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Introduction Tourniquets are routinely used during total knee replacement (TKR) surgery. They could increase the risk of thromboembolic events including cerebral emboli, cognitive decline, pain and other adverse events (AEs). A randomised controlled trial to assess whether tourniquet use might safely be avoided is therefore warranted but it is unclear whether such a trial would be feasible.Methods In a single-site feasibility study and pilot randomised controlled trial, adults having a TKR were randomised to surgery with an inflated tourniquet versus a non-inflated tourniquet. Participants underwent brain MRI preoperatively and within 2 days postoperatively. We assessed cognition using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Oxford Cognitive Screen (OCS) and thigh pain using a Visual Analogue Scale at baseline and days 1 and 2, and 1 week postsurgery. AEs related to surgery were recorded up to 12 months.Results We randomised 53 participants (27 tourniquet inflated and 26 tourniquet not inflated). Fifty-one participants received care per-protocol (96%) and 48 (91%) were followed up at 12 months. One new ischaemic brain lesion was detected. Of the cognitive tests, MoCA was easy to summarise, sensitive to change with lower ceiling effects compared with OCS and MMSE. There was a trend towards more thigh pain (mean 49.6 SD 30.4 vs 36.2 SD 28 at day 1) and more AEs related to surgery (21 vs 9) in participants with an inflated tourniquet compared with those with a tourniquet not inflated.Conclusion A full trial is feasible, but using MRI as a primary outcome is unlikely to be appropriate or feasible. Suitable primary outcomes would be cognition measured using MoCA, pain and AEs, all of which warrant investigation in a large multicentre trial.Trial registration number ISRCTN20873088.

A large number of hitherto unexplored elastic, thermophysical, acoustic, and optoelectronic properties of a double Weyl semimetal SrSi2 have been investigated in this study. Density functional theory (DFT) based methodology has been employed. Analyses of computed elastic parameters reveal that SrSi2 is a mechanically stable, ductile, moderately machinable, and relatively soft material. The compound is predicted to be dynamically stable and possesses significant metallic bonding. Study of thermophysical properties, namely, Debye temperature, Grüneisen parameter, acoustic parameters, melting temperature, heat capacity, thermal expansion coefficient, and dominant phonon mode is also indicative of soft nature of SrSi2. The electronic band structure calculations without and with spin–orbit coupling disclose semimetallic character with clear Weyl nodes close to the Fermi level. The electronic dispersion is anisotropic as evidenced by nearly flat and linear regions within the Brillouin zone. Optical parameters at different photon energies are investigated. SrSi2 shows excellent nonselective reflection spectrum across an extended range of energy encompassing the visible range implying that the compound under study has significant potential to be used as an efficient solar energy reflector. SrSi2 absorbs ultraviolet light quite efficiently. The compound also possesses high refractive index in the low energy. All these optical features can be useful in optoelectronic device applications.

Very recently carbon-rich NaC6 with sodalite-like structure has been predicted to show high superconducting transition temperature above 100 K at relatively low applied (compared to high-Tc hydrides) hydrostatic pressures. We have investigated the pressure dependent structural, elastic, electronic, superconducting state, and optoelectronic properties of NaC6in this study. Some important thermophysical properties have also been explored. The elastic properties along with Poisson’s and Pugh’s ratios and optoelectronic parameters are investigated for the first time. NaC6 was found to be structurally stable only at high pressures at and above 40 GPa, in agreement with previous study. The compound is highly ductile and the chemical bonding is prominently metallic in nature. The Debye temperature shows strong pressure dependence. The Grüneisen parameter also exhibits significant pressure dependence. The electronic band structure reveals metallic character and consists of highly dispersive and almost flat bands crossing the Fermi level. Both the electronic density of states at the Fermi level and repulsive Coulomb pseudopotential increase gradually with increasing pressure in the range 40 GPa to 70 GPa. The degree of dispersion in the E(k) curves depend weakly on pressure both in the valence and conduction bands. The optical parameters spectra, studied for the first time, correspond well with the electronic band structure. NaC6 absorbs and reflects electromagnetic radiation quite efficiently in the mid-ultraviolet region. Superconducting transition temperatures of NaC6 have been estimated at different pressures and compared with previously reported values. The effects of various parameters on Tc have been discussed in details.

