University of Education

The interest in the systematic study of the circadian typology (CT) is relatively recent and has developed rapidly in the two last decades. All the existing data suggest that this individual difference affects our biological and psychological functioning, not only in health, but also in disease. In the present study, we review the current literature concerning the psychometric properties and validity of CT measures as well as individual, environmental and genetic factors that influence the CT. We present a brief overview of the biological markers that are used to define differences between CT groups (sleep-wake cycle, body temperature, cortisol and melatonin), and we assess the implications for CT and adjustment to shiftwork and jet lag. We also review the differences between CT in terms of cognitive abilities, personality traits and the incidence of psychiatric disorders. When necessary, we have emphasized the methodological limitations that exist today and suggested some future avenues of work in order to overcome these. This is a new field of interest to professionals in many different areas (research, labor, academic and clinical), and this review provides a state of the art discussion to allow professionals to integrate chronobiological aspects of human behavior into their daily practice.

Abstract\n Collaborative inquiry learning is one of the most challenging and exciting ventures for today?s schools. It aims at bringing a new and promising culture of teaching and learning into the classroom where students in groups engage in self-regulated learning activities supported by the teacher. It is expected that this way of learning fosters students? motivation and interest in science, that they learn to perform steps of inquiry similar to scientists and that they gain knowledge on scientific processes. Starting from general pedagogical reflections and science standards the article reviews some prominent models of inquiry learning. This comparison results in a set of inquiry processes being the basis for cooperation in the scientific network NetCoIL. Inquiry learning is conceived in several ways with emphasis on different processes. For an illustration of the spectrum, some main conceptions of inquiry and their focuses are described. In the next step, the article describes exemplary computer tools and environments from within and outside the NetCoIL network that were designed to support processes of collaborative inquiry learning. These tools are analysed by describing their functionalities as well as effects on student learning known from the literature. The article closes with challenges for further developments elaborated by the NetCoIL network.

Water, a necessary component of cell protoplasm, plays an essential role in supporting life on Earth; nevertheless, extreme changes in climatic conditions limit water availability, causing numerous issues, such as the current water-scarce regimes in many regions of the biome. This review aims to collect data from various published studies in the literature to understand and critically analyze plants' morphological, growth, yield, and physio-biochemical responses to drought stress and their potential to modulate and nullify the damaging effects of drought stress via activating natural physiological and biochemical mechanisms. In addition, the review described current breakthroughs in understanding how plant hormones influence drought stress responses and phytohormonal interaction through signaling under water stress regimes. The information for this review was systematically gathered from different global search engines and the scientific literature databases Science Direct, including Google Scholar, Web of Science, related studies, published books, and articles. Drought stress is a significant obstacle to meeting food demand for the world's constantly growing population. Plants cope with stress regimes through changes to cellular osmotic potential, water potential, and activation of natural defense systems in the form of antioxidant enzymes and accumulation of osmolytes including proteins, proline, glycine betaine, phenolic compounds, and soluble sugars. Phytohormones modulate developmental processes and signaling networks, which aid in acclimating plants to biotic and abiotic challenges and, consequently, their survival. Significant progress has been made for jasmonates, salicylic acid, and ethylene in identifying important components and understanding their roles in plant responses to abiotic stress. Other plant hormones, such as abscisic acid, auxin, gibberellic acid, brassinosteroids, and peptide hormones, have been linked to plant defense signaling pathways in various ways.

