This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.

We delve into the intricate dynamics governing anisotropic fluids within the cosmological paradigm, elucidating the nuanced influence of conformal anomalies upon the stress-energy tensor. Through the presentation of a sophisticated theoretical framework, we seamlessly intertwine unconventional fluidic properties with profound cosmological considerations, thereby augmenting and refining our comprehension of the primordial universe.

This work is concerned with studying the change of some Optical and Electrical Properties of Ba_xFe_(1-x)TiO₄ thin films due to the change of the nano crystal size. Simple theoretical explanation based on the string theory was developed. The empirical results obtained indicated that the absorption coefficient, electrical conductivity, and the magnetic permeability increase upon decreasing the nano crystal size. However, the transmission coefficient, and the refractive index increase upon increasing the nano crystal size. All these changes are explained using a simple string model with the aid of Newton's laws. The capability of the string theory to explain this nano quantum behavior comes from the fact that the string theory recognize both the particle and the wave nature of the nano system. This is similar of course to the quantum approach which is based mainly on the dual wave particle nature of atomic systems and entities.

A new generalized definition of Mersenne numbers is proposed of the form (a^n - (a-1)^n), called Global Generalized Mersenne numbers, or simply Generalized Mersenne numbers and noted GM_{a,n} where a is the base and n is the exponent, both being positive integers. The properties are investigated for prime exponents n and several theorems on Mersenne numbers regarding their congruence properties are generalized and demonstrated. In particular, it is found that, for any base a, Generalized Mersenne numbers are in general such that (GM_{a,n} - 1) are even and divisible by n, a and (a-1) for any odd prime exponent n and by (a(a-1)+1) for any prime exponent n > 5. The remaining factor is a function of triangular numbers of (a-1), specific to each prime exponent n. Four theorems on Mersenne numbers are generalized and four new theorems are demonstrated, allowing to show first, that (GM_{a,n} - 1) are divisible by 6, and more precisely GM_{a,n} are congruent to 1(mod 12) or 7(mod 12) depending on the congruence of the base a(mod 4); second, that (GM_{a,n} - 1) are divisible by 10 if n == 1(mod 4) and, if n == 3(mod 4), GM_{a,n} == 1(mod 10), or 7(mod 10) or 9(mod 10) depending on the congruence of the base a(mod 5); third, that all factors c_{i} of GM_{a,n} are of the form (2nf_{i}+1) with f_{i} natural integers such that c_{i} is prime itself or the product of primes of the form (2nj+1) with j natural integer; fourth, that for odd prime exponents n, all GM_{a,n} are periodically congruent to either +/-1\(mod 8) or +/-3(mod 8) depending on the congruence of the base a(mod 8); and fifth, that the factors of a composite GM_{a,n} is of the form (2nf_{i}+1) with f_{i} == u(mod 4) and u being either 0, 1, 2 or 3 depending on the congruence of the exponent n(mod 4) and on the congruence of the base a(mod 8). The potential use of Generalized Mersenne primes in cryptography is shortly addressed.

Effective communication in English is crucial for marketing professionals in the highly competitive global business landscape. This article emphasizes the importance of English language proficiency and the need for a tailored needs analysis for marketing professionals in the field of Business English. The comprehensive exploration of the needs analysis process outlines its methods and steps, highlighting its relevance in designing targeted language programs. By utilizing surveys, interviews, and observations, trainers and language instructors gain valuable insights into the specific challenges faced by marketing professionals, enabling them to develop customized skill-building approaches. The article also underscores the pedagogical implications of the needs analysis, stressing the significance of a personalized training program that addresses identified language gaps. Recognizing the collaborative nature of the process, the article concludes by discussing the implementation of the training program and the importance of tracking progress, ultimately displaying the tangible benefits of conducting a needs analysis in Business English for marketing professionals in terms of enhancing competitiveness and driving business growth.

Since the introduction of Cross-laminated Timber (CLT) in Austria in the early 1990s, the adoption of this 90°-crosswise-laminated product has seen exponential growth worldwide. Compared to traditional laminated timber products (e.g. glulam), CLT provides improved dimensional stability, but with reduced out-of-plane bending stiffness. To improve the bending stiffness, while maintaining relative dimensional stability, a modified orientation of the inner layers in a diagonal direction can be used. This novel product is Diagonal-Cross-laminated Timber (DCLT), a composite timber product, consisting of inner layers which are rotated at different angle-ply orientations between 0 and 90 degrees to the outer layers. To properly model the out-of-plane bending behavior of the DCLT, analytical models and Finite Element Analysis (FEA) were used and the results were validated by third-point bending tests performed on DCLT panels with angle-ply orientations of 10°, 20°, 40°, 70°, and a conventional CLT 90° panel. The results indicate that DCLT panels with angle-ply cross-layers have a structural advantage in out-of-plane bending over traditional CLT (90°) panels. The development of DCLT and its introduction to the industry could enable the use of lower-quality timber that would not otherwise satisfy structural requirements for CLT.

ABO incompatibility is not considered a contraindication for hematopoietic stem cell transplantation (HSCT). Approximately 30% of transplants from related donors and up to 50% of transplants from unrelated donors are ABO-incompatible. The immuno-hematologic investigationsmay evaluate donor/recipient ABO mismatch and anti-A/B isohemoagglutinins (IHAs) titration in pre-HSCT phase. Immediate hemolysis or delayed complications (passenger lymphocyte syndrome and pure red cell aplasia) can appear post-HSCT. Some preventive treatmentstake into consideration the manipulation of the product and decision-making algorithms based on the titration of IHAs in the recipient with clinical protocols for the removal/reduction of IHAs through plasma exchange or immunoadsorption procedures. Currently, the best approach in the managementof ABO-incompatibletransplant is not defined in expert consensus documents or with solid evidence. In addition, the methods for IHAs titration are not standardized. The transfusion strategy must consider both the blood group systems of the recipient and the donor until the RBC engraftment and the confirmed ABO conversion (forward and reverse typing) on two consecutive and independent samples. Therefore, ABO incompatibility in the HSCT is an important immuno-hematologic challenge and request all necessary preventive measures including the appropriate selection of ABO blood components for transfusion.

Organic agriculture based on participatory guarantee system (PGS) has been promoted as a tool for sustainable ecosystem, self-reliance and poverty alleviation of smallholder farmers in Thailand. However, specific criteria are required to certify as organic products. In this paper, the researchers present the collaboration of smallholder farmers practicing organic farming based on participatory guarantee system in four Thai provinces: Nong Bua Lam Phu, Nakhon Phanom, Ubon Ratchathani and Nakhon Ratchasima. One hundred thirty-five smallholder farmers were purposively selected as the informants. In depth interviews were conducted to conceptualize. The participatory guarantee system management considering their diverse agricultural background and socio-economic conditions. Main findings concluded that motivations for PGS were restoration soil quality and decreasing yield. Key factors for sustainable adaptation are policy support in terms of financial measures and capacity building.

This study concerns calculation and comparison of Fe, Co, Ni and Cu deposition rates in tungsten codeposition process based on electrodeposition of numerous tungsten alloys. 8 different tungsten alloys containing from 2 to 5 metals were electrodeposited in constant conditions in order to compare exact reduction rates. Calculated rates enabled control of the alloy composition precise enough to obtain a high-entropy WFeCoNiCu alloy with well-balanced composition. Introduction of copper to form the quinternary alloy was found to catalyze the whole process, increasing deposition rates of all components of the high-entropy alloy.

This study aimed to identify the most beneficial attitudes adopted by enterprise participants during their engagement in open-source software (OSS) activities. For this purpose, we selected repositories from GitHub, featuring the top 10 with the highest stars and forks, classified the participants according to their sentiment states, and compared their productivity. The results indicated that participants with positive sentiments were more productive than those with negative sentiments. Consequently, it can be inferred that encouraging organizations to express positive sentiments towards their developers publicly may stimulate OSS community activities and enhance productivity, thereby potentially leading to increased open innovation and sustainability.

This study delves into the intricate interplay between cosmology and gravity in dilaton-based models, enhancing our understanding. Exploring boundary theories coupled to gravity, we derive a four-dimensional effective theory from the cosmological constant. Dilaton gravity is then scrutinized, revealing elegant solutions and insights into brane embedding. The analysis extends to probe scalar fields, employing the WKB approximation in both conformal and non-conformal cases. In summary, this work contributes to a nuanced understanding of the complex relationship between cosmology and gravity in dilaton-based frameworks.

Purpose: The pandemic, named COVID-19, which shook global economy, production and trade has equally affected the social and domestic lives of ours. In this study the COVID-19’s impact has been investigated on lack of social interaction, financial stress and how it influences the domestic violence in a developing country like Pakistan. The study focuses on some of the important factors that are root causes of increased domestic violence in Pakistan. Design/methodology/approach: The study is quantitative and survey questionnaire has been conducted for structural equation modelling (SEM) to evaluate the hypotheses. The modified scale was validated using confirmatory factor analysis, the Purposive and snowball sampling technique is used for collecting data from 200 respondents of Punjab Pakistan for data analysis through SPSS and SmartPLS. Findings: It is revealed that Covid 19 has a significant effect on lack of social interaction and financial stress. Moreover, lack of social interaction and financial stress also enhances domestic violence. Finally, lack of social interaction and financial stress mediates COVID-19 and domestic violence. Originality/value: This study appears to be frontier research which determines the impact of COVID-19 on lack of social interaction, financial stress and domestic violence particularly in a developing country like Pakistan.

Data obtained by Direct Numerical Simulations (DNS) of pressure-driven turbulent channel flow are studied in the range 180 ≤ Reτ≤ 10,000. Reynolds number effects on the Mean Velocity Profile (MVP) and second order statistics are analyzed with a view of finding logarithmic behavior in the overlap region or even further from the wall, well in the boundary layer’s outer region. Values of Kármán constant for the MVPs and the Townsend-Perry constants for the streamwise and spanwise fluctuation variances are determined for the Reynolds numbers considered. A data-driven model of the MVP, proposed and validated for zero pressure gradient flow over a flat plate, is employed for pressure-driven channel flow by appropriately adjusting Coles’ strength of the wake function parameter, Π. There is excellent agreement between the analytic model predictions of MVP and the DNS-computed MVP as well as of the Reynolds shear stress profile. The skin friction coefficient Cf is calculated analytically. The agreement between the analytical model predictions and the DNS-based computed discrete values of Cf is excellent.

