Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using the self-attention mechanism and linear layers. Experimental results show that the strategy we designed can effectively combine the advantages of the linear layer and self-attention mechanisms, compensating for the shortcomings of the Transformer. Additionally, we conducted an empirical study to explore the effectiveness of the linear layer and sparse self-attention mechanisms in trajectory prediction.

This article explores the relationship between Wang Yangming's philosophy and the new quality productivity, revealing how the new quality productivity, while inheriting the core ideas of Yangming's philosophy, achieves a higher level through innovation and development. The new quality productivity emphasizes the integration and innovation of knowledge, technology, and information, characterized by knowledge-driven innovation, information technology support, sustainable green development, and global collaborative development. The fusion of Yangming's philosophy and the new quality productivity holds significant theoretical and practical implications.

This systematic literature review meticulously explores the complexities of technological innovation and entrepreneurship, with a particular focus on the strategies and management practices involved in new product development from an interdisciplinary standpoint. By conducting an extensive analysis of 323 key articles from 11 top-tier journals published between 2020 and 2023, the review delineates the current research trajectories within these fields, underscoring significant scholarly interest and development. The study particularly emphasizes the emergence of artificial intelligence-generated content technology since 2022 and critically assesses its influence on innovation processes and product development practices among both nascent entrepreneurs and well-established corporations. This emergent technology's role in shaping the competitive landscape and its potential to drive significant shifts in market dynamics are thoroughly examined.

One of the relevant goals in forging technologies is simplifying the existing process and developing new processes that can retain the original or very close to the original dimensions and billet shape after the end of the deformation cycle. This study attempts to develop a forging equipment design that provides alternating cyclic loading; the alternate cyclic forging (ACF) method is proposed. To preliminary evaluate the ACF and study the plastic deformation behavior, numerical simulation via finite element modeling (FEM) was conducted using DEFORM-3D. The main attention was paid to the metal flow characteristics, effective strain and stress distribution, and the forging load. The ACF results in non-uniform metal flow with the formation of diagonal and vortex flows were demonstrated. Maximum effective strains and stresses zones are localized at two points of the billet contact with the surface of the tooling container and at two points of the interface between the punches. It was also assumed that reducing the contact area between the punch and the billet using separate semi-cylindrical punches can reduce the forging load. Preliminary finite element analysis (FEA) results of the proposed process after four successive steps of the deformation cycle confirmed the feasibility of its general implementation.

This article discusses the application of thermal quality control methods for plant tissues. The purpose of the work is to determine the optimal parameters of thermal impact on a spherical-shaped plant control object, ensuring the detection of surface and subsurface defects. The authors of the work proposed mathematical models of the temperature field for a spherical body with defects and a flat sample when exposed to the thermal influence of a pulsed source. As a result of the use of mathematical models, the thermophysical characteristics of plant tissues of varying degrees of disease damage were obtained, which made it possible to simulate the temperature field of the control object and obtain an image of classified tissues. The developed recommendations made it possible to select the optimal parameters of the thermal effect on the test object.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

The isotopic and nuclear techniques discussed, namely: Fallout Radionuclide, Fingerprint, and Isotope Hydrology, are linked to the evaluation of soil and water degradation processes. A traditional literature review was conducted to gain the most comprehensive understanding of both the current state of the art and the subject matter, with the aim of proposing a hypothesis that integrates the synergistic convergence of these techniques in order to offer results that are more reliable. A case study is presented as a synthesis of the preceding discussion.

There is a growing trend among consumers to seek out natural foods and products with natural ingredients. This shift in consumer preferences had a direct impact on both food and pharmaceutical industries, leading to a focus of scientific research and commercial efforts to meet these new demands. The aim of this work is to review recent available scientific data on foods of interest, such as artichoke, gooseberry, and polygonoideae plants, as well as olive oil and red raspberries. Interestingly, the urgency of solutions to the climate change emergency has brought new attention to by-products of grapevine bunch stem and cane, which have been found to contain bioactive compounds with potential health benefits. There is a pressing need for a faster process of translating scientific knowledge from the laboratory to real-world applications, especially in the face of the increasing societal burden associated to non-communicable diseases (NCDs), environmental crises, the post-pandemic world, and ongoing violent conflicts around the world.

This study examines stress distributions in adhesive joints under various loading and temperature conditions. Finite element analysis (FEA) was employed to compute the peel and shear stresses at the adhesive interface and bondline mid-section. Dependency analysis shows that mid-section peel stress significantly impacts the experimental shear strength of SLJs more than shear stress. This insight highlights the need to carefully analyze peel stress and bending moment factors. The analytical solutions proposed by Goland and Reissner were analyzed with modifications by Hart-Smith and Zhao. Hart-Smith’s approach performed more effectively, especially when the adhesive layer thickness (t_a) is 0.5 mm and the overlap length to thickness ratio (c/t_a) is ≥20. FEA revealed stress distributions at the adhesive/adherend interface and bondline mid-section. DP490 adhesive joints exhibited lower stresses than EA9696. Temperature variations significantly affected joint behavior, particularly above the adhesive’s glass transition temperature (T_g). Both EA9696 and DP490 adhesive joints displayed distinct responses to stress and temperature changes. The parabolic and biquadratic solutions for functionally graded adhesive (FGA) joints were compared. The biquadratic solution consistently yielded higher shear and peel stress values, with an increase ranging from 15% to 71% compared to the parabolic solution at various temperatures because of larger gradient of the Young's modulus distribution near the overlap ends. The ratio of peak peel stress to peak shear stress suggests selecting an adhesive with a superior peel strength or primarily reducing the peak peel stress by functionally grading is advisable, particularly if the adhesive is brittle. Comparison of stress distributions emphasize the importance of selecting adhesives based on stress type, temperature, and solution methods in optimizing adhesive bonding applications. These findings provide valuable insights for thermomechanical applications where thermal stimuli may be used for controlled debonding.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

In this paper, based on the cumulated damage generated by the cyclical application of the testing vibration profile, a methodology to determine the reliability index of the analyzed component is given. In the analysis, the testing time, component geometry, applied weight, and material resonant frequency are all of them considered by using a dynamic, and amplitude factor. Then, from the generated vibration response data of the analysis, the corresponding bending stress is determined for each one of the rows of the testing profile. And by the cyclical application of the whole testing profile, the corresponding damage is cumulated to each profile cycle until the cumulated damage is D=1. Finally, the reliability that each one of the cumulated blocks (cyclical profile application) is determined by considering, the random vibration is a stress variable. Thus, we use the cumulative damage model with the inverse power law stress/life relationship to determine the two parameter Weibull distribution used to estimate the reliability index of each cumulated block. As a numerical application, we analyze a cantilever aluminum beam Al 6061-T6.

Seventy-seven percent of Irish primary school children do not meet recommended physical activity (PA) guidelines [1]. The Daily Mile (TDM) is a primary school initiative designed to increase children’s PA output. This study evaluates TDM’s perceived effect on Irish primary school children’s health-related metrics. A mixed-methods study, comprising two data collection phases, used the RE-AIM framework to evaluate TDM [2]. Phase one involved teachers, principals and classroom assistants (n = 191) from TDM-registered schools completing a questionnaire. Two TDM-registered schools participated in phase two where, (i) interviews were conducted with each principal, (ii) a teacher sub-sample (n = 4) participated in a focus group and (iii) a child sub-sample (n = 14) participated in separate focus groups. TDM was perceived to positively impact markers of children’s health, including their PA behaviour, physical fitness and social wellbeing. Inclement weather (66.5%) and a lack of time (56.5%) were reported as the top-ranked implementation barriers. Moreover, TDM’s repetitive nature often left children feeling disinterested and resulted in some teachers modifying the initiative to maintain engagement levels. Maintaining the long-term implementation of TDM and its health benefits in primary schools may require bespoke amendments to the original format to preserve children’s engagement.

Introduction: The COVID-19 pandemic challenged people with spinal cord injury (SCI) regarding a variety of mental and physical issues. New challenges may arise as the effects of the pandemic continue. The objective of the present study was to explore the social participation of Canadians with SCI during the second wave of COVID-19. Methods: Participants with SCI from two Canadian provinces (Quebec and British Columbia) were interviewed. Results: Eighteen participants completed interviews. Facilitators of social participation remain similar since the first wave of COVID-19, such as the use of technology, help received by relatives and the use of delivery services to obtain groceries and other essentials. Obstacles to mobility due to winter conditions and lack of considerations related to COVID-19 public health measures specific to wheelchair users were also discussed by participants. Conclusions: People with SCI perceived participation restrictions, little changes in life habits, and uncertainty about the future during the second wave of COVID-19. The unique living conditions of people with SCI, ability to adapt life habits, and the lived experiences of people with SCI may have contributed to an overall resilience during the pandemic. Adaptive families, social contacts and technology made a difference during the pandemic.

The state of Oaxaca, Mexico obtained the “Pluma” designation of origin in 2022 and has stood out for the production of specialty coffee. Within its process, roasting is one of the stages that contributes to the physical, chemical and sensory quality of the coffee bean. Therefore, the microstructure and elemental chemical composition of special medium-roasted Coffea arabica L beans from six locations with this denomination were analyzed using Scanning Electron Microscopy. The average values of roughness Rz (25.93±2.69-34.18±1.15 and 13.85±1.66-21.85±6.63) and Ra (5.20±0.47- 8.27±0.82 and 6.55±0.94-11.53±1.65) were obtained at 400 and 2000x, respectively. The roasted Coffea arabica L. beans from MAP and HUA are the ones with the greatest roughness and both are located at a lower altitude compared to the rest. PLU and PTO roasted beans showed the highest physical and structural integrity. In all grains, 6C (66.48-81.14%), 8O (18.86-28.69%) and only 20Ca (4.5%) were found on the outside. This research constitutes a standard of authenticity and microstructural and elemental quality of Coffea arabica L. beans with designation of origin "Pluma".

