Present Research Research Articles

Agro-industrial waste management employing benefits of artificial intelligence.

By 2050, the world's population is predicted to reach over 9 billion, which requires 70% increased production in agriculture and food industries to meet demand. This presents a significant challenge for the agri-food sector in all aspects. Agro-industrial wastes are rich in bioactive substances and other medicinal properties. They can be used as a different source for manufacturing products like biogas, biofuels, mushrooms, and tempeh, the primary ingredients in various studies and businesses. Increased importance is placed on resource recovery, recycling, and reusing (RRR) any waste using advanced technology like IoT and artificial intelligence. AI algorithms offer alternate, creative methods for managing agro-industrial waste management (AIWM). There are contradictions and a need to understand how AI technologies work regarding their application to AIWM. This research studies the application of AI-based technology for the various areas of AIWM. The current work aims to discover AI-based models for forecasting the generation and recycling of AIWM waste. Research shows that agro-industrial waste generation has increased worldwide. Infrastructure needs to be upgraded and improved by adapting AI technology to maintain a balance between socioeconomic structures. The study focused on AI's social and economic impacts and the benefits, challenges, and future work in AIWM. The present research will increase recycling and reproduction with a balance of cost, efficiency, and human resources consumption in agro-industrial waste management.

Empowering children for better health with child-to-child approach: a systematic literature review

PurposeChild-to-Child (C-to-C) approach is an evidence-based approach that ensures the child’s participation in bringing about positive changes in healthcare. By systematically investigating the literature, the study aimed to evaluate the effectiveness of the approach and recognize associated themes, methodologies and outcomes.Design/methodology/approachAn extensive search on PubMed, ProQuest, Cochrane and Dimensions AI databases was performed for original research articles on C-to-C intervention, with no time and geographical restrictions. Following PRISMA and PICO, the Joanna Briggs Institute (JBI) critical appraisal tool assessed the studies. A data tabulation technique was used to summarize these studies.FindingsThe approach shows promising results in enhancing children’s understanding of health issues and their participation in community health promotion. This, in turn, encourages the adoption of better healthcare practices and shows improved health outcomes.Research limitations/implicationsFurther research is required to understand the long-term impact of the approach on populations from diverse socioeconomic statuses in different study settings.Practical implicationsThe findings will be helpful for practitioners, educators, policymakers and other stakeholders in creating more successful and effective C-to-C intervention programs to make informed decisions, achieve sustainable behavior change and improve health.Originality/valueThe originality of this review paper is evident in its unique focus on the C-to-C approach, which empowers children not only as recipients of health education but also as active contributors to promoting health. Further, the present research also explores the intricacies of how children learn from each other, offering new insights into effective educational practices.

Experimental and numerical settlement analysis of railway track over geogrid reinforced ballast

PurposeThe main objective of the present research is to investigate the benefits of using geogrid reinforcement in minimizing the rate of deterioration of ballasted rail track geometry resting on soft clay and to explore the effect of load amplitude, load frequency, presence of geogrid layer in ballast layer and ballast layer thickness on the behavior of track system. These variables are studied both experimentally and numerically. This paper examines the effect of geogrid reinforced ballast laying on a layer of clayey soil as a subgrade layer, where a half full scale railway tests are conducted as well as a theoretical analysis is performed.Design/methodology/approachThe experimental tests work consists of laboratory model tests to investigate the reduction in the compressibility and stress distribution induced in soft clay under a ballast railway reinforced by geogrid reinforcement subjected to dynamic load. Experimental model based on an approximate half scale for general rail track engineering practice is adopted in this study which is used in Iraqi railways. The investigated parameters are load amplitude, load frequency and presence of geogrid reinforcement layer. A half full-scale railway was constructed for carrying out the tests, which consists of two rails 800 mm in length with three wooden sleepers (900 mm × 90 mm × 90 mm). The ballast was overlying 500 mm thick clay layer. The tests were carried out with and without geogrid reinforcement, the tests were carried out in a well tied steel box of 1.5 m length × 1 m width × 1 m height. A series of laboratory tests were conducted to investigate the response of the ballast and the clay layers where the ballast was reinforced by a geogrid. Settlement in ballast and clay, was measured in reinforced and unreinforced ballast cases. In addition to the laboratory tests, the application of numerical analysis was made by using the finite element program PLAXIS 3D 2013.FindingsIt was concluded that the settlement increased with increasing the simulated train load amplitude, there is a sharp increase in settlement up to the cycle 500 and after that, there is a gradual increase to level out between, 2,500 and 4,500 cycles depending on the load frequency. There is a little increase in the induced settlement when the load amplitude increased from 0.5 to 1 ton, but it is higher when the load amplitude increased to 2 ton, the increase in settlement depends on the geogrid existence and the other studied parameters. Both experimental and numerical results showed the same behavior. The effect of load frequency on the settlement ratio is almost constant after 500 cycles. In general, for reinforced cases, the effect of load frequency on the settlement ratio is very small ranging between 0.5 and 2% compared with the unreinforced case.Originality/valueIncreasing the ballast layer thickness from 20 cm to 30 cm leads to decrease the settlement by about 50%. This ascertains the efficiency of ballast in spreading the waves induced by the track.