Through the development of the metaverse concept from the Sumerian myth (5500 - 1800 BC) and mind-altering novel, “Snow Crash” in 1992, to today’s information age, human- and society-centred urban metaverse worlds, an extension of residents and urban society where the virtual and the physically real blend and are more organically integrated, are meant to mirror the fabric of urban life with no harm to their residents. The success of urban metaverse cybercommunities depends on the quality of data-driven Smart City (SC) Digital Twins (DTs), the seamless exchange of data between cyber and physical worlds (e.g. between residents and their counterpart “Avatars’’) and the processing of the data effectively and efficiently with no vicious interventions. The potential risks in this ecosystem that incorporates Web3 can be extremer than the ones in Web2 since users are immersed with multiple tightly coupled wearable sensor-rich devices perceiving the blend of the real and the virtual with possible imminent negative experiences. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification technique, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars.

This paper introduces a novel multi-strategy enhanced dung beetle optimization (MSDBO) algorithm that is designed to address several issues identified in the standard dung beetle optimization algorithm. Specifically, the MSDBO aims to enhance convergence speed, reduce susceptibility to local optima, and increase search accuracy. By incorporating three strategies: tent chaotic mapping for population initialization, the golden sinusoidal strategy for position updating, and the Lévy flight strategy for balancing exploration and exploitation, the standard dung beetle optimization algorithm is enhanced. The MSDBO algorithm is evaluated using twelve benchmark test functions and compared against five state-of-the-art algorithms. The results consistently show that MSDBO exhibits faster convergence speeds and more accurate solutions than the other algorithms across most of the test functions. In addition, MSDBO is also applied to optimize the parameters of a valve plate, including the close angle, cross angle, triangle groove sizes, and wrap angle. The optimization outcomes reveal that MSDBO effectively minimizes pressure ripples in the piston chamber, resulting in reduced flow rate fluctuations and noise emission compared to the initial design. This study highlights the potential of the MSDBO algorithm in tackling complex nonlinear engineering optimization problems.

This papers objective is to describe a method of in-office 3D designing and 3D printing of a tooth-borne molar distalizer and create a library to easily reproduce the needed distalizers in a private orthodontic clinic. Research objectives were to design and 3D print molar distalizers, clinically use in orthodontic treatment settings, assess the strength and frequency of debonding and or breakage. The print resin used was Dental LT Clear V2 (RS-F2-DLCL-02) from formlabs. The 3D printer used was a Formlabs 3B+printer. 16 patients were treated with these 3D printed distalizers. Patients selected were between 11 Years and 49 Years old, Class II occlusion with no skeletal Class II values. The skeletal cephalometric values of the six patients were within the range of SNA = 81± 3º , SNB = 78 ± 3º , ANB = 3 ± 2º. The mean duration of the 3D printed appliance was 14.58 ± 4.31 weeks. The aim reached, was to position molars and canines in a dental Class I position. The combined failure rate was 0.94. A library of distalizers has been made of sizes between 16mm and 29mm, they are easy to print and easy to use in office.

We derive an uncertainty principle for Lipschitz maps acting on subsets of Banach spaces. We show that this nonlinear uncertainty principle reduces to the Heisenberg-Robertson-Schrodinger uncertainty principle for linear operators acting on Hilbert spaces.

This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1°C) and lower in February (10.3°C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance.

New onset diabetes mellitus after transplantation (NODAT) is a common complication following solid organ transplantation, with varying incidence rates due to discrepancies in defining NODAT and follow-up duration, alongside modifiable and non-modifiable risk factors. NODAT adversely affects patient survival and graft longevity, warranting early detection, especially in countries experiencing a rise in kidney transplant cases, emphasizing the need for understanding diagnostic criteria, risk factors, pathogenic mechanisms, and post-transplant outcomes.

Water reuse is critical to national development, sustenance, and survival in this era of climate, demographic, and social changes. There is no national systemic approach to address the challenge systematically. The paper presents a framework and a method to develop a national research strategy for water reuse. It presents an ontology of water reuse strategy that encapsulates the combinatorial complexity of the problem clearly, concisely, and comprehensively. Subsequently, it discusses the method to use the framework to develop a national strategy, adapt it through feedback and learning, and ultimately effect a revolutionary change in the strategy for water reuse.

Conventional MRI sequences are standard methods to monitor patients with brain tumors but have significant limitations especially after irradiation. Currently, the role of FET-PET in radiotherapy of glioblastoma is emerging, starting from target definition, response assessment and in distinguishing progression from post-irradiation changes. Recently a PET RANO criteria have been published providing optimal strategy for treatment evaluation with amino-acid PET. Earlier, a PET/RANO group reported contribution of PET imaging to radiotherapy planning and monitoring in glioma patients. Also, increasing evidence have showed advantages of amino-acid PET vs. RANO MRI for prediction of overall survival. In this narrative review we aimed to summarize published data on FET-PET based treatment response assessment to radiotherapy and focused on details in protocols.

In the context of precision agriculture (PA), geomatic surveys exploiting UAV platforms allow the trees’ dimensional characterization and crown identification. This paper focuses on the use of low-cost UAV photogrammetry to estimate the trees’ height, as part of a project for the phytoremediation of contaminated soils. Two study areas (Area 1; Area 2) have been chosen, having different characteristics in terms of mean trees’ height (5 m; 0.7 m), to test the procedure even in a challenging context. Three campaigns have been performed in Area 1 at different altitudes (30 m, 40 m, 50 m), and one UAV flight is available in Area 2 (42 m of altitude). The implemented workflow involves the elaboration of the UAV point clouds and DSMs using the standard SfM approach, the vegetation filtering, the generation of the DTM and a GIS-based analysis to obtain the CHMs for the extraction of the trees’ heights based on a local maxima approach. UAV-derived heights have been compared with in-field measurements obtaining promising results in Area 1, confirming the applicability of the procedure for trees’ height extraction, while the application in the context of low trees was more problematic.

Recent technological advances, including capsule endoscopy (CE) and balloon-assisted endoscopy (BAE), have revealed that small intestinal disease is more common than previously thought. CE has advantages including, high diagnostic yield, discom-fort-free, outpatient basis, and physiological images. BAE enabled endoscopic diagnosis and treatment in the deep small bowel. Computed tomography (CT) enterography with negative oral contrast can evaluate masses, wall thickening, and narrowing of the small intestine. In addition, Enhanced CT can detect abnormalities outside the gastrointestinal tract that endoscopy cannot evalu-ate. Each modality has its advantages and disadvantages, and a good combination of multiple modalities leads to an accurate diagnosis. As a first-line modality, three-phase enhanced CT is preferred. If CT shows a mass, stenosis, or wall thickening, a BAE should be selected. If there are no abnormal findings on CT and no obstructive symptoms, CE should be selected. If there are sig-nificant findings in the CE, determine the indication for BAE and its insertion route based on these findings. Early diagnosis of small intestinal tumors is essential for favorable outcomes. For early diagnosis, the possibility of small bowel lesions should be considered in patients with unexplained symptoms and signs after examination of the upper and lower gastro-intestinal tract.

Cardiotoxicity is an unwanted side-effect arising from different medications. The term describes a diminution of heart function, affecting especially left ventricular ejection fraction, which can progress to heart failure. Pediatric oncology patients treated with drugs of the anthracycline group, e.g., doxorubicin, can evolve to heart failure patients in adult age. Nuclear Medicine imaging of fatty acid oxidation and glycolysis accurately describes normal and altered heart metabolism. The most common finding is a state of cardiac glycolysis, which is reflected by increased uptake of 18F-desoxyglucose (18F-FDG) at a time when fatty acid oxidation is decreased. Glycolysis has recently been recognized as being the result of coenzyme Q10 deficiency as part of adaptation to hypoxia. Low LEVF correlates with impaired fatty acid oxidation. This deficiency can arise from chemotherapy and radiation and can be corrected by supplementation. Drugs like enalapril can improve fatty acid utilization. We will review relevant Nuclear Medicine imaging studies as well as fundamental biochemical data that demonstrate this pathogenetic path.

The purpose of this study is to analyze how global research cooperation patterns in the secondary battery industry have changed over recent years and to identify the evolution in the focus of research. To this end, network analysis was performed using the nationality information of the authors of a 10-year multinational joint research paper related to lithium-ion batteries. Furthermore, keyword analysis and topic modeling were performed using the abstract data from the study. The results of this study confirm that some countries that are not well-known in the field, such as Australia, Spain, and France, showed high centrality, compared with the level of cooperation scale. Additionally, six research topics were identified. According to a comparison over the first half of the decade, no difference was observed in the appearance of keywords indicating high energy density and conductivity with lithium, a key mineral. Keyword distribution was high for topics like battery charging and discharging in the first half of the decade, and for next-generation battery materials, such as solid electrolytes, lithium metal anodes, and lithium-sulfur batteries in the second. These results provide insights into the establishment of R&D cooperation strategies by countries and pre-planning by companies in the battery industry.

The global food system has three recognized challenges: (a)increasing the availability of food consumption, (b) reducing food loss, and (c) reducing food waste. The increasing demand for food consumption, increasing quantity of food loss, and corresponding increase in food waste are resulting in serious health, aesthetic, social, economic, and environmental problems due to a lack of appropriate planning and management. Despite its importance, there is no clear, concise, and comprehensive definition of food consumption, loss, and waste. Generally, food consumption, food loss, and food waste are dealt with separately. This article presents a logically constructed ontological framework of food consumption, loss, and waste. It gives equal importance to all three aspects of global food management. The systematic ontological framework is general, and the analysis can be applied to any country. The framework deconstructs the combinatorial complexity of the problem and explicates the pathways to manage the consumption, loss, and waste. The ontological framework encapsulates 19*8*7*4*5*6 = 25,536 possible components of the challenge. A critical analysis based on available data using the framework will help to develop strategies to deal with the problem. It can help to discover the gaps and to find ways to bridge the gaps. It is novel to conceptualize food consumption, loss, and waste together.