Using an exclusive data set from Indonesia in 2018–2020, this study aims to prove whether there is a relationship between the allocation of village funds and the level of hunger in the community. In particular, this study tries to find out whether the Village Fund allocation policy has an effect on the achievement of the United Nations Sustainable Development Goal 2 (SDG#2). Using a quantitative method with regression analysis, this study found that the allocation of village funds by the Indonesian government supported hunger and poverty alleviation in all areas of Indonesia’s villages. This research result has implications for policymaking on sustainable food inclusion, especially in Indonesian villages.

Using time-differential perturbed angular correlation of γ rays, we investigated the electric-field gradient of polycrystalline EuTiO3 and Eu2Ti2O7, with 181Hf(181Ta) as a probe, following different thermal treatments. The measurements were performed at ISOLDE-CERN following 80 keV implantation at the Bonn Radioisotope Separator. The experimental results indicated successful induction of different phases in the implantation recovery process at 1273 and 1373 K. These observations were combined with ab initio calculations and X-ray diffraction measurements. A comparison of ab initio calculated electric-field gradients with the measured values discriminates between different structures and defects and rules out many possible cases. The Ta probe at the Ti site in the Eu2Ti2O7 phase is found to be the most probable case of site occupation after annealing at 1373 K, while annealing at 1273 K keeps EuTiO3 in the vicinity of the Ta probe. A discussion of the hyperfine interactions that promote variation in the interaction strength at the 181Ta site is presented.

Introduction Electronic cigarettes (e-cigarettes) are new smoking devices that have gained popularity recently. However, there is limited evidence on e-cigarette consumption in Malaysia. This study aims to determine the prevalence, risk factors and perception associated with e-cigarette use among those attending government hospitals and health clinics in Malaysia. Methods A cross-sectional hospital-based study was conducted in seven public hospitals and health clinics in Malaysia, which were selected through a twostage cluster sampling. A validated questionnaire was used to obtain data from the selected participants. Multivariable logistic regression was employed to determine the association between sociodemographics and perceptions of e-cigarette use. Results Almost three-quarters (73.6%; n=923/1254) of participants were aware of e-cigarettes and 13.2% (n=122/923) reported having ever used e-cigarettes. The prevalence was significantly higher among males (18.1%), smokers (21.4%), and younger age group 18–34 years (30.2%). Ever users showed favourable perceptions towards e-cigarettes compared to non-users (23.3% vs 30.14%, p

Central District of Khodabandeh is an important agricultural zone of the Khodabandeh County and in the Zanjan province. Studying the role of agricultural cooperatives in economic development as well as identifying its possibilities and limitations can improve the performance of cooperatives and rural development projects. This paper investigates the effects on the development of agricultural cooperatives in the rural economy through SWOT analysis in 2010. Delphi method is used to identify strengths, weaknesses, opportunities and threats the cooperatives face and based on the results an appropriate strategy to improve the performance of cooperatives are presented. Statistical population includes 155 companies with 1,130 members of agricultural cooperatives in rural areas from which 35 Cooperatives and 250 members were randomly selected and 2 questionnaires were filled out in each cooperative. Also 70 questionnaires were distributed among non-member individuals to collect the necessary data. The results show that the cooperative sector of rural economy has the ability to solve problems, but some factors prevent the development of their work which in addition to rural economy stem from structural barriers and weaknesses in national and regional policy making. To improve the performance of cooperatives, Ministry of Cooperatives Cooperatives should increase its range of operations and a comprehensive revision of facilitating and monitoring policies, particularly in rural areas and agriculture, is also necessary.

