Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model.

The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide. However, public hospitals in Pakistan have yet to implement it and automate the coding process. In this research, we implemented ICD-10-CM coding for a private database and named it Clinical Pool of Liver Transplant (CPLT). Additionally, we proposed a novel deep learning model called Deep Recurrent-Convolution Neural Network with a lambda-scaled Attention module (DRCNN-ATT) using the CPLT database to achieve automatic ICD-10-CM coding. DRCNN-ATT combines a bi-directional long short-term memory network (bi-LSTM), a multi-scale convolutional neural network (MS-CNN), and a lambda-scaled attention module. Experimental results demonstrate that deep recurrent convolutional neural network (DRCNN) faces attention score vanishing problem with a standard attention module for automatic ICD coding. However, adding a lambda-scaled attention module resolves this issue. We evaluated DRCNN-ATT model using two distinct datasets: a private CPLT dataset and a public MIMIC III top 50 dataset. The results indicate that the DRCNN-ATT model outperformed various baselines by generating 0.862 micro F1 and 0.25 macro F1 scores on CPLT dataset and 0.705 micro F1 and 0.655 macro F1 scores on MIMIC III top 50 dataset. Furthermore, we also deployed our model for automatic ICD-10-CM coding using ngrok and the Flask APIs, which receives input, processes it, and then returns the results.

Malaria vaccine efficacy, safety, and community perception in Africa: a scoping review of recent empirical studies.

AIM: The review summarizes the recent empirical evidence on the efficacy, safety, and community perception of malaria vaccines in Africa. METHODS: Academic Search Complete, African Journals Online, CINAHL, Medline, PsychInfo, and two gray literature sources were searched in January 2023, and updated in June 2023. Relevant studies published from 2012 were included. Studies were screened, appraised, and synthesized in line with the review aim. Statistical results are presented as 95% Confidence Intervals and proportions/percentages. RESULTS: Sixty-six (N = 66) studies met the inclusion criteria. Of the vaccines identified, overall efficacy at 12 months was highest for the R21 vaccine (N = 3) at 77.0%, compared to the RTS,S vaccine (N = 15) at 55%. The efficacy of other vaccines was BK-SE36 (11.0-50.0%, N = 1), ChAd63/MVA ME-TRAP (- 4.7-19.4%, N = 2), FMP2.1/AS02A (7.6-9.9%, N = 1), GMZ2 (0.6-60.0%, N = 5), PfPZ (20.0-100.0%, N = 5), and PfSPZ-CVac (24.8-33.6%, N = 1). Injection site pain and fever were the most common adverse events (N = 26), while febrile convulsion (N = 8) was the most reported, vaccine-related Serious Adverse Event. Mixed perceptions of malaria vaccines were found in African communities (N = 17); awareness was generally low, ranging from 11% in Tanzania to 60% in Nigeria (N = 9), compared to willingness to accept the vaccines, which varied from 32.3% in Ethiopia to 96% in Sierra Leone (N = 15). Other issues include availability, logistics, and misconceptions. CONCLUSION: Malaria vaccines protect against malaria infection in varying degrees, with severe side effects rarely occurring. Further research is required to improve vaccine efficacy and community involvement is needed to ensure successful widespread use in African communities.

Novel acyl hydrazide derivatives of polyhydroquinoline as potent anti-diabetic and anti-glycating agents: Synthesis, in vitro α-amylase, α-glucosidase inhibition and anti-glycating activity with molecular docking insights.

In this study, eleven novel acyl hydrazides derivative of polyhydroquinoline were synthesized, characterized and screened for their in vitro anti-diabetic and anti-glycating activities. Seven compounds 2a, 2d, 2i, 2 h, 2j, 2f, and 2 g exhibited notable α-amylase inhibitory activity having IC50 values from 3.51 ± 2.13 to 11.92 ± 2.30 µM. Similarly, six compounds 2d, 2f, 2 h, 2i, 2j, and 2 g displayed potent α-glucosidase inhibitory activity compared to the standard acarbose. Moreover, eight derivatives 2d, 2 g, 2f, 2j, 2a, 2i, 2 g, and 2e showed excellent anti-glycating activity with IC50 values from 6.91 ± 2.66 to 15.80 ± 1.87 µM when compared them with the standard rutin (IC50 = 22.5 ± 0.90 µM). Molecular docking was carried out to predict the binding modes of all the compounds with α-amylase and α-glucosidase. The docking analysis revealed that most of the compounds established strong interactions with α-amylase and α-glucosidase. All compounds fitted well into the binding pockets of α-amylase and α-glucosidase. Among all compounds 2a and 2f were most potent based on docking score -8.2515 and -7.3949 against α-amylase and α-glucosidase respectively. These results hold promise for the development of novel candidates targeted at controlling postprandial glucose levels in individuals with diabetes.