Over the years, the vaste expansion of plastic manufacturing has dramatically increased the environmental impact of microplastics [MPs] and nanoplastics [NPs], making them a threat to marine and terrestrial biota because they contain endocrine disrupting chemicals [EDCs] and other harmful compounds. MPs and NPs have deleteriouse impacts on mammalian endocrine components such as hypothalamus, pituitary, thyroid, adrenal, testes, and ovaries. MPs and NPs absorb and act as a transport medium for harmful chemicals such as bisphenols, phthalates, polybrominated diphenyl ether, polychlorinated biphenyl ether, organotin, perfluorinated compounds, dioxins, polycyclic aromatic hydrocarbons, organic contaminants, and heavy metals, which are commonly used as additives in plastic production. As the EDCs are not covalently bonded to plastics, they can easily leach into milk, water, and other liquids affecting the endocrine system of mammals upon exposure. The toxicity induced by MPs and NPs is size-dependent, as smaller particles have better absorption capacity and larger surface area, releasing more EDC and toxic chemicals. Various EDCs contained or carried by MPs and NPs share structural similarities with specific hormone receptors; hence they interfere with normal hormone receptors, altering the hormonal action of the endocrine glands. This review demonstrates size-dependent MPs' bioaccumulation, distribution, and translocation with potential hazards to the endocrine gland. We reviewed that MPs and NPs disrupt hypothalamic-pituitary axes, including the hypothalamic-pituitary-thyroid/adrenal/testicular/ovarian axis leading to oxidative stress, reproductive toxicity, neurotoxicity, cytotoxicity, developmental abnormalities, decreased sperm quality, and immunotoxicity. The direct consequences of MPs and NPs on the thyroid, testis, and ovaries are documented. Still, studies need to be carried out to identify the direct effects of MPs and NPs on the hypothalamus, pituitary, and adrenal glands.

A MIL-53(Fe) analogue was successfully synthesized by a HF free-solvothermal method.

The accurate measurement of circadian typology (CT) is critical because the construct has implications for a number of health disorders. In this review, we focus on the evidence to support the reliability and validity of the more commonly used CT scales: the Morningness-Eveningness Questionnaire (MEQ), reduced Morningness-Eveningness Questionnaire (rMEQ), the Composite Scale of Morningness (CSM), and the Preferences Scale (PS). In addition, we also consider the Munich ChronoType Questionnaire (MCTQ). In terms of reliability, the MEQ, CSM, and PS consistently report high levels of reliability (>0.80), whereas the reliability of the rMEQ is satisfactory. The stability of these scales is sound at follow-up periods up to 13 mos. The MCTQ is not a scale; therefore, its reliability cannot be assessed. Although it is possible to determine the stability of the MCTQ, these data are yet to be reported. Validity must be given equal weight in assessing the measurement properties of CT instruments. Most commonly reported is convergent and construct validity. The MEQ, rMEQ, and CSM are highly correlated and this is to be expected, given that these scales share common items. The level of agreement between the MCTQ and the MEQ is satisfactory, but the correlation between these two constructs decreases in line with the number of "corrections" applied to the MCTQ. The interesting question is whether CT is best represented by a psychological preference for behavior or by using a biomarker such as sleep midpoint. Good-quality subjective and objective data suggest adequate construct validity for each of the CT instruments, but a major limitation of this literature is studies that assess the predictive validity of these instruments. We make a number of recommendations with the aim of advancing science. Future studies need to (1) focus on collecting data from representative samples that consider a number of environmental factors; (2) employ longitudinal designs to allow the predictive validity of CT measures to be assessed and preferably make use of objective data; (3) employ contemporary statistical approaches, including structural equation modeling and item-response models; and (4) provide better information concerning sample selection and a rationale for choosing cutoff points.

