Permafrost covers about 23% ~ 25% of the land in the northern hemisphere, mainly in Russia, Canada, Alaska, Northeast and Qinghai Tibet Plateau (QTP) in China. Many engineering especially the transportation has been in the permafrost regions above. Under the action of highways construction and global warming, permafrost under infrastructures is degraded and causes serious damage. Two phases closed thermosyphon (TPCT) is a widely accepted green countermeasure against the problem in permafrost regions. Although it has been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper focused on the thermosyphon technology and application in the permafrost. Moreover, the article highlighted the excellent working performance of the TPCT that improving the stability of the infrastructures and prevent it degrading due its excellent efficiency of heat transfer. The industrial applications of the TPCT were also summarized, along with their limitations. Finally, the results reported in this paper can provide a valuable guidance for the design of the TPCT and construction of the permafrost regions in the future.

In "genome editing," a type of genetic engineering, DNA is added, taken away, or changed in an organism's genome. Biomedicine, biotechnology, and synthetic biology, just to name a few, have all gained a lot from how often this method is used. Before the editing process can start, a Double-Strand Breaks (DSB) must be made in the DNA at a specific gene. Researchers have made nucleases, which are sometimes called "molecular scissors," to fix this DSB. Transcription activator-like effector nucleases (TALENs), Zinc-finger nucleases (ZFNs), and homing endonucleases are all examples of protein targets that have been studied and changed. The last part gave an overview of these study projects and explained how to make Cas12a for specific uses. The applications of Cas12a have been extensively studied in recent years, but the protein still holds a lot of promise as a treatment and screening tool. In order to give a quick review of CRISPR-Cas12a and its uses, we will briefly talk about the structure and function of the different parts of the reaction pathway that lead up to the catalysis of the target DNA. Cas12a uses a multistep process to make sure that it is selecting the right DNA. This is a good trait for a device that changes the DNA because it makes it less likely that something bad will happen. Even though data shows that a new CRISPR RNAs (crRNA) molecule can stop Cas12a from randomly destroying ssDNA, it may still hurt the host cell while trying to change the genome. The action of Cas12a catalysis, which is a powerful tool for changing the genome, needs to be changed so that it can be more easily controlled and managed. Using what we know about how Cas12a works, we have made mutants that are less active on ssDNA and more active on dsDNA. So far, only Cas9 and Cas12a, which are both part of the CRISPR family, have been used to change the genome. Because of how similar and different these two endonucleases are, CRISPR can now be used for multiple scientific purposes. Cas12a is better than Cas9 because it makes double-strand breaks (DSBs) that favor Homology-Directed Repair (HDR) over Non-homologous end joining (NHEJ) and can change more than one copy of the genome at the same time. Both Cas9 and Cas12a are being changed to make them better at detecting Protospacer Adjacent Motif (PAM) than they are in their natural state. This will make it possible to start focusing on more genes.

This article describes a novel topical composition constituted by gamma-amino butyric acid (GABA) and microbial chondroitin sulfate. This composition exhibits potential for non-invasive intervention in anxiety management, sleep quality enhancement, and promotion of brain-related functions like meditation and nonlocal consciousness. The core component comprises GABA, a key inhibitory neurotransmitter within the central nervous system, complexed with a suitable carrier molecule, microbial chondroitin sulfate. This complexation aims to enhance transdermal absorption and optimize delivery of GABA to target tissues. The proposed mechanism of action involves modulation of GABAergic signal transduction pathways. Application of the topical composition allows GABA penetration into the skin and potentially deeper tissues, where it interacts with GABA receptors. This interaction leads to mental calmness and relaxation as assessed by electroencephalography. The influence of GABAergic signaling on brain circuits demonstrated in this article, when associated with meditation and nonlocal consciousness, suggests potential for the composition to support these practices. The advantage of such an approach consists in avoiding the potential systemic side effects associated with oral or injectable administration of GABA, anxiolytics or hypnotics. Furthermore, the combination of GABA and microbial chondroitin sulfate may offer synergistic benefits, potentially enhancing the overall biological effects. In conclusion, this article presents a novel topical approach for managing anxiety, enhancing sleep quality, and potentially facilitating meditation and nonlocal consciousness through modulation of GABAergic signaling pathways. While further research is necessary, the potential for a non-invasive and potentially side-effect-free intervention in these areas is promising.

Nanoindentation load-depth (P-h) plots is the basis for determination of mechanical properties of the samples and is especially vital for thin coatings deposited on substrates of various mechanical makeup from amorphous-brittle (borosilicate glass) to Silicon (having different crystallographic texture) to ductile substrates like stainless steel as well other aspects like indenter tip shape. The elastic-plastic response by simulated P-h plots as per modified empirical relations have been presented. The outcomes of this computational studies enlighten the database on the P-h plots for various coating/substrate systems and shall aid in the analysis of experimental P-h plots obtained,

Among other fossil fuels, natural gas is a major energy source. It is generally created saturated with water vapor. Dehydration of natural gas is vital in the gas business to remove water vapor from the gas supply, which may cause hydrate development in pipes. Gas dehydration employs Tri ethylene glycol (TEG) to remove water vapor from natural gas flow. The wet gas is dehydrated with lean glycol in an absorber, and the rich glycol is recovered and reused. This work investigates the use of dry natural gas instead of nitrogen in the glycol dehydration re-generator, and compares the results using HYSYS modeling and simulation. The two techniques were compared for capital and utility expenses, while maintaining the identical glycol purity criteria. The wet gas from the stripping mechanisms may also be utilized to run steam pumps and compressors or recycled. The model is based on the real mechanism flow diagram. Finally, the findings of this model might be used to design a new heat and material balance for the plant.

Solid-state transformer (SST) is a promising technology for smart grids due to its flexible power control (better reliability) and high efficacy (by decreasing losses) compared with traditional transformers. The SST is designed in tandem with three stages, i.e., the input, isolation, and output stages. As three converter stages are used to design the SST to make a more reliable device in practice, it is highly prone to instabilities. Moreover, the key objective of SST design is to implement a modern power distribution system to make it more intelligent and reliable. The stability issue can be even more complicated by the presence of distributed energy resources in the distribution system. Thus, the stability of SST must be measured prior to /during the design, and to determine, a state-space model is used, which is developed in this paper. Each stage of SST is modeled with its controller, and the system's stability is measured through the controllability and observability test. Further, the conversion from state-space to transfer function model is done, and the stability of SST is shown using frequency and time-domain diagrams. With these, the different plots, namely, the Bode plot, Nyquist plot, Nichols chart, Root locus, pole-zero plot, and Eigen plot, are used to ascertain the relative and absolute stability. Finally, the SST Simulink model is tested and validated through hardwate-in-the-loop simulation, i.e., OPALRT, to show its effectiveness and applicability.

Traditional methods of non-metallic materials joining for industrial uses are limited to lasers, or induction or conduction heating. The precision and efficiency for large-scale applications are difficult to be achieved at the same time. Here in this paper, we report a precise microwave welding method induced by localized surface plasmon of metallic macro-gaps under microwave excitation. The proposed method utilizes energy from well-developed commercial microwaver, and benefits from the precision of localized surface plasmon, an oscillating electron cloud excited by electromagnetic waves, which is currently under hot investigation only at micro- or nano-scales. The method is applied to the welding of polymers to solve the problems of complex processing equipment and high processing energy consumption in the traditional welding process, so as to cope with the growing energy and environmental crisis. Finite element numerical simulations are used to study the distribution characteristics of electric fields and temperature fields. The results show that the process is a high quality, efficient, low consumption and environmentally friendly green process compared to traditional polymer welding methods.

The cycling of soil phosphorus (P) is inherently linked with soil organic carbon-iron (C-Fe) cycling, yet empirical integration of these processes within paddy soils remains scarce. In this study, we conducted a microcosm experiment using paddy soils subjected to six distinct fertilization regimes involving varying P inputs for five years. In addition to evaluating P activation under reflooding conditions, we assessed the Fe reduction process and characterized the properties of dissolved organic matter (DOM) at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), alongside profiling the composition of soil microbial communities with high-throughput sequencing. Our findings revealed that after 25 days of reflooding, soil Olsen-P content increased by an average of 73% compared to its initial state, showing a strong correlation with the Fe reduction process. Specifically, treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation, highlighting the role of organic matter in facilitating Fe reduction. Investigations on the relative abundance of typical iron-reducing microbes further supported their importance in P activation, but the rate of iron reduction is limited by soil organic matter content. Delving deeper into DOM properties, soil DOM composition profiling and network analysis suggested that high-molecular-weight DOM, particularly lignins, served as the primary resources driving Fe reduction by iron-reducing microbes, consequently promoting Fe reduction and P release. Taken together, our study assembled the C-Fe-P cycling dynamics in paddy soils, emphasizing the pivotal role of microbial-driven Fe reduction facilitated by soil DOM in P availability and subsequently sustainable agricultural practices.

This study investigated how broader parental factors including parental happiness, parental play engagement, and parenting stress are related to Korean children’s happiness and weight status across three years via indirect pathways through the children’s energy-related behaviors of healthy and unhealthy food intake, physical activity, and screen time. Data from 1551 Korean parent pairs and 7-yaer-old children in the Panel Study on Korean Children were analyzed. Path analysis and gender-based structural equation modeling were conducted. Maternal happiness was negatively related to child screen time. Maternal play engagement showed positive concurrent associations with child healthy food intake and physical activity and negative associations with screen time. Maternal parenting stress was negatively related to child healthy eating. There was one significant finding related to fathers’ role on children’s energy-related behaviors, happiness, and weight status: the positive association between parental happiness and boys’ unhealthy food intake. Child screen time was positively related to child weight status and negatively to child happiness at each age. Broader maternal parenting factors can serve as a protective factor for childhood happiness and weight status in 7-to 9-year-olds through being associated with a reduction in child screen time.

The decade of research concentrating on the area of Easter Slavonia revealed abundance of large on complex late Neolithic sites. It changed profoundly how we perceive late Neolithic settlement and space size and organization. As s first step in particular site detection we mostly conducted aerial reconnaissance and satellite image analysis but also data from the field such as excavation. On detected potential sites we conducted intensive field surveys and confirmed the attribution to the late Neolithic by surface finds. On those confirmed sites in the vicinity of Đakovo, Croatia, magnetic survey was conducted on 5 sites so far and the results confirmed presence of large scale late Neolithic settlements with complex spatial organization and enclosure(s). Most complex remains so far is site Gorjani Kremenjača and Topole which we present in this paper where a remains of one or two settlements covers the area of 70 hectares and is currently in the process of formal protection as a cultural landscape by the Ministry of Culture and Media of the Republic of Croatia. Special focus of the paper is the application of remote sensing in detection, archaeological confirmation and protection of the position Gorjani Topole.

Background: Poisoning is a growing public problem that has shown increased prevalence in many countries worldwide, contributing to increased costs and mortalities. A lack of or insufficient information regarding the circumstances, chemicals, drugs and people at risk due to imperfect reporting system is regarded as a barrier to successful poisoning prevention and intervention efforts. In Saudi Arabia, poisoning cases are monitored at the central level in the Ministry of Health, with meticulous follow-up of cases across country areas through public health departments. The current study's objective was to describe the poisoning notification and reporting system in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the environmental health and occupational safety department in Makkah Almukarramah over two years (2018-2019) were reviewed, together with verification of all systems, rules and guidelines organizing the performance of the department.Results: The total number of cases reported in 2018 was 209 cases, while it reached 42 cases in 2019. The overwhelming majority of the cases were Saudi (91.6%), with a slight predominance of males (57.4%) over females (42.6%). Generally, almost two-thirds of the cases occurred in children below 13 years (61.4%), most of the poisoning cases resulted from drug overdose (63.3%), while 34.7% were chemical poisoning. The most common drug poisoning was attributed to analgesics followed by antiepileptics, antihypertensive drugs, antipsychotics and antimicrobials, while cleaning and detergent agents were the most commonly poisoned chemicals. The majority of these agents were taken by the oral route. Children's chemical poisoning was significantly higher than that in older age groups (p<0.05). All intentional poisoning occurred in adults; the great majority of them (88.9%) used drug overdosing rather than chemical substances.Conclusion and recommendations: The current study results are consistent with most of the previous studies carried out in different regions in Saudi Arabia. Reviewing the performance and adherence of the “Environmental Health and Occupational Safety Department” to the guidelines and instruction regulating monitoring, notification and reporting of poison cases are satisfactory and the poisoning surveillance system is effective. Further in-depth studies are needed to elaborate other socioeconomic and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors, including prohibition over-the-counter pharmaceutical sales, selling the possibly harmful substances in kid-proof bottles and additional stringent rules governing chemical sale and storage. Intensive supervision of children is needed. Suicidal poisoning patients should seek psychiatric help to limit the possibility of attempting suicide again in the future. Furthermore, efforts are needed to plan and implement health education programs and campaigns about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning to raise public knowledge about exposure to chemicals and its consequences. Popular communication media, commercial malls, road ads, learning institutions and workplaces can be used to deliver health education campaigns and programs. Advice on how to store chemicals safely and how to keep kids safe should be focused on during such programs.

