Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater

43-50The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions

Potential Use of Agro/Food Wastes as Biosorbents in the Removal of Heavy Metals

The production of large quantities of agro/food wastes from food processing industries and the release of pollutants in the form of heavy metals from various metallurgical industries are the grave problems of the society as well as serious threats to the environment. It is estimated that approximately one–third of all food that is produced goes to waste, meaning thereby that nearly 1.3 billion tonnes of agro/food wastes are generated per year. This readily available and large amount waste can be utilized for the removal of toxic metals obtained from metallurgical industries by converting it into the adsorbents. For example, mango peel showed adsorption capacity of 68.92 mg/g in removing cadmium II ions. Similarly, coconut waste showed a higher adsorption capacity of 285 and 263 mg/g in removing cadmium and lead ion, respectively. Biosorption and bioaccumulation are recommended as novel, efficient, eco-friendly, and less costly alternative technologies over the conventional methods such as ion exchange, chemical precipitation, and membrane filtration, etc. for the removal of toxic metal ions. Because of the presence of metal-binding functional groups, the industrial by-products, agro-wastes and microbial biomass are considered as the potential biosorbents. Thus they can be used for the removal of toxic metal ions. This chapter highlights the available information and methods on utilizing the agro/food waste for the eradication of toxic and heavy metal ions. Furthermore, this chapter also focuses on the sorption mechanisms of different adsorbents as well as their adsorbing capacities

Antiviral evaluation of an Hsp90 inhibitor, gedunin, against dengue virus

Purpose: To evaluate the antiviral potential of a tetranortriterpenoid, gedunin, against dengue virus (DENV) replication by targeting the host chaperone, Hsp90.Methods: The compound, gedunin, was tested against the replication of DENV in vitro using BHK-15 cells transfected with DENV-2 subgenomic replicon. Molecular docking of gedunin with Hsp90 protein was performed for evaluation of mode of action, using the program, Autodock vina.Results: In vitro antiviral data showed that gedunin significantly (p < 0.05) reduced DENV replication with EC50 of 10 μM. Further, in silico molecular docking data revealed strong interaction of gedunin with the ATP/ADP binding site of the host protein, Hsp90, with an estimated average free binding energy of -8.9 kcal/mol.Conclusion: The results validate gedunin as a potential antiviral candidate. Further in vitro assays and in vivo viral challenge studies are required to confirm the exact mode of action and pharmacological profile of gedunin in DENV infections.Keywords: Dengue virus replication, Hsp90, Gedunin, Antiviral, Molecular dockin

Removal of Heavy Metals from Wastewater with Special Reference to Groundnut Shells: Recent Advances

Wastewater contains organic pollutants and heavy metals which presents a significant threat to aquatic life and impacts human health and animals. In the past few years, the incomplete remediation of wastewater has made living beings suffer from various problems, and many health diseases are being noticed at a peak rate. Different methods have been employed to remove heavy metals from wastewater to date. However, the adsorption technique is the most efficient and eco-friendly for removing heavy metals and pollutants in wastewater remediation. Many agricultural wastes have been used as adsorbents for removing toxic pollutants and heavy metals from wastewater. Groundnut shell is widely considered agro-industrial waste. Groundnut shells account for nearly 20% of the dried peanut pod by weight, and millions of tons of its quantity are wasted every year. An increase in groundnut production leads to accumulating these groundnut shells in colossal quantities, which is not utilized; thus, they are either burnt or buried. Groundnut shells undergo slow degradation in the natural environment because they are rich in lignin content. Therefore, these shells can be converted into a valuable bio-product to produce less waste. Groundnut shells and groundnut shell-derived biochar act as good biosorbents in the wastewater treatment

Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater

The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions

Frequency of hepatitis E and Hepatitis A virus in water sample collected from Faisalabad, Pakistan

Hepatitis E and Hepatitis A virus both are highly prevalent in Pakistan mainly present as a sporadic disease. The aim of the current study is to isolate and characterized the specific genotype of Hepatitis E virus from water bodies of Faisalabad, Pakistan. Drinking and sewage samples were qualitatively analyzed by using RT-PCR. HEV Genotype 1 strain was recovered from sewage water of Faisalabad. Prevalence of HEV and HAV in sewage water propose the possibility of gradual decline in the protection level of the circulated vaccine in the Pakistani population

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications

The Big Catch-up: Addressing Zero-Dose Children as a Surrogate of Vaccination Disruptions During Public Health Emergencies

The COVID-19 pandemic has significantly disrupted global immunization programs, resulting in a sharp increase in the number of zero-dose children-those who have not received any vaccinations. This disruption poses a critical threat to public health, exacerbating the risk of vaccine-preventable disease outbreaks. This paper investigates the pandemic's impact on routine childhood immunization, with a particular focus on zero-dose children. Through a comprehensive review of data from WHO, UNICEF, Gavi, and key informant interviews, we highlight evidence-based interventions aligned with the strategic framework of the Zero Dose Guidelines. Our findings emphasize the importance of context-specific approaches, particularly in vulnerable settings such as urban slums, remote rural areas, and conflict zones. We identified key thematic areas for intervention: community engagement, health systems strengthening, and technological innovations. These strategies are critical for reaching zero-dose children and rebuilding resilient immunization systems. However, gaps remain in the evidence surrounding the long-term effectiveness and cost-efficiency of these interventions, especially in low- and middle-income countries. This study underscores the urgency of addressing the growing number of zero-dose children through coordinated global efforts like "The Big Catch-Up" campaign, which aims to recover and strengthen immunization coverage worldwide. By focusing on equity, innovation, and tailored strategies, we can mitigate the pandemic’s long-term effects and ensure that no child is left unprotected

Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices

The prevalent use of the Internet of Things (IoT) devices over the Sea, such as, on oil and gas platforms, cargo, and cruise ships, requires high-speed connectivity of these devices. Although satellite based backhaul links provide vast coverage, but they are inherently constrained by low data rates and expensive bandwidth. If a signal propagated over the sea is trapped between the sea surface and the Evaporation Duct (ED) layer, it can propagate beyond the horizon, achieving long-range backhaul connectivity with minimal attenuation. This paper presents experimental measurements and simulations conducted in the Industrial, Scientific, and Medical (ISM) Band Wi-Fi frequencies, such as 5.8 GHz to provide hassle-free offshore wireless backhaul connectivity for IoT devices over the South China Sea in the Malaysian region. Real-time experimental measurements are recorded for 10 km to 80 km path lengths to determine average path loss values. The fade margin calculation for ED must accommodate additional slow fading on top of average path loss with respect to time and climate-induced ED height variations to ensure reliable communication links for IoT devices. Experimental results confirm that 99% link availability of is achievable with minimum 50 Mbps data rate and up to 60 km distance over the Sea to connect offshore IoT devices