Najran University

Because of the interesting and multifunctional properties, recently, ZnO nanostructures are considered as excellent material for fabrication of highly sensitive and selective gas sensors. Thus, ZnO nanomaterials are widely used to fabricate efficient gas sensors for the detection of various hazardous and toxic gases. The presented review article is focusing on the recent developments of NO2 gas sensors based on ZnO nanomaterials. The review presents the general introduction of some metal oxide nanomaterials for gas sensing application and finally focusing on the structure of ZnO and its gas sensing mechanisms. Basic gas sensing characteristics such as gas response, response time, recovery time, selectivity, detection limit, stability and recyclability, etc are also discussed in this article. Further, the utilization of various ZnO nanomaterials such as nanorods, nanowires, nano-micro flowers, quantum dots, thin films and nanosheets, etc for the fabrication of NO2 gas sensors are also presented. Moreover, various factors such as NO2 concentrations, annealing temperature, ZnO morphologies and particle sizes, relative humidity, operating temperatures which are affecting the NO2 gas sensing properties are discussed in this review. Finally, the review article is concluded and future directions are presented.

Renewable, cost-effective and eco-friendly electrode materials have attracted much attention in the energy conversion and storage fields. Bagasse, the waste product from sugarcane that mainly contains cellulose derivatives, can be a promising candidate to manufacture supercapacitor electrode materials. This study demonstrates the fabrication and characterization of highly porous carbon aerogels by using bagasse as a raw material. Macro and mesoporous carbon was first prepared by carbonizing the freeze-dried bagasse aerogel; consequently, microporous structure was created on the walls of the mesoporous carbon by chemical activation. Interestingly, it was observed that the specific surface area, the pore size and distribution of the hierarchical porous carbon were affected by the activation temperature. In order to evaluate the ability of the hierarchical porous carbon towards the supercapacitor electrode performance, solid state symmetric supercapacitors were assembled, and a comparable high specific capacitance of 142.1 F g(-1) at a discharge current density of 0.5 A g(-1) was demonstrated. The fabricated solid state supercapacitor displayed excellent capacitance retention of 93.9% over 5000 cycles. The high energy storage ability of the hierarchical porous carbon was attributed to the specially designed pore structures, i.e., co-existence of the micropores and mesopores. This research has demonstrated that utilization of sustainable biopolymers as the raw materials for high performance supercapacitor electrode materials is an effective way to fabricate low-cost energy storage devices.

Stretchable conductive fibers have received significant attention due to their possibility of being utilized in wearable and foldable electronics. Here, highly stretchable conductive fiber composed of silver nanowires (AgNWs) and silver nanoparticles (AgNPs) embedded in a styrene–butadiene–styrene (SBS) elastomeric matrix is fabricated. An AgNW‐embedded SBS fiber is fabricated by a simple wet spinning method. Then, the AgNPs are formed on both the surface and inner region of the AgNW‐embedded fiber via repeated cycles of silver precursor absorption and reduction processes. The AgNW‐embedded conductive fiber exhibits superior initial electrical conductivity ( σ 0 = 2450 S cm −1 ) and elongation at break (900% strain) due to the high weight percentage of the conductive fillers and the use of a highly stretchable SBS elastomer matrix. During the stretching, the embedded AgNWs act as conducting bridges between AgNPs, resulting in the preservation of electrical conductivity under high strain (the rate of conductivity degradation, σ / σ 0 = 4.4% at 100% strain). The AgNW‐embedded conductive fibers show the strain‐sensing behavior with a broad range of applied tensile strain. The AgNW reinforced highly stretchable conductive fibers can be embedded into a smart glove for detecting sign language by integrating five composite fibers in the glove, which can successfully perceive human motions.