This study investigates the spatial and temporal dynamics of Paomo restaurants in Xi'an's central urban area from 2012 to 2023, leveraging Point of Interest (POI) data through a series of sophisticated analytical techniques. Methods including nearest neighbor distance, kernel density estimation, and band set statistical analysis were employed to thoroughly examine the evolutionary patterns and key drivers influencing the spatial distribution of these eateries. Our results highlight a notable trend of expansion from the central city to the periphery, characterized by significant spatial agglomeration. Initially concentrated in the city center, the distribution of Paomo restaurants has progressively shifted towards multiple distinct cores, reflecting the dual influence of urban policy initiatives and the dynamics of market competition. Furthermore, the research identifies several pivotal factors shaping this spatial distribution, particularly transportation access, residential quality of life, and the availability of commercial services. These findings demonstrate the profound effects of urban spatial reconfiguration and socioeconomic development on the specialized catering industry. The insights gleaned from this analysis offer valuable guidance for urban planners and policymakers, providing strategic recommendations for the spatial planning of specialty catering services to optimize urban layouts and foster sustainable economic progress.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

Groundwater pollution in intensive horticultural areas is becoming an increasingly important problem. Therefore, this study evaluates nitrogen (N) fertilization in open field processing pepper crop in Extremadura, Spain. Previous research on the effect of N on sweet peppers grown in greenhouses is abundant, but data on outdoor cultivation, especially considering variety and site influences, are lacking. Field trials were conducted in 2020, 2021 and 2022 to determine the opti-mum N fertilizer rate for processing peppers, with the aim of reducing environmental impacts such as nitrate leaching while maintaining crop yields. Different N rates were applied in the trials, ranging from no N fertilizer to 300 kg N/ha. Meas-urements included crop yield, biomass, intercepted photosynthetically active radiation (PAR), and canopy cover. The study also developed a critical nitrogen curve (CNC) to determine the minimum N concentration required for optimal growth. The results showed that applying more than 200 kg N/ha did not significantly increase yield. Nitrogen-free treatments resulted in earlier fruit maturity, concentrating the harvest and reducing waste. In addition, excessive N application led to envi-ronmental problems such as groundwater contamination due to nitrate leaching. The study concludes that outdoor pepper crops in this region can achieve optimal yields with lower N rates (around 120 kg N/ha) compared to current practices, thereby reducing environmental risks and fertilizer costs. It also established relationships between biomass, canopy cover, and N uptake to improve fertilization strategies. These data support future crop modelling and sustainable fertili-zation practices.

In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

This study investigates the impact of single prolonged stress (SPS), a model of post-traumatic stress disorder (PTSD), on cardiovascular responses, hypothalamic paraventricular nucleus (PVN) activity, and vascular function to elucidate the mechanisms linking traumatic stress to hypertension. Although SPS did not directly cause chronic hypertension in male Sprague Dawley (SD) rats, it induced acute but transient increases in blood pressure and heart rate and significantly altered the expression of hypertension-associated genes, such as vasopressin, angiotensin II type 1 receptor (AT1R), and FOSL1 in the PVN. Notably, mitochondrial reactive oxygen species (mtROS) were predominantly elevated in the pre-autonomic regions of the PVN, colocalizing with AT1R- and FOSL1-expressing cells, suggesting that oxidative stress amplifies sympathetic activation and stress responses. SPS also triggered vascular inflammation, evidenced by increased mRNA levels of pro-inflammatory cytokines (TNFα and IL1β) and inducible nitric oxide synthase (iNOS) in the aorta, and impaired vascular reactivity to vasoconstrictor and vasodilator stimuli, reflecting compromised vascular function. These findings suggest that SPS sensitizes neuroendocrine, autonomic, and vascular pathways, creating a state of cardiovascular vulnerability that could pre-dispose individuals to hypertension when exposed to additional stressors. Understanding these mechanisms provides critical insights into the pathophysiology of stress-related cardiovascular disorders and underscores the need for targeted therapeutic interventions that address oxidative stress and modulate altered PVN pathways to mitigate the cardiovascular impact of PTSD and related conditions.

Cultural heritage is a fundamental part of the society’s identity, and its conservation is of great relevance as it transcends time and memory. To minimize its deterioration, cultural heritage has traditionally undergone diverse preservation and maintenance treatments and the attention of researchers to develop new and innovative methods for state diagnosis and protection treatments has been increasing in the recent decades. Therefore, in this study, a literature review using a bibliometric analysis and LDA methodology was conducted to systematically map scientific research outputs on cultural heritage conservation and protection. Data was retrieved from the Scopus database and the annual publications, countries, most-cited publications, authors, institutions, and keywords have been comprehensively analyzed, leading to the detection of research trends. Findings show an increasing number of studies in this field in the last decades, particularly since 2010. Italy, home to the largest number of UNESCO heritage sites, is the most prolific country. Most of the studies are related to metal, paper and stone as substrate to be protected. Significant progress has been made in understanding the deterioration processes through precise diagnosis and the development of innovative treatments for protection. In this sense, latest trends have been detected such as the use of non-invasive techniques for diagnosis and the use of nanotechnology and nature-based treatments for the conservation treatments.

CoCrFeNiMn high-entropy alloy coatings were prepared on compacted graphite iron（CGI） through plasma cladding to strength the CGI substrate. The effects of heat treatment temperatures and times on the microstructure and mechanical properties of the CoCrFeNiMn coatings were investigated. After heat treatment, a single FCC phase was remained in the coating as compared to the deposited one, with smaller dendrites, larger dendritic spacing and more Cr-rich compounds precipitated between dendrites. The coatings’ microhardness was increased after heat treatment. After heat treated at 660°C for 90 minutes, the coating had the highest microhardness of 563±6.9 HV0.2, the lowest frictional coefficient of 0.71 and the smallest wear mass loss of 1.12 mg, and it had the best wear resistance.

This study investigates the anthropogenic activities, water quality, and ichthyofaunal diversity of the Navotas Riverine Ecosystem. It examines the anthropogenic influences, analyzes the physicochemical and microbiological properties of water, identifies fish species, evaluates the water quality index, and determines ichthyofaunal diversity using various indices. Water samples and fish collections were conducted from January to April 2024 at three sites. Key findings indicate that water quality parameters such as temperature (23.20-37.5°C), dissolved oxygen (6.22-7.84 mg/L), phosphate concentration (0.55-3.72 mg/L), and fecal coliform (350-160,000 MPN/100 mL) failed to meet DENR standards for Class C water. The TDS (1153.33-10700 ppm) and conductivity (17653.33-21333.33 µS/cm) were also elevated, indicating significant pollution. The study identified low ichthyofaunal diversity, with six native and one invasive fish species. A total of 269 fish were collected: 161 from Site 1, 46 from Site 2, and 62 from Site 3. The most abundant species was Scatophagus argus (19% occurrence), while the invasive Sarotherodon melanotheron ranked third (17% occurrence). Notably, pH (r = 0.753) correlated highly with nitrate concentration. S. melanotheron significantly correlated with pH (r = 0.602, p = 0.019) and nitrates (r = 0.555, p = 0.031). Lutjanus argentimaculatus also had significant correlations with pH (r = 0.527, p = 0.039) and nitrates (r = 0.651, p = 0.011). Conversely, S. argus had a negative moderate correlation with temperature (r = -0.529, p = 0.039) and conductivity (r = -0.536, p = 0.036). These correlations suggest that the water parameters significantly influence the diversity and abundance of fish in the river. The study concludes that the river's water quality is heavily influenced by anthropogenic activities which negatively affect ichthyofaunal diversity. Recommendations include continuous monitoring, gut and heavy metal analysis of fish, assessment of avifauna diversity, and strict implementation of conservation regulations.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

The rapid integration of Information and Communication Technology (ICT) is transforming the traditional electrical grid into a {\em Smart Grid}. Smart grids enable two-way communication and improved monitoring and control between utilities and customers. However, due to its heterogeneous nature, public exposure, and weak security at low-powered devices, the Smart Grid has vulnerabilities to various malicious threats, adversaries, and cyber attacks, which may affect cost and service availability. Additionally, when the systems' confidentiality, integrity, or availability are compromised, the resulting fallout can threaten national security and have cascading effects on human lives. Given the extreme consequences of an attack, smart-grid technology must be thoroughly tested for correct operation and security {\em before} it is deployed. As a result, vulnerability testing of smart grids, not only for correctness but for security purposes, has been the subject of numerous studies by academics, government agencies, and private companies. This paper reviews the vulnerabilities associated with the smart grid and spotlights simulation as the vulnerability testing methodology conducted in recent pertinent research works. It also presents various security aspects of the smart grid, including grid applications, system and network infrastructure and components, cyber threats and attacks, simulation, and different mitigation techniques. Finally, we analyze the gaps in the current research works, focusing on simulation. We briefly present a real-time simulation testbed that mimics customer behaviour and integrates hardware in the loop to apply attack methods, analyze vulnerabilities and risk mitigation associated with the smart grid system, and propose future work to improve the current framework.