Integrating DFT and machine learning for the design and optimization of sodium alginate-based hydrogel adsorbents: Efficient removal of pollutants from wastewater.

This study presents a novel approach to design and optimize a sodium alginate-based hydrogel (SAH) for efficient adsorption of the model water pollutant methylene blue (MB) dye. Utilizing density functional theory (DFT) calculations, sodium alginate-g-poly (acrylamide-co-itaconic acid) was identified with the lowest adsorption energy (Eads) for MB dye among 14 different clusters. SAHs were prepared using selected monomers and sodium alginate combinations through graft co-polymerization, and swelling studies were conducted to optimize grafting conditions. Advanced characterization techniques, including FTIR, XRD, XPS, SEM, EDS, and TGA, were employed, and the process was optimized using statistical and machine learning tools. Screening tests demonstrated that Eads serves as an effective predicting indicator for adsorption capacity (qe) and MB removal efficiency (RRMB,%), with reasonable agreement between Eads and both responses under given conditions. Process modeling and optimization revealed that 5 mg of selected SAH achieves a maximum qe of 3244 mg g-1 at 84.4% RRMB under pH 8.05, 98.8 min, and MB concentration of 383.3 mg L-1, as identified by the desirability function approach. Moreover, SAH effectively eliminated various contaminants from aqueous solutions, including sulfasalazine (SFZ) and dibenzothiophene (DBT). MB adsorption onto selected SAH was exothermic, spontaneous, and followed the pseudo-first-order and Langmuir-Freundlich isotherm models. The remarkable ability of SAH to adsorb MB is attributed to its well-designed structure predicted through DFT and optimal operational conditions achieved by AI-based parametric optimization. By integrating DFT-based computations and machine-learning tools, this study contributes to the efficient design of adsorbent materials and optimization of adsorption processes, also showcasing the potential of SAH as an efficient adsorbent for the abatement of aqueous pollution.

Spatial distribution of physicochemical parameters and drinking and irrigation water quality indices in the Jhelum River, Pakistan.

Sustainable management of river systems is a serious concern, requiring vigilant monitoring of water contamination levels that could potentially threaten the ecological community. This study focused on the evaluation of water quality in the Jhelum River (JR), Azad Jammu and Kashmir, and northern Punjab, Pakistan. To achieve this, 60 water samples were collected from various points within the JR Basin (JRB) and subjected to a comprehensive analysis of their physicochemical parameters. The study findings indicated that the concentrations of physicochemical parameters in the JRB water remained within safety thresholds for both drinking and irrigation water, as established by the World Health Organization and Pakistan Environmental Protection Agency. These physicochemical parameters refer to various chemical and physical characteristics of the water that can have implications for both human health (drinking water) and agricultural practices (irrigation water). The spatial variations throughout the river course distinguished between the upstream, midstream, and downstream sections. Specifically, the downstream section exhibited significantly higher values for physicochemical parameters and a broader range, highlighting a substantial decline in its quality. Significant disparities in mean values and ranges were evident, particularly in the case of nitrates and total dissolved solids, when the downstream section was compared with its upstream and midstream counterparts. These variations indicated a deteriorating downstream water quality profile, which is likely attributable to a combination of geological and anthropogenic influences. Despite the observed deterioration in the downstream water quality, this study underscores that the JRB within the upper Indus Basin remains safe and suitable for domestic and agricultural purposes. The JRB was evaluated for various irrigation water quality indices. The principal component analysis conducted in this study revealed distinct covariance patterns among water quality variables, with the first five components explaining approximately 79% of the total variance. Recommending the continued utilization of the JRB for irrigation, we advocate for the preservation and enhancement of water quality in the downstream regions.

