Influence of agro-wastes derived biochar and their composite on reducing the mobility of toxic heavy metals and their bioavailability in industrial contaminated soils.

The agro-waste derived valuable products are prime interest for effective management of toxic heavy metals (THMs). The present study investigated the efficacy of biochars (BCs) on immobilization of THMs (Cr, Zn, Pb, Cu, Ni and Cd), bioaccumulation and health risk. Agro-wastes derived BCs including wheat straw biochar (WSB), orange peel biochar (OPB), rice husk biochar (RHB) and their composite biochar (CB) were applied in industrial contaminated soil (ICS) at 1% and 3% amendments rates. All the BCs significantly decreased the bioavailable THMs and significantly (p < 0.001) reduced bioaccumulation at 3% application with highest efficiency for CB followed by OPB, WSB and RHB as compared to control treatment. The bioaccumulation factor (BAF), concentration index (CI) and ecological risk were decreased with all BCs. The hazard quotient (HQ) and hazard index (HI) of all THMs were <1, except Cd, while carcer risk (CR) and total cancer risk index (TCRI) were decreased through all BCs. The overall results depicted that CB at 3% application rate showed higher efficacy to reduce significantly (p < 0.001) the THMs uptake and reduced health risk. Hence, the present study suggests that the composite of BCs prepared from agro-wastes is eco-friendly amendment to reduce THMs in ICS and minimize its subsequent uptake in vegetables.

The present study has a scientific research scope, based on reduction of bioavailability and bioaccumulation of toxic heavy metals (THMs) by the addition of biochars derived from agro-wastes and their composite biochar (CB), thereby decreasing the potential health risk. Limited study has been conducted, especially on the impact of CB in THMs-contaminated soil. This study could fill the scientific research gap and provides useful information for mitigation of THMs present in contaminated soil, which could be followed by the Environmental Protection Agency, Ministry of Agriculture and farmers in degraded lands.

Comprehensive pan-cancer analysis reveals VSIR as a candidate immunologic, diagnostic, and prognostic biomarker.

OBJECTIVES: Being a checkpoint, the expression level of V-set immunoregulatory receptor (VSIR) serves as an indicator of the extent of immunosuppression. Our objective was to undertake a pan-cancer analysis to examine the expression, genetic alterations, prognosis, and immunologic features associated with VSIR. METHODS: The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), GEPIA2, UALCAN, OncoDB, Human Protein Atlas (HPA), STRING, DAVID, cell culture, clinical sample collection, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used. RESULTS: This study comprehensively assessed VSIR across 33 cancers using TCGA and GTEx databases. Differential expression analysis revealed elevated VSIR in several cancers, notably in cholangiocarcinoma, esophageal carcinoma, kidney renal cell carcinoma, and liver hepatocellular carcinoma, while decreased expression was observed in various others. Prognostic analysis highlighted its significant association with reduced overall survival (OS) in ESCA and LIHC. Investigation into cancer stages demonstrated a correlation between VSIR expression and stage in ESCA and LIHC. Promoter methylation analysis indicated decreased VSIR methylation levels in tumors, implicating a role in oncogenesis. Furthermore, subcellular localization predictions, Tumor Mutational Burden (TMB), and Microsatellite Instability (MSI) correlations revealed intriguing insight into VSIR's function. Notably, a positive correlation was identified between VSIR expression and various immune cells in both cancers. Protein-protein interaction (PPI) network construction and gene enrichment analysis elucidated VSIR-associated dysregulated pathways, emphasizing its possible involvement in diverse pathways. Finally, experimental validation using LIHC clinical samples and cell lines confirmed elevated VSIR expression, supporting its oncogenic role. CONCLUSION: Collectively, these findings present a comprehensive understanding of VSIR's diverse roles and potential clinical implications in ESCA and LIHC.

Force and power requirement for development of cumin harvester: a dynamic approach.

