Understanding Telerehabilitation Technology to Evaluate Stakeholders' Adoption of Telerehabilitation Services: A Systematic Literature Review and Directions for Further Research.

OBJECTIVES: To examine the adoption of telerehabilitation services from the stakeholders' perspective and to investigate recent advances and future challenges. DATA SOURCES: A systematic review of English articles indexed by PubMed, Thomson Institute of Scientific Information's Web of Science, and Elsevier's Scopus between 1998 and 2020. STUDY SELECTION: The first author (N.N.) screened all titles and abstracts based on the eligibility criteria. Experimental and empirical articles such as randomized and nonrandomized controlled trials, pre-experimental studies, case studies, surveys, feasibility studies, qualitative descriptive studies, and cohort studies were all included in this review. DATA EXTRACTION: The first, second, and fourth authors (N.N., W.I., B.N.) independently extracted data using data fields predefined by the third author (M.B.). The data extracted through this review included study objective, study design, purpose of telerehabilitation, telerehabilitation equipment, patient/sample, age, disease, data collection methods, theory/framework, and adoption themes. DATA SYNTHESIS: A telerehabilitation adoption process model was proposed to highlight the significance of the readiness stage and to classify the primary studies. The articles were classified based on 6 adoption themes, namely users' perception, perspective, and experience; users' satisfaction; users' acceptance and adherence; TeleRehab usability; individual readiness; and users' motivation and awareness. RESULTS: A total of 133 of 914 articles met the eligibility criteria. The majority of papers were randomized controlled trials (27%), followed by surveys (15%). Almost 49% of the papers examined the use of telerehabilitation technology in patients with nervous system problems, 23% examined physical disability disorders, 10% examined cardiovascular diseases, and 8% inspected pulmonary diseases. CONCLUSION: Research on the adoption of telerehabilitation is still in its infancy and needs further attention from researchers working in health care, especially in resource-limited countries. Indeed, studies on the adoption of telerehabilitation are essential to minimize implementation failure, as these studies will help to inform health care personnel and clients about successful adoption strategies.

Emerging trends in hydrogen and synfuel generation: a state-of-the-art review.

The current work investigated emerging fields for generating and consuming hydrogen and synthetic Fischer-Tropsch (FT) fuels, especially from detrimental greenhouse gases, CO2 and CH4. Technologies for syngas generation ranging from partial oxidation, auto-thermal, dry, photothermal and wet or steam reforming of methane were adequately reviewed alongside biomass valorisation for hydrogen generation, water electrolysis and climate challenges due to methane flaring, production, storage, transportation, challenges and opportunities in CO2 and CH4 utilisation. Under the same conditions, dry reforming produces more coke than steam reforming. However, combining the two techniques produces syngas with a high H2/CO ratio, which is suitable for producing long-chain hydrocarbons. Although the steam methane reforming (SMR) process has been industrialised, it is well known to consume significant energy. However, coke production via catalytic methane decomposition, the prime hindrance to large-scale implementation of these techniques for hydrogen production, could be addressed by coupling CO with CO2 conversion to alter the H2/CO ratio of syngas, increasing the reaction temperatures in dry reforming, or increasing the steam content fed in steam reforming. Optimised hydrogen production and generation of green fuels from CO2 and CH4 can be achieved by implementing these strategies.

Water treatment and artificial intelligence techniques: a systematic literature review research.

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.

New insight into the kinetic study on the different loadings of the CuO/CNT catalyst and its optimization for p-chloroaniline photodegradation.

The effect of the copper (Cu) content on Cu oxide loaded onto a carbon nanotube (CuO/CNT) catalyst on the mechanistic, kinetic, and photonic efficiency of the photodegradation of p-chloroaniline (PCA) under visible (Vis) and ultraviolet (UV) light irradiation has been explored. For low-loading (1-5 wt%) CuO/CNTs, photodegradation performed better under UV (>84%) rather than the Vis system; this may be due to the presence of abundant defect sites on both CuO and CNTs, which allowed the multielectron reduction of oxygen at their impurity levels to generate more hydrogen peroxide and subsequent ·OH radicals. The active species under UV were in the following order: h+ ≫ e- > ·OH, while it was vice versa for the Vis system with a well-balanced 50 wt% CuO/CNT catalyst that exhibited a similar performance. The kinetic study showed the transition of the kinetic order from the zeroth to the first order on increasing the PCA concentration under the Vis system and vice versa for the UV system. The Thiele modulus (ϕ) further confirmed that the effect of internal mass transfer was negligible under UV light. In contrast, the transition from mass transfer to kinetic control limitation was observed under the Vis system. The optimum PCA degradation predicted from the response surface analysis was 97.36% at the reaction pH of 7.3, catalyst dosage of 0.45 g L-1, and initial PCA concentration of 11.02 mg L-1. The condition obtained was fairly close to the forecasted value with an error of 0.26%.

