PLpro. The fluorescent part included two bright fluorescent proteins - red mScarlet I and green mNeonGreen, separated by a linker with the PLpro cleavage site. Nuclear localization signal (NLS) was attached to ensure accumulation of mNeonGreen into the nucleus upon cleavage. We tested PLpro-ERNuc in the model of recombinant PLpro expressed in HeLa cells. The sensor demonstrated expected cytoplasmic reticular network in the red and green channels in the absence of protease, and efficient translocation of the green signal into nuclei in the PLpro-expressing cells (14-fold increase of the nucleus/cytoplasm ratio). Then, we used PLpro-ERNuc in the model of Huh7.5 cells infected with SARS-CoV-2 virus, where it showed robust ER-to-nucleus translocation of the green signal in the infected cells 24 h post infection. We believe that PLpro-ERNuc represents a useful tool for screening PLpro inhibitors as well as for monitoring virus spread in a culture.

The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high frequency of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, with a focus on image processing and machine learning techniques and concepts. The paper explores various algorithms, including traditional computer vision methods and advanced deep learning approaches, highlighting their strengths and limitations. Additionally, the paper examines the integration of these technologies with other sensing modalities such as infrared sensors, Wi-Fi, and RFID. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The paper also analyzed the current GDPR regulation that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field.

The aim of this study is to examine an efficient and environmental friendly process for hydrolyzing starch, an industrial-derived by-product of the potato processing industry, and to use the produced hydrolysates as substrates for methane production. A full factorial experiment was designed and the optimal concentrations of FeSO4·7H2O, H2O2 and temperature were determined obtaining a yield of 99.5%, with a total carbohydrates content equal to 28.65 g, when the input parameters were at their high level: FeSO4·7H2O 1.00g/L, H2O2 0.51 g/L and temperature 70.0oC. The hydrolysis kinetics were also examined revealing that temperature was the most important factor among the three optimization parameters. Furthermore, biodegradability tests were carried out to measure the methane production potential from the produced hydrolysates. The specific methanogenic activity was reached 0.669 g CODCH4/gVSS-day proving the effectiveness of the hydrolysis process and highlighting the potential of this food manufacturing by-product for bioenergy production. A preliminary cost analysis showed that a small investment for utilizing the starch in an existing wastewater treatment facility of a potato processing industry becomes profitable before the end of the 3rd year obtaining a NPV 37.5% higher than the NPV of the current utilization scenario.

In recent years, since flood disasters have brought immeasurable losses to the city, it is urgent to prevent and solve the flood of stagnant water. Considering the shortage of real-time and accuracy of hydrological analysis, Opencv technology is used in this paper to process the obtained data in real time. For improved Yolov5, BoTNet and GAMAttention Transformer are used to improve Yolov5 to enhance its ability of recognition and prediction to better identify surface gathered water. The prediction rate of the improved Yolov5 is 7.1% higher than that of Yolov7 and 1.7% higher than that of Yolov5.After that, contour preprocessing of the image is carried out through the cropping technology of the identification frame to eliminate relatively unstable factors. The principle of binocular distance measurement is used to measure the three-dimensional coordinates of the actual distance, better constrain the contour proportion of the picture, and then the Opencv technology is used to get the outline of the water, and HSV is combined with better color processing pictures for the identification of the water and contour generation, and the area is obtained to correspond to the corresponding parameters of flood to provide important help in flood prevention and storm drainage.

Abstract. Indonesia has developed several superior soybean varieties. Each soybean variety has different physical and chemical properties that can affect processed products and soybean quality. The purpose of this study was to determine the physical, chemical, and functional properties of three superior early-maturing soybean varieties. The research was conducted in Maros, Indonesia in 2022 by planting the Dega-1, Grobogan, and Gepak Kuning varieties and repeated them 6 times. The yields of the three soybean varieties Dega-1, Grobogan, and Gepak Kuning were taken at 1 kg each and 3 repeated, then further analysis of chemical compounds is carried out in the laboratory. The results showed that for physical characteristics, the largest seed size was in Dega-1 (18.57 g per 100 seeds) and the smallest in Gepak Kuning (8.29 g). The highest bulk density of soybean seeds was in the Grobogan and Dega-1 varieties (0.79 kg per m3) and the lowest was in Gepak Kuning (0.77 kg per m3 ), and the lowest number of damaged seeds was in Dega-1 (6.67%) and the highest in Grobogan (11.33). %). Chemical characteristics, highest protein content in Gepak Kuning (39.72%) and lowest in Dega-1 (36.62%), lowest fat content in Dega-1 (12.63%) and highest in Gepak Kuning (14.49%), highest phosphorus content in Gepak Kuning (0.59%) and the lowest was in Grobogan (0.53%), the highest calcium content was in Gepak Kuning (0.40%), and the highest FFA value was in Dega-1 (0.93) and the lowest in Gepak Kuning (0.81). Based on physical properties, Dega-1 is the best compared to the other two soybeans. Meanwhile, based on the size of starch granules, Gepak Kuning gave the best results. For chemical characteristics, in general, GepakKuning has the best quality compared to Grobogan and Dega-1. It can be concluded that Gepak Kuning has the most potential to be developed in supporting food and non-food products, especially in Indonesia.