The electric vehicle market is constantly evolving, with research and development efforts to improve motor technologies and address current challenges to meet the growing demand for sustainable transportation solutions well underway. Electric vehicles are crucial to the global initiative to reduce carbon emissions. The core component of an electric vehicle is its motor drive technology, which has seen significant advancements and diversification in recent years. Although alternating-current motors, particularly induction and synchronous motors, are widely used for their efficiency and low maintenance, direct-current motors provide high torque and cost-effectiveness advantages. This study examines various electric motor technologies used in electric vehicles and compares them using several parameters such as reliability, cost, and efficiency. This study presents a multi-criteria comparison of the various electric motors used in the electric traction system to provide a picture that enables selecting the appropriate electrical motor for the intended application. Although the permanent magnet synchronous motor appears to be the popular choice among electric car makers, the proposed comparative study demonstrates that the induction motor matches the essential requirements of electric vehicles.

Digital transformation in government services is seen as a progression towards offering more efficient and convenient services. In the wake of the COVID-19 pandemic, many governments worldwide have expedited the digitisation of their services to facilitate social distancing. Digital transformation encompasses the shift from manual, face-to-face services to automated, online-based systems. This process occurs in three stages: digitisation, digitalisation, and digital transformation. However, the varying stages of digital government implementation across countries impact service outcomes differently. Little research has been devoted to focusing on the outcome metrics crucial for the success of digital services, particularly in Kuwait. Moreover, although the existing literature has explored some metrics, specific metrics have been neglected. This study aimed to investigate current trends and preferences in digital government services usage using empirical quantitative data gathered from users of digital services in Kuwait. Specifically, the aim is fourfold: (i) to identify crucial outcome metrics for the success of digital services, (ii) to assess how users evaluate these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and validate a model for evaluating the success of digital transformation services. To achieve the study's objectives, 378 users of digital government services were surveyed quantitatively. This study found that users primarily access services through smartphones and applications, underscoring the need for mobile optimisation. There was a marked preference for real-time, automated services, with dissatisfaction for those requiring lengthy approval processes. Six key metrics were identified and grouped into three domains: service-related (customer satisfaction and service quality), convenience-related (accessibility and ease of use), and security-related (perceived security and perceived trust). Digital transformation services outperformed digitalisation services in five key areas: customer satisfaction, service quality, accessibility, ease of use, and perceived security, except perceived trust, which remained consistent across both service types. Several recommendations were proposed to advance government digital transformation services, boost efficiency, and enhance the user experience.

It is imperative to conduct experimental studies on the seepage behavior of gas-bearing coal under cyclic dynamic loading conditions. This paper focuses on the evolution of coal permeability under the combined effects of dynamic loading, static loading, and gas adsorption. The principal conclusions are as follows. (1) As the frequency and amplitude of dynamic loading increase, the development of pore and fissure structures within the coal body becomes increasingly pronounced during dynamic loading cycles, resulting in a gradual rise in permeability. Notably, as the coal approaches its yielding stage, the permeability can increase by up to 47%. (2) The permeability curve is divided into four regions: the compaction reduction zone, the oscillation zone, the gradual recovery zone, and the abrupt increase zone of failure. Ultimately, in the failure phase, the permeability surges dramatically, potentially reaching 4 to 5 times the initial permeability. (3) When the static load stage is constant, the rate of change in permeability of the coal under dynamic loading decreases with increasing adsorption amount. When the adsorption amount is constant, the rate of change in permeability of the coal under dynamic loading increases with the increase in static load stress stage, with the maximum increase reaching 75.2%. It can be concluded from the rate of change in permeability and the dynamic loading sensitivity coefficient that the permeability of the coal is highly sensitive to cyclic dynamic loading, with increased sensitivity associated with larger static load stages and decreased sensitivity with greater adsorption amounts.

This work aims to investigate the effect of process temperature and catalyst content by thermochemical degradation of municipal solid waste (MSW) fraction (organic matter + paper + plastic) on the yield of reaction products (bio-oil, biochar, H2O and gas), physi-cochemical properties and chemical composition of bio-oils, as well as on the morphol-ogy and crystalline phases of biochar in laboratory scale. The organic matter, paper and plastic segregated from the gravimetric composition of total waste sample were subject-ed to the pre-treatments of drying, crushing and sieving. Thermal pyrolysis was per-formed at 400, 450 and 475 °C and 1.0 atmosphere, and catalytic pyrolysis at 450°C and 1.0 atmosphere, using 5.0, 10.0 and 15.0% (wt.) of FCC zeolite, bath mode, using a laboratory-scale borosilicate glass reactor. The bio-oil was characterized for acidic val-ue. The chemical functions present in the bio-oil identified by FT-IR and the composi-tion by GC-MS. Biochar was characterized by SEM/EDS and XRD. Thermal pyrolysis of the MSW fraction shows bio-oil yields between 9.44 and 9.24% (wt), aqueous phase yields between 21.93 and 18.78% (wt), solid phase yields between 67.97 and 40.34% (wt) and gas yields between 28.27 and 5.92% (wt). The bio-oil yield decreased with in-creasing process temperature. For the experiments using FCC, the biochar and gas yields slightly increased with increasing FCC content, while that of bio-oil decreased and the H2O phase remained constant. GC-MS of bio-oils identified the presence of hy-drocarbons and oxygenates, as well as nitrogen-containing compounds including am-ides and amines. The acidity of the bio-oil increased with increasing temperature and with the aid of FCC as a catalyst. The presence of hydrocarbons in bio-oil was identified by the addition of FCC catalyst due to the deoxygenation of carboxylic acids, followed by decarboxylation and decarbonylation reactions, producing aliphatic and aromatic hydrocarbons.

: The activated carbons (ACs) derived from okara powder waste with high surface areas were modified with ammonia aqueous solution impregnation in an autoclave to enhance their CO2 adsorption properties toward CO2. The impregnated ACs were characterized in which surface chemical composition and properties of the ACs were analyzed by SEM-EDX, FTIR. Activated carbons were functionalized with ammonia aqueous solution (25%) through a hydrothermal process within 24; 48, and 72 hours. The adsorption performance of CO2 onto carbon samples was experimentally evaluated through a TPD CO2 measurement. FTIR spectra confirm the N-containing in N-modified activated carbons and the presence of the –C = O stretch and N-H groups. CO2 uptakes of N-modified activated carbons are 0.24; 1.78; 2.24 and 1.26 mmol/g, which are relatively comparable with those of activated carbons studied in in the literature.

The activated carbons (ACs) derived from okara powder waste with high surface areas were modified with ammonia aqueous solution impregnation in an autoclave to enhance their CO2 adsorption properties toward CO2. The impregnated ACs were characterized in which surface chemical composition and properties of the ACs were analyzed by SEM-EDS, FTIR. Activated carbons were functionalized with ammonia aqueous solution (25%) through a hydrothermal process within 24; 48, and 72 hours. The adsorption performance of CO2 onto carbon samples was experimentally evaluated through a breakthrough adsorption experimental setup at room temperature. FTIR spectra confirm the N-containing in N-modified activated carbons and the presence of the –C = O stretch and N-H groups. CO2 uptakes of N-modified activated carbons are 0.24; 1.47; and 1.20 mmol/g, which are relatively comparable with those of activated carbons studied in in the literature.

In the present research, it has been tried to use the applied research bases in the field of business management with marketing orientation. Since there were wide and diverse spectrums in the field of economic activities in Isfahan province and Isfahan city, the model of economic sectors was used to categorize these activities. In this regard, the specific goals of the research in all five sectors of the model of economic sectors, drawing and prioritizing the components (indices) of the 7P marketing mix model in each of these economic sectors by the related individuals and members of subgroups of guilds and non-governmental organizations in Isfahan province, have been formed, specified and determined. Collecting data for processing and answer the question of this research, after designing a questionnaire in accordance with the AHP model standard, by spending a period of 2 months from September 2022 and ended in November 2022 , from the statistical population has been done.

A comprehensive evaluation system of rural building energy consumption from the innovative composite perspective was established, which was suitable for southwest of China. The index system was established by Brainstorming method and Delphi method, the weights of the comprehensive evaluation model were calculated by Network Process (ANP) method, the scoring criteria of all evaluation indexes were leveld based on fuzzy evaluation theory. The system model was verified by case analysis, at the countryside around Chengdu Second Circle. With the highest weight, lowest comprehensive score, and broadest range of comprehensive scores taken into consideration, three key factors affecting the target layer, namely "Percentage of Clean Energy Use", "Thermal Performance of Exterior Walls", and "Implementation Rate of Energy saving Measures". The distribution of comprehensive indicators and evaluation factors has certain spatial distribution characteristics, and the overall spatial distribution shows a characteristic of "high in the southeast and low in the northwest". Finally, Based on key factors and regional distribution characteristics, energy-saving measures have been proposed from three aspects: increasing sunrooms, adding wall insulation layers, and standardizing air conditioning temperature settings.

Thermally-induced moisture flow in unsaturated soils is a complex coupled thermal-hydro process involving moisture flow both in vapor and liquid phases. Thermally-induced moisture flow in unsaturated sands is usually less pronounced than fine soils due to their low field capacity. It is affected by many factors, including soil density, particle size, moisture content, and gravity-induced flow. Besides, soil moisture sensors need to be highly accurate and calibrated for temperature effects in order to monitor the thermally induced moisture flow accurately. A silty sand with 35% non-plastic fines was selected to study the coupled thermo-hydro processes in a closed heat cell. The heat cell consists of two temperature-controlled heat exchanger plates at the top and bottom, heat flux sensors, moisture sensors, thermocouples, and heat conductivity sensors. The soil was prepared at various initial moisture content and tested under a constant 1-D temperature gradient. The soil moisture sensors were first calibrated in the test soil at room temperature and then at elevated temperatures. The soil moisture redistribution, temperature and thermal conductivity profiles were determined. The critical moisture content, corresponding to maximum moisture movement, was also determined. The results offer new insights into coupled moisture-heat flow in unsaturated sands.

This paper focuses on determining the State of Charge (SOC) of lithium-ion batteries when operating and the influence the State of Health (SOH) has on SOC determination. The paper studies the effects of the discharge rate on the battery performance and how it modifies the evaluation of the SOC. The article also analyzes the effects of battery aging on the determination of SOC. The online determination of SOC in lithium-ion batteries uses the linear behavior of battery discharge and how the discharge rate modifies the battery voltage slope. The paper develops a simple algorithm that relates the SOC of the battery with the online battery voltage. The proposed measurement method uses a very short controlled discharge at a specific rate that avoids unexpected interferences with the current operation of the battery during charge or discharge. We conducted a simulation process to evaluate the evolution of the SOC in Li-ion batteries for different operating stages. Experimental tests validate the proposed methodology, showing a close agreement between theoretical values and experimental results with 98% accuracy. The paper also deals with the determination of the aging factor in lithium batteries as a critical parameter to calculate the SOC. The method determines the aging factor using specific simulation for this goal. The results are compared to values obtained in an experimental test within high agreement (>93%). The proposed model applies to almost any lithium battery and is validated for a wide range of Battery State of Health, up to 50%, within accurate prediction, higher than 90%.