The article presents data on research of processes of obtaining concentrated phosphoric acid from Karatau phosphate rock. The research of purification of wet-process phosphoric acid by the method of liquid extraction with organic solvents is given. The influence of magnesium salts and sulfuric acid as impurities on the distribution factor of phosphoric acid is determined. It is shown that with increasing the initial concentration of phosphoric acid from 5.38 to 25.61% P2O5, the distribution factor at 25°C increases from 0.178 to 0.436 for n-butyl alcohol, from 0.06 to 0.183 for isoamyl alcohol, and from 0.120 to 0.320 for tributyl phosphate. Increasing the temperature from 25 to 60°C had little or no effect on the distribution factor. It was determined that the distribution factors of phosphoric acid depend on the initial concentration of phosphoric acid in the presence of sulfuric acid. It is shown that phosphoric acid is extracted better with n-butanol. Extraction of phosphoric acid with n-butanol on the principle of counterflow with subsequent azeotropic distillation of the solvent allows to obtain relatively pure and highly concentrated (56-63% of P2O5) phosphoric acid. The process of dehydration of phosphoric acid in the presence of sulfuric acid in the mixture was studied.

Foodborne illnesses can be infectious and dangerous and most of them are caused by bacteria. Some types of common food-related bacteria exist widely in nature and pose a serious threat to both humans and animals, and can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacteria detection are of paramount importance in food-safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical method have been widely used in food-safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical method for various bacteria detection (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)) considering different foods types, analytical performances and the reported limit of detection (LOD) are discussed. It is found that the bacteria type and food sample type contribute significantly to the analytical performance and LOD. Detection by LFIA has higher average LOD (24 CFU/ml) than detection by electrochemical method (12 CFU/ml) and PCR (6 CFU/ml). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. LFIA with gold and iron nanoparticles are prominent in the majority of articles of 26 CFU/ml and 12 CFU/ml respectively. Electrochemical method reveals that the average LOD is highest for cyclic voltammetry (CV) of 18 CFU/ml), followed by electrochemical impedance spectroscopy (EIS) of 12 CFU/ml and differential pulse voltammetry (DPV) of 8 CFU/ml. LOD usually decreases when replicate number increases until it remain unchanged. Exponential relations with (R²>0.95) between LOD of Listeria in milk by LFIA and electrochemical method with replicate number have been obtained. Finally, the review discusses the challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve the analytical performance for bacterial detection.

Tailings generated by mining account for the largest proportion of global waste from industrial activities. Copper is relatively uncommon world-wide, with low concentrations in sediments and waters, yet is very elevated around mining sites. On the Keweenaw Peninsula of Michigan, USA, jutting out into Lake Superior, 140 mines extracted native copper from the Portage Lake Volcanic Series, part of an intercontinental rift system. Between 1901-1932, two mills at Gay (Mohawk, Wolverine) sluiced 22.7 million metric tonnes (MMT) of copper-rich tailings (stamp sands) into Grand (Big) Traverse Bay. Here we examine: 1) 3D coastal elevation and reef bathymetry, 2) particle dispersal, 3) copper concentrations and leaching, plus 4) shoreline toxicity. About 10 MMT of tailings created a migrating coastal beach that stretches 7 km from the Gay pile to the Traverse River Seawall. Another 10 MMT are moving underwater on the coastal shelf, threatening Buffalo Reef, an important lake trout and whitefish breeding ground. Aerial photos, ALS (plane) LiDAR, and relatively inexpensive UAS (drone) surveys characterize shoreline structure and aid initial remediation. Because sands (natural beach quartz, stamp sand basalt) are silicates of similar density, particle dispersal is similar and challenging. However, stamp sand beaches contrast greatly with natural sand beaches in both physical profile and chemistry. Dispersing particles retain copper, and release toxic concentrations. Leaching is elevated by exposure to high DOC and low pH waters. Field experiments, shoreline seining, and benthic sampling confirm serious impacts on aquatic biota, supporting tailings removal.

In the former mining district of La Carolina (southern Spain), waste materials were accumulated without first waterproofing the substrate. Additionally, these mining dams were abandoned without any type of restoration, conditions that present a significant risk of contaminating the surrounding soils and waters. Prior to carry out the necessary works for isolating these residues, morphological characterization of the contact with the base substrate is needed. For that purpose, in recent years, different indirect study techniques have been used, among which electrical to-mography stands out. In this work, a less common geophysical technique, magnetometry, both in its magnetic and gra-diometric modalities, is used as an alternative method to analyse the geometry and structure of a mining dam. The results obtained with this methodology are compared with the results provided by electrical techniques, as well as with direct data from mechanical surveys and aerial photo-graphs prior to waste deposition. The magnetic anomaly curves obtained with geometric models generated with the Mag2dc program were also analysed. This comparative study shows the use-fulness of magnetic prospecting in characterizing the base geometry of these mining structures and therefore the interest in its application to environmental characterization studies.

Ballistic helmets are individual armor equipment designed to protect a soldier's head from projectiles and fragments. Although very common, these helmets are responsible for several causalities due to their low ballistic resistance and large back face deformation to stop the projectile. Therefore, to enhance helmet performance, studies have focused on the development of new materials and new ballistic protection solutions. The purpose of this study was to develop and evaluate a new ballistic solution using thermoplastic-based matrices. The first matrix was based on high-density polyethylene (HDPE). The second matrix was based on HDPE modified with ex-foliated montmorillonite (MMT). The main manufacturing processes of a thermoplastic-based ballistic helmet are presented, along with its ballistic performance, according to the National Institute of Justice (NIJ) standard 0106.01 and an investigation of its failure mechanisms via a non-destructive technique. All the helmets resulted in level III-A ballistic protection. The postimpact helmets were scanned to evaluate the back face deformation dimensions, which revealed that the global cone deformation was deeper in the HDPE than in the HDPE/MMT. The failure analysis revealed an overall larger deformation area in the HDPE and HDPE/MMT helmet delamination zones in regions with a large radius of curvature than in zones with the lowest radius, which is in accordance with previous simulations from the literature.

This study explored the effectiveness of natural additives in prolonging the life of cut roses. A Completely Randomized Design (CRD) was used, with four different treatments: distilled water (control), a sucrose solution, moringa leaf extracts, and a combination of sucrose and moringa. The roses were observed over eight days to assess various factors such as leaf drop, stem rotting, bent necks, petal drop, flower condition, and petal color. The results showed that the combined treatment of sucrose and moringa led to the highest number of leaf drops, with 15 leaves dropping by day five. Distilled water resulted in 12 dropped leaves, moringa extract caused four leaves to drop, and sucrose had the least impact with only two leaves dropping. The combination treatment also caused the most significant stem rotting (32.75 mm) and the most bent necks (14 roses). In contrast, roses treated with sucrose or moringa separately had better outcomes, with the least bent necks and minimal leaf drops. For petal drop, the combined treatment again had the worst results, with eight petals dropping, followed by distilled water with six petals. The overall flower condition was poorest with the combined treatment, scoring 1 (completely open/damaged), while roses treated with sucrose or moringa separately had better scores, indicating they remained fresher. Petal color was also most affected by the combined treatment, which resulted in a light brown color, whereas the separate treatments maintained a darker red hue. Hence, while sucrose and moringa are beneficial when used individually, their combination negatively impacts the longevity and appearance of cut roses.

A human eye has about 120 million rod cells and 6 million cone cells. This huge number of light sensing cells inside a human eye will continuously produce a huge quantity of visual signals which flow into a human brain for daily processing. However, the real-time processing of these visual signals does not cause any fatigue to a human brain. This fact tells us the truth which is to say that human-like vision processes do not rely on complicated formulas to compute depth, displacement, and colors, etc. On the other hand, a human eye is like a PTZ camera. Here, PTZ stands for pan, tilt and zoom. We all know that in computer vision, each set of PTZ parameters (i.e., coefficients of pan, tilt and zoom) requires a dedicated calibration to determine a camera’s projection matrix. Since there is an infinite number of PTZ parameters which could be produced by a human eye, it is unlikely that a human brain stores an infinite number of calibration matrices for each human eye. Therefore, it is an interesting question for us to answer, which is to say whether simpler formulas of computing depth and displacement exist or not. Moreover, these formulas must be calibration friendly (i.e., easy process on the fly or on the go). In this paper, we disclose an important discovery of a new solution to 3D projection in a human-like binocular vision system. The purpose of doing 3D projection in binocular vision is to undertake forward and inverse transformations (or mappings) between coordinates in 2D digital images and coordinates in a 3D analogue scene. The formulas underlying the new solution are accurate, easily computable, easily tunable (i.e., to be calibrated on the fly or on the go) and could be easily implemented by a neural system (i.e., a network of neurons). Experimental results have validated the discovered formulas.