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

COVID-19 pandemic has disrupted teaching in a vriety of institutions. It has tested the readiness of academic institutions to deal with such abrupt crisis. Online learning has become the main method of instruction during the pandemic in Jordan. After 4 months of online education, two online surveys were distributed to investigate faculty’s and Students’ perception of the learning process that took place over that period of time with no face to face education. In this regard, the study aimed to identify both faculty’s and students’ perceptions of online learning, utilizing two surveys one distributed to 50 faculty members and another 280 students were selected randomly to explore the effectiveness, challenges, and advantages of online education in Jordan. The analysis showed that the common online platforms in Jordan were Zoom, Microsoft Teams offering online interactive classes, and WhatsApp in communication with students outside the class. The study found that both faculty and students agreed that online education is useful during the current pandemic. At the same time, its efficacy is less effective than face-to-face learning and teaching. Faculty and students indicated that online learning challenges lie in adapting to online education, especially for deaf and hard of hearing students, lack of interaction and motivation, technical and Internet issues, data privacy, and security. They also agreed on the advantages of online learning. The benefits were mainly self-learning, low costs, convenience, and flexibility. Even though online learning works as a temporary alternative due to COVID-19, it could not substitute face-to-face learning. The study recommends that blended learning would help in providing a rigorous learning environment.

BACKGROUND: The emergence of COVID-19 global pandemic coupled with high transmission rate and mortality has created an unprecedented state of emergency worldwide. This global situation may have a negative impact on the psychological well-being of individuals which in turn impacts individuals' performance. This study aims to explore the prevalence of depression and anxiety among the GP, HCPs, and USs during COVID-19 outbreak, and to identify key population(s) who might need psychological intervention. METHODS: A cross-sectional study using an online survey was conducted in Jordan between 22 and 28 March 2020 to explore the mental health status (depression and anxiety) of the general population, healthcare professionals, and university students during the COVID-19 outbreak. The Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) were used to assess depression and anxiety among the study participants. Logistic regression analysis was used to identify predictors of depression and anxiety. RESULTS: The prevalence of depression and anxiety among the entire study participants was 23.8% and 13.1%, respectively. Anxiety was most prevalent across university students 21.5%, followed by healthcare professionals 11.3%, and general population 8.8%. Females among healthcare professionals and university students, divorced healthcare professionals, pulmonologists, and university students with history of chronic disease were at higher risk of developing depression. Females, divorced participants among the general population, and university students with history of chronic disease and those with high income (≥1,500 JD) were at higher risk of developing anxiety. CONCLUSIONS: During outbreaks, individuals are put under extreme stressful condition resulting in higher risk of developing anxiety and depression particularly for students and healthcare professionals. Policymakers and mental healthcare providers are advised to provide further mental support to these vulnerable groups during this pandemic.

An advanced development in construction industry was achieved by applying ultra-high-performance concrete technology (UHPC). Intensive research efforts had been concentrated in construction to produced amazing levels of qualities with strength greater than 150 MPa and high durability that had never been thought possible before. With this technology, it is possible to construct structures beyond the usual designs but with limited use in construction since it is not commercially viable to replace conventional concrete in most applications. This is attributed to the high cost of materials, the lack of their availability, limited design codes, and complicated manufacturing and curing techniques. This paper reviews the evolution of UHPC and the suggested ideas to replace its expensive composites by cementitious materials. However, concrete made with these alternative materials will not be of the same quality as the standard UHPC. Another promising choice, which seems to be more practical and easier to promote UHPC technology in construction, is looming on the horizon. It is based on the utilization of UHPC in hybrid structures by combining UHPC with other construction materials. The cost of production will hopefully be reduced with such composite structures that have the advantages of the combined materials. Therefore, it is recommended to continue research into this choice which will increase the potential of UHPC to be more accepted in many different construction applications.

A sensor can be called ideal or perfect if it is enriched with certain characteristics viz., superior detections range, high sensitivity, selectivity, resolution, reproducibility, repeatability, and response time with good flow. Recently, biosensors made of nanoparticles (NPs) have gained very high popularity due to their excellent applications in nearly all the fields of science and technology. The use of NPs in the biosensor is usually done to fill the gap between the converter and the bioreceptor, which is at the nanoscale. Simultaneously the uses of NPs and electrochemical techniques have led to the emergence of biosensors with high sensitivity and decomposition power. This review summarizes the development of biosensors made of NPssuch as noble metal NPs and metal oxide NPs, nanowires (NWs), nanorods (NRs), carbon nanotubes (CNTs), quantum dots (QDs), and dendrimers and their recent advancement in biosensing technology with the expansion of nanotechnology.