This work was inspired by a previous report [Janjua, M.R.S.A. Inorg. Chem. 2012, 51, 11306−11314] in which the nonlinear optical (NLO) response strikingly improved with double heteroaromatic rings. Herein, series of triphenylamine–dicyanovinylene based donor−π–acceptor dyes had been designed by structural tailoring of π-conjugated linkers and theoretical descriptions of their molecular NLO properties were reported. Density functional theory and time-dependent density functional theory calculations were performed on optimized geometries to elucidate the electronic structures, absorption spectra, and NLO properties and also to shed light on how structural modification influences the NLO properties. The simulated absorption spectra results indicate that all of the dyes showed the maximum absorbance wavelength in the visible region. The lowest unoccupied molecular orbital–highest occupied molecular orbital energy gaps of all of the dyes have been found smaller, which results in large NLO response. Calculation of natural bond orbital analysis reveals that electrons successfully migrated from donor to acceptor via π-conjugated linkers and a charge separation state was formed. High NLO response revealed that this class of metal free organic dyes possess eye-catching and remarkably large first hyperpolarizability values, especially D8 with highest ⟨α⟩ and βtot computed to be 771.80 and 139 075.05 au, respectively. Our research presented a vital confirmation for controlling the kinds of π-conjugated linker that was a significant approach for the design of new appealing NLO compounds. This theoretical framework also highlighted the NLO properties of organic dyes that can be valuable for their uses in modern hi-tech applications.

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

Abstract This paper explores the concept of smart cities and the role of the Internet of Things (IoT) and machine learning (ML) in realizing a data-centric smart environment. Smart cities leverage technology and data to improve the quality of life for citizens and enhance the efficiency of urban services. IoT and machine learning have emerged as key technologies for enabling smart city solutions that rely on large-scale data collection, analysis, and decision-making. This paper presents an overview of smart cities’ various applications and discusses the challenges associated with implementing IoT and machine learning in urban environments. The paper also compares different case studies of successful smart city implementations utilizing IoT and machine learning technologies. The findings suggest that these technologies have the potential to transform urban environments and enable the creation of more livable, sustainable, and efficient cities. However, significant challenges remain regarding data privacy, security, and ethical considerations, which must be addressed to realize the full potential of smart cities.

L.) varieties, it remains unclear whether this is true at the ecotype level. Here, an extensive dataset of the traits of 7686 rice varieties, released in China from 1978 to 2017, was used to study the relationship between yield and other agronomic traits. We assessed the association between yield and other agronomic traits for four different rice ecotypes, i.e., indica inbred, indica hybrid, japonica inbred, and japonica hybrid. We found that associations between agronomic traits and yield were ecotype-dependent. For both the indica inbred and indica hybrid ecotypes, we found that greater values of certain traits, including the filled grain number per panicle, 1000-grain-weight, plant height, panicle length, grains per panicle, seed setting rate, long growth period, low panicle number per unit area, and low seed length/width ratio, have accounted for high grain yield. In the japonica inbred and japonica hybrid ecotypes, we found that only high panicle number per unit area and long growth period led to high grain yield. Indirectly, growth period consistently had a positive effect on yield in all ecotypes, and plant height had a positive effect on yield for the indicas and japonica inbred only. Plant height had a negative effect for the japonica hybrid. Altogether, our findings potentially have valuable implications for improving the breeds of rice ecotypes.

This study aimed to investigate the direct influence of entrepreneurial education, entrepreneurial mindset, and creativity on the entrepreneurial intention with the indirect role of entrepreneurial self-efficacy. This study applied the structural equation model technique using AMOS software to verify the hypothesis relationships. This study collected self-administered survey data from 365 university students of Jiangsu and Zhejiang province of China. The findings indicated that entrepreneurial education, entrepreneurial mindset, and creativity have a positive and significant influence on entrepreneurial intention. Moreover, results revealed that entrepreneurial self-efficacy partially mediates in the relationship between entrepreneurial education, entrepreneurial mindset, and creativity on entrepreneurial intention. Further implications and limitations are also discussed in this article.

