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In order to protect the environment, manufacturing sectors have begun implementing a green supply chain (GSC) strategy. Governments are enacting increasingly stringent environmental regulations; consequently, industries must reduce the environmental impact of their supply chains. Our research investigates the barriers to implementing a GSC in the agriculture equipment manufacturing industries (AEMI). This research aims to discover and prioritize the barriers that impede the implementation of sustainable supply chain strategies in the AEMI. Through an in-depth literature review, contributions from experts, and empirical analysis, seventy-one barriers are identified across ten categories. The top barrier in each category is determined using the Delphi approach. The Fuzzy Technique for Order of Preference by Similarity to the Ideal Solution (F-TOPSIS) method creates an exhaustive framework that evaluates and ranks these barriers. The top five barriers are the lack of an environmental partnership with buyers and suppliers, the design complexity when reusing or recycling old goods or products, carbon emissions, paint shop emissions, lack of environmental education and training professionals that lack the necessary skills and less manpower available for the greening supply chain. This framework facilitates decision-makers to organize resources and create effective strategies for overcoming identified barriers. In addition, we proposed a blockchain IoT integrated architecture and strategies. This integrated architecture and strategies will help to mitigate all GSC barriers. It also increases the supply chain's transparency, traceability and effectiveness, fostering sustainability practices and reducing environmental impacts. Blockchain and IoT facilitate real-time data collaboration, computerized transactions and the implementation of smart contracts, thereby enhancing cooperation, trust and collaboration among stakeholders. Implementing GSC practices enables manufacturers to reduce waste and increase productivity, thereby saving funds. In addition, adopting sustainable practices improves these industries' reputation and brand image among environmentally conscious customers, investors and other stakeholders. [ABSTRACT FROM AUTHOR]

Presenters Lauren Clore, BS, Rajeev Agrawal, Janine [...], Context Social connectedness (SC) is an important social determinant of health (SDoH) associated with decreased mortality, improved mental health, and increased self-efficacy in adults and adolescents. Social isolation in families with young children has been linked to increased child abuse, and low-income families may be at higher risk of experiencing social isolation. More research is needed to understand the role of SC as a SDoH in low-income families with young children. Objective To examine SC in a low-income population using validated tools. To explore the role of SC as a SDoH for families with young children. Study Design This cross-sectional analysis utilized data from the Healthy Children and Families (HCF) program. HCF is a cohort intervention study conducted by an urban pediatric clinic serving low-income families with the primary objective of addressing food insecurity. Setting or Dataset 27 families enrolled in HCF completed the baseline Social Provisions Scale five question short form (SPS5) and other validated baseline assessments. Population Studied This study included families with at least one child 0-5 years who expressed food insecurity or at-risk for food insecurity during screening. Intervention/Instrument The SPS-5 is a validated tool used to evaluate SC. Other study instruments are divided into nutritional, child health, and parental health tools. Nutritional tools include food security status (USDA US Household Food Security Survey) and child/parental nutritional status (ASA-24 Dietary Assessment). Child health tools include body mass index (percentile for age) and developmental or behavioral diagnosis (ICD-10). Parental tools include parental stress (Parental Stress Index), parental mental health (BRFSS health perception), and financial health. Outcome Measures Outcome measures are the correlation coefficients between social connectedness and each study outcome determined by Pearson correlations. Results The average SPS-5 composite score 14.5 on a scale of 5-20. Strong correlations were identified between SC and parental stress and parental mental health. Moderate correlations were identified between SC and food insecurity total score, financial health, and childhood BMI. Conclusions Social connectedness is an important social determinant of health in low- income families with young children. This study identifies questions that may be a benefit to include in pediatric health screenings. Small sample size is a limitation.