Episcopic imaging using techniques such as High Resolution Episcopic Microscopy (HREM) and its variants, allows biological samples to be visualized in three dimensions over a large field of view. Quantitative analysis of episcopic image data is undertaken using a range of methods. In this systematic review, we look at trends in quantitative analysis of episcopic images and discuss avenues for further research. Papers published between 2011 and 2022 were analyzed for details about quantitative analysis approaches, methods of image annotation and choice of image processing software. It is shown that quantitative processing is becoming more common in episcopic microscopy and that manual annotation is the predominant method of image analysis. Our meta-analysis highlights where tools and methods require further development in this field, and we discuss what this means for the future of quantitative episcopic imaging, as well as how annotation and quantification may be automated and standardized across the field.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chro-matography (LC), but choosing the ‘correct’ pipeline isn’t straight-forward. This tutorial, in con-junction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide explor-atory analysis in order to inform decision-making regarding costly and time-consuming down-stream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Data could be in many forms like text, image, audio, video and many others. Amongst these data, processing image is of important concern even with sophisticated technologies. Generally, images consume more storage space and processing time. This work aims in attaining a novel image encryption strategy that consumes very less computational time with maximum security. The idea is to split the image into blocks and rearrange the pixels in diagonal fashion to achieve confusion. Next, the blocks are combined to form sub images and one block of the sub image is subjected to Secure Hash Algorithm (SHA 512 bits). The SHA bits are XORed with the remaining blocks of the sub image. Finally, the entire image is encrypted using Advanced Encryption Standard (AES). Decryption process is the exact reverse of encryption process. The computational overhead is very low, and security is efficient. The results are compared with existing encryption techniques.

The changes in consumer preferences and the increasingly competitive global market have demanded that food entrepreneurs engage in innovative value-added activities. The date is a delicatessen fruit known by its content of active compounds (e.g., dietary fiber and antioxidants) and its biological activity which has a vast potential in the design of new products such as bioactive ingredients, sugar substitutes, dietary supplements, functional foods, among others. In the current paper, innovative approaches to the value addition to date fruits and their processing by-products have been reviewed from recent high-quality scientific works. New processes such as ultrafiltration and hydrothermal treatments are shown as a useful alternative to obtain differentiated date-based ingredients (e.g., fiber concentrates, sap syrups, and date powders). Moreover, the use of date fruits and their byproducts as natural sources of value-added active compounds in the preparation of dairy, meat, and bakery and cereal products is also presented.

The study proposes laser shock peening without a coating of high strength ultrafine bainitic steel to mitigating the fatigue failures for automotive and structural engineering applications. Laser pulse density of 2500 pulses/cm2 (75% overlapping) was optimised based on the induced residual stresses for employing the wide range of characterisations. The roughness and topographic results showed that surface roughening was controlled by tuning the laser pulse density. The High-Resolution X-ray Diffraction analysis confirmed the lattice misorientation resulting peak shift and the trend towards martensite phase transformations. The electron microscopic micro/nanostructure analyses revealed the grain refinement features such as nano-twins, micro shear bands and shear cells. The work hardening depth and nanomechanical properties were significantly enhanced. A fully reversed (R= -1) high-cycle fatigue tests extended the lifespan by an average of fifteen times than the untreated. Also, it has potential to repair the structural components effectively.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

The editorial explores the realm of emerging translational research in neurology and psychiatry, providing insights into various subjects, including neurodegenerative disorders and innovative therapeutic methods. The main objective is to study neurological and psychiatric disorders, such as Alzheimer's disease, with a particular emphasis on techniques for neural regeneration and repair. The editorial highlights the importance of taking a comprehensive approach to studying and treating complex neurological and psychiatric conditions. This involves thoroughly reviewing research papers on the causes, development, biomarkers, and treatment options for these conditions. The editorial emphasizes the necessity of using multidisciplinary and integrative methods to gain a deeper understanding and improve the treatment of these conditions. The development of an animal model for recurrent primary dysmenorrhea showcases the innovative techniques employed in the investigation of gynecological disorders, illustrating the capacity of translational research to connect fundamental scientific knowledge with practical medical applications. The cooperative endeavors of researchers and clinicians emphasized in this editorial lay the foundation for revolutionary advancements that hold the potential to enhance outcomes for individuals afflicted with neurological and psychiatric disorders globally.

Micro and small wood-processing enterprises represent the heart of the European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the pro-vision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is nonparametric, linear-programming-based approach, commonly used to analyse the efficiency of organizational units. This method allows estimating the economic efficiency of a certain eco-nomic system without assumptions about the functional form between resources and products. Furthermore, DEA determines the efficiency frontier and gives results of whether an enterprise, i.e., a Decision Making Unit (DMU) is efficient or not. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterpris-es in the EU countries and reveal the hidden inputs that facilitate efficiency generation. The eco-nomic efficiency evaluation was carried out on the basis of the official statistical data for the mi-cro and small wood-processing companies in the EU member states for the period 2015-2020 by performing a two-stage DEA analysis. The data used were standardized by value per employee. In addition to the first stage of DEA, fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically-efficient entities. The only contextual variable affecting the eco-nomic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase of the investments.