Identifying physiological indicators of the cognitive, thermal, and combined (cognitive-thermal) stress conditions.

Physiologically based stress detection systems have proven to be effective in identifying different stress conditions in the body to determine the source of stress and be able to counteract it. However, some stress conditions have not been widely studied, including thermal stress, cognitive stress, and combined (thermal-cognitive) stress conditions, which are frequently encountered in work or school environments. In order to develop systems to detect and differentiate these conditions, it is necessary to identify the physiological indicators that characterize each of them. The present research aims to identify which physiological indicators (heart rate, respiratory rate, galvanic skin response, and local temperature) could differentiate different stress conditions (no-stress, cognitive stress, thermal stress, and combined (thermal-cognitive) stress conditions). Thirty participants were exposed to cognitive, thermal, and combined stress sources while recording their physiological signals. The findings indicate that both mean heart rate and mean galvanic skin response identify moderate thermal and cognitive stress conditions as distinct from a no-stress condition, yet they do not differentiate between the two stress conditions. Additionally, heart rate uniquely identifies the cognitive-thermal stress condition, effectively distinguishing this combined stress condition from the singular stress conditions and the no-stress condition. Mean local temperature specifically signals thermal stress conditions, whereas mean respiratory rate accurately identifies cognitive stress conditions, with both indicators effectively separating these conditions from each other and from the no-stress condition. This is the first basis for differentiating thermal and cognitive stress conditions through physiological indicators.

Mental Health in Institutionalized Older Adults: Influence of Emotional Intelligence on Psychological Well-Being

The present research work is grounded in the question: "Is there a significant relationship between emotional intelligence and psychological well-being in older adults?" In response to this, the hypothesis posits the existence of a significant relationship between emotional intelligence and psychological well-being in older adults. Consequently, the general objective of this study was to determine if there is a significant relationship between emotional intelligence and psychological well-being in older adults residing in a nursing home. This was accomplished through a Quantitative Correlational research design, using a sample of 85 older adults, encompassing both men and women, aged 60 years and older, residing in a nursing home. The assessment tools employed included Emily Sterret's Self-Assessment Test of Emotional Intelligence, which was adapted for use in Peru by Lorena Valdez in 2018, and Ryff's Psychological Well-Being Scale, originally created in the United States in 1995 and subsequently adapted for use in Peru by Madelinne Pérez-Basilio in 2017. The results revealed that in the realm of emotional intelligence, the predominant level was "optimal," accounting for 51%, followed by "outstanding," at 49%. Concerning psychological well-being, there was a predominance of the "high" level, comprising 61%, with 39% falling into the "medium" category. In conclusion, there was a strong correlation coefficient between emotional intelligence and psychological well-being, with r = 0.831** and significance at p < 0.01. This implies that higher emotional intelligence corresponds to greater psychological well-being among older adults residing in a nursing home.   Received: 7 September 2023 / Accepted: 9 April 2024 / Published: 5 May 2024