Cotton is the most ubiquitous and profitable fiber with diverse industrial and domestic applications. Grown in over 100 countries, it has a global market value of about $40 billion and employs over 350 million people from fields to textile mills, contributing about 7% of total labor-force recruitment in developing economies. Cotton has an indeterminate growth habit and an extensive tap root system affected by soil physicochemical and environmental conditions. There is a consensus among experts that conventional cotton cultivation still has a long way to attain sustainability, which is essential if cotton will maintain its competitive edge over other natural and synthetic fibers like hemp, polyesters, and rayon. Despite several efforts already committed to growing cotton sustainably, sustainability has eluded cotton cultivation globally because of the intense farm inputs (freshwater, pesticides, and heavy-duty equipment) needs, especially in developing economies. Some of the technological advancements towards achieving the goal of sustainable cotton cultivation include the development of new varieties, improved irrigation and mulching and precision agriculture techniques, application of remote sensing and Unmanned Aerial Systems (UAS) combined with image processing, and the introduction of autonomous and multi-purpose robotic platforms for growing and harvesting cotton. This review attempts to evaluate the successes already achieved by stakeholders in moving cotton towards sustainable production and identify areas where efforts are still needed to reach sustainable production and improved profitability goals for cotton with projections for future research directions.

Porous α-Al2O3 ceramics is a highly demanded material for multitude applications such as filters, substrates, biomedicine materials, etc. Despite the availability of the raw materials, a challenge of their technology is in a high energy budget caused by the sintering above 1500 °C. For a cold sintering processing (CSP) of ceramics, the lowering of α-Al2O3 sintering temperature is one of the most urgent challenges at the background of its rapid development. The current paper for the first time demonstrates the solution of the mentioned problem by the CSP of α-alumina ceramics in a presence of pure water as a transient liquid. The manufactured materials were examined by XRD analysis; the evolution of their microstructures during the CSP was revealed by SEM, and the porosity evaluated by the Archimedes method. The ceramics with an open porosity up to 36 % was produced at 380-450 °C, 220 MPa in 30 min. An increase in the pressure was found to impede α-Al2O3 formation from γ-AlOOH. The development of the microstructure was discussed within the frameworks of the dissolution-precipitation model and the homogenous nucleation. The results of the SEM study pointed to the coalescence of γ-AlOOH grains during the CSP.

Background/Objectives: The aim of the narrative review was to identify information on the impact of technological interventions (such as telehealth and mobile health) on the health promotion of cardiac patients from diverse populations; Methods: Online databases of PubMed, and Cochrane Library were searched for articles in the English language regarding technological interventions for health promotion in cardiac patients. In addition, a methodological quality control process was conducted. Exclusion was based on first reading the abstract and then the full manuscript confirming content was not related to cardiac patients and technological interventions; Results: 11 studies were included in the review after quality control analysis. The sample size reported in these studies ranged from 12 to 1424 subjects. In 8 studies mobile phones, smartphones and apps were used as mHealth intervention with tracking and texting component; 2 studies used videoconferencing as a digital intervention program, while one study focused on using physical activity trackers; Conclusions: This review highlights the digital divide as individuals with limited eHealth literacy and lack of technological knowledge are not motivated or able participate in these interventions. Finding methods to overcome these barriers are important and can be solved to some extent by providing the technology and technical support.

Multifunctional coatings based on Sn-Ni materials with and without titanium oxide nanoparticles (TiO2NPs) incorporation were prepared using the electrochemical deposition technique at 70°C. TiO2NPs were dispersed in the electrolyte bath, and their influence on both surface texture, crystalline phase, and properties was investigated. Various techniques such as X-ray phase analysis (XRD), scanning electron microscopy (SEM), and energy dispersive X-ray microanalysis (EDX) were used to characterize the prepared coatings. The anti-corrosion effectiveness of the coatings was assessed using potentiodynamic polarization curves and electrochemical impedance spectroscopy in an artificial sweat medium, while the bactericidal activity of the composite coatings (the ability to induce cell death) was evaluated in accordance with ISO 27447:2009 test. The influence of the TiO2NPs on the composition, structure and properties of the deposited coatings was established. Particular attention was paid to the relationship between the anticorrosive and bactericidal properties of the coatings and their structure and composition. The synergistic effect of the various properties was investigated and discussed.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). Multi-modal BCIs have been able to gain significant traction given the potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities including Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Single-Unit Activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature highlighting recent advances, challenges, and future directions for each of these modalities.

In this work, an attempt is made to the theoretically substantiate the experimentally known facts of the influence of halogen concentration on the catalytic properties of the neodymium-based Ziegler–Natta system. Based on the structural and thermochemical data obtained using modern methods of quantum chemistry, the process of the 1,3-butadiene cis-1,4-polymerization under the model active centers of the neodymium Ziegler-Natta catalysts with different content of chloride ions was studied. Results are presented that explain the increase in the cis-stereospecificity and activity of the polymerization system with an increase in the content of the chloride ions in the neodymium catalytic system. There were established the reasons for the decrease in the concentration of active centers relative to the introduced Nd(III) with an excess of chloride ions and the occurrence of the anti-syn isomerization as a source of the formation of the trans-1,4-structures in the cis-1,4-polybutadiene.

This study addresses the gap in methodological guidelines for Neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices including the Engagement Index (EI) and Beta/Alpha ratio alongside subjective ratings. A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, the study successfully demonstrated the effectiveness of the paradigm. Results indicate that both Beta/Alpha ratio and EI are sensitive to changes in attentional demands, with Beta/Alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to accurately monitor attention fluctuations and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining high attention level is crucial.

This study investigated the adaptability of Dunaliella salina to different salinity levels, with emphasis on growth, pigment concentration, and desalination potential. It was found that among the 21 salinity levels, salinity 75 produced consistently favorable results in cell count (13.08 x 10³ ± 1.41 x 10³ cells/mL), dry biomass (2.46 ± 0.06 g/L), pigment content (chlorophyll a = 97.5 ± 0.1 µg/L, chlorophyll b = 123.6 ± 0.3 µg/L), and desalination (9.32 ± 0.47 reduction). Therefore, salinity 75 was selected for the final trial (scale-up), which revealed unanticipatedly high cell counts (58.96 x 10³ ± 535.22 cells/mL), with dry biomass weight being statistically different (higher) than the expected (4.21 ± 0.02 g/L) (p<0.0001), most likely due to high cell count and energy reserve storage for high salinity adaption in the form of bio-compounds. Pigment growth continued (chlorophyll a = 95.4 ± 2.2 µg/L, chlorophyll b = 128.1 ± 5.1 µg/L), indicating pigment production under salt stress. Notably, desalination did not occur in this stage, possibly due to the necessity for a bigger initial inoculate, prolonged exposure or bioaccumulation becoming the prevailing mechanism over desalination. Nevertheless, the trial highlights D. salina's strong adaptation to various salinity levels. This suggests a promising future in halophyte research, particularly in understanding the mechanisms that prevent salt accumulation in cells and how to overcome this barrier. Additionally, these results suggest that microalgae could be a viable resource in saline-rich environments unsuitable for conventional agriculture, promoting industrial adaptation to adverse conditions.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication, the concept of the Internet of Vehicles (IoV) combined with Artificial Intelligence (AI) and big data technologies promotes the vision of an Intelligent Transportation System (ITS). By enabling a comprehensive data exchange platform, ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). Specifically, D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder-noise-decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. Then, BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing cus-tom-developed photogrammetry software, high-resolution images are processed to generate de-tailed 3D models, subsequently converted to STL files with precision. Adaptive meshing in COMSOL Multiphysics, controlled through a MATLAB API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL's advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

There are various approaches to managing polyethylene terephthalate waste, including methods for recycling PET. However, at present, only the cleanest wastes that can already be efficiently recycled using known methods are considered in the scientific literature. The study aims to look at other forms of polyethylene terephthalate waste that pose a challenge: low quality PET flakes, polyester tire cord, PET dust and prepolymer waste. The waste was characterized by FTIR spectroscopy, viscosimetry, differential scanning calorimetry, laser diffraction and sieve analysis. The findings describe the composition and properties of various forms of polyethylene terephthalate waste. Low quality PET flake waste and polyethylene terephthalate dust have been shown to be suitable for all chemical recycling methods. When recycling waste polyester tire cord, it is difficult to completely separate the rubber, so its presence in the final product must be taken into account. The most promising way to process a prepolymer is its depolymerization by hydrolysis, alcoholysis or glycolysis to produce monomers for the synthesis of PET, similar in properties to the primary one.

The production of a ton of steel in an integrated steel plant generates of approximately 1 ton of ferrous slag. A great part of ferrous slags is recycled in civil and road construction etc., but a huge amount is still dumped. Recycling ferrous slag as cement precursor was considered the upcycling route. Regulation (EU) 2024/1252 requires the Member States to provide information, until November 2026, on the critical raw materials (CRM) amount in their secondary resources and on quantification methods used for. This study addresses two synergic objectives: ensuring the reliability of XRFS results and screening for CRM in ferrous slags based on XRFS outcomes. The main novelty of the paper is the way of ensuring the reliability of the XRFS results based on weighted arithmetic mean and on the maximum likelihood approach. Secondly, the XRFS measurements carried on ferrous slags demonstrate that they contain significant amounts of CRM like Ba, Sr, Y, etc. XRFS cannot detect light CRM. Our preliminary LIBS measurements on ferrous slags disclosed the presence of Li and Be. The drawbacks of the XRFS technique impose further research to develop an integrated XRFS, LIBS and XRD procedure for comprehensive and trustworthy CRM screening in extractive waste piles.