Evaluation of irrigation, drinking, and risk indices for water quality parameters of alpine lakes.

Alpine lakes are aquatic ecosystems that maintain and regulate water supply for the downstream streams, rivers, and other reservoirs. This study examined the water characteristics of various alpine lakes in Gilgit-Baltistan, Northern Pakistan. For this purpose, water was sampled and investigated for basic parameters, anions, and cations using the multi-parameter analyzers and atomic absorption spectrophotometer. Physicochemical parameters of alpine lakes were noted under the World Health Organization water guidelines, except for fluoride (F-) and turbidity in 4.3% and 36% of samples, respectively. Water quality index (WQI) classified samples (93%) as excellent and good quality (7%). Results showed maximum chronic daily intake values (0.14 ± 0.01 mg/kg-day) for nitrate (NO3-) and hazard quotient (0.80 ± 0.24) for F- in children via water intake from Upper Kachura and Shausar Lakes, respectively. Statistical analyses of Piper and Gibbs's plots revealed that the water quality is mainly characterized by bedrock geology.

Adeno-Associated Virus-Mediated Gene Therapy.

Choice of vector is the most critical step in gene therapy. Adeno-associated viruses (AAV); third generation vectors, are getting much attention of scientists to be used as vehicles due to their non-pathogenicity, excellent safety profile, low immune responses, great efficiency to transduce non-dividing cells, large capacity to transfer genetic material and long-term expression of genetic payload. AAVs have multiple serotypes and each serotype shows tropism for a specific cell. Different serotypes are used to target liver, lungs, muscles, retina, heart, CNS, kidneys, etc. Furthermore, AAV based gene therapies have tremendous marketing applications that can be perfectly incorporated in the anticipated sites of the host target genome resulting in life long expression of transgenes. Some therapeutic products use AAV vectors that are used to treat lipoprotein lipase deficiency (LPLD) and it is injected intramuscularly, to treat mutated retinal pigment epithelium RPE65 (RPE65) that is introduced to subretinal space, an intravenous infusion to treat spinal muscular atrophy and rAAV2-CFTR vector is introduced into nasal epithelial cells to treat cystic fibrosis. AAV therapies and other such interdisciplinary methodologies can create the miracles for the generation of precision gene therapies for the treatment of most serious and sometimes fatal disorders.

Thiourea derivatives inhibit key diabetes-associated enzymes and advanced glycation end-product formation as a treatment for diabetes mellitus.

This study was designed to screen novel thiourea derivatives against different enzymes, such as α-amylase, α-glucosidase, protein tyrosine phosphatase 1 B, and advanced glycated end product (AGEs). A cytotoxicity analysis was performed using rat L6 myotubes and molecular docking analysis was performed to map the binding interactions between the active compounds and α-amylase and α-glucosidase. The data revealed the potency of five compounds, including E (1-(2,4-difluorophenyl)-3-(3,4-dimethyl phenyl) thiourea), AG (1-(2-methoxy-5-(trifluoromethyl) phenyl)-3-(3-methoxy phenyl) thiourea), AF (1-(2,4-dichlorophenyl)-3-(4-ethylphenyl) thiourea), AD (1-(2,4-dichlorophenyl)-3-(4-ethylphenyl) thiourea), and AH (1-(2,4-difluorophenyl)-3-(2-iodophenyl) thiourea), showed activity against α-amylase. The corresponding percentage inhibitions were found to be 85 ± 1.9, 82 ± 0.7, 75 ± 1.2, 72 ± 0.4, and 65 ± 1.1%, respectively. These compounds were then screened using in vitro assays. Among them, AH showed the highest activity against α-glucosidase, AGEs, and PTP1B, with percentage inhibitions of 86 ± 0.4% (IC50  = 47.9 µM), 85 ± 0.7% (IC50  = 49.51 µM), and 85 ± 0.5% (IC50  = 79.74 µM), respectively. Compound AH showed an increased glucose uptake at a concentration of 100 µM. Finally, an in vivo study was conducted using a streptozotocin-induced diabetic mouse model and PTP1B expression was assessed using real-time PCR. Additionally, we examined the hypoglycemic effect of compound AH in diabetic rats compared to the standard drug glibenclamide.

Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite.

