This study aims to investigate the key drivers of supply chain complexity in the footwear industry business sector. After conducting extensive literature review and consultation with experts, complexity drivers in the context of the footwear business sector were identified. The content validity of the identified drivers was checked by 13 experts using content validity ratio (CVR), which were used as the basis to select 20 key Supply Chain complexity drivers. Using a multi-criteria decision-making DEMATEL approach the cause and effect drivers were investigated. The findings of the study investigated 12 cause and 8 effect drivers in the footwear sector. Among the cause drivers, four linkage drivers that have strong driving power and dependency were identified. This study is the first in the developing county footwear business sector using DEMATEL approach to identify the key supply chain complexity drivers and their interrelationship. The study identified complexity drivers in the context of the footwear business sector that were not explored before in the existing studies. The outcome of this study will help SC complexity decision-makers in that case sector to control and manage the cause and effect drivers and thus improve the efficiency and effectiveness of their SC performance.

LM26 is a cast aluminum-silicon alloy that exhibits superior castability, moderate strength, and high corrosion resistance. Silicon is typically the primary alloying agent in aluminium alloy LM26. The present study investigates the development of hybrid composites and the machining of aluminium alloy LM26 reinforced with varying weight percentages (0.25%, 0.5%, and 0.75%) of multi-wall carbon nanotubes (MWCNTs), with a focus on sustainable machining. The casting was performed by stir casting at 700°C and 650 rpm. In this study, the Central Composite design is used to design the experiment, and Response Surface Methodology (RSM) is employed to develop a quadratic (polynomial) equation using Design-Expert software V13. The milling experiment was conducted on MWCNT-reinforced LM26 material using a milling machine, with spindle speed, feed rate, depth of cut, and MWCNT percentage as parameters, to measure surface roughness (Ra) and temperature (T). Microstructural characterization of the composite was performed. The performance characteristics were analyzed using ANOVA. This result shows that wt.% of MWCNT is an influential factor in minimizing Ra, while spindle speed affects Ra, and feed rate affects temperature. Based on the results, it has been concluded that adding 0.25% wt. of MWCNTs has improved machinability; however, increasing the MWCNT content increases Ra and temperature. To achieve better machining performance that meets sustainability criteria, a Multi-Objective Genetic Algorithm was employed to optimize the objectives. The MOGA was adopted to solve the optimization problem, yielding 21 non-dominated Pareto-optimal solutions. This study has identified several alternatives to help academics and industry develop environmentally friendly, sustainable machining techniques.

Manual labour in slaughterhouses is hazardous work. Workers suffer from injuries and occupational illnesses resulting from repetitive movements with sharp knives. There is a need for a robotic tool which can perform versatile tasks with a high level of precision. This knife must be able to imitate the same primary cuttings of a professional butcher and produce meat products which are acceptable to the end-market. This paper reports the results of a world-wide assessment of the fresh pork meat cuts as a performance indicator of knives specifically adapted for automated operation. These knives included Victorinox knife, bespoke double bladed Uddeholm knife, vibrating knife and novel smart knife with built-in sensing mechanism that detects in real time the contact with meat and cut depth. Physical appearances of cuts were assessed anonymously by independent responders with different backgrounds. All knives were deemed acceptable in terms of cutting quality. There was also no discernible difference of opinion between manual and robot cuts. This indicates that the new knife for robot-assisted cutting is acceptable by market.

Nonhomogeneous Continuous-Time Markov Chain (NH-CTMC) is a stochastic process that can be used to model problems where the future state depends only on the current state and is independent of the past. The transition intensity in NH-CTMC is not constant but is a function of time. In this paper, NH-CTMC is employed to model information dissemination on Twitter, where transitions occur only from followee groups to follower groups. Information is considered spread on Twitter when followers retweet posts from their followees. The tweet-retweet process on Twitter satisfies the Markov property, as a retweet from a follower depends only on the tweet posted just before by the corresponding followee. The probability of a tweet spreading is determined by the transition intensity, assumed to be a Sigmoid function whose parameters are estimated using Maximum Likelihood Estimation (MLE). This method is applied to Twitter data from Indonesia related to discussions on Covid-19 vaccination. The results indicate that information about Covid-19 vaccination on Twitter spreads rapidly from followees to followers in the first 20 hours, and then slows down after 40 hours. The NH-CTMC model outperforms the Homogeneous Continuous-Time Markov Chain (H-CTMC) approach, where the transition intensity (tweet spreading intensity) is assumed to be constant.

