The concept of distributed generation has made photovoltaic an integral source of energy in smart grid systems, especially in peer-to-peer energy trading frameworks that exploit excess power to fulfill the energy requirements of consumers in cost-efficient and eco-friendly manner. It is believed that P2P energy trading will dominate a significant portion of research in forthcoming power generation systems due to the excessive rise of energy demands across the globe. Despite a plethora of studies on energy optimization solutions in P2P trading, minimizing nanogrid energy trading cost and efficient energy sharing between consumers and prosumers are deemed among the challenging problems. This study overcomes essential issues overlooked by the contemporary P2P energy trading models by introducing a predictive optimization-oriented nanogrid energy trading model. The proposed study encompasses two stages: (1) predictive optimization model which harnesses BD-LSTM-based forecasted energy parameters (energy load, energy consumption, and PV generation) that are later incorporated in PSO-enabled objective function to reduce nanogrid trading cost, (2) optimal energy sharing plan is devised to decide the role of nanogrids as prosumers or consumers by emphasizing the use of PV-produced energy. The proposed model is validated on the case study containing nanogrid houses data. The simulation provides detailed experiments by comparing the energy demand and response using the proposed energy sharing model. The outcomes yield that the energy sharing plan holds a significant potential to fulfill maximum energy requirements of nanogrid house in P2P cluster and significantly reduces the energy cost compared to grid.

The present era of the Internet of Things (IoT) having intelligent functionalities in solving problems pertaining to realtime mission-critical systems has brought an immense revolution in diverse fields including healthcare and navigation systems. However, to the best of our knowledge, the potential of IoT has not been fully exploited yet in the field of the energy sector. We argue that there is an immense need to shift the traditional mission-critical electric power system architecture to IoT-based fully orchestrated architecture in order to increase efficiency, as billions of investment is reserved for the energy sector globally. Since network orchestration deals with auomating the interaction between multiple components involved to execute a particular service, therefore, scheduling the relevant processes within strict deadlines becomes the core pillar of the architecture. The mission-critical systems with urgent task execution often suffer from issues of missing task deadlines. In this study, we present a novel IoT task orchestration architecture for efficient energy management of a nanogrid system that focuses on minimizing the use of nonrenewable energy resources and maximizing the use of renewable energy resources. Moreover, major components of IoT task orchestration such as task mapping and task scheduling are also enhanced using NLP and PSO optimization modules. The proposed task scheduling algorithm incorporates the optimized surplus time, and efficiently executes the energy management-related tasks contemplating to their types. The study utilizes sensors to obtain data from physical IoT devices, including photovoltaic (PV), Energy Storage System (ESS), and diesel generator (DG). The performance of the proposed model is evaluated using data set of nanogrid houses. The outcomes revealed that IoT-task orchestration has played a pivotal role in efficient energy management for nanogrid mission-critical system. Furthermore, the comparison with state-of-the-art scheduling algorithms showed that the task starvation rate is reduced to 16% and 12% when compared with RR and FEF algorithms, respectively.

In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand space-time association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE-value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling to reduce the data acquisition period significantly. The extracted feature set is then inputted into fundamental classification algorithms, with the Support-Vector-Machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step of a large project aiming to identify specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Density Functional Theory (DFT) full potential linearized augmented plane wave (FP-LAPW) method with the Modified Becke-Johnson (mBJ) approximation is used to perform spin polarised calculations of the transition metal perovskites MoScO₃ and WScO₃. Both depict half metallic behaviour with semiconducting and metallic in the minority and majority spins respectively. MoScO₃ and WScO₃ have indirect R− Γ band gaps in the minority spin channels of 3.61 and 3.82eV respectively. Moreover, they both show substantial magnetic moments of 2.99μ_B. In addition, we calculate the dielectric function, optical conductivity and the optical constants, namely, the refractive index, the reflectivity, the extinction and absorption coefficients.

In this study, Acısu Basin, which is upper side of one of the most important basins (Gediz) in Türkiye, was modelled using three types of hydrological models and three different calibration algorithms. Lumped (GR4J), semi-distributed (SWAT+), and fully distributed (mHM) hydrological models were built and integrated with Parameter Estimation Tool (PEST) which is a model independent calibration tool including various algorithms. Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES) were selected as calibration algorithms. Calibration period was 1991-2000, and validation results were obtained for 2002-2005. The effect of model structure and calibration algorithm selection on discharge simulation was evaluated via comparison of 9 different model-algorithm combinations. Results have shown that mHM and CMA-ES combination showed the best discharge simulation performance according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the pattern comparison of discharge value shows that all of the models miss the peak values in significant amounts. This problem may be related to the interventions made in 2000-2001 years interval and possible to overcome by changing the calibration and validation periods, increasing the number of iterations or using the naturalized gauge data.

Globally, the fuel demand is increasing rapidly and the conventional method to produce fuel is toxic which causes environmental degradation and climate crisis. Therefore, biodiesel emerges as the substitute to overcome the conventional fuel problems because biodiesel is renewable and non-toxic. Biodiesel synthesis is the process of using fats and oils to produce biodiesel ester and glycerol. Biodiesel can be produced using edible and non-edible feedstock but using the edible feedstocks problems like fuel vs food, nutritional value and economic considerations were raised. Therefore, non-edible feedstocks are being used to overcome problems arising from edible feedstock. As non-edible feedstocks can easily be grown anywhere on land, doesn’t cause imbalance to food economy and has low cost. Many non-consumable seeds like Mahua, Jatropha, Castor, Neem etc. are used to produce biodiesel. Now a days, researchers are using nanoparticles to enhance the stability, quality, and properties of biodiesel. In future, researchers can use algae and microbial oils for production of biodiesel. Research can also use biojet fuel in such a way that it reduces the carbon emission. In future, production of biodiesel can be integrated into circular economy models to reduce environmental waste and impact. In this review we will discuss the production methods for biodiesel, catalysts for biodiesel production, and non-edible feedstock properties of biodiesel.

This study was conducted in the Dhanu River and adjacent waters at Mithamain upazila (sub-district) under Kishoreganj district of Bangladesh to prepare a check list of available native fishes with their availability status, conservation perspectives, habitat preferences, population trends and intimidations. Data were collected monthly by direct field survey, focus group discussions, and personal interviews with fishers by using a semi-structured questionnaire and a pictorial check list of fish species. A total of 91 indigenous fish species of 59 genera belonging to 29 families under 11 orders were documented where 17.58% species was abundantly available, 27.47% was commonly available, 31.87% was moderately available and 23.08% was rarely available. Cypriniformes was found as the dominant order, consisting 37.36% of the fish species aggregation and Cyprinidae was the most dominant family with 32.97% of the entire species assemblage. Twenty four piscine species (26.37%) were under threatened category in Bangladesh which subsumed 3 critically endangered (3.29%), 11 endangered (12.08%), and 10 vulnerable species (10.99%). Notably globally threatened Cirrhinus cirrhosus, Channa orientalis, and Wallago attu were available there. Fish population trends of 24.18% and 59.34% of the entire fish species was found in decreasing trends in global and national level, respectively. Leading intimidation to the fish diversity was indiscriminately overfishing, followed by fishing by dewatering of wetlands, katha fishing method, use of deprecated fishing gears, climate change, etc. Minimization of anthropogenic impacts, assuring the flux of water round the year, enactments of fish laws, installation and management of fish sanctuaries, and raising public awareness can be effective for the conservation of existing fisheries resources.