Through the eyes of grandparents: an in-depth exploration of the nexus between grandchild caring and the psychological well-being of older grandparents

BackgroundGrandchild caring has positive as well as negative impact on the grandparents’ psychological well-being and the findings are varied by culture and country.MethodsPresent study was intended to understand the relationship between caring for grandchildren and psychological well-being of grandparents living in skipped (SGH) and multi-generational households (MGH) in Indian demographical context. The present research involved In-depth Interviews (IDI) focusing on grandparents above 60 and grandchildren below 18, where the elder played a crucial role in caregiving. The study area was Malda, a district of West Bengal in India. Purposively 24 IDIs were selected. Psychological well-being was measured using open-ended questions. Thematic and content analyses were adopted to understand the perspective of grandparents.ResultsMost of the grandparents from SGH reported depression word frequently, while grandparents from MGH reported happy. In the content analysis, grandparents from SGH expressed tension, mental turmoil, and worry about grandchild’s future. On the contrary, grandparents from MGH expressed happy, companionship, and worry about grandchild’s future. Further, full time caring, compulsive reason behind grandchild caring, and working status were linked with living in SGH and grandchild caring, which were in turn connected with deteriorate psychological health. However, in MGH, a different scenario was observed, most grandparents were partially and non-compulsively engaged in grandchild caring and had expressed positive mental health.ConclusionsThe Findings provide an intervention implication, particularly in the context of India’s ageing population and their well-being by acknowledging the influence of household structure, caring intensity, motive behind grandchild caring, and working status on their psychological health. Understanding the importance of these key factors may help the policy maker and the individual to incorporate the most effective intervention to achieve sustainable development goal 3 and healthy ageing.

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Fear of Missing Out and Problematic Usage of Smartphones Concerning Adolescents

The development of technology and increase of common usage of mobile phone in recent years has caused the desire of the individual to share their lives with other individuals and the desire to be aware of their lives, that is, the fear of missing out regarding developments. By considering that the fear of missing out (fomo) and problematic cell phone use will affect adolescents, current study aimed to analyze the fomo and problematic mobile phone use of adolescents with regard to various variables. In present research, descriptive research design was conducted. The data of current study were collected with the fomo scale and the problematic smartphone usage scale. It was carried out with 673 adolescents who have been studying at high school. The results revealed that the fomo differs significantly in adolescents in terms of gender and class level and it was concluded that problematic smartphone usage differs significantly with regard to father education level, gender and class level. Moreover, it has been concluded that problematic mobile phone use did not differ with regard to mother and father education level, age, high school type and family income level. Additionally, the results of current research revealed that the fomo and problematic smartphone usage are positively and significantly related with each other regarding adolescents.

Students’ perceptions on different sources of self-feedback

ABSTRACT Feedback is crucial for improving student learning. In this regard, overcoming the transmissive conception of feedback in favour of its dialogic function introduces new reflections concerning the internal generative feedback process. In this regard, Nicol [(2020). The power of internal feedback: Exploiting natural comparator processes. Assessment & Evaluation in Higher Education, 46(5), 756–778. https://doi.org/10.1080/02602938.2020.1823314; (2021). Guiding learning by activating students’ inner feedback. Times Higher Education] outlines the concept of self-feedback, i.e., the continuous comparative process that students produce from various external information and sources triggering internal feedback. In this context, the present research aims to explore students’ perceptions concerning the activation of internal/self-feedback, investigating which sources are most effective from their point of view, and researching the relationships between different individual characteristics. The results show that students’ individual differences and contextual cultural factors might play a crucial role in creating ideal environments to enable the generation of self-feedback. Finally, students consider the teacher to be most effective in activating self-feedback, and many have never experienced self-feedback or automated feedback situations.