Environmental DNA (eDNA) sampling has emerged as a powerful approach for monitoring biodiversity in the context of diverse terrestrial and aquatic ecosystems, and has broad potential for both environmental surveys and species management. eDNA studies focusing on plant and animal species have capitalized on analytical methodologies developed originally for metagenomic studies of microorganisms, and on technological improvements in sample collection. However, while eDNA studies have proliferated rapidly, efforts to standardize sampling and analytical methods are still nascent. Critically, fundamental data concerning the distribution and persistence of eDNA in different ecological contexts are lacking, complicating both confidence in species detection and comparability between studies. We propose that key deficits could be addressed by using well-designed synthetic DNA standards or tracers, which are widely used in fields ranging from medical diagnostics to hydrology. In-field application of a standardized mixture of barcoded synthetic DNA fragments of varying lengths and concentrations that are recovered concommitantly with eDNA sampling should provide critical yet missing data on environmental DNA distribution and turnover, as well as an internal molecular standard to enhance both species detection and comparability between eDNA studies.

Technological advancements must keep pace with earth’s rising demand for energy, while minimizing the carbon footprint on earth. One such option is using space based solar (SBS) radio frequency (RF) microwave power beaming. This innovative and novel technology is currently being explored to address terrestrial photovoltaic (PV) intermittency and provide a method to transmit power to other spacecraft in various orbits. This modality of power generation and distribution presents a new way to power terrestrial off-grid applications and earth orbiting spacecraft. In 1968, Dr. Peter Glaser published "Power from the Sun: Its Future", which serves as the foundation of this paper to revitalize scientific interest and efforts in this topic. This paper will primarily focus on the reliance of SBS initiatives on reusable launch vehicles (RLV) to place SBS spacecraft in a designated orbit, as well as the technological, economic, and operational challenges associated with power beaming to earth and other spacecraft. This includes a discussion of the variables from the operational environment that contribute or accelerate spacecraft end of life.

The Casimir effect can either work over very short distances or at very low temperatures. At the cosmic scale, we naturally deal with long distances, but at the same time, we encounter extraordinarily low temperatures, namely the CMB (Cosmic Microwave Background) tem- perature. Recently, there has been increased interest in the Casimir effect and its implications in cosmology. Here, we briefly demonstrate that the luminosity and radiation pressure of the CMB is mathematical identical to a theoretical Casimir effect within the Hubble sphere, or actually our solution seems to be valid for any Schwarzschild black hole. The Casimir effect forevery Schwarzschild black hole is identical. This also include the Hubble sphere if we treat it as a Schwarzschild black hole that is related to increased interest in black hole cosmology.

The subject of the research are materials formed as a result of hydrothermal treatment, in this case cellular concretes, both traditional and modified with plastics (HIPS). There are two types of materials resulting from hydrothermal treatment: autoclaved sand-lime bricks and autoclaved concrete. Both in the case of ABK and silicates, the basic stubstrates used during their production are: lime, sand and water (cement is also added to cellular concrete). The article presents the methodology of testing the porous structure of autoclaved materials with the use of computed tomography. Aerated concrete (light autoclaved concrete) has a compressive strength of 2-6 MPa. The tests included aerated concrete modified with high-impact polystyrene, commonly known as HIPS. HIPS high-impact polystyrene is a thermoplastic polymer that is obtained by block-suspension polymerization of styrene with the addition of synthetic rubber. As a result of polymerization, small particles of polybutadiene remain in the polystyrene male, changing its physical and mechanical properties. The results from the content of air voids in the autoclaved concrete sample with HIPS were on average: 52.53%.

In the context of the coordinated development of digital economy and environmental protection, we take the strategy of "Broadband China" as a quasi-natural experiment, adopts the panel data of 284 cities from 2007 to 2020, and evaluates the policy effect of digital economy on total factor energy efficiency through the progressive DID model. The results show that: The construction of "Broadband China" demonstration city has significantly promoted the improvement of the total factor energy efficiency of the demonstration city, but there is also significant urban heterogeneity, which is manifested in the significant improvement of the total factor energy efficiency of the eastern city, the southern city, the big city and the non-resource-based city. It has no significant effect on the total factor energy efficiency of central and western cities, northern cities, small and medium-sized cities and resource-based cities. The construction of "Broadband China" demonstration cities can improve total factor energy efficiency by promoting technological progress and strengthening environmental regulations. Therefore, we should further strengthen the construction of digital infrastructure and give full play to its positive effects. Different cities should seek differentiated development paths based on their own resource endowments and development conditions, so that the digital economy can effectively improve the total factor energy efficiency.

The aim of the study is to explore how sharing status updated and receiving Likes on Facebook, as a form of positive social feedback, correlates with current and perceived changes in Quality of Life (QoL) Sample consisted of 1577 adult Facebook users (76.3% females; 65.9% aged 18-24, 21.9% aged 25-30, 12.3% aged > 30 years old). The study employed a longitudinal approach, collecting Facebook data from adult users over a 12-month period using the Graph API. Data were divided into 12 monthly segments. Two monthly indicators were calculated: the percentage of textual status updates and the average number of Likes per post. Current and perceived changes in Quality of Life (QoL) were administered as self-report measures. The findings indicated a positive correlation between the frequency of textual Facebook posts and the average received Likes. Furthermore, receivign more Likes was positively associated with a higher current QoL, and perceived improvements in QoL correlated with an increase in Likes over time. The study suggests that online positive social feedback, measured through Likes on Facebook, is associated with both current QoL and perceived improvements in QoL among adult Facebook users. This highlights the potential impact of online social interactions on individual well-being.

The arguments on the origin and evolution of the evergreen broad-leaved forest in eastern Asia, especially in the evolutionary process, exist generally, even contrary in some cases. The origin and evolution of the flora of eastern Asia, the formation time of Asian monsoon, and the implications from phylogenetic and molecular biogeographic studies in some important taxa, as well as in palaeobotanical evidences, are debatable. Most researches from different disciplines suggested that the monsoon in Miocene is a key to diversification of eastern Asian flora and its evergreen broad-leaved forest. The common view is that the evergreen broad-leaved forest of East Asia is closely related to the monsoon intensity and developments, which are caused by the uplift of Himalaya-Tibet, during or after the mid-Miocene. The analysis on the floristic elements show that the present subtropical evergreen broad-leaved forest in eastern Asia could have an early or ancient tropical origin and a tropical Asian affinity, but their species are dominated by the Chinese endemic or eastern Asian distribution, many of which are the tropical sister ones. The time of Himalayan uplift and intensity of monsoon climate is believed the key for the formation of the evergreen broad-leaved forest in eastern Asia. Combined existing palaeobotanical findings, the uplift of Himalayas and the formation of monsoon climate, as well as floristic elements of the subtropical evergreen broad-leaved forest, we believe that it was evolved from a Asian tropical rain forest after mid-Miocene in southeastern region of eastern Asia, while the ancient subtropical evergreen broad-leaved forests in southwestern region continuously evolved into the present subtropical ones.

One of the important issues being explored in Industry 4.0 is a collaborative mobile robots. This collaboration requires precise navigation systems, especially indoor navigation systems where GNSS (Global Navigation Satellite System) cannot be used. To enable precise localization of robots, different variations of navigation systems are being developed, mainly based on trilateration and triangulation methods. Triangulation systems are distinguished by the fact that they allow precise determination of an object’s orientation, which is important for mobile robots. An important feature of positioning systems is the frequency of position updates based on measurements. For most systems it is 10-20 Hz. In our work, we propose a high-speed 50 Hz positioning system based on the triangulation method with infrared transmitters and receivers. In addition, our system is completely static, i.e. it has no moving/rotating measurement sensors, which makes it more resistant to disturbances (caused by vibrations, wear and tear of components, etc.). In this paper we describe the principle of the system as well as its design. Finally, we present tests of the built system, which show centimeter positioning accuracy with a position update frequency of 50 Hz.

Social, environmental and economic problems are the concern in the agroecological communities. To address these issues, it is important to understand how communities think and act towards existing sustainable practices. There are various agriculture practices which are aboriginal to particular region, and have the knowledge of environment in deep sense thus, locals utilize it for coexisting with nature, by preserving environment and fulfilling the human needs for food alongside protecting the disappearing practices. At present, India has only three designated GIAHS sites and no proposed sites at present, despite having a diverse physical environment and area. Therefore, this paper acknowledges the need to recognize potential indigenous agricultural heritage site as GIAHS and its multidimensional Sustainability and vulnerability by climate change, Traditional value, ecological value, socio cultural values and value-added behavior which can increase the economic value and appeal towards agricultural commodities in later phase. Therefore, this paper aims to investigate the potential AHS site in the north east region of India, which is known as Zabö or Ruza and changes in the LULC of the area. Alongside assessing the present agricultural Heritage systems and changes GIAHS recognition brought to it.

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

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

Intratumoral immune cytolytic activity (CYT), measured as the geometric mean of the expression of Granzyme-A (GZMA) and Perforin-1 (PRF1), has emerged as a critical factor in cancer immunotherapy, with significant implications for patient prognosis and treatment outcomes. Here, we review how different immune checkpoint pathways, the composition of Tumor Microenvironment (TME), antigen presentation and metabolic pathways, all regulate CYT. We also discuss the various methods through which CYT can be assessed. The detection and analysis of tumor-infiltrating lymphocytes using flow cytometry or immunohistochemistry provide important information on the immune cell populations within the TME. Gene expression profiling and spatial analysis techniques, such as multiplex immunofluorescence and imaging mass cytometry, allow researchers to study CYT in the context of the TME. We further report the significant clinical implications that CYT has, as its increased levels are associated with positive clinical outcomes and a favorable prognosis. Moreover, CYT can be used as a prognostic biomarker and aid in patient stratification. Measuring CYT through different methods offers promising paths for improving treatment responses. Overall, we highlight that the understanding and modulation of CYT levels is critical for improving cancer immunotherapy. Research into CYT and the factors that influence it has the potential to transform cancer treatment and improve patient outcomes.