Active transportation, such as walking, cycling, and micro-mobility modes, has received a lot of attention in recent years due to its potential benefits to urban residents, such as less traffic, better air quality, more opportunities to get exercise, and an overall higher quality of life. In this study, we used Classification and Regression Trees (CART) to compare and contrast three mobility options: shared micro-mobility, individual micro-mobility, and walking. We surveyed 219 people living in Budapest, Hungary, to learn more about their travel habits and investigate the demographic elements that influence people's mode choice, such as age, gender, ownership of micro-mobility modes, education, job, and income. Results showed that ownership of personal micro-mobility modes, and age as important predictors of active travel mode choice. Males seem to prioritize cost and weather conditions when choosing shared micromobility modes, while females value safety and weather conditions. Our findings can guide policy decisions and urban planning initiatives by identifying the most significant predictors of mode choice and evaluating the possible benefits and drawbacks of each mode.

Ionic liquids (ILs) have gained interest among researchers due to its tunable properties that can be used in wide applications. However, toxicity and bio-degradation studies of ILs proved that most of the aromatic ILs, such as imidazolium is highly toxic and non-biodegradable. Several advance oxidation processes (AOPs) have been investigated by researchers to evaluate the efficiency of the systems for the removal of ILs from wastewater. However, the issue on relative high cost and environmental concern has limited the application of these AOPs in industry. In this research, photocatalytic study using hybrid nano-materials was conducted to evaluate the efficiency of this system as an alternative AOP system for removal of ILs from wastewater. The synergistic effect of adsorption-photodegradation was introduced by depositing Fe-TiO₂ onto the functionalized activated carbon (AC). Nano-TiO₂ was synthesized using micromulsion method before modification with transition metal and deposited onto the oxidized AC. Photodegradation reaction of 1-butyl-3-methylimidazolium chloride [bmim]Cl was investigated under simulated visible light irradiation. It was observed that the overall efficiency of the system was increased with the increasing amount of Fe dopant. Investigation on the extrinsic factors such as solution pH, initial concentration of ILs and photocatalyst dosage showed to significantly affect the overall efficiency of the systems where the optimum condition for the system was observed at pH 10, with initial ILs at 1mM at 1 g/L of photocatalyst. The best performance photocatalyst was 0.2Fe-TiO₂/AC.

By using Modified simple equation method, we study the Cahn Allen equation which arises in many scientific applications such as mathematical biology, quantum mechanics and plasma physics. As a result, the existence of solitary wave solutions of the Cahn Allen equation is obtained. Exact explicit solutions interms of hyperbolic solutions of the associated Cahn Allen equation are characterized with some free parameters. Finally, the variety of structure and graphical representation make the dynamics of the equations visible and provides the mathematical foundation in mathematical biology, quantum mechanics and plasma physics.

As Alberta increases solar power generation, land use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases increased) agricultural output is the co-locating of solar photovoltaics and agriculture: agrivoltaics. This study reviews policies that impact the growth of agri-voltaics in Alberta. Solar PV-based electricity generation is governed by three regula-tions based on system capacity. In addition, agrivoltaics falls under various legisla-tions, frameworks, and guidelines for land utilization. These include Land Use Frame-work, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Dis-position, Bill 22 and other laws/policies all of which are reviewed in the context of agrivoltaics. Several policies are recommended to support rapid diffusion of agrivolta-ics. First, open access research into agrivoltaics, which not only will help optimize agrivoltaic systems for the region, but also coupled with public education is expected to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaic technology, developing agrivoltaic standards, making agri-voltaic technology-friendly regulations/frameworks and developing programs and pol-icies to incentivize agrivoltaic deployment over conventional PV will all accelerate dif-fusion. Through these measures, Alberta can achieve conservation and sustainability in food and energy sector while simultaneously addressing the renewable energy and climate-related goals.

We investigate new graph characteristics namely total (vertex, edge) face irregularity strength of gen- eralized plane grid graphs Gmn under k-labeling Phi of type (Alpha, Beta, Gamma). The minimum integer k for which a vertex-edge labelled graph has distinct face weights is called the total face irregularity strength of the graph and is denoted by tfs(Gmn). In this article, the graphs G = (V;E; F) under consideration are simple, finite, undirected and planar. We will estimate the exact tight lower bounds for the total face irregularity strength of some families of generalized plane grid graphs.

Glucagon-like peptide-1 (GLP-1) agonists are gaining attention as a potential treatment for obesity. While studies have shown that these medications greatly impact dietary habits, their relationship to consumer food waste is unexplored. We analyzed data from 505 U.S. consumers who self-report GLP-1 medication use and found that 25% agree that they waste more food since beginning the medication with 61% disagreeing with this statement. Respondents are less likely to agree with this statement if they have been on the medication a longer time and more likely to agree if they report experiencing nausea since beginning the medication. Several dietary changes are also significantly associated with self-reported changes in food waste since starting the medication. With previous literature finding GLP-1’s significant impact on individual food consumption and dietary choices, our findings suggest that the effects of this novel class of anti-obesity medications may cast a significant impact on food waste patterns. With the potential for widespread adoption of these medications, and given that approximately one-third of food in the United States is wasted by consumers, understanding the interaction of these two consumer trends is critical for projecting their joint impact on the food system.

This cross-sectional study was aimed to investigate the relationship between gratitude and life satisfaction, and the mediation role of social support and enjoyment of life among older individuals. The Gratitude Resentment and Appreciation Test, the Enjoyment of Life Scale, the Satisfaction with Life Scale, and the Multidimensional Scale of Perceived Social Support Scale were administered to a sample of 260 older individuals aged between 60 and 80. The main findings revealed a positive association between gratitude and life satisfaction, and social support was a partial mediator in this relationship. Although enjoyment of life was not a direct mediator in the relationship between gratitude and life satisfaction, the final model indicated a significant serial pathway from gratitude to social support and then through enjoyment of life-to-life satisfaction. In conclusion, enhancing gratitude, promoting enjoyment of life, and social support in older individuals might increase their life satisfaction, which in turn might contribute to their successful aging

Recent legalization and normalization of euthanasia through the government program of medical assistance in dying (MAiD) is impacting Canada's population dynamics and is now the fifth leading cause of death. Ongoing proposals suggest expanding MAiD eligibility to mental illness. A knowledge gap exists to determine to what degree this expansion of MAiD may exacerbate the existing challenges associated with an aging population and declining birth rates. It may also complicate meeting national population targets of 100 million by 2100 set by the Century Initiative. To fill these knowledge gaps, this study conducts a comprehensive evaluation of the prevalence of mental illness in Canada. The results show that if MAiD is expanded to mental disorders under the high MAiD scenario by mid-century it will impact about 12.3% of the population. The low MAiD scenario projects an increase from 45,786 to 227,024 additional deaths/year. The likely scenario shows MAiD growing from 214,499 to approximately 896,312 by mid-century. Thus, if MAiD is expanded to mental illness under the likely scenario, more than half a million additional immigrants will be needed per year. These results emphasize the importance of integrating immigration policies with MAiD practices to ensure demographic stability and achieve long-term population objectives.

Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70% and 95% of all digital transformation projects fail due to signif-icant and varied challenges that sports companies face during the process of digital transfor-mation. This is because strategy, not digital technology, drives digital transformation, and suc-cess is unlikely without a mature digital transformation model. Moreover, the digital transfor-mation model of developing and emerging markets is not the same and cannot be adopted as a blueprint. Therefore, the aim of this research was to propose a framework for the digital trans-formation maturity of esports businesses in developing and emerging markets. Semi-structured interviews were conducted with a sample of 15 stakeholders of esports businesses in Iran. Thematic analysis was used to analyze the interviews, and three main perspectives were identified: (1) enablers, (2) digital resilience and capabilities, and (3) digital transformation ma-turity stages. The enablers were formed by six sub-themes, including governance and leader-ship, strategy, culture and skills, innovation, technology, and data. Digital resilience and capa-bilities were formed by four sub-themes, including business model and ecosystem, digital cus-tomer experience, digital employee experience, and digital processes. Additionally, it was found that digital transformation maturity stages are based on four stages of digital beginners, digital followers, digital conservatives, and digital leaders. The findings indicate that moving towards digital transformation and achieving digital resilience in esports businesses will be impossible without synergy between enablers and digital resilience and capabilities. This framework can be useful for esports businesses to evaluate their current digital status and effectively guide them towards a desirable digital status. Furthermore, esports businesses can prioritize their efforts and resources for digital transformation by focusing on targeted capabilities and enablers and ensure more effective and efficient allocation of resources towards digital transformation.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

Although solar photovoltaic (PV) system costs have declined, capital cost remains a barrier to widespread adoption. Do-it-yourself (DIY) system designs can decrease costs by about 50% by reducing labor costs, but if not attached to a building structure demand ground penetration for conventional footings. This is not technically and economically feasible at all sites. To overcome these challenges, this study details systems designed to eliminate drilling holes and pouring concrete. Two different designs for both fixed tilt and variable tilt PV racking are investigated to assess their structural integrity, constructability, and economic cost when fabricated with a ballast-supported foundation. The ballast-supported foundations are analyzed for eight such systems by proposing two separate ballast designs: one for single line of post systems, and one for double line of post systems built on a 4-kW basis. The results of the analysis found that both designs are slightly more expensive than typical in-ground concrete systems by 25%, but the overall DIY systems costs remain economically advantageous. Sensitivity analyses are conducted to show how modifications to the dimensions including clearances and distances between gabions influence the weight of the system and thus change the economic value of the design, so users can trade dimensional freedom for cost-savings, and vice versa. Overall, all the wood-based PV racking system designs provide users with cost-effective and easy DIY alternatives to conventional metal racking, and the novel ballast systems presented provide more versatility for PV systems installations.

Canada has committed to reducing greenhouse gas (GHG) emissions by increasing the non-emitting share of electricity generation to 90% by 2030. As solar energy costs have plummeted, agrivoltaics (co-development of solar photovoltaic (PV) systems and agriculture) provide an economic path to these goals. This study quantifies agrivoltaic potential in Canada by province using geographical information system analysis of agricultural areas and numerical simulations. Systems modeled would enable conventional farming of field crops to continue (and potentially increase yield) by using bifacial PV for single-axis tracking and vertical system configurations. Between a quarter (vertical) to more than one third (single axis tracking) of Canada’s electrical energy needs can be provided solely by agrivoltaics using only 1% of current agricultural lands. These results show that agrivoltaics could be a major contributor to sustainable electricity generation and provide the ability for Canada to render the power generation sector net zero/GHG emission free. It is clear that the potential of agrivoltaic-based solar energy production in Canada far outstrips current electric demand and can thus be used to electrify and decarbonize transportation, heating, expand economic opportunities by powering the burgeoning computing sector, and export green electricity to the U.S. to help eliminate their dependence on fossil fuels.

Keywords: Green human resource management1; Green supply chain management2; Regulation and legislation3; Sustainable development goals4.

Cyanobacterial mass developments in eutrophic ponds and lakes are a major concern for lake management, as many cyanobacteria produce a huge variety of toxic secondary metabolites, e.g. microcystins. The aim of this research was to culture a strain of the cyanobacterium Microcystis sp strain BM25, to observe its biomass production and to isolate and purify protease inhibitors from this cyanobacterial biomass. Different secondary metabolites were isolated following a standard bioassay-guideline. Isolation was performed, with an enzymatic protease assay as bioassay. High performance liquid chromatography was used to identify different fractions of secondary metabolite from the strain BM25. Moreover, protease homogenates were isolated from Daphnia magna in order to test the inhibitors against naturally occurring major digestive proteases trypsin and chymotrypsin. It was measured that 60% MeOH and the 80% MeOH C18-SPE fraction inhibits chymotrypsin activity 98% (6 nmol pNA min^-1 mg^-1) and 99 % (4 nmol pNA min^-1 mg^-1), respectively. In contrast, trypsin activity was not inhibited by methanolic extracts of this cyanobacterium strain.

City zones have become increasingly overcrowded as a result of the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a major issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication scheme for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed schemes support minimizing the end-to-end (E2E) delay and route request, in addition to reducing link failure. Our schemes’ leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The schemes are centred on short-range peer-to-peer (P2P) wireless communication in a 400-meter radius cluster. This includes innovative P2P wireless communications on VANET using minimized resources. The proposed schemes deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes, and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

Immediate load of full-arch rehabilitations over implants, specially over 4 implants are very well documented in the literature. More recently, the use of computed surgical guides to help the planning and the execution of these treatments, in many situations without the need of raising a flap, and diminishing the appearance of hematomas, bleeding and with less trauma to the patient has gained preference among implantologists. The objective of this case report is to show a clinical case of an immediate load full-arch fixed rehabilitation over 4 implants produced by 3D printing manufacturing technology, and placed with a computed surgical guide, in the same appointment, and the 24 months follow-up. The patient returned after 3 months to replace the immediate temporary fixed prosthesis for a new one, with metallic bar, better teeth and more delicate. Soft and hard tissue around the implants seemed to be stable, without signs of inflammation, either on the 3 or the 24 months follow-up. 3D printing of implants by direct metal laser sintering is a technology that seems to be promising for oral implantology, with solid studies showing reliable results, as seen in this single case. More clinical studies are necessary to testify its efficacy.

DEXTER (Detection of EXplosives and firearms to counter TERrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. The active microwave component of the DEXTER project is referred to as MIC (Microwave Imaging Curtain), supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real time to address non-checkpoint detection of explosives and fire-arms. The project, led by the ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra Wide-Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC's innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar im-ages using a deep-learning network. This paper will outline the project's objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results ob-tained during a live demonstration in a relevant environment.

This study aims to evaluate the patient’s dose exposure from Computed Tomography Pulmonary Angiography (CTPA) examination and to estimates the cancer risk induced from the examinations based on the patient’s size. One hundred patients were recruited, and data information was collected retrospectively. A multi-detector (MDCT) (Philips Brilliance 128, USA) scanner were utilized for the CTPA examination, and dose data were obtained from the system. The effective diameter of each subjects’ image was measured for the Size-specific dose estimates (SSDEs). All subjects divided into Group 1 (19 – 25 cm), Group 2 (25-28 cm) and Group 3 (28-38 cm), where the association between gender were analysed. Effective dose (E), SSDE, organ dose and cancer risk of each group were evaluated and compared statistically using independent t-test and one-way ANOVA. The range of mean CTDI_vol, DLP and E values were (6.44 – 17.42 mGy), (239 – 631 mGy), (5.19 – 13.90 mSv), respectively. In respective with the patient’s effective diameter, the mean SSDE value for Group 1, Group 2 and Group 3 were 9.93 ± 3.89 mGy, 13.70 ± 9.04 mGy and 22.29 ± 7.35 mGy. The organ dose and cancer risk attained for breast, lung and liver were 17.05 ± 10.40 mGy (194 per one million procedure), 17.55 ± 10.86 mGy (192 per one million procedure) and 15.04 ± 9.75 mGy (53 per one million procedure), respectively. Lung and breast with more massive patient’s effective diameter received the highest dose exposure which increases the probability of the cancer risk. CTDI_vol was found to be underestimated, and SSDE provides more accurate in describing the radiation dose and cancer risk. Body effective diameter found to be significant on the estimation except for gender. Therefore, it is essential to apply optimised protocols in order to reduce patient’s exposure during CTPA examination.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

Antibiotic resistance among bacterial pathogens is recognized as a major threat to human health worldwide. The emergence of multidrug-resistant bacteria can also be found in the community settings, apart from hospital environment, which indicates that reservoirs of antibiotic resistance genes do exist outside the hospital. The growth of antibiotic resistance is a consequence of bacterial adaptations in response to selective pressures. To survive in this hostile environment, bacteria develop defence mechanisms such as chemical modification of antibiotics, enzyme-catalysed antibiotic degradation, altered permeability, antibiotic efflux, mutation of target sites and biofilm formation, resulting in resistance to nearly all currently available antibiotics used in the clinical practice. The present review summarizes insights into the molecular mechanisms underlying the antibiotic resistance which is useful for planning strategies to combat antibiotic resistance and devise innovative therapeutic tools to fight against multidrug-resistant bacterial species.