As a clean, efficient, and renewable energy source, hydrogen has always been recognized as a favourable replacement of fossil fuel. A primary challenge is an efficient generation of hydrogen to fulfil the requirements of hydrogen on a commercial scale. The electrocatalytic process of HER (hydrogen evolution reaction), as primary phase in water electrolytic process for H 2 production, has undergone comprehensive observation from recent decades. Electrolytic water splitting presents a promised route to attain efficient hydrogen generation concerning energy conversion and storage, with electrolysis or catalysis playing a pivotal role. The advancement of catalyst or electrocatalysts that are effective, enduring and economical is necessary prerequisite for realizing the intended electrolytic hydrogen generation from water splitting for applicable considerations, embodying the primary emphasis of this article. In this extensive review, we initially summarize the basics of the Hydrogen evolution reaction and examine the latest cutting-edge progress in economical and highly efficiency catalysts utilizing both non-noble and noble metals. Moreover, the recent breakthroughs over the preceding years in electrolytic HER employing more affordable and widely available nanoparticles with a specific center of attention on economical and non-platinum electrocatalysts rooted in metal free (MF) and transition metal composite catalysts are deliberated here.

The need for high-performance and environmental friendly energy storage systems has prompted researchers to develop novel and improved electrode materials that can meet the rapidly expanding worldwide market in various applications of energy consumption. In this context, 2D graphene is one of the most promising candidates, attributed to a theoretical specific surface area of 2600 m2/g, high electrical charges mobility of ⁓230,000 cm2/Vs, high thermal conductivity value of 3000 W/mK along with high strength that has made it highly desirable for next-generation energy storage applications, particularly for supercapacitors. This extensive study offers a concise summary of recent developments by using graphene as a supercapacitor electrode in the forms of foams (3D), thin sheets (2D), Nano-fibers (1D), and Nano-dots (0D). This article provides a brief perspective on the discovery and advancement of graphene, followed by a study of the theoretical and experimental approaches employed for the production of superior-quality graphene. Additionally, the article focuses on the fabrication of electrodes while preserving their fundamental characteristics. An illustration of its potential applications is demonstrated by highlighting its efficacy as an anode in supercapacitors. The article concludes by identifying the main challenges encountered and the potential prospects for the subject matter.

Currently, the clinical use of sweat as biofluid is limited. The collection of sweat and its analysis for determining ethanol, drugs, ions, and metals have been encompassed in this review article to assess the merits of sweat compared to other biofluids, for example, blood or urine. Moreover, sweat comprises various biomarkers of different diseases including cystic fibrosis and diabetes. Additionally, the normalization of sampled volume of sweat is also necessary for getting efficient and useful results.

Materials with nonlinear optical properties have significant applications in nuclear science, biophysics, medicine, chemical dynamics, solid physics & materials science. We show how π bridges, donors & acceptors can be reconfigured to improve optical properties.

Both qualitative and quantitative paradigms try to find the same result; the truth. Qualitative studies are tools used in understanding and describing the world of human experience. Since we maintain our humanity throughout the research process, it is largely impossible to escape the subjective experience, even for the most experienced of researchers. Reliability and Validity are the issue that has been described in great deal by advocates of quantitative researchers. The validity and the norms of rigor that are applied to quantitative research are not entirely applicable to qualitative research. Validity in qualitative research means the extent to which the data is plausible, credible and trustworthy; and thus can be defended when challenged. Reliability and validity remain appropriate concepts for attaining rigor in qualitative research. Qualitative researchers have to salvage responsibility for reliability and validity by implementing verification strategies integral and self-correcting during the conduct of inquiry itself. This ensures the attainment of rigor using strategies inherent within each qualitative design, and moves the responsibility for incorporating and maintaining reliability and validity from external reviewers’ judgments to the investigators themselves. There have different opinions on validity with some suggesting that the concepts of validity is incompatible with qualitative research and should be abandoned while others argue efforts should be made to ensure validity so as to lend credibility to the results. This paper is an attempt to clarify the meaning and use of reliability and validity in the qualitative research paradigm.