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe3O4 nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained <1 for 90% predictions. GFC showed 86.29% NB removal for the given experimental matrix which can be elevated to 96.25% under optimum conditions. The NB adsorption was found to be physical, spontaneous, favorable and obeyed pseudo-2nd order kinetics. The results demonstrate the suitability of GFC as the promising cost-effective and efficient alternative for the decolourization of urban and drinking water streams and elucidate the potential of machine learning models for accurate prediction & elevation of adsorption processes with less experimentation in water purification applications.

Microbial communities associated with resin canal discoloration in mango fruit.

Resin canal discoloration (RCD) severely impacts the fruit quality of mango, diminishes consumer confidence, and reduces sales, but the biological cause is still unclear. Using next-generation sequencing, the overall microbial community composition of RCD+ and visually healthy mango fruits was determined for the first time to examine the possible role of bacterial and fungal pathogens in RCD. The diversity profile of bacterial and fungal communities was determined using primers targeting the 16S rRNA gene and Internal Transcribed Spacer (ITS) regions. Results showed that bacterial communities in healthy fruits are clustered together and significantly different from those in RCD+ fruits. Tatumella and Pantoea species were the most abundant bacterial taxa on RCD+ fruit, and both have been linked to disease outbreaks in a variety of fruit crops. Fungal communities were generally similar between RCD+ and normal samples, though non-pathogenic yeasts Meyerozyma and Naganishia tended to dominate the fungal communities on RCD+ fruit. The study indicates that bacteria rather than fungal organisms are more likely to be associated with RCD in mango. This finding will facilitate the isolation and confirmation of RCD-causing organisms and the development of control strategies to manage RCD problem in mango.

Topical Application of 0.5% Timolol Maleate Hydrogel for the Treatment of Superficial Infantile Hemangiomas.

BACKGROUND: Oral propranolol is considered the first line therapy in the treatment of infantile hemangiomas (IHs). However, there are considerable side effects due to its ability to penetrate the blood brain barrier. Alternatively, topical timolol, a non-selective beta blocker, has resulted in fewer side effects and is 4–10 times more potent in comparison to oral propranolol. This study evaluates the efficacy of 0.5% timolol maleate hydrogel for the treatment of IH. METHODS: This study was conducted via a quasi-experimental design from October 30, 2020 – April 29, 2021, at the Department of Dermatology Benazir Bhutto Hospital, Rawalpindi. 145 infants between 1–12 months in age diagnosed with superficial cutaneous hemangiomas were included in the study with a male to female ratio of 2.4:1. A thin layer of timolol maleate 0.5% hydrogel was applied to the entire surface of the patient’s IH three times daily. Digital photographs and measurements of the hemangiomas were taken at one-month intervals for a maximum of 6 months. RESULTS: The age range in this study was from 1–12 months with a mean age of 6.10 ± 2.52 months. The majority of the patients 89 (61.4%) were between 1–6 months of age. Of the 145 patients, 89 (61.4%) showed an excellent response, 44 (30.3%) showed a good response, and 12 (8.3%) showed no response to the topical 0.5% timolol maleate hydrogel treatment. CONCLUSION: The use of topical 0.5% timolol maleate hydrogel is a promising therapeutic option for the treatment of superficial IHs. Anwar F, Mahmood E, Sharif S, et al. Topical application of 0.5% timolol maleate hydrogel for the treatment of superficial infantile hemangiomas. J Drugs Dermatol. 2023;22(6):594-598. doi:10.36849/JDD.7054.

Joint Communication and Radar Sensing: RF Hardware Opportunities and Challenges-A Circuits and Systems Perspective.

This paper focuses on the topic of joint communication and radar sensing (JCRS) and its applications in the scope of upcoming sixth-generation (6G) technology. While the fifth-generation applications have reached the consumer market in the last few years, JCRS has been identified as one of the key technologies for next generation networks. The role of JCRS will vary, ranging from tasks such as radar coordination, context awareness for communication, enhanced security, increased availability, and improving the resilience and trustworthiness of future networks. In this work, the niche of JCRS technology in the future 6G ecosystem, as well as several potential applications, are discussed with a focus on RF hardware. The use of centimeter (cmWave) and millimeter (mmWave) frequency spectrums in the context of JCRS system implementation have been further elaborated. After presenting the near-term application scenarios, the circuit implementation perspectives are investigated in terms of radio frequency (RF) front-end architectures, antenna implementation, and phased arrays. Different communication and radar antenna options are compared, and the best candidates are identified. The packaging options are also presented. From circuit and system perspectives, link budget and self-interference cancellation (SIC) are highlighted. Furthermore, future directions including the next steps on the path to enabling JCRS technology are presented throughout this article. Prior works focused more on physical layers and network capabilities of JCRS systems, with less focus on hardware possibilities; to fill this gap, this article aims to contribute to this exciting research topic with a holistic review of RF hardware, highlighting the diversity of applications and the available technologies to tackle the near- and long-term needs of consumer applications.