Carbapenem resistant Enterobacteriaceae (CRE) has been public health risk in several countries and recent reports indicate the emergence of CRE in food animals. This study was conducted to investigate the occurrence, resistance patterns, and phylogenetic diversity of CRE E.coli from chicken. Routine bacteriology, PCR detection of E.coli species, multiplex PCR to detect carbapenemase encoding genes and phylogeny of CRE E. coli were conducted. The results show that 24.36 % (19/78) were identified as CRE based on the phenotypic identifications of which 17 were positive for the tested carabanemase genes. The majority, 57.99% (11/19) of the isolates harbored multiple carbapenemase genes. Four isolates harbored all blaNDM blaOXA, blaIMP, five and two different isolates harbored blaNDM and blaOXA, and blaOXA and blaIMP respectively. The Meropenem, Imipenem and Ertapenem MIC values for the isolates ranged from 2g/mL to ≥256g/mL. Phylogenetic grouping showed that the CRE E.coli isolates belonged to five different groups; groups A, B1, C, D and unknown. The detection of carbapenem resistant E.coli in this study shows that CRE is has become an emerging problem in farm animals, particularly, in poultry farms. This also implies the potential public health risks posed by CRE from chicken to the consumers.

Arsenic (As) is the most poisonous metalloid with negative effects on the plant and ecosystem. In the current study, observed that iron oxide nanoparticles (NPs) affected the molecular features of rice under arsenic (As) stress. The result showed that 1-D electrophoresis (SDS-PAGE) demonstrated variable expression of protein profiles. The polypeptide pattern was altered in the SDS-PAGE under arsenic stress. The findings also showed that low molecular weight polypeptides (MTs) expression was maximum under arsenic stress, while iron oxide NPs decreased the expression of these polypeptides and reduced the arsenic stress. RT-PCR was used to analyse the expression of the MYB transcriptional factor, WRKY, and OsGF14 genes in the rice plant. The results showed that these genes were up-regulated in response to arsenic stress, iron oxide NPs reduced arsenic stress, and Myeloblastosis (MYB), WRKY, and OsGF14 genes had very low expression levels. Our work showed that iron oxide nanoparticles (NPs) not only reduced the effects of arsenic stress but also decreased the expression of stress-related genes and metallothioneins (MTs).

Obesity is a universal health concern that can lead to serious diseases. The side effects of synthetic anti-obesity drugs necessitate the finding of suitable natural/herbal alternatives. Mother nature offers a wide range of plants with medicinal properties that include crude extracts and isolated compounds which are effective for controlling and reducing weight gain. Obesity was induced in 60, 3-week-old male ICR mice, using high-fat diet (60% dietary energy from fat) for 16-week. The mice were divided at random into six groups with 10 mice: mice fed with high-fat diet (HFD) only, mice fed normal diet only (NC), and orlistat at 15.9 mg/kg (HFD+Orlistat), and mice in three other high-fat diet groups treated with methanolic leaf extract of Clinacanthus nutans (MECN) at 500, 1000 and 1500 mg/kg. After 21-day of the treatment, MECN significantly reduced (P<0.05) the body weight, visceral fat and muscle saturated fatty acid compositions. There was also significant downregulation of HSL, PPAR α and PPAR γ and SCD genes expressions in the obese mice treated with 1500 mg/kg MECN compared to the HFD group. Therefore, MECN is a potentially useful natural supplement for alleviating obesity and obesity-mediated metabolic diseases.

Nanofibres have gained increasing attention due to their advantages of having a high porous structure, narrow pore size and distribution, and a high specific surface area. One of the most efficient techniques for producing nanofibres is via electrospinning. Nanofibres are currently being used in various fields, including water filtration. Although nanofibres possess the ability to filter various components, the fibres generally have a low mechanical strength that can mitigate the performance over time. Therefore, in overcoming this issue, several studies have focused on improving the strength of the nanofibre membranes for a water filtration application. This review discusses the advantages and challenges of nanofibres for water filtration membranes and summarises the methods to increase the strength of the membranes, including solvent vapour, thermal treatment, and chemical crosslinking.

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

In the era of Big Data and the Internet of Things (IoT), ensuring the security and privacy of patient data within distributed health systems is paramount. This research delves into the challenges posed by these advancements and explores viable solutions, focusing notably on blockchain technology, data encryption methods, and access control mechanisms. Employing a comprehensive literature review approach, the study synthesizes current research findings, complemented by analyses of existing frameworks and case studies from healthcare organizations, offering practical insights. Interviews with cybersecurity experts and healthcare IT professionals provide nuanced perspectives on current security issues and emerging trends. Through qualitative and quantitative data analyses, the study generates recommendations aimed at enhancing patient data security. These include the adoption of new frameworks and innovative technologies to fortify healthcare data systems. By addressing these critical aspects, this research aims to significantly contribute to the advancement of a more secure and efficient healthcare ecosystem in today's digital landscape.

Mucormycosis is an opportunistic fungal infection that is increasingly affecting patients with Coronavirus Disease (COVID-19). This connection is particularly pronounced in specific populations, including individuals with diabetes or those who have recently undergone corticosteroid therapy. Additionally, Cytomegalovirus (CMV) infection is a frequent complication among COVID-19 patients who have previously received corticosteroid or other immune-modulatory treatments. In this case report, we present the clinical details of a 56-year-old male who developed post-COVID pneumonia complicated by both cutaneous mucormycosis and CMV infection.

The present study explores the effect of using two porous deflectors on the performance of the Savonius wind turbine compared to only one porous deflector. The numerical simulation is performed to solve the unsteady Navier-Stokes equations using the SST k-

Pharmaceuticals contain biologically active components that can pollute water courses as a result of the excretions from individuals and/or uncontrolled release of residues from chemical plants, and they can pose a hazard to health. Pharmaceutical residues can persist at low concentrations in the environment, and thus may be potentially harmful to aquatic animals and to humans. Controlling and monitoring such residues are therefore a prime interest, for example, a solid-phase extraction uses solid sorbents to purify and preconcentrate the residues prior to their chemical analysis. In the present study, poly (acrylonitrile-co-divinylbenzene-80) sorbents are synthesised by varying the comonomer feed ratios under precipitation polymerisation conditions to deliver a family of porous polymer microspheres. Acrylonitrile confers polar characters onto the sorbents, and the acrylonitrile-derived nitrile groups can be chemically transformed via polymer-analogous reactions into thioamide and sulfonated residues which make the sorbents even more suitable for the capture of polar analytes, including selected pharmaceuticals. The utility of the porous thioamide-sulfonated containing sorbents is demonstrated via the dispersion-solid phase extraction of mefenamic acid from aqueous media; mefenamic acid is an anthranilic acid derivative which is a potent, non-steroidal anti-inflammatory drug which is found in environmental waters at low concentrations.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

To control the volatility and reduce the electricity costs for indoor farming, the agrivoltaics agrotunnel introduced here uses: 1) high insulation for a building dedicated to vertical growing, 2) high-efficiency LED lighting, 3) heat pumps (HPs), and 4), solar photovoltaics (PV) provide known electric costs for 25 years. In order to size the PV array, this study develops a thermal model for agrotunnel load calculations and validates it using the Hourly Analysis Program and measured data so the effect of plants’ evapotranspiration can be included. HPs are sized, plug loads (i.e., water pump energy needed to provide for the hybrid aeroponics/hydroponics system, DC power running the LEDs hung on grow walls, and dehumidifier assisting in moisture condensation in summer) are measured/modeled. Ultimately, all models are combined to establish an annual load profile for an agrotunnel that is then used to model the necessary PV to power the system throughout the year. The results find that agrivoltaics to power an agrotunnel range from 40 to 50kW and make up an areas from 3.2 to 10.48 m²/m² of an agrotunnel footprint. Net zero agrotunnels are technically viable although future work is needed to deeply explore the economics of localized vertical food growing systems.