This review details the extraction, characterization and utilization of seaweed-derived biopolymers for future packaging applications. The review is contextualized within the broader scope of the challenge of plastic pollution and the current urgent need for more sustainable packaging materials. Macroalgae (or seaweed) has been highlighted as a promising source of biopolymers, most commonly sodium alginate, agar and carrageenan, for reasons such as a rapid growth rate and decreased environmental impact when compared to terrestrial plant-life. Extraction methods detailed include the traditional solvent-based extraction to more sustainable developments such as ultrasound assisted extraction, microwave assisted extraction and bead milling. The review additionally presents the characterization techniques most pertinent in determining the applicability of these biopolymers in packaging applications. Properties of key importance to the development of sustainable packaging materials such as thermal properties, mechanical strength, barrier properties and biodegradability are highlighted in comparison to conventional petroleum-based plastics. This review concludes by realistically identifying the challenges faced by implementing seaweed-based biopolymers into packaging structures, such as cost-effectiveness, scalability, performance while suggesting future directions to mitigate these issues and improve the commercial viability of these materials for the packaging industry.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

This research investigates the energy consumption of buildings managed by traditional Building Management Systems (BMS) and proposes the integration of Internet of Things (IoT) technology to enhance energy efficiency. Conventional BMS often suffer from significant energy wastage and safety hazards due to sensor failures or malfunctions. These issues arise when building systems continue to operate under unknown conditions while the BMS is offline, leading to increased energy consumption and operational risks. The study demonstrates that integrating IoT systems with existing BMS can substantially improve energy efficiency in smart buildings. The research involved designing a system architecture prototype, performing MATLAB simulations, and real-life case study which revealed that IoT devices are effective in reducing energy waste, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems and lighting. Additionally, an auxiliary bypass system was incorporated in parallel with the IoT system to enhance reliability in the event of IoT system failures. Preliminary findings indicate that the integration of IoT systems with traditional BMS significantly boosts energy efficiency and safety in smart buildings. This integration offers a promising solution for advancing energy management practices and policies, thereby improving both operational performance and sustainability in building management.

Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of Precision Agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication, and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes low-cost “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.

The volatility and uncertainty associated with the high proportion of wind and PV output in the new power system significantly impact power and energy balance, making it challenging to accurately assess the risks related to renewable energy abandonment and supply guarante. Therefore, a probabilistic power and energy balance risk analysis method based on distributed robust optimization is proposed. Firstly, the affine factor and the flexible ramp reserve capacity of thermal power are combined to establish a probabilistic index, which serves to characterize the risk associated with power and energy balance. Drawing upon the principles of conditional value at risk theory, the risk indexes of load shedding power and curtailment power under a certain confidence probability are proposed. Secondly, the probability distribution fuzzy sets of uncertain variables are constructed using the distributionally robust method to measure the Wasserstein distance between different probability distributions. Finally, aiming at minimizing the operation cost of thermal power, the risk cost of power abandonment and the risk cost of load shedding, a distributed robust optimal scheduling model based on flexible ramp reserve of thermal power is established.

This study presents a novel approach combining computer vision with topology optimization for additive manufacturing. Using specialized photogrammetry software, we capture high-resolution images of the design domain to create precise 3D models through detailed scanning. These models are converted into STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, managed by a custom MATLAB 2023. This integration allows optimal mesh resolution and accuracy in analyses. We applied this method to design a concrete pillar for 3D printing, aiming for a 75% volume reduction to enhance material efficiency and stability, crucial for extraterrestrial environments. The design, scanned with a 360-degree camera array, informed the MATLAB-based topology optimization process. Combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element solver solver, we explored material-efficient configurations. The results show significant volume reduction, particularly in the central design area, optimizing material use while maintaining structural stability. The optimization algorithm demonstrated rapid, stable convergence to near-optimal solutions within about 20 iterations, highlighting the method’s efficiency and robustness in complex design optimization.

This article examines the diagnosis of the Agricultural Innovation System (SIA) in the department of Tolima, Colombia, as part of a project led by the Colombian Ministry of Agriculture and Rural Development and the Technological University of Pereira (TUP), which also evaluated the National Agricultural Innovation System (NAIS) in regions such as Chocó, Bolívar, and Meta. A methodology was used that involved a survey with 58 questions on a Likert scale to 300 participants, evaluated with congruence and relevance indexes, identifying key variables in the operation of the Agricultural Innovation System (AIS). A survey was also applied to actors of the National Innovation System for Higher Education in Agriculture, A survey was also applied to map the participating actors in the regional agricultural innovation systems of Tolima to identify gaps in extension, innovation, development, sustainability, ICT, education and training. Among the conclusions, it stands out that the associativity subsystem presents the lowest lag and is close to a score of two on the scale of gaps, evaluation carried out among the five subsystems including extension, ICT, environmental aspects, and pub-lic policy. The department of Tolima shows low performance in innovation and ICT, while the extension is less lagging. In Chaparral there is a general lag, with innovation being the worst evaluated subsystem. Rovira obtains high scores in most subsystems, although innovation and ICT have lower scores. At the national and regional levels, innovation is low, and articulation among NARS actors is insufficient, which highlights the need to promote good practices, strategies, and projects.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

This study addresses the gap in research on the application of thermochromic polymers (TPs) in food packaging and their potential for real-time temperature monitoring, aiding in the assessment of food quality and shelf-life. TPs exhibit a visible color change in response to temperature varia-tions. A comprehensive systematic review (SR) across multiple engineering peer-review databases using predefined terms was conducted. Additionally, international patent databases were inves-tigated using the same predefined terms. Independent experts reviewed the methodology to identify and address potential biases. A total of 288 eligible articles and 922 patents were identified. After a duplicate selection and extraction process according to the inclusion criteria, four related full-text publications were selected from the initial 288 articles, and five relevant patents were selected from the 922 patents. The qualitative review suggests that TPs hold significant promise as food packaging materials due to their unique physical properties. The study concludes that TPs offer valuable properties for the food packaging industry, meriting further investigation to exploit their benefits fully.

Graphene was synthesized through a two-step pyrolysis method using waste candlenut (Aleurites moluccanus) shells as the precursor. Cerium (Ce)/graphene composites were prepared via an impregnation technique. The resulting graphene and Ce/graphene were characterized using various analytical methods, including Scanning Electron Microscopy with Energy-Dispersive Spectroscopy (SEM-EDS), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscopy (TEM), Thermo Gravimetric Analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, Cyclic Voltammetry (CV), and Linear Sweep Voltammetry (LSV). The bio-carbon produced predominantly exhibited a graphene structure with flat carbon morphology and an interlayer distance of 0.33 nm. This structural information is supported by XRD data, which shows a broad and weak peak at 2θ = 26° corresponding to the C (002) plane, indicative of graphene presence. FTIR, XPS, and Raman spectroscopy further confirmed the presence of graphene through the detection of Csp2 aromatic bonds and the characteristic D, G, and 2D peaks. Notably, the performance of cerium can be enhanced by the incorporation of graphene, attributed to the large surface area and chemical interactions between Ce and graphene. Consequently, candlenut-derived graphene shows potential as a supportive material for modifying the properties of cerium, thereby opening avenues for various advanced applications, such as sustainable and high-performance energy storage systems.

The work presents the results of studying dilute aqueous solutions of commercial Ln(NO3)3 · xH2O salts with Ln=Ce-Lu using X-ray diffraction (XRD), IR spectroscopy, X-ray absorption spectroscopy (XAS: EXAFS/XANES) and pH measurements. As a reference point, XRD and XAS measurements for characterized Ln(NO3)3 · xH2O microcrystalline powder samples were performed. The local structure of Ln-nitrate complexes in 20 mM Ln(NO3)3 · xH2O aqueous solution was studied under total external reflection conditions and EXAFS geometry was applied to obtain high-quality EXAFS data for solutions with low concentrations of Ln3+ ions. Results obtained by EXAFS spectroscopy showed significant contraction of the first coordination sphere during the dissolution process for metal ions located in the middle of the lanthanide series. It was established that in Ln(NO3)3 · xH2O solutions with Ln=Ce, Sm, Gd, Yb (c=134, 100, 50 and 20 mM) there are coordinated and, to a greater extent, non-coordinated nitrate groups with bidentate and predominantly monodentate bonds with Ln ions, the number of which increases upon transition from cerium to ytterbium. Cross-relationships between the concentration of solutions, pH value and antimicrobial activity with the type of Ln = Ce, Sm, Gd, Yb were established, as well as the absence of biocidal properties of solutions with a concentration of 20 mM, except for Ln = Yb. The important role of experimental conditions in obtaining and interpreting the results was noted.

Lithium-ion batteries have gained immense popularity in electric vehicles(EVs) due to their high energy density, offering an extended driving range. This has made them the preferred choice in the automotive industry, driving the shift towards EVs. However, alongside their popularity, safety concerns have arisen. The main issues include the potential for thermal runaway leading to fires or explosions, which can occur due to overcharging, physical damage, or manufacturing defects. While safety features and rigorous testing aim to mitigate these risks, ongoing research and development focus on enhancing safety measures to further ensure the reliability and safety of lithium-ion batteries in EVs. This paper focuses on the development and deployment of a real-time monitoring dashboard for Li-ion battery health monitoring using STM32 Microcontroller within the framework of Industry 4.0. Leveraging advanced sensor technology and data analytics, the dashboard provides continuous, comprehensive insights into battery performance, including state of charge and temperature. Integrating with Industry 4.0 principles, such as IoT, the system enables predictive maintenance, optimizing production processes and ensuring efficient utilization of resources. The implementation showcases the integration of real-time monitoring tools for Li-ion batteries, aligning with the transformative paradigms of Industry 4.0 to enhance operational efficiency and reliability within industrial settings.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected was merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port's activities.