Influence of metals on lignocellulolytic enzyme production and metal removal by Phlebia brevispora in a wheat straw based fixed‐bed bioreactor

AbstractHeavy metals contamination poses a significant environmental threat, which requires the development of eco‐friendly bioremediation techniques. Present research work was conducted using wheat straw in submerged conditions for the growth of white rot fungus, Phlebia brevispora in the presence of varying concentrations of Cd, Pb, Ni, and Cr. The lignocellulolytic enzyme production ability of fungus was monitored. A wheat straw‐based fixed‐bed bioreactor was designed to treat metal‐containing water in continuous mode. The fungus showed a positive influence on laccase production, cellulase production, and lignin peroxidase activity. As observed through atomic absorption spectroscopy (AAS), Cr removal efficiency was 78%–80%, regardless of the initial metal concentration. The Cr was also found on the surface of fungal mycelium as per the results obtained from SEM–EDX. The continuous bioreactor achieved 98%–99% metal removal, making it a natural and cost‐effective solution for metal removal from wastewater using P. brevispora.

Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC)

AbstractPortland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders.

An enhanced MADM with Lq* q-Rung orthopair multi-fuzzy soft set in healthcare supplier selection

The global healthcare systems have encountered unparalleled difficulties due to the COVID-19 pandemic, underscoring the crucial significance of effective management within healthcare supply chains. This research contributes to the field of healthcare supply chain management by presenting a robust MADM methodology called lattice ordered(Lq*) q-rung orthopair multi-fuzzy soft set(Lq* q-ROMFS-MADM) for supplier evaluation and ranking amidst the challenges posed by the COVID-19 pandemic. Taking inspiration from multi-fuzzy soft set and q-rung orthopair fuzzy set, the present research article proposes a novel framework known as Lq* q-rung orthopair multi-fuzzy soft set (Lq* qROMFSS), which incorporates lattice ordering in q-rung orthopair multi-fuzzy soft set. The effectiveness of the proposed model is confirmed through successful experimentation on various important operations, including union, intersection, complement, restricted union and intersection. Moreover, the verification of De Morgan’s laws for Lq* qROMFSS is carried out specifically for these operations mentioned above. To highlight the significance of the proposed Lq* qROMFSS, a multi-attribute decision-making (MADM) problem is presented, showcasing its application in the domain of healthcare supply chain management. Furthermore, a comparative analysis is conducted to elucidate the advantages of this model in comparison to existing models.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Effect of Preoperative Administration of Dutasteride on Reducing Blood Loss in Perioperative Period of Transurethral Resection of Prostate

Background and Objective: Clinical benign prostatic hyperplasia (BPH), a condition that commonly occurs in aging men. It is characterized by the development of lower urinary tract symptoms (LUTS), which can lead to impairment of bladder function and potential renal complications. TURP is the gold standard surgical treatment of BPH. Dutasteride is a 5-reductase and is used for medical management. This study was planned to ascertain the effect of pre-treatment dutasteride in reducing blood loss in patients undergoing transurethral resection of prostate (TURP).Methods: It was a randomized clinical trial carried out at Department of Urology, Jinnah Hospital Lahore in which the total study sample was divided in two groups. One group received dutasteride and the second group was the control group. The study variables were Haemoglobin and Hematocrit values, clotting profile PT and INR, serum Ca levels and uric acid, BMI, prostate volume, residual volume of urine and hospital stay. Results were presented as mean ± SD or frequency (%). The main study outcome variable was mean blood loss (calculated by change in hemoglobin and hematocrit level).Results: 6 (10.3 %) patients needed blood transfusion in dutasteride group and 7 (12 %) in control group (p=0.14). Post-surgery change in haemoglobin and HCT levels were less in dutasteride groups as compared to the control group (ΔHb 1.94±1.27 g/dL vs. 2.16±0.73 g/dL, respectively, p=0.02; ΔHct 5.68 % ± 2.56% vs. 6.52 % ± 2.39 %, respectively, p = 0.04).Conclusion: The present research suggests that administering Dutasteride four weeks prior to the TURPprocedure results in a decrease in blood loss during the peri-operative period, ultimately leading to improved patient outcomes.Key words: Benign Prostatic Hyperplasia (BPH). Transurethral resection of Prostate (TURP), 5 Alpha Reductase Inhibitor (5-ARI)  

Synthesized Gold Nanoparticles with Moringa Oleifera leaf Extract Induce Mitotic Arrest (G2/M phase) and Apoptosis in Dalton's Lymphoma Cells.