Multi-access edge computing (MEC) has attracted the interest of the research and industrial community to support Internet of things (IoT) applications by enabling efficient data processing and minimizing latency. This paper presents significant contributions toward optimizing the resource allocation and enhancing the decision-making process in edge computing environments. Specifically, the TRUST-ME model is introduced, which consists of multiple edge servers and IoT devices, i.e., users, with varied computing tasks offloaded to the MEC servers. A utility function was designed to quantify the benefits in terms of latency and cost for the IoT device while utilizing the MEC servers’ computing capacities. The core innovation of our work is a novel trust model that was designed to evaluate the IoT devices’ confidence in MEC servers. This model integrates both direct and indirect trust and reflects the trustworthiness of the servers based on the direct interactions and social feedback from other devices using the same servers. This dual trust approach helps with accurately gauging the reliability of MEC services and ensuring more informed decision making. A reinforcement learning framework based on the optimistic Q-learning with an upper confidence bounds action selection algorithm enables the IoT devices to autonomously select a MEC server to process their computing tasks. Also, a multilateral bargaining model is proposed for fair resource allocation of the MEC servers’ computing resources to the users while accounting for their computing demands. Numerical simulations demonstrated the operational effectiveness, convergence, and scalability of the TRUST-ME model, which was validated through real-world scenarios and comprehensive comparative evaluations against existing approaches.

This paper introduces the INFLUTRUST framework that is designed to address challenges in trust-based influencer marketing campaigns on Online Social Networks (OSNs). The INFLUTRUST framework enables the influencers to autonomously select products across the OSN platforms for advertisement by employing a reinforcement learning algorithm. The Stochastic Learning Automata reinforcement algorithm considers the OSN platforms’ provided monetary rewards, the influencers’ advertising profit, and the influencers’ trust levels towards the OSN platforms to enable the influencers to autonomously select an OSN platform. The trust model for the influencers incorporates direct and indirect trust, which are derived from past interactions and social ties among the influencers and the OSN platforms, respectively. The OSN platforms allocate rewards through a multilateral bargaining model that supports competition among the influencers. Simulation-based results validate the INFLUTRUST framework’s effectiveness across diverse scenarios, with the scalability analysis demonstrating its robustness. Comparative evaluations highlight the INFLUTRUST framework’s superiority in considering trust levels and reward allocation fairness, benefiting both the influencers and the OSN platforms.

Diabetes mellitus (DM) comprises a range of metabolic disorders characterized by high blood glucose levels caused by defects in insulin release, insulin action, or both. DM is a widespread condition that affects a substantial portion of the global population, causing high morbidity and mortality rates. The prevalence of this major public health crisis is predicted to increase in the forthcoming years. Although several drugs are available to manage DM, these are associated with adverse side effects, which limits their use. In underdeveloped countries, where such drugs are often costly and not widely available, many people continue to rely on alternative traditional medicine, including medicinal plants. The latter serves as a source of primary healthcare and plant-based foods in many low- and middle-income countries. Interestingly, many of the phytochemicals they contain have been demonstrated to possess antidiabetic activity such as lowering blood glucose levels, stimulating insulin secretion, and alleviating diabetic complications. Therefore, such plants may provide protective effects that could be used in the management of DM. The purpose of this article was to review the medicinal plant-based foods traditionally used for the management of DM, including their therapeutic effects, pharmacologically active phytoconstituents, and antidiabetic mode of action at the molecular level. It also presents future avenues for research in this field.

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson’s disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

Td-WTe2 is a topological Weyl semimetal. WTe2 in the orthorhombic structure is stable at room temperature. Elastic, electronic, bonding, and optoelectronic properties of WTe2 have been investigated in detail in this work using the density functional theory. Elastic behaviour together with anisotropy indices of WTe2 have been investigated for the first time. Bonding nature among the constituent atoms and electric field polarization dependent optical constants are also explored theoretically for the first time. WTe2 is elastically anisotropic; optical anisotropy on the other hand is low. The electronic band structure reveals quasi-linear dispersions along certain direction in the Brillouin zone with semi-metallic features. The Fermi level is located at a pseudogap separating bonding and anti-bonding density of states. The electronic effective mass tensor is predicted to be highly direction dependent. The energy dispersion is significantly weaker in the c-direction. The bonding in WTe2 is an admixture of covalent and metallic bonds. Optoelectronic properties show strongly reflecting character over a wide band of photon energies. The compound is a strong absorber of ultraviolet radiation. The Debye temperature has been calculated from the elastic constants. We have compared all the calculated physical properties of WTe2 with those of isostructural MoTe2 Weyl semimetal studied in a previous work. The properties of WTe2 and MoTe2 have been compared and contrasted. The calculated parameters of WTe2 have also been compared with those already available in the literature. Good agreements have been found.