The evolution of photovoltaic cells is intrinsically linked to advancements in the materials from which they are fabricated. This review paper provides an in-depth analysis of the latest developments in silicon-based, organic, and perovskite solar cells, which are at the forefront of photovoltaic research. We scrutinize the unique characteristics, advantages, and limitations of each material class, emphasizing their contributions to efficiency, stability, and commercial viability. Silicon-based cells are explored for their enduring relevance and recent innovations in crystalline structures. Organic photovoltaic cells are examined for their flexibility and potential for low-cost production, while perovskites are highlighted for their remarkable efficiency gains and ease of fabrication. The paper also addresses the challenges of material stability, scalability, and environmental impact, offering a balanced perspective on the current state and future potential of these material technologies.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

In this paper we address the approximation of the coupling problem for the wave equation and Maxwell's equations of electromagnetism in the time domain in terms of the electric field, by means of a nodal linear finite-element discretization in space, combined with a classical'explicit finite-difference scheme for the time discretization. Our study applies to the particular case where the dielectric permittivity has a constant value outside a sub-domain, whose closure does not intersect the boundary of the domain where the problem is defined. Inside this sub-domain Maxwell's equations hold. Outside this sub-domain the wave equation holds, which may correspond to the Maxwell's equations with a constant permittivity under certain conditions. We consider as a model the case of first order absorbing boundary conditions. Optimal error estimates that hold in natural norms under reasonable assumptions are given, among which lies a typical CFL condition for hyperbolic equations.

Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, proposed by Cox. et al. , Chen and Cox , and Özen and Şahin , which we consider in this study, differ in terms of the duration of the jumps - transitory or permanent, frequency of the jumps, and size of the jumps. To illustrate the effect of jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter , Renshaw and Haberman and the Cairns-Blake-Dowd models. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also analyse the impact of mortality jump models on catastrophe bond pricing.

This research hypothesised that the use of Virtual Reality (VR) applications can be used to train people in the ability to acquire targets faster, with greater accuracy when using motion controllers than compared with a traditional pointing device (mouse) on a 2D display. User performance was defined and measured using Reaction Time and Accuracy. The research findings suggests that the use of motion controllers in VR improved Reaction Time but not Accuracy when compared against the traditional pointing device and 2D display. This suggests that VR can be used specifically in training scenarios where Reaction Times are more important than Accuracy. It is recommended that further research is conducted using a wider range of hardware and software configurations with a stratified sample that takes into account age and gender to expand the collective understanding of factors that impact on VR aiming and target acquisition in VR.

Background: Mechanical ventilation in preterm neonates aims for synchrony, preventing complications such as lung injury. Neurally Adjusted Ventilatory Assist (NAVA) stands out as a unique mode relying on diaphragmatic electrical signals for synchronization. We attempted to review studies that have explored the long-term consequence of using invasive NAVA in neonates with a specific focus on respiratory outcomes, namely bronchopulmonary dysplasia (BPD). Methods: A systematic review following PRISMA explored invasive NAVA in preterm neonates. Primary objectives compared NAVA to conventional ventilation, assessing BPD incidence, ventilation duration, length of stay, and adverse events. Secondary objectives analyzed ventilator parameters. Results: After screening 282 records, nine studies were included. NAVA showed reduced oxygen requirement at 28 days but no significant differences in oxygen need at 36 weeks postmenstrual age, total length of stay, or ventilator days. Adverse events such as air leak, mortality, patent ductus arteriosus, and intraventricular hemorrhage revealed no substantial variations. Ventilator variables favored NAVA, indicating decreased peak inspiratory pressure, tidal volume, work of breathing, and respiratory severity score. Conclusion: Despite short-term benefits, our study found no significant reduction in BPD with NAVA. Future large-scale trials are essential to comprehensively assess NAVA's impact on preterm infants' long-term outcomes.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

The use of hyperthermia is one of the effective methods of treating cancer. In this case, the problem arises of constructing and predicting the distribution of the thermal field inside tissues depending on their type. This paper discusses the use of linear and polynomial regression methods, as well as regression algorithms based on decision tree, random forest and K nearest neighbors methods to recover missing temperature values. The influence of the size of the training sample when performing regression on the quality of the reconstructed values is investigated. Experimental recommendations on sample size are given for the selected decision tree and random forest methods. The possibility of a significant (10 times or more) reduction in the initial sample size, without leading to a decrease in the R² metric by more than 0.05 for various tissues, has been shown.

Canadians overwhelmingly do not want to live in long-term-care (LTC) facilities when they age; yet many end up there for lack of homecare, because family caregivers burn out, or because they and their professional advisors are unaware of alternatives to institutions. Not only is institutional dementia care problem-driven, it segregates disabled people, thereby abrogating human rights. Because systemic ageism and ableism cloud elder care, institutions remain the default option for Canadians with dementia. Yet, decades of deinstitutionalization enabled younger disabled Canadians to live in the community with supports. Why not elders? We describe a plethora of noninstitutional dementia-care alternatives. We then present a roadmap for considering all relevant care options in service plans, one that incorporates supported decision-making by people with dementia. We propose a paradigm shift in how Canada serves its elder citizens—not just the current generation, but those to come, including ourselves.

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

Amidst escalating environmental concerns, short natural fiber thermoplastic (SNFT) biocomposites have emerged as sustainable materials for the eco-friendly production of mechanical components. However, their limited durability has prompted research into the experimental evaluation of the deterioration of the mechanical characteristics of SNFT biocomposites, particularly under the influence of ultraviolet rays. However, conducting tests to evaluate the mechanical properties can be time-consuming and expensive. In this study, an artificial neural network (ANN) model was employed to predict the mechanical properties (tensile strength) and the impact performance (resistance and absorbed energy) of polypropylene reinforced with 30 wt.% short flax or wood pine fibers (referred to as PP30-F or PP30-P, respectively). Eight parameters were collected from experimental studies. The ANN input parameters comprised nondestructive test results, including mass, hardness, roughness, and natural frequencies, while the output parameters were the tensile strength, the maximum impact load and absorbed energy. The model was developed using the ANN toolbox in MATLAB. The linear coefficient of correlation and mean squared error were selected as the metrics for evaluating the performance function and accuracy of the ANN model. They calculate the relationship and the average squared difference between the predicted and actual values. The data analysis conducted by the models demonstrated exceptional predictive capability, achieving an accuracy rate exceeding 96%, which was deemed satisfactory. For both the PP30-F and PP30-P biocomposites, the ANN predictions deviated from the experimental data by 3, 5, and 6% about the impact load, absorbed energy, and tensile strength, respectively.

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

This study focuses on predicting traffic speed using the simple Multilayer Perceptron (MLP) model, despite the availability of various models for traffic prediction, with artificial intelligence demonstrating superior predictive capabilities. Emphasizing that the quality of the data holds greater significance than the model itself, the research underscores the challenges posed by data containing significant errors and fluctuations. Unlike relying solely on criteria such as Mean Absolute Deviation (MAD) or coefficient of determination (r2), this study advocates for the use of MAD/range as a more robust metric for prediction accuracy, especially when dealing with data exhibiting substantial order.The study sets the speed limit and traffic value at 0.65 and 0.88, respectively. Notably, both criteria yield identical accuracy results of 13% for MAD/R in predicting speed and 14% for traffic. This parity suggests that the artificial intelligence model performs equally well for both variables, highlighting the importance of considering alternative evaluation metrics in the face of data complexities.

This comprehensive review delves into gold market business management, covering historical perspectives, market dynamics, mining models, investment strategies, technology, regulations, global trends, and sustainability. Starting with a historical overview, it traces the gold market's evolution and analyzes supply-demand dynamics, price fluctuations, and geopolitical influences. The article scrutinizes gold mining models, evaluating environmental and social responsibilities, and explores financial management strategies, emphasizing risk and gold's role in portfolios. Examining technology's role, it highlights innovations in exploration, mining, and processing for improved efficiency and sustainability. The regulatory landscape analysis encompasses compliance challenges, environmental regulations, and ethical considerations, emphasizing global impacts. Global market trends are explored, identifying potential opportunities and challenges, offering strategic insights. The review concludes by evaluating sustainability practices, emphasizing environmental conservation, community engagement, and ethical sourcing.

Video gamers can play to negate the psychological impact of stress (Pine et al., 2020), which may become problematic when users over-rely on the stress-relief potential of gaming. This study used a repeated-measures experimental design to investigate the relationships between stress, video gaming, and problematic video gaming behaviours in a convenience sample of 40 students at a UK university. Results indicated that positive affect increased and negative affect decreased, whilst a biological stress measure (Instantaneous Pulse Rate) also decreased after a short video gaming session (t(36) = 4.82, p < .001, d = 0.79). Results also suggest that video gaming can act as a short-term buffer against the physiological impact of stress. Further research should focus on testing individuals who have been tested for Gaming Disorder as opposed to a general population. Research could also utilise variations of the methodological framework used in this study to examine the intensity of a stress-relief effect under different social situations. The study’s findings in relation to published work are also discussed.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

Machine learning has become more prevalent as a tool used for biogeochemical analysis in agricultural management. However, a common drawback of machine learning models is the lack of interpretability, as they are black boxes that provide little insight into agricultural management. To overcome this limitation, we compared three tree-based models (decision tree, random forest, and gradient boosting) to explain soil organic matter content through Shapley additive explanations (SHAP). Here, we used nationwide data on field crops, soil, terrain, and climate across South Korea (n = 9,584). Using the SHAP method, we identified common primary controls of the models, for example, regions with precipitation levels above 1400 mm and exchangeable potassium levels exceeding 1 cmol+ kg-1, which favor enhanced organic matter in the soil. Different models identified different impacts of nutrients on the organic matter level in the soil. The SHAP method is practical for assessing whether different machine learning models yield consistent findings in addressing these inquiries. Increasing the explainability of these models means determining essential variables related to soil organic matter management and understanding their associations for specific instances.