An experimental setup was developed for simulating the field conditions to determine the force and power required for cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward speeds, and blade type on cutting force and power requirement for cutting cumin were also studied. Experiments were carried out at three levels: cutter bar speeds, forward speeds, and blade type. The results showed that all the factors significantly affected cutting force. The cutting force followed a decreasing trend with the increase in cutter bar speed. Whereas it followed an increasing trend with the increase in forward speed. The maximum cutting force for all three blades was observed at a cutter bar speed of 2.00 strokes.s-1 and forward speed of 0.46 m.s-1. The idle power and actual power required for cutting the cumin crop were also determined based on the cutting force. The results obtained were validated by the power drawn from the power source while operating the cutter bar blades. The R2 values for Blade-B1, Blade-B2, and Blade-B3 were 0.90, 0.82, and 0.88, respectively. The cutting force was primarily affected by the cutter bar speed, resulting in PCR values of 74.20%, 82.32%, and 81.75% for Blade-B1, Blade-B2, and Blade-B3, respectively, followed by the forward speed, which also had an impact on PCR values of 16.60%, 15.27%, and 18.25% for Blade-B1, Blade-B2, and Blade-B3, respectively. The cutting force for Blade-B1, Blade-B2, and Blade-B3 varied from 15.96 to 58.97 N, 21.08 to 76.64 N, and 30.22 to 85.31, respectively, for the selected range of cutter bar speed and forward speed. Blade-B1 had 18 and 30% less power consumption than Blade-B2 and Blade-B3, respectively.

High-order LP modes based Sagnac interference for temperature sensing with an enhanced optical Vernier effect.

In this paper, high-order LP modes based Sagnac interference for temperature sensing are proposed and investigated theoretically. Based on the specific high-order LP modes excited through the mode selective couplers (MSCs), we design a stress-induced Panda-type few-mode fiber (FMF) supporting 4 LP modes and construct a Sagnac interferometer to achieve a highly sensitive temperature sensor. The performances of different LP modes (LP01, LP11, LP21, and LP02) are explored under a single Sagnac interferometer and paralleled Sagnac interferometers, respectively. LP21 mode has the highest temperature sensitivity. Compared with fundamental mode (LP01), the temperature sensitivity based on LP21 mode improved by 18.2% at least. In addition, a way to achieve the enhanced optical Vernier effect is proposed. It should be noted that two Sagnac loops are located in two temperature boxes of opposite variation trends, respectively. Both two Sagnac interferometers act as the sensing element, which is different from the traditional optical Vernier effect. The temperature sensitivity of novel enhanced optical Vernier effect is magnified by 8 times, which is larger than 5 times the traditional Vernier effect. The novel approach avoids measurement errors and improves the stability of the sensing system. The focus of this research is on high-order mode interference, which has important guiding significance for the development of highly sensitive Sagnac sensors.

High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.

Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leaves that exhibit similar colors to spike regions. Consequently, accurate detection of grain spikes renders, in general, a non-trivial task even for advanced, state-of-the-art deep neural networks (DNNs). To improve pattern detection in spikes, we propose architectural changes to Faster-RCNN (FRCNN) by reducing feature extraction layers and introducing a global attention module. The performance of our extended FRCNN-A vs. conventional FRCNN was compared on images of different European wheat cultivars, including "difficult" bushy phenotypes from 2 different phenotyping facilities and optical setups. Our experimental results show that introduced architectural adaptations in FRCNN-A helped to improve spike detection accuracy in inner regions. The mean average precision (mAP) of FRCNN and FRCNN-A on inner spikes is 76.0% and 81.0%, respectively, while on the state-of-the-art detection DNNs, Swin Transformer mAP is 83.0%. As a lightweight network, FRCNN-A is faster than FRCNN and Swin Transformer on both baseline and augmented training datasets. On the FastGAN augmented dataset, FRCNN achieved a mAP of 84.24%, FRCNN-A attained a mAP of 85.0%, and the Swin Transformer achieved a mAP of 89.45%. The increase in mAP of DNNs on the augmented datasets is proportional to the amount of the IPK original and augmented images. Overall, this study indicates a superior performance of attention mechanisms-based deep learning models in detecting small and subtle features of grain spikes.

Triticale field phenotyping using RGB camera for ear counting and yield estimation.

Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018-2019 and 2019-2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.

Unlocking the diagnostic, prognostic roles, and immune implications of BAX gene expression in pan-cancer analysis.