Significant amount of oil remains in the reservoir after primary and secondary operations and to recover the remaining oil, enhanced oil recovery (EOR) can be applied as one of the feasible options remaining nowadays. In this study new nano polymeric materials have been prepared from purple yam and cassava starches. The yield of purple yam nanoparticles (PYNPs) was 85% and that of cassava nanoparticles (CSNPs) is 90.53%. Synthesized materials were characterized through particle size distribution (PSA), zeta potential distribution, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC) and transmission electron microscopy (TEM). The performance of PYNPs in recovering oil was better than CSNPs as found from recovery experiments. Zeta potential distribution results confirmed the stability of PYNPs over CSNPs (-36.3 mV for PYNPs and -10.7 mV for CSNPs). The optimum concentration for these nanoparticles has been found from interfacial tension measurements and rheological properties and it was 0.60 wt.% for PYNPs and 0.80 wt.% for CSNPs. More incremental recovery (33.46%) has achieved for the polymer that contained PYNPs in comparison to the other nano-polymer (31.3%). This opens the inception for a new technology for polymer flooding that may replace the conventional method that depends on partially hydrolyzed polyacrylamide (HPAM).

Primary stability is an important factor for dental implant success. In the past years, a new method for bone site preparation was introduced, named osseodensification (OD). OD produces a condensation of the trabecular portion of the bone, increasing bone-to-implant contact and primary stability. This study aims to compare the effect of OD in cylindrical and conical implants to conventional instrumentation. A total of 40 implants, divided in 4 groups, were placed in porcine tibia: cylindrical conventional (1a); cylindrical OD (1b); conical conventional (2a); and conical OD (2b). Each implant was measured for implant stability quotient (ISQ), insertion torque (IT) and removal torque (RT). Group 2b showed the higher values for each of the evaluated parameters; groups 1b and 2b showed better results than 1a and 2a, respectively. Regarding IT and RT, group 1b achieved higher values than group 2a, but not for ISQ. The inter-groups comparison showed significant difference between groups 1a vs 2a, 1a vs 2b and 1b vs 2b for ISQ, and 1a vs 1b and 1a vs 2b for RT analysis. OD resulted in improved ISQ, IT and RT of both cylindrical and conical implants. Conical implants groups showed higher ISQ, but cylindrical OD resulted in higher IT and RT, compared to conical conventional ones.

Plants are an important feature of urban ecosystems which provide numerous environmental and ecosystem benefits such as defenses against noise and air pollution and conservation of biodiversity. The aim of this study was to investigate the structure and composition of urban vegetation in different urban habitats like roadsides, parks, gardens and playgrounds in Dhaka South City area. Stratified random sampling method was used in this study. A total of 221 plant species belonging to 63 families were identified and recorded. Among all plant species Swietenia macrophylla, Polyalthia longifolia, Cocos nucifera, Samanea saman, and Artocarpus heterophyllus are recorded as the most dominant. Most of the tree and shrub population were found between 6-9 m and 1-3m height classes whereas most of tree and shrub population were found in between 10-15cm dbh classes. Highest IVI was found for Swietenia macrophylla (193.22%) followed by Polyalthia longifolia (184.59%), Samanea saman (138.37%), Cocos nucifera (79.9%) and Delonix regia (68.27%) respectively. Average frequency, density, dbh and basal area were found 46.82%, 138.28 tree ha^-1, 458.59 cm ha^-1 and 12.33 m² ha^-1 respectively. Findings of this study reveals that structural attributes of plant represent quite young and still developing vegetation. This research will help to plan for future green infrastructure which will maintain ecosystem function, therefore, providing longer term benefits for the city dwellers.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.

This study introduces a novel approach by proposing a dressing impregnated with vancomycin and chlorhexidine for postoperative care, specifically targeting patients undergoing orthopedic surgeries. The research design involved a pilot study with a randomized control group, aiming to evaluate the vancomycin with chlorhexidine dressing's efficacy and safety. A total of 12 patients used impregnated dressing and 9 used the same dressing without impregnation. Despite limitations, including the small sample size and single-center study location, our findings demonstrate the safety of the impregnated dressing in trauma surgeries, indicating potential applicability in broader surgical contexts. In conclusion, this study contributes to the discourse on preventive strategies for SSIs, presenting a pioneering approach with the vancomycin and chlorhexidine-impregnated dressing. Future research endeavors, incorporating larger-scale studies and addressing study limitations, are crucial for advancing the understanding and implementation of effective postoperative care strategies.

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.

This study assessed the performance of low-dose CT lung scans across various acquisition parameters and examined the impact of iterative reconstruction (IR) algorithms on image quality for lung cancer screening. The performance of multi-slice CT scanners was assessed by using a Catphan 600 phantom, specifically within the CTP404 test module that contains materials like Teflon, Delrin, Acrylic, Polystyrene, LDPE, PMP, Air, and water. Three protocols were selected and categorized into standard, low dose, and ultra-low dose. The standard protocol featured a tube voltage of 100 kVp for single-detector CT, a pitch of 0.75, detector collimation of 0.60 mm, and a gantry rotation time of 0.33 seconds, with 5 mm slice thickness. Images were reconstructed using standard kernels like B80f and 170f, combined with either FBP or various IR algorithms. Findings indicated that the noise power spectrum (NPS) peak value (HU2mm2) increased with higher IR levels, with FBP showing the highest peak. Additionally, NPS spatial frequency diminished as IR levels rose. Optimizing contrast and spatial resolution for both background and regions of interest involved adjusting the target transfer function (TTF) and contrast-to-noise ratio (CNR), crucial for protocol optimization. Proper adjustment of IR levels is vital, as higher levels can alter image texture and affect NPS spatial frequency. The results of this study showed that the use of IR algorithms in low-dose CT lung scans significantly improved image quality, particularly in terms of noise reduction and spatial resolution. However, the choice of acquisition parameters also played a crucial

Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we will discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.

Coronavirus Disease 2019 (COVID-19) exhibits a significant variation in presentation of symptoms ranging from mild to severe life-threatening symptoms, including death. COVID-19 infection susceptibility has been linked with various covariates, but studies in Qatar are limited. Data from Qatar Biobank (QBB) (n = 10,000; 18- to 79-year-old adults) were analyzed for associations between sociodemographic and lifestyle factors and susceptibility to COVID-19. Logistic regression was used to assess the associations between COVID-19 and age, gender, body mass index (BMI), smoking status, education level, dietary patterns, supplement use, physical activity, history of bariatric surgery, diabetes, and hypertension. In total, 10.5% (n = 1045) of the participants had COVID-19. Compared to non-smokers, current and ex-smokers had lower odds of having COVID-19 (odds ratio [OR]= 0.55; 95% CI: 0.44-0.68 and OR= 0.70; 95% CI: 0.57-0.86, respectively). Vitamin D supplement use was associated with an 18% reduction of the likelihood of contracting COVID-19 (OR = 0.82; 95% CI: 0.69-0.97). Obesity (BMI ≥30 kg/m2), history of bariatric surgery and higher adherence to the modern dietary pattern– characterized by consumption of foods high in saturated fat and refined carbohydrates– were positively associated with COVID-19. Our findings indicate that adopting a healthy lifestyle may be helpful in the prevention of COVID-19 infection.