This article studies a class of pursuit problems admitting both a simple geometric solution as well as a more general analytic treatment. The results are specialized to demonstrate solutions to certain elementary problems typically posed to high schoolers, although a full analytic treatment, requiring some more comfort with geometry and calculus, is typically avoided at that stage.

The high-quality development of urban-rural integration is a vital component of China's economy, serving as a strategic response to the in-depth deployment of new urbanization and rural revitalization strategies. This integration is underscored by the promotion of entrepreneurship in agriculture, rural areas, and among farmers, which acts as a driver for fostering high-quality development in urban-rural areas through market competition. The resulting influx of resources and commodities with similar characteristics plays a crucial role in bridging the urban-rural divide. To maintain objectivity, it is important to adopt a balanced approach and refrain from using biased language. This study delves into the connotation and internal mechanism of the high-quality development of urban-rural integration, centering on the principle of 'quality'. Specifically, it examines the role of entrepreneurship in agriculture, rural areas, and among farmers, and evaluates its direct impact and transmission mechanism. The research employs balanced panel data from 41 cities in the Yangtze River Delta region spanning from 2003 to 2021. The findings reveal that entrepreneurship in agriculture, rural areas, and among farmers serves as a catalyst for promoting high-quality urban-rural development through the construction of a virtuous cycle, whereby resource factors and commodity services are exchanged. Moreover, this deep integration yields diverse effects across economic, spatial, demographic, social, and ecological domains, underscoring the necessity of continued support for entrepreneurship in agriculture, rural areas, and among farmers to facilitate a seamless urban-rural economic cycle and fully realize the benefits of high-quality development.

This article provides a comprehensive exploration of the electric vehicle (EV) business, investigating its current state, market dynamics, and pivotal role in sustainable transportation. It delves into factors driving EV adoption, challenges faced by the industry, and the landscape of key players. The discussion extends to investments, infrastructure development, consumer trends, and the technological innovations shaping the future. The article concludes with insights into the anticipated growth and transformative impact of EVs, emphasizing their significance in fostering a cleaner, more sustainable future for global transportation.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

Approximately 300 million non-cardiac surgeries are performed each year. Perioperative mortality after non-cardiac surgery is estimated at 2% in patients over 45 years of age . Half of these deaths are attributable to cardiovascular complications, and most to perioperative myocardial infarction (MINS). Before introduction of cardiac biomarkers the diagnosis of postoperative myocardial infarction was based on symptoms and electrocardiographic changes and its incidence was largely underestimated. The incidence of MINS when standard troponin assay is used ranges between 8 and 19% but increases to 20-30% whit high sensitivity troponin assays. Higher troponin values sugegsting myocardial injury , both with or without definite diagnosis of myocardial infarction, are associated with increased 30 day and 1-year mortality . Diagnostic and therapeutic strategies are reported .

The paper proposes a new implementation of the PID algorithm in digital hardware. The proposed circuit implements an advanced PID formula, containing a non ideal derivative component, and weighting coefficients, which enable reducing influence of setpoint changes in the proportional and derivative components. The implementation operates on standard single precision (32 bit) floating-point numbers. The proposed circuit structure is optimized for cost. It uses just one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequential manner. The circuit was implemented in a Cyclone V FPGA device from Intel, using the Quartus Prime software. Proper operation of the circuit was verified by simulation. The proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on standard single precision floating-point numbers, while being significantly cheaper. However, it outperforms by several orders of magnitude the speed of any software-based implementation, including solutions using PLCs, and CPU/MCUs. The proposed circuit structure, together with the overall regulator device concept, suit well the SoC (System on Chip), or SoPC (System on Programmable Chip) idea, i. e. a device, that contains a CPU core immersed in “FPGA fabric” - logic resources characteristic for FPGA devices.

The altitude of the habitat is one of the important regulators of species survival. Kaçkar Mountains National Park is located in the Eastern Black Sea region of Turkey. This is the first study on the benthic diatom flora of the high mountain lakes in Kaçkar Mountains National Park, which is situated between 2782 and 3075 m a.s.l. A total of 84 diatom species were identified from benthic communities of 15 habitats in summer (19 July, 28 August) and autumn (10 September) months of 2020. The genus Pinnularia (13 species) formed the basis of the taxonomic list followed by Eunotia (5 species), Navicula (5 species), and Frustulia (4 species) genera, respectively. The waters in all the studied lakes were fresh, low-saline, with low-alkaline or circumneutral pH and organically uncontaminated as evidenced by prevailed bioindicator groups. Statistical methods and comparative floristic results confirm the role of the lake altitude for the diatom species distribution. Species richness was higher in lakes with lower altitude, but a statistical approach also revealed a tendency for it to increase with altitude if species richness increased. Further studies will be needed to continue exploring this pattern. In the first round of analysis, diatom species groups were categorized according to IUCN categories, revealing one potentially threatened and one rare species. To protect studied high mountain lakes, their ecological conditions must be constantly monitored in the Kaçkar Mountains National Park.

This work presents noble composites of AAILs@MCM-48 by functionalizing ordered mesopo-rous silicon MCM-48 with two amino acid-based ionic liquids (AAILs) ([Emim][Gly] and [Em-im][Ala]) to improve carbon capture and selectivity of CO2 over nitrogen. Thermogravimetric and XRD analyses of the composites show that the MCM-48 support's thermal and structural in-tegrity was preserved after the AAILs were encapsulated. An N2 adsorption-desorption study at 77 K also confirms AAIL encapsulation in the porous support. Under post-combustion flue gas conditions, both [Emim][Gly]@MCM-48 and [Emim][Ala]@MCM-48 demonstrated improved CO2 adsorption in comparison to unmodified MCM-48, with a CO2 partial pressure of around 0.15 bar. In terms of maximal CO₂ uptake, the 40wt%-[Emim][Gly] composite outperformed the others at 303K, with values of 0.74 and 0.82 mmol g-1, respectively, at 0.1 and 0.2 bar. These numbers show a 10x and 5x increase, respectively, as compared to the pure MCM-48 under iden-tical conditions. In addition, the selectivity of the composites was improved significantly, at 0.1 bar, the selectivity of composites containing 40wt% [Emim][Ala] increased to 17, compared to 2 for pristine MCM-48.

This exploration delves into the intricacies of decision-making in business management. It covers decision-making models, influencing factors, real-world case studies, and strategies for effectiveness. Emphasizing the role of data-driven approaches, the exploration highlights the transformative impact of effective decisions on organizational success. Challenges and pitfalls are addressed, providing insights for overcoming obstacles. The importance of mastering decision-making is underlined as a strategic advantage in the competitive landscape of business management. In summary, this exploration offers a nuanced understanding, empowering leaders to make informed choices that shape organizational destinies.

This analyses the impact of competition on liquidity creation in the banking industry of the United States. For the purposes of this paper panel data of US banks for the period of 1976-2000 is chosen as a sample. The plausible endogeneity issue is addressed by employing instrumental variables and then implementing two stages least squares method. Besides this, crisis period of 1990-1992 is also analyzed to compare effect with in “normal times”. The results show that decrease in the level of competition negatively affects the liquidity creation ability of banks in the US and this impact is even more negative during the crisis period.

With the first case of human immunodeficiency virus (HIV) infection confirmed in US in 1981, the efforts of discovering anti-HIV therapeutics have been continued ever since. Ten years later, the first HIV drug zidovudine (AZT) was developed to inhibit HIV reverse transcriptase. At meantime, scientists were enlightened to discover new drugs of different targets acting on HIV integrase, protease, and host receptors. The advent of combination antiretroviral therapy (cART) is completely a game changer, with high efficiency in suppressing viremia for people with HIV (PWH) by controlling the viral load below the detectable level. On the bright side, the ART treatment has made HIV a chronic infection rather than a fatal disease. However, it cannot eradicate integrated HIV DNA from the host cells, thus the latent viral reservoir has become a lifelong threaten. In this review, we first discuss the scientific history of conventional HIV drug discovery, with more and more anti-HIV agents have been developed to solve drug resistant issue and relieve the side effect. As a complementary therapy, advanced gene editing technologies have been applied to excise HIV provirus from host genome. Within four decades, novel research conducted on HIV treatment and their contributions to eliminate HIV have been altogether summarized in our review.

Here we explore the emerging role of NKG2D CAR-T cell therapy in the realm of solid tumor treatment. Unique capabilities of NKG2D CAR-T cells to recognize stress-induced ligands on cancer cells are discussed, highlighting the therapy's potential for targeted and precise antitumor responses. We also examine the challenges and opportunities associated with NKG2D CAR-T cell therapy, emphasizing the ongoing advancements and clinical trial progress shaping its future. Ultimately, the discussion underscores the transformative promise of NKG2D CAR-T cell therapy in revolutionizing the landscape of solid tumor treatment and enhancing the prospects for improved patient outcomes.

The Standard Model (SM) of particle physics successfully describes electromagnetic, weak, and strong forces but omits gravity. We introduce the Kinematrix Field Theory (KFT), integrating gravity into this fundamental force framework through a novel particle, the kinematon. This gauge boson, existing in both massless and massive forms, advances the SM's scope to address enigmas like dark matter and neutrino masses. We detail the mathematical structure of the KFT and establish Feynman rules for kinematon interactions with fermions and gauge bosons. The KFT, synergizing with the Higgs mechanism and general relativity, aspires to a unified quantum theory of gravity. We explore the kinematon's phenomenological implications and potential experimental signatures, forecasting significant advancements in fundamental physics and setting a course for future explorations in the SM.

Abdominal aortic aneurysm (AAA) is a chronic disease in which the abdominal aorta expands until it ruptures. AAA incidence: 10 million/year (India), deaths: 40%-90% (13th leading cause of death in the USA). Current treatment methods make diagnosis difficult due to the lack of noticeable symptoms and inaccurately planned operations. Rupture Scope is a mathematical model to dramatically reduce AAA deaths and improve treatment efficiency. Using a case-specific replica/simulation of real-time abdominal aortic blood flow for monitoring without frequent imaging scans and a novel derived mathematical linear regression model to predict rupture date and expansion rate with an overall accuracy of 95.64%.

