ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Management of chicken manure using black soldier fly (Diptera: Stratiomyidae) larvae assisted by companion bacteria

Black soldier fly (BSF) is used for the management of organic waste, but research has hardly explored the effect of companion bacteria when chicken manure (CHM) is converted to insect biomass. In this study, we isolated nine bacterial species (FE01, FE02, FE03, FE04, FE05, FE06, FE07, FE08, FE09) from BSF eggs and one (BSF-CL) from the larval gut. These companion bacteria were inoculated into CHM along with BSF larvae (BSFL). Larval growth and manure conversion rates were determined. Results indicated that almost all bacteria individual bacteria in this study significantly promote BSFL growth. BSFL reared in manure with the species Kocuria marina (FE01), Lysinibacillus boronitolerans (FE04), Proteus mirabilis (FE08) and Bacillus subtilis (BSF-CL) had higher weight gain and manure reduction rates compared to the control. These four strains used were then examined as a poly-bacteria community experiment to determine BSFL growth and manure conversion. Manure inoculated with the poly-bacteria Group3 (FE01:FE04:FE08:BSF-CL = 4:1:1:1) and then fed to BSFL resulted in 28.6% more weight gain than the control. The greatest manure reduction rate (52.91%) was reached when companion bacteria were mixed at a ratio of 1:1:1:4. Additionally, the companion bacteria influenced the nutritional value of BSFL. Crude protein content in Groupl (FE01:FE04:FE08:BSF-CL = 1:1:1:1) was significantly larger than that of the control. Crude fat content in Group3 was significantly larger than that of the control. BSFL companion bacteria and their poly-bacteria compound improved manure conversion efficiency and nutrient accumulation in BSFL, reduced CHM quantity, increased larvae biomass, with potential economic gains in CHM management. (C) 2019 Elsevier Ltd. All rights reserved.
Assessing the Potentials of Digitalization as a Tool for Climate Change Adaptation and Sustainable Development in Urban Centres

Digitalization is a key enabler of sustainable development of cities' socio-economic dynamics with the potential to foster climate-friendly urban environments and societies. The advent of the 4th industrial revolution has seen the increased application of digitalization in several fields and at different levels. High-tech digital devices, platforms and environments are increasingly being deployed to enhance productivity, efficiency and sustainability, and improve overall well-being of urban dwellers. Digitalization is projected to further impact cities in future, transform jobs and trigger life-style changes with far-reaching impacts that will ultimately affect cities' resilience and adaptation capacities. While a growing body of research has highlighted the significance of digitalization to climate change mitigation such as reducing GHG and CO2 emissions, comprehensive evaluations of the potentials of digitalization as an enabler of climate change adaptation remain scarce. This paper addresses this gap by analysing the current trend in digital revolution in relation to climate change adaptation and examines the likely challenges of digitalization. A desk research method was adopted, focusing on core digitalization concepts driving the Fourth Industrial Revolution (IR 4.0). Nine case studies in cities across various continents were selected to assess the potentials of digitalization in addressing climatic hazards and to highlight benefits from implementing digitalization, while considering the social-ecological-technological challenges and tensions around IR 4.0. Our findings reveal the capabilities of digitalization in supporting more effective early warning and emergency response systems, enhancing food and water security, improving power infrastructure performance, enabling citizen engagement and participatory adaptation measures and minimizing the impacts of climatic hazards. Finally, we recommend feasible pathways to overcome present risks and challenges in order to optimize the numerous opportunities offered by digitalization in support of climate change adaptation initiatives.
Systematic review and meta-analysis of the inter-recti distance on ultrasound measurement in nulliparas

