Social Robots in Retail: Emotional Experiences a Critical Driver of Purchase Intention

The purpose of the current study is to explore whether emotional experiences prompted due to human-social robot interaction in retail environments significantly influence consumers' purchase intentions. This present study focuses primarily on emotional experience, comprising factors, namely, enjoyment, arousal, and emotional involvement. The study tests the conceptual model on a sample of 229 respondents using the PLS-SEM (Partial Least Squares – Structural Equation Modeling) approach. The results reveal that emotional experiences significantly impact consumers’ purchase intentions in retail settings. All three emotional experiences, including enjoyment, emotional involvement, and arousal were significant in shaping consumers' purchase intentions. The study findings offer unique insights for manufacturers developing social robots for the retail sector. The present research extends the current body of work exploring hedonic predictors of consumers' purchase intentions in novel socio-technical contexts, such as social robotics

Persuasive Technology in Games: A Brief Review and Reappraisal

Persuasive technology is a new field of research that has attracted considerable attention from game designers since there is a growing interest in games promoting positive behavioral changes. Persuasive games have been exploited to tremendous effect with applications ranging from mobile healthcare, which persuade users to exercise more often and adopt a healthy lifestyle, to government programs encouraging civic engagement. Therefore, persuasive technologies have become an indispensable part of the modern game designer’s toolkit, and their importance is only set to grow with time. In this paper, we begin by reviewing the existing body of work in this field while also explaining the pros and cons of emerging design models and theoretical frameworks. We then uncover major pitfalls in the current work and suggest directions for future research. Hopefully, this article will prove instructive to game designers and leave them with a better understanding of the central concepts in the field of persuasive technology

5-Amino-1-phenyl-1H-pyrazole-4-carboxylic acid

In the mol­ecule of the title compound, C10H9N3O2, the pyrazole ring is approximately coplanar with the amino and carboxyl groups. The phenyl group is twisted by 48.13 (3)° relative to this plane. An intra­molecular N—H⋯O hydrogen bond stabilizes the planar conformation of the mol­ecule. The mol­ecules are linked into two-dimensional sheets by two strong inter­molecular N—H⋯N and O—H⋯O hydrogen bonds. The latter forms the classic carboxylic acid dimer motif

Impact of diabetes mellitus on nerves

Background: Involvement of the peripheral and autonomic nervous systems is probably the most common complication of diabetes. The main symptoms of diabetic polyneuropathy include negative symptoms (those related to nerve fiber loss or dysfunction) such as numbness and weakness, and positive symptoms (those related to abnormal function of surviving nerve fibers) such as tingling and pain.Methods: This was a cross-sectional study held in diabetic clinic of Nishter hospital, Multan, Pakistan. The study included any diabetic patients showing symptoms of neuropathy.Results: There were total of 140 in this study. This study included 85% of male and 15% of female. Most common symptoms of diabetic neuropathy were pain (70%) and tingling (70%) followed by numbness in 65% of patients. There were 28 patients in 5 years duration of diabetes, 35 people in 6-10 years duration, 21 patients in 11-15 years duration, and 14 patients in 20+ years duration.Conclusions: Neuropathy due to diabetes is crippling especially when pain is the prominent symptoms. Autonomic symptoms like constipation and lightheadedness are discomforting for the patients. The most commonly used screening test is vibrating tuning fork test which is east to perform is clinical setting and is not time consuming. Diabetic patients need to take special care of

Neural Network-Based Estimation of Flexural Performance for Polymer Permeable Concrete

Pervious concrete is increasingly used to reduce runoff water and improve water quality near pavements and parking lots, but highway pavement structures cannot use it due to its high porosity and reduced strength. To address the issue of lower flexural strength in permeable concrete, this study designs and conducts 11 different tests with varying mix ratios. The objective is to ensure that the resulting concrete satisfies both permeability and compression resistance requirements. The uniform test method is employed to measure the flexural strength of the concrete after a period of 28 days. This study employs neural networks to analyze the flexural performance of polymer permeable concrete by considering various input factors such as cement consumption, water consumption, STA (4.75 to 9.5 mm stones), STB (9.5 to 16 mm stones), VAE (vinyl acetate-ethylene) polymer content, and SAP polymer content. The objective is to optimize the mix proportion of polymer permeable concrete and identify a suitable ratio that satisfies the requirements of pavement structural flexural performance.&nbsp

Impact of SARS-CoV-2 on Ambient Air Quality in Northwest China (NWC)