In today's global economy, organizational effectiveness and innovation have become top priorities, putting pressure on all businesses worldwide. Therefore, this study aims to explore the impact of organizational culture on effectiveness through organizational innovation. We analyzed organizational resistance as a boundary condition on the relation of organizational innovation and effectiveness to seek whether organizational resistance enhances the positive effect of organizational innovation on effectiveness and on the indirect effect of organizational culture on the effectiveness of organization via organizational effectiveness. Organizational resistance is important because it occurs when employees understand how they fit into the new way of doing things, such that organizational innovation has a positive impact on organizational effectiveness. The data were collected in two waves from 280 manager-employee dyads operating in Pakistan's banking industry. The outcomes indicated that organizational culture positively influences organizational effectiveness; therefore, this relationship is mediated by organizational innovation. The positive influence of organizational innovation on organizational effectiveness is greater among individuals who embraced improvements rapidly than among those who did not. Additionally, organizational resistance reinforces the relationship between organizational culture and effectiveness through organizational innovation, such that the relationship is greater for those who embrace compliant advancement. Thus, the theoretical and practical implications of this study are discussed.

Abstract Background Cadmium (Cd) is amongst the most toxic heavy metals that severely affects crop growth, whereas application of nanoparticles (NPs) to negate the toxic effects of heavy metals could be an effective management approach. In the present study, the seeds of two fragrant rice varieties i.e., Yuxiangyouzhan and Xiangyaxiangzhan under normal and Cd stress conditions i.e., 0 and 100 mg L − 1 applied with four levels of ZnO NPs i.e., 0, 25, 50, and 100 mg L − 1 . Results Seed priming with ZnO NPs had no significant effect on the seed germination ( p > 0.05) however, it substantially improved the seedling growth and other related physiological attributes under the Cd stress. The mean fresh weight of the shoot, and whole seedling was increased by 16.92–27.88% and by 16.92–27.88% after ZnO NPs application. The root fresh weight, root-shoot length was also substantially improved under ZnO NPs treatment. Moreover, application of ZnO NPs induced modulations in physiological and biochemical attributes e.g., the superoxide dismutase (SOD) activity in root and shoot, the peroxidase (POD) activity and metallothionein contents in root were increased under low levels of ZnO NPs. The α-amylase and total amylase activity were improved by ZnO NPs application under Cd Stress. Besides, modulation in Zn concentration and ZnO NPs uptake in the seedling were detected. The metabolomic analysis indicated that various pathways such as alanine, aspartate and glutamate metabolism, phenylpropanoid biosynthesis, and taurine and hypotaurine metabolism were possibly important for rice response to ZnO NPs and Cd. Conclusion Overall, application of ZnO NPs substantially improved the early growth and related physio-biochemical attributes in rice. Our findings provide new insights regarding the effects of ZnO NPs on seed germination, and early growth of rice, and its potential applications in developing crop resilience against Cd contaminated soils.

Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent feature-extraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using ”Breast Cancer” keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

Hydrogels are three-dimensional polymer networks that are hydrophilic and capable of retaining a large amount of water. Hydrogels also can act as vehicles for the controlled delivery of active compounds. Bio-polymers are polymers that are derived from natural sources. Hydrogels prepared from biopolymers are considered non-toxic, biocompatible, biodegradable, and cost-effective. Therefore, bio-polymeric hydrogels are being extensively synthesized and used all over the world. Hydrogels based on biopolymers finds important applications in the agricultural field where they are used as soil conditioning agents as they can increase the water retention ability of soil and can act as a carrier of nutrients and other agrochemicals. Hydrogels are also used for the controlled delivery of fertilizer to plants. In this review, bio-polymeric hydrogels based on starch, chitosan, guar gum, gelatin, lignin, and alginate polymer have been discussed in terms of their synthesis method, swelling behavior, and possible agricultural application. The urgency to address water scarcity and the need for sustainable water management in agriculture necessitate the exploration and implementation of innovative solutions. By understanding the synthesis techniques and factors influencing the swelling behavior of these hydrogels, we can unlock their full potential in fostering sustainable agriculture and mitigating the challenges posed by an ever-changing environment.