High insertion torques increase the primary stability of dental implants, which might be beneficial for immediate loading protocols, but may lead to local ischemia and necrosis of the bone-implant interface. This study compared the outcomes of implants placed with low (&lt;45Ncm), conventional(45-70Ncm), and high (≥75Ncm) insertion torques in All-on-4 concept cases with up to 2-year follow-up. Patients included in this study presented edentulous or partially dentate arches with an indication for full-arch rehabilitation. Implants were installed following the All on four protocol and were divided into 3 groups: Low, Conventional, and High torque, and were followed periodically for 2 years with ISQ measurements. The results showed no significant differences in success rates between the three groups. This suggests that high insertion torques may maintain higher primary stability values for immediately loaded implants during the healing period. Further studies are necessary to confirm these findings.

Crop plants should be resilient to climatic factors in order to feed ever-increasing populations. Plants have developed stress-responsive mechanisms by changing their metabolic pathways and switching the stress-responsive genes. The discovery of plant transcriptional factors (TFs) as key regulators of different biotic and abiotic stresses have opened up new horizons for plant scientists. TFs perceive the signal and switch certain stress-responsive genes on and off by binding to different cis-regulatory elements. The above 50 species of plant TFs have been reported in nature. DREB, bZIP, MYB, NAC, Zinc-finger, HSF, Dof, WRKY, and NF-Y are important with respect to biotic and abiotic stresses whereas the role of many TFs is yet to explore. In this review, we summarize the role of different stress-responsive TFs with respect to biotic and abiotic stresses. Further, challenges and future opportunities linked with TFs for developing climate-resilient crops are also elaborated.

The popularity of functional gummies has increased, which is evident from the growing line of functional gummies from almost every nutraceutical companies. Sensory evaluation serves the purpose of determining which brand of functional gummy would capture the largest market share. Texture profile analysis was used to determine the mechanical properties of functional gummies. The brands of functional gummies that came under the scope of this study were denoted as Brand A, B, C, D, E, F and G. Fourier Transform Infrared Spectroscopy was utilised to detect organic material and functional groups in the functional gummies. Texture profile analysis gave valuable insights into the gummies’ mechanical properties which are cohesiveness, springiness, hardness, gumminess, and chewiness. Amongst the gummies that were studied, Brand F gummy has the highest value of cohesiveness of 0.92. Brand A gummy has a high springiness value of 1.0. Brand B gummy possesses the highest value of hardness, gumminess and chewiness of 12 532.2 g, 7617.6 N, and 6256.8 J, respectively. Qualitative sensory evaluation reveals that Brand G gummy has the best aesthetic qualities in terms of colour and appearance. Brand B gummy tastes the best while brand A gummy claims the top spot for gumminess and chewiness. Overall, the respondents in this study preferred brand A gummy over other brands.

Background: The UNHLM pledged to enroll 30 million in TB preventive treatment (TPT) by 2022, necessitating TPT expansion to all at TB risk. We assessed uptake and completion of a 12-dose, weekly isoniazid-rifapentine (3HP) TPT regimen. Methods: Between February 2018 - March 2019 in Dhaka, community-based TPT using 3HP targeted household contacts of 883 confirmed drug-sensitive pulmonary TB patients. Adhering to WHO guidelines, contacts underwent active TB screening before TPT initiation. Results: Of 3,193 contacts who advised health facility visits for screening, 67% (n=2,149) complied. Among these, 1,804 (84%) received chest X-rays. Active TB was diagnosed in 39 (2%) contacts; they commenced TB treatment. Over 97% of 1,216 contacts began 3HP TPT, with completion rates higher among females, those with more education and income, non-slum residents, and those without 3HP-related adverse events. Adverse events, mainly mild, occurred in 5% of participants. Conclusions: The 3HP weekly regimen, with its short duration, self-administered option, and minimal side effects, achieved satisfactory completion rates. A community-focused 3HP TPT approach is feasible, scalable nationally, and aligns with UNHLM targets.

Some global difficulties in the modern period include energy storage conversion. Researchers have looked to renewable energy resources to discover long-term answers to this dilemma. Fuel cells are one example of prospective energy producing technology. Although these technologies appear to be very enticing, they are known to be expensive due to high expenses. Catalyst costs, as well as concerns concerning energy density. Pt based Electrocatalysts have traditionally been used to drive ORR and offer efficient power outputs. However, cost constraints have limited the use of these electrocatalysts, rendering them inefficient. Apart from the high price, Pt-based Electrodes are prone to time-dependent drift and CO deactivation, both of which occur gradually. Electrocatalysts deteriorate over time, reducing the efficiency of fuel cells. This is why alternative electrocatalysts are in high demand for future energy applications. This review covers the comparative study of Platinium and Non-Platinium based electrocatalyst in fuel cells, their shortcomings and recent advancement for efficient and optimized electrocatalyst.

The issue of sufficient nutrition and appropriate distribution in school lunch programs presents a significant challenge that must be addressed. Many schools struggle to provide food that meets all of their students’ nutritional needs. Additionally, inconsistencies in nutritional information and administrative difficulties often hinder the efficient operation of these programs. Developing technology to better manage school lunch programs is critical to addressing these issues. The goal of this project is to create a web application that ensures the fair distribution of nutritional information while also increasing the efficiency of lunch program administration. The methodology used in this research is Agile. Agile approaches are employed throughout the development process, allowing for rapid iteration and responsiveness to user feedback. The end result is a web tool called Nourishify, designed to streamline the management and dissemination of nutritional information. Implementing Nourishify led to substantial improvements in access to nutrition information and operational efficiency, making it an innovative solution for school lunch programs. This project demonstrates that integrating technology can significantly enhance the administration and transparency of nutrition programs, ultimately benefiting students' health and well-being. Moreover, Nourishify’s user-friendly interface and real-time data capabilities ensure that all stakeholders, including parents, administrators, and nutritionists, can easily collaborate and access crucial information.

The orthodontic treatment brings numerous benefits and, in most cases, the benefits outweigh the possible disadvantages. Root resorption (RR) is a common adverse phenomenon associated with orthodontic treatment. This study evaluates the role of low-level laser emission / Photobiomodu-lation (LE/P) in quantitative measurements of root resorption (QRR). The application of LE/P was done after each orthodontic activation with 4 types of treatment intervention (TI) on the RR after fixed orthodontic treatment (FOT) of the upper arch with ectopic eye tooth/teeth [EET]. 32 Orthodontic patients scheduled for FOT were selected and assigned to the 4 groups. These were LE/P+Self ligating bracket (SLB), LE/P+Conventional bracket (CB), Non-Photobiomodulation (non-LE/P)+SLB, and non-LE/P+CB. Standard management stages of FOT were followed in the maxilla. Each patient received a single application of LE/P labially/buccally and palatally, a total of 5 different points during each activation or appointment. The main outcome measure was QRR in maxillary anteriors before and after FOT assessed via cone-beam computed tomography (CBCT) using 3D OnDemand software. Insignificant QRR was found between before and after FOT in SLB, CLB, and LE/P, non-LE/P groups (p > 0.05). QRR in the SLB vs CB and LE/P vs non-LE/P group was significantly different in 11, 13, and 23 (p < 0.05). QRR in the LE/P+SLB group (p < 0.05) was significantly different in 11, 13, and 23 than that in the other groups. The most severe QRR was found on the 13 (0.88 ± 0.28mm and 0.87±0.27mm) and 23 (1.19 ± 0.14 mm and 1.16±0.13mm) in the CB and non-LE/P group (p < 0.001). LE/P+SLB showed highly significant superior outcome (p < 0.001) in relation to non-LE/P+CB, the QRR of 23 were 0.813± 0.114mm and 1.156± 0.166mm respectively. Significantly higher amount of QRR found in EET patients after FOT treated with the CB, non-LE/P, and non-LE/P+CB system and warrants further investigation to explore potential specific causes.