SARS-CoV-2 was discovered in Wuhan (Hubei) in late 2019 and covered the globe by March 2020. To prevent the spread of the SARS-CoV-2 outbreak, China imposed a countrywide lockdown that significantly improved the air quality. To investigate the collective effect of SARS-CoV-2 on air quality, we analyzed the ambient air quality in five provinces of northwest China (NWC): Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX) and Qinghai (QH), from January 2019 to December 2020. For this purpose, fine particulate matter (PM2.5), coarse particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) were obtained from the China National Environmental Monitoring Center (CNEMC). In 2020, PM2.5, PM10, SO2, NO2, CO, and O3 improved by 2.72%, 5.31%, 7.93%, 8.40%, 8.47%, and 2.15%, respectively, as compared with 2019. The PM2.5 failed to comply in SN and XJ; PM10 failed to comply in SN, XJ, and NX with CAAQS Grade II standards (35 µg/m3, 70 µg/m3, annual mean). In a seasonal variation, all the pollutants experienced significant spatial and temporal distribution, e.g., highest in winter and lowest in summer, except O3. Moreover, the average air quality index (AQI) improved by 4.70%, with the highest improvement in SN followed by QH, GS, XJ, and NX. AQI improved in all seasons; significant improvement occurred in winter (December to February) and spring (March to May) when lockdowns, industrial closure etc. were at their peak. The proportion of air quality Class I improved by 32.14%, and the number of days with PM2.5, SO2, and NO2 as primary pollutants decreased while they increased for PM10, CO, and O3 in 2020. This study indicates a significant association between air quality improvement and the prevalence of SARS-CoV-2 in 2020.The National Natural Science Foundation of China (No. 21667026) and the Social Science Foundation of Xinjiang Production and Construction Corps (No. 18YB13) funded this work

Building Information Modeling and Artificial Intelligence Based Smart Construction Management: Materials and Electrical

With the development of society and technological progress, the requirements of government regulatory departments for engineering construction efficiency, quality, and safety are constantly increasing. The traditional extensive construction process can no longer meet the requirements of modern construction industry development. Based on the shortcomings of traditional construction processes, the concept of intelligent construction has been introduced. The construction of new smart and digital twin (DT) cities is entering an explosive period. The application of building rapid modeling technology based on artificial intelligence (AI) and building information modeling (BIM) integration in smart cities has gradually begun new explorations and attempts, and its application value is becoming increasingly prominent. A brand-new auto-machine learning (auto-ML) integrated algorithm technology platform for 3D building modeling is being developed and improved over time by combining AI and BIM technology in a deep way. This allows for fast and accurate modeling as well as high-value scenarios in the smart city industry, including architecture, municipal engineering, roads, and bridges.&nbsp

Development and testing of zinc sulfate and zinc oxide nanoparticle-coated urea fertilizer to improve N and Zn use efficiency

Nitrogen (N) losses from conventional fertilizers in agricultural systems are very high, which can lead to serious environmental pollution with economic loss. In this study, innovative slow-release fertilizers were prepared using zinc (Zn) [nanoparticles (NPs) or in bulk], using molasses as an environmentally friendly coating. Several treatments were prepared using Zn in different concentrations (i.e., 0.25%, 0.5%, and 4% elemental Zn). The zinc oxide nanoparticles (ZnO-NPs) were prepared from zinc sulfate heptahydrate (ZnSO4·7H2O), and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. Furthermore, the Zn-loaded urea samples were tested for urea N release rate, leaching of water from soil, and crushing strength to assess the impact of coating on the final finished product. Pot experiments were conducted simultaneously to check the agronomic effects of Zn-coated slow-release urea on the growth and development of wheat (Triticum aestivum L.). The laboratory and pot results confirmed that the ZnO-NP treatments boost wheat growth and yield as a result of reduced N and Zn release. UZnNPs2 (urea coated with 0.5% ZnO-NPs and 5% molasses) demonstrated the best results among all the treatments in terms of slow nutrient release, N and Zn uptake, and grain yield. The UZnNPs2 treatment increased plant yield by 34% (i.e., 4,515 vs. 3,345 kg ha–1) relative to the uncoated prill-treated crop because of the slower release of Zn and N

Effect of foliar application of nano-nutrients solution on growth and biochemical attributes of tomato (Solanum lycopersicum) under drought stress

IntroductionDrought stress has drastically hampered the growth and yield of many crops. Therefore, environmentally safe agricultural techniques are needed to mitigate drought stress impact. To this end, foliar spray of nano-nutrients solution to (NNS) alleviate harmful aspects of drought stress.MethodsIn a completely randomized design (CRD) experiment, seedlings were transplanted into pots at 2-3 leaf stage, each filled with loam-compost- organic manure soil (3:1:1). Plants were divided into two groups. (a) control group (b) applied stress group. Plants at vegetative stage were treated with 100% FC for control group and 60% FC for drought group, and these levels were maintained until harvesting. Three treatments of NNS with four levels i.e., 0%, 1%, 3% and 5% were given to all the pots after two weeks of drought stress treatment with a gap of 5 days at vegetative stage.Results and discussionApplication of 1% of nano-nutrient solution displayed an improvement in shoot length, shoot fresh and dry weight, number of leaves and flowers. Leaf chlorophylls and carotenoids and total phenolics contents were found maximum while minimum electrolyte leakage was observed at 3% application compared to control. Further, 1% application of NNS increased the Leaf RWC%, total soluble sugars, flavonoids contents. 5% NNS application exhibited higher total free amino acids with minimum lipid peroxidation rate in leaves of tomato under drought. Antioxidant enzyme activities increased in a concentration dependent manner as gradual increase was observed at 1%, 3% and 5%, respectively. Overall, this study introduced a new insights on using nano-nutrient solutions to maintain natural resources and ensure agricultural sustainabilit