Analysis of non-stationary and nonlinear sound signals obtained from dynamical processes is one of the greatest challenges in signal processing. Turning machine operation is a highly dynamic process persuaded by many events such as dynamical responses, chip formations and operational conditions of machining. Traditional and widely used fast Fourier transformation and spectrogram is not suitable for processing sound signals acquired from dynamical systems as its result have significant deficiencies because of stationary assumptions and having priori basis. The relatively new technique, Discrete Wavelet Transform, which uses Wavelet decomposition and the recently developed technique, Hilbert Huang Transform (HHT) which uses Empirical Mode Decomposition have notably better properties in the analysis of nonlinear and non-stationary sound signals. This paper presents a comparative study on the decomposition of multicomponent sound signals using Empirical Mode Decomposition and Wavelet Decomposition to highlight the suitability found in HHT to analyze tool-emitted sound signals received from turning processes. Apart from the short mathematical and theoretical foundations of the transformations, this paper demonstrates their decomposition strength using an experimental case study of tool flank wear monitoring in turning. It also concludes HHT is more suitable than DWT to analyze tool-emitted sound signals received from turning processes.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

Sweden has long been hailed as a role model in climate policy. But the new right-wing government supported by a far-right populist party is undertaking a paradigm shift in Swedish climate policy and governance. This paper critically analyses this paradigm shift and its impacts on legitimacy, accountability and justice. These are democratic norms important in the analysis of a just transition to climate neutrality. Severe democratic deficits of the policy process and the policies proposed and adopted are identified, suggesting ‘nasty politics’ characterised by populist, divisive, and contentious rhetoric that entrenches polarization and us/them narratives. Climate scientists and the climate justice movement are discriminated in the policy process, the latter also being accused of terrorism and seen as a threat to democracy by the government. The deficits are partly explained by the neo-corporatist political system of Sweden and the dominance of the (neo)liberal discourse of ecological modernization in Swedish environmental policy, but primarily with the ongoing process of autocratization driven by anti-democratic far-right populist parties throughout Europe. The anti-democratic climate policy reforms in Sweden are foreboding an unwelcome development in the EU given the boost of far-right populist parties in the upcoming elections to the European Parliament in June 2024.

Sweden has long been hailed as a role model in climate policy. But the new right-wing government supported by a far-right populist party is undertaking a paradigm shift in Swedish climate policy and governance. This paper critically analyses this paradigm shift from perspectives of legitimacy, accountability and justice. These are democratic norms important in the analysis of a just transition to climate neutrality. Severe democratic deficits of the policy process and the policies proposed and adopted are identified. The deficits are explained by the neo-corporatist political system of Sweden, the dominance of the (neo)liberal discourse of ecological modernization in Swedish environmental policy, and an ongoing process of autocratization driven by anti-democratic far-right populist parties throughout Europe. The paper ends by suggesting how climate policy and governance can be democratized and re-politicized, that also give room for climate justice advocates to take part in policy processes.

A comprehensive evaluation system of rural building energy consumption from a innovative composite perspective is established, which is suitable for southwest China. the brainstorming method and Delphi method were used to establish the indicator system, the ANP method was applied to calculate the weights of the comprehensive evaluation model, the scoring criteria of all evaluation indexes are constructed based on fuzzy evaluation theory, the applicability of the model is verified by an example of the countryside around Chengdu. The results showed that the score of some factors is low, whole or part area, just like the percentage of clean energy use(C24), the thermal performance of external walls(E21), the implementation of energy-saving measures(S22), were key factors affecting the target level, which has greater potential for improvement. The distribution of comprehensive indicators and evaluation factors have certain spatial distribution characteristics, the overall spatial distribution shows the characterwastic of "high in the southeast and low in the northwest". The key factors and spatial distribution characteristics found can be used as an important basis for the improvement of green energy-saving promotion measures, energy-saving retrofit programmes were proposed in terms of additional solar room, energy-saving of external walls and air-conditioning temperature setting guidance.

Laser-Powder Directed Energy Deposition (DED-LB/Powder) is an additive manufacturing process that is gaining popularity in the manufacturing industry due to its numerous advantages, particularly in repairing operations. However, its application is often limited to case studies due to some critical issues that need to be addressed, such as the degree of internal porosity. This paper investigates the effect of the most relevant process parameters of the DED-LB/Powder process on the level and distribution of porosity. Results indicate that, among the process parameters examined, porosity is less affected by travel speed and more influenced by powder mass flow rate and laser power. Additionally, a three-dimensional finite element transient model was introduced, which was able to predict the development and location of lack-of-fusion pores along the building direction.

Earthquake-triggered landslides are one of the most significant hazards worldwide. These landslides, involving small or large volumes, can develop in debris flow or avalanche with high mobility and long-runout distance. The motion processes of this kind of landslide also quite complicated. This study reproduced the motion processes of two landslides using the LS-RAPID program. The Aso Bridge landslide occurred on April 16, 2016, with rapid and long-runout distance characteristics. The Kataragai landslide occurred on April 9, 2018, travelling with flow-like behavior. Through field investigations and laboratory experiments, features and mechanical parameters of these two landslides were obtained. In the LS-RAPID program, the pre-failure models and motion processes of these two landslides were established and reproduced. Results indicated that sliding speed and travelling morphology at different intervals were considered as two indicators to describe their motion process. However, while sliding speed of these two landslides have significant differences with sliding speeds of actual landslides, the final morphologies after failure were consistent with those observed in the field investigation.

For a long time, a company's Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigated the effect on the market performance of rural products (banana) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results shows that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identified that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we concluded that rural families that develop economic and environmental practices with their products in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introduction and maturity phases in the market. As originality, our study contributed to demonstrate value on the product life cycle for the product development process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to the lack of research on these areas seen in an integrated way.

The research paper investigates the synthesis of metal matrix composites (MMCs) through the amalgamation of aluminum and silicon carbide particles, employing a two-step mixing methodology called the stir casting method. Metal matrix composites are materials that enhance the capabilities of a metallic matrix by incorporating another material. This leads to better characteristics such as tensile and shear moduli, fatigue and fracture properties, thermal expansion coefficient, and more. Aluminum-matrix composites are a class of materials that possess distinct characteristics that may be modified by altering factors such as the type of reinforcing material, the amount of reinforcement, and the fabrication technique employed to produce the composite. The concept aims to combine the desirable properties of metals and ceramics by incorporating durable refractory particles into a malleable metal structure. The fabrication process employs the stir casting technique, where reinforcing phases are mechanically stirred into a molten matrix to create cast composites with reinforcement volume percentages of up to 30%. The study utilizes Response Surface Methodology to enhance the mechanical properties of the composites through the optimization of manufacturing parameters, including stirring temperature, time, and speed. The study entails analyzing the correlation between different casting conditions and the resulting changes in hardness and tensile strength.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

The characteristics of excess aerobic granular sludge, related to its structure and chemical composition, limit the efficiency of anaerobic digestion. For this reason, pre-treatment methods and compositions with other organic substrates are used. The aim of the research was to determine the effects of co-digestion of pre-hydrodynamically cavitated aerobic granular sludge with waste fats on the efficiency of methane fermentation under mesophilic and thermophilic conditions. The addition of waste fats improved the C/N ratio and increased its value to 19. The greatest effects were observed in thermophilic anaerobic digestion at 55°C, where a 15% waste fat content in volatile soilds was ensured. This resulted in the production of 1278.2±40.2 mL/gVS biogas and 889.4±29.7 mL/gVS CH4. The CH4 content of the biogas was 69.6±1.3%. The increase in biogas and CH4 yield compared to pure AGS anaerobic digestion was 34.4% and 40.1%, respectively. An increase in the proportion of waste fats in the substrate had no significant effect on the efficiency of methane fermentation.

Climate warming profoundly affect vegetative growth, flowering phenology and sexual reproduction of plants, therefore the ornamental value of wild flowers. Despite this, the extent and mechanism of the impact remain unclear. Here, we conducted warming experiment for two growing seasons (1.89 ℃ in 2017 and 2.37 ℃ in 2018 increases) with infrared heaters to examine the effects of warming on ornamental value of wild flower I. oxyanthera, endemic to China, in Mount Emei. We fitted generalized linear mixed models (GLMMs) and generalized linear model (GLM) to examine how warming affected plant morphology and floral traits. We evaluated comprehensive ornamental value based on plant morphology and flowering characteristics using Analytic Hierarchy Process (AHP) and disentangled the impact of the two traits on ornamental value using principal component analysis (PCA) and partial least squares structural equation model (PLS-SEM) under the ambient and warming treatments. Warming decreased significantly plant height and crown width, while increased branch number and single leaf area. Warming also decreased vexillum length, corolla tube length, nectar spur length and pedicel length. In addition, warming shortened flowering duration per plant and reduced flower number, while there was no significant effect on flower longevity, flower color at full-bloom stage between the control and warming treatment. Therefore, comprehensive ornamental value under warming was lower than that under the control. Pedicel length, flower color, flower longevity and flowering duration per plant were the main factors of affecting comprehensive ornamental value. PLS-SEM showed that warming had an indirect negative effect on ornamental value via direct negative effects on flowering traits. Collectively, these results indicate that although promoting vegetative growth, short-term warming decreased significantly ornamental value of I. oxyanthera due to warming-caused smaller flowers and shorter flowering duration.

A La2FeCrO6 (LFCO) double perovskite powders were synthesized by hydrothermal method, which crystallized in an orthorhombic (Pnma) structure and exhibited a spherical morphology with average particle size of 900 nm. Fourier transformation infrared spectroscopy confirmed the octahedral coordination of Fe and Cr ions in the powders. XPS spectra revealed dual oxide states of Fe (Fe2+/Fe3+) and Cr (Cr3+/Cr4+) elements, and oxygen element appeared as lattice oxygen and defect oxygen, respectively. The LFCO powders exhibit ferromagnetic behavior at 5 K with saturation magnetization of 0.31 μB/f.u., coercive field of 8.0 kOe, and Curie temperature of 200 K. Griffiths phase appeared in the powders between 200 K and 223 K. The LFCO ceramics displayed a butterfly-like magnetoresistance (MR)-magnetic field (H) curve at 5 K with MR (5 K, 6 T) value of -4.07%. Temperature dependence of resistivity of the LFCO ceramics demonstrated their semiconducting nature. Electrical transport data were fitted by different conduction models. Strong frequency dispersion dielectric behavior was observed in the LFCO ceramics. A dielectric abnormality observed around 260 K, was ascribed to the jumping of electrons trapped in shallow levels created by oxygen vacancies. The dielectric relaxation behavior exhibited by dielectric loss between 160 K and 260 K, was contributed to the singly-ionized oxygen vacancies.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

This article is devoted to methods of processing random processes. Of particular relevance is the task of processing broadband non-stationary random processes. The processing of random processes is usually related to the assessment of their probabilistic characteristics. Very often, a non-stationary broadband random process is represented by a single implementation in a priori uncertainty about the type of distribution function. Such random processes occur in information and measuring communication systems in which information is transmitted at a real time pace (for example, radio telemetry systems of spacecraft). The use of methods of traditional mathematical statistics, for example, maximum likelihood methods to determine probability characteristics, in this case is not possible. The article discusses a method of processing non-stationary broadband random processes based on the use of non-parametric methods of decision theory. An algorithm for dividing the observation interval into stationary intervals using non-parametric Kendall statistics is considered, as well as methods for estimating probabilistic characteristics on the stationary interval using ordinal statistics. The article presents the results of statistical modeling using the Mathcad program.