The therapeutic potential of chemically synthesized AuNPs has been demonstrated in various types of cancer. However, gold nanoparticles (AuNPs) synthesized using typical chemical methods have concerns regarding their environmental safety and adverse impact on human well-being. To overcome this issue, we used an environmentally friendly approach in which gold nanoparticles were synthesized using Moringa oleifera leaf extract (MLE). The present research was mainly focused on the biosynthesis and characterization of gold nanoparticles (AuNPs) using Moringa oleifera leaf extract (MLE-AuNPs) and explore its anticancer potential against Dalton's Lymphoma (DL) cells. Characterization of the MLE-AuNPs was conducted using UV-Vis Spectroscopy to confirm the reduction process, FTIR analysis to ascertain the presence of functional groups, and XRD analysis to confirm the crystallinity. SEM and TEM images were used to examine size and morphology. After characterization, MLE-AuNPs were evaluated for their cytotoxic effects on Dalton's lymphoma cells, and the results showed an IC50 value of 75 ± 2.31 µg/mL; however, there was no discernible cytotoxicity towards normal murine thymocytes. Furthermore, flow cytometric analysis revealed G2/M phase cell cycle arrest mediated by the downregulation of cyclin B1 and Cdc2 and upregulation of p21. Additionally, apoptosis induction was evidenced by Annexin V Staining, accompanied by modulation of apoptosis-related genes including decreased Bcl-2 expression and increased expression of Bax, Cyt-c, and Caspase-3 at both the mRNA and protein levels. Collectively, our findings underscore the promising anti-cancer properties of MLE-AuNPs, advocating their potential as a novel therapeutic avenue for Dalton's lymphoma.

Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network

Abstract The present research develops and verifies a simpler numerical approach for analyzing the thermal transient state of rehabilitated concrete beams reinforced with various types of FRP (fiber-reinforced polymer) subjected to high temperatures and specifically built as under-reinforced concrete beams. This approach offers a straightforward, efficient, and exact instrument for numerical analysis. The proposed analytical technique has been validated by load-displacement curves and cross-section temperature data, indicating its dependability and practicality. Subsequently, the validated approach was used to examine the impact of significant variables on the outcome and restoration of FRP-reinforced concrete beams at high temperatures. The methodology gives the Comparing conventional and CFRP, GFRP, AFRP reinforced beams using beam, truss, and shell elements. Thermal and UDL loads were applied, mesh at 25mm x 25mm. Transient analysis contrasts performance via displacement and temperature. The temperature vs. displacement curve shows the FRP comparisons. Identifying the critical temperature before failure is crucial, emphasizing the curve's significance in assessing structural performance and potential failure points. Nodal temperatures ranged 939-963°C (rehabilitated) vs 958°C (conventional). 200°C difference affects thermal boundary conditions for structural analysis and Conventional peaks at 320°C, while AFRP, GFRP, and CFRP reach 358°C, 385°C, and 390°C respectively. CFRP lasts 2400 minutes. Neural network models demonstrate effective generalizability, enabling satisfactory predictions of RC beam rehabilitation with CFRP laminates within the study's parameter range.