In clinical applications, there is a lack of wound dressings that combine efficient resistance to drug-resistant bacteria with good self-healing properties. In this study, a series of adhesive self-healing conductive antibacterial hydrogel dressings based on oxidized sodium alginate-grafted dopamine/carboxymethyl chitosan/Fe3+ (OSD/CMC/Fe hydrogel)/polydopamine-encapsulated poly(thiophene-3-acetic acid) (OSD/CMC/Fe/PA hydrogel) were prepared for the repair of infected wound. The Schiff base and Fe3+ coordination bonds of the hydrogel structure are dynamic bonds that can be repaired automatically after the hydrogel network is disrupted. Macroscopically, the hydrogel exhibits self-healing properties, allowing the hydrogel dressing to adapt to complex wound surfaces. The OSD/CMC/Fe/PA hydrogel showed good conductivity and photothermal antibacterial properties under near-infrared (NIR) light irradiation. In addition, the hydrogels exhibit tunable rheological properties, suitable mechanical properties, antioxidant properties, tissue adhesion properties and hemostatic properties. Furthermore, all hydrogel dressings improved wound healing in the infected full-thickness defect skin wound repair test in mice. The wound size repaired by OSD/CMC/Fe/PA3 hydrogel + NIR was much smaller (12%) than the control group treated with Tegaderm™ film after 14 days. In conclusion, the hydrogels have high antibacterial efficiency, suitable conductivity, great self-healing properties, good biocompatibility, hemostasis and antioxidant properties, making them promising candidates for wound healing dressings for the treatment of infected skin wounds.

A flexible pressure sensor with high performances is one of the promising candidates for achieving electronic skins (E‐skin) related to various applications such as wearable devices, health monitoring systems, and artificial robot arms. The sensitive response for external mechanical stimulation is fundamentally required to develop the E‐skin which imitates the function of human skin. The performance of capacitive pressure sensors can be improved using morphologies and structures occurring in nature. In this work, highly sensitive capacitive pressure sensors based on a porous structure of polydimethylsiloxane (PDMS) thin film, inspired on the natural multilayered porous structures seen in mushrooms, diatoms, and spongia offilinalis , have been developed and evaluated. A bioinspired porous dielectric layer is used, resulting in high‐performance pressure sensors with high sensitivity (0.63 kPa −1 ), high stability over 10 000 cycles, fast response and relaxation times, and extremely low‐pressure detection of 2.42 Pa. Additionally, the resulting pressure sensors are demonstrated to fabricate multipixel arrays, thus achieving successful real‐time tactile sensing of various touch shapes. The developed high‐performance flexible pressure sensors may open new opportunities for innovative applications in advanced human‐machine interface systems, robotic sensory systems, and various wearable health monitoring devices.

The influence of digital industry and firm digitization on enterprise innovation has emerged as a critical research topic. To assess the impact of digital transformation on enhancing innovation output, we propose a game model of two organisations investing in digital transformation, analyze the index of enterprise digitalization level with Python tools for text analysis, and employ a fixed effect model. The findings indicate that firm digitalization and the level of regional digital industry innovation can both promote firm innovation. However, the regional digital industry innovation level can have a negative moderating effect on the firm digitalization innovation effect. Furthermore, the impact of firm digitalization on innovation is more visible in digital-related service industries. In other industries, the regional digital industry innovation level has a greater impact on innovation promotion. Due to firms' free-riding tendency in technology adoption, this study shows that the higher the level of digital industrialization in the region where the firm is located, the lower the marginal innovation efficiency of the firm's digital investment. When the level of development of digital industrialization in the region where a firm is located is higher, the "competitive effect" improves the marginal innovation efficiency of firms in adjacent areas, implying that digital industrialization has a spatial spillover effect. The relevant robustness test further verifies the conclusion of the empirical analysis. As a result, the digital industry should be given more attention and financial support.