Deep Eutectic Solvent Coated Cerium Oxide Nanoparticles Based Polysulfone Membrane to Mitigate Environmental Toxicology.

In this study, ceria nanoparticles (NPs) and deep eutectic solvent (DES) were synthesized, and the ceria-NP's surfaces were modified by DES to form DES-ceria NP filler to develop mixed matrix membranes (MMMs). For the sake of interface engineering, MMMs of 2%, 4%, 6% and 8% filler loadings were fabricated using solution casting technique. The characterizations of SEM, FTIR and TGA of synthesized membranes were performed. SEM represented the surface and cross-sectional morphology of membranes, which indicated that the filler is uniformly dispersed in the polysulfone. FTIR was used to analyze the interaction between the filler and support, which showed there was no reaction between the polymer and DES-ceria NPs as all the peaks were consistent, and TGA provided the variation in the membrane materials with respect to temperature, which categorized all of the membranes as very stable and showed that the trend of stability increases with respect to DES-ceria NPs filler loading. For the evaluation of efficiency of the MMMs, the gas permeation was tested. The permeability of CO2 was improved in comparison with the pristine Polysulfone (PSF) membrane and enhanced selectivities of 35.43 (αCO2/CH4) and 39.3 (αCO2/N2) were found. Hence, the DES-ceria NP-based MMMs proved useful in mitigating CO2 from a gaseous mixture.

Covid-19 vaccines and investment performance: Evidence from equity funds in European Union.

In this paper, we analyze the impact of the marketing authorization of EMA-approved vaccines on the returns of equity funds in the EU. Using the event study methodology, we report that the introduction of all vaccines had a positive impact on the funds' returns. Higher abnormal returns were associated with the earlier vaccines, indicating the first-mover advantage and the abnormal returns were persistent across several event windows. The findings imply that equity markets welcomed the vaccine administration as an important pharmaceutical intervention to support the quasi-revival of business activities. Consequently, there was a significant uplift in the economic bottom line.

TGF-ß1 signaling can worsen NAFLD with liver fibrosis backdrop.

Non-Alcoholic Fatty Liver Disease (NAFLD) is characterized by the accumulation of fats in the liver. Relatively benign NAFLD often progresses to fibrosis, cirrhosis, and liver malignancies. Although NAFLD precedes fibrosis, continuous lipid overload keeps fueling fibrosis and the process of disease progression remains unhindered. It is well known that TGF-ß1 plays its part in liver fibrosis, yet its effects on liver lipid overload remain unknown. As TGF-ß1 signaling has been increasingly attempted to manage liver fibrosis, its actions on the primary suspect (NAFLD) are easily ignored. The complex interaction of inflammatory stress and lipid accumulation aided by mediators scuh as pro-inflammatory interleukins and TGF-ß1 forms the basis of NAFLD progression. Anticipatorily, the inhibition of TGF-ß1 signaling during anti-fibrotic treatment should reverse the NAFLD though the data remain scattered on this subject to date. TGF-ß1 signaling pathway is an important drug target in liver fibrosis and abundant literature is available on it, but its direct effects on NAFLD are rarely studied. This review aims to cover the pathogenesis of NAFLD focusing on the role of the TGF-ß1 in the disease progression, especially in the backdrop of liver fibrosis.

Age in combination with gender is a valuable parameter in differential diagnosis of solid pseudopapillary tumors and pancreatic neuroendocrine neoplasm.