The weight loss process variables of alkali-treated micropolyester woven fabric were optimized and reported in this study. The grey relational analysis (GRA) with the help of the Taguchi technique was efficiently used to optimize the key variables of this process. The caustic soda concentration, treatment temperature, and weight loss machine speed were considered the control or design parameters. The weight reduction percentage, air permeability, tensile strength, and thermal resistance of alkali-treated woven polyester fabrics were also considered as responses in this study. The experiments were implemented according to a 33 full factorial design. The levels of the control parameters which yield the maximum weight reduction, tensile strength, air permeability, and minimum thermal resistance of the treated polyester fabrics were found to be NaOH concentration and treatment temperature with the highest levels, and machine speed with the lowest level. This means that a 27% caustic soda concentration, treatment temperature of 125 OC, and machine speed of 40 m/min exhibited the optimum properties of the treated micropolytester fabrics. It is also proved that the treatment temperature is the most influential factor affecting the micropolyester fabric’s properties. The confirmation test which was carried out in this study confirmed that the GRA improved the alkali-treated polyester fabric properties.

Municipal solid waste incineration fly ash (MSWI FA) predominantly consists of compounds comprising elements such as calcium, aluminum, silicon, sodium, and others. Additionally, it encompasses a complex mixture of heavy metals, chlorides, sulfates, organic pollutants, and other constituents. The effective and economically viable treatment of MSWI FA poses a formidable challenge for the current stage of resource cycling. In this research report, we will adopt a novel low-temperature sintering method called "Cold Sintering Process" (CSP) as a means to immobilize heavy metals within the fly ash. By utilizing a Taguchi orthogonal array, we will adjust five control factors in the CSP, including sintering temperature, uniaxial pressure, sintering time, initial water addition, and sodium carbonate dosage. The leaching of cadmium element from the fly ash, as measured by TCLP, will serve as the quality indicator. Through the application of CSP, the MSWI FA will be transformed into structurally stable ceramic blocks, effectively immobilizing the heavy metals within the blocks. The results of the experiments showed that MSWI FA under the conditions of a temperature of 300℃, uniaxial pressure of 312 MPa, sintering time in 60 minutes, 25 wt% water addition, and 9 wt% Na2CO3 addition could effectively reduce the leaching of cadmium by 77.71%, lead by 21.14%, zinc by 42.37%, and chromium by 99.99%, as compared to the original MSWI FA TCLP results.

Reductive amination is a powerful tool in sustainable organic synthesis that allows chemists to access a wide range of valuable amine products using renewable feedstocks, mild reaction con-ditions, and with minimal waste generation. Practical applications can be found in various fields, including pharmaceuticals, contributing to greener and more sustainable chemical processes. In this work we present a heterogeneous (Rh and Pt) catalysed protocol for the fast and efficient synthesis of ractopamine hydrochloride (-adrenergic drug) under MW-assisted reductive ami-nation protocol starting from raspberry ketone and octapamine. Microwave (MW) successfully accelerated the hydrogenation reaction and reduced the reaction time from 13 hours to only 3 hours under mild conditions (50°C at 10 bar). The best catalysts were Pt/C and Rh/C, which led to high conversion and selectivity towards ractopamine:HCl. The solvent has a significant effect on the reaction outcome. The replacement of methanol with other solvents had a negative impact on the reaction yield. We also replaced the raspberry ketone with other ketone substrates, especially cyclohexanone. These preliminary experiments may be useful for further process improvements in the synthesis of β-adrenergic agonists and related structures.

Recently, federated learning (FL) has been receiving great attention as an effective machine learning method to avoid the security issue in raw data collection as well as to distribute the computing load to edge devices. However, even though wireless communications is a essential part for implementing FL in edge networks, there have been a few works that analyze the effect of wireless networks on FL. In this paper, we investigate FL in small-cell networks where multiple base stations (BSs) and users are located according to homogeneous Poisson point process (PPP) with different densities. We comprehensively analyze the effects of geographic node deployment on the model aggregation in FL on the basis of stochastic geometry-based analysis. We derive the closed-form expressions of coverage probability with tractable approximations and discuss the minimum required BS density for achieving a target model aggregation rate in small-cell networks. Our analysis and simulation results provide insightful information for understanding the behaviors of FL in small-cell networks, and it can be exploited as a guideline for designing the network facilitating the wireless FL.

Abstract: (1) Background: The relationship between non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2DM) is bidirectional: NAFLD increases the risk of T2DM, and T2DM promotes the progression of the disease to non-alcoholic steatohepatitis (NASH). (2) Material and methods: We performed a retrospective, open study that included 59 patients with NAFLD and T2DM who were distributed into two groups: 44 (74.57%) patients were diagnosed with HS and 15 (25.42%) patients were diagnosed with NASH. (3) Results: Among the non-specific inflammatory biomarkers statistically significant (p=0.003 respectively p=0.03) higher mean values were recorded in patients with NASH and T2DM in the case of ferritin and the neutrophil percentage-to-albumin ratio (NPAR). (4) Conclusions: Consequently, it is essential to identify alternative "surrogate" markers for the inflammatory process, particularly in individuals with diabetes, as it is a key characteristic of NASH. This need arises from the desire to avoid the risks associated with liver biopsy procedures and to prevent the unpredictable and unfavorable progression of NAFLD in patients with T2DM.

Laser additively manufactured (LAM) Ti–6Al–4V alloy has huge application potential in aerospace structural parts such as turbine blades. However, there are few studies on the fatigue properties of such LAM parts under vibration loading, particularly with regard to anisotropy. In this paper, vibration fatigue properties of LAM Ti-6Al-4V by laser melted deposition process were investigated along the transversely deposited (TD) direction and parallelly deposited (PD) direction. Through the first-order bending vibration experiments, LAM Ti-6Al-4V alloy exhibits obvious anisotropic fatigue properties and significant dispersion in fracture position. The fracture morphology analysis reveals that the vibration fatigue failure was mainly dominated by the LAM process-induced defects and the microstructure. For the LAM samples without defects at crack initiation sites, the fatigue behavior is controlled by the prior β columnar grains with preferential orientation, which leads to enhanced fatigue crack propagation resistance for the PD samples. For the LAM samples with defects at initiation sites, having lower fatigue lives, the fatigue anisotropy strongly depends on the projection area of the lack-of-fusion defects relative to the loading direction, resulting in better fatigue performance for the TD samples.

Ehlers–Danlos syndromes (EDS) are a heterogeneous group of heritable connective tissue disorders (HCTDs) characterized by joint hypermobility, skin hyperextensibility, and tissue fragility. Among the different types, the hypermobile EDS (hEDS) is the most frequent, and includes generalized joint hypermobility as the major diagnostic criterion. Joint hypermobility in hEDS is often associated with pain that not always allow the use of effective pain-reducing treatments. Patients with hEDS always talk about pain using a lot of descriptions. Eighty-nine patients with diagnosis of hEDS were recruited and evaluated. They were asked to write down what grief was to them. The texts were analyzed through LIWC. Correlational analyses were conducted between pain perception and language. A comparison of high/low pain perception and quality of metaphors was carried out. The results showed, depending on the level of pain perception, different language quality is evidenced. The greater the pain the lesser the positive effects and the greater the negative effects and dehumanizing metaphors. Moreover, greater pain seems to be related to a verbal experience of greater isolation and less self-care. In conclusion, the use of metaphors is a useful tool for exploring illness experience and may help clinicians in the rehabilitation program.

In the Mediterranean Sea, there are two critical issues affecting marine benthic biocenosis: the sedimentation process and the increasing trend in marine biodiversity loss. These processes are very marked along the western side of Calabria coastline where siltation is one of the main reasons for the regression of Posidonia beds in the regional coastal waters. This research aims to investigate the relationships between the geomorphological features of the debris source areas, represented by fluvial basins, and the distribution of Posidonia meadows. So, a geomorphological study of the Tyrrhenian fluvial basins with area greater than 200 km2 and its correlation with the mapping of the meadows in the Calabria Tyrrhenian waters was carried out. Furthermore, in order to assess the increased level of burial in a Posidonia oceanica meadow and its health state over time, a program of Posidonia monitoring was undertaken between 2000 and 2010, in a test area located in the Marine Regional Park of Isca (Calabria, Southern Italy). The results of this survey highlighted that, at the beginning of the study period, for a silting rate of 4 - 5 cm/year, the meadow suffered a reduction of its photosynthetic ratio with a rate of mortality of 50%. In the following years, the siltation rate reach 12 cm/year in 2010 and the meadow began to disappear. Therefore, the marine pollution by sedimentation represents a serious factor for the regression of Posidonia oceanica meadows, enhancing the risk of a gradual loss in marine biodiversity.

The current work investigates the auxetic tensile deformation behavior of the inversehoneycomb structure with 5 × 5 cells made of biodegradable poly(butylene adipate-coterephthalate) (PBAT). Fused deposition modeling, an additive manufacturing method, produced such specimens. Residual stress (RS) and warpage, more or less, always exist in such specimens due to layer-by-layer fabrication, i.e., repeated heating and cooling. The RS influences the auxetic deformation behavior, but its measurement is challenging due to the very fine structure. Instead, the finite-element(FE)-based process simulation realized by an ABAQUS plug-in numerically predicts the RS and warpage. The predicted warpage shows a negligible slight deviation compared to the design topology. This process simulation also delivers the temperature evolution of a small volume material, revealing the local cyclic heating and cooling. The achieved RS serves as the initial condition for the FE model used to investigate the auxetic tensile behavior. With the outcomes from FE calculation without considering RS at hand, the effect of the RS on the deformation behavior is discussed for the global force-displacement curve, the structural Poisson’s ratio evolution, the deformed structural status, the stress distribution, and evolution, where the first three and the warpage are also compared with experimental results. Furthermore, the FE simulation can easily provide the global stress-strain flow curve with the total stress calculated from the elemental ones.

Cross-cultural mobility is a phenomenon that is increasingly growing, and the resulting stress has become a severe health issue. Thus, we conducted a systematic review of articles reporting research on stress measures of cross-cultural mobility or acculturative stress, emphasizing its internal structure. We adopted the PRISMA procedures for scoping reviews (e.g., searching articles in databases), resulting in the inclusion 20 articles in the final analysis that present evidence of validity based on the internal structure of 16 measures. The most common factors identified were related to cultural stressors, language stressors, and discrimination stressors. After analyzing a cross-cultural mobility stress concept implicit in the definitions used in the articles, we found that most measures of acculturative stress did not evaluate it according to the Stress and Coping Model of Lazarus and Folkman. Most measures only focused on part of the phenomena (some estimated the stressors, others the coping strategies). Few took into account the physiological and psychological responses to internal or external stressors, and even fewer considered its positive aspects (eustress). Consequently, it is pivotal to develop measures that take into account the multiple dimensions of the cross-cultural mobility stress. Other results (e.g., validity evidence of the measures) and limitations of this systematic scoping review are presented and discussed.

The global context of research for new sustainable energy storage technologies makes it a very active sector with significant scientific and economic challenges. Indeed, due to the irregular development of renewable energies and the shutdown of traditional power facilities, it is difficult to maintain a stable balance in terms of supply and demand: energy storage can help in particular for substantial changes in the latter. Metal air batteries have a higher energy density and are safer than other available energy storage devices. Based on the existing and proven lead-acid battery technology, this paper proposed an open cell foam manufactured by the Excess Salt Replication process for use as an anode for lead-air battery cellsies with sulphuric acid as the electrolyte. This will save lead and reduce the battery weight. A 25% antimonial lead alloy was used to produce open cell foams with a cell diameter between 2 mm and 5 mm for the antimonial lead-air battery. Preliminary results of the effective electrical conductivity of self-discharged primary battery cells, measured experimentally, showed that all antimonial lead foam-air battery cellsies performed better than that made from the same dense non porous antimonial lead alloy. This is generally due to their important specific surface area where oxidation-reduction reactions took place. A correlation between the effective electrical conductivity and the cell diameter has been established and the highest conductivity was obtained with a cell diameter of 5mm. The feasibility of such an electrical system has been demonstrated.