Flash flood in the cities led to high levels of water in the streets and roads, causing many problems such as bridge collapse, building damage and traffic problems. It is impossible to avoid risks of floods or prevent their occurrence, however it is plausible to work on the reduction of their effects and to reduce the losses which they may cause. Flash flood mapping to identify sites in high risk flood zones is one of the powerful tools for this purpose. Mapping flash flood will be beneficial to urban and infrastructure planners, risk managers and disaster response or emergency services during extreme and intense rainfall events. The objective of this paper is to generate flash flood map for Najran city, Saudi Arabia, using satellite images and GIS tools. To do so, we use SPOT and SRTM DEMs data for which accuracy assessment is achieved by using check points, obtained by GPS observations. Analytical Hierarchical Process (AHP) is used to determine relative impact weight of flood causative factors to get a composite flood hazard index (FHI). The causative factors in this study are runoff, soil type, surface slope, surface roughness, drainage density, distance to main channel and land use. All used data are finally integrated in an ArcMap to prepare a final flood hazard map for study area. The areas in high risk flood zones are obtained by overlaying the flood hazard index map with the zone boundaries layer. The affected population number and land area are determined and compared.

Histopathological examination is considered as gold standard procedure for arriving at a final diagnosis of various lesions of the human body. However, it is limited by a number of alterations of normal morphologic and cytological features that occur as a result of presence of artifacts. These artifacts may occur during surgical removal, fixation, tissue processing, embedding and microtomy and staining and mounting procedures. They can even lead to complete uselessness of the tissue. It is therefore essential to identify the commonly occurring artifacts during histopathological interpretations of tissue sections. This article reviews the common artifacts encountered during slide examination alongside the remedial measures which can be undertaken to differentiate between an artifact and tissue constituent.

OBJECTIVES: Depression and anxiety persist in cancer patients, creating an additional burden during treatment and making it more challenging in terms of management and control. Studies on the prevalence of depression and anxiety among cancer patients in the Middle East are limited and include many limitations such as their small sample sizes and restriction to a specific type of cancer in specific clinical settings. This study aimed to describe the prevalence and risk factors of depression and anxiety among cancer patients in the inpatient and outpatient settings. MATERIALS AND METHODS: A total of 1,011 patients (399 inpatients and 612 outpatients) formed the study sample. Patients' psychological status was assessed using the Hospital Anxiety and Depression Scale (HADS), the Patient Health Questionnaire (PHQ-9), and the Generalized Anxiety Disorder 7-item (GAD-7) scale. The prevalence rate of depressive and anxious symptomatology was estimated by dividing the number of patients who exceeded the borderline score: 10 or more for each subscale of the HADS scale, 15 or more for the GAD-7 scale, and 15 or more in the PHQ-9 by the total number of the patients. Risk factors were identified using logistic regression. RESULTS: < 0.001). In the inpatient setting, depressive symptomatology was more prevalent among patients with bladder cancer, while severe anxious symptomatology was more prevalent across patients with lung cancer. In the outpatient setting, depressive and anxious symptomatology was more prevalent among breast and prostate cancer patients, respectively. Despite that, around 42.7% and 24.8% of the patients, respectively, reported that they feel anxious and depressed, and only 15.5% of them were using medications to manage their conditions. CONCLUSION: Our study findings demonstrated a higher prevalence of depressive and anxious symptomatology in the inpatient setting and advanced disease stages. In addition, the underutilization of antidepressant therapy was observed. There is a need to consider mental disorders as part of the treatment protocol for cancer patients. Enhanced clinical monitoring and treatment of depression and anxiety of cancer patients are required.

The structural, optical and dielectric properties of as-grown Cr2O3 nanostructures are demonstrated in this paper. Powder X-ray diffractometry analysis confirmed the rhombohedral structure of the material with lattice parameter, a = b = 4.953 Å; c = 13.578 Å, and average crystallize size (62.40 ± 21.3) nm. FE-SEM image illustrated the mixture of different shapes (disk, particle and rod) of as-grown nanostructures whereas; EDS spectrum confirmed the elemental purity of the material. FTIR spectroscopy, revealed the characteristic peaks of Cr–O bond stretching vibrations. Energy band gap (3.2 eV) of the nanostructures has been determined using the results of UV-VIS-NIR spectrophotometer. The dielectric properties of the material were checked in the wide frequency region (100Hz-30 MHz). In the low frequency region, the matrix of the dielectric behaves like source as well as sink of electrical energy within the relaxation time. Low value of dielectric loss exhibits that the materials posses good optical quality with lesser defects. The ac conductivity of the material in the high frequency region was found according to frequency power law. The physical-mechanism and the theoretical-interpretation of dielectric-properties of Cr2O3 nanostructures attest the potential candidature of the material as an efficient dielectric medium.