Technological equipment in quarries that extract and deliver aggregates for different uses operates in a predetermined flow depending on the type of rocks exploited and the dimensional characteristics imposed on the final products. In this context, the interruptions in operation required to replace high-wear parts (such as the teeth of excavators and bucket loaders) must be limited as much as possible through technological solutions to increase their service life. The evolution of the wear of the teeth of the quarry equipment that come into direct contact with the rocks was concretely established in the production process, in parallel with the wear values obtained by simulating the wear phenomenon in laboratory conditions, in order to validate the data collected during the operation of the machines. Preventive-repetitive maintenance, within the activities of reconditioning the worn surfaces of the teeth, through the charging process by manual electric welding with covered electrodes, was applied directly to the machine, which led to the shortening of the interruptions in operation necessary to replace these spare parts.

This narrative review synthesizes findings from empirical studies to explore the factors influencing the adoption of ChatGPT in higher education. The review highlights several critical determinants, both confirmed and debatable, of ChatGPT acceptance among students and educators. Previous studies have predominantly utilized theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Diffusion of Innovations (DoI) theory. Confirmed factors include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust, which significantly predict user attitudes and intentions toward adopting ChatGPT. Perceived usefulness and ease of use are core components influencing adoption positively. However, some factors have shown inconsistent impacts. While generally critical, effort expectancy and facilitating conditions vary based on context. Social influence, demographic characteristics (age, gender), extrinsic motivation, and technology readiness exhibit inconsistent effects, suggesting that intrinsic motivations may be more critical. Data collection primarily used online surveys with structured questionnaires, varying widely in sample sizes from small pilot studies to large-scale surveys and covering diverse academic disciplines. Practical implications suggest enhancing perceived usefulness and ease of use through training and technical support, building trust by addressing privacy concerns, and leveraging social influence to drive adoption. The review underscores the need to refine technology acceptance models to incorporate broader psychological and contextual factors, integrate intrinsic motivation theories, and adopt a systems thinking approach. Addressing limitations such as potential selection bias and the quality and scope of existing studies, future research can develop robust models and provide deeper insights into ChatGPT adoption dynamics in higher education, supporting its effective integration to enhance learning outcomes and foster educational innovation.

The article presents the results of research on the production of effective inorganic fillers and chemically resistant composite polymer-mineral materials based on industrial waste. The aim of the work is to develop the technological foundations for the production of inorganic fillers and highly filled filling compositions for use in chlorine-containing media. Materials with high resistance to hydrochloric acid are graphite and quartz sand. Thermal power plant (TPP) ash was used as an acid-resistant filler in the compositions. The simplex lattice planning method was used to determine the areas of compositions with the best properties. Two components were selected: formaldehyde rubber resin and mineral filler (TPP ash) as a binder. As the third component, hydrolytically active fillers were used - anhydrite, phosphogypsum and phosphorus production slag. A preliminary operation was performed to remove hygroscopic moisture from the filler.The influence of the nature of mineral fillers on the chemical stability and durability of the obtained composites is investigated. The nature of the interaction of mineral fillers with a polymer binder was determined, and chemical interaction of the hydroxyl groups of the filler and the methyl groups of the polymer took place. The mathematical planning method was used to optimize the chemical resistance and durability of the compositions of the studied substances. Based on the results of this study, optimal compositions of compositions, anhydrite, phosphogypsum and phosphoric slag were selected. These composites, with the highest possible degree of filling, have high target characteristics.

Rice, as a staple crop feeding billions, faces constant threats from various diseases jeopardizing global food security. Precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. Integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with better understanding of their functional roles in disease resistance. Improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can give a transformative impact on rice breeding for enhancing disease resistance.

Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to SPOT/VEGETATION (SPOT VGT). In past years, important changes have been implemented in the SYN VGT processing baseline. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle a continuous series of data products from the combined data archives of SPOT VGT (1998 – 2014), PROBA-V (2013-2020) and Sentinel-3 SYN VGT (from 2018 onwards) is available to users, consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. Spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and the intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for 3 distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have resulted in better quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive: a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

Long gone was the time when tumors were thought to be insular masses of cells, residing independently at specific sites in an organ. Now, researchers gradually realize that tumors interact with the extracellular matrix (ECM), blood vessels, connective tissues and immune cells in their environment, which is now known as the tumor microenvironment (TME). It is found that the interactions between tumors and their surrounding promote tumor growth, invasion and metastasis. The dynamics and diversity of TME cause the tumors to be heterogeneous and thus pose a challenge for cancer diagnosis, drug design and therapy. As TME is significant in enhancing tumor progression, it is vital to identify the different components in the TME. This review explores how different factors in the TME supply tumors with the required growth factors and signaling molecules to proliferate, invade and metastasis. We also examine the development of TME-targeted nanotheranostics over the recent years for cancer therapy, diagnosis and anticancer drug delivery system. This review further discusses the limitations and future perspective of nanoparticle based theranostics when used in combination with current imaging modalities like Optical Imaging, Magnetic Resonance Imaging (MRI) and Nuclear Imaging (PET and SPECT).

Seaports are critical hubs in global trade, facing increasing pressure to accelerate green transi-tions while navigating evolving economic and environmental landscapes. This research analyses the intricate relationship between port organizational ecosystem resilience and the successful implementation of green transitions, highlighting the challenges and opportunities in moving towards a more sustainable future. The research employed a combined focus group interview and participatory observation methodology to gather insights from experts. Analysis revealed that while technological advancements are a primary driver for greening port operations, a lack of managerial capacity is a significant impediment to the successful integration of these innova-tions. It was identified several challenges, including outdated infrastructure, cybersecurity threats, and the need for ongoing investments in new technologies, along with the need to foster a sustainable behaviour culture. Experts emphasized the importance of developing managerial competencies and continuous learning. The research also highlights the role of strong stake-holder partnerships, including collaboration with government agencies, industry associations, and environmental organizations. This research provides valuable insights for policymakers, port managers, and other stakeholders seeking to navigate the challenges for ensuring a sus-tainable future for the maritime sport organizational ecosystems and maritime industry.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

Causation concepts for work-related musculoskeletal disorders vary among authors and academic disciplines. Major causation concepts are single-event and repetitive motion. The aim of this short communication is to share findings from a recent survey, conducted by the U.S. Bureau of Labor Statistics of United States employers about work-related musculoskeletal cases, and more specifically, about whether the employers regard the cause of their employee’s musculoskeletal problem as being from a single exposure or from multiple exposures. Recommendations are offered for using terminology consistent with employer understandings.

Electrochemical Impedance Spectroscopy (EIS) is a powerful non-invasive technique used to characterize Lithium-ion cells. Its application allows to identify and track the evolutions of cell degradation processes within a short testing time. This review collects all the available works which have used EIS spectra either to characterize Li-ion cell degradation or to develop Electric Circuit Models (ECMs). The objectives of this work are: (i) to highlight the influence of different aging test conditions on the EIS spectra; (ii) to find the correlations between EIS spectra variations and the underlying degradation mechanisms and (iii) to list the available options to formulate ECMs from EIS spectra of aged cells. After an exhaustive analysis of the state-of-the-art, a critical review is presented to discuss the existing links between degradation mechanisms and the most reliable solutions to model them.

In tissue engineering scaffolds take the place of the natural extra cellular matrix (ECM). The natural ECM is the extracellular part of animal tissue that usually provides structural support to the animal cells in addition to performing various other important functions. The design aspect along with the choice of the material for the artificial scaffold is very crucial to cell differentiation, adhesion, proliferation, and the transport of the growth factors or other bio molecular signals. In addition to the material and design of the scaffolds, it is necessary to replicate the normal physiological situation if the scaffold has to function as an implant. The cells have to be located in the porous scaffold to form a three dimensional assembly. The article discusses the important factors to be considered while designing a scaffold for tissue engineering and regenerative medicine.

Businesses use technological and social media marketing to respond to the Covid-19 pandemic. This study aims to understand better the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The study used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium-sized businesses participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium-sized enterprises. Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. Mediating effects between components may be better understood via internet/e-business technology and social media marketing, as shown by this study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the Covivirus-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

The large-scale introduction of renewable energy, replacing fossil fuels, is presented as an essential part of the energy transition; this substitution is being observed in electrical systems, but it will also be necessary to introduce them in other sectors, such as transportation, either by incorporating renewable energy sources in the sector's facilities, including automotive service centers, or by electrifying transportation technology. The introduction of electromobility in a country is associated with a group of technologies that are required to make this introduction feasible. In Cuba, the introduction of electric vehicles, electric charging stations and charging points, including residential charging points, could increase the current demand by about 330 MW, based on the prospects for the introduction of electrification in the country's transportation system. Taking all of the above as a point of reference, this article addresses the basic elements of an energy transition proposal in the transportation sector, arriving at a proposed Roadmap to achieve the required synergy between energy transition and electromobility.

Latin America and the Caribbean offer the most favorable natural and social conditions for the emergence of cattle ranching that regenerate and maintains the ecosystem services on which life on Earth depends, that is energy efficient and that contributes to meeting fundamental human food needs. Unlike other major global producers, cattle farming in Latin America and the Caribbean is fundamentally based on pasture. Therein lies both its strength (due to the regenerative potential of well-managed pastures) and its vulnerability (due to the pervasive link between deforestation and pasture formation, especially in the Amazon, where 40% of the Brazilian herd is concentrated). The diversification of pastures (today marked by impressive monotony, with brachiaria accounting for 85% of the species in Brazil), the introduction of legumes and tree plantations within them, and the proper management of herds, open the way to the possibility of drastically reducing the area currently occupied by the activity (especially in the Amazon) while maintaining the supply of its products. Agricultural research has disseminated forms of moderate intensification of cattle ranching, which represent a low opportunity cost of animal feed and, at the same time, regeneration of ecosystem services that have so far been destroyed by the prevailing methods of farming.

