Effectiveness of a “Whole of Chain” Approach in Linking Farmers to Market: A Case of Pakistan Mango Market

AbstractMango is the second major fruit crop in Pakistan. The domestic retail market for mango in Pakistan is dominated by small retail shops, street hawkers, and road side stalls. The fruit sold in these retail outlets is prescribed by the traditional quality standards of size, appearance and price. However the growth of superior outlets/supermarkets chain especially in the major cities are setting additional quality standards such as blemish free, improved packaging, prestige, convenience to deliver premium quality mangoes. Similarly the export market is mainly targeted to the expatriate Pakistani consumers rather than quality conscious foreign consumers. This is because of inadequate market information and understanding all along the chain. A whole of chain approach is undertaken to improve the market understanding in an ACIAR project. Since the approach is new, a conceptual framework is developed in order to asses the effectiveness of the approach. The results indicate that the participants all along the chain would change their practices if they find the compelling reason to change in their existing businesses

RETRACTED: The Heart of Silk Road “Xinjiang,” Its Genetic Portray, and Forensic Parameters Inferred From Autosomal STRs

The Xinjiang Uyghur Autonomous Region of China (XUARC) harbors almost 50 ethnic groups including the Uyghur (UGR: 45.84%), Han (HAN: 40.48%), Kazakh (KZK: 6.50%), Hui (HUI: 4.51%), Kyrgyz (KGZ: 0.86%), Mongol (MGL: 0.81%), Manchu (MCH: 0.11%), and Uzbek (UZK: 0.066%), which make it one of the most colorful regions with abundant cultural and genetic diversities. In our previous study, we established allelic frequency databases for 14 autosomal short tandem repeats (STRs) for four minority populations from XUARC (MCH, KGZ, MGL, and UZK) using the AmpFlSTR® Identifiler PCR Amplification Kit. In this study, we genotyped 2,121 samples using the GoldenEye™ 20A Kit (Beijing PeopleSpot Inc., Beijing, China) amplifying 19 autosomal STR loci for four major ethnic groups (UGR, HAN, KZK, and HUI). These groups make up 97.33% of the total XUARC population. The total number of alleles for all the 19 STRs in these populations ranged from 232 (HAN) to 224 (KZK). We did not observe any departures from the Hardy–Weinberg equilibrium (HWE) in these populations after sequential Bonferroni correction. We did find minimal departure from linkage equilibrium (LE) for a small number of pairwise combinations of loci. The match probabilities for the different populations ranged from 1 in 1.66 × 1023 (HAN) to 6.05 × 1024 (HUI), the combined power of exclusion ranged from 0.999 999 988 (HUI) to 0.999 999 993 (UGR), and the combined power of discrimination ranged from 0.999 999 999 999 999 999 999 983 (HAN) to 0.999 999 999 999 999 999 999 997 (UGR). Genetic distances, principal component analysis (PCA), STRUCTURE analysis, and the phylogenetic tree showed that genetic affinity among studied populations is consistent with linguistic, ethnic, and geographical classifications

Ensuring food safety with molecularly imprinted polymers: innovative methods for the detection of aflatoxins in food and feed samples

Aflatoxins, a group of mycotoxins, represent a heterogeneous class of secondary metabolites that pose a significant risk to food safety and public health due to their potent toxicity. Aflatoxins are widely distributed in the environment, with high levels frequently observed in hot and humid conditions. There is an ongoing development of various methods for detecting aflatoxins in food and feed samples. Herein, a review of these methods is presented with special emphasis on molecularly imprinted polymers (MIPs) as selective materials for aflatoxins’ detection. The key findings of various methods for real-time analysis of food and feed samples are presented and analyzed, providing a comparative assessment of their performance. Furthermore, the challenges and limitations of these methods are discussed, considering their commercialization prospects and real-world requirements

Enhancing spectrum sensing efficiency in multi-channel cognitive device-to-device networks: Medium Access Control layer strategies and analysis

The detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device-to-device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference-free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi-channel Cognitive D2D Networks (cD2DNs)

Composite model predictive control for the boost converter and two-phase interleaved boost converter

This article compares the conventional model predictive control (MPC) and active disturbance rejection control (ADRC) with a novel MPADRC technique for controlling a non-minimum phase behavior in the DC–DC boost converter. The control of the boost converter is challenging as it is nonlinear, and it shows non-minimum phase behavior in a continuous conduction mode (CCM). Moreover, in this article, the comparison is presented for the boost converter and the two-phase interleaved boost converter using MPC and ADRC, and the effectiveness of the interleaving technique is shown. Finally, it is proved that the interleaving method has much more efficiency and less output ripple than the simple boost converter. To conclude, a novel technique has been introduced that combines both the techniques, that is, MPC and ADRC, in the outer and inner loop with a boost converter, respectively, and the response is clearly the best when compared to the said techniques individually. The overall impact of this technique includes the advantages of both the techniques, that is, the use of MPC allows us to optimize the current value by predicting the future values, and the use of ADRC ensures that the disturbance factor is well tackled and cancels the effect caused by all the disturbances including ignored quantities as well

Blockchain adoption for sustainable supply chain management : economic, environmental, and social perspectives

Due to the rapid increase in environmental degradation and depletion of natural resources, the focus of researchers is shifted from economic to socio-environmental problems. Blockchain is a disruptive technology that has the potential to restructure the entire supply chain for sustainable practices. Blockchain is a distributed ledger that provides a digital database for recording all the transactions of the supply chain. The main purpose of this research is to explore the literature relevant to blockchain for sustainable supply chain management. The focus of this review is on the sustainability of the blockchain-based supply chain concerning environmental conservation, social equality, and governance effectiveness. Using a systematic literature review, a total of 136 articles were evaluated and categorized according to the triple bottom-line aspects of sustainability. Challenges and barriers during blockchain adoption in different industrial sectors such as aviation, shipping, agriculture and food, manufacturing, automotive, pharmaceutical, and textile industries were critically examined. This study has not only explored the economic, environmental, and social impacts of blockchain but also highlighted the emerging trends in a circular supply chain with current developments of advanced technologies along with their critical success factors. Furthermore, research areas and gaps in the existing research are discussed, and future research directions are suggested. The findings of this study show that blockchain has the potential to revolutionize the entire supply chain from a sustainability perspective. Blockchain will not only improve the economic sustainability of the supply chain through effective traceability, enhanced visibility through information sharing, transparency in processes, and decentralization of the entire structure but also will help in achieving environmental and social sustainability through resource efficiency, accountability, smart contracts, trust development, and fraud prevention. The study will be helpful for managers and practitioners to understand the procedure of blockchain adoption and to increase the probability of its successful implementation to develop a sustainable supply chain network