Ultraselective and sensitive detection of xylene and toluene with minimum interferences of other indoor air pollutants such as benzene, ethanol, and formaldehyde is achieved using NiO hierarchical nanostructures doped with Cr. Pure and 1.15–2.56 at% Cr-doped NiO flower-like hierarchical nanostructures assembled from nanosheets are prepared by a simple solvothermal reaction and their gas sensing characteristics toward o-xylene and toluene gases are investigated. The 1.15 at% Cr-doped NiO hierarchical nanostructures show high responses to 5 ppm of o-xylene and toluene (ratio of resistance to gas and air = 11.61 and 7.81, respectively) and negligible cross-responses to 5 ppm of benzene, formaldehyde, ethanol, hydrogen, and carbon monoxide. However, pure NiO nanostructures show low responses to 5 ppm of o-xylene and toluene (ratio of resistance to gas and air = 2.01 and 1.14, respectively) and no selectivity toward any specific gas is observed. Significant enhancement of the response and selectivity to o-xylene and toluene is attributed to the decrease in the hole concentration in NiO and the catalytic oxidation of methyl groups by Cr doping.

Recent advancement in nanoscience and nanotechnology has witnessed numerous triumphs of zinc oxide (ZnO) nanomaterials due to their various exotic and multifunctional properties and wide applications. As a remarkable and functional material, ZnO has attracted extensive scientific and technological attention, as it combines different properties such as high specific surface area, biocompatibility, electrochemical activities, chemical and photochemical stability, high-electron communicating features, non-toxicity, ease of syntheses, and so on. Because of its various interesting properties, ZnO nanomaterials have been used for various applications ranging from electronics to optoelectronics, sensing to biomedical and environmental applications. Further, due to the high electrochemical activities and electron communication features, ZnO nanomaterials are considered as excellent candidates for electrochemical sensors. The present review meticulously introduces the current advancements of ZnO nanomaterial-based chemical sensors. Various operational factors such as the effect of size, morphologies, compositions and their respective working mechanisms along with the selectivity, sensitivity, detection limit, stability, etc., are discussed in this article.

Nowadays, an increasing attention has been paid to the technologies for removing mercury from flue gases. Up to date, no optimal technology that can be broadly applied exists, but the heterogeneous catalytic oxidation of mercury is considered as a promising approach. Based on a brief introduction of the pros and cons of traditional existing technologies, a critical review on the recent advances in heterogeneous catalytic oxidation of elemental mercury is provided. In this contribution, four types of Hg oxidation catalysts including noble metals, selective catalytic reduction (SCR) catalysts, transition metals, and fly ash have been summarized. Both the advantages and disadvantages of these catalysts are described in detail. The influence of various acidic gases including SO2, SO3, NH3, NOx, HCl, Cl2, etc. have been discussed as well. We expect this work will shed light on the development of heterogeneous catalytic oxidation of elemental mercury technology in flue gases, particularly the synthesis of novel and highly efficient Hg0 oxidation catalysts.

Abstract Owing to extensive industrial revolutions, the harvesting of sunlight for environmental remediation has attracted extensive attention and a number of potential photocatalysts have been reported. These photocatalysts were prepared according to their effectiveness under various light irradiations, that is, from UV/Vis to near‐infrared (NIR) regions and finally to full solar light spectrum. This review briefly summarizes recent progress in the enhancement of photocatalytic activities of prepared photocatalysts under various light irradiations. To understand the photocatalytic process, photocatalytic mechanisms and band‐structure engineering are discussed in detail in this review. Moreover, various effective photocatalysts are taken as examples of the photocatalytic process under various light irradiations. Finally, the challenges and perspectives of photocatalysis under different lights irradiations are presented.

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons' weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.