In-plane strength and constitutive properties of composite materials is known as crucial problems. Although, several studies have been made for obtaining the in-plane mode II properties, the common test fixtures are blind in confrontation of shear zone. Furthermore, toughening mechanisms and consequently mode II fracture toughness cannot be evaluated, precisely. Also, proposing the convenient configuration of shear test fixture increase the accuracy of evaluation for the shear-zone energy dissipation, especially in orthotropic materials. Hence in the present research, a novel shear fixture configuration is proposed based on Iosipesque structural modification. Hereof, some arbitrary composite materials (e.g. graphite/epoxy and wood) were studies on the basis of DOE and E-691 ASTM. Furthermore, as the numerical method for indicating the comparison of the uniform and non-uniform shear stress distribution in the modified and common shear test fixture, a statistical procedure is used based on ASTM standard in addition to FEM analysis. The obtained results reveal that applying major amendments through the new scheme of the shear test fixture, would provide a remarkable precision and a reasonable estimation of the shear strength in comparison with the previous Iosipesque experiments.

In the present paper, the theory of generalized photo-thermoelasticity under fractional order derivative was used to study the coupled of thermal, plasma, and elastic waves on unbounded semiconductor medium with a cylindrical hole during the photo-thermoelastic process. The bounding surface of the cavity was traction free and loaded thermally by exponentially decaying pulse boundary heat flux. The medium was considered to be a semiconductor medium homogeneous, and isotropic. In addition, the elastic and thermal properties were considered without neglecting the coupling between the waves due to thermal, plasma and elastic conditions. Laplace transform techniques were used to obtain the exact solution of the problem in the transformed domain by the eigenvalue approach and the inversion of Laplace transforms were carried out numerically. The results were displayed graphically to estimate the effect of the thermal relaxation time and the fractional order parameters on the plasma, thermal and elastic waves.

In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-processing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer.

A review of some recent data from multiaxial fatigue data from different notched materials is carried out in the paper. The approach based on the strain energy density (SED) averaged over a control volume is first used to summarise uniaxial and multiaxial fatigue strength data of welded joints made of structural steel and aluminium alloys. In all welded joints, the weld toe and weld root regions are modelled like sharp, zero radius, V-notches with different opening angles. The control volume radius is 0.28 mm for structural steels, 0.12 mm for aluminium alloys. Afterwards the SED approach is applied to multi-axial fatigue data from plain and V-notched specimens made of AISI 416 stainless steel (hardened and tempered state), subjected to tension, torsion and combined tension and torsion, both in-phase and out-of-phase. Notched specimens are characterised by a very small root radius, ρ=0.1 mm, which results in high nonlinear effects ahead of the notch tips. All results are summarised in terms of Δ W-N (SED range versus cycles to failure) scatterbands. Together with the new results, a number of multi-axial fatigue data recently obtained from sharply notched specimens made of C40 steel (normalised state) and 39NiCrMo3 steel (hardened and tempered state) are represented with the aim to provide a global synthesis in terms of local SED. Finally a synthesis from Ti6Al4V alloy is carried out showing the capability of the SED approach to be applied also to light alloys.

The linear elastic analysis of homogeneous, isotropic cracked bodies started in the 1900s. The existence of three dimensional corner point effects in the vicinity of a corner point where a crack front intersects a free surface was investigated in the late 1970s. An approximate solution by Bažant and Estenssoro explained some features of corner point effects but there were various paradoxes and inconsistencies. Results derived from finite element models showed that the analysis is incomplete. The stress field in the vicinity of a corner point appears to be the sum of two different singularities (i.e. stress intensity factors and corner point singularities). In this paper some recent results for the corner point singularities under in and out of plane loadings is reviewed and discussed.

The present work summarizes some recent experimental, theoretical and numerical results on brittle fracture of isostatic polycrystalline graphite. The analyses have been carried out on V-notched samples under mixed mode (I+II), torsion and compression loading, considering various combinations of the notch tip radius, opening angle and notch tilt angle. The static strength of the considered specimens is assessed through an approach based on the strain energy density averaged over a control volume. The center of the control volume is located on the notch edge, where the principal stress reaches its maximum value. The correct orientation is obtained by a rigid rotation of the crescent-shaped volume while the size depends on the fracture toughness and the ultimate strength of the material. This methodology has been already used in the literature to analyze U- and V-shaped notches subject to mode I loading with very good results and advantages with respect to classic approaches. The results reported in this new work show, also under mixed mode loading conditions, good agreement between experimental data and theoretical predictions.