TiNbZrTa alloys are promising for multidisciplinary applications, such as refractory and biomedical purposes due to their high thermal stability and non-toxicity. Hardness and elastic modulus are among the key features for their adequate industrial applications. The influence of porosity and Ti/Ta ratio were investigated on TiNbZrTa alloys produced by three different processing routes, i.e., (i) blend element and posterior press and sintering (BE + P&S), (ii) mechanical alloying with press and sintering (MA + P&S), and (iii) arc melting and casting. Atomic diffusion was improved during processing, resulting in lower porosity in the following order: casting < MA + P&S < BE + P&S. Ta offered limited atomic diffusion due to its high melting point compared to other constituent elements. Thus, the total porosity of alloys increased with increasing the Ta contents, i.e., by lowering the Ti/Ta ratio. However, the Ti/Ta ratio did not considerably affect the bonding energy or the elastic modulus. Hardness was increased significantly in dense alloys compared to the porous ones. However, porosity and Ti/Ta ratio did not show a clear trend in hardness among the porous alloys.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long term illness. Out-of-the-body, non-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments has the potential to produce precise readings with comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81±0.97 and 0.86±0.61 beats/minute compared to a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore, reducing the data size by 40-140 times and requires 33% less power to process and transfer data. Further, we show that events hold enough information to reconstruct the original waveform, retaining pulse, and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies providing better user experience.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at the regional and local level. Climate and environmental databases are of critical importance to achieving sustainability goals (SDGs) and for the efficient planning and implementation of suitable mitigation measures: Available databases can serve municipalities as a vital starting points to determine requirements, prioritize resources and allocate investments under consideration of commonly tight budget restrictions. High-quality geo, climate and environmental data are now available – data from remote sensing, i.e. Copernicus services will be of crucial importance. Forward-looking approaches exist to using such data to derive forecasts for urban planning process optimization for municipal administrations. On municipal level, however, the existing data have so far only been used to a limited extent, since there are no practical tools for urban planning that can be used to merge and meaningfully combine remote sensing data with local data and to further process and apply in municipal planning processes. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services and procedures, such as the development of practice-oriented technical tools that acquire various remote sensing and in-situ data sets for validation and further processing.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

Speech recognition, or automatic speech recognition (ASR), is a technology designed to convert spoken language into text using software. However, conventional ASR methods involve several distinct components, including language, acoustic, and pronunciation models with dictionaries. This modular approach can be time-consuming and may influence performance. In this study, we propose a method that streamlines the speech recognition process by incorporating a unified recurrent neural network (RNN) architecture. Our architecture integrates a convolutional neural network (CNN) with an RNN and employs a connectionist temporal classification (CTC) loss function. Key experiments were carried out using a dataset comprising 576,656 valid sentences, using erosion techniques. Evaluation of the model performance, measured by the word error rate (WER) metric, demonstrated remarkable results, achieving a WER of 2%. This approach has significant implications for the realm of speech recognition, as it alleviates the need for labor-intensive dictionary creation, enhancing the efficiency and accuracy of ASR systems, and making them more applicable to real-world scenarios. For future enhancements, we recommend the inclusion of dialectal and spontaneous data in the dataset to broaden the model's adaptability. Additionally, fine-tuning the model for specific tasks can optimize its performance for targeted objectives or domains, further enhancing its effectiveness in those areas.

A reduced ability to work with information contained in a text is usually registered in low-achieving students. One important internal factor influencing reading comprehension concerns the child’s executive functioning. The current study investigated whether the domain-specific intervention ExeFun-READ targeting language abilities related to reading comprehension and executive functioning in primary school children would be effective in improving their scholastic performance in their reading domain and executive functioning. ExeFun-READ is designed to address the relations between domain-specific versus thinking-skill-oriented (domain-general) instruction. A child’s active learning is focused on semantic, phonetic, morphological and syntactical comprehension of linguistic material. In total, 151 students attending grade four from seven elementary schools took part in the project. The study concerned a pretest-intervention-posttest experimental design with three conditions: the experimental condition, an active, and a passive control group. To assess the children’s level of EF, the Delis–Kaplan Executive Function System test battery was used; to assess children’s language ability in reading domain, the Cognitive Abilities Test (the verbal battery, Thorndike &amp; Hagan, 1986 [1]), was used. In the current study, the intervention led to improved language abilities related to reading comprehension, but in terms of executive functioning the improvement only extended to switching fluency. Regarding the effects of the ExeFun-READ intervention on children’s reading abilities, specified as language abilities related to reading comprehension, significant improvements were found in vocabulary, completion of sentences, and classification of terms in the group of children that received the ExeFun-READ intervention.