There are many problems based on solving non-autonomous differential equations of the form x¨(t)+ω2(t)x(t)=0, where x(t) represents the coordinate of a material point and ω is the angular frequency. The inverse problem involves finding the bounded coefficient ω. Continuity of the function ω(t) is not required. The trajectory x(t) is unknown, but the initial and final values of the phase variables are given. The variation principle of minimum time for the entire dynamic process allows for the determination of the optimal solution {x(t),ω(t)}. Thus, the inverse problem is an optimal control problem. No simplifying assumptions were made.

As the most important component of soft robot, the pneumatic actuator can drive robot movement. However, the effect of the actuator pneumatic network geometry on the actuator load capacity is insufficient. In this paper, the finite element analysis method is applied to calculate the load force and stability torque of the pneumatic actuator under a wide range of length and height dimensions of the pneumatic network. The effect of the network size on the structure rigidity, load capacity and deformation of the actuator is analyzed. Based on the calculation results, the effect of network structure size on the deformation and load capacity is analyzed, and the optimal geometric results with the maximum load capacity are obtained. This study provides theoretical guidance for the structural design and optimization of the pneumatic actuator.

Alena Tensor is a recently discovered class of energy-momentum tensors that provides mathematical framework in which, as demonstrated in previous publications, the description of a physical system in curved spacetime and its description in flat spacetime with fields are equivalent. The description of a system with electromagnetic field based on Alena Tensor can be used to reconcile physical descriptions. 1) In curvilinear description, Einstein Field Equations were obtained with Cosmological Constant related to the invariant of the electromagnetic field tensor, which can be interpreted as negative pressure of vacuum, filled with electromagnetic field. 2) In classical description for flat spacetime, three densities of four-forces were obtained: electromagnetic, gravity, and the force responsible for the Abraham-Lorentz effect (radiation reaction force). Obtained connection of Einstein tensor with gravity and radiation reaction force, after transition to curvilinear description, excludes black hole singularities. There was obtained Lagrangian density and generalized canonical four-momentum, containing electromagnetic four-potential and a term responsible for other two forces. In this description charged particles cannot remain at complete rest and should have spin, their energy results from the existence of the energy of the magnetic moment and the density of this energy is part of the Poyting four-vector. The distribution of charged matter was expressed as polarization-magnetization stress-energy tensor, what may explain why gravity is invisible in QED. 3) In quantum picture, QED Lagrangian density simplification was obtained, and the Dirac, Schrödinger and Klein-Gordon equations may be considered as approximations of the obtained quantum solution. Farther use of Alena Tensor in unification applications was also discussed.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

In response to the COVID-19 crisis, decisionmakers in countries around the world adopted policies such as lockdowns, mask requirements, and once vaccines became available, vaccine mandates and vaccine passports. However, political polarization and differences in risk perceptions in the populous generated strong support for and opposition to such measures. This research identifies the factors with support for and against vaccine mandates and vaccine passports. An online survey of COVID-19 health experiences in the United States population was conducted. The survey collected information regarding potential reasons for why respondents might favor or oppose COVID-19 vaccine mandates and vaccine passports, including experiences with COVID-19 illness and COVID-19 vaccinations by respondents and those in their social circles. Analysis using a Logit regression technique was conducted to identify factors influencing the likelihood of opposing vaccine mandates and vaccine passports. The survey was completed by 2,840 participants between December 18 and 23, 2021. Respondents who knew someone who experienced a health problem following vaccination were more likely to oppose vaccine mandates (OR: 2.040, 95% CI: 1.635-2.254, and vaccine passports (OR: 1.691, 95% CI: 1.361-12.101)) Twenty-two percent (612 of 2,840) of respondents reported that they knew at least one person who had experienced a health problem following COVID-19 inoculation. Perceptions of vaccine safety based on personal experiences appear to be an important determinant of opposition to vaccine mandates and vaccine passports.

We are currently witnessing a strong global transition towards new, more sustainable models of development and consumption. This transition is both activating and highlighting a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight how, on a global level, the agri-marketing system is unable to provide adequate responses to the new principles of sustainability that are increasingly expected by civil society. This inability is often linked to the phenomena of opportunism, information asymmetries and lock-in effects (resulting from the slowness or inability of some sectors or actors to evolve technologically and culturally). The control of all these factors is often ineffective and often involves high transaction costs . These conditions lead the global market into an ongoing state of failure, creating structural holes, in which new forms of exchange are born, in response to the principles mentioned above. We identify these as nested markets: hybrid market forms that, thanks to new information technologies, create a new form of proximity that transcends the classical concept of physical proximity. This new proximity directly links producers and consumers in relationships based mainly on two substantial values: reciprocity and reputation. Both of these values significantly reduce transaction costs which today play an increasing role in determining the form and repetition of exchanges. This condition is difficult to interpret from classical and neo-classical economic theories. To do so it is necessary to use a multidisciplinary approach. In this article we combine neo-institutional theory and network analysis to interpret phenomena that are increasingly emerging in all areas of the world. Using these two theories, and case studies from Brasil, North America, China and Europe, we try to highlight how the market is not only the place where the price of products and of their quantities are formed. Market is a complex social spaces, where more-or-less stable relationships are formed, based on the values of reciprocity and reputation that determine behaviour and codes of conduct aimed at containing opportunism and encouraging sustainable, autonomous, forms of self-regulation. These relationships are also the driving forces that ensure the ongoing continuation of the exchanges and the diffusion and preservation of knowledge over time and space. These relationships represent the beating heart of nested markets and their dynamic ability to adapt to the continuous changes that occur in the socio-economic and environmental context in which they are rooted. In this way sustainability is built as an expression of the co-production between different actors (producers, consumers, institutions) and natural resources.

Chemical instability of liquid-repellent surfaces is one of the nontrivial hurdles that hinders their real-world applications. Although much efforts have been made to prepare chemically durable liquid-repellent surfaces, few attentions have been paid to exploit the instability for versatile use. Herein, we propose to create hydrophilic patterns on superhydrophobic surface by taking advantage of its chemical instability induced by acid solution treatment. Superhydrophobic Cu(OH)2 nanoneedles covered Cu plate that shows poor stability towards HCl solution (1.0 M) is taken as an example. Results show that 2.5 min of HCl solution exposure leads to the etching of Cu(OH)2 nanoneedles and partial removal of self-assembled fluoroalkyl silane molecular layer, resulting in the wettability transition from superhydrophobocity to hydrophilicity, and the water contact angle decreases from ~160° to ~30°. Hydrophilic dimples with different diameters are then created on the superhydrophobic surfaces by depositing HCl droplets with different volumes. Afterwards, the hydrophilic dimple patterned superhydrophobic surfaces are used for water droplet manipulations, including controlled transfer, merging and nanoliter droplets deposition. The results thereby verify the feasibility of creating wettability patterns on superhydrophobic surfaces by using their chemical instability towards corrosive solutions, which broadens the fabrication methods and applications of functional liquid-repellent surfaces.

Dementia education and training for workforce development is becoming increasingly important in bridging knowledge gaps among health and social care practitioners in the UK and internationally. Dementia Education for Workforce Excellence (DEWE) was developed during the COVID-19 pandemic blending both synchronous and asynchronous instruction and delivered across three different contexts: care homes, home care, and nurse education within the UK and India. This study aimed to evaluate DEWE using mixed methods with online survey data analyzed descriptively and interview data analyzed thematically. Integration of survey and interview data aimed toward a comprehensive evaluation of this novel approach for dementia workforce development. Thirty-four social care practitioners and nurse educators completed the online survey demonstrating high-level learner satisfaction; learning gains; behavioral change, and motivation to share new knowledge. Four key themes developed from the analysis of interviews (n=9) around participants’ pursuit of new knowledge; delivery modes in DEWE; learning gains and impact of DEWE; and adaptations for future program implementation. Findings suggest DEWE is an innovative resource that promotes person- and relationship-centered dementia care across all stages of one’s dementia journey. Cultural adaptations are recommended for international delivery to ensure contextual alignment and maximum impact.

Rapid population growth causes smallholders to practice unsustainable forms of land intensification to meet increasing food demand. Food insecurity is exacerbated by unreliable rainfall. We revisit family planning, smallholder irrigation, and agricultural cooperatives as potential sustainable solutions. We use descriptive statistics based on primary data from Kakamega Central and Navakholo in Kenya. Results from respondents indicate 83% had no family planning information, while 82% had no access to irrigation. The major reasons are poverty, illiteracy, misconceptions, gender inequality, constraints in accessing credit, lack of investment in water resources, and family planning. Lack of access to agricultural extension services limits the adoption of sustainable farming practices. Cooperatives’ principles and values make them suitable pathways to reach the poorest and facilitate members’ access to productive resources. Cooperatives can be used to train members in sustainable agricultural practices and educate members on family planning issues. Cooperatives have contributed to better yields, incomes, nutritional status, and reduced poverty. However are constrained by a lack of capital, credit, infrastructure, markets, training and technology, delayed payment, and low prices. Governments and development agencies should support the establishment and development of cooperatives with capacity building, market infrastructure, finance, and education in cooperative principles and values.

In this experiment, the fermented pineapple residue (FPR) was replaced with 0% (CON), 25% (T25) and 50% (T50) in the diet based on silage royal grass roxb for 30 days in the fattened fatten bulls to evaluate the effect of FPR on growth performance, serum indicators and rumen characteristics.Thirty Simmental calves were selected and divided into three groups according to a completely random design.The temperature and humidity of the cowshed shall be measured every day.Rectal temperature and blood of cattle were measured, and blood and immune markers were measured.The results showed that the rectal temperature decreased significantly when adding 25% fermented pineapple residue (P&lt;0.05), and the rectal temperature decreased significantly when adding 50% fermented pineapple residue (P&lt;0.01).The content of T4 and TC in serum of cattle in T50 group was significantly lower than that in the control group (P&lt;0.01), and the content of T3 was significantly higher than that in the control group (P&lt;0.05), and the content of CR was significantly lower than that in the control group (P&lt;0.01). Fermented pineapple residue can indeed improve the oxidative (heat) stress resistance of finishing cattle. In the correlation experiment, bacterial genera such as Succinniclassicum, Melainabacteria, Lachnospiraceae, and Fibrobacteres all showed significant or extremely significant correlations with heat stress-related indicators.