Vaccine hesitancy substantially impacts global vaccination rates. During the COVID-19 pandem-ic in Germany, vaccination uptake exhibited considerable regional disparities. To assess the fac-tors contributing to this variation, we examined the influence of sociodemographic variables on COVID-19, COVID-19 booster, and influenza vaccinations within a cohort of 37,078 participants from 13 German federal states in the digital health cohort study, DigiHero. Our findings re-vealed variations in vaccination rates based on sociodemographic factors. However, these fac-tors had limited explanatory power regarding regional differences in vaccine uptake. In contrast, we found substantial correlations between regional support of specific parties during last local elections and the vaccination uptake at Landkreis-level. In conclusion, sociodemographic factors alone did not suffice to explain the regional disparities in vaccine uptake. Political stances can play a major role, but the current investigation did not assess the individual political orienta-tion, but used only ecological approach.

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

In this review, we covered the recent advances in synthesis of gold nanoparticles and their uses in degradation of dyes. This study provides framework to develop a low cost, ecofriendly and highly efficient synthesis of gold nanoparticles. These synthetic methods do not produce any toxic byproduct. The present study is focus on removal of dyes by Au-NPs because gold nanoparticles act as good absorbent for dyes in short time. Synthesis of Au-NPs from plant extract e.g. marine alga, Scutellarin Barbata, A.nigra, Fruit peels, Bacillus marisflavi from raw silk cocoons, amylopectin and poly acrylic acid, L.asparagine, Grephene oxide, IPEI coated Au-NPs. The synthesizes Au-NPs were used further for removal of dyes like methylene blue (MB), Rhodamine B (RB) Degradation, methylene orange, acid red degradation, Congo red.

Objectives: There are limited data regarding the efficacy of methylprednisolone in patients with acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) requiring invasive mechanical ventilation. We aimed to determine whether methylprednisolone increases the number of ventilator-free days (VFDs) among these patients. Design: Retrospective single-center study Setting: Intensive care unit Patients: All patients with ARDS due to confirmed SARS-CoV-2 infection and requiring invasive mechanical ventilation between 1 March and 29 May 2020 were included Interventions: None Measurements and Main Results: The primary outcome was ventilator-free days (VFDs) during the first 28 days, defined as being alive and free from mechanical ventilation. The primary outcome was analyzed with competing-risks regression based on Fine and Gray’s proportional subhazards model. Death before day 28 was considered to be the competing event. A total of 77 patients met the inclusion criteria. Thirty-two patients (41.6%) received methylprednisolone. The median dose was 1 mg.kg-1 (IQR: 1-1.3 mg.kg-1) and median duration of 5 days (IQR:5-7 days). Patients who received methylprednisolone had a mean 18.8 VFDs (95% CI, 16.6-20.9) during the first 28 days vs. 14.2 VFDs (95% CI, 12.6-16.7) in patients who did not receive methylprednisolone (difference, 4.61; 95% CI, 1.10-8.12; P = 0.001). In the multivariable competing-risks regression analysis and after adjusting for potential confounders (ventilator settings, prone position, organ failure support, severity of the disease, tocilizumab, and inflammatory markers), methylprednisolone was independently associated with a higher number of VFDs (subhazards ratio: 0.10, 95%CI: 0.02-0.45; p=0.003). Hospital mortality did not differ between the two groups (31.2% vs. 28.9%, p=0.82). Hospital length of stay was significantly shorter in the methylprednisolone group (24 days [IQR:15-41 days] vs. 37 days [IQR:23-52 days], p=0.046). The incidence of positive blood cultures was higher in patients who received methylprednisolone (37.5% vs. 17.8%, p=0.052). However, 91% of patients who received methylprednisolone also received tocilizumab. The number of days with hyperglycemia was similar in the two groups. Conclusions: Methylprednisolone was independently associated with increased VFDs and shortened hospital length of stay. The combination of methylprednisolone and tocilizumab was associated with a higher rate of positive blood cultures. Further trials are needed to evaluate the benefits and safety of methylprednisolone in moderate or severe COVID-19 ARDS.

The objective of the research was to examine the determinants that determine farmer participation and shallot farming development in search of effective farm management practices. The study used Structural Equation Modeling data analysis. The primary data was collected from direct-structural interviews with 150 randomly chosen shallot farmers in Bantaeng Regency, Indonesia. It was found that the physical attributes of the land, the economic framework of the community, and the political system of the agricultural community were fundamental factors that exerted a positive and significant influence on farmer participation. Therefore, improvements in the physical aspects of land, the economic framework, and the political structure of agricultural communities could promote farmer participation. Furthermore, the farmers’ participation and the political structure of agricultural communities have a positive and significant impact on the development of shallot farming. Thus, by increasing the influence of government officials and community leaders, shallot farming can be promoted. The farmers can then enhance their participation in the shallot farming plan's formulation and implementation, providing the continued development of shallot farming. The findings of this study contribute significantly to the body of knowledge by validating previous research and proposing different ways to improve effective farm management practices in shallot farming.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p &lt; 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

The impact of interaction SS with MJO on rainfall along Java Island to East Nusa Tenggara has been studied using daily rainfall observation data from 2140 weather observation stations, which are also equipped with GSMaP, OLR, and ERA5 data. There were 61 SS events found, 17 during active MJO and 44 in inactive MJO. Rainfall increases more in the western study area when the active MJO and more in the eastern study area when the inactive MJO is due to SS. SS reduces rainfall in the Jakarta region, regardless of the activity or inactivity of the MJO. SS causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the IMC, SS predominates over MJO in supporting increased water vapor transport. Large-scale synoptic circulations like the monsoon, SS, and MJO can interact with local land-sea wind circulation to produce spatiotemporal variability in rainfall on Java Island. Rainfall mostly increases in the afternoon and decreases in the morning when SS occurs, whether there is MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by lowering CIN and raising VIMFC.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Pyrops candelaria is one of the common pests of fruit trees, but the research on the pathogenic microorganisms it may carry is very limited. Therefore, it is essential to reveal the pathogenic microbes it carries and their potential hazards. This study found a new virus from the transcriptome of P. candelaria, which was first reported in P. candelaria and named as PyCaV (Pyrops candelaria associated virus). RACE and bioinformatics assay revealed that the full-length of PyCaV is 10855 bp with the polyA tail, containing a single open reading frame (ORF) encoding a polyprotein consisting of 3171 amino acid (aa). The virus has a typical Iflavirus structure, including two rhv domains, a RNA helicase domain (HEL), a 3C cysteine protease domain (Pro), and a RNA-dependent RNA polymerase domain (RdRp). Further phylogenetic analysis revealed that this virus belongs to family Iflaviridae and sequence alignements analysis suggedted PyCaV is new member in unassigned genus of family Iflaviridae. Further in-depth analysis of virus-infection-part showed that PyCaV is distributing in the whole longan lanternfly, including its head, chest, and abdomen, but more PyCaV was identified in the chest. The distribution of PyCaV in different parts of P. candelaria was further explored, which showed that more PyCaV were detected in its piercing-sucking mouthparts and chest viscera. Statistical analysis showed that the PyCaV infection was affected by time and locations.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