Senegal central regions face rainfall deficit combined with scarcity of surface water and poor quality. Populations use groundwater for drinking and irrigation. A Groundwater assessment studiebased on 42 samples aims to determine their quality. Several parameters (TDS, TH, WQI, SAR, RSC, %N, PI, KR and MR) and ion chemistry determined . Samples exhibit an alcaline pH (7.6) and hydrochemical facies mainly Ca-Na-Mg-HCO3. The ionic dominance is Na+ ˃ Ca2+ ˃ Mg2+ ˃ K+ and HCO3- ˃ Cl- ˃ SO42- ˃ NO3- ˃ F-. From TDS and WQI index most of the groundwaters are suitable for drinking. Irrigation quality (based on SAR, %N, RSC, KR, MR) varie from excellent to good water type. Plotting chemical data (USSL salinity & Wilcox diagrams) reveals that the majority of the groundwater samples belong to the fields excellent to good and from good to permissible . Natural processes (rock weathering, mineral dissolution, evaporation and ion exchange) control groundwater quality. As the region faced a persistent rainfall deficit combine with fragile groundwater quality should alert the stakeholders. A sustainable development of the region can be faced if strong measures are planned to follow water quality evolution and quantity available for human purposes

The impetus for the development of coaching as a professional managerial activity is based on the needs and requirements of a modern sustainable society. The paper aims to explore the awareness of academic students about the coaching approach and to formu-late recommendations for its application in the academic environment. The article focuses on the views of experts in the field of coaching approach as a means of improving com-munication in the academic/educational setting, characterizes communication and coaching approach, coaching models and its benefits and barriers. The survey presents a survey of awareness of the coaching approach among college and university students of in Slovakia and the possibilities/options of its application in the academic environment. The research was carried out by the questionnaire method. The evaluation of the questionnaire and the statistical evaluation of established hypotheses and assumptions concerning the coaching approach. The proposion of students was the introduction of the subject "Basics of coaching" which could be taught for all students at University of Zilina, Slovakia. Acording to the survey, 468 students, representing 68%, have knowledge about coaching. Furthermore, 68% of students had or have doubts about achieving their goals during their studies. According to the results of the survey, only 24% of students had ex-perience with coaching. The cooperation with a coach was regarded as beneficial for 71% of participants of the survey, which had already experience the coaching lectures. The students of managerial fields would benefit in enhancing leadership skills and providing support of their working teams.Finally, suggest organizing an introductory full-day workshops for students of man-agement -future managers, followed by organizing individual thematic workshops for specific areas of coaching.Moreover, the usage of coaching skills is possible yet while they are students with applications in communication in their relationships, student jobs, searching the profes-sional job opportunities and accomplishing the final thesis.To raise awareness of coaching approach in the University of Zilina in a cooperation with The Counseling and Career Center and its activities.

The preparation of sulfur-single walled carbon nanohorns active material via a simple and sustainable evaporation method for application as cathode in lithium-sulfur batteries is reported. We show that the synthesis process enables the infiltration of elemental sulfur within the carbon nanohorns thus obtaining a morphology responsible for the ameliorating of the shuttle effect. The sulfur-carbon composite is characterized in terms of structure, morphology, and composition through x-ray diffraction, transmission electron microscopy, and thermogravimetric analyses. From the electrochemical point of view, cyclic voltammetry, rate capability, and galvanostatic cycling tests are performed employing a solution of bis(trifluoromethane)sulfonimide lithium salt and lithium nitrate in a mixture of 1,2-dimethoxyethane and 1,3-dioxolane in order to evaluate the electrode design applicability within lithium-sulfur cells. In this respect, further insights are provided by the estimation of the lithium-ion diffusion coefficient through the Randles-Sevcik equation, and by electrochemical impedance spectroscopy. The obtained results reveal a remarkable cycle life lasting around 800 cycles with a stable capacity of 520 mA h g-1 for the first 400 cycles at C/4, while reaching a value around 300 mA h g-1 at the 750th cycle. These results suggest sulfur-carbon nanohorns active material as a potential candidate for the next-generation battery technology.

The prospects of processing blast furnace and steelmaking sludge using Waelz process in a laboratory rotary kiln is shown. The influence of varying thermal treatment modes, furnace atmosphere and type of reducing agents on the level of zinc reduction from sludges was analyzed. In general, the blast furnace sludge contains a high portion of iron (approx.48 wt. %) and can be reused as a charge after satisfactory zinc reduction. It was found that N- atmosphere and high content of the graphite or coke oven reducing agent in combination with high temperature can reduce the content of Zn in the sludge to 0.08 wt. % at 1200 °C for mixture of steelmaking and blast furnace sludge. A significant reduction in the Zn content to 0.66 wt. % occurs at 1100 °C. The content and type of reducing agent play an important role; graphite has shown a better reducing ability compared to coke oven dust. When nitrogen is used, zinc is reduced even without an additional reducing agent, since the carbon contained in the sludge is made use of for the reduction. In an air atmosphere, without the use of a reducing agent, there was no reduction in the Zn content.

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

One of the key challenges of stock trading is the stock prices follow a random walk process, which is a special case of a stochastic process, and are highly sensitive to new information. A random walk process is difficult to predict in the short-term. Many linear process models that are being used to predict financial time series are structural models that provide an important decision boundary, albeit not adequately considering the correlation or causal effect of market sentiment on stock prices. This research seeks to increase the predictive capability of linear process models using the SPDR S\&P 500 ETF (SPY) and the CBOE Volatility (VIX) Index as a proxy for market sentiment. Three econometric models are considered to forecast SPY prices: (i) Auto-Regressive Integrated Moving Average (ARIMA), (ii) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH), and (iii) Vector Autoregression (VAR). These models are integrated into two technical indicators, Bollinger Bands and Moving Average Convergence Divergence (MACD), focusing on forecast performance. The profitability of various algorithmic trading strategies is compared based on a combination of these two indicators. This research finds that linear process models that incorporate the VIX Index do not improve the performance of algorithmic trading strategies.

This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver’s claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims as multiple binary classifications applying feedforward neural networks. The second stage is simulating values for aggregated amount of claims as regression using feedforward neural networks, with number of claims included in the set of feature variables. In the final stage, a synthetic portfolio of the space of feature variables is generated applying an extended SMOTE algorithm. The resulting dataset is evaluated by comparing the synthetic and real datasets when Poisson and gamma regression models are fitted to the respective data. Other visualization and data summarization produce remarkable similar statistics between the two datasets. We hope that researchers interested in obtaining telematics datasets to calibrate models or learning algorithms will find our work valuable.

In the existing group decision-making fuzzy analytic hierarchy process (FAHP) methods, the consensus among experts has rarely been fully reached. To fill this gap, in this study, a pre-aggregation fuzzy collaborative intelligence (FCI)-based FAHP approach is proposed. In the proposed pre-aggregation FCI-based FAHP approach, fuzzy intersection is applied to aggregate experts’ pairwise comparison results if these pairwise comparison results overlap. The aggregation result is a matrix of polygonal fuzzy numbers. Subsequently, alpha-cut operations are applied to derive the fuzzy priorities of criteria from the aggregation result. The pre-aggregation FCI-based FAHP approach has been applied to select suitable alternative suppliers for a wafer foundry in Taiwan amid the COVID-19 pandemic. The experimental results revealed that the pre-aggregation FCI-based FAHP approach significantly reduced the uncertainty inherent in the decision-making process by deriving fuzzy priorities with very narrow ranges.

A winemaking waste was used as a precursor of activated carbon used in hazardous Cr(VI) removal from solutions. The preparation process consisted of a hydrothermal process, and a chemical activation, of the resulting product, with KOH. The adsorption results showed that the adsorption of Cr(VI) on the obtained activated carbon is strongly dependent on the stirring speed applied to the carbon/solution mixture, pH of the solution, and temperature. The equilibrium isotherm was well fitted to the Langmuir type-II equation, whereas the kinetic can be described by the pseudo-second-order kinetic model. Thermodynamic studies revealed that Cr(VI) adsorption was an exothermic and spontaneous process. Finally, desorption experiments showed that Cr(VI) was effectively desorbed using hydrazine sulfate solutions, and at the same time, the element was reduced to the less hazardous Cr(III) oxidation state.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

This paper, the second in a series of two, describes the experimental results for removing arsenic from the dust collected in the electrostatic precipitators of a fluidized bed roasting oven (RP dust). The RP dust produced is discharged as a toxic waste, although its copper content is close to 20% by weight. This work aims to process the RP dust; until obtained a material with a low concentration of arsenic, which could be recirculated in smelting operations and recover the valuable metals. Based on the successful experience obtained in the first document with FS dust, this work proposes to use the reduction-sulphidation-volatilization processes to carry out the volatilization of arsenic from RP dust. A set of experiments was realized in the roasting different ratios of the mixture of copper concentrate / RP dust, sulfur / RP dust varying temperature and roasting time; to reach an arsenic concentration <0.5% by weight. The RP dust was characterized using different techniques, that confirmed more than 20% by weight of copper and describing its structure as a small particle agglomerate (<5 µm), with a complex sulfoxide morphology. The main results indicated: that for a 75/25 mixture of the copper concentrate / PR dust under 700 ° C, 15 minutes roasting time with air atmosphere, the arsenic volatilization was 96% by weight. Meanwhile, for a 5/95 mixture of sulfur / RP dust, at 650 ° C, the volatilization of arsenic was 67% by weight.

This paper (the first of a series of two) seeks to describe the experimental results of removing arsenic from dust collected in electrostatic precipitators of copper into the pyro-metallurgy gas cleaning systems. The first work corresponds to the treatment of dust contained in gas off generated in the copper Flash Smelting Furnace process (FS dust), while the second focuses on the treatment of dust generated from a Fluidized Bed Roasting Furnace (RP dust). The dust is complex sulphur-oxide-metals with concentrations of copper above 10 wt % and relatively high concentrations of silver. Due to the dust has an arsenic’s concentration above 5 wt %, makes it difficult to recover valuable metals through hydro-metallurgical processes or by direct recirculation of the dust into a smelting furnace. Thus, the development of pyrometallurgical processes aimed to reduce the concentration of arsenic in dust is the main purpose of this study, particularly the production of a material suitable for recirculation in smelting operations. The works conducted provide a detailed characterization of the dust, including Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN), Scanning electron microscope-Energy Dispersive X-ray analysis (SEM/EDS), X-ray diffraction (XR-D), elemental chemical analysis using Atomic Adsorption (A. A.) and X-Ray Fluorescence (X-RF). By considering that arsenic volatilization requires a process of reduction-sulphidation-volatilization, the works explore the roasting of mixtures of copper concentrate/dust, sulphur/dust and pyrrhotite/dust. The degree of volatilization was determined by elemental chemical analysis of arsenic in the mixture after and before the roasting process. The results indicated the effect of parameters such as temperature, gas flow, gas composition and the ratio of mixtures (concentrate, sulphur or pyrrhotite)/dust on the volatilization of arsenic. The findings indicate that the concentration of arsenic in the FS dust can be reduced to a relatively low level (<0.5 wt %), allowing to the material to be recirculated into smelting processes.

Failaka Island, located in the far east of Kuwait Bay, is about 20 km from the State of Kuwait’s coast, and represents a focal point for regional geography and history, with pristine beaches and archaeological sites dating to the Bronze, Iron, Hellenistic, Christian and Islamic periods. According to environmental data and in coordination with local authorities to develop an urban plan, the island is set to become the first tourist destination for the State of Kuwait. To achieve the Vision of Kuwait 2035, one of the planning objectives focuses around Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. The paper then, aims to propose the environmental urban plan for Failaka Island. Based around Environmental Analytical Hierarchical Processes (EAHP), and using the Field Calculator and ModelBuilder functions in ArcGIS, this research centres on the feasibility of carrying out an urban plan using suitability modelling that includes four factors and 13 criteria covering the island’s ecological and human composition. This study utilizes both remote sensing (UAVs for 3D imaging) and field study (ground truthing) to identify changes in land use and land cover – such as using sample analysis of the historical sites and soils for tracing evidence and creating/updating a soil map – and create the first GIS database for the island that can lead to generating a suitability model.