BACKGROUND: The clinicopathological characteristics of solid pseudopapillary tumor (SPT) and pancreatic neuroendocrine neoplasm (pNEN) are different. We, therefore, systematically investigated the performance of the clinicopathological characteristics in distinguishing SPT from pNEN. METHODS: We collected the cases from the Surveillance, Epidemiology, and End Results Program. The International Classification of Diseases for Oncology, third edition (ICD-O-3) for tumors was used to identify patients with pNEN or patients with SPT. To determine the performance of age in combination with gender in distinguishing SPT from pNEN, a nomogram was developed and the performance of this nomogram was evaluated by the receiver operating characteristic curve and the area under the curve (AUC). RESULTS: In the training cohort, 563 patients with pNENs and 30 patients with SPTs were recruited. The logistic regression and receiver operating characteristic curves suggest that age, gender, T-stage, N-stage, and M-stage could discriminate SPT and pNEN. The AUC of age, gender, T-stage, N-stage, and M-stage was 0.82, 0.75, 0.65, 0.69, and 0.70, respectively. Based on the nomogram, we observed that the AUC of age and gender is significantly high than that of the T-stage, N-stage, and M-stage. CONCLUSIONS: The present study proposes a non-invasive nomogram that could aid in the differential diagnosis of pNEN and SPT. This might help the clinicians to distinguish SPT from pNEN and choose the appropriate treatments for the patients.

Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks.

The seamless operation of inter-connected smart devices in Internet of Things (IoT) wireless sensor networks (WSNs) requires consistently available end-to-end routes. However, the sensor nodes that rely on a very limited power source tend to cause disconnection in multi-hop routes due to power shortages in the WSNs, which eventually results in the inefficiency of the overall IoT network. In addition, the density of the available sensor nodes affects the existence of feasible routes and the level of path multiplicity in the WSNs. Therefore, an efficient routing mechanism is expected to extend the lifetime of the WSNs by adaptively selecting the best routes for the data transfer between interconnected IoT devices. In this work, we propose a novel routing mechanism to balance the energy consumption among all the nodes and elongate the WSN lifetime, which introduces a score value assigned to each node along a path as the combination of evaluation metrics. Specifically, the scoring scheme considers the information of the node density at a certain area and the node energy levels in order to represent the importance of individual nodes in the routes. Furthermore, our routing mechanism allows for incorporating non-cooperative nodes. The simulation results show that the proposed work gives comparatively better results than some other experimented protocols.

An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks.

Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.

COVID-19 impact on multifractality of energy prices: Asymmetric multifractality analysis.

This article assesses the asymmetric multifractality of the energy prices in the different periods during the coronavirus pandemic (COVID-19) through asymmetric multifractality detrended fluctuation analysis. The higher (lower) multifractality shows a rapid rise (fall), which has different consequences for the energy prices. The findings explore strong multifractality in the downward movements for crude oil, heating oil, diesel, gasoline, propane and kerosene oil returns. The upside multifractality for coal and natural gas returns are bigger than the downside in both periods. Furthermore, the access asymmetry is more pronounced during the COVID-19, implying increased market inefficiency. The outcomes explore if energy prices are inefficient during the pandemic. A special attention is required in order to observe such unexpected fluctuations in the price dynamic and guidelines are vital. The level of efficiency can be improved by a greater transference in information while the government must play its role in regulations. Such aspects can increase stability and decrease the expected risks and price movements.

Save the environment, get financing! How China is protecting the environment with green credit policies?

Green credit policy (GCP) can achieve economic growth and environmental conservation, notably by lowering air pollutants (PM2.5). Green credit is a significant component of China's green financing for environmental regulation and achieving carbon neutrality. In this paper, to understand the causal relationship between GCP and PM2.5, we apply a bootstrap full-sample Granger causality test, parameter stability test, and quantile-on-quantile test for the period between 2003:M01 to 2019:M12. The result shows a bidirectional relationship and reveals that GCP has varied environmental implications in its early and mature stages because of a low percentage of green credit and a lack of motivation for financial institutions. In the long run, GCP and PM2.5 interaction confirm the favorable effects of GCP on PM2.5 as the green credit system improves. For robustness, we used quantile-based granger causality to evaluate the causative link between GCP and PM2.5. In light of the findings, this research advises legislative reforms and stresses the relevance of green credit in improving air quality. This study adds additional evidence that air pollution affects green credit policies. Air quality being a leading indicator helps firms anticipate changes in bank credit preferences and alter financing techniques.