Our previous studies demonstrated a tight connection between simvastatin (SIM) inhibition of B16.F10 murine melanoma cell proliferation in vivo and in vitro and strong suppression of the subunit α of hypoxia inducible factor 1 (HIF-1) (HIF-1 α) production in these cancer cells. Since the translation of HIF-1α is modulated by signaling pathways coordinated by Akt, SIM effects on the link between these two regulatory factors and cancer cell metabolism under normoxia was investigated, which to our knowledge have never been described before. To achieve the proposed aim, SIM effects on cell metabolism were analysed at mRNA (via RNA-seq and RT-qPCR) as well as protein level (via western blot and catalytic activity of the key enzymes) Our data suggested that SIM might orientate glucose transport and metabolism to ensure replenishment of tricarboxylic acid (TCA) cycle activity. Moreover, it seems that SIM enhanced biosynthetic role of TCA over its energy role to further support synthesis of lipid-derived signaling molecules. Thus, under normoxia SIM treatment orientated lipid metabolism to stimulate isoprenoid and prostaglandin synthesis which might favor melanoma cell survival and aggressiveness.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

In this paper, we extend the KdVSKR equations in (1+1)-dimension and (2+1)-dimension to fractional KdVSKR equations with modified Riemann-Liouville derivative. The (2+1)-dimensional KdVSKR equation, which is a recent extension of (1+1)-dimensional KdVSKR equation, can model the resonances of solitons in shallow water. By means of the Hirota bilinear method, finite symmetry group method and consistent Riccati expansion method, many new interaction solutions have been derived. Soliton-cnoidal interaction solution of the (1+1)-dimensional fractional KdVSKR equation has been derived for the first time. For the (2+1)-dimensional frac-tional KdVSKR equation, two-wave interaction solutions and three-wave interaction solutions, including dark-soliton-sine interaction solution, bright-soliton-elliptic interaction solution, and lump-hyperbolic-sine interaction solution, have been derived. Impact of the fractional order on the shapes of these solutions has been illustrated by figures. Three-wave interaction solutions of fractional systems have not been reported in the existing references. The research idea in this paper can be applied to other fractional differential equations.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

Antimicrobial peptides represent a promising alternative to traditional drugs in relation to cost, toxicity and, primarily, the growing problem of drug resistance. Here we report on the activity against HSV-1 and HSV-2 of a previously described wide-spectrum synthetic decapeptide, Killer Peptide (KP). As determined by plaque reduction assays, treatment with KP at 100 μg/mL resulted in a reduction in the viral yield titer of 3.5 Logs for HSV-1 and 4.1 Logs for HSV-2. Further evaluation of KP antiviral activity focused on the early stages of virus replicative cycle, including the determination of the residual infectivity of viral suspensions treated with KP. A direct effect of the peptide on viral particles, impairing virus absorption and penetration, was shown. The toxicity profile proved to be extremely good with a selectivity index of 29.6 for HSV-1 and 156 for HSV-2. KP was also active against an acyclovir (ACV)-resistant HSV-1 strain, while HSV-1 subcultures in the presence of sub-inhibitory doses of KP did not lead to the emergence of resistant strains. Finally, the antiviral action of KP proved to be synergistic with that of ACV. Overall, these results demonstrate that KP could represent an interesting addition/alternative to acyclovir for antiviral treatment.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

In electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel EMAT (electromagnetic acoustic transducer)-PEC (pulse eddy current) composite sensor is designed. We use amplitude of bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on amplitude of bottom echo are distinguished combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect the bottom crack as small as 0.1mm* 0.3mm. The signal fusion method can accurately correct the amplitude of defect signal and the relative error is less than ±8%.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

Abstract: Transcription factors are crucial pre-transcriptional regulatory mechanisms that can modulate the expression of downstream genes by binding to their promoter regions. DOF (DNA binding with One Finger) proteins are a unique class of transcription factors with extensive roles in plant growth and development. Our previous research indicated a decrease in iron content in chlorotic bamboo leaves. However, to our knowledge, genes related to iron metabolism pathways in bamboo species have not yet been studied. Therefore, in the current study, we identified iron metabolism-related (IMR) genes in bamboo and determined the transcription factors that significantly influence them. Among these, DOFs were found to have widespread effects and potentially significant impacts on their expression. We identified specific DOF members in Dendrocalamus latiflorus with binding abilities through homology with Arabidopsis DOF proteins, and established connections between some of these members and IMR genes using RNA-seq data. Additionally, molecular docking confirmed the binding interactions between these DlDOFs and the DOF binding sites in the promoter regions of IMR genes. The co-expression relationship between the two gene sets was further validated using q-PCR experiments. This study paves the way for research into iron metabolism pathways in bamboo and lays the foundation for understanding the role of DOF transcription factors in D. latiflorus.

Tussilago farfara L. is a traditional medicinal plant valued for its potentially health-promoting metabolites. Its herbal raw material has been recognized and used since ancient times and continues to be widely used in traditional medicine. Introducing these plants to in vitro cultivation is a challenging task, but once the protocol is developed, such cultures can provide an abundant and inexhaustible source of plant material. In this study, we report the successful induction and growth of vigorous T. farfara L. callus in vitro. Callus induction was achieved on solid media in darkness, whereas it appeared inefficient under light conditions and in suspension culture. We present a detailed description of callus growth kinetics, morphological analysis, photosynthetic activity and biochemical parameters (including protein content and photosynthetic pigments) supported by histological studies. Furthermore, we observed potential for organogenesis and somatic embryogenesis. This method for in vitro propagation of T. farfara, along with callus culture maintenance, offers a wide range of applications in pharmacy and could benefit the environment by minimizing the depletion of natural populations of this species.

Surgical site infections (SSIs) are a major concern in public healthcare, leading to increased morbidity, pro-longed hospital stays length, and significant economic costs. Understanding the epidemiology and risk fac-tors of SSIs is crucial for developing effective prevention and management strategies. In the last century, CDC ‘s HICPAC improved a bundle for prevention of HAIs based on the use of aseptic procedures, disin-fection of the skin and correct use of antibiotics for SSIs. In our country, Gelli-Bianco law introduced the national health guidelines system that include the prophylaxis antibiotic therapy pre- and post-surgery. The aim of our study is to evaluate adherence and appropriateness of the use of antibiotic-preoperative prophylaxis in a province of southern Italy and to identify risk factors that may influence the development of an SSI. The study found many statistically significant associations among the factors examined, i.e. the timing of administration adherent to guidelines, with 47% of the analyzed files administering antibiotics within 60 min before the intervention and 62.7% using intravenous administration.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) continues to be a global threat due to its ability to evolve and generate new subvariants, leading to new waves of infection. Additionally, other coronaviruses like Middle East respiratory syndrome coronavirus (MERS-CoV, formerly known as hCoV-EMC) which first emerged in 2012, persists and continues to present a threat of severe illness to humans. The continued identification of novel coronaviruses, coupled with the potential for genetic recombination between different strains, raises the possibility of new coronavirus clades of global concern emerging. As a result, there is a pressing need for pan-CoV therapeutic drugs and vaccines. After extensive optimization of an HCV protease inhibitor screening hit, a novel 3CLPro inhibitor (MK-7845) was discovered and subsequently profiled. MK-7845 exhibited nanomolar in vitro potency with broad spectrum activity against a panel of clinical SARS-CoV-2 subvariants and MERS-CoV. Furthermore, when administered orally, MK-7845 demonstrated a notable reduction of viral burdens by &gt;6 log orders in the lungs of transgenic mice infected with SARS-CoV-2 (K18-hACE2 mice) and MERS-CoV (K18-hDDP4 mice).

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.