OBJECTIVES: Cancer, a formidable disease, continues to challenge our understanding and therapeutic approaches. This study delves into the pan-cancer analysis of BCL2 Associated X (BAX) gene expression, seeking to unravel its significance in cancer development, prognosis, and potential therapeutic strategies. METHODS: A combination of bioinformatics and molecular experiments. RESULTS: Our pan-cancer investigation into BAX expression encompassed 33 distinct cancer types, revealing a remarkable and uniform increase in BAX expression. This groundbreaking finding emphasizes the potential universality of BAX's role in cancer development and progression. Further, our study explored the prognostic implications of BAX expression, highlighting a consistent association between up-regulated BAX and poor overall survival (OS) in Liver Hepatocellular Carcinoma (LIHC) and Skin Cutaneous Melanoma (SKCM). These results suggest that BAX may serve as an adverse prognostic indicator in these malignancies, emphasizing the importance of personalized treatment strategies. Epigenetic and genetic analyses of BAX provided valuable insights. Hypomethylation of the BAX promoter region was evident in LIHC and SKCM, which likely contributes to the up-regulation of BAX, while genetic mutations in the BAX gene itself were infrequent in these cancers. Our exploration of BAX-associated signaling pathways and the correlation between BAX expression and CD8+ T cell infiltration shed light on the intricate molecular landscape of cancer. BAX's interaction with key apoptotic and immune-related pathways reinforces its role as a central player in tumor development and the immune microenvironment. Moreover, our drug prediction analysis identified potential therapeutic agents for modulating BAX expression in the context of LIHC and SKCM, bridging the gap between research and clinical application. CONCLUSION: In sum, our comprehensive BAX study not only enhances our understanding of its significance as a biomarker gene but also offers novel avenues for therapeutic interventions, contributing to the ongoing quest for more effective cancer treatments and improved patient care.

Do institutional contingencies matter for green investment?-An institution based view of Chinese listed companies.

Current research in the field of environmental management has placed significant emphasis on understanding the reasons behind varying organizational responses to environmental responsibilities. Governance scholars emphasize the central role of institutional factors in shaping environmental responsibilities, primarily due to the substantial influence exerted by regulatory institutions. Drawing on institutional theory, we investigate how sub-national institutional factors impact a firm's green investment intensity and explore their moderating influence on the relationship between green investment and a firm's financial performance. Using a database of Chinese listed companies from 2012 to 2019, this study employs fixed effect model as a baseline regression. Our analysis demonstrates that sub-national institutions, such as state-owned enterprises (SOEs), regional development, and cross-listing, have significant and positive impact on corporate green investment. Our study further provide an evidence that green investment significantly improve firms' financial performance. Moreover, the positive effect of green investment on financial performance is stronger in SOEs and in firms of developed regions as compared to their counterparts, and weaker in cross listed firms than those of non-cross listed peers. Our study suggest that subnational institutions play an imperative role in improving environmental quality and financial performance by promoting corporate green investment. To make sure that our findings remain robust to endogeneity, we applied generalized method of moments (GMM) and propensity score matching (PSM) method. Our findings further provide implications for emerging economies with similar shareholding patterns and unbalanced regional development.

PTPN3 in cancer: unveiling its immune-mediated impact on prognosis and dysregulated signaling pathways.

OBJECTIVES: Oncogenic processes in cancer are frequently marked by the dysregulation of critical genes, and PTPN3 (Protein Tyrosine Phosphatase, Non-Receptor Type 3) has emerged as a gene of interest due to its potential involvement in various cellular processes. This study delves into the diagnostic and prognostic implications of PTPN3 in a pan-cancer context. METHODS: Leveraging comprehensive genomic datasets and experimental validation, we aimed to shed light on the role of PTPN3 in cancer. RESULTS: Our findings revealed the pervasive up-regulation of PTPN3 across 33 cancer types, making it a ubiquitous player in tumorigenesis. Of particular note, PTPN3 up-regulation exhibited a strong association with reduced overall survival in breast cancer (BRCA) and lung adenocarcinoma (LUAD). This underscores PTPN3's potential as a valuable prognostic marker in these cancers. While genetic mutations often drive oncogenic processes, our mutational analysis demonstrated the relative stability of PTPN3 in BRCA and LUAD. Promoter methylation analysis showed that hypomethylation plays a predominant role in PTPN3 dysregulation in BRCA and LUAD. Furthermore, our study unveiled positive correlations between PTPN3 expression and CD8+ T cell infiltration, offering insights into the gene's influence on the tumor immune microenvironment. Pathway enrichment analysis highlighted the involvement of PTPN3-associated genes in crucial signaling pathways. In addition, drug prediction analysis pinpointed potential drugs capable of modulating PTPN3 expression, opening avenues for personalized treatment strategies. CONCLUSION: In summary, our study elucidates the multifaceted roles of PTPN3 in BRCA and LUAD, underlining its significant up-regulation, prognostic relevance, epigenetic regulation, and its impact on the tumor immune microenvironment.