Forest fires can occur suddenly and have significant environmental, economic, and social consequences. Timely and accurate evaluation and prediction of their progression, particularly the spread speed in difficult-to-access areas, are essential for emergency management departments to proactively implement prevention strategies and scientifically extinguish fires. This paper provides a comprehensive analysis of advanced technologies for predicting forest fire spread in China and globally. Incorporating remote sensing (RS) technology and forest fire science as the theoretical foundation, and utilizing the Wang Zhengfei forest fire spread model as the technical framework, this study constructs a forest fire spread model based on remote sensing interpretation. The model optimizes the method for acquiring parameters and enhances algorithm accuracy. By considering regional landforms (ridge lines, valley lines, slopes) and vegetation coverage, this paper establishes visual interpretation markers for identifying hotspots within the spread model. Utilizing statistical data from pixels within the fire line zone transforms the methodology for predicting forest fire spread speed, resulting in greater accuracy compared to conventional fixed-point predictions. Finally, the model was applied to the case of a forest fire in Mianning County, Sichuan Province, China and verified using high-time resolution mid-infrared image data and previous research findings. The results demonstrate that the predicted fire spread area from this RS-enabled model is consistent with both high-time resolution geostationary satellite data and previous research outcomes, indicating high reliability. This model can provide crucial fire spread information to relevant emergency departments, enabling effective preemptive measures and scientific firefighting strategies.

Abstract: Field studies with large-stemmed plants Artemisia dubia (wormwood) have been carried out at Vėžaičiai Branch LAMMC since 2018. According to three years of experimental results, annual dry matter (DM) yield varied from 7.94 to 10.14 t ha-1. Growing conditions, nitrogen ap-plication level and harvesting time had significant impacts on Artemisia dubia productivity. This article presents the results of research on preparation and use of Artemisia dubia biomass for en-ergy conversion and evaluation of technological-technical and environmental parameters of these plants utilization for solid biofuel. For experiments were used 6 variants of Artemisia dubia samples, which were grown in 2021: plants were cut three times and two fertilization options were used: (1) no fertilization and (2) fertilization with 180 kg ha-1 of nitrogen fertilizer. These harvested plants were chopped, milled and were pressed into the pellets. The physi-cal-mechanical characteristics (mоisture content, density and strength) of Artemisia dubia pellets were investigated. During this study, it was found that the density in dry mass (DM) of pellets ranged from 1119.86 to 1192.44 kg m-3. The pellet moisture content ranged from 8.80 to 10.49%. After testing of pellet strength, it was found that the pellets which were made from plant bio-mass PK-1-1, were the most resistant to compression, they withstood 560.36 N of pressure. Dry fuel lower heating value (LHV) of pellets was sufficiently high and was very close to pine saw-dust pellets, it varied from 17.46±0.25 MJ kg-1 to 18.14±0.28 MJ kg-1. The ash content of burned pellets ranged from 3.62±0.02% to 6.47±0.09%. Emissions of harmful pollutants – CO2, CO, NOx and unburnt hydrocarbons (CxHy) did not exceed the maximum permissible levels. Summariz-ing the results of investigated properties, cоmbustion and emissiоns of Artemisia dubia pellets, it can be concluded that this biofuel can be used for production of pressed biofuel and it is charac-terized by sufficient high quality, efficient combustion and permissible emissions to the envi-ronment.

This paper designed a new type of generator, transgenerator, that integrates the wind turbine and flywheel into one system, aiming to make the flywheel distributed energy storage (FDES) more modular and scalable than the conventional FDES. The transgenerator is a three-member dual-mechanical-port (DMP) machine with two rotating members (inner and outer rotors) and one stationary member (stator). The transgenerator-flywheel system is introduced with the system configuration, transgenerator overview, flywheel operation principle and power management strategies, and control system. Simulations are performed in MATLAB/ Simulink to verify the system viability, including control system verification and the flywheel storage performance evaluation. Results show that the inner and outer rotors can be controlled independently with accurate and fast control response, and the grid-side control works properly. The flywheel performs well with considerable charging power and storage capacity.

An increase in plant-sourced (PS) nutrient intake is promoted in support of a sustainable diet. PS dietary minerals and proteins have bioactive properties that can affect bone health and the risk of fracture. In a group randomised, cross-over, design, this study evaluated the post-ingestion, temporal pattern of change in arterialised ionised calcium (iCa), parathyroid hormone (PTH), C-terminal crosslinked telopeptide of type I collagen (CTX) and procollagen type 1 amino-terminal propeptide (P1NP) for 4 hours following ingestion of a novel supplement (SUPP) containing a PS marine multi-mineral + PS protein isolate. A diurnally matched intake of mineral water was used as a control (CON). Compared to baseline, the change in iCa concentration was 0.022 (95%CI, 0.006 to 0.038, p = 0.011) mmol/l greater in SUPP than CON, resulting in a -4.214 (95%CI, -8.244 to -.183, p = 0.042) pg/mL mean reduction in PTH, a -0.64 (95%CI, -0.199 to -0.008, p = .029) ng/mL decrease in the biomarker of bone resorption, CTX, and no change in biomarker of bone formation, P1NP. Used as a dietary supplement, or by incorporation into a food matrix, the promotion of PS marine multi-mineral and PS protein isolates may contribute to a more sustainable diet and overall bone health.

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Utilizing the comprehensive international EM-DAT database, our analysis reveals significant trends and disparities in disaster occurrences and their widespread consequences. Results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991-2000 and 2001-2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9,269). The most significant increase in natural disasters occurred from 1961-1970, while man-made (technological) disasters surged substantially from 1981-1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001-2010 period, driven by catastrophic natural disasters in Asia and North America. Our research highlights Asia as the most disaster-prone continent, accounting for 41.75% of global events, with natural disasters making up 61.89% of these events. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent's disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the most frequently affected by both natural and man-made (technological) disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010-2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Particularly, the decade from 2001 to 2010 experienced the greatest economic losses, primarily driven by natural disasters in Asia and North America. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies.

We developed a protein to rapidly and accurately diagnose Chagas disease, a life-threatening illness identified by the WHO as a critical worldwide public health risk. Complicating the current health situation are serological test limitations that contribute to most infected persons being unaware of their condition and therefore untreated. To improve diagnostic testing, we developed an immunological mimic of the etiological agent, Trypanosoma cruzi, by combining ten pathogen specific epitopes within the beta-barrel protein structure of Thermal Green Protein. The resulting multi-epitope protein, DxCruziV3, displayed high specificity and sensitivity as the antibody capture reagent in an ELISA platform with an analytical sensitivity that exceeds WHO recommendations. Within an immunochromatographic platform, DxCruziV3 showed excellent performance for the point of application diagnosis in a region endemic for multiple diseases, the municipality of Barcelos in the state of Amazonas, Brazil. In total, 167 individuals were rapidly tested using whole blood from a finger stick. As recommended by the Brazilian Ministry of Health, venous blood samples were laboratory tested by conventional assays for comparison. Test results suggest utilizing DxCruziV3 in different assay platforms can confidently diagnosis chronic infections by T. cruzi. Rapid and more accurate results will benefit everyone but will have the most noticeable impact in resource-limited rural areas where the disease is endemic.

The interest in the Spark Plasma Sintering (SPS) technique has continuously increased over the last few years. This article shows the possibility of the development of a SPS device used for material processing and synthesis both in both scientific and industrial applications. This work presents an example of processing Arc-Meleted (half-Heusler, cobalt triantimonide) and SHS-synthesized semiconductors (bismuth telluride) materials with SPS device. The system functionalities expansion is presented, showing the possible way of increasing the information amount obtained about SPS processes and using the SPS apparatus for conducting synthesis of materials and increasing reproducibility and accuracy of process parameters control.

The construction industry is shifting towards more sustainable practices to address environmental concerns. This review examines the impact of LEED certification on design-build projects, highlighting how the two work together to promote sustainability. By analyzing the article under review, we explore how LEED standards—such as energy efficiency, water conservation, and the use of sustainable materials—are incorporated into design-build projects to create environmentally responsible buildings. The article also discusses the benefits of the design- build approach, which encourages collaboration and early integration of sustainability principles. The review critically assesses the strengths and weaknesses of the article, emphasizing the importance of cost considerations and scalability. Ultimately, it underscores the value of combining LEED certification with design-build to promote green building practices.