The objective is to evaluate the inter-recti distance on ultrasound measurement at different locations in healthy nulliparas. Electronic databases were searched for studies describing the inter-recti distance measured by ultrasound in healthy nulliparas. We excluded studies without descriptions of the measurement position or the condition of the abdominal wall. A meta-analysis was performed to evaluate the inter-recti distance on ultrasound measurement. Seven eligible studies with 295 healthy nulliparas were included. The location of the inter-recti distance measurement by ultrasound was not uniform. The pooled data divided the measurement locations into three areas. The meta-analytic summary values of the umbilical inter-recti distance of the nulliparas was 8.77 mm (6.56-10.99 mm), the distance at the epigastric area was 7.22 mm (2.76-11.68 mm), and that at the infraumbilical area was 4.09 mm (1.55-6.64 mm). The maximal reported inter-recti distance in healthy nulliparous women is smaller than 10 mm on ultrasound measurement at all locations and the range in the umbilical area is larger than that in the epigastric, infraumbilical areas. The values for the inter-recti distance reported in this systematic review can be used as the reference of feasible and desirable distance of the rectus muscles after rectus fascia plication. The limitation was that the methodological quality of the assessment in most studies was unclear or low.
State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis

Accurate state-of-health estimation can ensure the safe and reliable operation of Lithium-ion batteries in any given application. Nevertheless, most of the state-of-health estimation methods require a large amount of laboratory aging data to offer precise results. As obtaining battery aging data under laboratory conditions requires a considerable amount of time and incurs high economic costs, in this paper, a method based on transfer learning is proposed to monitor state-of-health of batteries. A novel data processing method based on maximum mean discrepancy is considered to eliminate redundant information and minimize the difference between different data distributions. Then, mutual information is used to prove that the correlation between processed data is not decreased. To validate the developed transfer learning method, the data sets of four batteries in different working conditions are considered. Different error-detection methods, maximum average error, mean squared error and root mean squared error, which are utilized to evaluate the proposed model. The state of health is estimated effectively with less than 2.5% error considering the aforementioned errors after processed by using semi-supervised transfer component analysis algorithm, although the training set only accounts for about 35% of the entire set. The results indicate that transfer learning plays an important role in improving the estimation accuracy of a battery state-of-health.
Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people's lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman's rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.
A novel multi-objective spiral optimization algorithm for an innovative solar/biomass-based multi-generation energy system: 3E analyses, and optimization algorithms comparison

A novel multi-generation energy system is proposed consisting of a solar gas turbine system, multi-effect seawater desalination, LNG cold energy recovery unit, and a double effect absorption chiller. In addition, different working fluids of the ORC system are examined to select the suitable working fluid in terms of global warming potential and exergy efficiency of the system. Subsequently, energy, exergy, and economic (3E) analyses are performed to comprehensively evaluate the energy system. Besides, a parametric study is conducted to assess the effect of the most influential decision variables on the proposed system. Afterward, the novel multi-objective spiral optimization (MOSPO) algorithm is introduced to minimize total cost rate of the system while maximizing the exergy efficiency as the conflicting objective functions. The proposed algorithm is developed to optimize the decision variables effectively. To ascertain the final optimum solution point, three conventional methods i.e. TOPSIS, LINMAP and Shannon's entropy are implemented. The results revealed that exergy efficiency and total cost rate of the system at the baseline are 60.05%, and 36.75 $/h, respectively. Furthermore, the net power output of the system would be 106.5 kW in addition to 0.7703 kW heating load, 56.01 kW cooling capacity, and 35.74 kg/h fresh water production capacity. The eco-environmental assessment revealed the fact that the proposed renewable-based energy system is capable of avoiding 485 tons CO2 emissions annually, and product cost rate reduction up to 6 $/hr in comparison to coal and natural gas-based energy systems. Besides, the proposed MOSPO algorithm is compared with common optimization methods; accordingly, the conventional algorithms are selected for the comparison including non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO) algorithm, the Pareto envelope-based selection algorithm II (PESA-II), and the strength Pareto evolutionary algorithm II (SPEA-II). The comparison results show that the proposed MOSPO algorithm is preferable according to the Taylor Diagrams showing the performance of the algorithms.
Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression (GWR) model