Identification of useful biomolecular markers in kidney renal clear cell carcinoma: an in silico and in vitro analysis-based study.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) is the most prevalent type of renal cell carcinoma (RCC), with a high incidence and mortality rate. There is a lack of sensitive biomarkers. Therefore, the discovery of accurate biomarkers for KIRC patients is critical to improve prognosis. METHODS: We determined hub genes and their associated pathways involved in the pathogenesis of KIRC from the GSE66272 dataset consisting of KIRC (n = 26) and corresponding control (n = 26) samples and later validated the expression and methylation level of the identified hub genes on The Cancer Genomic Atlas (TCGA) datasets and Human RCC 786-O and normal HK-2 cell lines through RNA sequencing (RNA-seq), Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and targeted bisulfite sequencing (bisulfite-seq) analyses. RESULTS: The identified up-regulated four hub genes include TYROBP (Transmembrane Immune Signaling Adaptor TYROBP), PTPRC (Protein tyrosine phosphatase, receptor type, C), LCP2 (Lymphocyte cytosolic protein 2), and ITGB2 (Integrin Subunit Beta 2). Moreover, the higher expression of TYROBP, PTPRC, LCP2, and ITGB2 in KIRC patients insignificantly correlates with a poor prognosis in KIRC patients. In addition, hub genes were involved in the "Fc epsilon RI signaling pathway, asthma, natural cell killer mediated cytotoxicity, T cell receptor signaling pathway, primary immunodeficiency, Fc gamma R-mediated phagocytosis, malaria, leukocyte transendothelial migration, and legionellosis" pathways and associated with the infiltration level of CD8+ T, CD4+ T, and macrophage cells. CONCLUSION: Our integrated in silico and in vitro analysis identified important hub genes (TYROBP, PTPRC, LCP2, and ITGB2) involved in the pathogenesis of KIRC as possible diagnostic biomarkers.

Occurrence and Distribution of Heavy Metals in Mining Degraded Soil and Medicinal Plants: A Case Study of Pb/Zn Sulfide Terrain Northern Areas, Pakistan.

Mining activities have serious environmental impacts, thus releasing heavy metals (HMs) such as cadmium (Cd), lead (Pb), chromium (Cr), zinc (Zn) and nickel (Ni) into the surrounding environment. The current paper investigated the impacts of mining activities of Pb-Zn sulfide on soil and medicinal plants. Hence, soil samples (n = 36) and medicinal plants (n = 36) samples were collected, acid extracted and were analyzed through Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for HMs quantification. Our results showed that mineralized zones showed high HMs enrichment levels as compared to non-mineralized zones. Various Indices for HMs assessment revealed that the contaminated soil of the study area had low to extreme level. The mean concentrations of HMs in mining degraded soil and medicinal plants were significantly higher (p ≤ 0.01) and were found in order of Zn > Pb > Cr > Ni > Cd and Cr > Zn > Pb > Ni > Cd respectively. Similarly, some widely consumable medicinal plants showed good metal accumulation for Cd, Cr and Pb i.e., 3.57 mg kg1, 350 mg kg-1 and 335 mg kg-1 in C. sativa, while 5.9 mg kg-1, 276.9 mg kg-1 and 188.7 mg kg-1 in R. hestatus respectively. Hence, the present study recommended that medicinal plants grown in mining areas should be analyzed for HMs concentration before consumption.

Modeling Key Strategies for Reducing Socio-Economic and Health Crisis: Perspective from COVID-19 Pandemic.

The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that "strong leadership and control" and "awareness on social media" play significant roles in reducing pandemic challenges, while "promoting online purchase behavior" and "online education" are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.

Multi-geostatistical analyses of the spatial distribution and source apportionment of potentially toxic elements in urban children's park soils in Pakistan: A risk assessment study.

In the past few decades, contamination of urban children's parks (UCPs) with potentially toxic elements (PTEs) has been attracting more and more interest; however, assessment of eco-environmental and child exposure risks particularly in developing countries remains limited. The current study investigated PTE (Cr, Ni, Zn, As, Cd, and Pb) concentrations, potential sources, and their health risk assessment in UCP soils of 12 major cities in Pakistan. The results showed that the mean concentration of Ni exceeded the SEPA-permissible limit in all UCP sites, while other PTEs were found to be within acceptable limits. The soil properties such as pH, electrical conductivity, organic matter, and soil particles size were determined in UCPs soils. The contamination factor and pollution load index results indicated low to moderate pollution levels (CF < 3) and (PLI<1) for all PTEs except Ni in some of the selected cities. Quantile-quantile (Q-Q) plotting determined the normal distribution line for all PTEs in the UCPs. Principal component analysis showed the mixed sources of contamination from industrial emissions, fossil fuel combustion, vehicular emissions, wastewater irrigation, as well as solid waste disposal and natural sources of soil parent materials in all park sites. ANOVA results showed that all the PTEs except Cd had moderate to higher contamination values than the reference site. The risk assessment study revealed that children had high exposure to the selected PTEs via all exposure pathways. The hazard index (HI) mean value (1.82E+00) of Ni for all exposure pathways was greater than 1, while total risk value of Cr (1.00E-03) had exceeded USEPA limit, indicating cancer risk. Consequently, the study of UCPs soils revealed PTEs contamination that could pose a potential health risk to the local population in the studied UCPs regions of Pakistan. Thus, the present study recommends that the influx of PTEs originating from natural and anthropogenic sources should be mitigated and government should implement strict enforcement of environmental regulations and proper management, as well as air quality monitoring guidelines for public health should be strictly adopted to reduce traffic- and industrial emission-related to PTEs in metropolitan areas.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables.