Automatic facial expression analysis is a fascinating and difficult subject that has implications in a wide range of fields, including human-computer interaction and data-driven approaches. Based on face traits, a variety of techniques are employed to identify emotions. This article examines various recent explorations into automatic data-driven approaches and handcrafted approaches for recognising face emotions. These approaches offer computationally complex solutions that provide good accuracy when training and testing are conducted on the same datasets, but they perform less well on the most difficult realistic dataset, FER-2013. The article's goal is to present a robust model with lower computational complexity that can predict emotion classes more accurately than current methods and aid society in finding a realistic, all-encompassing solution for the facial expression system. A crucial step in good facial expression identification is extracting appropriate features from the face images. In this paper, we examine how well-known deep learning techniques perform when it comes to facial expression recognition and propose a convolutional neural network-based enhanced version of a spatial deep learning model for the most relevant feature extraction with less computational complexity. That gives a significant improvement on the most challenging dataset, FER-2013, which has the problems of occlusions, scale, and illumination variations, resulting in the best feature extraction and classification and maximizing the accuracy, i.e., 74.92%. It also maximizes the correct prediction of emotions at 99.47%, and 98.5% for a large number of samples on the CK+ and FERG datasets, respectively. It is capable of focusing on the major features of the face and achieving greater accuracy over previous fashions. [ABSTRACT FROM AUTHOR]

Wireless systems in the future will have to provide multimedia services where different users have different physical-layer quality of service (QoS) requirements [e.g., bit energy per interference power density ([E.sub.b]/[N.sub.o]) or bit error rate and power constraints] and network-layer QoS requirements (e.g., delay bound, delay-jitter, throughput, and loss). We investigate the use of power control, processing gain and/or multiple codes, and scheduling in CDMA systems to accommodate these diverse service requirements. We first show that the instantaneous capacity region, given in terms of the set of user bit rates that can be supported simultaneously subject to peak power and [E.sub.b]/[N.sub.o] constraints, is nonconvex. This suggests that by time-sharing the channel, one may be able to get better system throughput. We define the capacity region as the convex hull of the instantaneous capacity region and we show that it may be obtained by time sharing between operating points, where each user either uses full power or is silent (bang-bang control). We then consider the problem of scheduling so as to meet pre-specified delay bounds or minimum service curve requirements for traffic streams, which are specified in terms of a traffic profile such as a sigma-rho constraint (enforced by a leaky bucket) and a guarantee that the system is stable. Index Terms--Multirate CDMA, power control, service curve scheduling, sigma-rho scheduling.

This paper considers a multiclass routing and server allocation problem in a queueing system of multiple stations in parallel with penalties for switching the server between stations. We proceed by dynamically ranking the queues so that if all arrivals ceased, switches of the server between queues would be maximally delayed by serving queues in order of rank. The main result of this paper shows that we may improve any policy by constructing a greedy version that routes arriving customers to the queue of highest acceptable rank and allocates the server to the queue of highest rank whenever it switches. We also establish analogous results for a variant of the problem that encourages fairness in server allocation by restricting attention to pseudocyclic policies which visit each queue periodically.

Consistently rising issues through food supply chain like food safety, security along with point of origin, traceability is becoming the most demanding characteristic. Numerous incidents like food adulteration, misinterpreting food characteristics and recently Covid-19 disease spread incidents in the FSC have shaken the consumer's trust and supported the need of creating traceability in the FSC. Food recalls creates huge losses. However financial losses can be catered but regaining consumer trust and recovering a bad reputation associated with product is often considered as a challenging task by the responsible authorities. A sound traceability system tends to minimize unsafe and low quality goods during production, processing and distribution phases. For implementing tracking and tracing, one should understand what drives, inhibits, and enables the tracking and tracing feature through the food supply chain. In continuation, the research is an effort to create a robust set of the drivers and prioritise them to create traceability in food supply chain. Sixteen drivers have been identified by means of literature review supported by expert's opinion. For getting priority among drivers, authors have implemented fuzzy AHP to determine the robust set of drivers along with their priorities. The results can directly contribute to the effective decision making while creating traceability in FSC. [ABSTRACT FROM AUTHOR]