The objective of this study is to evaluate how two varying amounts of sepiolite-based powder, infused with indigo extract, affect the appearance and durability of a water-based white primer. To examine the influence of this eco-friendly pigment on the coatings' overall appearance, assessments were performed for color, gloss, and surface roughness. Additionally, the coatings were investigated through optical and electron microscopic observations, to evaluate the distribution of the pigment within the polymer matrix. The effect of the pigment on the coating's durability was assessed through accelerated tests, including exposure in a salt spray chamber and a UV-B chamber. These tests aimed to evaluate the emergence of defects and changes in the appearance of the samples over time. Furthermore, the impact of different quantities of sepiolite-based powder on the coating's ability to act as a barrier was assessed using liquid resistance tests and contact angle measurements. These evaluations aimed to understand how the coating responded to various liquids and its surface properties concerning repellency or absorption. In essence, this study underscores the considerable influence of the eco-friendly pigment, demonstrating its capacity to introduce unique color and texture variations to the paint. Moreover, the inclusion of the pigment has enhanced the coating's color stability, its ability to act as a barrier, and overall durability when exposed to harsh environments.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

The world of education has changed. And for all the trials and challenges of managing education during a pandemic, we have also learned a few things that can make our teaching and learning experiences even better in the future.The pandemic imposed constant balance exercises in all aspects of our daily life, including education. Suddenly the living room of the house turned into a work space, the kitchen replaced the canteen, the children's office became a student seat. There was a new area of unknown, irregular and unpredictable forced experimentation. Now what is right, beneficial and effective is an unanswerable question in the absence of previous experience, and the only way to answer it is by trial and error.Universities, in the context of "emergency" remote teaching, also had to experiment. Professors and students found themselves inside virtual classrooms, facing digital boards, closed cameras and microphones.How could a professor not be frustrated by low levels of interaction during the lesson, and how could a student understand a problem without disrupting the fragile digital teaching flow? What was the learning outcome after each two-hour e-teaching? Was the learner's attention maintained, the learning objective achieved, knowledge transferred, new skills acquired? The answers had to be given through experimentation, creativity and innovation.To stop digital teaching from being monotonous and boring, we tried to turn it into a game in order to engage students and lead to better learning outcomes. Gamification is the use of game mechanics in non-game situations. It involves the use of video game elements, such as leaderboards, levels and badges in non-game activities aimed at improving the user experience and increasing user engagement. We find examples of gamification in various fields, e.g. in physical exercise and health (Pereira et al., 2014; Johnson et al., 2016), in the promotion of products and services (Huotari, & Hamari, 2012) and, of course, in education (Triantafyllou & Georgiadis, 2022). For example, airlines enable travelers to earn points with each trip that they can later redeem, or sports equipment companies have developed apps on smart phones and watches so that users can celebrate their performance with digital trophies, compete and challenge their running friends.In the learning process, gamification includes direct feedback mechanisms for learners, point systems, ranking tables, prizes, badges, progress bars and avatars among others, with the main objectives of mobilizing, encouraging and guiding the participant to achieve learning progress. Game-based learning makes the most of these mechanisms with an a priori architecture, which clearly defines the goal of the game, its basic functions and scenario, the rules of progression, competition and interaction with the application and teammates (Triantafyllou & Sapounidis, 2023).The reason why gamification is of particular interest is that research shows an increased degree of involvement and interest of participants when an environment or activity incorporates elements of gamification (Triantafyllou & Georgiadis, 2022). Game-based learning therefore activates not only cognitive functions, such as attention or perception, but also feelings of excitement, surprise, joy, sadness or even anger. This experience results in the achievement of meaningful learning objectives, such as deepening knowledge and developing intrapersonal (e.g. project scheduling) and interpersonal skills (e.g. collaboration).

This article offers a pragmatic ‘epistemology of wargaming’ which views wargames as immersive ‘thought experiments’ in which the human players involved use their experiential, empirical, and theoretical knowledge – together with whatever cognitive models they are able to deploy, or develop anew – to generate a conceptual, operational understanding of the adversarial scenario in which they are immersed; and exploit this understanding to craft tactical decisions designed to optimise the likelihood they will achieve their strategic objectives. From this perspective, contemporary interest in the use of ‘AI’-enabled tools to augment the validity of wargaming outputs – where these outputs constitute the decisions players make and the insights such decisions reveal – might most purposefully focus on: the design and implementation of wargames (to strengthen the architecture these provide to support immersive decision-making); and the analysis of players’ decisions (to better understand the cognitive models these involve and reflect). While the focus we suggest might disappoint those keen to replace human players with (semi-) autonomous decision-making machines, as long as the principal objectives of wargaming are to assess and enhance the decision-making capabilities of human players and human personnel, ‘AI’-enabled applications can only ever play a supporting role (albeit a potentially invaluable one) in the design, presentation, implementation, and analysis of wargames. As Irving Berlin might have it: ‘AI’ might soon be able to do most things better than us, but it can never replace humans when only a human will do.

In his Cynefin classification of system behaviours, Snowden (2007), posits that his Simple (Clear ) and even Complicated system categories are sufficiently understandable to be amenable to the traditional methods of system analysis and modelling. Here, relationships between entities in the system are clear and unambiguous and they behave predictably. So, we can build mathematical “models” to describe how the system is expected to function in different situations. Leaving aside his fourth category, Chaotic, as too hard for the moment, this paper focusses on the third category, Complex systems. Yaneer Bar-Yam [1998] defined "complex systems" as systems that "have multiple interacting components, whose collective behaviour cannot be simply inferred from the behaviour of components." So, the traditional system modelling approach of decomposition into components and attempting to build up a picture of system performance from individual pieces, alone, is no longer appropriate for this class. The problem is that most of the methods we currently employ, and particularly the methods that attempt to make the case that these systems are safe, (such as Fault trees, FMEA. and Root Cause Analysis), rely on this decomposition into components and attempt to make the parts more reliable individually. Even the more systemic approaches, such as Reason’s (1990) Swiss Cheese Model, and Leveson’s System Theoretic Analysis (STPA) (2004), encourage adding layers of protection, barriers, strengthened control loops and better safety critical systems, in the hope that they are making the systems safer. But without a valid model to test the effectiveness of these add-ons, we cannot be sure that they will not have an opposite effect, of making the systems even more complex, unreliable and unpredictable. For complex systems, the careful gathering and documentation of system components and their relationships is still a necessary first step, but we need an additional capacity to model these complex systems to test out and prove they work safely and successfully in the real world. (not just draw fixed component relationships as system “models” (wiring diagrams)) In the last 10 years, an original approach developed by Hollnagel, (2012), to model systems as sets of interactive, interdependent “functions”, (abstracted from and independent of component details), has now been developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, such as MBSE( Goldberg (1994)), and can pull it all together in a dynamic system model from which analysts can discern how it really works in practice, and predict the emergent behaviours characteristic of complex systems. The paper describes how this methodology developed and how it is currently used to model real situations, to analyse incidents and anticipate events.

There is a deep link existing between symmetry and nature that may be best apprehended by using group theory. In physics, giving a symmetry group is entirely equivalent to fix natural laws (Noether’s theorem). Within such a framework, it follows that all fundamental mechanical laws may be given by defining a group of symmetry operations belonging to the Gal(3,1) symmetry group having ten generators (3 + 3 + 3 + 1 = 10). The move to special relativity amounts simply to substitute another group ISO(3,1) to Gal(3,1) and having the same number of generators. In both cases, one immediately perceives that we live in universe with 3 dimensions of space and 1 dimension of time. A further progress could be made by looking for the symmetry group that leaves Maxwell’s equations invariant. The answer to this fundamental question is the group ISO(4,2)⊗U(2)⊗U(2) characterized by 23 generators : 15 = 10 + 5 for the conformal group ISO(4,2), 4 for U(2) and hence 8 = 4 + 4 for the compact part U(2)⊗U(2) that could be related to the existence of quantum physics besides relativity. As ISO(3,1) is a sub-group of ISO(4,2), the two numbers 4 and 2 suggests that, taking into account the existence of light besides matter, we live in an universe having 4 spacelike dimensions and 2 time-like dimensions. Here, it is proposed that, as music is characterized by a scale-invariant symmetry operation (transposition by octaves) and that as time may be perceived differently in shamanic trance for instance, the two “new” dimensions of our universe may be related to music and consciousness. Related to the fifth dimension of music, the notion of diapason linking physical frequencies to a musical note is revisited. It is thus proposed to adopt a new A4 = 429.62 Hz value based on universal physical constants of our universe and on the mass of the water molecule H₂O, the most abundant molecule in the universe. Demonstration is done that this new diapason is superior to other diapasons based on frequencies of 440 Hz or even 432 Hz

Continuous Distributed Constraint Optimization Problem (C-DCOPs) are a powerful framework to model problems with continuous variables in multi-agent systems. Previous works about C-DCOP algorithms mainly use pseudo-trees to guarantee the anytime property or are without anytime property guarantees. However, there is a risk of privacy leakage in the pseudo-tree due to utility passing between agents. Therefore, based on the basic constraint graph instead of the pseudo-tree, we (i) extend the Maximum Gain Message (MGM) algorithm by combining the local search strategy to solve C-DCOPs, named Continuous MGM (C-MGM), and it’s able to guarantee the monotonicity of the solution quality; (ii) propose a Parallel C-MGM (C-PMGM) algorithm to improve the solution quality through parallel random search; and (iii) introduce the differential search into C-PMGM to design a Parallel Differential Search C-MGM (C-PDSM) algorithm, which constructs a heuristic method to speed up convergence and improve solution quality. Compared to other anytime C-DCOP algorithms using pseudo-trees, the proposed three algorithms can exhibit better performance in avoiding privacy violations. We theoretically prove that the proposed algorithms are the anytime algorithms and empirically demonstrate that our algorithms outperform the state-of-the-art C-DCOP algorithms.