The nitrogen cycle and mitigation strategies for nitrogen loss during organic waste composting: A review.

Composting is a promising technology to decompose organic waste into humus-like high-quality compost, which can be used as organic fertilizer. However, greenhouse gases (N2O, CO2, CH4) and odorous emissions (H2S, NH3) are major concerns as secondary pollutants, which may pose adverse environmental and health effects. During the composting process, nitrogen cycle plays an important role to the compost quality. This review aimed to (1) summarizes the nitrogen cycle of the composting, (2) examine the operational parameters, microbial activities, functions of enzymes and genes affecting the nitrogen cycle, and (3) discuss mitigation strategies for nitrogen loss. Operational parameters such as moisture, oxygen content, temperature, C/N ratio and pH play an essential role in the nitrogen cycle, and adjusting them is the most straightforward method to reduce nitrogen loss. Also, nitrification and denitrification are the most crucial processes of the nitrogen cycle, which strongly affect microbial community dynamics. The ammonia-oxidizing bacteria or archaea (AOB/AOA) and the nitrite-oxidizing bacteria (NOB), and heterotrophic and autotrophic denitrifiers play a vital role in nitrification and denitrification with the involvement of ammonia monooxygenase (amoA) gene, nitrate reductase genes (narG), and nitrous oxide reductase (nosZ). Furthermore, adding additives such as struvite salts (MgNH4PO4·6H2O), biochar, and zeolites (clinoptilolite), and microbial inoculation, namely Bacillus cereus (ammonium strain), Pseudomonas donghuensis (nitrite strain), and Bacillus licheniformis (nitrogen fixer) can help control nitrogen loss. This review summarized critical issues of the nitrogen cycle and nitrogen loss in order to help future composting research with regard to compost quality and air pollution/odor control.

Synthesis and catalytic properties of calcium oxide obtained from organic ash over a titanium nanocatalyst for biodiesel production from dairy scum.

The fatty acid methyl ester (FAME) production from dairy effluent scum as a sustainable energy source using CaO obtained from organic ash over titanium dioxide nanoparticles (TNPs) as the transesterification nano-catalyst has been studied. The physical and chemical properties of the synthesized catalysts were characterized, and the effect of different experimental factors on the biodiesel yield was studied. It was revealed that the CaO-TiO2 nano-catalyst displayed bifunctional properties, has both basic and acid phases, and leads to various effects on the catalyst activity in the transesterification process. These bifunctional properties are critical for achieving simultaneous transesterification of dairy scum oil feedstock. According to the reaction results, the catalyst without and with a low ratio of TNPs showed a low catalytic activity. In contrast, the 3Ca-3Ti nano-catalyst had the highest catalytic activity and a strong potential for reusability, producing a maximum biodiesel yield of 97.2% for a 3 wt% catalyst, 1:20 oil to methanol molar ratio for the dairy scum, and a reaction temperature of 70 °C for a period of 120 min under a 300 kPa pressure. The physical properties of the produced biodiesel are within the EN14214 standards.

Transforming traditional CRM into social CRM: An empirical investigation in Iraqi healthcare industry.

The unprecedented growth of social media usage questions the conventional customer relationship management (CRM). Social CRM strategy is a novel version of CRM empowered by social media technology that offers a new way of managing relationships with customers effectively. The aims of this study are two-fold, examining the important determinants of the successful adoption of the social CRM; and to validate the outcomes on this novel social CRM adoption in the healthcare industry. The proposed adoption model of this study derived with theoretical support from TOE, DOI and ISS theories in IS/IT, social media, and CRM literatures. This undertaking focuses on the use of structural equations modelling, to examine a theoretical social CRM (Social Customer Relationship Management) model involving 17 Iraqi hospitals, and a sample total of 428. The model's principal independent constructs are associated to the viewpoint of top management, IT staff and operational staff, regarding the grounds for social CRM adoption, the operations performed on social CRM, and the themes employed. PLS-SEM was applied for statistical analysis, to evaluate the hypnotized linkages between the variables. The results show that, social CRM adoption has a tremendous impact on healthcare organizations with its perceived benefits. According to the results attained, all constructs have significant impact on social CRM adoption except for leadership knowledge. Consequently, adoption results in remarkable outcomes that gives credence to the intervening performance of social CRM. Following an examination of the model, which included a scrutiny of its pathways, we are of the view that the concerns, past history, and potential let-downs with regards to social CRM adoption, need to be thoroughly investigated. This study is one of the few researches that provide the in-depth knowledge about the constructs impacting CRM transformation and the benefits attained. The results can guide healthcare providers during their efforts to develop effective marketing techniques, and advance the perceived benefits, particularly in the healthcare profession. Moreover, this study contributes to the IS literature by suggesting the empirically extended TOE model which advances the conventional TOE model.