Owing to the time-varying characteristics and nonlinearities of industrial processes, control has higher difficulties and results in challenges for advanced technology. In this paper, a self-tuning controller that includes a nonlinear proportional-integral-derivative (NPID) control function as well as a self-tuning function is proposed for first-order plus time delay (FOPTD) process control. The NPID control function is implemented using the nonlinear PID controller whose optimum parameters are adapted by a neural network (NN). The self-tuning function is able to identify the process dynamics using a short period of process behavior and tune NPID parameters based on the identified parameters. The advantage of the proposed method is validated with a set of simulation works on three processes and the comparison results are presented.

In this study, we first present a time-fractional L$\hat{e}$vy diffusion equation of the exponential option pricing models of European option pricing and the risk-neutral parameter. Then, we modify a particular L$\hat{e}$vy-time fractional diffusion equation of European-style options. Introduce a more general model from the models based on the L$\hat{e}$vy-time fractional diffusion equation and review some recent findings regarding of the Europe option pricing of risk-neutral free.

Abstract: Purpose – In this article we lay out the change management practices adopted by financial firms in small states within the Eurozone. We determine whether these organisations have the ability to identify triggers for change (Red Flags) and subject them to eight thematic elements to understand whether management practices can continue to exist and support operational environments, even when unexpected circumstances affect their day to day operations and processes. In doing this we examine the extent to which the eight thematic elements from the model designed by Dalli Gonzi, (2019) (The Dali Model) can assist organisations in risk identification and business continuity planning. Design/methodology/approach – A self-administered questionnaire purposely designed for this study was administered to personnel working in internal controls within financial institutions of small Eurozone states. The participants were asked to grade statements using a 5-point Likert scale, ‘1’ being ‘totally disagree’ and ‘5’ being totally agree’ to the statement posed under the thematic elements forming the basis of the Dali Model. Findings – Factor analysis provided support for the eight hypothesised dimensions of the decision-making model: connection, capacity, governance, network, policy, training, process improvement, standards. Originality/value – The study provides a better understanding and support of “best practice” in change management through an understanding and assessment of the eight factors that are the basis of this model. It addresses practical recommendations to ensure application to a wider frame of use.

A portable, electrical impedance spectroscopy device to monitor the bioimpedance’s resistive component of beef meat by injecting a sinusoidal current of 1mA at 65.5 kHz was developed. In 4 slaughtered beef both right and left longissimus dorsi muscles where trimmed and left muscle portion was frozen to -18° C up to 7^th day while right one was meantime maintained at 5° C. Median value of specific resistivity of not-frozen sample was about twice Ω cm^-1 with respect of that of frozen-thawed sample (P = 0.004). It was concluded that the device is reliable to monitoring the ripening of beef meat in situ.

This report describes the collection of a large dataset (6930 measurement) on dimensional error in the fused deposition modeling (FDM) additive manufacturing process for full-density parts. Three different print orientations were studied, as well as seven raster angles (0°, 15°, 30°, 45°, 60°, 75°, and 90°) for the rectilinear infill pattern. All measurements were replicated ten times on ten different samples to ensure a comprehensive dataset. Eleven polymer materials were considered: acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), high-temperature PLA, wood-composite PLA, carbon-fiber-composite PLA, copper-composite PLA, aluminum-composite PLA, high-impact polystyrene (HIPS), polyethylene terephthalate glycol-enhanced (PETG), polycarbonate, and synthetic polyamide (nylon). The samples were ASTM-standard impact testing samples, since this geometry allows the measurement of error on three different scales; the nominal dimensions were 3.25mm thick, 63.5mm long, and 12.7mm wide. This dataset is intended to give engineers and product designers a benchmark for judging the accuracy and repeatability of the FDM process for use in manufacturing of end-user products.

A phenomenological constitutive model is developed to describe the uniaxial transformation ratcheting behaviors of super–elastic shape memory alloy (SMA) by employing a cosine–type phase transformation equation with the initial martensite evolution coefficient that can capture the feature of the predictive residual martensite accumulation evolution and the nonlinear hysteresis loop on a finite element (FE) analysis framework. The effect of the applied loading level on transformation ratcheting are considered in the proposed model. The evolutions of transformation ratcheting and transformation stresses are constructed as the function of the accumulated residual martensite volume fraction. The FE implementation of the proposed model is carried out for the numerical analysis of transformation ratcheting of the SMA bar element. The integration algorithm and the expression of consistent tangent modulus are deduced in a new form for the forward and reverse transformation. The numerical results are compared with those of existing model and the experimental results to show the validity of the proposed model and its FE implementation in transformation ratcheting. Finally, a FE modeling is established for a repeated preload analysis of SMA bolted joint

Polyphenols are well-known health promoting agents, but they have some limitations due to their spontaneous oxidation. This evidence has limited their use as drugs in the last years. In this field, several chemical modifications have been proposed to overcome these restrictions; among these, the esterification seems to be the preferred. Ester derivatives could be able to reduce the bioavailability problems connected to polyphenols. On the other hand, the presence of the esterase enzymes in the body guarantees the ester hydrolysis, which in turn frees the two molecules that make it up. Lipase-catalyzed esterifications afforded several derivatives of flavonoids glycosides, in green conditions. In this short note, pancreatic porcine lipase was firstly used as a cheap bio-catalyst, to synthesize oleoyl derivatives of quercetin in aglycone form. Results demonstrated how the enzyme acyl regioselective in position C-3, with high yields and easy purification processes

The unprecedented demand for services and applications, coupled with large network architecture calls for a radical melioration in evolving wireless networks. Massive multiple-input and multiple-out (massive MIMO) systems, small cell networks (SCN) and Heterogeneous networks (HetNet) are envisioned to meet the new quality of service (QoS) objectives of evolving networks. In this paper, we investigate massive MIMO with HetNet, where the intended macro BS signal follows Ricean fading and interfering Femto BS signals follow Rayleigh fading. Subject to the Ricean assumption, strong line-of-sight (LOS) channel exists in the coverage area, when the high power macro BS is mounted at a high altitude. Then, by exploiting matrix and stochastic geometric tools, we evaluate the signal-to-interference (SIR). And obtain QOS objectives: coverage and outage probabilities, and area spectral efficiency (ASE). Further, we investigate the role of multiple antenna system in improving the SIR. This involves massive MIMO beamforming coordination of the macro BSs through power control with max-min optimization for users at the cell-edge. Numerical results show that the coverage and outage performance converge for different user locations, pathloss and Ricean factor. Also, the monotonic increase in Ricean factor improves the SIR of a user located within coverage region. That is, a user is in an outage at a distance where the Ricean factor is very small. And, optimal macro BS antennas and Ricean factor that achieve the ASE performance guarantee a rate-fairness between the cooperating macrocells, and avoid strong Ricean channel correlation. The performance gain is dependent on Ricean factor and user location, but independent of cell size.

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.

Reducing fertilizer-N rate, applying nitrification inhibitor (NI), and incorporating straw are widely recommended to improve N use efficiency of crops and decrease N losses. However, how these practices affect crop N uptake by regulating the fertilizer-N fate during the growing season is not well understood. We conducted a field 15N tracer study to explore the effectiveness of reduced N application, NI, and straw incorporation on fertilizer-N fates for maize cultivation in Northeast China. The following six treatments were used: 1) no N fertilization (control); 2) 200 kg urea-N ha−1 (100%N); 3) 200 kg urea-N ha−1 and 2400 kg dry-straw ha−1 (100%N+S); 4) 160 kg urea-N ha−1 (80%N, i.e., 20% reduction of N fertilization); 5) 160 kg urea-N ha−1 and nitrification inhibitor (Nitrapyrin) (80%N+NI); 6) 160 kg urea-N ha−1, nitrification inhibitor and 2400 kg dry-straw ha−1 (80%N+NI+S). Our results showed that the five N fertilization treatments yielded 16%–25% more grain and 39%–60% more crop N uptake than the control receiving no N fertilizer, but the differences among the five treatments were not statistically significant. In contrast to plant growth, changes in fertilizer-N fates in soil and plant pools were more pronounced. Compared with the 100%N treatment, 20% fertilizer-N reduction (80%N) decreased 15N concentration in topsoil and plant pools but increased the proportion of plant 15N recovery at harvesting (N use efficiency, NUE15N, 60% vs. 50%) (P &lt; 0.05). Nitrapyrin co-application (80%N+NI) delayed soil nitrification and increased soil 15N retention at harvesting (52% vs. 36%), thereby decreasing NUE15N significantly (P &lt; 0.05). Straw incorporation (100%N+S) decreased fertilizer-N retention compared with NI co-application because it promoted NUE15N significantly (P &lt; 0.05). The combined application of NI and straw (80%N+NI+S) was more conducive to fertilizer-N retention in soil. Our findings demonstrate that NI and straw additions are efficient strategies for stabilizing fertilizer-N in soils and potentially minimizing N loss; however, their effects on NUE15N vary and the related mechanism must be further clarified in long-term trials.

a Special Issue of Microorganisms devoted to ‘Microbial Biocatalysis and Biodegradation’ would be incomplete without some form of acknowledgement of the many important roles that dioxygen-dependent enzymes (principally mono- and dioxygenases) play in relevant aspects of biooxygenation. This is reflected by the multiple strategic roles that dioxygen -dependent microbial enzymes play both in generating valuable synthons for chemoenzymatic synthesis, and in facilitating key reactions that drive the global geochemical Carbon Cycle. A useful insight into this relationship can be gained by reviewing the evolution of the current status of 2,5-diketocamphane 1,2-monooxygenase (EC 1.14.14.108) from camphor-grown Pseudomonas putida ATCC 17453. Over the last sixty years, the perceived nature of this monooxygenase has transmogrified significantly. Commencing in the 1960s, extensive initial studies consistently reported that the enzyme was a monomeric true flavoprotein dependent on both FMNH₂ and nonheme iron as bound cofactors. However, over the last decade all those criteria have changed absolutely, and the enzyme is currently acknowledged to be a metal ion-independent homodimeric flavin-dependent two-component monooxygenase deploying FMNH₂ as a cosubstrate. That transition is a paradigm of the ever evolving nature of scientific knowledge.

Radiation therapy remains to be one of the main treatment modalities for the management of breast cancer. Several reports have asserted that low dose photon irradiation, which is the most available radiation modality, can promote migration and invasion of malignant cells. Also, some approved chemotherapy drugs used in the management of breast cancer are implicated in promoting metastasis. Therefore, it has become critical to unravel novel therapies that are effective on tumour cells and can reduce the metastatic potential. Methods: Malignant (MCF-7), triple-negative (MDA-MB-231) and spontaneously immortalized MCF-10A human breast cancer cell lines were pre-treated with pan-HDACi SAHA or multi-target inhibitor CUDC-101 and exposed to 2Gy and 6Gy 148 MeV mid-SOBP protons or 250KeV X-rays. Wound healing and trans-well invasion assays were performed. Imaging was done at 4-hour intervals for wound healing assays, and at 24-hours post-irradiation for trans-well migration assays. Results: Significant reduction in migration was observed after treatment with proton alone at lower doses (2Gy) and at higher (6Gy) doses in the MCF-7 cell line. In the triple negative cell line MDA-MB-231 cell line, reduction in migration was evident at higher doses(6Gy) protons. Combination therapy of CUDC-101 or SAHA and protons also inhibited migration and invasion in the MDA-MB-231 cell line. Treatment with CUDC-101 monotherapy showed benefit in reducing migration and invasion in the MDA-MB-231 cell line. Treatment with SAHA, either as monotherapy or in combination with X-rays, promoted migration in the MCF-7 cell line. Conclusions: CUDC-101 or proton monotherapies, as well as combination therapy with radiation, inhibits migration as monotherapy particularly in the MDA-MB-231 which has high metastatic potential.