A global geological phenomenon caused by natural or human activities is described as land subsidence. Groundwater extraction plays a significant part in causing land subsidence. Due to economic development, urban expansion, and rapid population expansion, the unscientific exploitation of groundwater in Beijing has been accelerated, whichmakes it the regionwith the fastest land subsidence rate in China. To study the spatial heterogeneity of land subsidence caused by groundwater aquifers level changes, the monitoring results of land subsidence in 2003-2010 yearswere analyzed by using PS-InSAR, based on ENVISAT ASAR in Beijing plain area. Themaximum value of accumulated land subsidence in the study area is 707mm, and in this study areamultiple subsidence center areas have been formed. AGWRmodel based on a regular grid has been established by exploring the effects of unconfined aquifer (UA), first confined aquifer (FCA), second confined aquifer (SCA), third confined aquifer (TCA) on land subsidence and their spatial non-stationarity. The change of subsidence in all subsidence areas is positively related to the change of SCA water level. Except the fact that the main control factors of Liyuan and Songzhuang are the change of UA layer, the change of SCA is the main control factor of land subsidence in most subsidence areas. Though the contribution rate of SCA to land subsidence is the highest, the contribution rate of TCA has been increasing. It is predicted that the impact on land subsidence will increase year by year. The results of thiswill not only help to understand the spatial impact patterns of aquifers on land subsidence zones, but also to formulate optimal groundwater regulation and recharge policies. There is a scarcity of the consideration of the compressible layer in the study and it will become more comprehensive if further datasets are obtained. (c) 2020 Published by Elsevier B.V.
Therapy Delayed is Therapy Denied: A Case Report of Melanoma Misdiagnosed as Diabetic Foot Ulcer

Malignant melanoma is a deadly form of skin cancer, and prompt diagnosis is a key factor in providing adequate, life-saving therapy. A 75-year-old man, with long-standing type 2 diabetes mellitus, presented with a 2- to 3-year history of right heel ulcer. He had received various therapies for a diagnosis of diabetic foot ulcer, to no avail. Physical examination showed a black, fungating ulcerated lesion on his right heel, with minimal bleeding. No inguinal lymphadenopathy was palpated. A biopsy was done, which revealed BRAF-negative malignant melanoma, with a vertical growth phase, Breslow 3.1 mm, ulceration, 11 mitoses/mm2, Clark level IV, no lymphatic or vascular invasion observed. Right inguinal lymph node sampling suggested no involvement, but PET-CT suggested pulmonary, right inguinal lymph node and bone involvement. The patient was referred to the oncologists. Written informed consent for publication was given by the patient. Diabetic foot ulcers are a frequently encountered, but serious complication of diabetes mellitus. Delayed healing is often seen, despite adequate therapy. The differential diagnosis of diabetic foot ulcers is vast and includes neoplasia. When a foot ulcer fails to heal, other differential diagnoses must be considered, in order for the patient to receive adequate therapy. Here specialist consultations, including dermatology consultations, could improve chances of delivering the right therapy promptly. This is a factor underlying the emphasis on a multidisciplinary approach to foot ulcer therapy. Our presentation - reported according to the CARE guidelines - also illustrates the fact that failure to reach a timely diagnosis may deny the patient the opportunity to receive adequate treatment. In such cases, "delayed therapy becomes denied (or failed) therapy", paraphrasing the old adage "Justice delayed is justice denied".
How does energy poverty affect economic development? A panel data analysis of South Asian countries

This study examines the interaction between energy poverty, employment, education, per capita income, inflation, and economic development using panel data for seven South Asian countries. The present study uses panel data spanning the period from 1995 to 2017, panel cointegration, autoregressive distributed lag (ARDL), and penalized quantile regression (PQR) estimators to test for cointegration in the long-run. The estimated results reveal that both panel cointegration approaches (Pedroni and Johansen-Fisher) demonstrate the existence of the long-term relationship between energy poverty, employment, education, per capita income, inflation, and economic development. The ARDL estimates show that energy poverty has a negative influence on economic development in both the long-run and the short-run. The results provide support for economic, social, and environmental policymakers in their decision-making. This study suggests that, in relation to financing the green and low-carbon economy concept, both the public sector and private industries need to make further efforts to use modern, energy-efficient, and green technologies, which are beneficial both for economic progress as well as managing the ecological degradation process.
Foreign direct investment and education as determinants of environmental quality: The importance of post Paris Agreement (COP21)