Let Them Shine: Associations of Schools’ Support for Strengths Use with Teachers’ Sense of Meaning, Engagement, and Satisfaction

The present research builds on the psychology of sustainability and sustainable development and on research demonstrating the contribution of perceived organizational support for strengths use (POSSU) to the quality of employees’ work life. Specifically, we focus on teachers, whose training and development in schools often aim to narrow competence gaps and correct deficits. We propose that focusing on the development of their strengths may more positively impact their engagement, satisfaction, and sense of meaning at work. A total of 47 school principals (30 women; Mage = 48.37, SD = 7.31) and 235 of their teachers (197 women; Mage = 40.73, SD = 7.78) reported perceptions of their schools’ organizational support for strengths use (POSSU), and of their school support for deficit correction (POSDC). In addition, teachers completed measures of their strengths use, sense of meaning at work, work engagement, and job satisfaction. Teachers’ POSSU was associated (more strongly than POSDC) with teachers’ strength use and positive work-related well-being. Teachers’ strengths use mediated the associations of POSSU with the other variables. Principals’ POSSU was not associated with teachers’ POSSU or with teachers’ strengths use, but was associated with teachers’ sense of meaning and satisfaction at work. The findings highlight the potential benefits of a strengths-supporting school culture to teachers’ work-life quality.

Green synthesized AgNPs as a probe for colorimetric detection of Hg (II) ions in aqueous medium and fluorescent imaging in liver cell lines and its antibacterial activity

The present research aimed at green synthesis of Ag nanoparticles (AgNPs) based colorimetric sensor using persimmon leaf extract (PLE) for selective detection of mercuric ion (Hg2+). Optimization of reaction conditions viz. pH, concentration of PLE, time was done and further AgNPs were characterized using UV, IR, FE-SEM, EDX, XRD and TEM analysis. The developed AgNPs were evaluated for the selective colorimetric detection of Hg2+ in aqueous medium and fluorescence imaging of Hg2+ ions in liver cell lines. Later, the antibacterial activity of AgNPs was performed against S. aureus and E. coli. The findings of the study revealed that PLE mediated AgNPs exhibited notable limit of detection up to 0.1 ppb, high efficiency, and stability. The antibacterial study indicated that developed AgNPs has impressive bacterial inhibiting properties against the tested bacterial strains. In conclusion, developed biogenic AgNPs has high selectivity and notable sensitivity towards Hg2+ ions and may be used as key tool water remediation.Graphical abstract

Impact of different irrigation & trace metals treatments on onion (Allium cepa L.) plant growth cultivated in rural and urban soils

AbstractThe present research attempts to evaluate the response of Allium cepa L. to different irrigation treatments and to indicate the optimum scheme along with plant growth, throughout a pot experiment during Spring 2021. The experimental procedure consisted of two different soil types, three treatments of irrigation and two levels of Cu and Zn (low and high), in four replications each. Irrigation events started when the lower allowable limit (LAL) reached a defined percentage of filed capacity (FC): 40% FC, 60% FC, and 75% FC and an irrigation event occurred with irrigation doses (Dn) equal to 60% FC, 40% FC, and 25% FC, respectively, to reach the value of FC. According to the results, the minimum yield was achieved by both soil types when Cu and Zn solution concentration and water stress were at their highest levels, although light texture soil (loamy sand—LS) allowed for superior growth. The optimum scheme was: the lower concentration of Cu and Zn solution along with LAL equal to 60% FC at the loamy sand soil. The outcomes suggest that frequent short irrigation doses at light-texture soils can result in yield response indicators when planted in pots. Furthermore, the influence of Cu and Zn cations at low concentrations can be advantageous for onions because Cu cations provide protection against fungal diseases, while Zn cations serve as nutrient support reducing the risk of metals deficiency.

Negative life events and college students’ adjustment: the mediating role of self-esteem and the moderating role of grade

The present research explored the relationship among negative life events, self-esteem, grade, and adjustment of college students. In total, 1,717 college students were investigated by the adolescent life events scale, Rosenberg self-esteem inventory, and Chinese college students’ adjustment scale. The results showed that negative life events were negatively correlated with self-esteem and college students’ adjustment. Moreover, self-esteem was positively correlated with college students’ adjustment. Negative life events significantly negatively predicted college students’ adjustment, and self-esteem mediated the relationship between negative life events and college students’ adjustment. Grade moderated the effects of negative life events on college students’ adjustment. These findings have broad implications for college students’ mental health.