Classical Optimal Power Flow (OPF) is one of the complex and challenging problem in power system that includes non-convex, nonlinear and large-scale structure. With the use of incorporation of uncertain and intermittent wind energy sources in the OPF problem, the complexity of the problem escalates. In a power system, Flexible Alternating Current Transmission Systems (FACTS) devices can mitigate most of the problems associated with power quality and overload in the network. However, determining the placement and sizing of facts devices is an additional problem to minimize the total cost of production of the power system. As a result, in order to solve OPF problem which includes all of these conditions, an artificial intelligence based optimization algorithm needs to have an unusual exploration ability as well as exploitation–exploration balance. Weighted mean of vectors (INFO) is a new heuristic optimizer, which can help finding a more effective solutions in engineering design optimization problems. In this study, firstly, INFO algorithm was improved by using the Fitness–Distance Balance (FDB) method with its abilities. Then, the algorithm developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success-History based Adaptive Differential Evolution (LSHADE). Finally, the developed algorithm has been applied for solving optimal placement and sizing of facts devices for optimal power flow problem incorporating wind energy source. The obtained results showed that the proposed algorithm is more effective solver for the problems cases while compared to the literature.

Introduction: The COVID-19 pandemic prompted a global shift to distance learning, profoundly affecting children's mental health. In Saudi Arabia, remote education was implemented on March 8, 2020, impacting students across various factors, such as age, vaccination status, and school system. This shift lasted between 1.5 to 2.5 years, influencing students’ experiences differently. This study explores the psychological impact of distance learning on children and adolescents, with the goal of informing mental health advocacy and guiding educational strategies in scenarios where distance learning becomes essential. Methods: We conducted a cross-sectional study using an online survey distributed to parents of children aged 6 to 18 in the major metropolitan areas of Jeddah and Riyadh. The survey collected demographic information and included the validated Arabic version of the Vanderbilt ADHD Diagnostic Rating Scale. This tool assesses behavioral challenges, anxiety, and symptoms of attention deficit hyperactivity disorder (ADHD). Results: The study found that 71.6% of families reported a positive experience with distance learning. A significant correlation was identified between parents' marital status and children's ability to cope with remote education. Interestingly, students without ADHD symptoms experienced three times more negative outcomes than those with ADHD. However, despite reporting fewer negative experiences, children with ADHD symptomatology faced heightened symptom severity and academic difficulties. The results further indicated that 5.4% of students were classified with ADHD predominantly inattentive presentation, 1.8% with predominantly hyperactive/impulsive presentation, and 3.9% with combined ADHD presentation. Additionally, 7.2% of students screened positive for oppositional defiant disorder, 1.5% for conduct disorder, and 6.6% for anxiety or depression. Conclusion: This study is the first comprehensive, multi-city investigation in Saudi Arabia to analyze the relationship between distance learning experiences, sociodemographic factors, and mental health symptoms in children. The findings provide essential insights for future research and offer a foundation for developing targeted interventions to support children’s mental health and education in the region.

Combining genomics with digital healthcare information is set to transform per- sonalized medicine. However, this integration is challenging due to the differing nature of the data modalities. The large size of the genome makes it impossible to store it as part of the standard electronic health record (EHR) system. Rep- resenting the genome as a condensed representation containing biomarkers and usable features is required to make the genome interoperable with EHR data. This systematic review examines both conventional and state-of-the-art methods for genome language modeling (GLM), which involves representing and extract- ing features from genomic sequences. Feature extraction is an essential step for applying machine learning (ML) models to large genomic datasets, especially within integrated workflows. We first provide a step-by-step guide to various genomic sequence pre-processing and representation techniques. Then we explore feature extraction methods including tokenization, and transformation of tokens using frequency, embedding, and neural network-based approaches. In the end, we discuss ML applications in genomics, focusing on classification, prediction, and language processing algorithms. Additionally, we explore the role of GLM in func- tional annotation, emphasizing how advanced ML models, such as Bidirectional encoder representations from transformers (BERT), enhance the interpretation of genomic data. To the best of our knowledge, we compile the first end-to-end analytic guide to convert complex genomic data into biologically interpretable information using GLM, thereby facilitating the development of novel data-driven hypotheses.

TikTok has rapidly become one of the most popular social media platforms, and, thus, scholars should pay attention to its association with users’ mental health. Our aim was to synthesize and evaluate the association between problematic TikTok use and mental health. We applied the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines in our review. The review protocol was registered with PROSPERO (CRD42024582054). We searched PubMed, Scopus, Web of Science, PsycINFO, ProQuest, and CINAHL until September 02, 2024. We identified 16 studies with 15,821 individuals. All studies were cross-sectional and were conducted after 2019. Our meta-analysis showed a statistically significant positive association between TikTok use and depression (β = 0.321, 95% confidence interval [CI]: 0.261 to 0.381, p &lt; 0.001), and anxiety (β = 0.406, 95% CI: 0.279 to 0.533, p &lt; 0.001). Also, we found a positive association between TikTok use and body image issues, poor sleep, anger, distress intolerance, narcissism, and stress. Our findings suggest that problematic TikTok use has a negative impact on several mental health issues. Given the high levels of TikTok use especially among young adults, our findings are essential to further enhance our understanding of the impact of TikTok use on mental health. Finally, there is a need for further studies of better quality to assess the impact of problematic TikTok use on mental health in a more valid way.

Our findings illustrate that CS should be employed as a means of popularizing scientific output, facilitating the promotion of political decision-making processes, and acting as a knowledge broker to reverse consumer behavior towards more environmentally friendly and conducive to SUP usage.

Flexible surgical robots are emerging as advanced tools for minimally invasive surgeries, offering greater versatility compared to traditional rigid robots. Unlike commercial endoscopes, the overtube should remain fixed to maintain stability, ensure a clear field of view, and allow surgical tools to perform tasks efficiently. While constant curvature bending of the overtube is sufficient for some lesions, certain lesions require the overtube to bend into specific shapes to achieve appropriate positioning. Various methods for creating different bending shapes have been proposed in previous research, typically involving connecting multiple segments. However, this approach complicates control and reduces both space and cost efficiency. This study proposed a conceptual method for adding a shaping tendon to control the bending shape and mathematically analyzed the effect of this shaping tendon, inserted along an arbitrary path in addition to the main driving tendons for constant curvature bending, on the bending shape of the hyper-redundant manipulator with rolling joints. The overall system was modeled and analyzed from an energy perspective, and the validity of the proposed mathematical modeling was verified through comparison with results obtained from physical experiments. In addition, it was identified that the design parameter determining the tendon path is significant element in defining bending shape of the overtube.

As modern technology enables instant access to virtually limitless information, students may perceive memorization of information as lacking in practical importance. The current study investigated the relationship between attitudes toward remembering and metamemory as well as objective memory performance. University students completed the Importance of Remembering questionnaire (IORQ) as a measure of attitudes toward remembering. Subjective components of memory were measured by immediate and delayed judgements of learning (JOLs), global judgements of learning (global JOLs), retroactive confidence judgements (RCJs), as well as subjective mental workload. Objective memory performance was measured using a cued recall test. The IORQ was only significantly correlated with absolute accuracy of delayed judgements of learning for words and pictures such that higher IORQ ratings were associated with less accurate judgments about how well they learned the items. No other correlations were significant. This suggests that a student’s lack of belief in the importance of remembering, at least as conceptualized on the IORQ, may not affect most aspects of memory performance, including those related to academic outcomes.

Background. One of the most important prognostic factors in periampullary and pancreatic cancers is perineural infiltration, whose preoperative detection could be decisive in selecting which patients should not undergo upfront surgery. We evaluated CA 19.9 and the Neutrophil-to-lymphocyte ratio (NLR) as preoperative predictors of perineural invasion (PNI). Methods: Retrospective analysis including patients with periampullary and pancreatic cancers who underwent curative resection from January 2013 to August 2023 in our institution. A univariate analysis and multivariate analysis were performed to analyze the association between the CA 19.9 and NLR with the existence of PNI. Results: A total of 136 patients were included in the study. PNI was observed in 95 (69.9%) patients. The selected cut-off points were NLR: 2.2 and CA 19.9: 37 U/mL. In univariate analysis, NLR (p=0.001) and CA 19.9 (p=0.006) were statistically significantly associated with PNI. In multivariate analysis, baseline NLR levels (p=0.012; OR: 1.95; 95%CI: 1.16-3.29) as well as CA 19.9 levels (p=0.026; OR: 1.67; 95%CI: 1.06-2.64) remained independent prognostic factors for PNI. The area under de ROC curves was 0.67 for CA 19.9 (p=0.004) and 0.72 for NLR (p<0.001). A sensitivity of 65.9% with a positive predictive value of 81.7% were obtained for CA 19.9, and a sensitivity of 62.1% with a positive predictive value of 84.3% were achieved for NLR. Conclusion: Preoperative CA 19.9 and NLR levels seem to be good predictors of PNI. In patients with normal levels of CA 19.9, preoperative NLR above of 2.2 should be an indication for neoadjuvant therapy.

.Citizen or community science (CS) projects in the marine environment rarely consider carbon footprint and sustainability. In this case study, we assessed the effectiveness of ten CS methods used by tourists in the Great Barrier Reef Marine Park (GBRMP) and Coral Sea Marine Park (CSMP). We also assessed baseline coral reef knowledge of the tourists, observations of marine species and the communication of our results to the public. Specifically, the tourists utilised up to ten methods: iNaturalist, CoralWatch, Great Barrier Reef Census, Eye on the Reef (EoR), environ-mental DNA (eDNA) testing kits, photogrammetry, social surveys, and Red Map, as well as ma-rine debris and marine vegetation collections. A total of 10,421 data points were collected across 14 days, including 5,390 records (52 % of the total) uploaded to iNaturalist, comprising 640 plant animal species. Public awareness of the CS expedition reached over 700,000 people based on estimates from advertising, media, social media, family and friends, and conference presenta-tions. We estimated the total carbon footprint for the expedition as 268.7 tonnes of CO2 or 4.47 tonnes of CO2 per person, equivalent to $112 AUD needed to offset this input. Based on these re-sults, our recommendations to leverage CS methods include governmental review strategies, temporal replication to allow the measurement of changes through time, integrating sustainabil-ity into CS ecotourism platforms, and encouraging broad participation.

Pastırma is an ethnic meat product defined as dry-curing, drying, pressing the whole muscles of cattle and buffalo, and coating them with a special paste containing fenugreek seed flour, garlic, milled red capia pepper, and water. In this narrative literature review, historical aspects of pastırma, its definition and classification, detailed production steps, composition and yield, chemical and microbiological properties, pastırma fraud and customer concerns are mentioned. In the shape of the narrative review, relevant studies were identified by searching Scopus, Science Direct, Web of Science, Trdizin, and Google Scholar Search included English- or Turkish-based articles, online reports, books, and electronic books. The keywords "pastırma, pastirma, çemen, cemen, cemening, cemen paste, Fenugreek" were used. The results of this review indicate that future studies on pastırma may focus on related cultural aspects, elimination of unpleasant odour from fenugreek, histological and chemical effects of pressing meat parts, kinetics of drying, osmotic dehydration, and developing new starter combinations.

A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.

The basic helix-loop-helix (bHLH) transcription factor family, is the second-largest superfamily in plants after MYB, which plays a significant role in the physiological processes of plant growth, tissue development, and environmental variation. However, the systematized study of the bHLH transcription factor family has not yet been conducted in A. catechu. Herein, we conducted genome genome-wide investigation of AcbHLH genes located on 16 chromosomes of A. catechu. A phylogenic tree was constructed to adjudge their homology of genes, and 24 subgroups were identified. Finally, we analyzed the dynamic changes of gene-expression levels of nine AcbHLH genes in response to drought and salt in leaves and roots. The expression patterns of 9 AcbHLH genes show differences in leaves and roots. Under stress conditions induced by salt and drought, AcbHLH22, AcbHLH62 and AcbHLH45 are significantly upregulated in both leaves and roots. In conclusion, this study will substantially contribute to the foundation for exploring the role of the bHLH superfamily in A.catechu in dealing with abiotic stresses.