This paper studied the dynamic characteristics of the deployment process of the replaceable interface mast and the vibration generated at the end of the mast. The interface is discretized by the absolute nodal coordinate thin shell element with gradient reduction, and the three-dimensional motion of the reconfigurable rigid body module is described by the natural coordinate method. The dynamic equations of the interface and the reconfigurable module without external constraints are established by the principle of virtual work. Finally, the dynamic models of each part are assembled into the dynamic model of the mast system deployment process through the system constraint equation. According to the established dynamic model of the mast system deployment process, the dynamic behavior changes of the replaceable interface mast are analyzed. At the same time, the differences in the deployment behavior of the replaceable interface mast under different system configuration schemes, flexible interface geometric parameters, and different driving laws are studied and compared. It provides guidance for the scheme configuration of the mast system, the structural design of the interface, and the deployment motion planning of the mast. According to the physical prototype and the assembly of the mast system model, the deployment process of the replaceable interface mast is experimentally analyzed. It shows that the established dynamic model of the mast system can correctly analyze the dynamic characteristics of the deployment behavior of the replaceable interface mast, which provides a reference for the design and behavior analysis of the mast system.

Claims arising frequently in the Architecture, Engineering and Construction (AEC) industry usually result in disputes, leading to all sorts of negative implications for a project. Claims need to be managed efficiently, and in case they evolve into disputes, they need to be resolved as soon as possible so that the construction process can resume. In this paper, a bibliometric review is carried out to explore the existing literature regarding construction claims and dispute resolution methods employed in construction projects. Using VOSviewer and the Scopus database, relevant literature is retrieved and analyzed using keyword searches, including "construction claim” and "construction management". The detected research themes provide future researchers with potential research directions. A gap in the research regarding the emergence of terms such as “blockchain”, “smart contracts”, and "building information modelling (BIM)" appearing only recently after 2020 in the literature regarding the construction claims and disputes research area was determined. Therefore, a content analysis of the most recent publications employing these and other novel technologies is conveyed. Ultimately, the main research trends and potential research directions for assisting researchers and construction professionals in their efforts to address construction claims and disputes in sustainable and efficient ways are discussed.

In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

Experimental tests and numerical analyses were carried out on short, thin-walled channel columns with modified cross-sectional shape. The columns were loaded with an axial compressive force applied at the center of gravity of the cross section. Tests were carried out on a universal testing machine, while numerical analyses were performed in ANSYS software. The purpose of the tests was to determine the values of critical forces for the compressed columns and to determine the values of maximum forces at which failure of the columns occurs. Critical forces were determined based on the strain-averaged method. Based on the study, it was found that the strength and resistance to loss of stability of columns is primarily affected by their stiffness, and therefore by the shape of the cross-sections. In addition, for short columns, it seems more important to determine the value of maximum forces than the values of critical forces.

The application of numerical simulation methods for studying vertical-axis wind turbines offers significant advantages over conventional horizontal-axis wind turbines regarding wind energy utilisation, start-up wind speed, and environmental impact. This paper aims to investigate and establish the aerodynamic design performance of asymmetrical (NACA 4412) airfoils used in the application of vertical-axis wind turbines. The computational fluid dynamics (CFD) simulation uses Ansys software based on the Reynolds Averaged Navier-Stokes (RANS) equations. The aerodynamic parameters (pressure and velocity) are evaluated and compared within a range of angles of attack (AoA) 5-12 degrees at a fixed Reynolds number, as is a usual condition for wind turbine application. The lift coefficient (Cl), drag coefficient (Cd), and Cl/Cd ratio are obtained within the range AoA for the asymmetrical airfoils, and the performances have been predicted using the potential flow panel and CFD methods. This study suggests that while Xfoil may not be as precise as computational fluid dynamics (CFD), its predicted lift and drag coefficients generally align with experimental results. These help us to understand the best air-foil selection in a wind turbine application and predict its optimum AoA at which it performs at its best.

This research investigates the impact of social media monitoring on Online Reputation Management (ORM) within Zimbabwe’s life assurance industry. Employing a mixed-methods approach, data was collected through questionnaires from 464 participants, yielding a response rate of 92.8%. Ethical considerations were meticulously addressed, with informed consent and confidentiality safeguards. Data analysis involved organizational and statistical techniques, including Structural Equation Modeling (SEM) for theoretical exploration. Descriptive statistics unveiled demographic insights and patterns in social media platform usage. Internal consistency assessments, Exploratory Factor Analysis (EFA), and Principal Component Analysis (PCA) informed the refinement of research instruments. The SEM analysis established significant relationships and moderation effects, providing empirical support for several hypotheses. Findings indicate that employees’ attitudes toward social media monitoring positively influence ORM, while perceived behavioural control exhibits a non-significant impact. Intriguingly, management commitment, though positively related to ORM, moderates the relationship with a negative effect. The study contributes to the understanding of ORM determinants, emphasizing the need for nuanced strategies considering both individual and organizational factors. The recommendations underscore the importance of management commitment and employee training for effective social media utilization. Future research avenues include exploring training interventions, delving into the dynamics of perceived behavioural control, and investigating emerging social media platforms’ impact on ORM.

Cadmium (Cd) is an environmental toxicant of worldwide public health significance. Diet is the main non-workplace Cd exposure source other than smoking. The intestinal absorption of Cd involves the transporters for iron, zinc, copper, and calcium. These metal transporters essentially determine the body burden of Cd because only 0.001-0.005% of Cd accumulated in the body can be excreted in urine each day. The International Agency for Research on Cancer listed Cd as a human lung carcinogen. Current evidence, however, suggests that environmental Cd may increase the prevalence of many types of cancer, notably lung, liver, breast, pancreas, and kidney. A two-year bioassay that mimics a lifelong exposure demonstrates Cd caused neoplasms in multiple tissues of mice. Also, several non-tumorigenic human cell lines underwent malignant cell transformation, when they were exposed to a sublethal dose of Cd for a prolonged time. Cd does not directly damage DNA, but it can profoundly affect gene expression through its interactions with various proteins, and Cd can thus be viewed as a transcription modulator. The present review highlights epidemiological studies that connected an enhanced risk of various neoplastic diseases to chronic exposure to environmental Cd. Special emphasis is on the impact of body iron stores on the absorption rate of Cd, and its implications for breast cancer prevention in highly susceptible subpopulation groups of women. Resistance to cell death and other cancer phenotypes acquired during Cd-induced cancer-cell transformation, under in vitro conditions, are briefly discussed. The potential role for ZnT1 efflux transporter in cellular acquisition of tolerance to Cd cytotoxicity are highlighted.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

This paper investigates nonverbal communication in human interactions, with a specific focus on facial expressions. Employing a Long Short-Term Memory (LSTM) architecture and a custom-ized facial expression framework, our approach aims to improve virtual agent interactions by incorporating subtle nonverbal cues. The paper contributes to the emerging field of facial expres-sion generation, addressing gaps in current research and presenting a novel framework within Unreal Engine 5. The model's architecture, trained on the CANDOR corpus, captures temporal dynamics, and refines hyperparameters for optimal performance. During testing, the trained model showed a cosine similarity of -0.95. This enables the algorithm to accurately respond to non-verbal cues and interact with humans in a way that is comparable to human-human interac-tion. Unlike other approaches in the field of facial expression generation, the presented method is more comprehensive and enables the integration of a multi-modal approach for generating facial expressions. Future work involves integrating blendshape generation, real-world testing, and the inclusion of additional modalities to create a comprehensive framework for seamless hu-man-agent interactions beyond facial expressions.

The present study focuses on the investigation of fatigue, social support and quality of life that characterizes social workers. Furthermore, the study aims to highlight the effect of demographic and occupational characteristics on the above factors. A total of 400 social workers with an average age of 39.05 years took part in the research. Most were women, single, residents of cities of more than 100,000, and health care workers. Participants were asked to answer online questionnaires (FAS for fatigue, MSPSS for social support and GHQ-28 for quality of life). From the analysis of the data, it was found that the level of fatigue of the social workers is at low levels. At the same time, the level of occurrence of negative symptoms is also low, with physical symptoms appearing more often than the rest. Participants receive a high level of support from their environment, and more so from the "significant other" in their life. Finally, it was found that the fatigue, social support and quality of life of social workers is affected by the age of the social workers, the total years of service, gender, marital status, place of residence, employer and position of responsibility.

Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

Power grid fault diagnosis technology has been widely used at home and abroad. With the access of distributed power sources and diversified loads, the distribution network has changed from a passive network to an active network, and the current has changed from "one-way" to "two-way". The structure of distribution network, equipment environment and operating conditions are becoming more and more complex, and the fault diagnosis and localization of active distribution network is becoming more and more difficult. With the rapid development of artificial intelligence, power grid fault diagnosis based on intelligence is unprecedented. This paper introduces the research status of distributed photovoltaic grid-connected fault diagnosis technology, and expounds the history of fault diagnosis technology, which mainly includes expert system, artificial neural network, Bayesian network, fuzzy set theory, rough set theory, Petri net analytical model and multi-source information fusion, etc. The applicability and characteristics of these diagnoses are described, and the existing defects and the overall direction of improvement are briefly described Finally, the key technical problems and future directions in the field of power system fault diagnosis are pointed out by combining theory with practice, so as to promote the further development of this field.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string.

The main goal of the research is to assess the effectiveness of aerial ground-penetrating radar (GPR) in delineating boundaries between disturbed clayey ground and rock formations along the old Wadi. The study also tackles technical challenges associated with drone-based GPR applications in urban open areas, considering geological heterogeneity, topographic variations, and environmental conditions. The potential of using drone-based GPR equipped with an unshielded dipole antenna as a transformative tool in geospatial analysis was demonstrated. The research encompasses academic insights and field borehole drilling experiments to validate the accuracy and reliability of drone-based GPR data in real-world scenarios. Anticipated contributions include advancements in understanding drone-based GPR technology for mapping disturbed soil boundaries in foundation engineering applications and other relevant areas and recommendations for optimizing its performance in challenging terrains.