This study investigates the effect of foreign direct investment and education on environmental quality for Asian countries by controlling income, energy consumption, and urbanization for the period of 1990-2018. We have applied panel cointegration techniques to probe for long-run associations among the variables. The empirical results indicate the existence of cointegration between the variables. Dynamic ordinary least square and fully modified least square methods are applied to estimate long-run elasticities. The empirical results confirm that environmental quality is sensitive to foreign direct investment, education, and urbanization. Income and energy consumption deteriorate environmental quality by increasing CO2 emissions. In the long-run, bidirectional causal associations are found for emissions-foreign direct investment, emissions-energy use, income-emissions, foreign direct investment-income, and energy-income nexus. Furthermore, there is a unidirectional causality running from education and urbanization to emissions, foreign direct investment, income, and energy use. Policymakers in Asian economies are encouraged to establish policies that increase the education budget, promote the use of green energy, attract foreign direct investment with green technology, and expand cities to limit the urbanization effects on environmental quality.
Effect of COVID-19 virus on reducing GHG emission and increasing energy generated by renewable energy sources: A brief study in Malaysian context

Coronavirus 2019 (COVID-19) has globally affected the human mortality rate and economic history of the modern world. According to the World Health Organization, COVID-19 has caused a severe threat to the health of the vulnerable groups, notably the elderly. There is still some disagreements regarding the source of the virus and its intermediate host. However, the spread of this disease has caused most countries to enforce strict curfew laws and close most industrial and recreational centres. This study aims to show the potential positive effects of COVID-19 on the environment and the increase of renewable energy generation in Malaysia. To prevent the spread of this disease, Malaysia enacted the Movement Control Order (MCO) law in March 2020. Implementation of this law led to a reduction in environmental pollution, especially air pollution, in this country. The greenhouse gases (GHG) emission , which was 8 Mt CO2 eq. from January 2020 to March 2020, reduced to <1 Mt CO2 eq. for April and May. The reduction of GHG emission and pollutant gases allowed more sunlight to reach photovoltaic panels, hence increasing the renewable energy generation. (C) 2020 Elsevier B.V. All rights reserved.
Novel insights on premature progesterone elevation: a mini-review

In vitro fertilization (IVF) success involves ovarian stimulation with conventional or mild stimulation protocols aimed to yield an adequate number of mature oocytes. These oocytes can be further fertilized and generate good quality embryos to be transferred, ideally in the same cycle. Since 2013, following the results of a meta-analysis including more than 60,000 IVF cycles, the negative effects of premature progesterone elevation on reproductive outcomes have been demonstrated. Nowadays, evidence is emerging on the limited regulation on serum progesterone values, demonstrating significantly lower live birth rates in patients with either low (<0.5ng/ml) late follicular phase progesterone or higher levels decreasing sharply. This review discusses and summarizes the different mechanisms of progesterone elevation and its impact on IVF treatments. Different approaches to diminish the impact of progesterone on fertility outcomes are also addressed.
Perspectives of online anatomy teachers: A neglected study population struggles with the invisible student

Online teachers are an under-researched population, but their perspectives are crucial to the successful implementation of online education. A fully online section of an established face-to-face (F2F) two-semester undergraduate anatomy course with a prosection laboratory commenced in 2012 at The University of Western Ontario, Canada. Professors' lectures for F2F students were broadcast in live and archived format to online students using Blackboard Collaborate (BBC) video conferencing software. Teaching assistants (TAs) delivered online laboratories using BBC and three-dimensional (3D) anatomical computer models. This study explored the common experiences and issues faced by the course teachers from 2012 to 2014. Transcripts from open-ended, individual interviews with professors (n = 4) and TAs (n = 5) were coded and analyzed thematically. The teachers' concern for their inability to see the students during sessions to assess class engagement and their teaching effectiveness, and to develop social relationships, was the main finding. However, video conferencing software and email were sufficient communication methods for the students' questions and the teachers' answers. The TAs noted usability challenges and anatomical inaccuracies in the 3D models compared to cadavers. Due to limitations of BBC's screen sharing function, live manipulation for the 3D computer models was not possible; however, the TAs found pedagogical value in using screen captures of the models for drawing activities with the students. Overall, preparation time for teaching online was longer than for F2F. The study's findings provide science educators with issues to consider when preparing for online teaching and recommendations to optimize the teaching experience.