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Do Tournament Incentives Matter for CEOs to Be Environmentally Responsible? Evidence from Chinese Listed Companies.

Relying on tournament theory and environmental management research, we examine how CEO tournament incentives induce top executives to invest more in green innovation. Using a sample of Chinese listed companies from 2010 to 2016, we find evidence that CEO tournament incentives are positively associated with green innovation. In addition, we find that a positive relationship between CEO tournament incentives and green innovation is stronger in state-owned enterprises than in non-state-owned enterprises. These results support tournament theory, which proposes that better incentives induce top executives' efforts to win the tournament incentives, and such efforts are subject to fiercer competition among employees, which improves firms' social and financial performance. Moreover, our findings have implications for policy makers and regulators who wish to enhance environmental legitimacy by providing tournament incentives to top executives.

Promoting sustainability through green innovation adoption: a case of manufacturing industry.

Green innovation is becoming more common among researchers and practitioners around the world due to environmental and social issues. Green innovation minimizes wastes and pollution, and also leads to financial gains and better image if implemented effectively. Nowadays, developing countries pay greater emphasis on environmental issues as their manufacturing industries are considered major contributors to pollution. Considering the case of a developing country (Pakistan), the study empirically identified the drivers of green innovation in the manufacturing industry. A hybrid methodology-Fuzzy Delphi method (FDM), interpretive structural modeling (ISM), and cross-impact matrix multiplication applied to classification (MICMAC)-was used to develop a novel framework for analyzing the green innovation drivers. At first, green innovation drivers were selected from past studies; they were further screened by applying Fuzzy Delphi approach. The MICAMAC and ISM results indicate that "cost reduction" and "government support" are the most important drivers motivating green innovation implementation in the Pakistani manufacturing industry, while a green image appeared as the least significant driver of green innovation adoption. The study's findings have significant implications for managers and policymakers to develop green strategies for manufacturing sector.

Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.

Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top of the grain plants growing in field conditions. However, accurate and automated analysis of all fully and partially visible spikes in greenhouse images renders a more challenging task, which was rarely addressed in the past. A particular difficulty for image analysis is represented by leaf-covered, occluded but also matured spikes of bushy crop cultivars that can hardly be differentiated from the remaining plant biomass. To address the challenge of automated analysis of arbitrary spike phenotypes in different grain crops and optical setups, here, we performed a comparative investigation of six neural network methods for pattern detection and segmentation in RGB images, including five deep and one shallow neural network. Our experimental results demonstrate that advanced deep learning methods show superior performance, achieving over 90% accuracy by detection and segmentation of spikes in wheat, barley and rye images. However, spike detection in new crop phenotypes can be performed more accurately than segmentation. Furthermore, the detection and segmentation of matured, partially visible and occluded spikes, for which phenotypes substantially deviate from the training set of regular spikes, still represent a challenge to neural network models trained on a limited set of a few hundreds of manually labeled ground truth images. Limitations and further potential improvements of the presented algorithmic frameworks for spike image analysis are discussed. Besides theoretical and experimental investigations, we provide a GUI-based tool (SpikeApp), which shows the application of pre-trained neural networks to fully automate spike detection, segmentation and phenotyping in images of greenhouse-grown plants.

Mapping Interactions among Green Innovations Barriers in Manufacturing Industry Using Hybrid Methodology: Insights from a Developing Country.