This study presents a fit-for-purpose evaluation of commercially available portable sensor systems, measuring PM, NO2 and/or BC, through extensive lab- and field benchmarking. The main aim of the study is to identify sensor systems that can be used for mobile monitoring by citizens to assess dynamic exposure in ambient air. After an initial literature study and market search resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked in laboratory and real-word conditions. We evaluated comparability to reference analyzers, sensor precision and sensitivity to temperature, humidity and O3. Moreover, we evaluated if accuracy can be improved by the lab and field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM and BC, but maturity of the tested NO2 sensors is still low and additional efforts are needed in terms of signal noise and calibration. The horizontal accuracy of the build-in GPS was generally good achieving a &lt;10m horizontal accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and exposome impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

The use of fuel material and different sources of ignition causes unwanted fires, producing and spreading in different spaces. Fire fighting. systems are measures taken to minimize the effects of fire. This struggle is not only on land, but also in the maritime world, through maritime security that has become a very important element for the International Maritime Organization (IMO). Therefore, maritime accidents caused by fire are one of the challenges to be taken into account, because they require immediate control and response. This article presents a historical interpretation of the concept of maritime safety through first responders on ships. It will be demonstrate how the technological advances of such equipment have been very important for their arrival on ships. In addition, the article reveals the legislative changes of International Convention for the Safety of Life at Sea (SOLAS), which reflects the IMO requirements to prevent fire risks on board, requiring a reading of the primary documents. Finally, the importance of the evolution of first intervention equipment for safety on board has been shown.

The paper analyzes the relationship between urban waste recycling and technological innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013-2022 was used. To analyze the characteristics of European countries in terms of waste recy-cling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coef-ficient was used. The results show that the optimal number of clusters is 3. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffu-sion of Information and Communication Technologies (ICTs), while it decreases with the infra-structure index, business sophistication index, and the average expenditure in research and devel-opment of large companies.

Since the 1980s, a growing interest in goat milk was noticed due to the nutritional values and health benefits of this milk, which resulted in increasing goat populations and milk production worldwide. This comprehensive review elaborates on goat milk composition compared to other types of milk. It is also an overview of goat milk production, properties, nourishment-value, applications in dairy products, and potential health benefits. Goat milk composition and its characteristics are slightly different from other types of milk and can be utilized to manufacture many dairy products. Fresh goat milk and goat milk products (e.g., yogurt and kefir were found to provide various potential health benefits, such as anti-inflammatory, prevent cardiovascular disease, anti-diabetic and antihypertensive, strengthen bones, boost immunity, and improve metabolism.

This paper provides attempts to formalize Hayek’s theory of knowledge. It has been theoretically shown that exponential income distribution is a spontaneous order of the well-functioning market economy. We show that this theoretical result is supported by the empirical evidence from the United Kingdom and China. In particular, we empirically show how the income structure of China evolved towards an exponential distribution after the market-oriented economic reformation. Furthermore, we strictly prove that, if the income structure of an economy obeys an exponential distribution, the income summation over all households leads to an aggregate production function with Hicks-neutral-like technical progress, in which the technology factor is exactly equal to society’s information stock that is a result of combining all of decentralized decisions.

Organosilicate glass (OSG) films are a vital component in modern electronic devices, with their electrical properties playing a crucial role in device performance. This comprehensive review systematically examines the influence of chemical composition, vacuum ultraviolet (VUV) irradiation, and plasma treatment on the electrical properties of OSG films. Through an extensive survey of literature and experimental findings, we elucidate the intricate interplay between these factors and the resultant alterations in electrical conductivity, dielectric constant, and breakdown strength of OSG films. Key focus areas include the impact of diverse organic moieties incorporated into the silica matrix, the effects of VUV irradiation on film properties, and the modifications induced by various plasma treatment techniques. Furthermore, we discuss the underlying mechanisms governing these phenomena, shedding light on the intricate molecular interactions and structural rearrangements occurring within OSG films under different environmental conditions. This review not only consolidates existing knowledge but also identifies gaps in understanding and suggests avenues for future research, thereby advancing our comprehension of OSG film behavior and paving the way for enhanced performance in next-generation electronic and photonic devices.

As technology advances, people are increasingly adopting IoT (Internet of Things) devices in smart cities. These devices are equipped with sensors that are connected everywhere, from energy infrastructure and transportation to telecommunications, health and human services, waste management, public safety, and more. People want to access sensor data anytime and everywhere to make better decisions and improve their lives. To achieve this, a sensor integration model has been developed where multiple sensors are integrated under one gateway to manage and share sensor data. However, effective sharing of sensor data requires data security, privacy, confidentiality, fairness, effective architectural design, and low communication and computation costs. These issues need to be addressed in the sensor integration model in smart cities. Existing storage models face challenges in managing sensor data effectively. To overcome these issues and hurdles, a data storage framework leveraging blockchain technology has been proposed within the sensor integration model. This framework ensures data security, privacy, confidentiality, and a fair and transparent financial model, all while minimizing communication and computation costs.

The properties of coatings obtained using two surface preparation methods were compared: heating and etching by ion bombardment with plasma generation by arc evaporators and heating and etching by a glow discharge. A Ti-TiN-(Ti,Cr,Al)N coating was deposited. The use of a glow discharge provides better resistance of the coating to destruction during the scratch test and wear resistance of metal-cutting tools when turning steel. As the cutting speed increases, the advantage in wear resistance of the coating deposited using a glow discharge increases. During the process of heating and etching by ion bombardment with metal ions, a nanolayer rich in cobalt and tooling elements (iron, molybdenum) is formed in the area of the interface of the coating and the carbide substrate. When heated and etched by a glow discharge, such a layer does not form. When using both methods, there is identical diffusion of tungsten into the coating and diffusion of chromium and possibly titanium into the substrate. Thus, the glow discharge heating and etching method can be effectively used in the process of PVD coating deposition.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

This qualitative research explores the transformative impact of technological innovations on retail marketing effectiveness. Through thematic analysis of interviews with industry professionals, the study identifies key themes including enhanced customer engagement, personalized marketing strategies driven by AI and machine learning, data-driven decision-making leveraging big data and analytics, the rise of e-commerce platforms, integration of mobile technologies, adoption of augmented reality (AR) and virtual reality (VR), development of omnichannel strategies, and implementation of loyalty programs. These themes illustrate how technology has reshaped retail marketing by enabling real-time customer interactions, personalized shopping experiences, and targeted marketing campaigns that enhance customer satisfaction and loyalty. The findings highlight the critical role of digital platforms like social media in fostering community engagement and brand loyalty. AI and machine learning facilitate personalized marketing efforts, tailoring product recommendations and enhancing customer experiences. Data-driven strategies optimize marketing ROI by providing insights into consumer behavior and market trends. E-commerce platforms expand market reach and streamline transactions, while mobile technologies and AR/VR innovations create immersive shopping experiences that captivate consumers. Omnichannel strategies integrate physical and digital channels, delivering seamless brand experiences and enhancing customer retention through CRM systems and loyalty programs. Looking ahead, retailers must navigate challenges such as rapid technological change, the need for continuous adaptation, and concerns about data privacy. By embracing technological advancements strategically and maintaining ethical standards, retailers can sustain competitive advantage, meet evolving consumer expectations, and drive growth in the digital era.

Hydrogen is undoubtedly one of the most promising alternatives for generating energy with low environmental impact. However, most hydrogen produced today is derived from fossil fuels, which negatively impacts the environment. In this context, biomass conversion technologies emerge as a sustainable and environmentally friendly alternative to conventional fossil-based processes for hydrogen production. The main objective of this study is to underscore the potential of biomass-derived hydrogen, as one of the key sustainable energy solutions. This work reviews diverse biological and thermochemical pathways for biomass-derived hydrogen production, noting challenges in technology and feedstock diversity. Biomass offers local availability and economic benefits, potentially yielding negative emissions. Biological methods like bio-photolysis and fermentation promise low-energy, waste-recycling solutions despite operational constraints. Thermochemical processes, influenced by biomass type and energy content, favor economies of scale and conventional gasification for economic viability. Hydrothermal gasification shows promise for high-moisture feedstocks. Overall, while thermochemical processes suit centralized large-scale hydrogen production, biological pathways offer decentralized options, necessitating continued innovation for integration into future energy strategies.

In this paper we investigate into the possible resurrection for the aether and it’s compatibility with the theory of relativity. We revisit the Michelson-Morley experiment and expose some of the major inadequacies. In this regard, we have presented the true/corrected form of the Michelson-Morley experiment. We have tried to revise the interpretational aspect of the mathematical formalism regarding the metric of Minkowskian space-time in addendum with it’s relationship to the two theories of time. We herein have also tried to restrain some of the quantum mechanical issues arising from the mainstream understanding of the mathematical formalism of the Minkowskian manifold. Essentially, we have argued in favour of aether to be incorporated into our mathematical formalism as well the physical understanding of the universe.

Human longevity is an important but difficult goal due to the extreme complication of human body. If people could repeatedly transfer their consciousness from old bodies to new ones, their lifespan might be prolonged extremely. However, there are several difficulties which prevent the achievement of such a technique. In this paper, we propose an approach that can transfer human consciousness indirectly, and avoid or significantly relieve those difficulties. The strategy of our approach is to integrate the consciousnesses of two bodies (old and new) into a unique consciousness. By doing so, the consciousness is extended to the new body. After that, the consciousness shrinks and remains in the new body when the old body dies. This may also apply to integrating more than two bodies of human, or animal, or even across different species. After investigating thousands of literatures, we find quite strong evidence (related discoveries and technologies) that can support the proposed approach and its advantages. Beside prolonging human lifespan, the approach could also have other meaningful applications.

Form deviation generated during the milling profile process poses challenges to the precision and functionality of industrial fixtures and product manufacturing across various sectors. The inspection of contour profile quality often relies on commonly employed contact methods for measuring form deviation, but these methods frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system (RAM−StarliteTM), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The RAM−StarliteTM vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations when compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. Results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, quality classification.