This study presents a novel investigation into the use of Light Emitting Diodes (LEDs) for the in vitro rooting of 'Marubakaido' and 'M9' apple tree rootstocks, marking the first time this approach has been reported in the literature. The research compares four different light sources: blue LED (450 nm), red LED (660 nm), a combination of red and blue LEDs, and traditional fluorescent lamps as a control. Mini-cuttings were inoculated in Murashige &amp; Skoog (MS) medium with reduced nutrient concentrations and supplemented with indoleacetic acid (IAA) and sucrose. The explants were incubated under controlled conditions for 30 days, allowing for a thorough evaluation of the effects of different light sources on various growth metric. The study found that blue LEDs significantly increased dry mass accumulation in 'Marubakaido' seedlings compared to both red LEDs and fluorescent lamps, demonstrating the effectiveness of LEDs in promoting plant growth. Utilizing LEDs not only enhances seedling development but also offers economic benefits over fluorescent lamps. LEDs are known for their high luminous efficiency, low energy consumption, and long lifespan, which can lead to reduced operational costs in plant production systems. This research not only contributes to the understanding of light effects on plant tissue culture but also suggests that the combination of blue and red LEDs can serve as a viable alternative to fluorescent lamps, potentially transforming practices in the field of horticulture and plant propagation.

This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. While traditional centralized data processing methods limit effective collaboration across agencies due to data privacy and compliance concerns, federated learning avoids the risk of privacy breaches through a distributed architecture that allows participants to train artificial intelligence (AI) models together without sharing raw data. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy. In addition, this study explores the scalability and generality of federated learning in the medical field, and points out that the technology is not only suitable for drug development, but also has broad cross-industry application potential, especially in areas such as finance and insurance, where data privacy is critical.

Parental willingness is a prerequisite for childhood immunization. This study evaluated whether parental vaccination readiness and willingness, measured using the modified Japanese 7C Vaccination Readiness Scale, predicted vaccination behavior. In December 2021, 2 months before the approval of COVID-19 vaccination for children aged 5–11 years in Japan, we asked 1,190 parents of children in this age groups about their intention regarding vaccinating their children against COVID-19 using an online modified 7C Vaccination Readiness Scale, consisting of seven components, namely, Confidence, Complacency, Constraints, Calculation, Collective responsibility, Compliance, and Conspiracy. The modified 7C components were analyzed for model fit and association with parental intention using structural equation modeling. The model fit of the modified 7C scale was good (goodness-of-fit index: 0.98), and the factor loadings and model fit for parental readiness were high (standardized factor loadings: 0.84). In January 2024, we asked the same parents about their children's COVID-19 vaccination status (valid responses: 536). The components of the modified 7C scale were compared with actual vaccination behavior. Parents with a low readiness to vaccinate their children against COVID-19 had a high actual non-vaccination rate. The use of the modified 7C scale may assist in the development of childhood vaccination interventions.

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WT), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-time scale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ES, and distributed WT are analyzed. Based on this analysis, then a multi-time scale voltage optimization scheme for distribution networks using MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of proposed method, simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results that are more realistic compared to the day-ahead optimization method.

The learning system is primarily based on teaching, and teaching through digital resources is referred to as e-learning. As the world progresses towards success day by day, the use of technology, digital devices, and modern techniques is becoming increasingly prevalent. The global shift from traditional paperwork to digital learning is one of the most significant changes in the education sector. However, many factors have created numerous hurdles during the online setup. The Covid-19 pandemic has led to serious problems all over the world, with the education sector being one of the most affected. One of the greatest challenges students face during online education is the lack of digital infrastructure, especially in remote areas of Pakistan. The biggest challenge is connecting students with teachers due to limited internet access. Without proper internet connectivity, both students and teachers cannot access educational materials. In this context, the government plays a crucial role in providing adequate facilities to students, particularly in primary schools. If proper infrastructure is not made available during this pandemic, students will be deprived of a good quality education. Therefore, good infrastructure is essential for effective online education. With the proper resources in place, students will be able to receive a quality education.

Chitosan spherical scaffolds, derived from the deacetylation of chitin, are natural polymers with significant potential, though challenges remain in optimizing their properties. The use of chitosan as a carrier for the controlled release of iron chloride is not yet fully established, despite iron’s critical role in various essential biological processes, including as a component of metalloenzymes and in the maintenance of life. Methods: This study aimed to develop a novel protocol for synthesing chitosan scaffolds and to characterize their properties using analytical techniques. Additionally, the release profile of iron was modeled using two primary controlled-release mathematical models. Results: The results demonstrated successful synthesis and physicochemical characterization of the chitosan sphere scaffolds. These scaffolds exhibited appropriate topography for iron loading and cellular interaction, as confirmed by SEM analysis, and contained functional groups as identified by FTIR. The scaffolds were also semi-crystalline (XRD), showed controlled degradation, and were confirmed to be non-toxic biomaterials. Furthermore, the modeling of the controlled release of iron chloride adsorbed on the scaffold surface, as well as the modeling of iron chloride specimens using the MEDUZA software, was successfully conducted. A theoretical curve was generated from the experimental dose-response data collected over time, considering variations in pH and medium concentration. Conclusions: In conclusion, a novel protocol for chitosan-based carriers was successfully developed, enabling thorough physicochemical characterization and modeling of both absorption and controlled release.

The modern world is gradually moving towards a global civilisation that is automated and propelled by technology. Artificial intelligence (AI) is a highly advanced technology that has been acknowledged as a major innovation. The incorporation of this technology into the banking sector has enhanced operational procedures in relation to customer satisfaction, virtual assistance, and risk reduction. This machine is employed to streamline activities and improve how decisions are made in this sector. This study investigates the potential benefits and challenges of implementing artificial intelligence in the banking sector. The methodology ensures a comprehensive understanding of the implementation of artificial intelligence in banking by showcasing its effectiveness, precision, and efficiency, as well as the opportunities it presents. The study data were collected from secondary sources, such as recent publications that demonstrate applications of artificial intelligence from five different banks, as well as the examination of industry reports. The main findings of this study demonstrate the substantial improvement of administrative operations through AI-driven technology, which effectively automates activities that include inputting data and handling information. As a result, the frequency of mistakes caused by people has reduced, leading to a 30% decrease in operational expenditures for all evaluated institutions. However, the banking industry admits the challenges it encounters, such as issues with confidentiality and a scarcity of skilled employees. In the financial services industry, the integration of artificial intelligence into the banking sector is perceived as an advantageous chance instead of a harmful danger, with a greater potential for beneficial impact.

Urbanization degrades natural habitats and creates new urban ecosystems, like domestic gardens. The plant composition of these gardens varies with socio-economic factors and urban planning levels. However, the diversity and impact of introduced species are often poorly assessed, causing potential ecological imbalances, particularly in Lubumbashi (DR Congo). The objective was to analyze the spatial structure, plant diversity, propagation strategies, and ecological functions of domestic gardens. Three distinct neighborhoods were selected: planned, unplanned and residential neighborhood. 20 avenues (with 5 plots per avenue) were chosen to represent the diversity within each neighborhood, and stratified random sampling of plots was conducted to analyze gardening practices. Gardens were classified into types, and their vegetation was evaluated based on species origin and ecological impact. The analysis of domestic gardens in Lubumbashi reveals significant variations across different neighborhood types. Residential neighborhoods exhibit larger average garden sizes (315.1 m²), higher species richness (22 species), and larger plot sizes (1032 m²) compared to unplanned and planned neighborhoods, where garden areas and species richness are notably lower. Rectangular gardens dominate in unplanned areas, while planned neighborhoods feature more intentional landscaping elements, such as flowerbeds and hedges. The use of gardens for food production is prominent in planned areas (40.7%), whereas residential neighborhoods prioritize ornamentation (51.4%). The study identifies 232 species across 68 families, with a predominance of exotic species (80%) in all neighborhoods, particularly in unplanned areas (82.25%). The data reveals that Mangifera indica and Persea americana are abundant in all neighborhoods, illustrating their adaptability to different urban contexts. Herbaceous species are most common, followed by woody plants, with vines being sparse. Species dispersal is primarily driven by human activities (anthropochory), accounting for over 85% in all neighborhoods. These findings highlight the strong human influence on the composition and structure of domestic gardens in Lubumbashi, emphasizing the dominance of exotic species and the importance of anthropogenic factors in shaping urban green spaces. Urban policies should incorporate strategies to minimize the negative impacts of exotic species on native flora.

Recent developments in metallic additive manufacturing (AM) processes for the production of high-performance industrial pieces have been hampered by the limited availability of reliably processable or printable alloys. To date, most of the alloys used in AM are commercial grades that have been previously optimized for different manufacturing techniques. This study aims to design new alloys specifically tailored for AM processes, to minimize defects in the final products and optimize their properties. A computational approach is proposed to design novel and optimized austenitic alloy compositions. This method integrates a suite of predictive tools, including machine learning, calculation of phase diagrams (CALPHAD) and physical models, all piloted by a multi-objective genetic algorithm. Within this framework, several material-dependent criteria are examined and their impact on properties and on the occurrence of defects is identified. To validate our approach, experimental tests are performed on a selected alloy composition: powder is produced by gas atomization and samples are fabricated by laser powder bed fusion. The microstructure and mechanical properties of the alloys are evaluated and its printability is compared to a commercial 316L stainless steel taken as a reference.

Aortic transection, a near-complete tear through the layers of the aorta, is a critical condition often resulting from trauma such as motor vehicle collisions. The urgency of managing aortic transection underscores the critical need for effective interventions. We report the case of a male in his early 50’s with no significant medical history who presented to the emergency department following a motor vehicle collision, sustaining multiple injuries including a descending thoracic aortic transection. Rapid diagnostic assessment confirmed the severity of the injury, necessitating immediate intervention. Endovascular aortic repair (EVAR) was successfully employed, highlighting its efficacy in managing acute aortic injuries. The patient responded well to treatment, underscoring the importance of timely intervention in improving patient outcomes. This case emphasizes the critical role of rapid diagnostic assessment and endovascular intervention in managing life-threatening thoracic aortic injuries, particularly in the acute setting.

The fundamental divergence in foundation of modern physics lies in the incompatibility between quantum theory and relativity. This study created a relative continuum, found a mathematical tool for the cosmic continuum; proposed dark particle hypothesis, filled gap on minimum existence quantity particle; discovered new equivalence principle, bridged differences in foundation of physics; and established an ideal model, revealed deep essence of cosmic and material structure. In this model, any cosmic system is a continuum composed of existence continuum relative to the wavelength of bosons energy waves and its dimension continuum. This model provides new perspectives on the fundamental problems of physics and cosmology. Firstly, it elucidated the physical mechanism of bosons in fundamental interactions. Secondly, it reconstructed the understanding of the basic unit of the cosmic and material structure. Thirdly, it updated the inherent concepts about the existence form and existence dimension. Fourthly, it restored the causality truth of wave function collapse in quantum mechanics.

This study examines the aggregate consumption function of Saudi Arabia from 2000 to 2022, focusing on identifying key determinants of household consumption and evaluating the impacts of disposable income, household wealth, government expenditure, interest rates, and oil revenues. The research uses advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) model and Johansen cointegration test, to analyze the relationships among these variables. The findings reveal that disposable income, household wealth, and government expenditure significantly and positively influence consumption, whereas interest rates show a negative correlation. Oil revenues also play a critical role, reflecting the country's economic reliance on oil. The study highlights the necessity for economic diversification to reduce the impact of oil price volatility on household income and consumption stability. The results offer crucial insights for policymakers, emphasizing the need for strategies that enhance household income and wealth, maintain robust public sector spending, and effectively manage interest rates. These findings also support the importance of consistent and predictable income sources for sustaining consumption. Additionally, this study suggests directions for future research, including developing sophisticated forecasting models to predict consumption trends and exploring other influencing factors such as demographic shifts and technological progress.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.