Recent years have witnessed continuous rise in adopting green innovations which is considered as an important organizational instrument to achieve profits by reducing environmental deterioration. However, green innovation in developing countries, especially in Pakistan, is surprisingly scant as compared to developed countries. This paper empirically investigated obstacles to green innovations in Pakistani manufacturing firms. Specifically, a novel three phase methodological framework was applied to investigate significant barriers and filtration by integrating Delphi method (DM), interpretive structural modeling (ISM), and cross-impact matrix multiplication applied to classification (MICMAC). Our results highlighted that lack of enforceable laws regarding returned goods and recycled products, lack of rules and regulations for green practices, and lack of collaboration with government and environmental institutions are most critical barriers. However, fear of failure about green innovation is least important barriers to green innovations adoption. This study offers interesting clues to promote green innovation in manufacturing industry.

Occurrence, distribution, and pollution indices of potentially toxic elements within the bed sediments of the riverine system in Pakistan.

Potentially toxic elements (PTEs) are a major source of pollution due to their toxicity, persistence, and bio-accumulating nature in riverine bed sediments. The sediment, as the largest storage and source of PTEs, plays an important role in transformation of mercury (Hg), lead (Pb), nickel (Ni), chromium (Cr), copper (Cu), zinc (Zn), and other toxic PTEs. Several important industrial hubs that contain a large population along the banks of different rivers, such as Kabul, Sutlej, Ravi, Jhelum, and Chenab in Pakistan, are acting as major sources of PTEs. In this study, 150 bed sediment samples (n=30 from each river) were collected from different sites. Total (acid extracted) PTE (Hg, Cu, Cr, Ni, Zn, and Pb) concentrations in bed sediments were determined using inductively coupled plasma mass spectrometry (ICP-MS). Sediment pollution indices were calculated in the major rivers of Pakistan. The results demonstrated high levels of Hg and Ni concentrations which exceeded the guideline standards of river authorities in the world. The contamination factor (CF) and contamination degree (CD) indices for Hg, Ni, and Pb showed a moderate to high (CF≥6 and CD≥24) contamination level in all the selected rivers. The values of geo-accumulation index (Igeo) were also high (Igeo≥5) for Hg and Pb and heavily polluted for Ni, while Cr, Cu, and Zn showed low to unpolluted (Igeo) values. Similarly, the enrichment factor (EF) values were moderately severe (5≤EF≤10) for Hg, Pb, and Ni in Sutlej, Ravi, and Jhelum, and severe (10≤EF≤25) in Kabul and Jhelum. Moreover, Hg and Ni showed severe to very severe enrichment in all the sampling sites. The ecological risk index (ERI) values represented considerable, moderate, and low risks, respectively, for Hg (The ERI value should not be bold. Please unbold the  ERI in the whole paper. It should be same like RI, CD and EF. [Formula: see text]≥160), Pb and Ni (40≤[Formula: see text]≤80), and Cr, Cu, and Zn ([Formula: see text]≤40). Similarly, potential ecological risk index (PERI) values posed considerable (300≤RI≤600) risk in Ravi and moderate (150≤RI≤300) in Kabul and Jhelum, but low (RI≤150) risk in Ravi and Chenab. On the basis of the abovementioned results, it is concluded that bed sediment pollution can be dangerous for both ecological resources and human beings. Therefore, PTE contamination should be regularly monitored and a cost-effective and environmentally friendly wastewater treatment plant should be installed to ensure removal of PTEs before the discharge of effluents into the freshwater ecosystems.

CaO-Promoted Graphene-Supported Palladium Nanocrystals as a Universal Electrocatalyst for Direct Liquid Fuel Cells.

Here, we present the fabrication of a reduced graphene oxide-supported PdCa (PdCa/rGO) alloyed catalyst via a NaBH4 reduction method for direct alcohol fuel cells in basic medium and direct formic acid fuel cells in acidic medium. Powder X-ray diffraction, energy-dispersive X-ray spectroscopy, scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, Brunauer-Emmett-Teller, inductively coupled plasma mass spectrometry, and Raman spectroscopy are used to characterize the PdCa/rGO catalyst. We proved that the calcium oxide significantly enhances the electrocatalytic methanol, ethanol, and formic acid oxidation over the Pd/rGO surface. The obtained mass activities for PdCa/rGO are 4838.06, 4674.70, and 3906.49 mA mg-1 for formic acid, methanol, and ethanol, respectively. Long-term stability, high activity, and high level of tolerance to CO poisoning of the PdCa/rGO electrocatalyst are attributed to the presence of calcium oxide. These results prove that the PdCa/rGO catalyst has improved electrocatalytic performance for the oxidation of formic acid, methanol, and ethanol with reference to the Pd/rGO.